From 076b131efb1d72c14f5e750abda12ced9fe44e56 Mon Sep 17 00:00:00 2001
From: Damoon <damoon.shah3@gmail.com>
Date: Thu, 27 Feb 2025 16:35:13 +0300
Subject: [PATCH 1/5] moe layer

---
 examples/vision/ipynb/mnist_moe.ipynb         |   624 +
 examples/vision/md/mnist_moe.md               | 22871 ++++++++++++++++
 examples/vision/mnist_moe.py                  |   424 +
 .../examples/audio/vocal_track_separation.md  |   921 +
 4 files changed, 24840 insertions(+)
 create mode 100644 examples/vision/ipynb/mnist_moe.ipynb
 create mode 100644 examples/vision/md/mnist_moe.md
 create mode 100644 examples/vision/mnist_moe.py
 create mode 100644 templates/examples/audio/vocal_track_separation.md

diff --git a/examples/vision/ipynb/mnist_moe.ipynb b/examples/vision/ipynb/mnist_moe.ipynb
new file mode 100644
index 0000000000..0d4c4a0ad4
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@@ -0,0 +1,624 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "# MoE for MNIST\n",
+    "\n",
+    "**Author:** [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)<br>\n",
+    "**Date created:** 2015/06/19<br>\n",
+    "**Last modified:** 2020/04/21<br>\n",
+    "**Description:** Showcasing concepts relates to Mixture of Experts (MoE)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "In this example, we implement an adaptation of the Mixture of Experts (MoE) architecture\n",
+    "([Shazeer et al.](https://arxiv.org/abs/1701.06538)).\n",
+    "The idea is to use conditional computation to increases model capacity without increasing computation.\n",
+    "Experts are identical blocks within a layer where each are trained to specialize in different parts of the input space.\n",
+    "At each forward pass, a gating network selects a subset of experts to apply to the input.\n",
+    "\n",
+    "The components to implement are:\n",
+    "- Gating network: A dense layer that outputs a probability distribution over the experts.\n",
+    "- MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.\n",
+    "- Model: A simple model that uses the MoE layer.\n",
+    "\n",
+    "In this example, we will first implement a linear MoE layer and then a CNN-based MoE layer. Lastly we will combine the two using an abstract implementation to showcase its capacties."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import keras\n",
+    "from keras import layers, models\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import backend as K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "### Data Prepration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "# Model / data parameters\n",
+    "num_classes = 10\n",
+    "input_shape = (28, 28, 1)\n",
+    "\n",
+    "# Load the data and split it between train and test sets\n",
+    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
+    "\n",
+    "# Scale images to the [0, 1] range\n",
+    "x_train = x_train.astype(\"float32\") / 255\n",
+    "x_test = x_test.astype(\"float32\") / 255\n",
+    "# Make sure images have shape (28, 28, 1)\n",
+    "x_train = np.expand_dims(x_train, -1)\n",
+    "x_test = np.expand_dims(x_test, -1)\n",
+    "print(\"x_train shape:\", x_train.shape)\n",
+    "print(x_train.shape[0], \"train samples\")\n",
+    "print(x_test.shape[0], \"test samples\")\n",
+    "\n",
+    "\n",
+    "# convert class vectors to binary class matrices\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "## Constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "NUM_EXPERTS = 5\n",
+    "TOP_K = 3\n",
+    "BATCH_SIZE = 128\n",
+    "NUM_EPOCHS = 20\n",
+    "LEARNING_RATE = 0.001\n",
+    ""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "## Base architecture\n",
+    "\n",
+    "The most basic [MNIST classifier](https://keras.io/examples/vision/mnist_convnet/) consists of a stack of convolutional layers followed by a dense layer. In this tutorial, we will first replace the dense layer with a MoE layer. Then do the same for convolutional layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "model = keras.Sequential(\n",
+    "    [\n",
+    "        keras.Input(shape=input_shape),\n",
+    "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
+    "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
+    "        layers.Flatten(),\n",
+    "        layers.Dropout(0.5),\n",
+    "        layers.Dense(num_classes, activation=\"softmax\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "# Linear MoE using Dense layers\n",
+    "\n",
+    "For this layer, we will create multiple dense layers that will be used as experts. Then a simple gating network will select at each step which exerts should be utilized for the current input. We will keep track of the number of times each expert is used. Then the selected experts will be combined using a weighted sum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "class LinearMoE(layers.Layer):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        hidden_size,\n",
+    "        num_experts=NUM_EXPERTS,\n",
+    "        top_k=TOP_K,\n",
+    "    ):\n",
+    "        super(LinearMoE, self).__init__()\n",
+    "\n",
+    "        # Initialize experts\n",
+    "        self.experts = [\n",
+    "            layers.Dense(\n",
+    "                hidden_size,\n",
+    "                kernel_initializer=tf.keras.initializers.RandomNormal(\n",
+    "                    mean=0.0, stddev=0.001\n",
+    "                ),\n",
+    "                bias_initializer=\"zeros\",\n",
+    "            )\n",
+    "            for _ in range(num_experts)\n",
+    "        ]\n",
+    "        # Initialize gating network\n",
+    "        self.gating_network = layers.Dense(\n",
+    "            NUM_EXPERTS,\n",
+    "            kernel_initializer=tf.keras.initializers.RandomNormal(\n",
+    "                mean=0.0, stddev=0.001\n",
+    "            ),\n",
+    "            bias_initializer=\"zeros\",\n",
+    "        )\n",
+    "\n",
+    "        self.num_experts = num_experts\n",
+    "        self.top_k = top_k\n",
+    "        # Keep track of how many times each expert is used\n",
+    "        self.expert_usage_count = tf.Variable(\n",
+    "            tf.zeros((num_experts,), dtype=tf.float32)\n",
+    "        )\n",
+    "\n",
+    "    def call(self, x):\n",
+    "        # Get gating weights\n",
+    "        gating_weights = self.gating_network(x)\n",
+    "\n",
+    "        # Get the top k experts based on the gating weights\n",
+    "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
+    "\n",
+    "        # Count usage of each expert symbolically\n",
+    "        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)\n",
+    "        # Use tf.tensor_scatter_nd_add to increment the usage count\n",
+    "        self.expert_usage_count.assign(\n",
+    "            tf.tensor_scatter_nd_add(\n",
+    "                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        # Get outputs from only the top-k experts\n",
+    "        top_k_expert_outputs = tf.stack(\n",
+    "            [\n",
+    "                self.experts[expert_index](x)\n",
+    "                for expert_index in top_k_indices.numpy()[0]\n",
+    "            ],\n",
+    "            axis=1,\n",
+    "        )  # Stack outputs along axis 1\n",
+    "\n",
+    "        # Combine outputs using top-k weights\n",
+    "        combined_output = tf.einsum(\"ijk,ij->ik\", top_k_expert_outputs, top_k_weights)\n",
+    "\n",
+    "        return combined_output\n",
+    ""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "Output of the top 3 experts out of 10 for one layer of MoE:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "sample_data = tf.random.uniform((1, 10))\n",
+    "linear_mode = LinearMoE(32, 10, 3)\n",
+    "linear_mode(sample_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "## Routing Collapse\n",
+    "\n",
+    "Routing collapse is a problem that occurs with MoE layers. The route terminology refers to the selection process of which expert to use for a given input.\n",
+    "\n",
+    "Route collapse happens when a routing model, early in training, starts favoring just a few experts because they perform slightly better due to random starting conditions. This leads to most examples being sent to these experts, leaving others unused and reducing the model\u2019s overall capacity.\n",
+    "\n",
+    "Code below demonstrates the randomness of expert selection:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def check_expert_usage(runs):\n",
+    "    # Running the later multiple times to show randomness of expert selection\n",
+    "    for i in range(runs):\n",
+    "        sample_data = tf.random.uniform((1, 10))\n",
+    "        linear_mode = LinearMoE(10, 5)\n",
+    "        _ = linear_mode(sample_data)\n",
+    "        print(f\"Run {i}, Expert usage: {linear_mode.expert_usage_count.numpy()}\")\n",
+    "\n",
+    "\n",
+    "check_expert_usage(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "### Adding loss functions to prevent route collapse\n",
+    "To fix this, the authors use extra rules (importance and load losses), ideas borrowed from [Shazeer et al.](https://arxiv.org/abs/1701.06538), to ensure all experts get used evenly.\n",
+    "\n",
+    "The importance_loss calculates how much the usage of each expert (tracked in batch_importance_sum) deviates from the average usage (mean_importance) by using mean squared error, aiming to balance expert utilization. This helps prevent route collapse by discouraging the model from overloading a few experts, instead promoting an even distribution of examples across all experts to maintain diverse and effective routing.\n",
+    "\n",
+    "#### Load losses:\n",
+    "    - Diversity loss: Diversity loss helps prevent route collapse by encouraging the routing model to evenly distribute examples across all experts, rather than favoring just a few due to their initial performance. It does this by maximizing the entropy of the gating weights, ensuring balanced expert utilization and improving the model's overall capacity.\n",
+    "    - Overflow loss: The batch_overflow_sum measures how much the usage of experts exceeds a set capacity by applying ReLU to the difference between usage_counts (how many examples each expert handles) and batch_capacity (the allowed limit), then summing the excesses. This helps prevent route collapse by penalizing situations where certain experts are overused, encouraging a more even spread of examples across all experts to keep the model's capacity balanced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "class LinearMoE(layers.Layer):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        hidden_size,\n",
+    "        num_experts=NUM_EXPERTS,\n",
+    "        top_k=TOP_K,\n",
+    "    ):\n",
+    "        super(LinearMoE, self).__init__()\n",
+    "\n",
+    "        # Initialize experts\n",
+    "        self.experts = [\n",
+    "            layers.Dense(\n",
+    "                hidden_size,\n",
+    "                kernel_initializer=tf.keras.initializers.RandomNormal(\n",
+    "                    mean=0.0, stddev=0.001\n",
+    "                ),\n",
+    "                bias_initializer=\"zeros\",\n",
+    "            )\n",
+    "            for _ in range(num_experts)\n",
+    "        ]\n",
+    "        # Initialize gating network\n",
+    "        self.gating_network = layers.Dense(\n",
+    "            num_experts,  # Match output to num_experts\n",
+    "            kernel_initializer=tf.keras.initializers.RandomNormal(\n",
+    "                mean=0.0, stddev=0.001\n",
+    "            ),\n",
+    "            bias_initializer=\"zeros\",\n",
+    "        )\n",
+    "\n",
+    "        self.num_experts = num_experts\n",
+    "        self.top_k = top_k\n",
+    "        # Keep track of how many times each expert is used as a layer weight\n",
+    "        self.expert_usage_count = tf.Variable(\n",
+    "            tf.zeros((num_experts,), dtype=tf.float32)\n",
+    "        )\n",
+    "\n",
+    "        self.batch_capacity = BATCH_SIZE // num_experts\n",
+    "\n",
+    "    def _diversity_loss(self, weights):\n",
+    "        entropy = -K.sum(weights * K.log(weights + 1e-10), axis=1)\n",
+    "        self.diversity_loss = -K.mean(entropy)\n",
+    "\n",
+    "    def _importance_loss(self, gating_weights):\n",
+    "        batch_importance_sum = K.sum(gating_weights, axis=0)\n",
+    "        mean_importance = K.mean(batch_importance_sum)\n",
+    "        self.importance_loss = K.mean(\n",
+    "            K.square(\n",
+    "                batch_importance_sum\n",
+    "                - mean_importance * tf.ones_like(batch_importance_sum)\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    def call(self, x):\n",
+    "        # Get gating weights and normalize\n",
+    "        gating_weights = self.gating_network(x)\n",
+    "        gating_weights = K.softmax(gating_weights)  # Ensure weights are probabilities\n",
+    "        self._diversity_loss(gating_weights)\n",
+    "        self._importance_loss(gating_weights)\n",
+    "\n",
+    "        # Get the top k experts based on the gating weights\n",
+    "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
+    "\n",
+    "        # Count usage of each expert symbolically\n",
+    "        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)\n",
+    "        # Use tf.tensor_scatter_nd_add to increment the usage count\n",
+    "        self.expert_usage_count.assign(\n",
+    "            tf.tensor_scatter_nd_add(\n",
+    "                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        # Calculate overflow using updated usage count\n",
+    "        self.batch_overflow_sum = K.sum(\n",
+    "            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)\n",
+    "        )\n",
+    "\n",
+    "        # Compute all expert outputs\n",
+    "        expert_outputs = tf.stack(\n",
+    "            [expert(x) for expert in self.experts], axis=1\n",
+    "        )  # Shape: (batch_size, num_experts, hidden_size)\n",
+    "\n",
+    "        # Gather the top-k expert outputs using top_k_indices\n",
+    "        batch_size = tf.shape(x)[0]\n",
+    "        batch_indices = tf.expand_dims(\n",
+    "            tf.range(batch_size), 1\n",
+    "        )  # Shape: (batch_size, 1)\n",
+    "        batch_indices = tf.tile(\n",
+    "            batch_indices, [1, self.top_k]\n",
+    "        )  # Shape: (batch_size, top_k)\n",
+    "\n",
+    "        # Create indices for gathering\n",
+    "        indices = tf.stack(\n",
+    "            [batch_indices, top_k_indices], axis=2\n",
+    "        )  # Shape: (batch_size, top_k, 2)\n",
+    "        top_k_expert_outputs = tf.gather_nd(\n",
+    "            expert_outputs, indices\n",
+    "        )  # Shape: (batch_size, top_k, hidden_size)\n",
+    "\n",
+    "        # Combine outputs using top-k weights\n",
+    "        combined_output = tf.reduce_sum(\n",
+    "            top_k_expert_outputs * tf.expand_dims(top_k_weights, axis=-1), axis=1\n",
+    "        )\n",
+    "\n",
+    "        return combined_output\n",
+    "\n",
+    "    def compute_total_loss(self, load_balance_coef=0.01):\n",
+    "        return load_balance_coef * (\n",
+    "            self.diversity_loss + self.batch_overflow_sum + self.importance_loss\n",
+    "        )\n",
+    ""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "## MNIST classification with MoE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "class MoEModel(keras.Model):\n",
+    "    def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_K):\n",
+    "        super(MoEModel, self).__init__()\n",
+    "\n",
+    "        # Define the convolutional block\n",
+    "        self.conv_block = keras.Sequential(\n",
+    "            [\n",
+    "                layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+    "                layers.MaxPooling2D(pool_size=(2, 2)),\n",
+    "                layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+    "                layers.MaxPooling2D(pool_size=(2, 2)),\n",
+    "                layers.Flatten(),\n",
+    "                layers.Dropout(0.5),\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
+    "        # MoE classifier\n",
+    "        self.moe_classifier = LinearMoE(\n",
+    "            hidden_size=num_classes, num_experts=num_experts, top_k=top_k\n",
+    "        )\n",
+    "\n",
+    "        # Softmax layer\n",
+    "        self.softmax = layers.Softmax()\n",
+    "\n",
+    "    def call(self, inputs, training=False):\n",
+    "        conv_flatten = self.conv_block(inputs)\n",
+    "        moe_output = self.moe_classifier(conv_flatten)\n",
+    "        outputs = self.softmax(moe_output)\n",
+    "        return outputs\n",
+    "\n",
+    "    def train_step(self, data):\n",
+    "        x, y = data  # Unpack input data and labels\n",
+    "\n",
+    "        with tf.GradientTape() as tape:\n",
+    "            y_pred = self(x, training=True)\n",
+    "            classification_loss = self.compute_loss(x, y, y_pred)\n",
+    "            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)\n",
+    "            total_loss = classification_loss + moe_loss\n",
+    "\n",
+    "        # Compute gradients\n",
+    "        gradients = tape.gradient(total_loss, self.trainable_variables)\n",
+    "\n",
+    "        # Update weights\n",
+    "        self.optimizer.apply_gradients(\n",
+    "            zip(gradients, self.trainable_variables)\n",
+    "        )  # Update metrics (e.g., accuracy)\n",
+    "        self.compiled_metrics.update_state(y, y_pred)\n",
+    "        # Return a dict of metrics for monitoring\n",
+    "        return {\n",
+    "            \"loss\": total_loss,\n",
+    "            \"moe_loss\": moe_loss,\n",
+    "            **{m.name: m.result() for m in self.metrics},\n",
+    "        }\n",
+    "\n",
+    "    def test_step(self, data):\n",
+    "        x, y = data\n",
+    "        y_pred = self(x, training=False)\n",
+    "        classification_loss = self.compute_loss(x, y, y_pred)\n",
+    "        moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)\n",
+    "        total_loss = classification_loss + moe_loss\n",
+    "\n",
+    "        self.compiled_metrics.update_state(y, y_pred)\n",
+    "        return {\n",
+    "            \"loss\": total_loss,\n",
+    "            \"moe_loss\": moe_loss,\n",
+    "            **{m.name: m.result() for m in self.metrics},\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "# Instantiate and compile the model\n",
+    "inputs = keras.Input(shape=input_shape)\n",
+    "model = MoEModel(\n",
+    "    input_shape=input_shape, num_classes=num_classes, num_experts=6, top_k=4\n",
+    ")\n",
+    "\n",
+    "model.compile(\n",
+    "    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),\n",
+    "    loss=keras.losses.CategoricalCrossentropy(),  # Assumes one-hot encoded labels\n",
+    "    metrics=[\"accuracy\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "###  Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "history = model.fit(\n",
+    "    x_train,\n",
+    "    y_train,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    epochs=NUM_EPOCHS,\n",
+    "    validation_data=(x_test, y_test),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "### Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab_type": "code"
+   },
+   "outputs": [],
+   "source": [
+    "score = model.evaluate(x_test, y_test, verbose=0)\n",
+    "print(\"Test loss:\", score[0])\n",
+    "print(\"Test accuracy:\", score[1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "mnist_moe",
+   "private_outputs": false,
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/vision/md/mnist_moe.md b/examples/vision/md/mnist_moe.md
new file mode 100644
index 0000000000..9165321e16
--- /dev/null
+++ b/examples/vision/md/mnist_moe.md
@@ -0,0 +1,22871 @@
+# MoE for MNIST
+
+**Author:** [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)<br>
+**Date created:** 2015/06/19<br>
+**Last modified:** 2020/04/21<br>
+**Description:** Showcasing concepts relates to Mixture of Experts (MoE).
+
+
+<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/mnist_moe.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/vision/mnist_moe.py)
+
+
+
+# Introduction
+
+In this example, we implement an adaptation of the Mixture of Experts (MoE) architecture
+([Shazeer et al.](https://arxiv.org/abs/1701.06538)).
+The idea is to use conditional computation to increases model capacity without increasing computation.
+Experts are identical blocks within a layer where each are trained to specialize in different parts of the input space.
+At each forward pass, a gating network selects a subset of experts to apply to the input.
+
+The components to implement are:
+- Gating network: A dense layer that outputs a probability distribution over the experts.
+- MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.
+- Model: A simple model that uses the MoE layer.
+
+In this example, we will first implement a linear MoE layer and then a CNN-based MoE layer. Lastly we will combine the two using an abstract implementation to showcase its capacties.
+
+---
+## Imports
+
+
+```python
+import numpy as np
+import keras
+from keras import layers, models
+import tensorflow as tf
+from tensorflow.keras import backend as K
+```
+
+### Data Prepration
+
+
+```python
+# Model / data parameters
+num_classes = 10
+input_shape = (28, 28, 1)
+
+# Load the data and split it between train and test sets
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+# Scale images to the [0, 1] range
+x_train = x_train.astype("float32") / 255
+x_test = x_test.astype("float32") / 255
+# Make sure images have shape (28, 28, 1)
+x_train = np.expand_dims(x_train, -1)
+x_test = np.expand_dims(x_test, -1)
+print("x_train shape:", x_train.shape)
+print(x_train.shape[0], "train samples")
+print(x_test.shape[0], "test samples")
+
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+```
+
+<div class="k-default-codeblock">
+```
+x_train shape: (60000, 28, 28, 1)
+60000 train samples
+10000 test samples
+
+```
+</div>
+---
+## Constants
+
+
+```python
+NUM_EXPERTS = 5
+TOP_K = 3
+BATCH_SIZE = 128
+NUM_EPOCHS = 20
+LEARNING_RATE = 0.001
+
+```
+
+---
+## Base architecture
+
+The most basic [MNIST classifier](https://keras.io/examples/vision/mnist_convnet/) consists of a stack of convolutional layers followed by a dense layer. In this tutorial, we will first replace the dense layer with a MoE layer. Then do the same for convolutional layers.
+
+
+```python
+model = keras.Sequential(
+    [
+        keras.Input(shape=input_shape),
+        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+        layers.MaxPooling2D(pool_size=(2, 2)),
+        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+        layers.MaxPooling2D(pool_size=(2, 2)),
+        layers.Flatten(),
+        layers.Dropout(0.5),
+        layers.Dense(num_classes, activation="softmax"),
+    ]
+)
+
+model.summary()
+```
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "sequential"</span>
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+┃<span style="font-weight: bold"> Layer (type)                    </span>┃<span style="font-weight: bold"> Output Shape           </span>┃<span style="font-weight: bold">       Param # </span>┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+│ conv2d (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                 │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">26</span>, <span style="color: #00af00; text-decoration-color: #00af00">26</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>)     │           <span style="color: #00af00; text-decoration-color: #00af00">320</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ max_pooling2d (<span style="color: #0087ff; text-decoration-color: #0087ff">MaxPooling2D</span>)    │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">13</span>, <span style="color: #00af00; text-decoration-color: #00af00">13</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>)     │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ conv2d_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)               │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">11</span>, <span style="color: #00af00; text-decoration-color: #00af00">11</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)     │        <span style="color: #00af00; text-decoration-color: #00af00">18,496</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ max_pooling2d_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">MaxPooling2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">5</span>, <span style="color: #00af00; text-decoration-color: #00af00">5</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)       │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ flatten (<span style="color: #0087ff; text-decoration-color: #0087ff">Flatten</span>)               │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1600</span>)           │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dropout (<span style="color: #0087ff; text-decoration-color: #0087ff">Dropout</span>)               │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1600</span>)           │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ dense (<span style="color: #0087ff; text-decoration-color: #0087ff">Dense</span>)                   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">10</span>)             │        <span style="color: #00af00; text-decoration-color: #00af00">16,010</span> │
+└─────────────────────────────────┴────────────────────────┴───────────────┘
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">34,826</span> (136.04 KB)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">34,826</span> (136.04 KB)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">0</span> (0.00 B)
+</pre>
+
+
+
+# Linear MoE using Dense layers
+
+For this layer, we will create multiple dense layers that will be used as experts. Then a simple gating network will select at each step which exerts should be utilized for the current input. We will keep track of the number of times each expert is used. Then the selected experts will be combined using a weighted sum.
+
+
+```python
+
+class LinearMoE(layers.Layer):
+    def __init__(
+        self,
+        hidden_size,
+        num_experts=NUM_EXPERTS,
+        top_k=TOP_K,
+    ):
+        super(LinearMoE, self).__init__()
+
+        # Initialize experts
+        self.experts = [
+            layers.Dense(
+                hidden_size,
+                kernel_initializer=tf.keras.initializers.RandomNormal(
+                    mean=0.0, stddev=0.001
+                ),
+                bias_initializer="zeros",
+            )
+            for _ in range(num_experts)
+        ]
+        # Initialize gating network
+        self.gating_network = layers.Dense(
+            NUM_EXPERTS,
+            kernel_initializer=tf.keras.initializers.RandomNormal(
+                mean=0.0, stddev=0.001
+            ),
+            bias_initializer="zeros",
+        )
+
+        self.num_experts = num_experts
+        self.top_k = top_k
+        # Keep track of how many times each expert is used
+        self.expert_usage_count = tf.Variable(
+            tf.zeros((num_experts,), dtype=tf.float32)
+        )
+
+    def call(self, x):
+        # Get gating weights
+        gating_weights = self.gating_network(x)
+
+        # Get the top k experts based on the gating weights
+        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+
+        # Count usage of each expert symbolically
+        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
+        # Use tf.tensor_scatter_nd_add to increment the usage count
+        self.expert_usage_count.assign(
+            tf.tensor_scatter_nd_add(
+                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
+            )
+        )
+
+        # Get outputs from only the top-k experts
+        top_k_expert_outputs = tf.stack(
+            [
+                self.experts[expert_index](x)
+                for expert_index in top_k_indices.numpy()[0]
+            ],
+            axis=1,
+        )  # Stack outputs along axis 1
+
+        # Combine outputs using top-k weights
+        combined_output = tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
+
+        return combined_output
+
+```
+
+Output of the top 3 experts out of 10 for one layer of MoE:
+
+
+```python
+sample_data = tf.random.uniform((1, 10))
+linear_mode = LinearMoE(32, 10, 3)
+linear_mode(sample_data)
+```
+
+
+
+
+<div class="k-default-codeblock">
+```
+<tf.Tensor: shape=(1, 32), dtype=float32, numpy=
+array([[ 7.8205157e-06, -9.6895346e-06, -1.3567289e-06, -1.1132732e-05,
+        -2.8532945e-06,  2.5079655e-06, -1.9245979e-06, -1.1963858e-06,
+         7.0713571e-07, -6.1005521e-06, -1.4404541e-06,  8.7282970e-06,
+        -1.2715462e-05, -6.8048112e-06, -1.4842963e-06, -1.1498453e-06,
+        -1.2819509e-06,  2.1932642e-06,  9.3587869e-06, -2.8901491e-07,
+         4.9251821e-06,  6.3183566e-06,  4.5951542e-06,  2.1709191e-06,
+        -9.7254415e-06, -9.1735465e-06, -1.9290899e-06,  1.2122581e-07,
+         4.6307746e-06,  1.4301847e-06, -8.8591560e-06, -1.0252905e-06]],
+      dtype=float32)>
+
+```
+</div>
+---
+## Routing Collapse
+
+Routing collapse is a problem that occurs with MoE layers. The route terminology refers to the selection process of which expert to use for a given input.
+
+Route collapse happens when a routing model, early in training, starts favoring just a few experts because they perform slightly better due to random starting conditions. This leads to most examples being sent to these experts, leaving others unused and reducing the model’s overall capacity.
+
+Code below demonstrates the randomness of expert selection:
+
+
+```python
+
+def check_expert_usage(runs):
+    # Running the later multiple times to show randomness of expert selection
+    for i in range(runs):
+        sample_data = tf.random.uniform((1, 10))
+        linear_mode = LinearMoE(10, 5)
+        _ = linear_mode(sample_data)
+        print(f"Run {i}, Expert usage: {linear_mode.expert_usage_count.numpy()}")
+
+
+check_expert_usage(4)
+```
+
+<div class="k-default-codeblock">
+```
+Run 0, Expert usage: [1. 0. 1. 1. 0.]
+Run 1, Expert usage: [0. 1. 1. 0. 1.]
+Run 2, Expert usage: [1. 1. 0. 1. 0.]
+Run 3, Expert usage: [1. 0. 1. 1. 0.]
+
+```
+</div>
+### Adding loss functions to prevent route collapse
+To fix this, the authors use extra rules (importance and load losses), ideas borrowed from [Shazeer et al.](https://arxiv.org/abs/1701.06538), to ensure all experts get used evenly.
+
+The importance_loss calculates how much the usage of each expert (tracked in batch_importance_sum) deviates from the average usage (mean_importance) by using mean squared error, aiming to balance expert utilization. This helps prevent route collapse by discouraging the model from overloading a few experts, instead promoting an even distribution of examples across all experts to maintain diverse and effective routing.
+
+#### Load losses:
+    - Diversity loss: Diversity loss helps prevent route collapse by encouraging the routing model to evenly distribute examples across all experts, rather than favoring just a few due to their initial performance. It does this by maximizing the entropy of the gating weights, ensuring balanced expert utilization and improving the model's overall capacity.
+    - Overflow loss: The batch_overflow_sum measures how much the usage of experts exceeds a set capacity by applying ReLU to the difference between usage_counts (how many examples each expert handles) and batch_capacity (the allowed limit), then summing the excesses. This helps prevent route collapse by penalizing situations where certain experts are overused, encouraging a more even spread of examples across all experts to keep the model's capacity balanced.
+
+
+```python
+
+class LinearMoE(layers.Layer):
+    def __init__(
+        self,
+        hidden_size,
+        num_experts=NUM_EXPERTS,
+        top_k=TOP_K,
+    ):
+        super(LinearMoE, self).__init__()
+
+        # Initialize experts
+        self.experts = [
+            layers.Dense(
+                hidden_size,
+                kernel_initializer=tf.keras.initializers.RandomNormal(
+                    mean=0.0, stddev=0.001
+                ),
+                bias_initializer="zeros",
+            )
+            for _ in range(num_experts)
+        ]
+        # Initialize gating network
+        self.gating_network = layers.Dense(
+            num_experts,  # Match output to num_experts
+            kernel_initializer=tf.keras.initializers.RandomNormal(
+                mean=0.0, stddev=0.001
+            ),
+            bias_initializer="zeros",
+        )
+
+        self.num_experts = num_experts
+        self.top_k = top_k
+        # Keep track of how many times each expert is used as a layer weight
+        self.expert_usage_count = tf.Variable(
+            tf.zeros((num_experts,), dtype=tf.float32)
+        )
+
+        self.batch_capacity = BATCH_SIZE // num_experts
+
+    def _diversity_loss(self, weights):
+        entropy = -K.sum(weights * K.log(weights + 1e-10), axis=1)
+        self.diversity_loss = -K.mean(entropy)
+
+    def _importance_loss(self, gating_weights):
+        batch_importance_sum = K.sum(gating_weights, axis=0)
+        mean_importance = K.mean(batch_importance_sum)
+        self.importance_loss = K.mean(
+            K.square(
+                batch_importance_sum
+                - mean_importance * tf.ones_like(batch_importance_sum)
+            )
+        )
+
+    def call(self, x):
+        # Get gating weights and normalize
+        gating_weights = self.gating_network(x)
+        gating_weights = K.softmax(gating_weights)  # Ensure weights are probabilities
+        self._diversity_loss(gating_weights)
+        self._importance_loss(gating_weights)
+
+        # Get the top k experts based on the gating weights
+        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+
+        # Count usage of each expert symbolically
+        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
+        # Use tf.tensor_scatter_nd_add to increment the usage count
+        self.expert_usage_count.assign(
+            tf.tensor_scatter_nd_add(
+                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
+            )
+        )
+
+        # Calculate overflow using updated usage count
+        self.batch_overflow_sum = K.sum(
+            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
+        )
+
+        # Compute all expert outputs
+        expert_outputs = tf.stack(
+            [expert(x) for expert in self.experts], axis=1
+        )  # Shape: (batch_size, num_experts, hidden_size)
+
+        # Gather the top-k expert outputs using top_k_indices
+        batch_size = tf.shape(x)[0]
+        batch_indices = tf.expand_dims(
+            tf.range(batch_size), 1
+        )  # Shape: (batch_size, 1)
+        batch_indices = tf.tile(
+            batch_indices, [1, self.top_k]
+        )  # Shape: (batch_size, top_k)
+
+        # Create indices for gathering
+        indices = tf.stack(
+            [batch_indices, top_k_indices], axis=2
+        )  # Shape: (batch_size, top_k, 2)
+        top_k_expert_outputs = tf.gather_nd(
+            expert_outputs, indices
+        )  # Shape: (batch_size, top_k, hidden_size)
+
+        # Combine outputs using top-k weights
+        combined_output = tf.reduce_sum(
+            top_k_expert_outputs * tf.expand_dims(top_k_weights, axis=-1), axis=1
+        )
+
+        return combined_output
+
+    def compute_total_loss(self, load_balance_coef=0.01):
+        return load_balance_coef * (
+            self.diversity_loss + self.batch_overflow_sum + self.importance_loss
+        )
+
+```
+
+---
+## MNIST classification with MoE
+
+
+```python
+
+class MoEModel(keras.Model):
+    def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_K):
+        super(MoEModel, self).__init__()
+
+        # Define the convolutional block
+        self.conv_block = keras.Sequential(
+            [
+                layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+                layers.MaxPooling2D(pool_size=(2, 2)),
+                layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+                layers.MaxPooling2D(pool_size=(2, 2)),
+                layers.Flatten(),
+                layers.Dropout(0.5),
+            ]
+        )
+
+        # MoE classifier
+        self.moe_classifier = LinearMoE(
+            hidden_size=num_classes, num_experts=num_experts, top_k=top_k
+        )
+
+        # Softmax layer
+        self.softmax = layers.Softmax()
+
+    def call(self, inputs, training=False):
+        conv_flatten = self.conv_block(inputs)
+        moe_output = self.moe_classifier(conv_flatten)
+        outputs = self.softmax(moe_output)
+        return outputs
+
+    def train_step(self, data):
+        x, y = data  # Unpack input data and labels
+
+        with tf.GradientTape() as tape:
+            y_pred = self(x, training=True)
+            classification_loss = self.compute_loss(x, y, y_pred)
+            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
+            total_loss = classification_loss + moe_loss
+
+        # Compute gradients
+        gradients = tape.gradient(total_loss, self.trainable_variables)
+
+        # Update weights
+        self.optimizer.apply_gradients(
+            zip(gradients, self.trainable_variables)
+        )  # Update metrics (e.g., accuracy)
+        self.compiled_metrics.update_state(y, y_pred)
+        # Return a dict of metrics for monitoring
+        return {
+            "loss": total_loss,
+            "moe_loss": moe_loss,
+            **{m.name: m.result() for m in self.metrics},
+        }
+
+    def test_step(self, data):
+        x, y = data
+        y_pred = self(x, training=False)
+        classification_loss = self.compute_loss(x, y, y_pred)
+        moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
+        total_loss = classification_loss + moe_loss
+
+        self.compiled_metrics.update_state(y, y_pred)
+        return {
+            "loss": total_loss,
+            "moe_loss": moe_loss,
+            **{m.name: m.result() for m in self.metrics},
+        }
+
+
+# Instantiate and compile the model
+inputs = keras.Input(shape=input_shape)
+model = MoEModel(
+    input_shape=input_shape, num_classes=num_classes, num_experts=6, top_k=4
+)
+
+model.compile(
+    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),
+    loss=keras.losses.CategoricalCrossentropy(),  # Assumes one-hot encoded labels
+    metrics=["accuracy"],
+)
+```
+
+###  Training
+
+
+```python
+history = model.fit(
+    x_train,
+    y_train,
+    batch_size=BATCH_SIZE,
+    epochs=NUM_EPOCHS,
+    validation_data=(x_test, y_test),
+)
+```
+
+<div class="k-default-codeblock">
+```
+Epoch 1/20
+
+/opt/homebrew/Caskroom/miniforge/base/envs/keras-io/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:642: UserWarning: `model.compiled_metrics()` is deprecated. Instead, use e.g.:
+```
+for metric in self.metrics:
+    metric.update_state(y, y_pred)
+```
+```
+</div>
+    
+<div class="k-default-codeblock">
+```
+  return self._compiled_metrics_update_state(
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  8:21 1s/step - accuracy: 0.1406 - loss: 0.1000 - moe_loss: 3.8421
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 17ms/step - accuracy: 0.1637 - loss: 0.1000 - moe_loss: 11.5298
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   8/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2071 - loss: 0.1000 - moe_loss: 21.7700
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  12/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2438 - loss: 0.1000 - moe_loss: 32.0082
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2744 - loss: 0.1000 - moe_loss: 42.2476
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2988 - loss: 0.1000 - moe_loss: 52.4873
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  24/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3206 - loss: 0.1000 - moe_loss: 62.7278
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3409 - loss: 0.1000 - moe_loss: 72.9716
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  32/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3598 - loss: 0.1000 - moe_loss: 83.2221
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3774 - loss: 0.1000 - moe_loss: 93.4818
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.3938 - loss: 0.1000 - moe_loss: 103.7338
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  44/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4092 - loss: 0.1000 - moe_loss: 113.9789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4237 - loss: 0.1000 - moe_loss: 124.2205
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4373 - loss: 0.1000 - moe_loss: 134.4638
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4503 - loss: 0.1000 - moe_loss: 144.7069
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4626 - loss: 0.1000 - moe_loss: 154.9452
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4744 - loss: 0.1000 - moe_loss: 165.1867
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4828 - loss: 0.1000 - moe_loss: 172.8672
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  71/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4937 - loss: 0.1000 - moe_loss: 183.1078
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  75/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5039 - loss: 0.1000 - moe_loss: 193.3479
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5137 - loss: 0.1000 - moe_loss: 203.5896
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5207 - loss: 0.1000 - moe_loss: 211.2682
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  86/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5297 - loss: 0.1000 - moe_loss: 221.5096
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  89/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5362 - loss: 0.1000 - moe_loss: 229.1885
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  93/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5445 - loss: 0.1000 - moe_loss: 239.4279
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5524 - loss: 0.1000 - moe_loss: 249.6689
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5581 - loss: 0.1000 - moe_loss: 257.3486
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5654 - loss: 0.1000 - moe_loss: 267.5893
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 107/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5707 - loss: 0.1000 - moe_loss: 275.2696
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 110/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5758 - loss: 0.1000 - moe_loss: 282.9488
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 113/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5808 - loss: 0.1000 - moe_loss: 290.6284
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 116/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5856 - loss: 0.1000 - moe_loss: 298.3105
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 119/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5903 - loss: 0.1000 - moe_loss: 305.9899
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5963 - loss: 0.1000 - moe_loss: 316.2303
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6007 - loss: 0.1000 - moe_loss: 323.9091
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6049 - loss: 0.1000 - moe_loss: 331.5898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6104 - loss: 0.1000 - moe_loss: 341.8302
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6143 - loss: 0.1000 - moe_loss: 349.5105
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6195 - loss: 0.1000 - moe_loss: 359.7512
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 143/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6232 - loss: 0.1000 - moe_loss: 367.4311
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6281 - loss: 0.1000 - moe_loss: 377.6704
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6316 - loss: 0.1000 - moe_loss: 385.3510
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6361 - loss: 0.1000 - moe_loss: 395.5921
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6394 - loss: 0.1000 - moe_loss: 403.2722
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6427 - loss: 0.1000 - moe_loss: 410.9522
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6458 - loss: 0.1000 - moe_loss: 418.6319
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 167/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6499 - loss: 0.1000 - moe_loss: 428.8718
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6539 - loss: 0.1000 - moe_loss: 439.1111
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6578 - loss: 0.1000 - moe_loss: 449.3512
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6615 - loss: 0.1000 - moe_loss: 459.5908
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 182/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6643 - loss: 0.1000 - moe_loss: 467.2707
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 186/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6678 - loss: 0.1000 - moe_loss: 477.5105
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6713 - loss: 0.1000 - moe_loss: 487.7509
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 194/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6747 - loss: 0.1000 - moe_loss: 497.9902
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 197/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6771 - loss: 0.1000 - moe_loss: 505.6700
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6804 - loss: 0.1000 - moe_loss: 515.9094
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6827 - loss: 0.1000 - moe_loss: 523.5893
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6850 - loss: 0.1000 - moe_loss: 531.2690
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6880 - loss: 0.1000 - moe_loss: 541.5093
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 215/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6910 - loss: 0.1000 - moe_loss: 551.7495
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6938 - loss: 0.1000 - moe_loss: 561.9893
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6959 - loss: 0.1000 - moe_loss: 569.6691
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6980 - loss: 0.1000 - moe_loss: 577.3488
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7007 - loss: 0.1000 - moe_loss: 587.5882
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7033 - loss: 0.1000 - moe_loss: 597.8292
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7059 - loss: 0.1000 - moe_loss: 608.0698
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7084 - loss: 0.1000 - moe_loss: 618.3093
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7102 - loss: 0.1000 - moe_loss: 625.9894
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7121 - loss: 0.1000 - moe_loss: 633.6691
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 251/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7144 - loss: 0.1000 - moe_loss: 643.9095
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7167 - loss: 0.1000 - moe_loss: 654.1490
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7184 - loss: 0.1000 - moe_loss: 661.8292
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7207 - loss: 0.1000 - moe_loss: 672.0692
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7223 - loss: 0.1000 - moe_loss: 679.7494
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7245 - loss: 0.1000 - moe_loss: 689.9895
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7266 - loss: 0.1000 - moe_loss: 700.2294
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7286 - loss: 0.1000 - moe_loss: 710.4691
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7306 - loss: 0.1000 - moe_loss: 720.7094
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7326 - loss: 0.1000 - moe_loss: 730.9502
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7346 - loss: 0.1000 - moe_loss: 741.1905
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 293/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7365 - loss: 0.1000 - moe_loss: 751.4304
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7374 - loss: 0.1000 - moe_loss: 756.5504
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7388 - loss: 0.1000 - moe_loss: 764.2300
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7406 - loss: 0.1000 - moe_loss: 774.4699
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7424 - loss: 0.1000 - moe_loss: 784.7094
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7441 - loss: 0.1000 - moe_loss: 794.9492
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 314/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7458 - loss: 0.1000 - moe_loss: 805.1893
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7475 - loss: 0.1000 - moe_loss: 815.4291
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7488 - loss: 0.1000 - moe_loss: 823.1090
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7504 - loss: 0.1000 - moe_loss: 833.3490
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7520 - loss: 0.1000 - moe_loss: 843.5892
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7532 - loss: 0.1000 - moe_loss: 851.2693
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7548 - loss: 0.1000 - moe_loss: 861.5092
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7563 - loss: 0.1000 - moe_loss: 871.7490
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7575 - loss: 0.1000 - moe_loss: 879.4288
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7590 - loss: 0.1000 - moe_loss: 889.6685
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7601 - loss: 0.1000 - moe_loss: 897.3481
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7612 - loss: 0.1000 - moe_loss: 905.0280
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7626 - loss: 0.1000 - moe_loss: 915.2678
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7640 - loss: 0.1000 - moe_loss: 925.5076
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7654 - loss: 0.1000 - moe_loss: 935.7476
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7664 - loss: 0.1000 - moe_loss: 943.4277
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 372/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7678 - loss: 0.1000 - moe_loss: 953.6683
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 375/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7688 - loss: 0.1000 - moe_loss: 961.3480
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7698 - loss: 0.1000 - moe_loss: 969.0279
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7711 - loss: 0.1000 - moe_loss: 979.2679
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7724 - loss: 0.1000 - moe_loss: 989.5076
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7736 - loss: 0.1000 - moe_loss: 999.7477
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7749 - loss: 0.1000 - moe_loss: 1009.9877
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7761 - loss: 0.1000 - moe_loss: 1020.2275
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7773 - loss: 0.1000 - moe_loss: 1030.4677
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7785 - loss: 0.1000 - moe_loss: 1040.7075
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7797 - loss: 0.1000 - moe_loss: 1050.9473
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 414/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7808 - loss: 0.1000 - moe_loss: 1061.1871
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7820 - loss: 0.1000 - moe_loss: 1071.4269
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7828 - loss: 0.1000 - moe_loss: 1079.1069
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7839 - loss: 0.1000 - moe_loss: 1089.3467
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7850 - loss: 0.1000 - moe_loss: 1099.5865
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7861 - loss: 0.1000 - moe_loss: 1109.8267
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7869 - loss: 0.1000 - moe_loss: 1117.5068
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7877 - loss: 0.1000 - moe_loss: 1125.1870
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7887 - loss: 0.1000 - moe_loss: 1135.4268
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7895 - loss: 0.1000 - moe_loss: 1143.1067
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 450/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7905 - loss: 0.1000 - moe_loss: 1153.3466
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7916 - loss: 0.1000 - moe_loss: 1163.5869
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7926 - loss: 0.1000 - moe_loss: 1173.8270
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7933 - loss: 0.1000 - moe_loss: 1181.5070
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7940 - loss: 0.1000 - moe_loss: 1189.1869
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 468/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7950 - loss: 0.1000 - moe_loss: 1199.4266
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 9s 18ms/step - accuracy: 0.7956 - loss: 0.1000 - moe_loss: 1204.5302 - val_loss: 0.1000 - val_moe_loss: 2798.7275
+
+
+<div class="k-default-codeblock">
+```
+Epoch 2/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  14s 30ms/step - accuracy: 0.9688 - loss: 0.1000 - moe_loss: 2803.8604
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 2814.1450 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 2824.3696
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 2834.6130
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  17/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9693 - loss: 0.1000 - moe_loss: 2844.8762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9693 - loss: 0.1000 - moe_loss: 2852.5579
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  23/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9692 - loss: 0.1000 - moe_loss: 2860.2383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  26/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9691 - loss: 0.1000 - moe_loss: 2867.9192
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9688 - loss: 0.1000 - moe_loss: 2875.5964
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 2885.8335
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 2893.5164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 2903.7554
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  44/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 2913.9944
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9668 - loss: 0.1000 - moe_loss: 2924.2329
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 2934.4727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 2944.7114
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 2954.9500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 2965.1897
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  68/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 2975.4287
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  72/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 2985.6675
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  75/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 2993.3472
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3003.5850
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  83/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3013.8240
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3024.0654
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3034.3062
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3044.5454
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  99/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3054.7854
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 3065.0247
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 107/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 3075.2642
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 110/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 3082.9436
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 3093.1829
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 3100.8628
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 3108.5425
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3116.2224
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3126.4617
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 131/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3136.7017
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 134/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3144.3816
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3154.6211
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3164.8611
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3172.5408
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3180.2202
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3187.8999
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3195.5798
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 158/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3205.8191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 162/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3216.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3226.2993
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 170/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3236.5393
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 174/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3246.7805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 178/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3257.0203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 182/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3267.2603
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3274.9407
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 188/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3282.6201
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 192/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3292.8596
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3300.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3310.7786
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3318.4583
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 206/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9666 - loss: 0.1000 - moe_loss: 3328.6982
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9666 - loss: 0.1000 - moe_loss: 3338.9380
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9666 - loss: 0.1000 - moe_loss: 3346.6179
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3356.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 221/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3367.0972
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3377.3372
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3387.5769
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9668 - loss: 0.1000 - moe_loss: 3397.8169
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9668 - loss: 0.1000 - moe_loss: 3408.0564
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3418.2964
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3425.9768
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3433.6567
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 251/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3443.8967
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9670 - loss: 0.1000 - moe_loss: 3454.1365
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9670 - loss: 0.1000 - moe_loss: 3464.3762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 263/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9670 - loss: 0.1000 - moe_loss: 3474.6157
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9671 - loss: 0.1000 - moe_loss: 3484.8552
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9671 - loss: 0.1000 - moe_loss: 3495.0950
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9671 - loss: 0.1000 - moe_loss: 3502.7751
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 278/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 3513.0149
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 3523.2546
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 3533.4944
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 290/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9673 - loss: 0.1000 - moe_loss: 3543.7341
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9673 - loss: 0.1000 - moe_loss: 3553.9744
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9674 - loss: 0.1000 - moe_loss: 3564.2141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9674 - loss: 0.1000 - moe_loss: 3574.4539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9674 - loss: 0.1000 - moe_loss: 3584.6936
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9675 - loss: 0.1000 - moe_loss: 3594.9331
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 314/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9675 - loss: 0.1000 - moe_loss: 3605.1729
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9675 - loss: 0.1000 - moe_loss: 3615.4126
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3625.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3633.3323
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 328/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3641.0125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3651.2524
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 3661.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 3671.7319
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 3681.9717
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9678 - loss: 0.1000 - moe_loss: 3692.2117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9678 - loss: 0.1000 - moe_loss: 3702.4514
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 356/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9678 - loss: 0.1000 - moe_loss: 3712.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3722.9312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3733.1711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3740.8511
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3748.5310
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3758.7710
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3769.0112
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3776.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3784.3713
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3794.6113
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3802.2913
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3804.8511
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3812.5310
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3820.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3827.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3835.5706
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3843.2505
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3850.9304
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3858.6106
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 417/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3868.8503
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3879.0901
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3889.3303
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3899.5706
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 432/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3907.2507
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3914.9309
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3922.6106
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3930.2908
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3940.5305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3950.7703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3958.4500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3968.6899
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3976.3699
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 462/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 3984.0498
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 3994.2898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 4004.5132 - val_loss: 0.1000 - val_moe_loss: 5598.7266
+
+
+<div class="k-default-codeblock">
+```
+Epoch 3/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 28ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 5603.8740
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9725 - loss: 0.1000 - moe_loss: 5614.1147 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9711 - loss: 0.1000 - moe_loss: 5624.3594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  12/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9713 - loss: 0.1000 - moe_loss: 5632.0366
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9720 - loss: 0.1000 - moe_loss: 5642.2812
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9727 - loss: 0.1000 - moe_loss: 5652.5317
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  24/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9731 - loss: 0.1000 - moe_loss: 5662.7671
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9734 - loss: 0.1000 - moe_loss: 5673.0073
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9736 - loss: 0.1000 - moe_loss: 5680.6851
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  35/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9738 - loss: 0.1000 - moe_loss: 5690.9282
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  39/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9740 - loss: 0.1000 - moe_loss: 5701.1680
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9742 - loss: 0.1000 - moe_loss: 5711.4087
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  47/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9742 - loss: 0.1000 - moe_loss: 5721.6470
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  51/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9743 - loss: 0.1000 - moe_loss: 5731.8843
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  54/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9743 - loss: 0.1000 - moe_loss: 5739.5645
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9744 - loss: 0.1000 - moe_loss: 5749.8052
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9745 - loss: 0.1000 - moe_loss: 5757.4844
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9746 - loss: 0.1000 - moe_loss: 5767.7251
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  69/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9747 - loss: 0.1000 - moe_loss: 5777.9648
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9748 - loss: 0.1000 - moe_loss: 5788.2041
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  77/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9749 - loss: 0.1000 - moe_loss: 5798.4434
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  81/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9750 - loss: 0.1000 - moe_loss: 5808.6831
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  84/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9751 - loss: 0.1000 - moe_loss: 5816.3623
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9752 - loss: 0.1000 - moe_loss: 5826.6025
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  92/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9752 - loss: 0.1000 - moe_loss: 5836.8413
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  96/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9753 - loss: 0.1000 - moe_loss: 5847.0811
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9753 - loss: 0.1000 - moe_loss: 5857.3213
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9754 - loss: 0.1000 - moe_loss: 5867.5610
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9755 - loss: 0.1000 - moe_loss: 5877.8013
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9755 - loss: 0.1000 - moe_loss: 5885.4810
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9756 - loss: 0.1000 - moe_loss: 5895.7212
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 119/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9756 - loss: 0.1000 - moe_loss: 5905.9614
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 122/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9757 - loss: 0.1000 - moe_loss: 5913.6421
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9758 - loss: 0.1000 - moe_loss: 5923.8813
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9758 - loss: 0.1000 - moe_loss: 5931.5615
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9758 - loss: 0.1000 - moe_loss: 5939.2412
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9759 - loss: 0.1000 - moe_loss: 5949.4810
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9759 - loss: 0.1000 - moe_loss: 5959.7207
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9760 - loss: 0.1000 - moe_loss: 5969.9600
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9760 - loss: 0.1000 - moe_loss: 5980.2007
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 152/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9761 - loss: 0.1000 - moe_loss: 5990.4404
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9761 - loss: 0.1000 - moe_loss: 6000.6802
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9762 - loss: 0.1000 - moe_loss: 6010.9199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 164/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9762 - loss: 0.1000 - moe_loss: 6021.1602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9763 - loss: 0.1000 - moe_loss: 6031.3994
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9763 - loss: 0.1000 - moe_loss: 6041.6392
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 173/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9763 - loss: 0.1000 - moe_loss: 6044.1992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 176/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9764 - loss: 0.1000 - moe_loss: 6051.8794
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9764 - loss: 0.1000 - moe_loss: 6059.5596
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9765 - loss: 0.1000 - moe_loss: 6069.7998
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9765 - loss: 0.1000 - moe_loss: 6080.0405
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 191/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9765 - loss: 0.1000 - moe_loss: 6090.2808
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 6100.5200
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 6110.7603
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 203/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9767 - loss: 0.1000 - moe_loss: 6120.9995
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9767 - loss: 0.1000 - moe_loss: 6131.2402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9768 - loss: 0.1000 - moe_loss: 6141.4800
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 215/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9768 - loss: 0.1000 - moe_loss: 6151.7197
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9769 - loss: 0.1000 - moe_loss: 6161.9600
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9769 - loss: 0.1000 - moe_loss: 6172.1992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 227/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9769 - loss: 0.1000 - moe_loss: 6182.4390
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9770 - loss: 0.1000 - moe_loss: 6192.6792
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9770 - loss: 0.1000 - moe_loss: 6202.9194
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 239/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 6213.1592
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 6223.3989
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 6231.0786
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 6241.3188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 6248.9990
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 6256.6792
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 260/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6266.9189
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6277.1587
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6284.8384
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6292.5186
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6300.1987
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6307.8789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6315.5586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6323.2388
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6333.4790
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 290/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 6343.7188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 6353.9590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 6364.1992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6374.4390
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6382.1191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6392.3589
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6402.5991
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 317/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 6412.8389
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 6423.0786
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 6433.3184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6443.5581
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6453.7983
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6461.4780
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6471.7178
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6481.9580
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6492.1978
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6502.4375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 356/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6512.6777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6522.9180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6533.1577
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6540.8379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6551.0776
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 375/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6561.3174
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6571.5576
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6581.7974
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6592.0371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6602.2773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6612.5176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6620.1973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6630.4375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6638.1172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6648.3569
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6658.5972
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6666.2769
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6673.9570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6684.1973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 426/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6691.8770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6699.5571
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6709.7969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6720.0366
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6730.2764
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6740.5166
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6750.7563
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6760.9961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6771.2363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6781.4766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6791.7163
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6801.9536
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9785 - loss: 0.1000 - moe_loss: 6804.5000 - val_loss: 0.1000 - val_moe_loss: 8398.7275
+
+
+<div class="k-default-codeblock">
+```
+Epoch 4/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 26ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 8403.8486
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 8414.1064 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 8424.3496
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 8434.5850
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  17/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 8444.8232
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  21/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9770 - loss: 0.1000 - moe_loss: 8455.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 8465.3047
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 8472.9844
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  32/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 8483.2256
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 8493.4678
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 8503.7090
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  44/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 8513.9502
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 8524.1924
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 8534.4336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 8544.6738
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9786 - loss: 0.1000 - moe_loss: 8554.9131
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9787 - loss: 0.1000 - moe_loss: 8565.1514
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  68/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9789 - loss: 0.1000 - moe_loss: 8575.3916
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  72/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9790 - loss: 0.1000 - moe_loss: 8585.6318
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  75/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9791 - loss: 0.1000 - moe_loss: 8593.3125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9792 - loss: 0.1000 - moe_loss: 8603.5527
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  83/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9793 - loss: 0.1000 - moe_loss: 8613.7930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9793 - loss: 0.1000 - moe_loss: 8624.0332
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  90/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9794 - loss: 0.1000 - moe_loss: 8631.7139
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9794 - loss: 0.1000 - moe_loss: 8641.9541
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  98/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9795 - loss: 0.1000 - moe_loss: 8652.1943
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 101/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9795 - loss: 0.1000 - moe_loss: 8659.8740
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9796 - loss: 0.1000 - moe_loss: 8667.5547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9797 - loss: 0.1000 - moe_loss: 8677.7939
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9797 - loss: 0.1000 - moe_loss: 8685.4736
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9798 - loss: 0.1000 - moe_loss: 8695.7139
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 119/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9799 - loss: 0.1000 - moe_loss: 8705.9541
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 122/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9799 - loss: 0.1000 - moe_loss: 8713.6348
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9800 - loss: 0.1000 - moe_loss: 8723.8750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9800 - loss: 0.1000 - moe_loss: 8734.1143
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 134/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9801 - loss: 0.1000 - moe_loss: 8744.3545
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9802 - loss: 0.1000 - moe_loss: 8754.5947
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9802 - loss: 0.1000 - moe_loss: 8764.8350
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 146/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9803 - loss: 0.1000 - moe_loss: 8775.0742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 149/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9803 - loss: 0.1000 - moe_loss: 8782.7549
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 152/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9804 - loss: 0.1000 - moe_loss: 8790.4346
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9805 - loss: 0.1000 - moe_loss: 8800.6738
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9805 - loss: 0.1000 - moe_loss: 8810.9141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 164/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9806 - loss: 0.1000 - moe_loss: 8821.1533
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9806 - loss: 0.1000 - moe_loss: 8831.3936
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9806 - loss: 0.1000 - moe_loss: 8841.6328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 176/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 8851.8730
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 180/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 8862.1123
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 184/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9808 - loss: 0.1000 - moe_loss: 8872.3525
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 188/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9808 - loss: 0.1000 - moe_loss: 8882.5928
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 192/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 8892.8330
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 8903.0732
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 200/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 8913.3135
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8923.5537
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8931.2334
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8938.9131
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8949.1523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 218/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8959.3926
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8969.6318
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8979.8721
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8987.5518
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 8997.7920
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 236/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 9005.4717
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 9015.7119
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 9025.9521
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 248/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9036.1914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9046.4316
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9054.1113
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9061.7910
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9072.0312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 266/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9082.2715
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9089.9512
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9100.1914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9110.4307
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9118.1113
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 284/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9128.3516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9138.5908
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 292/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9148.8311
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 296/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9159.0713
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9169.3105
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 304/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9179.5508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9187.2305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 311/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9197.4707
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 314/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9205.1504
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9215.3906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9225.6309
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 326/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9235.8711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9243.5508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9251.2314
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9261.4707
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9271.7109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9281.9512
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9292.1914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 351/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9299.8711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9310.1113
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9320.3516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9330.5908
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9340.8311
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9348.5107
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9358.7510
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9368.9912
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9376.6709
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9386.9111
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9397.1514
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9404.8311
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9415.0713
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9422.7510
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9432.9912
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9440.6709
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9448.3506
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9458.5908
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 417/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9468.8311
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9479.0703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9489.3105
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9499.5498
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9509.7900
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9520.0303
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9527.7100
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9535.3896
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9545.6299
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 450/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9553.3096
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9560.9902
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9568.6699
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9578.9102
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9586.5898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9594.2695
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 9604.4932 - val_loss: 0.1000 - val_moe_loss: 11198.7256
+
+
+<div class="k-default-codeblock">
+```
+Epoch 5/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 26ms/step - accuracy: 0.9688 - loss: 0.1000 - moe_loss: 11203.8506
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9796 - loss: 0.1000 - moe_loss: 11214.1025 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   8/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9802 - loss: 0.1000 - moe_loss: 11221.7900
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  11/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 11229.4717
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  15/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 11239.7148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  18/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 11247.3945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  21/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 11255.0732
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 11265.3115
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 11275.5498
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 11285.7881
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 11296.0273
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 11303.7070
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  42/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 11308.8281
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 11311.3887
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 11319.0693
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 11326.7500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  53/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 11336.9912
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 11344.6709
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 11354.9111
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 11365.1504
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 11372.8301
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 11380.5098
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  74/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11390.7490
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  77/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11398.4287
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  81/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11408.6680
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  84/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11416.3486
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11424.0283
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  90/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11431.7080
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11441.9473
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11449.6270
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9824 - loss: 0.1000 - moe_loss: 11457.3076
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 104/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9824 - loss: 0.1000 - moe_loss: 11467.5479
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9824 - loss: 0.1000 - moe_loss: 11477.7871
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 112/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9825 - loss: 0.1000 - moe_loss: 11488.0273
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 116/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9825 - loss: 0.1000 - moe_loss: 11498.2666
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9825 - loss: 0.1000 - moe_loss: 11508.5078
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9826 - loss: 0.1000 - moe_loss: 11518.7480
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9826 - loss: 0.1000 - moe_loss: 11526.4277
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 131/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 11536.6680
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 134/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 11544.3477
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 11554.5879
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9828 - loss: 0.1000 - moe_loss: 11564.8271
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 146/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9828 - loss: 0.1000 - moe_loss: 11575.0674
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9829 - loss: 0.1000 - moe_loss: 11585.3066
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9829 - loss: 0.1000 - moe_loss: 11592.9873
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9830 - loss: 0.1000 - moe_loss: 11603.2266
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 161/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9830 - loss: 0.1000 - moe_loss: 11613.4668
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9830 - loss: 0.1000 - moe_loss: 11623.7061
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9831 - loss: 0.1000 - moe_loss: 11633.9463
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9831 - loss: 0.1000 - moe_loss: 11641.6270
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9831 - loss: 0.1000 - moe_loss: 11649.3066
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9832 - loss: 0.1000 - moe_loss: 11659.5459
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 182/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9832 - loss: 0.1000 - moe_loss: 11667.2256
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9832 - loss: 0.1000 - moe_loss: 11674.9062
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9833 - loss: 0.1000 - moe_loss: 11685.1455
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9833 - loss: 0.1000 - moe_loss: 11695.3857
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 197/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9833 - loss: 0.1000 - moe_loss: 11705.6260
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11715.8662
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11723.5459
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11733.7861
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11741.4658
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 215/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11751.7061
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 218/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11759.3857
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 221/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11767.0654
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11777.3057
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11787.5459
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11797.7861
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 236/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11805.4658
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11815.7051
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11823.3857
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11831.0654
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 249/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11838.7461
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11848.9854
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 257/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11859.2256
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 260/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11866.9053
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 263/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11874.5859
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11884.8252
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11892.5059
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11902.7451
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 278/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11912.9854
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11920.6650
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11930.9053
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11938.5850
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11946.2656
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11953.9453
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11964.1855
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11974.4248
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 11982.1055
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 308/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 11989.7852
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12000.0254
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12007.7051
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12015.3848
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12023.0654
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12030.7451
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12038.4248
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12046.1055
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12053.7852
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12061.4648
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12071.7051
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12081.9453
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12092.1846
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12102.4248
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12110.1055
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12120.3447
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12128.0254
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12135.7051
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12143.3848
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 372/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12153.6250
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12163.8652
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12174.1055
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12184.3447
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12192.0254
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12202.2646
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12212.5049
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12222.7451
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12232.9854
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12243.2256
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12253.4658
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 414/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12261.1455
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12271.3857
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12279.0654
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12286.7451
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12294.4248
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12304.6650
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12312.3447
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12322.5850
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12330.2646
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12340.5049
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12350.7451
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12358.4248
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12368.6650
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12378.9053
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12386.5850
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9843 - loss: 0.1000 - moe_loss: 12396.8252
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9843 - loss: 0.1000 - moe_loss: 12404.4883 - val_loss: 0.1000 - val_moe_loss: 13998.7246
+
+
+<div class="k-default-codeblock">
+```
+Epoch 6/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 29ms/step - accuracy: 0.9609 - loss: 0.1000 - moe_loss: 14003.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9740 - loss: 0.1000 - moe_loss: 14014.0947 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   8/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 14021.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  12/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 14032.0244
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 14042.2637
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 14049.9434
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 14057.6221
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 14065.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9843 - loss: 0.1000 - moe_loss: 14075.5439
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9848 - loss: 0.1000 - moe_loss: 14085.7842
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9852 - loss: 0.1000 - moe_loss: 14096.0244
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9854 - loss: 0.1000 - moe_loss: 14103.7041
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9856 - loss: 0.1000 - moe_loss: 14111.3838
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9858 - loss: 0.1000 - moe_loss: 14119.0635
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9859 - loss: 0.1000 - moe_loss: 14126.7432
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9860 - loss: 0.1000 - moe_loss: 14134.4229
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9861 - loss: 0.1000 - moe_loss: 14142.1035
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  59/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9862 - loss: 0.1000 - moe_loss: 14152.3428
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  62/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9863 - loss: 0.1000 - moe_loss: 14160.0225
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9864 - loss: 0.1000 - moe_loss: 14167.7021
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  68/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9865 - loss: 0.1000 - moe_loss: 14175.3818
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  71/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9865 - loss: 0.1000 - moe_loss: 14183.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  74/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9866 - loss: 0.1000 - moe_loss: 14190.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  77/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9866 - loss: 0.1000 - moe_loss: 14198.4219
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  80/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9867 - loss: 0.1000 - moe_loss: 14206.1016
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  83/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9867 - loss: 0.1000 - moe_loss: 14213.7812
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  86/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9868 - loss: 0.1000 - moe_loss: 14221.4619
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  89/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9868 - loss: 0.1000 - moe_loss: 14229.1416
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  92/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14236.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14244.5029
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  98/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14252.1826
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 102/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14262.4229
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 106/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14272.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14280.3428
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 112/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14288.0225
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14295.7021
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 118/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14303.3818
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14311.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14318.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14326.4229
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14334.1035
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14341.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14349.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14359.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 143/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14367.3838
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 146/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14375.0635
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14385.3037
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14392.9844
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14400.6641
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14408.3447
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14418.5840
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14426.2637
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14433.9443
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14441.6240
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14449.3037
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14459.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 182/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14467.2236
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14474.9033
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 188/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14482.5830
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 191/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14490.2627
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 194/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14497.9434
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 197/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14505.6230
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 200/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14513.3037
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14523.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14531.2236
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14538.9033
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14546.5840
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 216/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14554.2637
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14561.9434
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14569.6230
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14577.3037
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 228/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14584.9834
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14592.6631
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14600.3428
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14608.0234
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14615.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14623.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14631.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 249/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14638.7432
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14646.4229
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14654.1025
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14661.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 261/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14669.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14677.1426
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14684.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14692.5029
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14700.1826
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14707.8623
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14715.5420
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14723.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14730.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14738.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14746.2627
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14753.9424
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14761.6221
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14769.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14776.9824
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14784.6631
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14792.3428
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14800.0234
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14807.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14815.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14823.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14830.7432
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14838.4229
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14846.1025
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14853.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14861.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14869.1426
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14876.8232
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 345/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14884.5029
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14892.1826
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 351/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14899.8623
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 354/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14907.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14915.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14922.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14930.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14940.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14948.5029
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14956.1826
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14963.8623
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14971.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14979.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14986.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14994.5830
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15002.2627
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15009.9424
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15020.1826
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15027.8623
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15035.5420
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15043.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15050.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15058.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15066.2627
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15073.9424
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 422/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15081.6221
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15089.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15094.4229
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15104.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15112.3428
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15120.0225
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15127.7021
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15135.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15143.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15150.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15158.4219
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15166.1025
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15173.7822
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15181.4619
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15189.1426
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15196.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15204.4854 - val_loss: 0.1000 - val_moe_loss: 16798.7246
+
+
+<div class="k-default-codeblock">
+```
+Epoch 7/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 29ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 16803.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 16811.5371 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 16819.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 16826.9043
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 16834.5898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 16842.2676
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 16849.9531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 16857.6309
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  23/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 16860.1934
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  24/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 16862.7539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  26/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9904 - loss: 0.1000 - moe_loss: 16867.8711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9902 - loss: 0.1000 - moe_loss: 16875.5527
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  10s 24ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 16885.7910
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9899 - loss: 0.1000 - moe_loss: 16893.4688
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  39/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 16901.1484 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  42/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9897 - loss: 0.1000 - moe_loss: 16908.8281
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  45/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9896 - loss: 0.1000 - moe_loss: 16916.5078
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 16924.1875
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  51/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 16931.8691
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  54/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 16939.5488
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  57/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 16947.2285
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 16954.9082
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  63/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 16962.5898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  66/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9892 - loss: 0.1000 - moe_loss: 16970.2676
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  69/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9892 - loss: 0.1000 - moe_loss: 16977.9473
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  72/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9891 - loss: 0.1000 - moe_loss: 16985.6270
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  75/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9891 - loss: 0.1000 - moe_loss: 16993.3086
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  78/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9890 - loss: 0.1000 - moe_loss: 17000.9863
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  81/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9890 - loss: 0.1000 - moe_loss: 17008.6660
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  84/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 17016.3457
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 17024.0273
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  90/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9888 - loss: 0.1000 - moe_loss: 17031.7070
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  93/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9888 - loss: 0.1000 - moe_loss: 17039.3867
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  96/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9888 - loss: 0.1000 - moe_loss: 17047.0664
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  99/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17054.7461
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 102/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17062.4277
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17070.1074
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17077.7871
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17085.4668
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17093.1465
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17100.8262
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17108.5059
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17116.1855
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17123.8652
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17131.5449
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17139.2246
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17146.9043
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17154.5840
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17162.2637
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17169.9434
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17177.6230
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17185.3047
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17192.9844
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17200.6641
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17208.3438
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 162/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17216.0234
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17223.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17231.3848
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17239.0645
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 174/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17246.7441
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17254.4238
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 180/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17262.1035
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17269.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 186/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17277.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17285.1445
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 192/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17292.8242
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17300.5039
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 198/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17308.1836
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17315.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17323.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17331.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17338.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17346.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 216/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17354.2617
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17361.9434
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17369.6230
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17377.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 228/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17384.9824
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17392.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17400.3438
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17408.0234
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17415.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17423.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17431.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 249/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17438.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17446.4219
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17454.1035
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17461.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 261/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17469.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17477.1426
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17484.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17492.5020
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17500.1816
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17507.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17515.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17523.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17530.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17538.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17546.2617
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17553.9414
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17561.6211
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17569.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17576.9824
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17584.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17592.3418
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17600.0234
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17607.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17615.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17623.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17630.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17638.4219
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17646.1016
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17653.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17661.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17669.1426
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17676.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 345/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17684.5020
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17692.1816
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 351/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17699.8613
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17707.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17715.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17722.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17730.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 366/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17738.2617
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 369/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17745.9414
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 372/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17753.6230
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 375/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17761.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17768.9824
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17776.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17784.3418
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17792.0215
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17799.7012
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17807.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17815.0625
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17822.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17830.4219
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17838.1016
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 408/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17845.7832
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17853.4629
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 414/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17861.1426
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 417/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17868.8223
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 420/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17876.5020
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17884.1816
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 426/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17891.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17899.5430
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 432/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17907.2227
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17914.9023
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17922.5820
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17930.2617
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 444/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17937.9414
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17945.6230
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 450/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17953.3027
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17960.9824
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17968.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17976.3418
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 462/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17984.0215
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17991.7031
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 468/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17999.3828
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 18004.4863 - val_loss: 0.1000 - val_moe_loss: 19598.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 8/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 28ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 19603.8477
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 19611.5293 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9864 - loss: 0.1000 - moe_loss: 19619.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 19626.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9877 - loss: 0.1000 - moe_loss: 19634.5723
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9880 - loss: 0.1000 - moe_loss: 19642.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 19649.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 19657.6152
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 19665.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 19672.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 19680.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  34/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 19688.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9890 - loss: 0.1000 - moe_loss: 19696.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9891 - loss: 0.1000 - moe_loss: 19703.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9892 - loss: 0.1000 - moe_loss: 19711.3809
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19719.0605
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19726.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19734.4219
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19742.1016
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19749.7812
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19757.4609
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19765.1406
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19772.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19780.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19788.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 19795.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 19803.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 19811.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19818.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19826.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19834.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19841.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19849.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19857.3008
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19864.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 106/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19872.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19880.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 112/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19888.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19895.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 118/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19903.3809
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19911.0605
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19918.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19926.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19934.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19941.7812
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19949.4609
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19957.1406
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19964.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19972.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19980.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19987.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19995.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20003.2207
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20010.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20018.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20026.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20033.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20041.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20049.3008
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20056.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20064.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20072.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20080.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20087.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20095.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20103.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20110.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20118.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 205/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20126.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20133.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20141.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20149.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20156.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20164.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20172.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20179.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20187.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20195.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20202.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20210.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20218.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20225.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20233.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20241.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20248.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20256.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20264.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20272.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20279.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 268/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20287.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20295.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20302.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20310.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20318.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20325.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20333.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20341.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20348.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20356.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20364.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 301/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20371.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 304/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20379.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20387.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20394.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20402.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 316/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20410.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20417.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 320/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20420.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20428.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 326/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20435.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20443.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20451.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20458.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20466.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 341/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20474.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20481.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20489.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20497.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20504.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 356/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20512.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20520.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20528.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20535.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20543.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20551.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20558.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20566.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20574.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20581.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20589.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20597.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20604.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20607.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20609.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20612.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20620.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20627.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20635.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20643.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20650.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20658.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20666.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20673.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 422/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20681.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20689.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 428/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20696.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20704.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20712.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20720.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20727.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20735.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20743.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20750.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20758.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20766.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20773.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20781.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20789.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20796.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 10s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20804.4824 - val_loss: 0.1000 - val_moe_loss: 22398.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 9/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  15s 33ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 22403.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9959 - loss: 0.1000 - moe_loss: 22411.5312 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9964 - loss: 0.1000 - moe_loss: 22419.2129
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9967 - loss: 0.1000 - moe_loss: 22426.8926
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9965 - loss: 0.1000 - moe_loss: 22434.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9960 - loss: 0.1000 - moe_loss: 22442.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 22449.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 22457.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 22465.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 22472.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  30/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 22478.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  32/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 22483.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  35/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 22490.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  38/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 22498.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  41/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 22506.2559
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  44/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 22513.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  47/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 22521.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  50/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 22529.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  53/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 22536.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 22544.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  59/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 22552.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  62/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 22560.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22567.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  68/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22575.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  71/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22583.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  74/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22590.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  77/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22598.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  80/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22606.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  83/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22613.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  86/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 22621.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  89/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 22629.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  92/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 22636.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22644.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22649.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  99/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22654.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 102/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22662.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22670.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22677.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22685.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22693.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22700.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22708.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22716.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22723.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22731.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22739.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22746.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22754.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22762.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22769.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22777.6211
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22785.3008
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22792.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22800.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22808.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 162/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22816.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22823.7012
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22831.3809
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22839.0605
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 174/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22846.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22854.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 180/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22862.1016
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22869.7812
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 186/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22877.4609
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22885.1406
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 192/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22892.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22900.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 198/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22908.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22915.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22923.5410
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22931.2207
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22938.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22946.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 216/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22954.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22961.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22969.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22977.3008
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 228/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22984.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22992.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23000.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23008.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23015.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23023.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23031.0605
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 249/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23038.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23046.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23054.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23061.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 261/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23069.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23077.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23084.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23092.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23100.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23107.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23115.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23123.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23130.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23138.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23146.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23153.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23161.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23169.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23176.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23184.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23192.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23200.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23207.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23215.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23223.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23230.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23238.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23246.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23253.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23261.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23269.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23276.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 345/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23284.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23292.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 351/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23299.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23307.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23315.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23322.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23330.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23335.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23343.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23348.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23356.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23363.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23371.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23379.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23386.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23394.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23402.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23409.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23417.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23425.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23432.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23440.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23448.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23456.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23463.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23471.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23479.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23486.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23494.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23502.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23509.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23517.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23525.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23532.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23540.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23548.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23555.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23563.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23571.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23578.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23586.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23594.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23601.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23604.4824 - val_loss: 0.1000 - val_moe_loss: 25198.7246
+
+
+<div class="k-default-codeblock">
+```
+Epoch 10/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  15s 33ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 25203.8496
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9963 - loss: 0.1000 - moe_loss: 25211.5312 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9955 - loss: 0.1000 - moe_loss: 25219.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9951 - loss: 0.1000 - moe_loss: 25226.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 25234.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9946 - loss: 0.1000 - moe_loss: 25242.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 25249.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 25257.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 25265.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 25272.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 25280.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  34/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 25288.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 25296.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 25303.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 25311.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 25319.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 25326.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 25334.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 25342.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 25349.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 25357.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 25365.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 25372.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 25380.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 25388.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 25395.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 25403.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 25411.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 25418.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 25426.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 25434.2559
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 25441.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 25449.6152
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 25457.2949
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 25464.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 106/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25472.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25480.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 112/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25488.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25495.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 118/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25503.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25511.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25518.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25526.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25534.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25541.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25549.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25557.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25564.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25572.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25580.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25587.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 154/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25595.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25603.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25610.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25618.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25626.2559
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25633.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25641.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25649.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25656.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25664.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25672.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25680.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25687.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25695.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25703.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25710.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25718.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 205/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25726.0957
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25733.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25741.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25749.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25756.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 220/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25764.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25772.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25779.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25787.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25795.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25802.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25810.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25818.2559
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25825.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25833.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25841.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25848.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25856.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25864.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25872.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25879.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 268/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25887.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25895.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25902.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25910.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25918.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25925.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25933.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25938.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25941.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 290/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25943.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 293/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25951.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 296/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25959.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 299/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25966.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25974.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25982.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 308/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25989.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 311/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 25997.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 314/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26005.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 317/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26012.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 320/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26020.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26028.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 326/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26035.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26043.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26051.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26058.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26066.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 341/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26074.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26081.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26089.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26097.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26104.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 356/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26112.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26120.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 362/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26128.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26135.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26143.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26151.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 374/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26158.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26166.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26174.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26181.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26186.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26194.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26202.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26209.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26217.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26225.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26232.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26240.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26248.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26256.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26263.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26271.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26279.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26286.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26294.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26302.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26309.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26317.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26325.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26332.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26340.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26348.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26355.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26360.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26368.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26376.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 462/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26384.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26391.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26396.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26404.4805 - val_loss: 0.1000 - val_moe_loss: 27998.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 11/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  18s 39ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28003.8535
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  10s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 28011.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  10s 22ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 28019.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9955 - loss: 0.1000 - moe_loss: 28026.8926
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 28034.5723
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9952 - loss: 0.1000 - moe_loss: 28042.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9950 - loss: 0.1000 - moe_loss: 28049.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9947 - loss: 0.1000 - moe_loss: 28057.6152
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 28065.2949
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 28072.9766 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 28080.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  34/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 28088.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 28096.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 28103.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 28111.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 28119.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 28126.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 28134.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 28142.0957
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 28149.7754
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 28157.4551
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28165.1348
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28172.8145
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28180.4941
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28188.1738
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28195.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28203.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28211.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28218.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28226.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28234.2559
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28241.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28249.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28257.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 102/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28262.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28270.0957
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28277.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28285.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28293.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28300.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28308.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28316.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28323.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28331.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28339.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28346.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28354.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28362.2559
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28369.9355
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28377.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28385.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28392.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28400.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28408.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 162/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28416.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28423.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28431.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28439.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 174/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28446.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28454.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28459.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28464.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28469.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28474.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 188/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28482.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 191/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28490.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 194/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28497.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 197/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28505.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 200/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28513.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 203/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28520.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 206/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28528.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 209/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28536.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 212/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28544.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 215/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28551.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 218/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28559.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 221/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28567.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 224/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28574.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 227/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28582.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 230/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28590.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28597.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 236/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28605.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 239/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28613.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 242/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28620.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 245/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28628.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28633.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28641.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28648.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28656.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28664.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28672.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28679.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28684.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28689.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 272/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28697.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 275/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28705.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28710.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 280/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28718.0957
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28725.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28733.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28741.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 292/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28748.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28756.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28761.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 299/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28766.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28774.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28782.0957
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 308/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28789.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 311/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28797.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 314/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28805.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 317/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28812.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 320/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28820.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28828.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28833.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 328/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28840.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28848.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28853.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28861.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 338/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28866.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 341/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28874.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28879.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28887.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28894.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28902.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28910.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 358/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28917.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28925.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 364/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28933.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28940.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28948.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28956.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 376/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28963.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28971.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28976.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 384/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28984.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28992.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28999.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29004.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29009.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29015.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29020.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29025.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29030.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29035.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29040.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 408/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29045.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29050.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29056.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 414/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29061.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29066.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29071.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29079.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29084.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29089.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29094.4160
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29099.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29104.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29112.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29117.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29122.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29127.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29132.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 444/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29137.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29143.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29150.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29155.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29160.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29166.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29171.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29176.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29181.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29186.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29191.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29196.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 12s 25ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29204.4805 - val_loss: 0.1000 - val_moe_loss: 30798.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 12/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  20s 45ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 30803.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   3/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 30808.9746
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9880 - loss: 0.1000 - moe_loss: 30814.0918
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 30819.2129
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9896 - loss: 0.1000 - moe_loss: 30826.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  12/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 30832.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  15/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9900 - loss: 0.1000 - moe_loss: 30839.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  18/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9903 - loss: 0.1000 - moe_loss: 30847.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  21/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9904 - loss: 0.1000 - moe_loss: 30855.0605
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  24/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 30862.7422
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  26/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 30867.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 30872.9824
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 30880.6621
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 30885.7812
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 30893.4609
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  38/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 30898.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 30903.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  42/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 30908.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  44/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 30913.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 30919.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30924.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  50/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30929.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30934.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  54/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30939.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30944.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30949.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30954.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  62/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30960.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30965.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30967.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30972.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  69/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30977.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  71/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30983.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  74/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30990.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30995.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  78/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 31000.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  80/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 31006.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31011.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31018.9004
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31024.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  89/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31029.1406
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31034.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  93/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31039.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31044.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31049.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  99/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31054.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 101/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31059.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31064.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31070.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 107/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31075.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31080.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31085.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 113/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31090.5801 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31095.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31100.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 119/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31105.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31111.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31116.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 125/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31121.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31126.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31131.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 131/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31136.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31141.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31146.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 137/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31152.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31157.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31162.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 143/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31167.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31172.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31177.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 149/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31182.7402
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31187.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31192.9805
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 155/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31198.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31203.2207
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31208.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 161/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31213.4609
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31218.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31223.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31226.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 167/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31228.8203
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31233.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31239.0605
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 173/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31244.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31249.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31254.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31259.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31264.6602
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31269.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31274.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31280.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31285.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 191/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31290.2598
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31295.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31300.5000
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 197/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31305.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31310.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31315.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 203/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31320.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 205/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31326.0996
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31331.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 209/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31336.3398
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31341.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31346.5801
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 215/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31351.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31356.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31361.9395
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 221/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31367.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31372.1797
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31377.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 227/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31382.4199
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31387.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31392.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31397.7793
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31402.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31408.0195
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 239/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31413.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31418.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31423.3789
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 245/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31428.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31433.6191
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 249/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31438.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 251/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31443.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31448.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31454.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 257/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31459.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31464.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 261/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31469.4590
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 263/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31474.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31479.6992
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31484.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31489.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31495.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31500.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 275/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31505.2988
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31510.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31515.5391
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 281/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31520.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31525.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31530.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 287/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31536.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31541.1387
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31546.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 293/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31551.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31556.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31561.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 299/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31566.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 301/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31571.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31576.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31582.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31587.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31592.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 311/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31597.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31602.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31607.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 317/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31612.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31617.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31623.0586
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31628.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31633.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31638.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31643.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31648.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31653.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31658.8984
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 337/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31664.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31669.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 341/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31674.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31679.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 345/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31684.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 347/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31689.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31694.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 351/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31699.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31704.9785
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31710.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31715.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31720.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31725.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31730.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31735.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31740.8184
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 369/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31745.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31751.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31756.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 375/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31761.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31766.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31771.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31776.6582
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 383/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31781.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31786.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31792.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31797.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31802.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31807.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31812.4980
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31817.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31822.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31827.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31832.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31838.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31843.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31848.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31853.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31858.5781
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31863.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 417/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31868.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31873.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31879.0566
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31884.1777
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31889.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31894.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31899.5371
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31904.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31909.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31914.8965
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31920.0176
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31925.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31930.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31935.3770
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31940.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31945.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31950.7363
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31955.8574
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31960.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31966.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31971.2168
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31976.3379
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31981.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31986.5762
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31991.6973
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31996.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 14s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 32004.4805 - val_loss: 0.1000 - val_moe_loss: 33598.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 13/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  21s 45ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33603.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  11s 24ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 33611.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   6/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9953 - loss: 0.1000 - moe_loss: 33616.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 33624.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  11/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 33629.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 33634.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  15/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 33639.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  17/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 33644.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 33649.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  21/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 33655.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  23/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 33660.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 33665.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  27/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33670.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33675.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33680.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33685.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  35/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33690.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33696.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  39/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33701.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  41/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33706.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33711.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  45/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 33716.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  47/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 33721.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 33726.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  51/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 33731.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  53/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 33736.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 33742.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  57/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 33747.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  59/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 33752.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33757.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  63/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33762.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33767.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33772.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  69/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33777.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  71/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33783.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33788.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  75/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33793.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  77/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33798.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33803.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  81/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33808.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  83/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33813.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33818.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33824.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  89/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33829.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33834.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  93/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33839.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33844.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33849.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  99/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33854.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 101/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33859.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33864.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33870.0938 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 107/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33875.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33880.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33885.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 113/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33890.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33895.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33900.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 119/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33905.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33911.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33916.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 125/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33921.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33926.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33931.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 131/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33936.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33941.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33946.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 137/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33952.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33957.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33962.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 143/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33967.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33972.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33977.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 149/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33982.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33987.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33992.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 155/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33998.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34003.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34008.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 161/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34013.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34018.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34023.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 167/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34028.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34033.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34039.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 173/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34044.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34049.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34054.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34059.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34064.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34069.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34074.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 186/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34077.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34080.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 188/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34082.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34087.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 192/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34092.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 194/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34097.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34103.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 198/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34108.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 200/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34113.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34118.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34123.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 206/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34128.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34133.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34138.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 212/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34144.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34149.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 216/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34154.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 218/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34159.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 220/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34164.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34169.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 224/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34174.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34179.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 228/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34184.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 230/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34190.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 232/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34195.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34200.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 236/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34205.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34210.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34215.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 242/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34220.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34225.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34231.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 248/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34236.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34241.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34246.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 254/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34251.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34256.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34261.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 260/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34266.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34272.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 263/469 ━━━━━━━━━━━━━━━━━━━━  6s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34274.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  6s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34279.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34284.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34289.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34295.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34300.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 275/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34305.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34310.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34315.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 281/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34320.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34325.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34330.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 287/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34336.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34341.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34346.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 293/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34351.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34356.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34361.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 299/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34366.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 301/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34371.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34376.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34382.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34387.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34392.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 311/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34397.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34402.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34407.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 317/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34412.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34417.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34423.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34428.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34433.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34438.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34443.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34448.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34453.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34458.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 337/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34464.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34469.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 341/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34474.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34479.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 345/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34484.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 347/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34489.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34494.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 351/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34499.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34504.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34510.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34515.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34520.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34525.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34530.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34535.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34540.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 369/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34545.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34551.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34556.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 375/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34561.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34566.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34571.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34576.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 383/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34581.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34586.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34592.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34597.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34602.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34607.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34612.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34617.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 399/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34622.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34627.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34632.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34638.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34643.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34648.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34653.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34658.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34663.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 417/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34668.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34673.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34679.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34684.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34689.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34694.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34699.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34704.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34709.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 435/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34714.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34720.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34725.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34730.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34735.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34740.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34745.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34750.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34755.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34760.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34766.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34771.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34776.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34781.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34786.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34791.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34796.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34801.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 15s 32ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34804.4766 - val_loss: 0.1000 - val_moe_loss: 36398.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 14/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  23s 50ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 36403.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   3/469 ━━━━━━━━━━━━━━━━━━━━  13s 28ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 36408.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9905 - loss: 0.1000 - moe_loss: 36414.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 36419.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 36424.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  11/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9899 - loss: 0.1000 - moe_loss: 36429.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9899 - loss: 0.1000 - moe_loss: 36434.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  15/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9900 - loss: 0.1000 - moe_loss: 36439.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  17/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 36444.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 36449.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  21/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9902 - loss: 0.1000 - moe_loss: 36455.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  23/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9903 - loss: 0.1000 - moe_loss: 36460.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9903 - loss: 0.1000 - moe_loss: 36465.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  27/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9904 - loss: 0.1000 - moe_loss: 36470.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9905 - loss: 0.1000 - moe_loss: 36475.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 36480.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 36485.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  35/469 ━━━━━━━━━━━━━━━━━━━━  12s 29ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 36490.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  13s 32ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 36493.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  14s 33ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 36496.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  39/469 ━━━━━━━━━━━━━━━━━━━━  14s 34ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 36501.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  41/469 ━━━━━━━━━━━━━━━━━━━━  14s 34ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 36506.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  42/469 ━━━━━━━━━━━━━━━━━━━━  14s 35ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 36508.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  44/469 ━━━━━━━━━━━━━━━━━━━━  15s 37ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 36513.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  45/469 ━━━━━━━━━━━━━━━━━━━━  16s 39ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 36516.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 36519.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 36524.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  50/469 ━━━━━━━━━━━━━━━━━━━━  18s 43ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 36529.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  18s 43ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 36534.4102
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  54/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 36539.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 36542.0898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  56/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 36544.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  17s 44ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 36549.7695
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  60/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 36554.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  62/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 36560.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36565.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  66/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36570.2500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  68/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36575.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36580.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  72/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36585.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  74/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36590.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36595.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  78/469 ━━━━━━━━━━━━━━━━━━━━  16s 41ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36600.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  80/469 ━━━━━━━━━━━━━━━━━━━━  16s 41ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36606.0898
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36611.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  84/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 36616.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  86/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 36621.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 36626.5703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  90/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36631.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36634.2500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  93/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36639.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  15s 40ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36644.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  15s 40ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36649.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  99/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36654.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 101/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36659.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36664.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36670.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 107/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36675.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36680.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36685.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 113/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36690.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36695.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  13s 40ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36700.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 119/469 ━━━━━━━━━━━━━━━━━━━━  13s 40ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36705.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36711.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36716.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 125/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36721.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36726.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36731.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 131/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36736.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36741.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36746.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 137/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36752.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36757.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36762.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 143/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36767.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36772.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36777.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 149/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36782.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36787.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36792.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 155/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36798.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36803.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36808.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 161/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36813.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36818.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36823.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 167/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36828.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36833.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36839.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 173/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36844.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36849.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36854.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36859.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36864.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36869.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 185/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36874.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36880.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36885.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 191/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36890.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36895.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36900.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 197/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36905.6133 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 198/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36908.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 200/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36913.2930 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36918.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36923.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 206/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36928.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36933.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 209/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36936.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36941.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36946.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 215/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36951.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36956.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36961.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 221/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36967.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36969.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 224/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36974.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  26s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36977.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  26s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36979.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 227/469 ━━━━━━━━━━━━━━━━━━━━  26s 109ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36982.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 228/469 ━━━━━━━━━━━━━━━━━━━━  26s 109ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36984.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  27s 113ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36987.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 230/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36990.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36992.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 232/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36995.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 233/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36997.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  26s 111ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37002.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 236/469 ━━━━━━━━━━━━━━━━━━━━  25s 111ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37005.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  25s 111ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37010.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  25s 110ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37015.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 242/469 ━━━━━━━━━━━━━━━━━━━━  24s 110ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37020.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  24s 109ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37025.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  24s 108ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37031.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 248/469 ━━━━━━━━━━━━━━━━━━━━  23s 108ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37036.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  23s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37041.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  23s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37046.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 254/469 ━━━━━━━━━━━━━━━━━━━━  22s 106ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37051.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  22s 106ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37056.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  22s 105ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37061.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 260/469 ━━━━━━━━━━━━━━━━━━━━  21s 105ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37066.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  21s 104ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37072.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  21s 104ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37077.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 266/469 ━━━━━━━━━━━━━━━━━━━━  20s 103ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37082.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 268/469 ━━━━━━━━━━━━━━━━━━━━  20s 103ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37087.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  20s 102ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37092.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 272/469 ━━━━━━━━━━━━━━━━━━━━  19s 101ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37097.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  19s 101ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37102.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  19s 100ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37107.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  18s 100ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37115.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  18s 99ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37123.2148 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  18s 98ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37130.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  17s 97ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37138.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  17s 96ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37146.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  16s 95ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37153.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  16s 95ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37161.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  15s 94ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37169.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 304/469 ━━━━━━━━━━━━━━━━━━━━  15s 93ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37179.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 308/469 ━━━━━━━━━━━━━━━━━━━━  14s 92ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37189.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  14s 91ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37200.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 316/469 ━━━━━━━━━━━━━━━━━━━━  13s 90ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37210.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 320/469 ━━━━━━━━━━━━━━━━━━━━  13s 89ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37220.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  12s 88ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37228.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 326/469 ━━━━━━━━━━━━━━━━━━━━  12s 88ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37235.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  12s 87ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37243.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  11s 86ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37251.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  11s 86ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37258.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 338/469 ━━━━━━━━━━━━━━━━━━━━  11s 85ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37266.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 342/469 ━━━━━━━━━━━━━━━━━━━━  10s 84ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37276.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  10s 84ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37287.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 350/469 ━━━━━━━━━━━━━━━━━━━━  9s 83ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37297.2969 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  9s 82ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37304.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  9s 81ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37315.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  8s 81ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37322.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  8s 80ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37330.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  8s 80ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37340.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  7s 79ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37348.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  7s 79ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37356.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  7s 78ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37366.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  6s 77ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37376.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 384/469 ━━━━━━━━━━━━━━━━━━━━  6s 77ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37384.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 387/469 ━━━━━━━━━━━━━━━━━━━━  6s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37392.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 390/469 ━━━━━━━━━━━━━━━━━━━━  6s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37399.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 392/469 ━━━━━━━━━━━━━━━━━━━━  5s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37404.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  5s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37412.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  5s 75ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37420.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  5s 75ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37427.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  4s 74ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37435.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  4s 74ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37443.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 411/469 ━━━━━━━━━━━━━━━━━━━━  4s 73ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37453.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 414/469 ━━━━━━━━━━━━━━━━━━━━  4s 73ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37461.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 417/469 ━━━━━━━━━━━━━━━━━━━━  3s 73ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37468.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  3s 72ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37479.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  3s 72ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37489.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 429/469 ━━━━━━━━━━━━━━━━━━━━  2s 71ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37499.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  2s 70ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37509.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  2s 70ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37520.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  2s 70ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37527.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 444/469 ━━━━━━━━━━━━━━━━━━━━  1s 69ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37537.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 448/469 ━━━━━━━━━━━━━━━━━━━━  1s 69ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37548.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  1s 68ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37558.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 454/469 ━━━━━━━━━━━━━━━━━━━━  1s 68ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37563.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 68ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37571.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 67ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37581.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 67ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37591.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37601.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 32s 67ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37604.4766 - val_loss: 0.1000 - val_moe_loss: 39198.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 15/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 39203.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 39214.0938 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 39224.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 39234.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  17/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39244.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  21/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39255.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39265.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  29/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 39275.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 39285.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 39296.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  41/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39306.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  45/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39316.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39326.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  53/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39336.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  57/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39347.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39357.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39367.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  69/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39377.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  72/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39385.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39395.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  80/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39406.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  84/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39416.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39426.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  92/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39436.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  95/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39444.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  98/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39452.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 101/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39459.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 102/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39462.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39464.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39470.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39477.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39485.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39493.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39500.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39508.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39516.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39523.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39531.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39539.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39546.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39554.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39562.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39569.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39577.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39585.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39592.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39600.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39608.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 162/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39616.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39623.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39631.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39641.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39649.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39659.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39669.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39680.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39687.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 194/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39697.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 198/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39708.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39715.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39723.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39733.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 212/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39744.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 216/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39754.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39761.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39772.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39779.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 230/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39790.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39800.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39810.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 242/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39820.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 245/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39828.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 248/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39836.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39846.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39856.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39864.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39872.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39879.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39889.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39900.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39910.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39920.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39930.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39941.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39948.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 296/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39959.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 299/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39966.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39976.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39987.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 311/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39997.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40007.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40017.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 323/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40028.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40038.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40048.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40058.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40069.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40079.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40087.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40097.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40107.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 358/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40117.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40128.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 366/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40138.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 369/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40145.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40156.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40166.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40176.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40186.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40197.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40207.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40215.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40225.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40235.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 408/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40245.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 412/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40256.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40266.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 420/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40276.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40284.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40294.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40304.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40314.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40325.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40335.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40345.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40355.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40363.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40371.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40378.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40386.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40394.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40404.4766 - val_loss: 0.1000 - val_moe_loss: 41998.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 16/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 42003.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 42014.1016 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 42024.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 42034.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 42042.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 42052.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  24/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 42062.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 42072.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  32/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 42083.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 42093.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  39/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 42101.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  42/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 42108.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  45/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 42116.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 42126.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  53/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 42136.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  57/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 42147.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 42157.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42167.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  69/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42177.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42188.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  77/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42198.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  81/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42208.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42218.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  89/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42229.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  93/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42239.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42249.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42257.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42267.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42277.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 112/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42288.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 116/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42298.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42308.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42318.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 128/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42328.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42339.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42349.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42359.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42369.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42380.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42385.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 152/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42390.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 155/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42398.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42408.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42418.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 167/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42428.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42439.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42449.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42459.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42469.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42480.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 191/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42490.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42500.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42510.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 203/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42520.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42531.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42541.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42549.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 218/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42559.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42569.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42579.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 230/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42590.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42600.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42610.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 242/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42620.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42631.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42641.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 254/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42651.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42661.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42672.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 266/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42682.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42689.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42700.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42707.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42715.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42720.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42723.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42730.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42738.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42746.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42753.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42764.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42774.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 305/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42782.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42792.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42800.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42807.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42815.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42823.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42830.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42838.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42848.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42858.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42866.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42876.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42887.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42894.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42904.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42915.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42925.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42933.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42943.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42951.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42958.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42968.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42979.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42989.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42999.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43007.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43017.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43027.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43038.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43048.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43058.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43066.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 420/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43076.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43084.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 426/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43091.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43102.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43112.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43122.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43132.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43143.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43150.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43160.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43171.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43181.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43191.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43201.9297
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43204.4766 - val_loss: 0.1000 - val_moe_loss: 44798.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 17/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 26ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 44803.8477
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9947 - loss: 0.1000 - moe_loss: 44814.0938 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 44824.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 44834.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 44842.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 44849.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  23/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 44860.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  27/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 44870.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 44880.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 44885.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 44896.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  41/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 44906.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  45/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44916.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  48/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44924.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  51/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44931.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44942.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  59/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44952.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  63/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44962.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44972.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  71/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44983.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  74/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44990.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  78/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 45000.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  81/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 45008.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  84/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45016.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  87/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45024.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  90/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45031.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45041.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45049.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 101/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45059.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45070.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45077.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45085.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45093.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 118/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45103.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45111.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45118.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45126.4102
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45134.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45141.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45149.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45157.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45164.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45172.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 149/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45182.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45192.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45200.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45208.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45218.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 167/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45228.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 170/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45236.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 174/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45246.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45254.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 180/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45262.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45269.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 186/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45277.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45285.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45295.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45303.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45310.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45318.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 205/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45326.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45333.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45341.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45349.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45356.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45364.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45372.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 227/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45382.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45392.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45400.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45408.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45418.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45425.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45433.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45441.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45448.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 257/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45459.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 261/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45469.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45477.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45484.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45492.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45500.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45507.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45515.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45523.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45530.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45538.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45546.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45553.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45561.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45569.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45576.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45584.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45587.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45594.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45602.5703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 316/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45610.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45617.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45625.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 326/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45635.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45643.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45651.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45658.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45666.5703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 341/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45674.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45681.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45689.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45697.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 353/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45704.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 356/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45712.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 359/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45720.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45728.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45735.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45743.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45751.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45758.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 377/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45766.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45774.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45781.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45789.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45797.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45804.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45812.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45820.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45827.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45835.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45843.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45850.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45858.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45866.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45873.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 422/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45881.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45889.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 428/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45896.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45904.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45912.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45920.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45927.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45935.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45943.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45950.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45958.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45966.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45973.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45981.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45989.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45996.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 46004.4766 - val_loss: 0.1000 - val_moe_loss: 47598.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 18/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 27ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 47603.8477
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9967 - loss: 0.1000 - moe_loss: 47611.5312 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9965 - loss: 0.1000 - moe_loss: 47619.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9965 - loss: 0.1000 - moe_loss: 47626.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9966 - loss: 0.1000 - moe_loss: 47634.5703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9964 - loss: 0.1000 - moe_loss: 47642.2500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9961 - loss: 0.1000 - moe_loss: 47649.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9957 - loss: 0.1000 - moe_loss: 47657.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 47665.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9952 - loss: 0.1000 - moe_loss: 47672.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9950 - loss: 0.1000 - moe_loss: 47680.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  34/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9950 - loss: 0.1000 - moe_loss: 47688.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9949 - loss: 0.1000 - moe_loss: 47696.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 47703.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9947 - loss: 0.1000 - moe_loss: 47711.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9946 - loss: 0.1000 - moe_loss: 47719.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9946 - loss: 0.1000 - moe_loss: 47726.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47734.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47742.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47749.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47757.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47765.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47772.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47780.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47788.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47795.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47803.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47811.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47818.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47826.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47834.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47841.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47849.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47857.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47864.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 106/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47872.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 107/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47875.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47877.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47885.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47893.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47900.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47911.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47918.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47926.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47934.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47941.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47949.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47957.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47964.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47972.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47980.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47987.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47995.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48003.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48010.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48018.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48026.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48033.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48041.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48049.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48056.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48064.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48072.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48080.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48087.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48095.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48103.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48110.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48118.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 205/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48126.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48133.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48141.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48149.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48156.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48164.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48172.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48179.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48187.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48195.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48202.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48210.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48218.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 244/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48225.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48233.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48241.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48248.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48256.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48264.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48272.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48279.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 268/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48287.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48295.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48302.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48310.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48318.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48325.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48333.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48341.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48348.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48356.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48364.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 301/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48371.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 304/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48379.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48387.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48394.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48402.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 316/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48410.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48417.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48425.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48433.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 328/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48440.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48448.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 334/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48456.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 337/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48464.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48471.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48479.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48487.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48494.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48502.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48510.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 358/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48517.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48525.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48533.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48540.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48548.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48556.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48563.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48571.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48579.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48586.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48594.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48602.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48609.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48617.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48625.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48632.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48640.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48648.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48656.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 415/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48663.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48671.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48679.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48686.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48694.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48702.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48709.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48717.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48725.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48732.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48740.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48748.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48755.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48763.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48771.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48778.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48786.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48794.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48801.9297
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48804.4766 - val_loss: 0.1000 - val_moe_loss: 50398.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 19/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  14s 31ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 50403.8594
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50411.5312 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50419.2070
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50426.8867
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50434.5703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50442.2500
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50449.9297
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50457.6094
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50465.2891
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50472.9688
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50480.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  34/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50488.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50496.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50503.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50511.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50519.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50526.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50534.4102
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50542.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50549.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50557.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50565.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50572.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50580.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50588.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50595.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50603.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50611.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50618.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50626.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50634.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50641.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50649.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50657.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 103/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50664.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 106/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50672.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 109/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50680.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 112/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50688.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 115/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50695.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 118/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50703.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 121/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50711.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50718.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50726.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50734.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50741.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50749.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 139/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50757.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 142/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50764.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 145/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50772.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 148/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50780.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 151/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50787.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 154/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50795.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 157/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50803.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 160/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50810.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 163/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50818.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 166/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50826.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50833.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50841.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50849.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50856.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50864.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50872.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50880.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50887.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50895.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 196/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50903.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 199/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50910.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 202/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50918.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 205/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50926.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 208/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50933.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 211/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50941.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50949.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50956.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50964.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50972.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50979.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50987.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 50995.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51002.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51010.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51018.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 242/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51020.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51023.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 245/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51028.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51033.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 250/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51041.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 253/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51048.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 256/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51056.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 259/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51064.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 262/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51072.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 265/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51079.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 268/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51087.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 271/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51095.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51102.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51110.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51118.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51125.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51133.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51141.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51148.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 295/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51156.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 298/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51164.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 301/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51171.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 304/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51179.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 307/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51187.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 310/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51194.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 313/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51202.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 316/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51210.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 319/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51217.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51225.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51233.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 328/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51240.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51248.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 334/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51256.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 337/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51264.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51271.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51279.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51287.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51294.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51302.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 355/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51310.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 358/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51317.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 361/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51325.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 364/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51333.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 367/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51340.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 370/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51348.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51356.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51363.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51371.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51379.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51386.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51394.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51402.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51409.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51417.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51425.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51432.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51440.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51448.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51456.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51463.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51471.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51479.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51486.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51494.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51502.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51509.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51517.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51525.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51532.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51540.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51548.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51555.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51563.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51571.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51576.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51578.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51586.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51594.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51601.9297
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51604.4766 - val_loss: 0.1000 - val_moe_loss: 53198.7227
+
+
+<div class="k-default-codeblock">
+```
+Epoch 20/20
+
+```
+</div>
+    
+   1/469 ━━━━━━━━━━━━━━━━━━━━  18s 40ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 53203.8633
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   4/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 53211.5391 
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+   7/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 53219.2188
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  10/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 53226.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  13/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 53234.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  16/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 53242.2578
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  19/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 53249.9375
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  22/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 53257.6172
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  25/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 53265.2969
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  28/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53272.9766
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  31/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53280.6562
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  34/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53288.3359
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  37/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53296.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  40/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 53303.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  43/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 53311.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  46/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 53319.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 53326.7383
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  52/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 53334.4180
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  55/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 53342.0977
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  58/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 53349.7773
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  61/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 53357.4570
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  64/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 53365.1367
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  67/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 53372.8164
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  70/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 53380.4961
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  73/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 53388.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  76/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 53395.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  79/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 53403.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  82/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 53411.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  85/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 53418.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  88/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 53426.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  91/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 53434.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  94/469 ━━━━━━━━━━━━━━━━━━━━  8s 22ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 53441.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+  97/469 ━━━━━━━━━━━━━━━━━━━━  8s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53449.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 100/469 ━━━━━━━━━━━━━━━━━━━━  8s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53457.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 102/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53462.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 105/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53470.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 108/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53477.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 111/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53485.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 114/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53493.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 117/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53500.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 120/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53508.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 123/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53516.1758
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 126/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53523.8555
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 129/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53531.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 132/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53539.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 135/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53546.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 138/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53554.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 141/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53562.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 144/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53569.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 147/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53577.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 150/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53585.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 153/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53592.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 156/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53600.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 159/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53608.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 162/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53616.0156
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 165/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53623.6953
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 168/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53631.3750
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 171/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53639.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 174/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53646.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 177/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53654.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 180/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53662.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 183/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53669.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 186/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53677.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 189/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53685.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 192/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53692.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 195/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53700.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 198/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53708.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 201/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53715.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 204/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53723.5352
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 207/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53731.2148
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 210/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53738.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 213/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53746.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 214/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53749.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 216/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53754.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 219/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53761.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 222/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53769.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 225/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53777.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 228/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53784.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 231/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53792.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 234/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53800.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 237/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53808.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 240/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53815.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 243/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53823.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 246/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53831.0547
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 249/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53838.7344
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 252/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53846.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 255/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53854.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 258/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53861.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 261/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53869.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 264/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53877.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 267/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53884.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 270/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53892.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 273/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53900.1719
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 276/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53907.8516
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 279/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53915.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 282/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53923.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 285/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53930.8945
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 288/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53938.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53946.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53953.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 297/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53961.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 300/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53969.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 303/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53976.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 306/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53984.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 309/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53992.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 312/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54000.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 315/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54007.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 318/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54015.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 321/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54023.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 324/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54030.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 327/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54038.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 330/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54046.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 333/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54053.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54061.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54069.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54076.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54081.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54087.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54094.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54102.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54107.5312
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 357/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54115.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 360/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54122.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 363/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54130.5703
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 366/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54138.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 369/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54145.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 372/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54153.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 375/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54161.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 378/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54168.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54176.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54184.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54189.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54197.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54202.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54207.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54212.4922
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54217.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54222.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54230.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54238.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54243.2109
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54250.8906
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54258.5742
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 416/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54266.2539
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 419/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54273.9336
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 422/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54281.6133
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54289.2930
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 428/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54296.9727
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54304.6523
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54312.3320
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54320.0117
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54327.6914
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54335.3711
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54343.0508
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54350.7305
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54358.4141
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54366.0938
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54373.7734
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54381.4531
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54389.1328
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54396.8125
+
+<div class="k-default-codeblock">
+```
+
+```
+</div>
+ 469/469 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54404.4766 - val_loss: 0.1000 - val_moe_loss: 55998.7227
+
+
+### Evaluation
+
+
+```python
+score = model.evaluate(x_test, y_test, verbose=0)
+print("Test loss:", score[0])
+print("Test accuracy:", score[1])
+```
+
+<div class="k-default-codeblock">
+```
+Test loss: tf.Tensor(0.10000026, shape=(), dtype=float32)
+Test accuracy: {'accuracy': <tf.Tensor: shape=(), dtype=float32, numpy=0.9932000041007996>}
+
+```
+</div>
\ No newline at end of file
diff --git a/examples/vision/mnist_moe.py b/examples/vision/mnist_moe.py
new file mode 100644
index 0000000000..8b1911511d
--- /dev/null
+++ b/examples/vision/mnist_moe.py
@@ -0,0 +1,424 @@
+"""
+Title: MoE for MNIST
+Author: [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)
+Date created: 2015/06/19
+Last modified: 2020/04/21
+Description: Showcasing concepts relates to Mixture of Experts (MoE).
+Accelerator: GPU
+"""
+
+"""
+# Introduction
+
+In this example, we implement an adaptation of the Mixture of Experts (MoE) architecture
+([Shazeer et al.](https://arxiv.org/abs/1701.06538)).
+The idea is to use conditional computation to increases model capacity without increasing computation.
+Experts are identical blocks within a layer where each are trained to specialize in different parts of the input space.
+At each forward pass, a gating network selects a subset of experts to apply to the input.
+
+The components to implement are:
+- Gating network: A dense layer that outputs a probability distribution over the experts.
+- MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.
+- Model: A simple model that uses the MoE layer.
+
+In this example, we will first implement a linear MoE layer and then a CNN-based MoE layer. Lastly we will combine the two using an abstract implementation to showcase its capacties.
+"""
+
+"""
+## Imports
+"""
+
+import numpy as np
+import keras
+from keras import layers, models
+import tensorflow as tf
+from tensorflow.keras import backend as K
+
+"""
+### Data Prepration
+"""
+
+# Model / data parameters
+num_classes = 10
+input_shape = (28, 28, 1)
+
+# Load the data and split it between train and test sets
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+
+# Scale images to the [0, 1] range
+x_train = x_train.astype("float32") / 255
+x_test = x_test.astype("float32") / 255
+# Make sure images have shape (28, 28, 1)
+x_train = np.expand_dims(x_train, -1)
+x_test = np.expand_dims(x_test, -1)
+print("x_train shape:", x_train.shape)
+print(x_train.shape[0], "train samples")
+print(x_test.shape[0], "test samples")
+
+
+# convert class vectors to binary class matrices
+y_train = keras.utils.to_categorical(y_train, num_classes)
+y_test = keras.utils.to_categorical(y_test, num_classes)
+
+"""
+## Constants
+
+"""
+NUM_EXPERTS = 5
+TOP_K = 3
+BATCH_SIZE = 128
+NUM_EPOCHS = 20
+LEARNING_RATE = 0.001
+
+
+"""
+## Base architecture
+
+The most basic [MNIST classifier](https://keras.io/examples/vision/mnist_convnet/) consists of a stack of convolutional layers followed by a dense layer. In this tutorial, we will first replace the dense layer with a MoE layer. Then do the same for convolutional layers.
+"""
+
+model = keras.Sequential(
+    [
+        keras.Input(shape=input_shape),
+        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+        layers.MaxPooling2D(pool_size=(2, 2)),
+        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+        layers.MaxPooling2D(pool_size=(2, 2)),
+        layers.Flatten(),
+        layers.Dropout(0.5),
+        layers.Dense(num_classes, activation="softmax"),
+    ]
+)
+
+model.summary()
+
+"""
+# Linear MoE using Dense layers
+
+For this layer, we will create multiple dense layers that will be used as experts. Then a simple gating network will select at each step which exerts should be utilized for the current input. We will keep track of the number of times each expert is used. Then the selected experts will be combined using a weighted sum.
+"""
+
+
+class LinearMoE(layers.Layer):
+    def __init__(
+        self,
+        hidden_size,
+        num_experts=NUM_EXPERTS,
+        top_k=TOP_K,
+    ):
+        super(LinearMoE, self).__init__()
+
+        # Initialize experts
+        self.experts = [
+            layers.Dense(
+                hidden_size,
+                kernel_initializer=tf.keras.initializers.RandomNormal(
+                    mean=0.0, stddev=0.001
+                ),
+                bias_initializer="zeros",
+            )
+            for _ in range(num_experts)
+        ]
+        # Initialize gating network
+        self.gating_network = layers.Dense(
+            NUM_EXPERTS,
+            kernel_initializer=tf.keras.initializers.RandomNormal(
+                mean=0.0, stddev=0.001
+            ),
+            bias_initializer="zeros",
+        )
+
+        self.num_experts = num_experts
+        self.top_k = top_k
+        # Keep track of how many times each expert is used
+        self.expert_usage_count = tf.Variable(
+            tf.zeros((num_experts,), dtype=tf.float32)
+        )
+
+    def call(self, x):
+        # Get gating weights
+        gating_weights = self.gating_network(x)
+
+        # Get the top k experts based on the gating weights
+        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+
+        # Count usage of each expert symbolically
+        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
+        # Use tf.tensor_scatter_nd_add to increment the usage count
+        self.expert_usage_count.assign(
+            tf.tensor_scatter_nd_add(
+                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
+            )
+        )
+
+        # Get outputs from only the top-k experts
+        top_k_expert_outputs = tf.stack(
+            [
+                self.experts[expert_index](x)
+                for expert_index in top_k_indices.numpy()[0]
+            ],
+            axis=1,
+        )  # Stack outputs along axis 1
+
+        # Combine outputs using top-k weights
+        combined_output = tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
+
+        return combined_output
+
+
+"""
+Output of the top 3 experts out of 10 for one layer of MoE:
+"""
+sample_data = tf.random.uniform((1, 10))
+linear_mode = LinearMoE(32, 10, 3)
+linear_mode(sample_data)
+
+"""
+## Routing Collapse
+
+Routing collapse is a problem that occurs with MoE layers. The route terminology refers to the selection process of which expert to use for a given input.
+
+Route collapse happens when a routing model, early in training, starts favoring just a few experts because they perform slightly better due to random starting conditions. This leads to most examples being sent to these experts, leaving others unused and reducing the model’s overall capacity.
+
+Code below demonstrates the randomness of expert selection:
+"""
+
+
+def check_expert_usage(runs):
+    # Running the later multiple times to show randomness of expert selection
+    for i in range(runs):
+        sample_data = tf.random.uniform((1, 10))
+        linear_mode = LinearMoE(10, 5)
+        _ = linear_mode(sample_data)
+        print(f"Run {i}, Expert usage: {linear_mode.expert_usage_count.numpy()}")
+
+
+check_expert_usage(4)
+
+"""
+### Adding loss functions to prevent route collapse
+To fix this, the authors use extra rules (importance and load losses), ideas borrowed from [Shazeer et al.](https://arxiv.org/abs/1701.06538), to ensure all experts get used evenly.
+
+The importance_loss calculates how much the usage of each expert (tracked in batch_importance_sum) deviates from the average usage (mean_importance) by using mean squared error, aiming to balance expert utilization. This helps prevent route collapse by discouraging the model from overloading a few experts, instead promoting an even distribution of examples across all experts to maintain diverse and effective routing.
+
+#### Load losses
+    - Diversity loss: Diversity loss helps prevent route collapse by encouraging the routing model to evenly distribute examples across all experts, rather than favoring just a few due to their initial performance. It does this by maximizing the entropy of the gating weights, ensuring balanced expert utilization and improving the model's overall capacity.
+    - Overflow loss: The batch_overflow_sum measures how much the usage of experts exceeds a set capacity by applying ReLU to the difference between usage_counts (how many examples each expert handles) and batch_capacity (the allowed limit), then summing the excesses. This helps prevent route collapse by penalizing situations where certain experts are overused, encouraging a more even spread of examples across all experts to keep the model's capacity balanced.
+"""
+
+
+class LinearMoE(layers.Layer):
+    def __init__(
+        self,
+        hidden_size,
+        num_experts=NUM_EXPERTS,
+        top_k=TOP_K,
+    ):
+        super(LinearMoE, self).__init__()
+
+        # Initialize experts
+        self.experts = [
+            layers.Dense(
+                hidden_size,
+                kernel_initializer=tf.keras.initializers.RandomNormal(
+                    mean=0.0, stddev=0.001
+                ),
+                bias_initializer="zeros",
+            )
+            for _ in range(num_experts)
+        ]
+        # Initialize gating network
+        self.gating_network = layers.Dense(
+            num_experts,  # Match output to num_experts
+            kernel_initializer=tf.keras.initializers.RandomNormal(
+                mean=0.0, stddev=0.001
+            ),
+            bias_initializer="zeros",
+        )
+
+        self.num_experts = num_experts
+        self.top_k = top_k
+        # Keep track of how many times each expert is used as a layer weight
+        self.expert_usage_count = tf.Variable(
+            tf.zeros((num_experts,), dtype=tf.float32)
+        )
+
+        self.batch_capacity = BATCH_SIZE // num_experts
+
+    def _diversity_loss(self, weights):
+        entropy = -K.sum(weights * K.log(weights + 1e-10), axis=1)
+        self.diversity_loss = -K.mean(entropy)
+
+    def _importance_loss(self, gating_weights):
+        batch_importance_sum = K.sum(gating_weights, axis=0)
+        mean_importance = K.mean(batch_importance_sum)
+        self.importance_loss = K.mean(
+            K.square(
+                batch_importance_sum
+                - mean_importance * tf.ones_like(batch_importance_sum)
+            )
+        )
+
+    def call(self, x):
+        # Get gating weights and normalize
+        gating_weights = self.gating_network(x)
+        gating_weights = K.softmax(gating_weights)  # Ensure weights are probabilities
+        self._diversity_loss(gating_weights)
+        self._importance_loss(gating_weights)
+
+        # Get the top k experts based on the gating weights
+        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+
+        # Count usage of each expert symbolically
+        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
+        # Use tf.tensor_scatter_nd_add to increment the usage count
+        self.expert_usage_count.assign(
+            tf.tensor_scatter_nd_add(
+                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
+            )
+        )
+
+        # Calculate overflow using updated usage count
+        self.batch_overflow_sum = K.sum(
+            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
+        )
+
+        # Compute all expert outputs
+        expert_outputs = tf.stack(
+            [expert(x) for expert in self.experts], axis=1
+        )  # Shape: (batch_size, num_experts, hidden_size)
+
+        # Gather the top-k expert outputs using top_k_indices
+        batch_size = tf.shape(x)[0]
+        batch_indices = tf.expand_dims(
+            tf.range(batch_size), 1
+        )  # Shape: (batch_size, 1)
+        batch_indices = tf.tile(
+            batch_indices, [1, self.top_k]
+        )  # Shape: (batch_size, top_k)
+
+        # Create indices for gathering
+        indices = tf.stack(
+            [batch_indices, top_k_indices], axis=2
+        )  # Shape: (batch_size, top_k, 2)
+        top_k_expert_outputs = tf.gather_nd(
+            expert_outputs, indices
+        )  # Shape: (batch_size, top_k, hidden_size)
+
+        # Combine outputs using top-k weights
+        combined_output = tf.reduce_sum(
+            top_k_expert_outputs * tf.expand_dims(top_k_weights, axis=-1), axis=1
+        )
+
+        return combined_output
+
+    def compute_total_loss(self, load_balance_coef=0.01):
+        return load_balance_coef * (
+            self.diversity_loss + self.batch_overflow_sum + self.importance_loss
+        )
+
+
+"""
+## MNIST classification with MoE
+"""
+
+
+class MoEModel(keras.Model):
+    def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_K):
+        super(MoEModel, self).__init__()
+
+        # Define the convolutional block
+        self.conv_block = keras.Sequential(
+            [
+                layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+                layers.MaxPooling2D(pool_size=(2, 2)),
+                layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+                layers.MaxPooling2D(pool_size=(2, 2)),
+                layers.Flatten(),
+                layers.Dropout(0.5),
+            ]
+        )
+
+        # MoE classifier
+        self.moe_classifier = LinearMoE(
+            hidden_size=num_classes, num_experts=num_experts, top_k=top_k
+        )
+
+        # Softmax layer
+        self.softmax = layers.Softmax()
+
+    def call(self, inputs, training=False):
+        conv_flatten = self.conv_block(inputs)
+        moe_output = self.moe_classifier(conv_flatten)
+        outputs = self.softmax(moe_output)
+        return outputs
+
+    def train_step(self, data):
+        x, y = data  # Unpack input data and labels
+
+        with tf.GradientTape() as tape:
+            y_pred = self(x, training=True)
+            classification_loss = self.compute_loss(x, y, y_pred)
+            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
+            total_loss = classification_loss + moe_loss
+
+        # Compute gradients
+        gradients = tape.gradient(total_loss, self.trainable_variables)
+
+        # Update weights
+        self.optimizer.apply_gradients(
+            zip(gradients, self.trainable_variables)
+        )  # Update metrics (e.g., accuracy)
+        self.compiled_metrics.update_state(y, y_pred)
+        # Return a dict of metrics for monitoring
+        return {
+            "loss": total_loss,
+            "moe_loss": moe_loss,
+            **{m.name: m.result() for m in self.metrics},
+        }
+
+    def test_step(self, data):
+        x, y = data
+        y_pred = self(x, training=False)
+        classification_loss = self.compute_loss(x, y, y_pred)
+        moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
+        total_loss = classification_loss + moe_loss
+
+        self.compiled_metrics.update_state(y, y_pred)
+        return {
+            "loss": total_loss,
+            "moe_loss": moe_loss,
+            **{m.name: m.result() for m in self.metrics},
+        }
+
+
+# Instantiate and compile the model
+inputs = keras.Input(shape=input_shape)
+model = MoEModel(
+    input_shape=input_shape, num_classes=num_classes, num_experts=6, top_k=4
+)
+
+model.compile(
+    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),
+    loss=keras.losses.CategoricalCrossentropy(),  # Assumes one-hot encoded labels
+    metrics=["accuracy"],
+)
+
+"""
+###  Training
+"""
+history = model.fit(
+    x_train,
+    y_train,
+    batch_size=BATCH_SIZE,
+    epochs=NUM_EPOCHS,
+    validation_data=(x_test, y_test),
+)
+
+"""
+### Evaluation
+"""
+
+score = model.evaluate(x_test, y_test, verbose=0)
+print("Test loss:", score[0])
+print("Test accuracy:", score[1])
diff --git a/templates/examples/audio/vocal_track_separation.md b/templates/examples/audio/vocal_track_separation.md
new file mode 100644
index 0000000000..af44162d78
--- /dev/null
+++ b/templates/examples/audio/vocal_track_separation.md
@@ -0,0 +1,921 @@
+# Vocal Track Separation with Encoder-Decoder Architecture
+
+**Author:** [Joaquin Jimenez](https://github.com/johacks/)<br>
+**Date created:** 2024/12/10<br>
+**Last modified:** 2024/12/10<br>
+**Description:** Train a model to separate vocal tracks from music mixtures.
+
+
+<div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div>
+<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/audio/ipynb/vocal_track_separation.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/audio/vocal_track_separation.py)
+
+
+
+---
+## Introduction
+
+In this tutorial, we build a vocal track separation model using an encoder-decoder
+architecture in Keras 3.
+
+We train the model on the [MUSDB18 dataset](https://doi.org/10.5281/zenodo.1117372),
+which provides music mixtures and isolated tracks for drums, bass, other, and vocals.
+
+Key concepts covered:
+
+- Audio data preprocessing using the Short-Time Fourier Transform (STFT).
+- Audio data augmentation techniques.
+- Implementing custom encoders and decoders specialized for audio data.
+- Defining appropriate loss functions and metrics for audio source separation tasks.
+
+The model architecture is derived from the TFC_TDF_Net model described in:
+
+W. Choi, M. Kim, J. Chung, D. Lee, and S. Jung, “Investigating U-Nets with various
+intermediate blocks for spectrogram-based singing voice separation,” in the 21st
+International Society for Music Information Retrieval Conference, 2020.
+
+For reference code, see:
+[GitHub: ws-choi/ISMIR2020_U_Nets_SVS](https://github.com/ws-choi/ISMIR2020_U_Nets_SVS).
+
+The data processing and model training routines are partly derived from:
+[ZFTurbo/Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training/tree/main).
+
+---
+## Setup
+
+Import and install all the required dependencies.
+
+
+```python
+!pip install -qq audiomentations soundfile ffmpeg-binaries
+!pip install -qq "keras==3.7.0"
+!sudo -n apt-get install -y graphviz >/dev/null 2>&1  # Required for plotting the model
+```
+
+
+```python
+import glob
+import os
+
+os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
+
+import random
+import subprocess
+import tempfile
+import typing
+from os import path
+
+import audiomentations as aug
+import ffmpeg
+import keras
+import numpy as np
+import soundfile as sf
+from IPython import display
+from keras import callbacks, layers, ops, saving
+from matplotlib import pyplot as plt
+```
+
+---
+## Configuration
+
+The following constants define configuration parameters for audio processing
+and model training, including dataset paths, audio chunk sizes, Short-Time Fourier
+Transform (STFT) parameters, and training hyperparameters.
+
+
+```python
+# MUSDB18 dataset configuration
+MUSDB_STREAMS = {"mixture": 0, "drums": 1, "bass": 2, "other": 3, "vocals": 4}
+TARGET_INSTRUMENTS = {track: MUSDB_STREAMS[track] for track in ("vocals",)}
+N_INSTRUMENTS = len(TARGET_INSTRUMENTS)
+SOURCE_INSTRUMENTS = tuple(k for k in MUSDB_STREAMS if k != "mixture")
+
+# Audio preprocessing parameters for Short-Time Fourier Transform (STFT)
+N_SUBBANDS = 4  # Number of subbands into which frequencies are split
+CHUNK_SIZE = 65024  # Number of amplitude samples per audio chunk (~4 seconds)
+STFT_N_FFT = 2048  # FFT points used in STFT
+STFT_HOP_LENGTH = 512  # Hop length for STFT
+
+# Training hyperparameters
+N_CHANNELS = 64  # Base channel count for the model
+BATCH_SIZE = 3
+ACCUMULATION_STEPS = 2
+EFFECTIVE_BATCH_SIZE = BATCH_SIZE * (ACCUMULATION_STEPS or 1)
+
+# Paths
+TMP_DIR = path.expanduser("~/.keras/tmp")
+DATASET_DIR = path.expanduser("~/.keras/datasets")
+MODEL_PATH = path.join(TMP_DIR, f"model_{keras.backend.backend()}.keras")
+CSV_LOG_PATH = path.join(TMP_DIR, f"training_{keras.backend.backend()}.csv")
+os.makedirs(DATASET_DIR, exist_ok=True)
+os.makedirs(TMP_DIR, exist_ok=True)
+
+# Set random seed for reproducibility
+keras.utils.set_random_seed(21)
+```
+
+<div class="k-default-codeblock">
+```
+WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
+E0000 00:00:1734318393.806217   81028 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+E0000 00:00:1734318393.809885   81028 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+
+```
+</div>
+---
+## MUSDB18 Dataset
+
+The MUSDB18 dataset is a standard benchmark for music source separation, containing
+150 full-length music tracks along with isolated drums, bass, other, and vocals.
+The dataset is stored in .mp4 format, and each .mp4 file includes multiple audio
+streams (mixture and individual tracks).
+
+### Download and Conversion
+
+The following utility function downloads MUSDB18 and converts its .mp4 files to
+.wav files for each instrument track, resampled to 16 kHz.
+
+
+```python
+
+def download_musdb18(out_dir=None):
+    """Download and extract the MUSDB18 dataset, then convert .mp4 files to .wav files.
+
+    MUSDB18 reference:
+    Rafii, Z., Liutkus, A., Stöter, F.-R., Mimilakis, S. I., & Bittner, R. (2017).
+    MUSDB18 - a corpus for music separation (1.0.0) [Data set]. Zenodo.
+    """
+    ffmpeg.init()
+    from ffmpeg import FFMPEG_PATH
+
+    # Create output directories
+    os.makedirs((base := out_dir or tempfile.mkdtemp()), exist_ok=True)
+    if path.exists((out_dir := path.join(base, "musdb18_wav"))):
+        print("MUSDB18 dataset already downloaded")
+        return out_dir
+
+    # Download and extract the dataset
+    download_dir = keras.utils.get_file(
+        fname="musdb18",
+        origin="https://zenodo.org/records/1117372/files/musdb18.zip",
+        extract=True,
+    )
+
+    # ffmpeg command template: input, stream index, output
+    ffmpeg_args = str(FFMPEG_PATH) + " -v error -i {} -map 0:{} -vn -ar 16000 {}"
+
+    # Convert each mp4 file to multiple .wav files for each track
+    for split in ("train", "test"):
+        songs = os.listdir(path.join(download_dir, split))
+        for i, song in enumerate(songs):
+            if i % 10 == 0:
+                print(f"{split.capitalize()}: {i}/{len(songs)} songs processed")
+
+            mp4_path_orig = path.join(download_dir, split, song)
+            mp4_path = path.join(tempfile.mkdtemp(), split, song.replace(" ", "_"))
+            os.makedirs(path.dirname(mp4_path), exist_ok=True)
+            os.rename(mp4_path_orig, mp4_path)
+
+            wav_dir = path.join(out_dir, split, path.basename(mp4_path).split(".")[0])
+            os.makedirs(wav_dir, exist_ok=True)
+
+            for track in SOURCE_INSTRUMENTS:
+                out_path = path.join(wav_dir, f"{track}.wav")
+                stream_index = MUSDB_STREAMS[track]
+                args = ffmpeg_args.format(mp4_path, stream_index, out_path).split()
+                assert subprocess.run(args).returncode == 0, "ffmpeg conversion failed"
+    return out_dir
+
+
+# Download and prepare the MUSDB18 dataset
+songs = download_musdb18(out_dir=DATASET_DIR)
+```
+
+<div class="k-default-codeblock">
+```
+MUSDB18 dataset already downloaded
+
+```
+</div>
+### Custom Dataset
+
+We define a custom dataset class to generate random audio chunks and their corresponding
+labels. The dataset does the following:
+
+1. Selects a random chunk from a random song and instrument.
+2. Applies optional data augmentations.
+3. Combines isolated tracks to form new synthetic mixtures.
+4. Prepares features (mixtures) and labels (vocals) for training.
+
+This approach allows creating an effectively infinite variety of training examples
+through randomization and augmentation.
+
+
+```python
+
+class Dataset(keras.utils.PyDataset):
+    def __init__(
+        self,
+        songs,
+        batch_size=BATCH_SIZE,
+        chunk_size=CHUNK_SIZE,
+        batches_per_epoch=1000 * ACCUMULATION_STEPS,
+        augmentation=True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.augmentation = augmentation
+        self.vocals_augmentations = [
+            aug.PitchShift(min_semitones=-5, max_semitones=5, p=0.1),
+            aug.SevenBandParametricEQ(-9, 9, p=0.25),
+            aug.TanhDistortion(0.1, 0.7, p=0.1),
+        ]
+        self.other_augmentations = [
+            aug.PitchShift(p=0.1),
+            aug.AddGaussianNoise(p=0.1),
+        ]
+        self.songs = songs
+        self.sizes = {song: self.get_track_set_size(song) for song in self.songs}
+        self.batch_size = batch_size
+        self.chunk_size = chunk_size
+        self.batches_per_epoch = batches_per_epoch
+
+    def get_track_set_size(self, song: str):
+        """Return the smallest track length in the given song directory."""
+        sizes = [len(sf.read(p)[0]) for p in glob.glob(path.join(song, "*.wav"))]
+        if max(sizes) != min(sizes):
+            print(f"Warning: {song} has different track lengths")
+        return min(sizes)
+
+    def random_chunk_of_instrument_type(self, instrument: str):
+        """Extract a random chunk for the specified instrument from a random song."""
+        song, size = random.choice(list(self.sizes.items()))
+        track = path.join(song, f"{instrument}.wav")
+
+        if self.chunk_size <= size:
+            start = np.random.randint(size - self.chunk_size + 1)
+            audio = sf.read(track, self.chunk_size, start, dtype="float32")[0]
+            audio_mono = np.mean(audio, axis=1)
+        else:
+            # If the track is shorter than chunk_size, pad the signal
+            audio_mono = np.mean(sf.read(track, dtype="float32")[0], axis=1)
+            audio_mono = np.pad(audio_mono, ((0, self.chunk_size - size),))
+
+        # If the chunk is almost silent, retry
+        if np.mean(np.abs(audio_mono)) < 0.01:
+            return self.random_chunk_of_instrument_type(instrument)
+
+        return self.data_augmentation(audio_mono, instrument)
+
+    def data_augmentation(self, audio: np.ndarray, instrument: str):
+        """Apply data augmentation to the audio chunk, if enabled."""
+
+        def coin_flip(x, probability: float, fn: typing.Callable):
+            return fn(x) if random.uniform(0, 1) < probability else x
+
+        if self.augmentation:
+            augmentations = (
+                self.vocals_augmentations
+                if instrument == "vocals"
+                else self.other_augmentations
+            )
+            # Loudness augmentation
+            audio *= np.random.uniform(0.5, 1.5, (len(audio),)).astype("float32")
+            # Random reverse
+            audio = coin_flip(audio, 0.1, lambda x: np.flip(x))
+            # Random polarity inversion
+            audio = coin_flip(audio, 0.5, lambda x: -x)
+            # Apply selected augmentations
+            for aug_ in augmentations:
+                aug_.randomize_parameters(audio, sample_rate=16000)
+                audio = aug_(audio, sample_rate=16000)
+        return audio
+
+    def random_mix_of_tracks(self) -> dict:
+        """Create a random mix of instruments by summing their individual chunks."""
+        tracks = {}
+        for instrument in SOURCE_INSTRUMENTS:
+            # Start with a single random chunk
+            mixup = [self.random_chunk_of_instrument_type(instrument)]
+
+            # Randomly add more chunks of the same instrument (mixup augmentation)
+            if self.augmentation:
+                for p in (0.2, 0.02):
+                    if random.uniform(0, 1) < p:
+                        mixup.append(self.random_chunk_of_instrument_type(instrument))
+
+            tracks[instrument] = np.mean(mixup, axis=0, dtype="float32")
+        return tracks
+
+    def __len__(self):
+        return self.batches_per_epoch
+
+    def __getitem__(self, idx):
+        # Generate a batch of random mixtures
+        batch = [self.random_mix_of_tracks() for _ in range(self.batch_size)]
+
+        # Features: sum of all tracks
+        batch_x = ops.sum(
+            np.array([list(track_set.values()) for track_set in batch]), axis=1
+        )
+
+        # Labels: isolated target instruments (e.g., vocals)
+        batch_y = np.array(
+            [[track_set[t] for t in TARGET_INSTRUMENTS] for track_set in batch]
+        )
+
+        return batch_x, ops.convert_to_tensor(batch_y)
+
+
+# Create train and validation datasets
+train_ds = Dataset(glob.glob(path.join(songs, "train", "*")))
+val_ds = Dataset(
+    glob.glob(path.join(songs, "test", "*")),
+    batches_per_epoch=int(0.1 * train_ds.batches_per_epoch),
+    augmentation=False,
+)
+```
+
+### Visualize a Sample
+
+Let's visualize a random mixed audio chunk and its corresponding isolated vocals.
+This helps to understand the nature of the preprocessed input data.
+
+
+```python
+
+def visualize_audio_np(audio: np.ndarray, rate=16000, name="mixup"):
+    """Plot and display an audio waveform and also produce an Audio widget."""
+    plt.figure(figsize=(10, 6))
+    plt.plot(audio)
+    plt.title(f"Waveform: {name}")
+    plt.xlim(0, len(audio))
+    plt.ylabel("Amplitude")
+    plt.show()
+    # plt.savefig(f"tmp/{name}.png")
+
+    # Normalize and display audio
+    audio_norm = (audio - np.min(audio)) / (np.max(audio) - np.min(audio) + 1e-8)
+    audio_norm = (audio_norm * 2 - 1) * 0.6
+    display.display(display.Audio(audio_norm, rate=rate))
+    # sf.write(f"tmp/{name}.wav", audio_norm, rate)
+
+
+sample_batch_x, sample_batch_y = val_ds[None]  # Random batch
+visualize_audio_np(ops.convert_to_numpy(sample_batch_x[0]))
+visualize_audio_np(ops.convert_to_numpy(sample_batch_y[0, 0]), name="vocals")
+```
+
+
+    
+![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_12_0.png)
+    
+
+
+
+
+<audio  controls="controls" >
+    <source src="data:audio/wav;base64,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+![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_12_2.png)
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" type="audio/wav" />
+    Your browser does not support the audio element.
+</audio>
+
+
+
+---
+## Model
+
+### Preprocessing
+
+The model operates on STFT representations rather than raw audio. We define a
+preprocessing model to compute STFT and a corresponding inverse transform (iSTFT).
+
+
+```python
+
+def stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
+    """Compute the STFT for the input audio and return the real and imaginary parts."""
+    real_x, imag_x = ops.stft(inputs, fft_size, sequence_stride, fft_size)
+    real_x, imag_x = ops.expand_dims(real_x, -1), ops.expand_dims(imag_x, -1)
+    x = ops.concatenate((real_x, imag_x), axis=-1)
+
+    # Drop last freq sample for convenience
+    return ops.split(x, [x.shape[2] - 1], axis=2)[0]
+
+
+def inverse_stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
+    """Compute the inverse STFT for the given STFT input."""
+    x = inputs
+
+    # Pad back dropped freq sample if using torch backend
+    if keras.backend.backend() == "torch":
+        x = ops.pad(x, ((0, 0), (0, 0), (0, 1), (0, 0)))
+
+    real_x, imag_x = ops.split(x, 2, axis=-1)
+    real_x = ops.squeeze(real_x, axis=-1)
+    imag_x = ops.squeeze(imag_x, axis=-1)
+
+    return ops.istft((real_x, imag_x), fft_size, sequence_stride, fft_size)
+
+```
+
+### Model Architecture
+
+The model uses a custom encoder-decoder architecture with Time-Frequency Convolution
+(TFC) and Time-Distributed Fully Connected (TDF) blocks. They are grouped into a
+`TimeFrequencyTransformBlock`, i.e. "TFC_TDF" in the original paper by Choi et al.
+
+We then define an encoder-decoder network with multiple scales. Each encoder scale
+applies TFC_TDF blocks followed by downsampling, while decoder scales apply TFC_TDF
+blocks over the concatenation of upsampled features and associated encoder outputs.
+
+
+```python
+
+@saving.register_keras_serializable()
+class TimeDistributedDenseBlock(layers.Layer):
+    """Time-Distributed Fully Connected layer block.
+
+    Applies frequency-wise dense transformations across time frames with instance
+    normalization and GELU activation.
+    """
+
+    def __init__(self, bottleneck_factor, fft_dim, **kwargs):
+        super().__init__(**kwargs)
+        self.fft_dim = fft_dim
+        self.hidden_dim = fft_dim // bottleneck_factor
+
+    def build(self, *_):
+        self.group_norm_1 = layers.GroupNormalization(groups=-1)
+        self.group_norm_2 = layers.GroupNormalization(groups=-1)
+        self.dense_1 = layers.Dense(self.hidden_dim, use_bias=False)
+        self.dense_2 = layers.Dense(self.fft_dim, use_bias=False)
+
+    def call(self, x):
+        # Apply normalization and dense layers frequency-wise
+        x = ops.gelu(self.group_norm_1(x))
+        x = ops.swapaxes(x, -1, -2)
+        x = self.dense_1(x)
+
+        x = ops.gelu(self.group_norm_2(ops.swapaxes(x, -1, -2)))
+        x = ops.swapaxes(x, -1, -2)
+        x = self.dense_2(x)
+        return ops.swapaxes(x, -1, -2)
+
+
+@saving.register_keras_serializable()
+class TimeFrequencyConvolution(layers.Layer):
+    """Time-Frequency Convolutional layer.
+
+    Applies a 2D convolution over time-frequency representations and applies instance
+    normalization and GELU activation.
+    """
+
+    def __init__(self, channels, **kwargs):
+        super().__init__(**kwargs)
+        self.channels = channels
+
+    def build(self, *_):
+        self.group_norm = layers.GroupNormalization(groups=-1)
+        self.conv = layers.Conv2D(self.channels, 3, padding="same", use_bias=False)
+
+    def call(self, x):
+        return self.conv(ops.gelu(self.group_norm(x)))
+
+
+@saving.register_keras_serializable()
+class TimeFrequencyTransformBlock(layers.Layer):
+    """Implements TFC_TDF block for encoder-decoder architecture.
+
+    Repeatedly apply Time-Frequency Convolution and Time-Distributed Dense blocks as
+    many times as specified by the `length` parameter.
+    """
+
+    def __init__(
+        self, channels, length, fft_dim, bottleneck_factor, in_channels=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.channels = channels
+        self.length = length
+        self.fft_dim = fft_dim
+        self.bottleneck_factor = bottleneck_factor
+        self.in_channels = in_channels or channels
+        self.blocks = []
+
+    def build(self, *_):
+        # Add blocks in a flat list to avoid nested structures
+        for i in range(self.length):
+            in_channels = self.channels if i > 0 else self.in_channels
+            self.blocks.append(TimeFrequencyConvolution(in_channels))
+            self.blocks.append(
+                TimeDistributedDenseBlock(self.bottleneck_factor, self.fft_dim)
+            )
+            self.blocks.append(TimeFrequencyConvolution(self.channels))
+            # Residual connection
+            self.blocks.append(layers.Conv2D(self.channels, 1, 1, use_bias=False))
+
+    def call(self, inputs):
+        x = inputs
+        # Each block consists of 4 layers:
+        # 1. Time-Frequency Convolution
+        # 2. Time-Distributed Dense
+        # 3. Time-Frequency Convolution
+        # 4. Residual connection
+        for i in range(0, len(self.blocks), 4):
+            tfc_1 = self.blocks[i](x)
+            tdf = self.blocks[i + 1](x)
+            tfc_2 = self.blocks[i + 2](tfc_1 + tdf)
+            x = tfc_2 + self.blocks[i + 3](x)  # Residual connection
+        return x
+
+
+@saving.register_keras_serializable()
+class Downscale(layers.Layer):
+    """Downscale time-frequency dimensions using a convolution."""
+
+    conv_cls = layers.Conv2D
+
+    def __init__(self, channels, scale, **kwargs):
+        super().__init__(**kwargs)
+        self.channels = channels
+        self.scale = scale
+
+    def build(self, *_):
+        self.conv = self.conv_cls(self.channels, self.scale, self.scale, use_bias=False)
+        self.norm = layers.GroupNormalization(groups=-1)
+
+    def call(self, inputs):
+        return self.norm(ops.gelu(self.conv(inputs)))
+
+
+@saving.register_keras_serializable()
+class Upscale(Downscale):
+    """Upscale time-frequency dimensions using a transposed convolution."""
+
+    conv_cls = layers.Conv2DTranspose
+
+
+def build_model(
+    inputs,
+    n_instruments=N_INSTRUMENTS,
+    n_subbands=N_SUBBANDS,
+    channels=N_CHANNELS,
+    fft_dim=(STFT_N_FFT // 2) // N_SUBBANDS,
+    n_scales=4,
+    scale=(2, 2),
+    block_size=2,
+    growth=128,
+    bottleneck_factor=2,
+    **kwargs,
+):
+    """Build the TFC_TDF encoder-decoder model for source separation."""
+    # Compute STFT
+    x = stft(inputs)
+
+    # Split mixture into subbands as separate channels
+    mix = ops.reshape(x, (-1, x.shape[1], x.shape[2] // n_subbands, 2 * n_subbands))
+    first_conv_out = layers.Conv2D(channels, 1, 1, use_bias=False)(mix)
+    x = first_conv_out
+
+    # Encoder path
+    encoder_outs = []
+    for _ in range(n_scales):
+        x = TimeFrequencyTransformBlock(
+            channels, block_size, fft_dim, bottleneck_factor
+        )(x)
+        encoder_outs.append(x)
+        fft_dim, channels = fft_dim // scale[0], channels + growth
+        x = Downscale(channels, scale)(x)
+
+    # Bottleneck
+    x = TimeFrequencyTransformBlock(channels, block_size, fft_dim, bottleneck_factor)(x)
+
+    # Decoder path
+    for _ in range(n_scales):
+        fft_dim, channels = fft_dim * scale[0], channels - growth
+        x = ops.concatenate([Upscale(channels, scale)(x), encoder_outs.pop()], axis=-1)
+        x = TimeFrequencyTransformBlock(
+            channels, block_size, fft_dim, bottleneck_factor, in_channels=x.shape[-1]
+        )(x)
+
+    # Residual connection and final convolutions
+    x = ops.concatenate([mix, x * first_conv_out], axis=-1)
+    x = layers.Conv2D(channels, 1, 1, use_bias=False, activation="gelu")(x)
+    x = layers.Conv2D(n_instruments * n_subbands * 2, 1, 1, use_bias=False)(x)
+
+    # Reshape back to instrument-wise STFT
+    x = ops.reshape(x, (-1, x.shape[1], x.shape[2] * n_subbands, n_instruments, 2))
+    x = ops.transpose(x, (0, 3, 1, 2, 4))
+    x = ops.reshape(x, (-1, n_instruments, x.shape[2], x.shape[3] * 2))
+
+    return keras.Model(inputs=inputs, outputs=x, **kwargs)
+
+```
+
+---
+## Loss and Metrics
+
+We define:
+
+- `spectral_loss`: Mean absolute error in STFT domain.
+- `sdr`: Signal-to-Distortion Ratio, a common source separation metric.
+
+
+```python
+
+def prediction_to_wave(x, n_instruments=N_INSTRUMENTS):
+    """Convert STFT predictions back to waveform."""
+    x = ops.reshape(x, (-1, x.shape[2], x.shape[3] // 2, 2))
+    x = inverse_stft(x)
+    return ops.reshape(x, (-1, n_instruments, x.shape[1]))
+
+
+def target_to_stft(y):
+    """Convert target waveforms to their STFT representations."""
+    y = ops.reshape(y, (-1, CHUNK_SIZE))
+    y_real, y_imag = ops.stft(y, STFT_N_FFT, STFT_HOP_LENGTH, STFT_N_FFT)
+    y_real, y_imag = y_real[..., :-1], y_imag[..., :-1]
+    y = ops.stack([y_real, y_imag], axis=-1)
+    return ops.reshape(y, (-1, N_INSTRUMENTS, y.shape[1], y.shape[2] * 2))
+
+
+@saving.register_keras_serializable()
+def sdr(y_true, y_pred):
+    """Signal-to-Distortion Ratio metric."""
+    y_pred = prediction_to_wave(y_pred)
+    # Add epsilon for numerical stability
+    num = ops.sum(ops.square(y_true), axis=-1) + 1e-8
+    den = ops.sum(ops.square(y_true - y_pred), axis=-1) + 1e-8
+    return 10 * ops.log10(num / den)
+
+
+@saving.register_keras_serializable()
+def spectral_loss(y_true, y_pred):
+    """Mean absolute error in the STFT domain."""
+    y_true = target_to_stft(y_true)
+    return ops.mean(ops.absolute(y_true - y_pred))
+
+```
+
+---
+## Training
+
+### Visualize Model Architecture
+
+
+```python
+# Load or create the model
+if path.exists(MODEL_PATH):
+    model = saving.load_model(MODEL_PATH)
+else:
+    model = build_model(keras.Input(sample_batch_x.shape[1:]), name="tfc_tdf_net")
+
+# Display the model architecture
+model.summary()
+img = keras.utils.plot_model(model, path.join(TMP_DIR, "model.png"), show_shapes=True)
+display.display(img)
+```
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "tfc_tdf_net"</span>
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
+┃<span style="font-weight: bold"> Layer (type)        </span>┃<span style="font-weight: bold"> Output Shape      </span>┃<span style="font-weight: bold">    Param # </span>┃<span style="font-weight: bold"> Connected to      </span>┃
+┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
+│ input_layer         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">65024</span>)     │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ -                 │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">InputLayer</span>)        │                   │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ stft (<span style="color: #0087ff; text-decoration-color: #0087ff">STFT</span>)         │ [(<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,      │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ input_layer[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">1025</span>), (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>,     │            │                   │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>)]       │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ expand_dims         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ stft[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]        │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">ExpandDims</span>)        │ <span style="color: #00af00; text-decoration-color: #00af00">1</span>)                │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ expand_dims_1       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ stft[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">1</span>]        │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">ExpandDims</span>)        │ <span style="color: #00af00; text-decoration-color: #00af00">1</span>)                │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ concatenate         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ expand_dims[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">2</span>)                │            │ expand_dims_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ split (<span style="color: #0087ff; text-decoration-color: #0087ff">Split</span>)       │ [(<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,      │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ concatenate[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">1024</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>), (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>,  │            │                   │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)]       │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ reshape (<span style="color: #0087ff; text-decoration-color: #0087ff">Reshape</span>)   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ split[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]       │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">8</span>)                │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ conv2d (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │        <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ reshape[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │    <span style="color: #00af00; text-decoration-color: #00af00">287,744</span> │ conv2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ downscale           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │     <span style="color: #00af00; text-decoration-color: #00af00">49,536</span> │ time_frequency_t… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │  <span style="color: #00af00; text-decoration-color: #00af00">1,436,672</span> │ downscale[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ downscale_1         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │    <span style="color: #00af00; text-decoration-color: #00af00">246,400</span> │ time_frequency_t… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">3,904,512</span> │ downscale_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ downscale_2         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │    <span style="color: #00af00; text-decoration-color: #00af00">574,336</span> │ time_frequency_t… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">7,635,968</span> │ downscale_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ downscale_3         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">8</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>,     │  <span style="color: #00af00; text-decoration-color: #00af00">1,033,344</span> │ time_frequency_t… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">576</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">8</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>,     │ <span style="color: #00af00; text-decoration-color: #00af00">12,617,216</span> │ downscale_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">576</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ upscale (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>)   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">1,033,088</span> │ time_frequency_t… │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ concatenate_1       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">896</span>)              │            │ time_frequency_t… │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │ <span style="color: #00af00; text-decoration-color: #00af00">15,065,600</span> │ concatenate_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ upscale_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │    <span style="color: #00af00; text-decoration-color: #00af00">574,080</span> │ time_frequency_t… │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ concatenate_2       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],  │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">640</span>)              │            │ time_frequency_t… │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">7,695,872</span> │ concatenate_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ upscale_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │    <span style="color: #00af00; text-decoration-color: #00af00">246,144</span> │ time_frequency_t… │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ concatenate_3       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],  │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">384</span>)              │            │ time_frequency_t… │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │  <span style="color: #00af00; text-decoration-color: #00af00">2,802,176</span> │ concatenate_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ upscale_3 (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │     <span style="color: #00af00; text-decoration-color: #00af00">49,280</span> │ time_frequency_t… │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ concatenate_4       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],  │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">128</span>)              │            │ time_frequency_t… │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │    <span style="color: #00af00; text-decoration-color: #00af00">439,808</span> │ concatenate_4[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ multiply (<span style="color: #0087ff; text-decoration-color: #0087ff">Multiply</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ time_frequency_t… │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │ conv2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ concatenate_5       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ reshape[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">72</span>)               │            │ multiply[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]    │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ conv2d_59 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │      <span style="color: #00af00; text-decoration-color: #00af00">4,608</span> │ concatenate_5[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ conv2d_60 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │        <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv2d_59[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">8</span>)                │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ reshape_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Reshape</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2d_60[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)             │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ transpose           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ reshape_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
+│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Transpose</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">1024</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)          │            │                   │
+├─────────────────────┼───────────────────┼────────────┼───────────────────┤
+│ reshape_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">Reshape</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ transpose[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
+│                     │ <span style="color: #00af00; text-decoration-color: #00af00">2048</span>)             │            │                   │
+└─────────────────────┴───────────────────┴────────────┴───────────────────┘
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">222,789,634</span> (849.88 MB)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">55,697,408</span> (212.47 MB)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">0</span> (0.00 B)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Optimizer params: </span><span style="color: #00af00; text-decoration-color: #00af00">167,092,226</span> (637.41 MB)
+</pre>
+
+
+
+
+    
+![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_20_6.png)
+    
+
+
+### Compile and Train the Model
+
+
+```python
+# Compile the model
+optimizer = keras.optimizers.Adam(5e-05, gradient_accumulation_steps=ACCUMULATION_STEPS)
+model.compile(optimizer=optimizer, loss=spectral_loss, metrics=[sdr])
+
+# Define callbacks
+cbs = [
+    callbacks.ModelCheckpoint(MODEL_PATH, "val_sdr", save_best_only=True, mode="max"),
+    callbacks.ReduceLROnPlateau(factor=0.95, patience=2),
+    callbacks.CSVLogger(CSV_LOG_PATH),
+]
+
+if not path.exists(MODEL_PATH):
+    model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=cbs, shuffle=False)
+else:
+    # Demonstration of a single epoch of training when model already exists
+    model.fit(train_ds, validation_data=val_ds, epochs=1, shuffle=False, verbose=2)
+```
+
+<div class="k-default-codeblock">
+```
+2000/2000 - 490s - 245ms/step - loss: 0.2977 - sdr: 5.6497 - val_loss: 0.1720 - val_sdr: 6.0508
+
+```
+</div>
+---
+## Evaluation
+
+Evaluate the model on the validation dataset and visualize predicted vocals.
+
+
+```python
+model.evaluate(val_ds, verbose=2)
+y_pred = model.predict(sample_batch_x, verbose=2)
+y_pred = prediction_to_wave(y_pred)
+visualize_audio_np(ops.convert_to_numpy(y_pred[0, 0]), name="vocals_pred")
+```
+
+<div class="k-default-codeblock">
+```
+200/200 - 8s - 41ms/step - loss: 0.1747 - sdr: 5.9374
+
+1/1 - 4s - 4s/step
+
+```
+</div>
+    
+![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_24_2.png)
+    
+
+
+
+
+<audio  controls="controls" >
+    <source 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" type="audio/wav" />
+    Your browser does not support the audio element.
+</audio>
+
+
+
+---
+## Conclusion
+
+We built and trained a vocal track separation model using an encoder-decoder
+architecture with custom blocks applied to the MUSDB18 dataset. We demonstrated
+STFT-based preprocessing, data augmentation, and a source separation metric (SDR).
+
+**Next steps:**
+
+- Train for more epochs and refine hyperparameters.
+- Separate multiple instruments simultaneously.
+- Enhance the model to handle instruments not present in the mixture.
+

From ee5be7d1b58f040be0f0f67cdacb95c0f21fbbce Mon Sep 17 00:00:00 2001
From: Damoon <damoon.shah3@gmail.com>
Date: Thu, 27 Feb 2025 21:21:53 +0300
Subject: [PATCH 2/5] added by mistake

---
 examples/audio/vocal_track_separation.py | 673 -----------------------
 1 file changed, 673 deletions(-)
 delete mode 100644 examples/audio/vocal_track_separation.py

diff --git a/examples/audio/vocal_track_separation.py b/examples/audio/vocal_track_separation.py
deleted file mode 100644
index 25574e0ab8..0000000000
--- a/examples/audio/vocal_track_separation.py
+++ /dev/null
@@ -1,673 +0,0 @@
-"""
-Title: Vocal Track Separation with Encoder-Decoder Architecture
-Author: [Joaquin Jimenez](https://github.com/johacks/)
-Date created: 2024/12/10
-Last modified: 2024/12/10
-Description: Train a model to separate vocal tracks from music mixtures.
-Accelerator: GPU
-"""
-
-"""
-## Introduction
-
-In this tutorial, we build a vocal track separation model using an encoder-decoder
-architecture in Keras 3.
-
-We train the model on the [MUSDB18 dataset](https://doi.org/10.5281/zenodo.1117372),
-which provides music mixtures and isolated tracks for drums, bass, other, and vocals.
-
-Key concepts covered:
-
-- Audio data preprocessing using the Short-Time Fourier Transform (STFT).
-- Audio data augmentation techniques.
-- Implementing custom encoders and decoders specialized for audio data.
-- Defining appropriate loss functions and metrics for audio source separation tasks.
-
-The model architecture is derived from the TFC_TDF_Net model described in:
-
-W. Choi, M. Kim, J. Chung, D. Lee, and S. Jung, “Investigating U-Nets with various
-intermediate blocks for spectrogram-based singing voice separation,” in the 21st
-International Society for Music Information Retrieval Conference, 2020.
-
-For reference code, see:
-[GitHub: ws-choi/ISMIR2020_U_Nets_SVS](https://github.com/ws-choi/ISMIR2020_U_Nets_SVS).
-
-The data processing and model training routines are partly derived from:
-[ZFTurbo/Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training/tree/main).
-"""
-
-"""
-## Setup
-
-Import and install all the required dependencies.
-"""
-
-"""shell
-pip install -qq audiomentations soundfile ffmpeg-binaries
-pip install -qq "keras==3.7.0"
-sudo -n apt-get install -y graphviz >/dev/null 2>&1  # Required for plotting the model
-"""
-
-import glob
-import os
-
-os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
-
-import random
-import subprocess
-import tempfile
-import typing
-from os import path
-
-import audiomentations as aug
-import ffmpeg
-import keras
-import numpy as np
-import soundfile as sf
-from IPython import display
-from keras import callbacks, layers, ops, saving
-from matplotlib import pyplot as plt
-
-"""
-## Configuration
-
-The following constants define configuration parameters for audio processing
-and model training, including dataset paths, audio chunk sizes, Short-Time Fourier
-Transform (STFT) parameters, and training hyperparameters.
-"""
-
-# MUSDB18 dataset configuration
-MUSDB_STREAMS = {"mixture": 0, "drums": 1, "bass": 2, "other": 3, "vocals": 4}
-TARGET_INSTRUMENTS = {track: MUSDB_STREAMS[track] for track in ("vocals",)}
-N_INSTRUMENTS = len(TARGET_INSTRUMENTS)
-SOURCE_INSTRUMENTS = tuple(k for k in MUSDB_STREAMS if k != "mixture")
-
-# Audio preprocessing parameters for Short-Time Fourier Transform (STFT)
-N_SUBBANDS = 4  # Number of subbands into which frequencies are split
-CHUNK_SIZE = 65024  # Number of amplitude samples per audio chunk (~4 seconds)
-STFT_N_FFT = 2048  # FFT points used in STFT
-STFT_HOP_LENGTH = 512  # Hop length for STFT
-
-# Training hyperparameters
-N_CHANNELS = 64  # Base channel count for the model
-BATCH_SIZE = 3
-ACCUMULATION_STEPS = 2
-EFFECTIVE_BATCH_SIZE = BATCH_SIZE * (ACCUMULATION_STEPS or 1)
-
-# Paths
-TMP_DIR = path.expanduser("~/.keras/tmp")
-DATASET_DIR = path.expanduser("~/.keras/datasets")
-MODEL_PATH = path.join(TMP_DIR, f"model_{keras.backend.backend()}.keras")
-CSV_LOG_PATH = path.join(TMP_DIR, f"training_{keras.backend.backend()}.csv")
-os.makedirs(DATASET_DIR, exist_ok=True)
-os.makedirs(TMP_DIR, exist_ok=True)
-
-# Set random seed for reproducibility
-keras.utils.set_random_seed(21)
-
-"""
-## MUSDB18 Dataset
-
-The MUSDB18 dataset is a standard benchmark for music source separation, containing
-150 full-length music tracks along with isolated drums, bass, other, and vocals.
-The dataset is stored in .mp4 format, and each .mp4 file includes multiple audio
-streams (mixture and individual tracks).
-
-### Download and Conversion
-
-The following utility function downloads MUSDB18 and converts its .mp4 files to
-.wav files for each instrument track, resampled to 16 kHz.
-"""
-
-
-def download_musdb18(out_dir=None):
-    """Download and extract the MUSDB18 dataset, then convert .mp4 files to .wav files.
-
-    MUSDB18 reference:
-    Rafii, Z., Liutkus, A., Stöter, F.-R., Mimilakis, S. I., & Bittner, R. (2017).
-    MUSDB18 - a corpus for music separation (1.0.0) [Data set]. Zenodo.
-    """
-    ffmpeg.init()
-    from ffmpeg import FFMPEG_PATH
-
-    # Create output directories
-    os.makedirs((base := out_dir or tempfile.mkdtemp()), exist_ok=True)
-    if path.exists((out_dir := path.join(base, "musdb18_wav"))):
-        print("MUSDB18 dataset already downloaded")
-        return out_dir
-
-    # Download and extract the dataset
-    download_dir = keras.utils.get_file(
-        fname="musdb18",
-        origin="https://zenodo.org/records/1117372/files/musdb18.zip",
-        extract=True,
-    )
-
-    # ffmpeg command template: input, stream index, output
-    ffmpeg_args = str(FFMPEG_PATH) + " -v error -i {} -map 0:{} -vn -ar 16000 {}"
-
-    # Convert each mp4 file to multiple .wav files for each track
-    for split in ("train", "test"):
-        songs = os.listdir(path.join(download_dir, split))
-        for i, song in enumerate(songs):
-            if i % 10 == 0:
-                print(f"{split.capitalize()}: {i}/{len(songs)} songs processed")
-
-            mp4_path_orig = path.join(download_dir, split, song)
-            mp4_path = path.join(tempfile.mkdtemp(), split, song.replace(" ", "_"))
-            os.makedirs(path.dirname(mp4_path), exist_ok=True)
-            os.rename(mp4_path_orig, mp4_path)
-
-            wav_dir = path.join(out_dir, split, path.basename(mp4_path).split(".")[0])
-            os.makedirs(wav_dir, exist_ok=True)
-
-            for track in SOURCE_INSTRUMENTS:
-                out_path = path.join(wav_dir, f"{track}.wav")
-                stream_index = MUSDB_STREAMS[track]
-                args = ffmpeg_args.format(mp4_path, stream_index, out_path).split()
-                assert subprocess.run(args).returncode == 0, "ffmpeg conversion failed"
-    return out_dir
-
-
-# Download and prepare the MUSDB18 dataset
-songs = download_musdb18(out_dir=DATASET_DIR)
-
-"""
-### Custom Dataset
-
-We define a custom dataset class to generate random audio chunks and their corresponding
-labels. The dataset does the following:
-
-1. Selects a random chunk from a random song and instrument.
-2. Applies optional data augmentations.
-3. Combines isolated tracks to form new synthetic mixtures.
-4. Prepares features (mixtures) and labels (vocals) for training.
-
-This approach allows creating an effectively infinite variety of training examples
-through randomization and augmentation.
-"""
-
-
-class Dataset(keras.utils.PyDataset):
-    def __init__(
-        self,
-        songs,
-        batch_size=BATCH_SIZE,
-        chunk_size=CHUNK_SIZE,
-        batches_per_epoch=1000 * ACCUMULATION_STEPS,
-        augmentation=True,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.augmentation = augmentation
-        self.vocals_augmentations = [
-            aug.PitchShift(min_semitones=-5, max_semitones=5, p=0.1),
-            aug.SevenBandParametricEQ(-9, 9, p=0.25),
-            aug.TanhDistortion(0.1, 0.7, p=0.1),
-        ]
-        self.other_augmentations = [
-            aug.PitchShift(p=0.1),
-            aug.AddGaussianNoise(p=0.1),
-        ]
-        self.songs = songs
-        self.sizes = {song: self.get_track_set_size(song) for song in self.songs}
-        self.batch_size = batch_size
-        self.chunk_size = chunk_size
-        self.batches_per_epoch = batches_per_epoch
-
-    def get_track_set_size(self, song: str):
-        """Return the smallest track length in the given song directory."""
-        sizes = [len(sf.read(p)[0]) for p in glob.glob(path.join(song, "*.wav"))]
-        if max(sizes) != min(sizes):
-            print(f"Warning: {song} has different track lengths")
-        return min(sizes)
-
-    def random_chunk_of_instrument_type(self, instrument: str):
-        """Extract a random chunk for the specified instrument from a random song."""
-        song, size = random.choice(list(self.sizes.items()))
-        track = path.join(song, f"{instrument}.wav")
-
-        if self.chunk_size <= size:
-            start = np.random.randint(size - self.chunk_size + 1)
-            audio = sf.read(track, self.chunk_size, start, dtype="float32")[0]
-            audio_mono = np.mean(audio, axis=1)
-        else:
-            # If the track is shorter than chunk_size, pad the signal
-            audio_mono = np.mean(sf.read(track, dtype="float32")[0], axis=1)
-            audio_mono = np.pad(audio_mono, ((0, self.chunk_size - size),))
-
-        # If the chunk is almost silent, retry
-        if np.mean(np.abs(audio_mono)) < 0.01:
-            return self.random_chunk_of_instrument_type(instrument)
-
-        return self.data_augmentation(audio_mono, instrument)
-
-    def data_augmentation(self, audio: np.ndarray, instrument: str):
-        """Apply data augmentation to the audio chunk, if enabled."""
-
-        def coin_flip(x, probability: float, fn: typing.Callable):
-            return fn(x) if random.uniform(0, 1) < probability else x
-
-        if self.augmentation:
-            augmentations = (
-                self.vocals_augmentations
-                if instrument == "vocals"
-                else self.other_augmentations
-            )
-            # Loudness augmentation
-            audio *= np.random.uniform(0.5, 1.5, (len(audio),)).astype("float32")
-            # Random reverse
-            audio = coin_flip(audio, 0.1, lambda x: np.flip(x))
-            # Random polarity inversion
-            audio = coin_flip(audio, 0.5, lambda x: -x)
-            # Apply selected augmentations
-            for aug_ in augmentations:
-                aug_.randomize_parameters(audio, sample_rate=16000)
-                audio = aug_(audio, sample_rate=16000)
-        return audio
-
-    def random_mix_of_tracks(self) -> dict:
-        """Create a random mix of instruments by summing their individual chunks."""
-        tracks = {}
-        for instrument in SOURCE_INSTRUMENTS:
-            # Start with a single random chunk
-            mixup = [self.random_chunk_of_instrument_type(instrument)]
-
-            # Randomly add more chunks of the same instrument (mixup augmentation)
-            if self.augmentation:
-                for p in (0.2, 0.02):
-                    if random.uniform(0, 1) < p:
-                        mixup.append(self.random_chunk_of_instrument_type(instrument))
-
-            tracks[instrument] = np.mean(mixup, axis=0, dtype="float32")
-        return tracks
-
-    def __len__(self):
-        return self.batches_per_epoch
-
-    def __getitem__(self, idx):
-        # Generate a batch of random mixtures
-        batch = [self.random_mix_of_tracks() for _ in range(self.batch_size)]
-
-        # Features: sum of all tracks
-        batch_x = ops.sum(
-            np.array([list(track_set.values()) for track_set in batch]), axis=1
-        )
-
-        # Labels: isolated target instruments (e.g., vocals)
-        batch_y = np.array(
-            [[track_set[t] for t in TARGET_INSTRUMENTS] for track_set in batch]
-        )
-
-        return batch_x, ops.convert_to_tensor(batch_y)
-
-
-# Create train and validation datasets
-train_ds = Dataset(glob.glob(path.join(songs, "train", "*")))
-val_ds = Dataset(
-    glob.glob(path.join(songs, "test", "*")),
-    batches_per_epoch=int(0.1 * train_ds.batches_per_epoch),
-    augmentation=False,
-)
-
-"""
-### Visualize a Sample
-
-Let's visualize a random mixed audio chunk and its corresponding isolated vocals.
-This helps to understand the nature of the preprocessed input data.
-"""
-
-
-def visualize_audio_np(audio: np.ndarray, rate=16000, name="mixup"):
-    """Plot and display an audio waveform and also produce an Audio widget."""
-    plt.figure(figsize=(10, 6))
-    plt.plot(audio)
-    plt.title(f"Waveform: {name}")
-    plt.xlim(0, len(audio))
-    plt.ylabel("Amplitude")
-    plt.show()
-    # plt.savefig(f"tmp/{name}.png")
-
-    # Normalize and display audio
-    audio_norm = (audio - np.min(audio)) / (np.max(audio) - np.min(audio) + 1e-8)
-    audio_norm = (audio_norm * 2 - 1) * 0.6
-    display.display(display.Audio(audio_norm, rate=rate))
-    # sf.write(f"tmp/{name}.wav", audio_norm, rate)
-
-
-sample_batch_x, sample_batch_y = val_ds[None]  # Random batch
-visualize_audio_np(ops.convert_to_numpy(sample_batch_x[0]))
-visualize_audio_np(ops.convert_to_numpy(sample_batch_y[0, 0]), name="vocals")
-
-"""
-## Model
-
-### Preprocessing
-
-The model operates on STFT representations rather than raw audio. We define a
-preprocessing model to compute STFT and a corresponding inverse transform (iSTFT).
-"""
-
-
-def stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
-    """Compute the STFT for the input audio and return the real and imaginary parts."""
-    real_x, imag_x = ops.stft(inputs, fft_size, sequence_stride, fft_size)
-    real_x, imag_x = ops.expand_dims(real_x, -1), ops.expand_dims(imag_x, -1)
-    x = ops.concatenate((real_x, imag_x), axis=-1)
-
-    # Drop last freq sample for convenience
-    return ops.split(x, [x.shape[2] - 1], axis=2)[0]
-
-
-def inverse_stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
-    """Compute the inverse STFT for the given STFT input."""
-    x = inputs
-
-    # Pad back dropped freq sample if using torch backend
-    if keras.backend.backend() == "torch":
-        x = ops.pad(x, ((0, 0), (0, 0), (0, 1), (0, 0)))
-
-    real_x, imag_x = ops.split(x, 2, axis=-1)
-    real_x = ops.squeeze(real_x, axis=-1)
-    imag_x = ops.squeeze(imag_x, axis=-1)
-
-    return ops.istft((real_x, imag_x), fft_size, sequence_stride, fft_size)
-
-
-"""
-### Model Architecture
-
-The model uses a custom encoder-decoder architecture with Time-Frequency Convolution
-(TFC) and Time-Distributed Fully Connected (TDF) blocks. They are grouped into a
-`TimeFrequencyTransformBlock`, i.e. "TFC_TDF" in the original paper by Choi et al.
-
-We then define an encoder-decoder network with multiple scales. Each encoder scale
-applies TFC_TDF blocks followed by downsampling, while decoder scales apply TFC_TDF
-blocks over the concatenation of upsampled features and associated encoder outputs.
-"""
-
-
-@saving.register_keras_serializable()
-class TimeDistributedDenseBlock(layers.Layer):
-    """Time-Distributed Fully Connected layer block.
-
-    Applies frequency-wise dense transformations across time frames with instance
-    normalization and GELU activation.
-    """
-
-    def __init__(self, bottleneck_factor, fft_dim, **kwargs):
-        super().__init__(**kwargs)
-        self.fft_dim = fft_dim
-        self.hidden_dim = fft_dim // bottleneck_factor
-
-    def build(self, *_):
-        self.group_norm_1 = layers.GroupNormalization(groups=-1)
-        self.group_norm_2 = layers.GroupNormalization(groups=-1)
-        self.dense_1 = layers.Dense(self.hidden_dim, use_bias=False)
-        self.dense_2 = layers.Dense(self.fft_dim, use_bias=False)
-
-    def call(self, x):
-        # Apply normalization and dense layers frequency-wise
-        x = ops.gelu(self.group_norm_1(x))
-        x = ops.swapaxes(x, -1, -2)
-        x = self.dense_1(x)
-
-        x = ops.gelu(self.group_norm_2(ops.swapaxes(x, -1, -2)))
-        x = ops.swapaxes(x, -1, -2)
-        x = self.dense_2(x)
-        return ops.swapaxes(x, -1, -2)
-
-
-@saving.register_keras_serializable()
-class TimeFrequencyConvolution(layers.Layer):
-    """Time-Frequency Convolutional layer.
-
-    Applies a 2D convolution over time-frequency representations and applies instance
-    normalization and GELU activation.
-    """
-
-    def __init__(self, channels, **kwargs):
-        super().__init__(**kwargs)
-        self.channels = channels
-
-    def build(self, *_):
-        self.group_norm = layers.GroupNormalization(groups=-1)
-        self.conv = layers.Conv2D(self.channels, 3, padding="same", use_bias=False)
-
-    def call(self, x):
-        return self.conv(ops.gelu(self.group_norm(x)))
-
-
-@saving.register_keras_serializable()
-class TimeFrequencyTransformBlock(layers.Layer):
-    """Implements TFC_TDF block for encoder-decoder architecture.
-
-    Repeatedly apply Time-Frequency Convolution and Time-Distributed Dense blocks as
-    many times as specified by the `length` parameter.
-    """
-
-    def __init__(
-        self, channels, length, fft_dim, bottleneck_factor, in_channels=None, **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.channels = channels
-        self.length = length
-        self.fft_dim = fft_dim
-        self.bottleneck_factor = bottleneck_factor
-        self.in_channels = in_channels or channels
-
-    def build(self, *_):
-        self.blocks = []
-        # Add blocks in a flat list to avoid nested structures
-        for i in range(self.length):
-            in_channels = self.channels if i > 0 else self.in_channels
-            self.blocks.append(TimeFrequencyConvolution(in_channels))
-            self.blocks.append(
-                TimeDistributedDenseBlock(self.bottleneck_factor, self.fft_dim)
-            )
-            self.blocks.append(TimeFrequencyConvolution(self.channels))
-            # Residual connection
-            self.blocks.append(layers.Conv2D(self.channels, 1, 1, use_bias=False))
-
-    def call(self, inputs):
-        x = inputs
-        # Each block consists of 4 layers:
-        # 1. Time-Frequency Convolution
-        # 2. Time-Distributed Dense
-        # 3. Time-Frequency Convolution
-        # 4. Residual connection
-        for i in range(0, len(self.blocks), 4):
-            tfc_1 = self.blocks[i](x)
-            tdf = self.blocks[i + 1](x)
-            tfc_2 = self.blocks[i + 2](tfc_1 + tdf)
-            x = tfc_2 + self.blocks[i + 3](x)  # Residual connection
-        return x
-
-
-@saving.register_keras_serializable()
-class Downscale(layers.Layer):
-    """Downscale time-frequency dimensions using a convolution."""
-
-    conv_cls = layers.Conv2D
-
-    def __init__(self, channels, scale, **kwargs):
-        super().__init__(**kwargs)
-        self.channels = channels
-        self.scale = scale
-
-    def build(self, *_):
-        self.conv = self.conv_cls(self.channels, self.scale, self.scale, use_bias=False)
-        self.norm = layers.GroupNormalization(groups=-1)
-
-    def call(self, inputs):
-        return self.norm(ops.gelu(self.conv(inputs)))
-
-
-@saving.register_keras_serializable()
-class Upscale(Downscale):
-    """Upscale time-frequency dimensions using a transposed convolution."""
-
-    conv_cls = layers.Conv2DTranspose
-
-
-def build_model(
-    inputs,
-    n_instruments=N_INSTRUMENTS,
-    n_subbands=N_SUBBANDS,
-    channels=N_CHANNELS,
-    fft_dim=(STFT_N_FFT // 2) // N_SUBBANDS,
-    n_scales=4,
-    scale=(2, 2),
-    block_size=2,
-    growth=128,
-    bottleneck_factor=2,
-    **kwargs,
-):
-    """Build the TFC_TDF encoder-decoder model for source separation."""
-    # Compute STFT
-    x = stft(inputs)
-
-    # Split mixture into subbands as separate channels
-    mix = ops.reshape(x, (-1, x.shape[1], x.shape[2] // n_subbands, 2 * n_subbands))
-    first_conv_out = layers.Conv2D(channels, 1, 1, use_bias=False)(mix)
-    x = first_conv_out
-
-    # Encoder path
-    encoder_outs = []
-    for _ in range(n_scales):
-        x = TimeFrequencyTransformBlock(
-            channels, block_size, fft_dim, bottleneck_factor
-        )(x)
-        encoder_outs.append(x)
-        fft_dim, channels = fft_dim // scale[0], channels + growth
-        x = Downscale(channels, scale)(x)
-
-    # Bottleneck
-    x = TimeFrequencyTransformBlock(channels, block_size, fft_dim, bottleneck_factor)(x)
-
-    # Decoder path
-    for _ in range(n_scales):
-        fft_dim, channels = fft_dim * scale[0], channels - growth
-        x = ops.concatenate([Upscale(channels, scale)(x), encoder_outs.pop()], axis=-1)
-        x = TimeFrequencyTransformBlock(
-            channels, block_size, fft_dim, bottleneck_factor, in_channels=x.shape[-1]
-        )(x)
-
-    # Residual connection and final convolutions
-    x = ops.concatenate([mix, x * first_conv_out], axis=-1)
-    x = layers.Conv2D(channels, 1, 1, use_bias=False, activation="gelu")(x)
-    x = layers.Conv2D(n_instruments * n_subbands * 2, 1, 1, use_bias=False)(x)
-
-    # Reshape back to instrument-wise STFT
-    x = ops.reshape(x, (-1, x.shape[1], x.shape[2] * n_subbands, n_instruments, 2))
-    x = ops.transpose(x, (0, 3, 1, 2, 4))
-    x = ops.reshape(x, (-1, n_instruments, x.shape[2], x.shape[3] * 2))
-
-    return keras.Model(inputs=inputs, outputs=x, **kwargs)
-
-
-"""
-## Loss and Metrics
-
-We define:
-
-- `spectral_loss`: Mean absolute error in STFT domain.
-- `sdr`: Signal-to-Distortion Ratio, a common source separation metric.
-"""
-
-
-def prediction_to_wave(x, n_instruments=N_INSTRUMENTS):
-    """Convert STFT predictions back to waveform."""
-    x = ops.reshape(x, (-1, x.shape[2], x.shape[3] // 2, 2))
-    x = inverse_stft(x)
-    return ops.reshape(x, (-1, n_instruments, x.shape[1]))
-
-
-def target_to_stft(y):
-    """Convert target waveforms to their STFT representations."""
-    y = ops.reshape(y, (-1, CHUNK_SIZE))
-    y_real, y_imag = ops.stft(y, STFT_N_FFT, STFT_HOP_LENGTH, STFT_N_FFT)
-    y_real, y_imag = y_real[..., :-1], y_imag[..., :-1]
-    y = ops.stack([y_real, y_imag], axis=-1)
-    return ops.reshape(y, (-1, N_INSTRUMENTS, y.shape[1], y.shape[2] * 2))
-
-
-@saving.register_keras_serializable()
-def sdr(y_true, y_pred):
-    """Signal-to-Distortion Ratio metric."""
-    y_pred = prediction_to_wave(y_pred)
-    # Add epsilon for numerical stability
-    num = ops.sum(ops.square(y_true), axis=-1) + 1e-8
-    den = ops.sum(ops.square(y_true - y_pred), axis=-1) + 1e-8
-    return 10 * ops.log10(num / den)
-
-
-@saving.register_keras_serializable()
-def spectral_loss(y_true, y_pred):
-    """Mean absolute error in the STFT domain."""
-    y_true = target_to_stft(y_true)
-    return ops.mean(ops.absolute(y_true - y_pred))
-
-
-"""
-## Training
-
-### Visualize Model Architecture
-"""
-
-# Load or create the model
-if path.exists(MODEL_PATH):
-    model = saving.load_model(MODEL_PATH)
-else:
-    model = build_model(keras.Input(sample_batch_x.shape[1:]), name="tfc_tdf_net")
-
-# Display the model architecture
-model.summary()
-img = keras.utils.plot_model(model, path.join(TMP_DIR, "model.png"), show_shapes=True)
-display.display(img)
-
-"""
-### Compile and Train the Model
-"""
-
-# Compile the model
-optimizer = keras.optimizers.Adam(5e-05, gradient_accumulation_steps=ACCUMULATION_STEPS)
-model.compile(optimizer=optimizer, loss=spectral_loss, metrics=[sdr])
-
-# Define callbacks
-cbs = [
-    callbacks.ModelCheckpoint(MODEL_PATH, "val_sdr", save_best_only=True, mode="max"),
-    callbacks.ReduceLROnPlateau(factor=0.95, patience=2),
-    callbacks.CSVLogger(CSV_LOG_PATH),
-]
-
-if not path.exists(MODEL_PATH):
-    model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=cbs, shuffle=False)
-else:
-    # Demonstration of a single epoch of training when model already exists
-    model.fit(train_ds, validation_data=val_ds, epochs=1, shuffle=False, verbose=2)
-
-"""
-## Evaluation
-
-Evaluate the model on the validation dataset and visualize predicted vocals.
-"""
-
-model.evaluate(val_ds, verbose=2)
-y_pred = model.predict(sample_batch_x, verbose=2)
-y_pred = prediction_to_wave(y_pred)
-visualize_audio_np(ops.convert_to_numpy(y_pred[0, 0]), name="vocals_pred")
-
-"""
-## Conclusion
-
-We built and trained a vocal track separation model using an encoder-decoder
-architecture with custom blocks applied to the MUSDB18 dataset. We demonstrated
-STFT-based preprocessing, data augmentation, and a source separation metric (SDR).
-
-**Next steps:**
-
-- Train for more epochs and refine hyperparameters.
-- Separate multiple instruments simultaneously.
-- Enhance the model to handle instruments not present in the mixture.
-"""

From 790b008504e829d1246d80c476085fa39e0dd572 Mon Sep 17 00:00:00 2001
From: Damoon <damoon.shah3@gmail.com>
Date: Fri, 28 Feb 2025 01:45:13 +0300
Subject: [PATCH 3/5] Optimized the code, more details added

---
 examples/vision/ipynb/mnist_moe.ipynb |   235 +-
 examples/vision/md/mnist_moe.md       | 22592 +-----------------------
 examples/vision/mnist_moe.py          |   228 +-
 3 files changed, 443 insertions(+), 22612 deletions(-)

diff --git a/examples/vision/ipynb/mnist_moe.ipynb b/examples/vision/ipynb/mnist_moe.ipynb
index 0d4c4a0ad4..931cb764c6 100644
--- a/examples/vision/ipynb/mnist_moe.ipynb
+++ b/examples/vision/ipynb/mnist_moe.ipynb
@@ -11,7 +11,7 @@
     "**Author:** [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)<br>\n",
     "**Date created:** 2015/06/19<br>\n",
     "**Last modified:** 2020/04/21<br>\n",
-    "**Description:** Showcasing concepts relates to Mixture of Experts (MoE)."
+    "**Description:** Simple MoE implementation for MNIST classification."
    ]
   },
   {
@@ -29,11 +29,10 @@
     "At each forward pass, a gating network selects a subset of experts to apply to the input.\n",
     "\n",
     "The components to implement are:\n",
+    "\n",
     "- Gating network: A dense layer that outputs a probability distribution over the experts.\n",
     "- MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.\n",
-    "- Model: A simple model that uses the MoE layer.\n",
-    "\n",
-    "In this example, we will first implement a linear MoE layer and then a CNN-based MoE layer. Lastly we will combine the two using an abstract implementation to showcase its capacties."
+    "- Model: A simple model that uses the MoE layer."
    ]
   },
   {
@@ -120,7 +119,7 @@
     "NUM_EXPERTS = 5\n",
     "TOP_K = 3\n",
     "BATCH_SIZE = 128\n",
-    "NUM_EPOCHS = 20\n",
+    "NUM_EPOCHS = 12\n",
     "LEARNING_RATE = 0.001\n",
     ""
    ]
@@ -207,6 +206,7 @@
     "                mean=0.0, stddev=0.001\n",
     "            ),\n",
     "            bias_initializer=\"zeros\",\n",
+    "            activation=\"softmax\",\n",
     "        )\n",
     "\n",
     "        self.num_experts = num_experts\n",
@@ -216,33 +216,50 @@
     "            tf.zeros((num_experts,), dtype=tf.float32)\n",
     "        )\n",
     "\n",
-    "    def call(self, x):\n",
-    "        # Get gating weights\n",
-    "        gating_weights = self.gating_network(x)\n",
+    "    def get_top_outputs(self, x, top_k_indices, top_k_weights):\n",
+    "        batch_size = tf.shape(x)[0]\n",
+    "        flat_indices = tf.reshape(top_k_indices, [-1])\n",
+    "        repeated_x = tf.repeat(x, repeats=self.top_k, axis=0)\n",
+    "\n",
+    "        # Compute outputs for unique experts\n",
+    "        unique_expert_ids = tf.unique(flat_indices)[0]  # Get unique expert indices\n",
+    "        expert_outputs_dict = {}\n",
+    "        for idx in unique_expert_ids:\n",
+    "            mask = tf.equal(flat_indices, idx)\n",
+    "            selected_inputs = tf.boolean_mask(repeated_x, mask)\n",
+    "            expert_outputs_dict[idx.numpy()] = self.experts[idx](selected_inputs)\n",
+    "\n",
+    "        # Gather outputs back into the correct shape\n",
+    "        output_size = self.experts[0].compute_output_shape(input_shape=(None, 10))[-1]\n",
+    "        flat_outputs = tf.zeros(\n",
+    "            [batch_size * self.top_k, output_size], dtype=tf.float32\n",
+    "        )\n",
+    "        for idx in unique_expert_ids:\n",
+    "            mask = tf.equal(flat_indices, idx)\n",
+    "            indices = tf.where(mask)\n",
+    "            flat_outputs = tf.tensor_scatter_nd_update(\n",
+    "                flat_outputs, indices, expert_outputs_dict[idx.numpy()]\n",
+    "            )\n",
+    "        top_k_expert_outputs = tf.reshape(\n",
+    "            flat_outputs, [batch_size, self.top_k, output_size]\n",
+    "        )\n",
     "\n",
-    "        # Get the top k experts based on the gating weights\n",
-    "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
+    "        # Combine outputs using top-k weights\n",
+    "        return tf.einsum(\"ijk,ij->ik\", top_k_expert_outputs, top_k_weights)\n",
     "\n",
-    "        # Count usage of each expert symbolically\n",
-    "        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)\n",
-    "        # Use tf.tensor_scatter_nd_add to increment the usage count\n",
+    "    def update_usage_counts(self, indices):\n",
+    "        updates = tf.ones_like(tf.reshape(indices, [-1]), dtype=tf.float32)\n",
     "        self.expert_usage_count.assign(\n",
     "            tf.tensor_scatter_nd_add(\n",
-    "                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates\n",
+    "                self.expert_usage_count, tf.reshape(indices, [-1, 1]), updates\n",
     "            )\n",
     "        )\n",
     "\n",
-    "        # Get outputs from only the top-k experts\n",
-    "        top_k_expert_outputs = tf.stack(\n",
-    "            [\n",
-    "                self.experts[expert_index](x)\n",
-    "                for expert_index in top_k_indices.numpy()[0]\n",
-    "            ],\n",
-    "            axis=1,\n",
-    "        )  # Stack outputs along axis 1\n",
-    "\n",
-    "        # Combine outputs using top-k weights\n",
-    "        combined_output = tf.einsum(\"ijk,ij->ik\", top_k_expert_outputs, top_k_weights)\n",
+    "    def call(self, x):\n",
+    "        gating_weights = self.gating_network(x)\n",
+    "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
+    "        combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)\n",
+    "        self.update_usage_counts(top_k_indices)\n",
     "\n",
     "        return combined_output\n",
     ""
@@ -278,9 +295,12 @@
    "source": [
     "## Routing Collapse\n",
     "\n",
-    "Routing collapse is a problem that occurs with MoE layers. The route terminology refers to the selection process of which expert to use for a given input.\n",
+    "One common challenge with MoE architectures is \"routing collapse\". The \"route\" refers to the selection process of which expert to use for a given input where the model falls into a pattern of only using a small subset of experts. This happens because:\n",
     "\n",
-    "Route collapse happens when a routing model, early in training, starts favoring just a few experts because they perform slightly better due to random starting conditions. This leads to most examples being sent to these experts, leaving others unused and reducing the model\u2019s overall capacity.\n",
+    "1. Early in training, some experts may perform slightly better by chance\n",
+    "2. These better-performing experts get selected more frequently\n",
+    "3. With more practice, these experts improve further, creating a feedback loop\n",
+    "4. Other experts become neglected and never improve\n",
     "\n",
     "Code below demonstrates the randomness of expert selection:"
    ]
@@ -312,14 +332,24 @@
     "colab_type": "text"
    },
    "source": [
-    "### Adding loss functions to prevent route collapse\n",
-    "To fix this, the authors use extra rules (importance and load losses), ideas borrowed from [Shazeer et al.](https://arxiv.org/abs/1701.06538), to ensure all experts get used evenly.\n",
+    "### Load Balancing Solutions\n",
+    "\n",
+    "To prevent routing collapse, we implement three types of losses that were introduced in various MoE research:\n",
+    "\n",
+    "1. Diversity Loss: Encourages the gating network to use all experts by maximizing the entropy\n",
+    "   of expert selection probabilities\n",
+    "   [Shazeer et al., \"Outrageously Large Neural Networks\" (2017)](https://arxiv.org/abs/1701.06538)\n",
     "\n",
-    "The importance_loss calculates how much the usage of each expert (tracked in batch_importance_sum) deviates from the average usage (mean_importance) by using mean squared error, aiming to balance expert utilization. This helps prevent route collapse by discouraging the model from overloading a few experts, instead promoting an even distribution of examples across all experts to maintain diverse and effective routing.\n",
+    "2. Importance Loss: Ensures each expert handles a similar total amount of input across the batch\n",
+    "   by penalizing deviations from the mean usage\n",
+    "   [Lepikhin et al., \"GShard: Scaling Giant Models with Conditional Computation\" (2020)](https://arxiv.org/abs/2006.16668)\n",
     "\n",
-    "#### Load losses:\n",
-    "    - Diversity loss: Diversity loss helps prevent route collapse by encouraging the routing model to evenly distribute examples across all experts, rather than favoring just a few due to their initial performance. It does this by maximizing the entropy of the gating weights, ensuring balanced expert utilization and improving the model's overall capacity.\n",
-    "    - Overflow loss: The batch_overflow_sum measures how much the usage of experts exceeds a set capacity by applying ReLU to the difference between usage_counts (how many examples each expert handles) and batch_capacity (the allowed limit), then summing the excesses. This helps prevent route collapse by penalizing situations where certain experts are overused, encouraging a more even spread of examples across all experts to keep the model's capacity balanced."
+    "3. Overflow Loss: Prevents individual experts from being overloaded by penalizing usage above\n",
+    "   a specified capacity threshold\n",
+    "   [Fedus et al., \"Switch Transformers\" (2021)](https://arxiv.org/abs/2101.03961)\n",
+    "\n",
+    "These losses are combined with the main classification loss during training to ensure balanced expert utilization.\n",
+    "The combination of these techniques has proven effective in large-scale models like GShard and Switch Transformers."
    ]
   },
   {
@@ -358,6 +388,7 @@
     "                mean=0.0, stddev=0.001\n",
     "            ),\n",
     "            bias_initializer=\"zeros\",\n",
+    "            activation=\"softmax\",\n",
     "        )\n",
     "\n",
     "        self.num_experts = num_experts\n",
@@ -383,60 +414,52 @@
     "            )\n",
     "        )\n",
     "\n",
-    "    def call(self, x):\n",
-    "        # Get gating weights and normalize\n",
-    "        gating_weights = self.gating_network(x)\n",
-    "        gating_weights = K.softmax(gating_weights)  # Ensure weights are probabilities\n",
-    "        self._diversity_loss(gating_weights)\n",
-    "        self._importance_loss(gating_weights)\n",
-    "\n",
-    "        # Get the top k experts based on the gating weights\n",
+    "    # Replace the current get_top_outputs method with this vectorized version\n",
+    "    def get_top_outputs(\n",
+    "        self, x, gating_weights\n",
+    "    ):  # Changed to take gating_weights directly\n",
+    "        \"\"\"Compute outputs from top-k experts.\"\"\"\n",
     "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
     "\n",
-    "        # Count usage of each expert symbolically\n",
-    "        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)\n",
-    "        # Use tf.tensor_scatter_nd_add to increment the usage count\n",
-    "        self.expert_usage_count.assign(\n",
-    "            tf.tensor_scatter_nd_add(\n",
-    "                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "        # Calculate overflow using updated usage count\n",
-    "        self.batch_overflow_sum = K.sum(\n",
-    "            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)\n",
-    "        )\n",
+    "        # Store indices and updates for usage count\n",
+    "        self.indices = tf.reshape(top_k_indices, [-1, 1])\n",
+    "        self.updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)\n",
     "\n",
-    "        # Compute all expert outputs\n",
-    "        expert_outputs = tf.stack(\n",
-    "            [expert(x) for expert in self.experts], axis=1\n",
-    "        )  # Shape: (batch_size, num_experts, hidden_size)\n",
-    "\n",
-    "        # Gather the top-k expert outputs using top_k_indices\n",
+    "        # Compute expert outputs symbolically\n",
+    "        expert_outputs = tf.stack([expert(x) for expert in self.experts], axis=1)\n",
     "        batch_size = tf.shape(x)[0]\n",
-    "        batch_indices = tf.expand_dims(\n",
-    "            tf.range(batch_size), 1\n",
-    "        )  # Shape: (batch_size, 1)\n",
-    "        batch_indices = tf.tile(\n",
-    "            batch_indices, [1, self.top_k]\n",
-    "        )  # Shape: (batch_size, top_k)\n",
-    "\n",
-    "        # Create indices for gathering\n",
-    "        indices = tf.stack(\n",
-    "            [batch_indices, top_k_indices], axis=2\n",
-    "        )  # Shape: (batch_size, top_k, 2)\n",
-    "        top_k_expert_outputs = tf.gather_nd(\n",
-    "            expert_outputs, indices\n",
-    "        )  # Shape: (batch_size, top_k, hidden_size)\n",
+    "        batch_indices = tf.tile(tf.range(batch_size)[:, tf.newaxis], [1, self.top_k])\n",
+    "        gather_indices = tf.stack([batch_indices, top_k_indices], axis=-1)\n",
+    "        top_k_expert_outputs = tf.gather_nd(expert_outputs, gather_indices)\n",
     "\n",
-    "        # Combine outputs using top-k weights\n",
     "        combined_output = tf.reduce_sum(\n",
-    "            top_k_expert_outputs * tf.expand_dims(top_k_weights, axis=-1), axis=1\n",
+    "            top_k_expert_outputs * top_k_weights[:, :, tf.newaxis], axis=1\n",
     "        )\n",
+    "        return combined_output\n",
+    "\n",
+    "    def update_usage_counts(self):\n",
+    "        updates = tf.ones_like(tf.reshape(self.indices, [-1]), dtype=tf.float32)\n",
+    "        self.expert_usage_count.assign(\n",
+    "            tf.tensor_scatter_nd_add(\n",
+    "                self.expert_usage_count, tf.reshape(self.indices, [-1, 1]), updates\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    def call(self, x):\n",
+    "        # Get gating weights and normalize\n",
+    "        gating_weights = self.gating_network(x)\n",
+    "        # top_k_weights, top_k_indices = tf.nn.top_k(gating_weights, k=self.top_k)\n",
+    "        combined_output = self.get_top_outputs(x, gating_weights)\n",
+    "        self.update_usage_counts()\n",
+    "        self._diversity_loss(gating_weights)\n",
+    "        self._importance_loss(gating_weights)\n",
     "\n",
     "        return combined_output\n",
     "\n",
     "    def compute_total_loss(self, load_balance_coef=0.01):\n",
+    "        self.batch_overflow_sum = K.sum(\n",
+    "            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)\n",
+    "        )\n",
     "        return load_balance_coef * (\n",
     "            self.diversity_loss + self.batch_overflow_sum + self.importance_loss\n",
     "        )\n",
@@ -462,7 +485,13 @@
    "source": [
     "\n",
     "class MoEModel(keras.Model):\n",
-    "    def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_K):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        num_classes,\n",
+    "        num_experts=NUM_EXPERTS,\n",
+    "        top_k=TOP_K,\n",
+    "        moe_loss_considered=True,\n",
+    "    ):\n",
     "        super(MoEModel, self).__init__()\n",
     "\n",
     "        # Define the convolutional block\n",
@@ -484,6 +513,7 @@
     "\n",
     "        # Softmax layer\n",
     "        self.softmax = layers.Softmax()\n",
+    "        self.moe_loss_considered = moe_loss_considered\n",
     "\n",
     "    def call(self, inputs, training=False):\n",
     "        conv_flatten = self.conv_block(inputs)\n",
@@ -497,17 +527,21 @@
     "        with tf.GradientTape() as tape:\n",
     "            y_pred = self(x, training=True)\n",
     "            classification_loss = self.compute_loss(x, y, y_pred)\n",
-    "            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)\n",
-    "            total_loss = classification_loss + moe_loss\n",
+    "            if self.moe_loss_considered:\n",
+    "                moe_loss = self.moe_classifier.compute_total_loss(\n",
+    "                    load_balance_coef=0.01\n",
+    "                )\n",
+    "                total_loss = classification_loss + moe_loss\n",
+    "            else:\n",
+    "                total_loss = classification_loss\n",
     "\n",
     "        # Compute gradients\n",
     "        gradients = tape.gradient(total_loss, self.trainable_variables)\n",
     "\n",
     "        # Update weights\n",
-    "        self.optimizer.apply_gradients(\n",
-    "            zip(gradients, self.trainable_variables)\n",
-    "        )  # Update metrics (e.g., accuracy)\n",
-    "        self.compiled_metrics.update_state(y, y_pred)\n",
+    "        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n",
+    "        for metric in self.metrics:\n",
+    "            metric.update_state(y, y_pred)\n",
     "        # Return a dict of metrics for monitoring\n",
     "        return {\n",
     "            \"loss\": total_loss,\n",
@@ -522,7 +556,8 @@
     "        moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)\n",
     "        total_loss = classification_loss + moe_loss\n",
     "\n",
-    "        self.compiled_metrics.update_state(y, y_pred)\n",
+    "        for metric in self.metrics:\n",
+    "            metric.update_state(y, y_pred)\n",
     "        return {\n",
     "            \"loss\": total_loss,\n",
     "            \"moe_loss\": moe_loss,\n",
@@ -532,9 +567,7 @@
     "\n",
     "# Instantiate and compile the model\n",
     "inputs = keras.Input(shape=input_shape)\n",
-    "model = MoEModel(\n",
-    "    input_shape=input_shape, num_classes=num_classes, num_experts=6, top_k=4\n",
-    ")\n",
+    "model = MoEModel(num_classes=num_classes, num_experts=5, top_k=3)\n",
     "\n",
     "model.compile(\n",
     "    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),\n",
@@ -566,6 +599,7 @@
     "    batch_size=BATCH_SIZE,\n",
     "    epochs=NUM_EPOCHS,\n",
     "    validation_data=(x_test, y_test),\n",
+    "    verbose=0,\n",
     ")"
    ]
   },
@@ -590,6 +624,33 @@
     "print(\"Test loss:\", score[0])\n",
     "print(\"Test accuracy:\", score[1])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text"
+   },
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "This example demonstrated how Mixture of Experts (MoE) can be used to increase model capacity without a proportional increase in computation cost. The key benefits are:\n",
+    "\n",
+    "1. Conditional Computation: Only a subset of experts (TOP_K=3 out of NUM_EXPERTS=5) process each input,\n",
+    "   making the model more computationally efficient than a model that uses all parameters for every input.\n",
+    "\n",
+    "2. Specialized Processing: Each expert learns to handle different aspects of the input space,\n",
+    "   allowing for more sophisticated processing without requiring a larger dense network.\n",
+    "\n",
+    "In our implementation, we:\n",
+    "1. Created a basic MoE layer using dense networks as experts\n",
+    "2. Implemented three types of load balancing losses to prevent routing collapse\n",
+    "3. Applied the MoE architecture to MNIST classification by replacing the final dense layer\n",
+    "4. Achieved comparable accuracy to the baseline model while using experts conditionally\n",
+    "\n",
+    "This approach is particularly valuable for large-scale models where computational efficiency\n",
+    "is crucial. The same principles demonstrated here are used in much larger language models\n",
+    "and other applications where model capacity needs to scale efficiently"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/vision/md/mnist_moe.md b/examples/vision/md/mnist_moe.md
index 9165321e16..2696176d0c 100644
--- a/examples/vision/md/mnist_moe.md
+++ b/examples/vision/md/mnist_moe.md
@@ -3,7 +3,7 @@
 **Author:** [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)<br>
 **Date created:** 2015/06/19<br>
 **Last modified:** 2020/04/21<br>
-**Description:** Showcasing concepts relates to Mixture of Experts (MoE).
+**Description:** Simple MoE implementation for MNIST classification.
 
 
 <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/mnist_moe.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/vision/mnist_moe.py)
@@ -19,12 +19,11 @@ Experts are identical blocks within a layer where each are trained to specialize
 At each forward pass, a gating network selects a subset of experts to apply to the input.
 
 The components to implement are:
+
 - Gating network: A dense layer that outputs a probability distribution over the experts.
 - MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.
 - Model: A simple model that uses the MoE layer.
 
-In this example, we will first implement a linear MoE layer and then a CNN-based MoE layer. Lastly we will combine the two using an abstract implementation to showcase its capacties.
-
 ---
 ## Imports
 
@@ -80,7 +79,7 @@ x_train shape: (60000, 28, 28, 1)
 NUM_EXPERTS = 5
 TOP_K = 3
 BATCH_SIZE = 128
-NUM_EPOCHS = 20
+NUM_EPOCHS = 12
 LEARNING_RATE = 0.001
 
 ```
@@ -188,6 +187,7 @@ class LinearMoE(layers.Layer):
                 mean=0.0, stddev=0.001
             ),
             bias_initializer="zeros",
+            activation="softmax",
         )
 
         self.num_experts = num_experts
@@ -197,33 +197,50 @@ class LinearMoE(layers.Layer):
             tf.zeros((num_experts,), dtype=tf.float32)
         )
 
-    def call(self, x):
-        # Get gating weights
-        gating_weights = self.gating_network(x)
+    def get_top_outputs(self, x, top_k_indices, top_k_weights):
+        batch_size = tf.shape(x)[0]
+        flat_indices = tf.reshape(top_k_indices, [-1])
+        repeated_x = tf.repeat(x, repeats=self.top_k, axis=0)
+
+        # Compute outputs for unique experts
+        unique_expert_ids = tf.unique(flat_indices)[0]  # Get unique expert indices
+        expert_outputs_dict = {}
+        for idx in unique_expert_ids:
+            mask = tf.equal(flat_indices, idx)
+            selected_inputs = tf.boolean_mask(repeated_x, mask)
+            expert_outputs_dict[idx.numpy()] = self.experts[idx](selected_inputs)
+
+        # Gather outputs back into the correct shape
+        output_size = self.experts[0].compute_output_shape(input_shape=(None, 10))[-1]
+        flat_outputs = tf.zeros(
+            [batch_size * self.top_k, output_size], dtype=tf.float32
+        )
+        for idx in unique_expert_ids:
+            mask = tf.equal(flat_indices, idx)
+            indices = tf.where(mask)
+            flat_outputs = tf.tensor_scatter_nd_update(
+                flat_outputs, indices, expert_outputs_dict[idx.numpy()]
+            )
+        top_k_expert_outputs = tf.reshape(
+            flat_outputs, [batch_size, self.top_k, output_size]
+        )
 
-        # Get the top k experts based on the gating weights
-        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+        # Combine outputs using top-k weights
+        return tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
 
-        # Count usage of each expert symbolically
-        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
-        # Use tf.tensor_scatter_nd_add to increment the usage count
+    def update_usage_counts(self, indices):
+        updates = tf.ones_like(tf.reshape(indices, [-1]), dtype=tf.float32)
         self.expert_usage_count.assign(
             tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
+                self.expert_usage_count, tf.reshape(indices, [-1, 1]), updates
             )
         )
 
-        # Get outputs from only the top-k experts
-        top_k_expert_outputs = tf.stack(
-            [
-                self.experts[expert_index](x)
-                for expert_index in top_k_indices.numpy()[0]
-            ],
-            axis=1,
-        )  # Stack outputs along axis 1
-
-        # Combine outputs using top-k weights
-        combined_output = tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
+    def call(self, x):
+        gating_weights = self.gating_network(x)
+        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+        combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)
+        self.update_usage_counts(top_k_indices)
 
         return combined_output
 
@@ -244,14 +261,14 @@ linear_mode(sample_data)
 <div class="k-default-codeblock">
 ```
 <tf.Tensor: shape=(1, 32), dtype=float32, numpy=
-array([[ 7.8205157e-06, -9.6895346e-06, -1.3567289e-06, -1.1132732e-05,
-        -2.8532945e-06,  2.5079655e-06, -1.9245979e-06, -1.1963858e-06,
-         7.0713571e-07, -6.1005521e-06, -1.4404541e-06,  8.7282970e-06,
-        -1.2715462e-05, -6.8048112e-06, -1.4842963e-06, -1.1498453e-06,
-        -1.2819509e-06,  2.1932642e-06,  9.3587869e-06, -2.8901491e-07,
-         4.9251821e-06,  6.3183566e-06,  4.5951542e-06,  2.1709191e-06,
-        -9.7254415e-06, -9.1735465e-06, -1.9290899e-06,  1.2122581e-07,
-         4.6307746e-06,  1.4301847e-06, -8.8591560e-06, -1.0252905e-06]],
+array([[ 4.5012074e-04,  3.6236792e-04, -3.6404550e-04, -1.9282849e-04,
+         1.1771411e-03, -4.8739690e-05,  4.8100296e-04,  2.9840387e-04,
+         1.3504212e-03,  4.3620504e-04,  4.2805035e-04,  6.2110950e-04,
+         8.0633757e-04,  3.1539859e-04, -6.5098109e-04,  9.3904360e-05,
+         2.4753943e-04, -3.7851423e-05, -8.6873386e-04,  1.2532169e-04,
+        -5.0137896e-04, -9.8181982e-04, -2.3387320e-04,  1.7193856e-04,
+        -7.3027433e-05, -1.1662465e-03,  3.3941280e-04,  8.5291412e-04,
+        -3.9347171e-04,  6.5931707e-04,  3.8440802e-04,  4.5856956e-04]],
       dtype=float32)>
 
 ```
@@ -259,9 +276,12 @@ array([[ 7.8205157e-06, -9.6895346e-06, -1.3567289e-06, -1.1132732e-05,
 ---
 ## Routing Collapse
 
-Routing collapse is a problem that occurs with MoE layers. The route terminology refers to the selection process of which expert to use for a given input.
+One common challenge with MoE architectures is "routing collapse". The "route" refers to the selection process of which expert to use for a given input where the model falls into a pattern of only using a small subset of experts. This happens because:
 
-Route collapse happens when a routing model, early in training, starts favoring just a few experts because they perform slightly better due to random starting conditions. This leads to most examples being sent to these experts, leaving others unused and reducing the model’s overall capacity.
+1. Early in training, some experts may perform slightly better by chance
+2. These better-performing experts get selected more frequently
+3. With more practice, these experts improve further, creating a feedback loop
+4. Other experts become neglected and never improve
 
 Code below demonstrates the randomness of expert selection:
 
@@ -282,21 +302,31 @@ check_expert_usage(4)
 
 <div class="k-default-codeblock">
 ```
-Run 0, Expert usage: [1. 0. 1. 1. 0.]
-Run 1, Expert usage: [0. 1. 1. 0. 1.]
-Run 2, Expert usage: [1. 1. 0. 1. 0.]
-Run 3, Expert usage: [1. 0. 1. 1. 0.]
+Run 0, Expert usage: [1. 1. 0. 0. 1.]
+Run 1, Expert usage: [1. 1. 1. 0. 0.]
+Run 2, Expert usage: [0. 1. 1. 0. 1.]
+Run 3, Expert usage: [0. 1. 1. 1. 0.]
 
 ```
 </div>
-### Adding loss functions to prevent route collapse
-To fix this, the authors use extra rules (importance and load losses), ideas borrowed from [Shazeer et al.](https://arxiv.org/abs/1701.06538), to ensure all experts get used evenly.
+### Load Balancing Solutions
 
-The importance_loss calculates how much the usage of each expert (tracked in batch_importance_sum) deviates from the average usage (mean_importance) by using mean squared error, aiming to balance expert utilization. This helps prevent route collapse by discouraging the model from overloading a few experts, instead promoting an even distribution of examples across all experts to maintain diverse and effective routing.
+To prevent routing collapse, we implement three types of losses that were introduced in various MoE research:
 
-#### Load losses:
-    - Diversity loss: Diversity loss helps prevent route collapse by encouraging the routing model to evenly distribute examples across all experts, rather than favoring just a few due to their initial performance. It does this by maximizing the entropy of the gating weights, ensuring balanced expert utilization and improving the model's overall capacity.
-    - Overflow loss: The batch_overflow_sum measures how much the usage of experts exceeds a set capacity by applying ReLU to the difference between usage_counts (how many examples each expert handles) and batch_capacity (the allowed limit), then summing the excesses. This helps prevent route collapse by penalizing situations where certain experts are overused, encouraging a more even spread of examples across all experts to keep the model's capacity balanced.
+1. Diversity Loss: Encourages the gating network to use all experts by maximizing the entropy
+   of expert selection probabilities
+   [Shazeer et al., "Outrageously Large Neural Networks" (2017)](https://arxiv.org/abs/1701.06538)
+
+2. Importance Loss: Ensures each expert handles a similar total amount of input across the batch
+   by penalizing deviations from the mean usage
+   [Lepikhin et al., "GShard: Scaling Giant Models with Conditional Computation" (2020)](https://arxiv.org/abs/2006.16668)
+
+3. Overflow Loss: Prevents individual experts from being overloaded by penalizing usage above
+   a specified capacity threshold
+   [Fedus et al., "Switch Transformers" (2021)](https://arxiv.org/abs/2101.03961)
+
+These losses are combined with the main classification loss during training to ensure balanced expert utilization.
+The combination of these techniques has proven effective in large-scale models like GShard and Switch Transformers.
 
 
 ```python
@@ -328,6 +358,7 @@ class LinearMoE(layers.Layer):
                 mean=0.0, stddev=0.001
             ),
             bias_initializer="zeros",
+            activation="softmax",
         )
 
         self.num_experts = num_experts
@@ -353,60 +384,52 @@ class LinearMoE(layers.Layer):
             )
         )
 
-    def call(self, x):
-        # Get gating weights and normalize
-        gating_weights = self.gating_network(x)
-        gating_weights = K.softmax(gating_weights)  # Ensure weights are probabilities
-        self._diversity_loss(gating_weights)
-        self._importance_loss(gating_weights)
-
-        # Get the top k experts based on the gating weights
+    # Replace the current get_top_outputs method with this vectorized version
+    def get_top_outputs(
+        self, x, gating_weights
+    ):  # Changed to take gating_weights directly
+        """Compute outputs from top-k experts."""
         top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
 
-        # Count usage of each expert symbolically
-        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
-        # Use tf.tensor_scatter_nd_add to increment the usage count
-        self.expert_usage_count.assign(
-            tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
-            )
-        )
-
-        # Calculate overflow using updated usage count
-        self.batch_overflow_sum = K.sum(
-            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
-        )
-
-        # Compute all expert outputs
-        expert_outputs = tf.stack(
-            [expert(x) for expert in self.experts], axis=1
-        )  # Shape: (batch_size, num_experts, hidden_size)
+        # Store indices and updates for usage count
+        self.indices = tf.reshape(top_k_indices, [-1, 1])
+        self.updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
 
-        # Gather the top-k expert outputs using top_k_indices
+        # Compute expert outputs symbolically
+        expert_outputs = tf.stack([expert(x) for expert in self.experts], axis=1)
         batch_size = tf.shape(x)[0]
-        batch_indices = tf.expand_dims(
-            tf.range(batch_size), 1
-        )  # Shape: (batch_size, 1)
-        batch_indices = tf.tile(
-            batch_indices, [1, self.top_k]
-        )  # Shape: (batch_size, top_k)
+        batch_indices = tf.tile(tf.range(batch_size)[:, tf.newaxis], [1, self.top_k])
+        gather_indices = tf.stack([batch_indices, top_k_indices], axis=-1)
+        top_k_expert_outputs = tf.gather_nd(expert_outputs, gather_indices)
 
-        # Create indices for gathering
-        indices = tf.stack(
-            [batch_indices, top_k_indices], axis=2
-        )  # Shape: (batch_size, top_k, 2)
-        top_k_expert_outputs = tf.gather_nd(
-            expert_outputs, indices
-        )  # Shape: (batch_size, top_k, hidden_size)
-
-        # Combine outputs using top-k weights
         combined_output = tf.reduce_sum(
-            top_k_expert_outputs * tf.expand_dims(top_k_weights, axis=-1), axis=1
+            top_k_expert_outputs * top_k_weights[:, :, tf.newaxis], axis=1
+        )
+        return combined_output
+
+    def update_usage_counts(self):
+        updates = tf.ones_like(tf.reshape(self.indices, [-1]), dtype=tf.float32)
+        self.expert_usage_count.assign(
+            tf.tensor_scatter_nd_add(
+                self.expert_usage_count, tf.reshape(self.indices, [-1, 1]), updates
+            )
         )
 
+    def call(self, x):
+        # Get gating weights and normalize
+        gating_weights = self.gating_network(x)
+        # top_k_weights, top_k_indices = tf.nn.top_k(gating_weights, k=self.top_k)
+        combined_output = self.get_top_outputs(x, gating_weights)
+        self.update_usage_counts()
+        self._diversity_loss(gating_weights)
+        self._importance_loss(gating_weights)
+
         return combined_output
 
     def compute_total_loss(self, load_balance_coef=0.01):
+        self.batch_overflow_sum = K.sum(
+            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
+        )
         return load_balance_coef * (
             self.diversity_loss + self.batch_overflow_sum + self.importance_loss
         )
@@ -420,7 +443,13 @@ class LinearMoE(layers.Layer):
 ```python
 
 class MoEModel(keras.Model):
-    def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_K):
+    def __init__(
+        self,
+        num_classes,
+        num_experts=NUM_EXPERTS,
+        top_k=TOP_K,
+        moe_loss_considered=True,
+    ):
         super(MoEModel, self).__init__()
 
         # Define the convolutional block
@@ -442,6 +471,7 @@ class MoEModel(keras.Model):
 
         # Softmax layer
         self.softmax = layers.Softmax()
+        self.moe_loss_considered = moe_loss_considered
 
     def call(self, inputs, training=False):
         conv_flatten = self.conv_block(inputs)
@@ -455,17 +485,21 @@ class MoEModel(keras.Model):
         with tf.GradientTape() as tape:
             y_pred = self(x, training=True)
             classification_loss = self.compute_loss(x, y, y_pred)
-            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
-            total_loss = classification_loss + moe_loss
+            if self.moe_loss_considered:
+                moe_loss = self.moe_classifier.compute_total_loss(
+                    load_balance_coef=0.01
+                )
+                total_loss = classification_loss + moe_loss
+            else:
+                total_loss = classification_loss
 
         # Compute gradients
         gradients = tape.gradient(total_loss, self.trainable_variables)
 
         # Update weights
-        self.optimizer.apply_gradients(
-            zip(gradients, self.trainable_variables)
-        )  # Update metrics (e.g., accuracy)
-        self.compiled_metrics.update_state(y, y_pred)
+        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
+        for metric in self.metrics:
+            metric.update_state(y, y_pred)
         # Return a dict of metrics for monitoring
         return {
             "loss": total_loss,
@@ -480,7 +514,8 @@ class MoEModel(keras.Model):
         moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
         total_loss = classification_loss + moe_loss
 
-        self.compiled_metrics.update_state(y, y_pred)
+        for metric in self.metrics:
+            metric.update_state(y, y_pred)
         return {
             "loss": total_loss,
             "moe_loss": moe_loss,
@@ -490,9 +525,7 @@ class MoEModel(keras.Model):
 
 # Instantiate and compile the model
 inputs = keras.Input(shape=input_shape)
-model = MoEModel(
-    input_shape=input_shape, num_classes=num_classes, num_experts=6, top_k=4
-)
+model = MoEModel(num_classes=num_classes, num_experts=5, top_k=3)
 
 model.compile(
     optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),
@@ -511,22361 +544,42 @@ history = model.fit(
     batch_size=BATCH_SIZE,
     epochs=NUM_EPOCHS,
     validation_data=(x_test, y_test),
+    verbose=0,
 )
 ```
 
-<div class="k-default-codeblock">
-```
-Epoch 1/20
-
-/opt/homebrew/Caskroom/miniforge/base/envs/keras-io/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:642: UserWarning: `model.compiled_metrics()` is deprecated. Instead, use e.g.:
-```
-for metric in self.metrics:
-    metric.update_state(y, y_pred)
-```
-```
-</div>
-    
-<div class="k-default-codeblock">
-```
-  return self._compiled_metrics_update_state(
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  8:21 1s/step - accuracy: 0.1406 - loss: 0.1000 - moe_loss: 3.8421
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 17ms/step - accuracy: 0.1637 - loss: 0.1000 - moe_loss: 11.5298
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   8/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2071 - loss: 0.1000 - moe_loss: 21.7700
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  12/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2438 - loss: 0.1000 - moe_loss: 32.0082
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2744 - loss: 0.1000 - moe_loss: 42.2476
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.2988 - loss: 0.1000 - moe_loss: 52.4873
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  24/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3206 - loss: 0.1000 - moe_loss: 62.7278
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3409 - loss: 0.1000 - moe_loss: 72.9716
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  32/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3598 - loss: 0.1000 - moe_loss: 83.2221
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.3774 - loss: 0.1000 - moe_loss: 93.4818
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.3938 - loss: 0.1000 - moe_loss: 103.7338
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  44/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4092 - loss: 0.1000 - moe_loss: 113.9789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4237 - loss: 0.1000 - moe_loss: 124.2205
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4373 - loss: 0.1000 - moe_loss: 134.4638
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4503 - loss: 0.1000 - moe_loss: 144.7069
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4626 - loss: 0.1000 - moe_loss: 154.9452
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4744 - loss: 0.1000 - moe_loss: 165.1867
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4828 - loss: 0.1000 - moe_loss: 172.8672
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  71/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.4937 - loss: 0.1000 - moe_loss: 183.1078
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  75/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5039 - loss: 0.1000 - moe_loss: 193.3479
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5137 - loss: 0.1000 - moe_loss: 203.5896
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5207 - loss: 0.1000 - moe_loss: 211.2682
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  86/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5297 - loss: 0.1000 - moe_loss: 221.5096
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  89/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5362 - loss: 0.1000 - moe_loss: 229.1885
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  93/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5445 - loss: 0.1000 - moe_loss: 239.4279
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5524 - loss: 0.1000 - moe_loss: 249.6689
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.5581 - loss: 0.1000 - moe_loss: 257.3486
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5654 - loss: 0.1000 - moe_loss: 267.5893
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 107/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5707 - loss: 0.1000 - moe_loss: 275.2696
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 110/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5758 - loss: 0.1000 - moe_loss: 282.9488
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 113/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5808 - loss: 0.1000 - moe_loss: 290.6284
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 116/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5856 - loss: 0.1000 - moe_loss: 298.3105
+### Evaluation
 
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 119/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5903 - loss: 0.1000 - moe_loss: 305.9899
 
-<div class="k-default-codeblock">
-```
-
+```python
+score = model.evaluate(x_test, y_test, verbose=0)
+print("Test loss:", score[0])
+print("Test accuracy:", score[1])
 ```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.5963 - loss: 0.1000 - moe_loss: 316.2303
 
 <div class="k-default-codeblock">
 ```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6007 - loss: 0.1000 - moe_loss: 323.9091
+Test loss: tf.Tensor(0.9811909, shape=(), dtype=float32)
+Test accuracy: {'accuracy': <tf.Tensor: shape=(), dtype=float32, numpy=0.9919000267982483>}
 
-<div class="k-default-codeblock">
-```
-
 ```
 </div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6049 - loss: 0.1000 - moe_loss: 331.5898
+# Conclusion
 
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6104 - loss: 0.1000 - moe_loss: 341.8302
+This example demonstrated how Mixture of Experts (MoE) can be used to increase model capacity without a proportional increase in computation cost. The key benefits are:
 
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6143 - loss: 0.1000 - moe_loss: 349.5105
+1. Conditional Computation: Only a subset of experts (TOP_K=3 out of NUM_EXPERTS=5) process each input,
+   making the model more computationally efficient than a model that uses all parameters for every input.
 
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6195 - loss: 0.1000 - moe_loss: 359.7512
+2. Specialized Processing: Each expert learns to handle different aspects of the input space,
+   allowing for more sophisticated processing without requiring a larger dense network.
 
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 143/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6232 - loss: 0.1000 - moe_loss: 367.4311
+In our implementation, we:
+1. Created a basic MoE layer using dense networks as experts
+2. Implemented three types of load balancing losses to prevent routing collapse
+3. Applied the MoE architecture to MNIST classification by replacing the final dense layer
+4. Achieved comparable accuracy to the baseline model while using experts conditionally
 
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.6281 - loss: 0.1000 - moe_loss: 377.6704
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6316 - loss: 0.1000 - moe_loss: 385.3510
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6361 - loss: 0.1000 - moe_loss: 395.5921
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6394 - loss: 0.1000 - moe_loss: 403.2722
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6427 - loss: 0.1000 - moe_loss: 410.9522
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.6458 - loss: 0.1000 - moe_loss: 418.6319
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 167/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6499 - loss: 0.1000 - moe_loss: 428.8718
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6539 - loss: 0.1000 - moe_loss: 439.1111
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6578 - loss: 0.1000 - moe_loss: 449.3512
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6615 - loss: 0.1000 - moe_loss: 459.5908
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 182/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6643 - loss: 0.1000 - moe_loss: 467.2707
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 186/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6678 - loss: 0.1000 - moe_loss: 477.5105
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6713 - loss: 0.1000 - moe_loss: 487.7509
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 194/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6747 - loss: 0.1000 - moe_loss: 497.9902
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 197/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6771 - loss: 0.1000 - moe_loss: 505.6700
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6804 - loss: 0.1000 - moe_loss: 515.9094
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6827 - loss: 0.1000 - moe_loss: 523.5893
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6850 - loss: 0.1000 - moe_loss: 531.2690
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6880 - loss: 0.1000 - moe_loss: 541.5093
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 215/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6910 - loss: 0.1000 - moe_loss: 551.7495
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6938 - loss: 0.1000 - moe_loss: 561.9893
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6959 - loss: 0.1000 - moe_loss: 569.6691
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.6980 - loss: 0.1000 - moe_loss: 577.3488
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7007 - loss: 0.1000 - moe_loss: 587.5882
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7033 - loss: 0.1000 - moe_loss: 597.8292
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7059 - loss: 0.1000 - moe_loss: 608.0698
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7084 - loss: 0.1000 - moe_loss: 618.3093
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7102 - loss: 0.1000 - moe_loss: 625.9894
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7121 - loss: 0.1000 - moe_loss: 633.6691
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 251/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7144 - loss: 0.1000 - moe_loss: 643.9095
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7167 - loss: 0.1000 - moe_loss: 654.1490
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7184 - loss: 0.1000 - moe_loss: 661.8292
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7207 - loss: 0.1000 - moe_loss: 672.0692
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7223 - loss: 0.1000 - moe_loss: 679.7494
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7245 - loss: 0.1000 - moe_loss: 689.9895
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7266 - loss: 0.1000 - moe_loss: 700.2294
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7286 - loss: 0.1000 - moe_loss: 710.4691
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7306 - loss: 0.1000 - moe_loss: 720.7094
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.7326 - loss: 0.1000 - moe_loss: 730.9502
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7346 - loss: 0.1000 - moe_loss: 741.1905
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 293/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7365 - loss: 0.1000 - moe_loss: 751.4304
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7374 - loss: 0.1000 - moe_loss: 756.5504
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7388 - loss: 0.1000 - moe_loss: 764.2300
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7406 - loss: 0.1000 - moe_loss: 774.4699
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7424 - loss: 0.1000 - moe_loss: 784.7094
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7441 - loss: 0.1000 - moe_loss: 794.9492
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 314/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7458 - loss: 0.1000 - moe_loss: 805.1893
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7475 - loss: 0.1000 - moe_loss: 815.4291
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7488 - loss: 0.1000 - moe_loss: 823.1090
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7504 - loss: 0.1000 - moe_loss: 833.3490
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7520 - loss: 0.1000 - moe_loss: 843.5892
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7532 - loss: 0.1000 - moe_loss: 851.2693
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7548 - loss: 0.1000 - moe_loss: 861.5092
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7563 - loss: 0.1000 - moe_loss: 871.7490
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7575 - loss: 0.1000 - moe_loss: 879.4288
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7590 - loss: 0.1000 - moe_loss: 889.6685
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.7601 - loss: 0.1000 - moe_loss: 897.3481
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7612 - loss: 0.1000 - moe_loss: 905.0280
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7626 - loss: 0.1000 - moe_loss: 915.2678
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7640 - loss: 0.1000 - moe_loss: 925.5076
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7654 - loss: 0.1000 - moe_loss: 935.7476
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7664 - loss: 0.1000 - moe_loss: 943.4277
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 372/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7678 - loss: 0.1000 - moe_loss: 953.6683
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 375/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7688 - loss: 0.1000 - moe_loss: 961.3480
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7698 - loss: 0.1000 - moe_loss: 969.0279
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7711 - loss: 0.1000 - moe_loss: 979.2679
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7724 - loss: 0.1000 - moe_loss: 989.5076
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7736 - loss: 0.1000 - moe_loss: 999.7477
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7749 - loss: 0.1000 - moe_loss: 1009.9877
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7761 - loss: 0.1000 - moe_loss: 1020.2275
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7773 - loss: 0.1000 - moe_loss: 1030.4677
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.7785 - loss: 0.1000 - moe_loss: 1040.7075
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7797 - loss: 0.1000 - moe_loss: 1050.9473
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 414/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7808 - loss: 0.1000 - moe_loss: 1061.1871
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7820 - loss: 0.1000 - moe_loss: 1071.4269
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7828 - loss: 0.1000 - moe_loss: 1079.1069
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7839 - loss: 0.1000 - moe_loss: 1089.3467
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7850 - loss: 0.1000 - moe_loss: 1099.5865
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7861 - loss: 0.1000 - moe_loss: 1109.8267
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7869 - loss: 0.1000 - moe_loss: 1117.5068
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7877 - loss: 0.1000 - moe_loss: 1125.1870
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7887 - loss: 0.1000 - moe_loss: 1135.4268
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7895 - loss: 0.1000 - moe_loss: 1143.1067
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 450/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7905 - loss: 0.1000 - moe_loss: 1153.3466
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7916 - loss: 0.1000 - moe_loss: 1163.5869
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7926 - loss: 0.1000 - moe_loss: 1173.8270
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7933 - loss: 0.1000 - moe_loss: 1181.5070
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7940 - loss: 0.1000 - moe_loss: 1189.1869
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 468/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.7950 - loss: 0.1000 - moe_loss: 1199.4266
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 9s 18ms/step - accuracy: 0.7956 - loss: 0.1000 - moe_loss: 1204.5302 - val_loss: 0.1000 - val_moe_loss: 2798.7275
-
-
-<div class="k-default-codeblock">
-```
-Epoch 2/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  14s 30ms/step - accuracy: 0.9688 - loss: 0.1000 - moe_loss: 2803.8604
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 2814.1450 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 2824.3696
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 2834.6130
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  17/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9693 - loss: 0.1000 - moe_loss: 2844.8762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9693 - loss: 0.1000 - moe_loss: 2852.5579
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  23/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9692 - loss: 0.1000 - moe_loss: 2860.2383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  26/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9691 - loss: 0.1000 - moe_loss: 2867.9192
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9688 - loss: 0.1000 - moe_loss: 2875.5964
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 2885.8335
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 2893.5164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 2903.7554
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  44/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 2913.9944
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9668 - loss: 0.1000 - moe_loss: 2924.2329
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 2934.4727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 2944.7114
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 2954.9500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 2965.1897
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  68/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 2975.4287
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  72/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 2985.6675
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  75/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 2993.3472
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3003.5850
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  83/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3013.8240
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3024.0654
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3034.3062
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3044.5454
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  99/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9659 - loss: 0.1000 - moe_loss: 3054.7854
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 3065.0247
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 107/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 3075.2642
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 110/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9660 - loss: 0.1000 - moe_loss: 3082.9436
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 3093.1829
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 3100.8628
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9661 - loss: 0.1000 - moe_loss: 3108.5425
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3116.2224
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3126.4617
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 131/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3136.7017
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 134/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3144.3816
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9662 - loss: 0.1000 - moe_loss: 3154.6211
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3164.8611
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3172.5408
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3180.2202
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3187.8999
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3195.5798
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 158/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3205.8191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 162/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3216.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3226.2993
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 170/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9663 - loss: 0.1000 - moe_loss: 3236.5393
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 174/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3246.7805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 178/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3257.0203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 182/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3267.2603
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3274.9407
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 188/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9664 - loss: 0.1000 - moe_loss: 3282.6201
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 192/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3292.8596
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3300.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3310.7786
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9665 - loss: 0.1000 - moe_loss: 3318.4583
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 206/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9666 - loss: 0.1000 - moe_loss: 3328.6982
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9666 - loss: 0.1000 - moe_loss: 3338.9380
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9666 - loss: 0.1000 - moe_loss: 3346.6179
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3356.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 221/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3367.0972
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3377.3372
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9667 - loss: 0.1000 - moe_loss: 3387.5769
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9668 - loss: 0.1000 - moe_loss: 3397.8169
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9668 - loss: 0.1000 - moe_loss: 3408.0564
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3418.2964
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3425.9768
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3433.6567
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 251/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9669 - loss: 0.1000 - moe_loss: 3443.8967
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9670 - loss: 0.1000 - moe_loss: 3454.1365
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9670 - loss: 0.1000 - moe_loss: 3464.3762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 263/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9670 - loss: 0.1000 - moe_loss: 3474.6157
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9671 - loss: 0.1000 - moe_loss: 3484.8552
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9671 - loss: 0.1000 - moe_loss: 3495.0950
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9671 - loss: 0.1000 - moe_loss: 3502.7751
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 278/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 3513.0149
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 3523.2546
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9672 - loss: 0.1000 - moe_loss: 3533.4944
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 290/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9673 - loss: 0.1000 - moe_loss: 3543.7341
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9673 - loss: 0.1000 - moe_loss: 3553.9744
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9674 - loss: 0.1000 - moe_loss: 3564.2141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9674 - loss: 0.1000 - moe_loss: 3574.4539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9674 - loss: 0.1000 - moe_loss: 3584.6936
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9675 - loss: 0.1000 - moe_loss: 3594.9331
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 314/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9675 - loss: 0.1000 - moe_loss: 3605.1729
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9675 - loss: 0.1000 - moe_loss: 3615.4126
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3625.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3633.3323
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 328/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3641.0125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9676 - loss: 0.1000 - moe_loss: 3651.2524
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 3661.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 3671.7319
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9677 - loss: 0.1000 - moe_loss: 3681.9717
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9678 - loss: 0.1000 - moe_loss: 3692.2117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9678 - loss: 0.1000 - moe_loss: 3702.4514
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 356/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9678 - loss: 0.1000 - moe_loss: 3712.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3722.9312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3733.1711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3740.8511
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9679 - loss: 0.1000 - moe_loss: 3748.5310
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3758.7710
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3769.0112
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3776.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3784.3713
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9680 - loss: 0.1000 - moe_loss: 3794.6113
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3802.2913
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3804.8511
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3812.5310
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3820.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9681 - loss: 0.1000 - moe_loss: 3827.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3835.5706
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3843.2505
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3850.9304
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3858.6106
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 417/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9682 - loss: 0.1000 - moe_loss: 3868.8503
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3879.0901
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3889.3303
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3899.5706
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 432/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9683 - loss: 0.1000 - moe_loss: 3907.2507
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3914.9309
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3922.6106
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3930.2908
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9684 - loss: 0.1000 - moe_loss: 3940.5305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3950.7703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3958.4500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3968.6899
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9685 - loss: 0.1000 - moe_loss: 3976.3699
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 462/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 3984.0498
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 3994.2898
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9686 - loss: 0.1000 - moe_loss: 4004.5132 - val_loss: 0.1000 - val_moe_loss: 5598.7266
-
-
-<div class="k-default-codeblock">
-```
-Epoch 3/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 28ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 5603.8740
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9725 - loss: 0.1000 - moe_loss: 5614.1147 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9711 - loss: 0.1000 - moe_loss: 5624.3594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  12/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9713 - loss: 0.1000 - moe_loss: 5632.0366
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9720 - loss: 0.1000 - moe_loss: 5642.2812
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9727 - loss: 0.1000 - moe_loss: 5652.5317
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  24/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9731 - loss: 0.1000 - moe_loss: 5662.7671
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9734 - loss: 0.1000 - moe_loss: 5673.0073
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9736 - loss: 0.1000 - moe_loss: 5680.6851
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  35/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9738 - loss: 0.1000 - moe_loss: 5690.9282
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  39/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9740 - loss: 0.1000 - moe_loss: 5701.1680
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9742 - loss: 0.1000 - moe_loss: 5711.4087
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  47/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9742 - loss: 0.1000 - moe_loss: 5721.6470
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  51/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9743 - loss: 0.1000 - moe_loss: 5731.8843
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  54/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9743 - loss: 0.1000 - moe_loss: 5739.5645
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9744 - loss: 0.1000 - moe_loss: 5749.8052
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9745 - loss: 0.1000 - moe_loss: 5757.4844
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9746 - loss: 0.1000 - moe_loss: 5767.7251
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  69/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9747 - loss: 0.1000 - moe_loss: 5777.9648
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9748 - loss: 0.1000 - moe_loss: 5788.2041
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  77/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9749 - loss: 0.1000 - moe_loss: 5798.4434
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  81/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9750 - loss: 0.1000 - moe_loss: 5808.6831
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  84/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9751 - loss: 0.1000 - moe_loss: 5816.3623
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9752 - loss: 0.1000 - moe_loss: 5826.6025
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  92/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9752 - loss: 0.1000 - moe_loss: 5836.8413
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  96/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9753 - loss: 0.1000 - moe_loss: 5847.0811
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9753 - loss: 0.1000 - moe_loss: 5857.3213
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9754 - loss: 0.1000 - moe_loss: 5867.5610
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9755 - loss: 0.1000 - moe_loss: 5877.8013
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9755 - loss: 0.1000 - moe_loss: 5885.4810
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9756 - loss: 0.1000 - moe_loss: 5895.7212
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 119/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9756 - loss: 0.1000 - moe_loss: 5905.9614
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 122/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9757 - loss: 0.1000 - moe_loss: 5913.6421
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9758 - loss: 0.1000 - moe_loss: 5923.8813
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9758 - loss: 0.1000 - moe_loss: 5931.5615
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9758 - loss: 0.1000 - moe_loss: 5939.2412
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9759 - loss: 0.1000 - moe_loss: 5949.4810
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9759 - loss: 0.1000 - moe_loss: 5959.7207
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9760 - loss: 0.1000 - moe_loss: 5969.9600
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9760 - loss: 0.1000 - moe_loss: 5980.2007
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 152/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9761 - loss: 0.1000 - moe_loss: 5990.4404
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9761 - loss: 0.1000 - moe_loss: 6000.6802
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9762 - loss: 0.1000 - moe_loss: 6010.9199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 164/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9762 - loss: 0.1000 - moe_loss: 6021.1602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9763 - loss: 0.1000 - moe_loss: 6031.3994
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9763 - loss: 0.1000 - moe_loss: 6041.6392
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 173/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9763 - loss: 0.1000 - moe_loss: 6044.1992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 176/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9764 - loss: 0.1000 - moe_loss: 6051.8794
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9764 - loss: 0.1000 - moe_loss: 6059.5596
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9765 - loss: 0.1000 - moe_loss: 6069.7998
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9765 - loss: 0.1000 - moe_loss: 6080.0405
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 191/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9765 - loss: 0.1000 - moe_loss: 6090.2808
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 6100.5200
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 6110.7603
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 203/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9767 - loss: 0.1000 - moe_loss: 6120.9995
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9767 - loss: 0.1000 - moe_loss: 6131.2402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9768 - loss: 0.1000 - moe_loss: 6141.4800
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 215/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9768 - loss: 0.1000 - moe_loss: 6151.7197
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9769 - loss: 0.1000 - moe_loss: 6161.9600
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9769 - loss: 0.1000 - moe_loss: 6172.1992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 227/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9769 - loss: 0.1000 - moe_loss: 6182.4390
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9770 - loss: 0.1000 - moe_loss: 6192.6792
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9770 - loss: 0.1000 - moe_loss: 6202.9194
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 239/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 6213.1592
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 6223.3989
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 6231.0786
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 6241.3188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 6248.9990
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 6256.6792
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 260/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6266.9189
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6277.1587
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6284.8384
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6292.5186
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 6300.1987
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6307.8789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6315.5586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6323.2388
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 6333.4790
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 290/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 6343.7188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 6353.9590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 6364.1992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6374.4390
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6382.1191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6392.3589
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 6402.5991
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 317/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 6412.8389
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 6423.0786
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 6433.3184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6443.5581
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6453.7983
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6461.4780
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9778 - loss: 0.1000 - moe_loss: 6471.7178
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6481.9580
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6492.1978
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6502.4375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 356/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 6512.6777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6522.9180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6533.1577
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6540.8379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6551.0776
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 375/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 6561.3174
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6571.5576
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6581.7974
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6592.0371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6602.2773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 6612.5176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6620.1973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6630.4375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6638.1172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6648.3569
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6658.5972
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 6666.2769
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6673.9570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6684.1973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 426/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6691.8770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6699.5571
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6709.7969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6720.0366
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 6730.2764
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6740.5166
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6750.7563
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6760.9961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6771.2363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6781.4766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6791.7163
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 6801.9536
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9785 - loss: 0.1000 - moe_loss: 6804.5000 - val_loss: 0.1000 - val_moe_loss: 8398.7275
-
-
-<div class="k-default-codeblock">
-```
-Epoch 4/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 26ms/step - accuracy: 0.9766 - loss: 0.1000 - moe_loss: 8403.8486
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 8414.1064 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9777 - loss: 0.1000 - moe_loss: 8424.3496
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9775 - loss: 0.1000 - moe_loss: 8434.5850
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  17/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9771 - loss: 0.1000 - moe_loss: 8444.8232
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  21/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9770 - loss: 0.1000 - moe_loss: 8455.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9772 - loss: 0.1000 - moe_loss: 8465.3047
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9773 - loss: 0.1000 - moe_loss: 8472.9844
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  32/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9774 - loss: 0.1000 - moe_loss: 8483.2256
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9776 - loss: 0.1000 - moe_loss: 8493.4678
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9779 - loss: 0.1000 - moe_loss: 8503.7090
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  44/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9781 - loss: 0.1000 - moe_loss: 8513.9502
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9782 - loss: 0.1000 - moe_loss: 8524.1924
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9783 - loss: 0.1000 - moe_loss: 8534.4336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9784 - loss: 0.1000 - moe_loss: 8544.6738
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9786 - loss: 0.1000 - moe_loss: 8554.9131
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9787 - loss: 0.1000 - moe_loss: 8565.1514
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  68/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9789 - loss: 0.1000 - moe_loss: 8575.3916
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  72/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9790 - loss: 0.1000 - moe_loss: 8585.6318
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  75/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9791 - loss: 0.1000 - moe_loss: 8593.3125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9792 - loss: 0.1000 - moe_loss: 8603.5527
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  83/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9793 - loss: 0.1000 - moe_loss: 8613.7930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9793 - loss: 0.1000 - moe_loss: 8624.0332
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  90/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9794 - loss: 0.1000 - moe_loss: 8631.7139
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9794 - loss: 0.1000 - moe_loss: 8641.9541
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  98/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9795 - loss: 0.1000 - moe_loss: 8652.1943
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 101/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9795 - loss: 0.1000 - moe_loss: 8659.8740
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9796 - loss: 0.1000 - moe_loss: 8667.5547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9797 - loss: 0.1000 - moe_loss: 8677.7939
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9797 - loss: 0.1000 - moe_loss: 8685.4736
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9798 - loss: 0.1000 - moe_loss: 8695.7139
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 119/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9799 - loss: 0.1000 - moe_loss: 8705.9541
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 122/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9799 - loss: 0.1000 - moe_loss: 8713.6348
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9800 - loss: 0.1000 - moe_loss: 8723.8750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9800 - loss: 0.1000 - moe_loss: 8734.1143
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 134/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9801 - loss: 0.1000 - moe_loss: 8744.3545
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9802 - loss: 0.1000 - moe_loss: 8754.5947
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9802 - loss: 0.1000 - moe_loss: 8764.8350
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 146/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9803 - loss: 0.1000 - moe_loss: 8775.0742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 149/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9803 - loss: 0.1000 - moe_loss: 8782.7549
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 152/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9804 - loss: 0.1000 - moe_loss: 8790.4346
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9805 - loss: 0.1000 - moe_loss: 8800.6738
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9805 - loss: 0.1000 - moe_loss: 8810.9141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 164/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9806 - loss: 0.1000 - moe_loss: 8821.1533
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9806 - loss: 0.1000 - moe_loss: 8831.3936
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9806 - loss: 0.1000 - moe_loss: 8841.6328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 176/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 8851.8730
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 180/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 8862.1123
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 184/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9808 - loss: 0.1000 - moe_loss: 8872.3525
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 188/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9808 - loss: 0.1000 - moe_loss: 8882.5928
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 192/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 8892.8330
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 8903.0732
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 200/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 8913.3135
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8923.5537
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8931.2334
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8938.9131
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 8949.1523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 218/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8959.3926
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8969.6318
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8979.8721
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9811 - loss: 0.1000 - moe_loss: 8987.5518
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 8997.7920
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 236/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 9005.4717
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 9015.7119
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9812 - loss: 0.1000 - moe_loss: 9025.9521
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 248/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9036.1914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9046.4316
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9054.1113
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9061.7910
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 9072.0312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 266/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9082.2715
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9089.9512
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9100.1914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9110.4307
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9118.1113
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 284/469 ━━━━━━━━━━━━━━━━━━━━  3s 16ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 9128.3516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9138.5908
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 292/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9148.8311
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 296/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9159.0713
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 9169.3105
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 304/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9179.5508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9187.2305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 311/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9197.4707
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 314/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9205.1504
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9215.3906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 9225.6309
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 326/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9235.8711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9243.5508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9251.2314
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9261.4707
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9271.7109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 16ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 9281.9512
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9292.1914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 351/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9299.8711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9310.1113
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9320.3516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9330.5908
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 9340.8311
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9348.5107
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9358.7510
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9368.9912
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9376.6709
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9386.9111
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9397.1514
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9404.8311
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9415.0713
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 16ms/step - accuracy: 0.9819 - loss: 0.1000 - moe_loss: 9422.7510
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9432.9912
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9440.6709
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9448.3506
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9458.5908
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 417/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9468.8311
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9479.0703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9489.3105
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9499.5498
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 9509.7900
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9520.0303
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9527.7100
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9535.3896
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9545.6299
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 450/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9553.3096
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9560.9902
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9568.6699
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9578.9102
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9586.5898
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 9594.2695
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 17ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 9604.4932 - val_loss: 0.1000 - val_moe_loss: 11198.7256
-
-
-<div class="k-default-codeblock">
-```
-Epoch 5/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 26ms/step - accuracy: 0.9688 - loss: 0.1000 - moe_loss: 11203.8506
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9796 - loss: 0.1000 - moe_loss: 11214.1025 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   8/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9802 - loss: 0.1000 - moe_loss: 11221.7900
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  11/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 11229.4717
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  15/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 11239.7148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  18/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9809 - loss: 0.1000 - moe_loss: 11247.3945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  21/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9810 - loss: 0.1000 - moe_loss: 11255.0732
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 11265.3115
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9813 - loss: 0.1000 - moe_loss: 11275.5498
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 11285.7881
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 11296.0273
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9814 - loss: 0.1000 - moe_loss: 11303.7070
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  42/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 11308.8281
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9815 - loss: 0.1000 - moe_loss: 11311.3887
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9816 - loss: 0.1000 - moe_loss: 11319.0693
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9817 - loss: 0.1000 - moe_loss: 11326.7500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  53/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 11336.9912
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9818 - loss: 0.1000 - moe_loss: 11344.6709
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9820 - loss: 0.1000 - moe_loss: 11354.9111
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 11365.1504
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 11372.8301
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 11380.5098
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  74/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11390.7490
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  77/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11398.4287
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  81/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11408.6680
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  84/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9822 - loss: 0.1000 - moe_loss: 11416.3486
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11424.0283
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  90/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11431.7080
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11441.9473
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9823 - loss: 0.1000 - moe_loss: 11449.6270
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9824 - loss: 0.1000 - moe_loss: 11457.3076
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 104/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9824 - loss: 0.1000 - moe_loss: 11467.5479
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9824 - loss: 0.1000 - moe_loss: 11477.7871
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 112/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9825 - loss: 0.1000 - moe_loss: 11488.0273
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 116/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9825 - loss: 0.1000 - moe_loss: 11498.2666
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9825 - loss: 0.1000 - moe_loss: 11508.5078
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9826 - loss: 0.1000 - moe_loss: 11518.7480
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9826 - loss: 0.1000 - moe_loss: 11526.4277
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 131/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 11536.6680
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 134/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 11544.3477
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 11554.5879
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9828 - loss: 0.1000 - moe_loss: 11564.8271
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 146/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9828 - loss: 0.1000 - moe_loss: 11575.0674
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9829 - loss: 0.1000 - moe_loss: 11585.3066
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9829 - loss: 0.1000 - moe_loss: 11592.9873
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9830 - loss: 0.1000 - moe_loss: 11603.2266
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 161/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9830 - loss: 0.1000 - moe_loss: 11613.4668
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9830 - loss: 0.1000 - moe_loss: 11623.7061
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9831 - loss: 0.1000 - moe_loss: 11633.9463
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9831 - loss: 0.1000 - moe_loss: 11641.6270
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9831 - loss: 0.1000 - moe_loss: 11649.3066
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9832 - loss: 0.1000 - moe_loss: 11659.5459
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 182/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9832 - loss: 0.1000 - moe_loss: 11667.2256
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9832 - loss: 0.1000 - moe_loss: 11674.9062
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9833 - loss: 0.1000 - moe_loss: 11685.1455
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9833 - loss: 0.1000 - moe_loss: 11695.3857
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 197/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9833 - loss: 0.1000 - moe_loss: 11705.6260
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11715.8662
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11723.5459
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11733.7861
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 11741.4658
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 215/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11751.7061
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 218/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11759.3857
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 221/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11767.0654
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9835 - loss: 0.1000 - moe_loss: 11777.3057
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11787.5459
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11797.7861
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 236/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11805.4658
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9836 - loss: 0.1000 - moe_loss: 11815.7051
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11823.3857
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11831.0654
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 249/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11838.7461
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11848.9854
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 257/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9837 - loss: 0.1000 - moe_loss: 11859.2256
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 260/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11866.9053
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 263/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11874.5859
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11884.8252
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11892.5059
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 11902.7451
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 278/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11912.9854
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11920.6650
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11930.9053
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11938.5850
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11946.2656
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11953.9453
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11964.1855
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9839 - loss: 0.1000 - moe_loss: 11974.4248
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 11982.1055
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 308/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 11989.7852
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12000.0254
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12007.7051
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12015.3848
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12023.0654
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12030.7451
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12038.4248
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12046.1055
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12053.7852
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12061.4648
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9840 - loss: 0.1000 - moe_loss: 12071.7051
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12081.9453
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12092.1846
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12102.4248
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12110.1055
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12120.3447
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12128.0254
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12135.7051
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12143.3848
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 372/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12153.6250
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12163.8652
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12174.1055
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12184.3447
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12192.0254
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12202.2646
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12212.5049
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9841 - loss: 0.1000 - moe_loss: 12222.7451
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12232.9854
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12243.2256
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12253.4658
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 414/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12261.1455
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12271.3857
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12279.0654
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12286.7451
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12294.4248
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12304.6650
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12312.3447
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12322.5850
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12330.2646
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12340.5049
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12350.7451
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12358.4248
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12368.6650
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12378.9053
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9842 - loss: 0.1000 - moe_loss: 12386.5850
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9843 - loss: 0.1000 - moe_loss: 12396.8252
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9843 - loss: 0.1000 - moe_loss: 12404.4883 - val_loss: 0.1000 - val_moe_loss: 13998.7246
-
-
-<div class="k-default-codeblock">
-```
-Epoch 6/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 29ms/step - accuracy: 0.9609 - loss: 0.1000 - moe_loss: 14003.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9740 - loss: 0.1000 - moe_loss: 14014.0947 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   8/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9780 - loss: 0.1000 - moe_loss: 14021.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  12/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9807 - loss: 0.1000 - moe_loss: 14032.0244
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9821 - loss: 0.1000 - moe_loss: 14042.2637
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9827 - loss: 0.1000 - moe_loss: 14049.9434
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9834 - loss: 0.1000 - moe_loss: 14057.6221
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9838 - loss: 0.1000 - moe_loss: 14065.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9843 - loss: 0.1000 - moe_loss: 14075.5439
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9848 - loss: 0.1000 - moe_loss: 14085.7842
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9852 - loss: 0.1000 - moe_loss: 14096.0244
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9854 - loss: 0.1000 - moe_loss: 14103.7041
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9856 - loss: 0.1000 - moe_loss: 14111.3838
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9858 - loss: 0.1000 - moe_loss: 14119.0635
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9859 - loss: 0.1000 - moe_loss: 14126.7432
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9860 - loss: 0.1000 - moe_loss: 14134.4229
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9861 - loss: 0.1000 - moe_loss: 14142.1035
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  59/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9862 - loss: 0.1000 - moe_loss: 14152.3428
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  62/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9863 - loss: 0.1000 - moe_loss: 14160.0225
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9864 - loss: 0.1000 - moe_loss: 14167.7021
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  68/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9865 - loss: 0.1000 - moe_loss: 14175.3818
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  71/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9865 - loss: 0.1000 - moe_loss: 14183.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  74/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9866 - loss: 0.1000 - moe_loss: 14190.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  77/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9866 - loss: 0.1000 - moe_loss: 14198.4219
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  80/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9867 - loss: 0.1000 - moe_loss: 14206.1016
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  83/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9867 - loss: 0.1000 - moe_loss: 14213.7812
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  86/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9868 - loss: 0.1000 - moe_loss: 14221.4619
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  89/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9868 - loss: 0.1000 - moe_loss: 14229.1416
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  92/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14236.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14244.5029
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  98/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14252.1826
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 102/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14262.4229
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 106/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9869 - loss: 0.1000 - moe_loss: 14272.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14280.3428
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 112/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14288.0225
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14295.7021
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 118/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9870 - loss: 0.1000 - moe_loss: 14303.3818
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14311.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14318.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14326.4229
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14334.1035
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14341.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14349.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9871 - loss: 0.1000 - moe_loss: 14359.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 143/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14367.3838
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 146/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14375.0635
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14385.3037
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14392.9844
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14400.6641
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14408.3447
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14418.5840
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14426.2637
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14433.9443
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 14441.6240
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14449.3037
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14459.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 182/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14467.2236
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14474.9033
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 188/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14482.5830
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 191/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14490.2627
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 194/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14497.9434
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 197/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14505.6230
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 200/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14513.3037
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14523.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14531.2236
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14538.9033
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14546.5840
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 216/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14554.2637
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14561.9434
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14569.6230
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14577.3037
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 228/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14584.9834
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14592.6631
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14600.3428
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14608.0234
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14615.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14623.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14631.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 249/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14638.7432
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14646.4229
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14654.1025
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14661.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 261/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14669.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14677.1426
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14684.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14692.5029
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14700.1826
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14707.8623
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14715.5420
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14723.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14730.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14738.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14746.2627
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14753.9424
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14761.6221
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14769.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14776.9824
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14784.6631
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14792.3428
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14800.0234
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14807.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14815.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14823.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14830.7432
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14838.4229
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14846.1025
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14853.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14861.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14869.1426
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14876.8232
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 345/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14884.5029
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14892.1826
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 351/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14899.8623
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 354/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14907.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14915.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14922.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14930.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14940.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14948.5029
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14956.1826
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14963.8623
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14971.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14979.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14986.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 14994.5830
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15002.2627
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15009.9424
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15020.1826
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15027.8623
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15035.5420
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15043.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15050.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15058.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9873 - loss: 0.1000 - moe_loss: 15066.2627
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15073.9424
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 422/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15081.6221
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15089.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15094.4229
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15104.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15112.3428
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15120.0225
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15127.7021
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15135.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15143.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15150.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15158.4219
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15166.1025
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15173.7822
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15181.4619
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15189.1426
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15196.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 15204.4854 - val_loss: 0.1000 - val_moe_loss: 16798.7246
-
-
-<div class="k-default-codeblock">
-```
-Epoch 7/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 29ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 16803.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 16811.5371 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 16819.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 16826.9043
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 16834.5898
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 16842.2676
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 16849.9531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 16857.6309
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  23/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 16860.1934
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  24/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 16862.7539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  26/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9904 - loss: 0.1000 - moe_loss: 16867.8711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9902 - loss: 0.1000 - moe_loss: 16875.5527
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  10s 24ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 16885.7910
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9899 - loss: 0.1000 - moe_loss: 16893.4688
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  39/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 16901.1484 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  42/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9897 - loss: 0.1000 - moe_loss: 16908.8281
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  45/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9896 - loss: 0.1000 - moe_loss: 16916.5078
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 16924.1875
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  51/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 16931.8691
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  54/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 16939.5488
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  57/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 16947.2285
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 16954.9082
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  63/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 16962.5898
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  66/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9892 - loss: 0.1000 - moe_loss: 16970.2676
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  69/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9892 - loss: 0.1000 - moe_loss: 16977.9473
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  72/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9891 - loss: 0.1000 - moe_loss: 16985.6270
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  75/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9891 - loss: 0.1000 - moe_loss: 16993.3086
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  78/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9890 - loss: 0.1000 - moe_loss: 17000.9863
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  81/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9890 - loss: 0.1000 - moe_loss: 17008.6660
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  84/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 17016.3457
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 17024.0273
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  90/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9888 - loss: 0.1000 - moe_loss: 17031.7070
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  93/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9888 - loss: 0.1000 - moe_loss: 17039.3867
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  96/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9888 - loss: 0.1000 - moe_loss: 17047.0664
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
-  99/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17054.7461
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 102/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17062.4277
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17070.1074
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 17077.7871
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17085.4668
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17093.1465
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17100.8262
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17108.5059
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 17116.1855
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17123.8652
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17131.5449
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17139.2246
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17146.9043
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9885 - loss: 0.1000 - moe_loss: 17154.5840
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17162.2637
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17169.9434
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17177.6230
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17185.3047
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17192.9844
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17200.6641
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17208.3438
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 162/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17216.0234
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17223.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17231.3848
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17239.0645
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 174/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17246.7441
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17254.4238
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 180/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17262.1035
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17269.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 186/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17277.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17285.1445
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 192/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17292.8242
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17300.5039
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 198/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17308.1836
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17315.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17323.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17331.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17338.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17346.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 216/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17354.2617
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 19ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17361.9434
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17369.6230
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17377.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 228/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17384.9824
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17392.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17400.3438
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17408.0234
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17415.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17423.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17431.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 249/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17438.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17446.4219
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17454.1035
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17461.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 261/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17469.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17477.1426
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17484.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17492.5020
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17500.1816
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17507.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17515.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17523.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17530.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17538.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17546.2617
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17553.9414
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17561.6211
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17569.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17576.9824
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17584.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17592.3418
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17600.0234
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17607.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17615.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17623.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17630.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17638.4219
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17646.1016
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17653.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17661.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17669.1426
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17676.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 345/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17684.5020
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17692.1816
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 351/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17699.8613
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17707.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17715.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17722.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17730.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 366/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17738.2617
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 369/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17745.9414
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 372/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 17753.6230
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 375/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17761.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17768.9824
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17776.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17784.3418
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17792.0215
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17799.7012
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17807.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17815.0625
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17822.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17830.4219
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17838.1016
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 408/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17845.7832
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17853.4629
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 414/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17861.1426
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 417/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17868.8223
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 420/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17876.5020
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17884.1816
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 426/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17891.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17899.5430
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 432/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 17907.2227
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17914.9023
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17922.5820
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17930.2617
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 444/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17937.9414
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17945.6230
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 450/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17953.3027
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17960.9824
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17968.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17976.3418
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 462/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17984.0215
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17991.7031
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 468/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 17999.3828
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 18004.4863 - val_loss: 0.1000 - val_moe_loss: 19598.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 8/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  13s 28ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 19603.8477
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 19611.5293 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9864 - loss: 0.1000 - moe_loss: 19619.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9872 - loss: 0.1000 - moe_loss: 19626.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9877 - loss: 0.1000 - moe_loss: 19634.5723
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9880 - loss: 0.1000 - moe_loss: 19642.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 19649.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  8s 18ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 19657.6152
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9884 - loss: 0.1000 - moe_loss: 19665.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9886 - loss: 0.1000 - moe_loss: 19672.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9887 - loss: 0.1000 - moe_loss: 19680.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  34/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 19688.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9890 - loss: 0.1000 - moe_loss: 19696.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9891 - loss: 0.1000 - moe_loss: 19703.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9892 - loss: 0.1000 - moe_loss: 19711.3809
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19719.0605
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19726.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19734.4219
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19742.1016
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 19749.7812
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19757.4609
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19765.1406
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19772.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19780.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19788.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 19795.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  7s 18ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 19803.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9895 - loss: 0.1000 - moe_loss: 19811.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19818.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19826.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19834.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19841.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19849.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19857.3008
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19864.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 106/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19872.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19880.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 112/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19888.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19895.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 118/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19903.3809
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19911.0605
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19918.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19926.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19934.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19941.7812
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19949.4609
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19957.1406
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19964.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19972.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19980.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19987.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 19995.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20003.2207
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20010.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20018.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20026.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20033.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20041.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20049.3008
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20056.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20064.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20072.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20080.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20087.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20095.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20103.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20110.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20118.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 205/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20126.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20133.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20141.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20149.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20156.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20164.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20172.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20179.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20187.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20195.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20202.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20210.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20218.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20225.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20233.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20241.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20248.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20256.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20264.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20272.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20279.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 268/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20287.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20295.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20302.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20310.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20318.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20325.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20333.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20341.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20348.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20356.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20364.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 301/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20371.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 304/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20379.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20387.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20394.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20402.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 316/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20410.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20417.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 320/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20420.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20428.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 326/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20435.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20443.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20451.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20458.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20466.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 341/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20474.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20481.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20489.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20497.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20504.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 356/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20512.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  2s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20520.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9894 - loss: 0.1000 - moe_loss: 20528.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20535.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20543.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20551.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20558.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20566.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20574.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20581.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20589.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20597.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20604.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20607.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20609.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20612.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20620.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20627.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20635.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20643.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20650.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20658.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20666.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20673.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 422/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20681.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20689.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 428/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20696.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20704.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20712.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20720.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20727.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20735.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20743.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20750.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20758.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20766.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20773.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20781.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20789.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 19ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20796.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 10s 20ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 20804.4824 - val_loss: 0.1000 - val_moe_loss: 22398.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 9/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  15s 33ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 22403.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9959 - loss: 0.1000 - moe_loss: 22411.5312 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9964 - loss: 0.1000 - moe_loss: 22419.2129
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9967 - loss: 0.1000 - moe_loss: 22426.8926
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9965 - loss: 0.1000 - moe_loss: 22434.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9960 - loss: 0.1000 - moe_loss: 22442.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 22449.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 22457.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 22465.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 22472.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  30/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 22478.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  32/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 22483.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  35/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 22490.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  38/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 22498.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  41/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 22506.2559
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  44/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 22513.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  47/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 22521.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  50/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 22529.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  53/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 22536.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 22544.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  59/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 22552.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  62/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 22560.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22567.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  68/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22575.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  71/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22583.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  74/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22590.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  77/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22598.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  80/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22606.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  83/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 22613.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  86/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 22621.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  89/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 22629.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  92/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 22636.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22644.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22649.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  99/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22654.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 102/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 22662.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22670.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22677.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22685.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22693.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 22700.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22708.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22716.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22723.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22731.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 22739.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22746.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22754.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22762.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22769.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22777.6211
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 22785.3008
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22792.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22800.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22808.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 162/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22816.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22823.7012
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22831.3809
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 22839.0605
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 174/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22846.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22854.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 180/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22862.1016
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22869.7812
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 186/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22877.4609
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22885.1406
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 192/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22892.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22900.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 198/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22908.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22915.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22923.5410
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 22931.2207
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22938.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22946.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 216/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22954.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22961.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22969.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22977.3008
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 228/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22984.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 22992.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23000.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23008.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23015.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23023.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23031.0605
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 249/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23038.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23046.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 23054.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23061.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 261/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23069.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23077.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23084.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23092.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23100.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23107.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23115.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23123.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23130.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23138.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23146.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23153.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23161.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23169.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23176.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23184.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 23192.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23200.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23207.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23215.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23223.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23230.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23238.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 330/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23246.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23253.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23261.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23269.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23276.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 345/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23284.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 348/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23292.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 351/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23299.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23307.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23315.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23322.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23330.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 23335.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23343.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23348.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23356.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23363.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23371.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23379.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23386.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23394.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23402.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23409.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23417.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23425.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23432.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23440.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23448.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23456.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23463.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23471.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23479.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 23486.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23494.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23502.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23509.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23517.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23525.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23532.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23540.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23548.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23555.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23563.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23571.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23578.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23586.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23594.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23601.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 23604.4824 - val_loss: 0.1000 - val_moe_loss: 25198.7246
-
-
-<div class="k-default-codeblock">
-```
-Epoch 10/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  15s 33ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 25203.8496
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9963 - loss: 0.1000 - moe_loss: 25211.5312 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9955 - loss: 0.1000 - moe_loss: 25219.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9951 - loss: 0.1000 - moe_loss: 25226.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 25234.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9946 - loss: 0.1000 - moe_loss: 25242.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 25249.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  9s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 25257.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 25265.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 25272.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 25280.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  34/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 25288.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 25296.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 25303.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 25311.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 25319.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 25326.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 25334.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 25342.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 25349.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 25357.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 25365.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 25372.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 25380.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 25388.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 25395.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 25403.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 25411.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 25418.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 25426.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 25434.2559
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 25441.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 25449.6152
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 25457.2949
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 25464.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 106/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25472.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25480.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 112/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25488.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25495.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 118/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25503.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 25511.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25518.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  7s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25526.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25534.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25541.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25549.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25557.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25564.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 25572.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25580.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25587.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 154/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25595.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25603.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25610.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25618.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25626.2559
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25633.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25641.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  6s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25649.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25656.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25664.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25672.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 25680.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25687.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25695.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25703.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25710.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25718.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 205/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25726.0957
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25733.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25741.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25749.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25756.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 220/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25764.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25772.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  5s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25779.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25787.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25795.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25802.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25810.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25818.2559
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25825.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25833.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25841.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25848.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25856.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25864.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25872.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25879.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 268/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25887.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25895.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25902.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  4s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25910.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25918.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25925.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25933.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 21ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25938.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25941.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 290/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25943.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 293/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25951.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 296/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25959.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 299/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25966.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25974.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25982.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 308/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 25989.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 311/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 25997.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 314/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26005.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 317/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26012.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 320/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26020.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26028.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 326/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26035.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26043.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26051.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26058.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26066.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 341/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26074.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26081.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26089.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26097.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26104.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 356/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26112.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26120.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 362/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26128.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26135.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26143.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26151.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 374/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26158.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26166.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26174.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26181.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26186.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26194.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26202.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26209.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26217.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26225.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26232.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26240.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26248.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26256.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26263.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26271.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26279.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26286.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26294.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26302.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26309.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26317.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26325.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26332.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26340.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26348.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26355.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26360.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 456/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26368.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26376.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 462/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26384.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26391.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 22ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26396.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 11s 23ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 26404.4805 - val_loss: 0.1000 - val_moe_loss: 27998.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 11/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  18s 39ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28003.8535
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  10s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 28011.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  10s 22ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 28019.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9955 - loss: 0.1000 - moe_loss: 28026.8926
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 28034.5723
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9952 - loss: 0.1000 - moe_loss: 28042.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9950 - loss: 0.1000 - moe_loss: 28049.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9947 - loss: 0.1000 - moe_loss: 28057.6152
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  10s 23ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 28065.2949
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 28072.9766 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 28080.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  34/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 28088.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 28096.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 28103.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 28111.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 28119.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 28126.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  9s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 28134.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 28142.0957
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 28149.7754
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 28157.4551
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28165.1348
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28172.8145
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  9s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28180.4941
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28188.1738
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 28195.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28203.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28211.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28218.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28226.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28234.2559
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28241.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28249.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28257.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 102/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28262.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 28270.0957
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28277.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28285.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  8s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28293.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28300.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28308.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28316.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 28323.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28331.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28339.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28346.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 28354.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28362.2559
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28369.9355
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28377.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28385.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28392.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 28400.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  7s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28408.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 162/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28416.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28423.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28431.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28439.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 174/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 28446.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28454.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28459.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28464.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28469.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28474.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 188/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28482.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 191/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28490.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 194/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28497.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 197/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28505.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 200/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 28513.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 203/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28520.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 206/469 ━━━━━━━━━━━━━━━━━━━━  6s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28528.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 209/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28536.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 212/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28544.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 215/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28551.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 218/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28559.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 221/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28567.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 224/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28574.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 227/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28582.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 230/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28590.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28597.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 236/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28605.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 239/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28613.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 242/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28620.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 245/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28628.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28633.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  5s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28641.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28648.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28656.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28664.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28672.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28679.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28684.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28689.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 272/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28697.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 275/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 28705.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28710.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 280/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28718.0957
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28725.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28733.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28741.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 292/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28748.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28756.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  4s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28761.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 299/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28766.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28774.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28782.0957
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 308/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28789.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 311/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28797.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 314/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28805.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 317/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28812.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 320/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28820.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28828.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28833.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 328/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28840.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28848.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28853.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28861.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 338/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28866.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 341/469 ━━━━━━━━━━━━━━━━━━━━  3s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28874.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28879.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28887.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 28894.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28902.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28910.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 358/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28917.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28925.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 364/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28933.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28940.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28948.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28956.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 376/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28963.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28971.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28976.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 384/469 ━━━━━━━━━━━━━━━━━━━━  2s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28984.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28992.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 28999.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29004.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29009.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29015.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29020.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 29025.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29030.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29035.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29040.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 408/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29045.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29050.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29056.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 414/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29061.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29066.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29071.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29079.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29084.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29089.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  1s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29094.4160
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29099.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29104.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29112.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29117.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29122.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29127.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29132.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 444/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29137.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29143.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29150.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29155.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29160.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 29166.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29171.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29176.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29181.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29186.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29191.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 24ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29196.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 12s 25ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 29204.4805 - val_loss: 0.1000 - val_moe_loss: 30798.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 12/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  20s 45ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 30803.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   3/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9883 - loss: 0.1000 - moe_loss: 30808.9746
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9880 - loss: 0.1000 - moe_loss: 30814.0918
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9889 - loss: 0.1000 - moe_loss: 30819.2129
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9896 - loss: 0.1000 - moe_loss: 30826.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  12/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 30832.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  15/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9900 - loss: 0.1000 - moe_loss: 30839.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  18/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9903 - loss: 0.1000 - moe_loss: 30847.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  21/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9904 - loss: 0.1000 - moe_loss: 30855.0605
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  24/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 30862.7422
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  26/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 30867.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 30872.9824
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 30880.6621
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 30885.7812
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 30893.4609
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  38/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 30898.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 30903.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  42/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 30908.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  44/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 30913.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 30919.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30924.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  50/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30929.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30934.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  54/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 30939.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30944.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30949.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30954.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  62/469 ━━━━━━━━━━━━━━━━━━━━  10s 25ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30960.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30965.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 30967.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30972.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  69/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30977.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  71/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30983.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  74/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30990.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 30995.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  78/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 31000.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  80/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 31006.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  11s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31011.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31018.9004
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31024.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  89/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31029.1406
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31034.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  93/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31039.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 31044.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31049.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  99/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31054.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 101/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31059.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31064.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31070.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 107/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31075.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31080.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  10s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31085.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 113/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31090.5801 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31095.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31100.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 119/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31105.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31111.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31116.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 125/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31121.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31126.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31131.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 131/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31136.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31141.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31146.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 137/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31152.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31157.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31162.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 143/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31167.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31172.5000
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31177.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 149/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31182.7402
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31187.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31192.9805
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 155/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31198.0996
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31203.2207
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31208.3398
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 161/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31213.4609
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31218.5801
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  8s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31223.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31226.2598
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 167/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31228.8203
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31233.9395
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31239.0605
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 173/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31244.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31249.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31254.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31259.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31264.6602
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31269.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31274.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31280.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31285.1387
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 191/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31290.2598
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31295.3789
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31300.5000
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 197/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31305.6191
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31310.7383
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31315.8594
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 203/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31320.9785
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 205/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31326.0996
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31331.2188
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 209/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31336.3398
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31341.4590
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31346.5801
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 215/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31351.6992
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31356.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31361.9395
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 221/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31367.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31372.1797
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31377.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 227/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31382.4199
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31387.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31392.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31397.7793
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31402.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31408.0195
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 239/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31413.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31418.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31423.3789
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 245/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31428.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31433.6191
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 249/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31438.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 251/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31443.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  6s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31448.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31454.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 257/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31459.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31464.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 261/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 31469.4590
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 263/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31474.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31479.6992
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31484.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31489.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31495.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31500.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 275/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31505.2988
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31510.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31515.5391
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 281/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31520.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31525.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31530.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 287/469 ━━━━━━━━━━━━━━━━━━━━  5s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31536.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31541.1387
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31546.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 293/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31551.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31556.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31561.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 299/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31566.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 301/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31571.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31576.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31582.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31587.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31592.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 311/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31597.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31602.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31607.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 317/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31612.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31617.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31623.0586
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  4s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31628.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31633.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31638.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31643.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31648.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31653.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31658.8984
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 337/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31664.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31669.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 341/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31674.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31679.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 345/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31684.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 347/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31689.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31694.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 351/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31699.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31704.9785
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31710.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31715.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  3s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31720.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31725.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31730.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31735.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31740.8184
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 369/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31745.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31751.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31756.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 375/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31761.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31766.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31771.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 31776.6582
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 383/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31781.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31786.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31792.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31797.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31802.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31807.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  2s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31812.4980
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31817.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31822.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31827.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31832.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31838.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31843.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31848.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31853.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31858.5781
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31863.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 417/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31868.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31873.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31879.0566
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31884.1777
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31889.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31894.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31899.5371
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  1s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31904.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31909.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31914.8965
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31920.0176
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31925.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31930.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31935.3770
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31940.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31945.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31950.7363
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31955.8574
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31960.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31966.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31971.2168
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31976.3379
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31981.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31986.5762
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31991.6973
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 28ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 31996.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 14s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 32004.4805 - val_loss: 0.1000 - val_moe_loss: 33598.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 13/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  21s 45ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33603.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  11s 24ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 33611.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   6/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9953 - loss: 0.1000 - moe_loss: 33616.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 33624.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  11/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 33629.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  11s 25ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 33634.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  15/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 33639.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  17/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 33644.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 33649.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  21/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 33655.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  23/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 33660.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 33665.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  27/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33670.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33675.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33680.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33685.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  35/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33690.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 33696.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  39/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33701.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  41/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33706.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 33711.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  45/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 33716.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  47/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 33721.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 33726.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  51/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 33731.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  53/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 33736.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 33742.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  57/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 33747.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  59/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 33752.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33757.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  63/469 ━━━━━━━━━━━━━━━━━━━━  11s 27ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33762.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33767.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33772.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  69/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33777.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  71/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33783.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33788.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  75/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33793.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  77/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33798.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33803.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  81/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33808.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  83/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33813.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33818.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33824.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  89/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33829.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33834.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  93/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33839.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33844.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33849.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  99/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33854.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 101/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33859.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  10s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33864.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33870.0938 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 107/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33875.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33880.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33885.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 113/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33890.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33895.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33900.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 119/469 ━━━━━━━━━━━━━━━━━━━━  9s 27ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 33905.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33911.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33916.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 125/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33921.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33926.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33931.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 131/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33936.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33941.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33946.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 137/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33952.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33957.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33962.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 143/469 ━━━━━━━━━━━━━━━━━━━━  9s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33967.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33972.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 33977.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 149/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33982.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33987.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33992.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 155/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 33998.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34003.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34008.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 161/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34013.4570
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34018.5742
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34023.6953
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 167/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34028.8164
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34033.9336
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34039.0547
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 173/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 34044.1758
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34049.2969
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34054.4141
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  8s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34059.5352
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34064.6562
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34069.7734
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34074.8945
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 186/469 ━━━━━━━━━━━━━━━━━━━━  7s 28ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34077.4531
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34080.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 188/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34082.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34087.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 192/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34092.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 194/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34097.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  8s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34103.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 198/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34108.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 200/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34113.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34118.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34123.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 206/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34128.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34133.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34138.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 212/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34144.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34149.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 216/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34154.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 218/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34159.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 220/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34164.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34169.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 224/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34174.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34179.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 228/469 ━━━━━━━━━━━━━━━━━━━━  7s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34184.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 230/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34190.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 232/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34195.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34200.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 236/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34205.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34210.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34215.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 242/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34220.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34225.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34231.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 248/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34236.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34241.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34246.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 254/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34251.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34256.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34261.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 260/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34266.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  6s 29ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34272.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 263/469 ━━━━━━━━━━━━━━━━━━━━  6s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34274.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  6s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34279.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34284.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34289.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34295.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34300.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 275/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34305.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34310.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34315.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 281/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34320.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34325.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34330.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 287/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34336.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34341.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34346.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 293/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34351.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34356.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34361.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 299/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34366.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 301/469 ━━━━━━━━━━━━━━━━━━━━  5s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34371.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34376.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34382.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34387.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34392.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 311/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34397.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34402.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34407.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 317/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34412.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34417.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34423.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34428.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34433.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34438.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34443.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34448.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34453.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  4s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34458.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 337/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34464.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34469.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 341/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34474.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34479.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 345/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34484.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 347/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34489.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34494.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 351/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34499.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34504.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34510.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34515.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34520.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34525.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34530.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34535.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34540.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 369/469 ━━━━━━━━━━━━━━━━━━━━  3s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34545.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34551.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34556.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 375/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34561.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34566.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34571.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34576.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 383/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34581.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34586.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34592.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34597.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34602.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34607.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34612.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34617.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 399/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34622.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  2s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34627.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34632.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 34638.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34643.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34648.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 411/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34653.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34658.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34663.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 417/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34668.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34673.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34679.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34684.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34689.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34694.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34699.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34704.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34709.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 435/469 ━━━━━━━━━━━━━━━━━━━━  1s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34714.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34720.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34725.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 441/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34730.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34735.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34740.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34745.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34750.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34755.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34760.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34766.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34771.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34776.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34781.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34786.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34791.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34796.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34801.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 15s 32ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 34804.4766 - val_loss: 0.1000 - val_moe_loss: 36398.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 14/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  23s 50ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 36403.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   3/469 ━━━━━━━━━━━━━━━━━━━━  13s 28ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 36408.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9905 - loss: 0.1000 - moe_loss: 36414.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 36419.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9898 - loss: 0.1000 - moe_loss: 36424.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  11/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9899 - loss: 0.1000 - moe_loss: 36429.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9899 - loss: 0.1000 - moe_loss: 36434.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  15/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9900 - loss: 0.1000 - moe_loss: 36439.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  17/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 36444.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 36449.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  21/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9902 - loss: 0.1000 - moe_loss: 36455.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  23/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9903 - loss: 0.1000 - moe_loss: 36460.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9903 - loss: 0.1000 - moe_loss: 36465.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  27/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9904 - loss: 0.1000 - moe_loss: 36470.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9905 - loss: 0.1000 - moe_loss: 36475.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 36480.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  12s 28ms/step - accuracy: 0.9906 - loss: 0.1000 - moe_loss: 36485.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  35/469 ━━━━━━━━━━━━━━━━━━━━  12s 29ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 36490.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  13s 32ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 36493.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  14s 33ms/step - accuracy: 0.9907 - loss: 0.1000 - moe_loss: 36496.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  39/469 ━━━━━━━━━━━━━━━━━━━━  14s 34ms/step - accuracy: 0.9908 - loss: 0.1000 - moe_loss: 36501.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  41/469 ━━━━━━━━━━━━━━━━━━━━  14s 34ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 36506.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  42/469 ━━━━━━━━━━━━━━━━━━━━  14s 35ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 36508.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  44/469 ━━━━━━━━━━━━━━━━━━━━  15s 37ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 36513.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  45/469 ━━━━━━━━━━━━━━━━━━━━  16s 39ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 36516.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 36519.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9910 - loss: 0.1000 - moe_loss: 36524.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  50/469 ━━━━━━━━━━━━━━━━━━━━  18s 43ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 36529.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  18s 43ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 36534.4102
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  54/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 36539.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 36542.0898
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  56/469 ━━━━━━━━━━━━━━━━━━━━  18s 44ms/step - accuracy: 0.9912 - loss: 0.1000 - moe_loss: 36544.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  17s 44ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 36549.7695
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  60/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 36554.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  62/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9913 - loss: 0.1000 - moe_loss: 36560.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36565.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  66/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36570.2500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  68/469 ━━━━━━━━━━━━━━━━━━━━  17s 43ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36575.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36580.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  72/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 36585.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  74/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36590.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  16s 42ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36595.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  78/469 ━━━━━━━━━━━━━━━━━━━━  16s 41ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36600.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  80/469 ━━━━━━━━━━━━━━━━━━━━  16s 41ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36606.0898
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9915 - loss: 0.1000 - moe_loss: 36611.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  84/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 36616.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  86/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 36621.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9916 - loss: 0.1000 - moe_loss: 36626.5703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  90/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36631.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36634.2500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  93/469 ━━━━━━━━━━━━━━━━━━━━  15s 41ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36639.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  15s 40ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36644.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  15s 40ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 36649.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  99/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36654.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 101/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36659.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36664.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36670.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 107/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36675.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36680.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36685.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 113/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36690.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  14s 40ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 36695.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  13s 40ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36700.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 119/469 ━━━━━━━━━━━━━━━━━━━━  13s 40ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36705.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36711.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36716.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 125/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36721.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36726.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36731.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 131/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36736.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  13s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36741.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36746.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 137/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36752.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36757.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  12s 39ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36762.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 143/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36767.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36772.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36777.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 149/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 36782.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36787.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  12s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36792.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 155/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36798.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36803.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36808.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 161/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36813.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36818.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  11s 38ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36823.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 167/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36828.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36833.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36839.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 173/469 ━━━━━━━━━━━━━━━━━━━━  11s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36844.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36849.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36854.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36859.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36864.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36869.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 185/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36874.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36880.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36885.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 191/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36890.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36895.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36900.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 197/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36905.6133 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 198/469 ━━━━━━━━━━━━━━━━━━━━  10s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36908.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 200/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36913.2930 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36918.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36923.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 206/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36928.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  9s 37ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 36933.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 209/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36936.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36941.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36946.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 215/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36951.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36956.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36961.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 221/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36967.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36969.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 224/469 ━━━━━━━━━━━━━━━━━━━━  9s 38ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36974.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  26s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36977.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  26s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36979.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 227/469 ━━━━━━━━━━━━━━━━━━━━  26s 109ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36982.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 228/469 ━━━━━━━━━━━━━━━━━━━━  26s 109ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36984.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  27s 113ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36987.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 230/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36990.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36992.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 232/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36995.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 233/469 ━━━━━━━━━━━━━━━━━━━━  26s 112ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 36997.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  26s 111ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37002.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 236/469 ━━━━━━━━━━━━━━━━━━━━  25s 111ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37005.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  25s 111ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37010.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  25s 110ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37015.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 242/469 ━━━━━━━━━━━━━━━━━━━━  24s 110ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37020.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  24s 109ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37025.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  24s 108ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37031.0547
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 248/469 ━━━━━━━━━━━━━━━━━━━━  23s 108ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37036.1758
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  23s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37041.2930
-
-<div class="k-default-codeblock">
-```
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-```
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- 252/469 ━━━━━━━━━━━━━━━━━━━━  23s 107ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37046.4141
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 254/469 ━━━━━━━━━━━━━━━━━━━━  22s 106ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37051.5352
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  22s 106ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37056.6562
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  22s 105ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37061.7734
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 260/469 ━━━━━━━━━━━━━━━━━━━━  21s 105ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37066.8945
-
-<div class="k-default-codeblock">
-```
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-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  21s 104ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37072.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  21s 104ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37077.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 266/469 ━━━━━━━━━━━━━━━━━━━━  20s 103ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37082.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 268/469 ━━━━━━━━━━━━━━━━━━━━  20s 103ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37087.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  20s 102ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37092.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 272/469 ━━━━━━━━━━━━━━━━━━━━  19s 101ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37097.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  19s 101ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37102.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  19s 100ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37107.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  18s 100ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37115.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  18s 99ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37123.2148 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  18s 98ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37130.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  17s 97ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 37138.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  17s 96ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37146.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  16s 95ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37153.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  16s 95ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37161.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  15s 94ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37169.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 304/469 ━━━━━━━━━━━━━━━━━━━━  15s 93ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37179.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 308/469 ━━━━━━━━━━━━━━━━━━━━  14s 92ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37189.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  14s 91ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37200.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 316/469 ━━━━━━━━━━━━━━━━━━━━  13s 90ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37210.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 320/469 ━━━━━━━━━━━━━━━━━━━━  13s 89ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37220.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  12s 88ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37228.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 326/469 ━━━━━━━━━━━━━━━━━━━━  12s 88ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37235.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  12s 87ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37243.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  11s 86ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37251.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  11s 86ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37258.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 338/469 ━━━━━━━━━━━━━━━━━━━━  11s 85ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37266.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 342/469 ━━━━━━━━━━━━━━━━━━━━  10s 84ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37276.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  10s 84ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37287.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 350/469 ━━━━━━━━━━━━━━━━━━━━  9s 83ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37297.2969 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  9s 82ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37304.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  9s 81ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37315.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  8s 81ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37322.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  8s 80ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37330.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  8s 80ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37340.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  7s 79ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37348.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  7s 79ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37356.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  7s 78ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37366.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  6s 77ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37376.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 384/469 ━━━━━━━━━━━━━━━━━━━━  6s 77ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37384.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 387/469 ━━━━━━━━━━━━━━━━━━━━  6s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37392.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 390/469 ━━━━━━━━━━━━━━━━━━━━  6s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37399.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 392/469 ━━━━━━━━━━━━━━━━━━━━  5s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37404.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  5s 76ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37412.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  5s 75ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37420.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  5s 75ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37427.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  4s 74ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37435.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  4s 74ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37443.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 411/469 ━━━━━━━━━━━━━━━━━━━━  4s 73ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37453.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 414/469 ━━━━━━━━━━━━━━━━━━━━  4s 73ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37461.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 417/469 ━━━━━━━━━━━━━━━━━━━━  3s 73ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37468.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  3s 72ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37479.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  3s 72ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37489.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 429/469 ━━━━━━━━━━━━━━━━━━━━  2s 71ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37499.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  2s 70ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37509.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  2s 70ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37520.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  2s 70ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37527.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 444/469 ━━━━━━━━━━━━━━━━━━━━  1s 69ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37537.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 448/469 ━━━━━━━━━━━━━━━━━━━━  1s 69ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37548.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  1s 68ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37558.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 454/469 ━━━━━━━━━━━━━━━━━━━━  1s 68ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37563.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 68ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37571.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 67ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37581.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 67ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37591.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37601.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 32s 67ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 37604.4766 - val_loss: 0.1000 - val_moe_loss: 39198.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 15/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 39203.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 39214.0938 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 39224.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 39234.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  17/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39244.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  21/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39255.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39265.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  29/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 39275.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 39285.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 39296.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  41/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39306.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  45/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39316.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39326.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  53/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39336.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  57/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39347.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39357.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39367.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  69/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39377.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  72/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39385.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39395.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  80/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39406.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  84/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39416.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39426.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  92/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39436.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  95/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39444.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  98/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39452.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 101/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39459.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 102/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39462.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39464.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39470.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39477.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39485.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39493.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 39500.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39508.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39516.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39523.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39531.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39539.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39546.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39554.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39562.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39569.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39577.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39585.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39592.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39600.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39608.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 162/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39616.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39623.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39631.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39641.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39649.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39659.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39669.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39680.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39687.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 194/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39697.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 198/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39708.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39715.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39723.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39733.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 212/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39744.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 216/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39754.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39761.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39772.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39779.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 230/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39790.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39800.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39810.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 242/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39820.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 245/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39828.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 248/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39836.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39846.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39856.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39864.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39872.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39879.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39889.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39900.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39910.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 39920.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39930.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39941.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39948.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 296/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39959.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 299/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39966.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39976.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39987.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 311/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 39997.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40007.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40017.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 323/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40028.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40038.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40048.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40058.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40069.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40079.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40087.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40097.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40107.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 358/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40117.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40128.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 366/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40138.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 369/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40145.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40156.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40166.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40176.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40186.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40197.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40207.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 396/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40215.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40225.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40235.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 408/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40245.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 412/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40256.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40266.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 420/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40276.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40284.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40294.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40304.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 435/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40314.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40325.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40335.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 447/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40345.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40355.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40363.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40371.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40378.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40386.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40394.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 40404.4766 - val_loss: 0.1000 - val_moe_loss: 41998.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 16/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  11s 26ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 42003.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9882 - loss: 0.1000 - moe_loss: 42014.1016 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9901 - loss: 0.1000 - moe_loss: 42024.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9909 - loss: 0.1000 - moe_loss: 42034.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9914 - loss: 0.1000 - moe_loss: 42042.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  20/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 42052.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  24/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 42062.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 42072.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  32/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 42083.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  36/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 42093.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  39/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 42101.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  42/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 42108.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  45/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 42116.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 42126.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  53/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 42136.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  57/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 42147.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 42157.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  65/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42167.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  69/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42177.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42188.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  77/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42198.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  81/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42208.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42218.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  89/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42229.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  93/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42239.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42249.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42257.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 104/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42267.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42277.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 112/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42288.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 116/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42298.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42308.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42318.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 128/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42328.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42339.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42349.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 140/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42359.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42369.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 16ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42380.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42385.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 152/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42390.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 155/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42398.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42408.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42418.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 167/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42428.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42439.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42449.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 179/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42459.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 42469.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42480.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 191/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42490.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42500.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42510.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 203/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42520.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42531.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42541.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42549.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 218/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42559.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42569.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42579.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 230/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42590.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42600.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42610.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 242/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 42620.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42631.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42641.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 254/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42651.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42661.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42672.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 266/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42682.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 269/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42689.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42700.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42707.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42715.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 281/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42720.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42723.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42730.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42738.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 42746.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42753.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42764.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 302/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42774.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 305/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42782.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42792.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42800.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42807.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42815.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42823.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 324/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42830.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42838.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42848.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42858.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42866.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42876.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42887.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42894.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42904.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 42915.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42925.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42933.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42943.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42951.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42958.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 378/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42968.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42979.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42989.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 390/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 42999.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43007.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43017.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43027.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43038.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43048.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43058.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43066.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 420/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43076.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 423/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43084.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 426/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43091.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43102.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43112.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 438/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43122.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43132.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43143.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43150.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 453/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43160.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43171.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43181.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 465/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43191.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43201.9297
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 43204.4766 - val_loss: 0.1000 - val_moe_loss: 44798.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 17/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 26ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 44803.8477
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   5/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9947 - loss: 0.1000 - moe_loss: 44814.0938 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   9/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 44824.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 16ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 44834.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 44842.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 44849.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  23/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 44860.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  27/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 44870.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 44880.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  33/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 44885.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 44896.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  41/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 44906.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  45/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44916.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  48/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44924.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  51/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44931.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 44942.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  59/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44952.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  63/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44962.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44972.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  71/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44983.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  74/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 44990.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  78/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 45000.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  81/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 45008.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  84/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45016.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  87/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45024.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  90/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45031.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45041.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45049.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 101/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 45059.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45070.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45077.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45085.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45093.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 118/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45103.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 45111.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45118.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45126.4102
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45134.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45141.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45149.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 45157.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45164.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45172.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 149/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45182.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 45192.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45200.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45208.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45218.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 167/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45228.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 170/469 ━━━━━━━━━━━━━━━━━━━━  5s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45236.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 174/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45246.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45254.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 180/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45262.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45269.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 186/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45277.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45285.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45295.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45303.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45310.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45318.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 205/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45326.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45333.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45341.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45349.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45356.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45364.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45372.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 227/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45382.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  4s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45392.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45400.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45408.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45418.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45425.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45433.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45441.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45448.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 257/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45459.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 261/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45469.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45477.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45484.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45492.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45500.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45507.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45515.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45523.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45530.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  3s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45538.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45546.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45553.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45561.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45569.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45576.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45584.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45587.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45594.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45602.5703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 316/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45610.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45617.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45625.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 326/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45635.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 329/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45643.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 332/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45651.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 335/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45658.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 338/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45666.5703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 341/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45674.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45681.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 347/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45689.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 350/469 ━━━━━━━━━━━━━━━━━━━━  2s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45697.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 353/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45704.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 356/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45712.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 359/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45720.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 362/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45728.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 365/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45735.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 368/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45743.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 371/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45751.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 374/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45758.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 377/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45766.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 380/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45774.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 383/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45781.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45789.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 45797.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 392/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45804.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45812.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 398/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45820.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 401/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45827.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 404/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45835.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45843.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45850.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45858.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45866.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45873.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 422/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45881.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45889.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 428/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45896.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45904.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45912.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45920.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45927.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45935.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45943.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45950.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45958.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45966.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45973.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45981.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45989.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 45996.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 8s 18ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 46004.4766 - val_loss: 0.1000 - val_moe_loss: 47598.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 18/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  12s 27ms/step - accuracy: 1.0000 - loss: 0.1000 - moe_loss: 47603.8477
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9967 - loss: 0.1000 - moe_loss: 47611.5312 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9965 - loss: 0.1000 - moe_loss: 47619.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9965 - loss: 0.1000 - moe_loss: 47626.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9966 - loss: 0.1000 - moe_loss: 47634.5703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9964 - loss: 0.1000 - moe_loss: 47642.2500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9961 - loss: 0.1000 - moe_loss: 47649.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9957 - loss: 0.1000 - moe_loss: 47657.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9954 - loss: 0.1000 - moe_loss: 47665.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9952 - loss: 0.1000 - moe_loss: 47672.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9950 - loss: 0.1000 - moe_loss: 47680.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  34/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9950 - loss: 0.1000 - moe_loss: 47688.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9949 - loss: 0.1000 - moe_loss: 47696.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9948 - loss: 0.1000 - moe_loss: 47703.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9947 - loss: 0.1000 - moe_loss: 47711.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9946 - loss: 0.1000 - moe_loss: 47719.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9946 - loss: 0.1000 - moe_loss: 47726.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47734.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47742.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47749.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47757.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47765.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  7s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47772.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9945 - loss: 0.1000 - moe_loss: 47780.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47788.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47795.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47803.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47811.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47818.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47826.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9944 - loss: 0.1000 - moe_loss: 47834.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47841.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47849.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47857.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47864.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 106/469 ━━━━━━━━━━━━━━━━━━━━  6s 17ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47872.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 107/469 ━━━━━━━━━━━━━━━━━━━━  6s 18ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47875.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47877.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47885.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47893.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47900.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 47911.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47918.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47926.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47934.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47941.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47949.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47957.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47964.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  6s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47972.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47980.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47987.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 154/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 47995.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48003.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48010.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  5s 19ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48018.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48026.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48033.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48041.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48049.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48056.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48064.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48072.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48080.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48087.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48095.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  5s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48103.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48110.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48118.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 205/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48126.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48133.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48141.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48149.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48156.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 48164.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48172.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48179.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48187.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48195.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48202.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48210.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48218.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 244/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48225.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48233.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48241.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48248.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48256.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48264.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48272.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48279.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 268/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48287.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48295.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48302.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48310.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48318.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48325.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48333.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48341.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48348.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48356.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48364.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 301/469 ━━━━━━━━━━━━━━━━━━━━  3s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48371.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 304/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48379.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48387.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 310/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48394.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48402.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 316/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48410.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48417.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48425.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48433.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 328/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48440.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48448.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 334/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48456.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 337/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48464.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48471.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48479.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48487.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48494.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48502.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48510.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 358/469 ━━━━━━━━━━━━━━━━━━━━  2s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48517.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48525.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 364/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48533.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48540.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48548.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48556.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48563.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48571.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48579.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48586.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48594.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48602.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48609.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48617.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48625.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48632.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48640.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48648.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48656.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 415/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48663.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48671.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48679.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48686.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48694.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48702.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48709.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 48717.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48725.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48732.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48740.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48748.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48755.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48763.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48771.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48778.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48786.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48794.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 18ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48801.9297
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 9s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 48804.4766 - val_loss: 0.1000 - val_moe_loss: 50398.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 19/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  14s 31ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 50403.8594
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50411.5312 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50419.2070
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50426.8867
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50434.5703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50442.2500
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50449.9297
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50457.6094
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50465.2891
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50472.9688
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50480.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  34/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50488.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50496.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50503.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  8s 19ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50511.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50519.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 50526.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50534.4102
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50542.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  8s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50549.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50557.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  7s 19ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50565.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 50572.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50580.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50588.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50595.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50603.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50611.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 50618.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50626.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50634.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50641.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50649.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50657.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 103/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50664.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 106/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50672.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 109/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50680.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 112/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50688.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 115/469 ━━━━━━━━━━━━━━━━━━━━  7s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50695.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 118/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50703.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 121/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 50711.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 124/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50718.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 127/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50726.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 130/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50734.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 133/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50741.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 136/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50749.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 139/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50757.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 142/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50764.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 145/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50772.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 148/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 50780.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 151/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50787.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 154/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50795.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 157/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50803.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 160/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50810.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 163/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50818.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 166/469 ━━━━━━━━━━━━━━━━━━━━  6s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50826.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 169/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50833.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 172/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 50841.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 175/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50849.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 178/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50856.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 181/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50864.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 184/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50872.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 187/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50880.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 190/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50887.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 193/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50895.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 196/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50903.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 199/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 50910.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 202/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50918.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 205/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50926.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 208/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50933.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 211/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50941.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  5s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50949.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 217/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50956.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 220/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50964.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 223/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50972.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 226/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50979.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 229/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 50987.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 232/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 50995.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 235/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51002.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 238/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51010.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 241/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51018.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 242/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51020.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51023.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 245/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51028.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 247/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51033.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 250/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51041.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 253/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51048.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 256/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51056.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 259/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51064.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 262/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51072.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 265/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51079.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 268/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 51087.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 271/469 ━━━━━━━━━━━━━━━━━━━━  4s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51095.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 274/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51102.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 277/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51110.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 280/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51118.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 283/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51125.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 286/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51133.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 289/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51141.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 292/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51148.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 295/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51156.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 298/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51164.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 301/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51171.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 304/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51179.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 307/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51187.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 310/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51194.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 313/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51202.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 316/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51210.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 319/469 ━━━━━━━━━━━━━━━━━━━━  3s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51217.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 322/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51225.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 325/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51233.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 328/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51240.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 331/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51248.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 334/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 51256.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 337/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51264.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 340/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51271.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 343/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51279.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51287.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51294.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51302.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 355/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51310.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 358/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51317.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 361/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51325.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 364/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51333.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 367/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51340.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 370/469 ━━━━━━━━━━━━━━━━━━━━  2s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51348.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 373/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51356.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 376/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51363.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 379/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51371.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 382/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51379.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 385/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51386.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 388/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51394.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51402.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 394/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51409.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51417.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 400/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51425.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 403/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51432.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 406/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51440.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 409/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51448.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 412/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51456.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 415/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51463.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 418/469 ━━━━━━━━━━━━━━━━━━━━  1s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51471.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 421/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51479.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 424/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51486.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 427/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51494.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 430/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51502.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 433/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51509.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 436/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51517.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 439/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51525.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 442/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51532.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 445/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51540.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 448/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51548.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 451/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51555.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 454/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51563.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 457/469 ━━━━━━━━━━━━━━━━━━━━  0s 20ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51571.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 459/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51576.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 460/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51578.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 463/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51586.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 466/469 ━━━━━━━━━━━━━━━━━━━━  0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51594.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51601.9297
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 10s 22ms/step - accuracy: 0.9943 - loss: 0.1000 - moe_loss: 51604.4766 - val_loss: 0.1000 - val_moe_loss: 53198.7227
-
-
-<div class="k-default-codeblock">
-```
-Epoch 20/20
-
-```
-</div>
-    
-   1/469 ━━━━━━━━━━━━━━━━━━━━  18s 40ms/step - accuracy: 0.9844 - loss: 0.1000 - moe_loss: 53203.8633
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   4/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9893 - loss: 0.1000 - moe_loss: 53211.5391 
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-   7/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9911 - loss: 0.1000 - moe_loss: 53219.2188
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  10/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 53226.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  13/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 53234.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  16/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 53242.2578
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  19/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9917 - loss: 0.1000 - moe_loss: 53249.9375
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  22/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9918 - loss: 0.1000 - moe_loss: 53257.6172
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  25/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9919 - loss: 0.1000 - moe_loss: 53265.2969
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  28/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53272.9766
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  31/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53280.6562
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  34/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53288.3359
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  37/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9920 - loss: 0.1000 - moe_loss: 53296.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  40/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 53303.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  43/469 ━━━━━━━━━━━━━━━━━━━━  9s 21ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 53311.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  46/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9921 - loss: 0.1000 - moe_loss: 53319.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  49/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 53326.7383
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  52/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 53334.4180
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  55/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9922 - loss: 0.1000 - moe_loss: 53342.0977
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  58/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9923 - loss: 0.1000 - moe_loss: 53349.7773
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  61/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 53357.4570
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  64/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9924 - loss: 0.1000 - moe_loss: 53365.1367
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  67/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9925 - loss: 0.1000 - moe_loss: 53372.8164
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  70/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 53380.4961
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  73/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9926 - loss: 0.1000 - moe_loss: 53388.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  76/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 53395.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  79/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9927 - loss: 0.1000 - moe_loss: 53403.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  82/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 53411.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  85/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9928 - loss: 0.1000 - moe_loss: 53418.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  88/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 53426.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  91/469 ━━━━━━━━━━━━━━━━━━━━  8s 21ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 53434.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  94/469 ━━━━━━━━━━━━━━━━━━━━  8s 22ms/step - accuracy: 0.9929 - loss: 0.1000 - moe_loss: 53441.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
-  97/469 ━━━━━━━━━━━━━━━━━━━━  8s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53449.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 100/469 ━━━━━━━━━━━━━━━━━━━━  8s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53457.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 102/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53462.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 105/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9930 - loss: 0.1000 - moe_loss: 53470.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 108/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53477.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 111/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53485.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 114/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53493.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 117/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53500.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 120/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9931 - loss: 0.1000 - moe_loss: 53508.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 123/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53516.1758
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 126/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53523.8555
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 129/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53531.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 132/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53539.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 135/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9932 - loss: 0.1000 - moe_loss: 53546.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 138/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53554.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 141/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53562.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 144/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53569.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 147/469 ━━━━━━━━━━━━━━━━━━━━  7s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53577.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 150/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9933 - loss: 0.1000 - moe_loss: 53585.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 153/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53592.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 156/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53600.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 159/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53608.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 162/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53616.0156
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 165/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9934 - loss: 0.1000 - moe_loss: 53623.6953
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 168/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53631.3750
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 171/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53639.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 174/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53646.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 177/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53654.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 180/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53662.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 183/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9935 - loss: 0.1000 - moe_loss: 53669.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 186/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53677.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 189/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53685.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 192/469 ━━━━━━━━━━━━━━━━━━━━  6s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53692.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 195/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53700.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 198/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53708.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 201/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53715.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 204/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53723.5352
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 207/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9936 - loss: 0.1000 - moe_loss: 53731.2148
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 210/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53738.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 213/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53746.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 214/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53749.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 216/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53754.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 219/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53761.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 222/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53769.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 225/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53777.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 228/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53784.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 231/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9937 - loss: 0.1000 - moe_loss: 53792.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 234/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53800.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 237/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53808.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 240/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53815.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 243/469 ━━━━━━━━━━━━━━━━━━━━  5s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53823.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 246/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53831.0547
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 249/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53838.7344
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 252/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53846.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 255/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53854.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 258/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53861.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 261/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9938 - loss: 0.1000 - moe_loss: 53869.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 264/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53877.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 267/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53884.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 270/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53892.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 273/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53900.1719
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 276/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53907.8516
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 279/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53915.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 282/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53923.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 285/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53930.8945
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 288/469 ━━━━━━━━━━━━━━━━━━━━  4s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53938.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 291/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53946.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 294/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53953.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 297/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9939 - loss: 0.1000 - moe_loss: 53961.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 300/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53969.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 303/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53976.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 306/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53984.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 309/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 53992.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 312/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54000.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 315/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54007.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 318/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54015.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 321/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54023.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 324/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54030.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 327/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54038.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 330/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54046.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 333/469 ━━━━━━━━━━━━━━━━━━━━  3s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54053.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 336/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54061.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 339/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54069.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 342/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54076.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 344/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54081.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 346/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9940 - loss: 0.1000 - moe_loss: 54087.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 349/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54094.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 352/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54102.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 354/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54107.5312
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 357/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54115.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 360/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54122.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 363/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54130.5703
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 366/469 ━━━━━━━━━━━━━━━━━━━━  2s 22ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54138.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 369/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54145.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 372/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54153.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 375/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54161.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 378/469 ━━━━━━━━━━━━━━━━━━━━  2s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54168.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 381/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54176.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 384/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54184.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 386/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54189.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 389/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54197.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 391/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54202.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 393/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54207.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 395/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54212.4922
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 397/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54217.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 399/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54222.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 402/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54230.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 405/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54238.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 407/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54243.2109
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 410/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54250.8906
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 413/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54258.5742
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 416/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54266.2539
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 419/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9941 - loss: 0.1000 - moe_loss: 54273.9336
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 422/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54281.6133
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 425/469 ━━━━━━━━━━━━━━━━━━━━  1s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54289.2930
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 428/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54296.9727
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 431/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54304.6523
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 434/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54312.3320
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 437/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54320.0117
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 440/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54327.6914
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 443/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54335.3711
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 446/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54343.0508
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 449/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54350.7305
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 452/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54358.4141
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 455/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54366.0938
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 458/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54373.7734
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 461/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54381.4531
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 464/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54389.1328
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 467/469 ━━━━━━━━━━━━━━━━━━━━  0s 23ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54396.8125
-
-<div class="k-default-codeblock">
-```
-
-```
-</div>
- 469/469 ━━━━━━━━━━━━━━━━━━━━ 11s 24ms/step - accuracy: 0.9942 - loss: 0.1000 - moe_loss: 54404.4766 - val_loss: 0.1000 - val_moe_loss: 55998.7227
-
-
-### Evaluation
-
-
-```python
-score = model.evaluate(x_test, y_test, verbose=0)
-print("Test loss:", score[0])
-print("Test accuracy:", score[1])
-```
-
-<div class="k-default-codeblock">
-```
-Test loss: tf.Tensor(0.10000026, shape=(), dtype=float32)
-Test accuracy: {'accuracy': <tf.Tensor: shape=(), dtype=float32, numpy=0.9932000041007996>}
-
-```
-</div>
\ No newline at end of file
+This approach is particularly valuable for large-scale models where computational efficiency
+is crucial. The same principles demonstrated here are used in much larger language models
+and other applications where model capacity needs to scale efficiently
diff --git a/examples/vision/mnist_moe.py b/examples/vision/mnist_moe.py
index 8b1911511d..bcec4edf87 100644
--- a/examples/vision/mnist_moe.py
+++ b/examples/vision/mnist_moe.py
@@ -3,7 +3,7 @@
 Author: [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)
 Date created: 2015/06/19
 Last modified: 2020/04/21
-Description: Showcasing concepts relates to Mixture of Experts (MoE).
+Description: Simple MoE implementation for MNIST classification.
 Accelerator: GPU
 """
 
@@ -17,11 +17,10 @@
 At each forward pass, a gating network selects a subset of experts to apply to the input.
 
 The components to implement are:
+
 - Gating network: A dense layer that outputs a probability distribution over the experts.
 - MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.
 - Model: A simple model that uses the MoE layer.
-
-In this example, we will first implement a linear MoE layer and then a CNN-based MoE layer. Lastly we will combine the two using an abstract implementation to showcase its capacties.
 """
 
 """
@@ -67,7 +66,7 @@
 NUM_EXPERTS = 5
 TOP_K = 3
 BATCH_SIZE = 128
-NUM_EPOCHS = 20
+NUM_EPOCHS = 12
 LEARNING_RATE = 0.001
 
 
@@ -126,6 +125,7 @@ def __init__(
                 mean=0.0, stddev=0.001
             ),
             bias_initializer="zeros",
+            activation="softmax",
         )
 
         self.num_experts = num_experts
@@ -135,33 +135,50 @@ def __init__(
             tf.zeros((num_experts,), dtype=tf.float32)
         )
 
-    def call(self, x):
-        # Get gating weights
-        gating_weights = self.gating_network(x)
+    def get_top_outputs(self, x, top_k_indices, top_k_weights):
+        batch_size = tf.shape(x)[0]
+        flat_indices = tf.reshape(top_k_indices, [-1])
+        repeated_x = tf.repeat(x, repeats=self.top_k, axis=0)
+
+        # Compute outputs for unique experts
+        unique_expert_ids = tf.unique(flat_indices)[0]  # Get unique expert indices
+        expert_outputs_dict = {}
+        for idx in unique_expert_ids:
+            mask = tf.equal(flat_indices, idx)
+            selected_inputs = tf.boolean_mask(repeated_x, mask)
+            expert_outputs_dict[idx.numpy()] = self.experts[idx](selected_inputs)
+
+        # Gather outputs back into the correct shape
+        output_size = self.experts[0].compute_output_shape(input_shape=(None, 10))[-1]
+        flat_outputs = tf.zeros(
+            [batch_size * self.top_k, output_size], dtype=tf.float32
+        )
+        for idx in unique_expert_ids:
+            mask = tf.equal(flat_indices, idx)
+            indices = tf.where(mask)
+            flat_outputs = tf.tensor_scatter_nd_update(
+                flat_outputs, indices, expert_outputs_dict[idx.numpy()]
+            )
+        top_k_expert_outputs = tf.reshape(
+            flat_outputs, [batch_size, self.top_k, output_size]
+        )
 
-        # Get the top k experts based on the gating weights
-        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+        # Combine outputs using top-k weights
+        return tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
 
-        # Count usage of each expert symbolically
-        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
-        # Use tf.tensor_scatter_nd_add to increment the usage count
+    def update_usage_counts(self, indices):
+        updates = tf.ones_like(tf.reshape(indices, [-1]), dtype=tf.float32)
         self.expert_usage_count.assign(
             tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
+                self.expert_usage_count, tf.reshape(indices, [-1, 1]), updates
             )
         )
 
-        # Get outputs from only the top-k experts
-        top_k_expert_outputs = tf.stack(
-            [
-                self.experts[expert_index](x)
-                for expert_index in top_k_indices.numpy()[0]
-            ],
-            axis=1,
-        )  # Stack outputs along axis 1
-
-        # Combine outputs using top-k weights
-        combined_output = tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
+    def call(self, x):
+        gating_weights = self.gating_network(x)
+        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+        combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)
+        self.update_usage_counts(top_k_indices)
 
         return combined_output
 
@@ -176,9 +193,12 @@ def call(self, x):
 """
 ## Routing Collapse
 
-Routing collapse is a problem that occurs with MoE layers. The route terminology refers to the selection process of which expert to use for a given input.
+One common challenge with MoE architectures is "routing collapse". The "route" refers to the selection process of which expert to use for a given input where the model falls into a pattern of only using a small subset of experts. This happens because:
 
-Route collapse happens when a routing model, early in training, starts favoring just a few experts because they perform slightly better due to random starting conditions. This leads to most examples being sent to these experts, leaving others unused and reducing the model’s overall capacity.
+1. Early in training, some experts may perform slightly better by chance
+2. These better-performing experts get selected more frequently
+3. With more practice, these experts improve further, creating a feedback loop
+4. Other experts become neglected and never improve
 
 Code below demonstrates the randomness of expert selection:
 """
@@ -196,14 +216,24 @@ def check_expert_usage(runs):
 check_expert_usage(4)
 
 """
-### Adding loss functions to prevent route collapse
-To fix this, the authors use extra rules (importance and load losses), ideas borrowed from [Shazeer et al.](https://arxiv.org/abs/1701.06538), to ensure all experts get used evenly.
+### Load Balancing Solutions
 
-The importance_loss calculates how much the usage of each expert (tracked in batch_importance_sum) deviates from the average usage (mean_importance) by using mean squared error, aiming to balance expert utilization. This helps prevent route collapse by discouraging the model from overloading a few experts, instead promoting an even distribution of examples across all experts to maintain diverse and effective routing.
+To prevent routing collapse, we implement three types of losses that were introduced in various MoE research:
 
-#### Load losses
-    - Diversity loss: Diversity loss helps prevent route collapse by encouraging the routing model to evenly distribute examples across all experts, rather than favoring just a few due to their initial performance. It does this by maximizing the entropy of the gating weights, ensuring balanced expert utilization and improving the model's overall capacity.
-    - Overflow loss: The batch_overflow_sum measures how much the usage of experts exceeds a set capacity by applying ReLU to the difference between usage_counts (how many examples each expert handles) and batch_capacity (the allowed limit), then summing the excesses. This helps prevent route collapse by penalizing situations where certain experts are overused, encouraging a more even spread of examples across all experts to keep the model's capacity balanced.
+1. Diversity Loss: Encourages the gating network to use all experts by maximizing the entropy
+   of expert selection probabilities
+   [Shazeer et al., "Outrageously Large Neural Networks" (2017)](https://arxiv.org/abs/1701.06538)
+
+2. Importance Loss: Ensures each expert handles a similar total amount of input across the batch
+   by penalizing deviations from the mean usage
+   [Lepikhin et al., "GShard: Scaling Giant Models with Conditional Computation" (2020)](https://arxiv.org/abs/2006.16668)
+
+3. Overflow Loss: Prevents individual experts from being overloaded by penalizing usage above
+   a specified capacity threshold
+   [Fedus et al., "Switch Transformers" (2021)](https://arxiv.org/abs/2101.03961)
+
+These losses are combined with the main classification loss during training to ensure balanced expert utilization.
+The combination of these techniques has proven effective in large-scale models like GShard and Switch Transformers.
 """
 
 
@@ -234,6 +264,7 @@ def __init__(
                 mean=0.0, stddev=0.001
             ),
             bias_initializer="zeros",
+            activation="softmax",
         )
 
         self.num_experts = num_experts
@@ -259,60 +290,52 @@ def _importance_loss(self, gating_weights):
             )
         )
 
-    def call(self, x):
-        # Get gating weights and normalize
-        gating_weights = self.gating_network(x)
-        gating_weights = K.softmax(gating_weights)  # Ensure weights are probabilities
-        self._diversity_loss(gating_weights)
-        self._importance_loss(gating_weights)
-
-        # Get the top k experts based on the gating weights
+    # Replace the current get_top_outputs method with this vectorized version
+    def get_top_outputs(
+        self, x, gating_weights
+    ):  # Changed to take gating_weights directly
+        """Compute outputs from top-k experts."""
         top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
 
-        # Count usage of each expert symbolically
-        updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
-        # Use tf.tensor_scatter_nd_add to increment the usage count
-        self.expert_usage_count.assign(
-            tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(top_k_indices, [-1, 1]), updates
-            )
-        )
-
-        # Calculate overflow using updated usage count
-        self.batch_overflow_sum = K.sum(
-            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
-        )
-
-        # Compute all expert outputs
-        expert_outputs = tf.stack(
-            [expert(x) for expert in self.experts], axis=1
-        )  # Shape: (batch_size, num_experts, hidden_size)
+        # Store indices and updates for usage count
+        self.indices = tf.reshape(top_k_indices, [-1, 1])
+        self.updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
 
-        # Gather the top-k expert outputs using top_k_indices
+        # Compute expert outputs symbolically
+        expert_outputs = tf.stack([expert(x) for expert in self.experts], axis=1)
         batch_size = tf.shape(x)[0]
-        batch_indices = tf.expand_dims(
-            tf.range(batch_size), 1
-        )  # Shape: (batch_size, 1)
-        batch_indices = tf.tile(
-            batch_indices, [1, self.top_k]
-        )  # Shape: (batch_size, top_k)
-
-        # Create indices for gathering
-        indices = tf.stack(
-            [batch_indices, top_k_indices], axis=2
-        )  # Shape: (batch_size, top_k, 2)
-        top_k_expert_outputs = tf.gather_nd(
-            expert_outputs, indices
-        )  # Shape: (batch_size, top_k, hidden_size)
+        batch_indices = tf.tile(tf.range(batch_size)[:, tf.newaxis], [1, self.top_k])
+        gather_indices = tf.stack([batch_indices, top_k_indices], axis=-1)
+        top_k_expert_outputs = tf.gather_nd(expert_outputs, gather_indices)
 
-        # Combine outputs using top-k weights
         combined_output = tf.reduce_sum(
-            top_k_expert_outputs * tf.expand_dims(top_k_weights, axis=-1), axis=1
+            top_k_expert_outputs * top_k_weights[:, :, tf.newaxis], axis=1
+        )
+        return combined_output
+
+    def update_usage_counts(self):
+        updates = tf.ones_like(tf.reshape(self.indices, [-1]), dtype=tf.float32)
+        self.expert_usage_count.assign(
+            tf.tensor_scatter_nd_add(
+                self.expert_usage_count, tf.reshape(self.indices, [-1, 1]), updates
+            )
         )
 
+    def call(self, x):
+        # Get gating weights and normalize
+        gating_weights = self.gating_network(x)
+        # top_k_weights, top_k_indices = tf.nn.top_k(gating_weights, k=self.top_k)
+        combined_output = self.get_top_outputs(x, gating_weights)
+        self.update_usage_counts()
+        self._diversity_loss(gating_weights)
+        self._importance_loss(gating_weights)
+
         return combined_output
 
     def compute_total_loss(self, load_balance_coef=0.01):
+        self.batch_overflow_sum = K.sum(
+            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
+        )
         return load_balance_coef * (
             self.diversity_loss + self.batch_overflow_sum + self.importance_loss
         )
@@ -324,7 +347,13 @@ def compute_total_loss(self, load_balance_coef=0.01):
 
 
 class MoEModel(keras.Model):
-    def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_K):
+    def __init__(
+        self,
+        num_classes,
+        num_experts=NUM_EXPERTS,
+        top_k=TOP_K,
+        moe_loss_considered=True,
+    ):
         super(MoEModel, self).__init__()
 
         # Define the convolutional block
@@ -346,6 +375,7 @@ def __init__(self, input_shape, num_classes, num_experts=NUM_EXPERTS, top_k=TOP_
 
         # Softmax layer
         self.softmax = layers.Softmax()
+        self.moe_loss_considered = moe_loss_considered
 
     def call(self, inputs, training=False):
         conv_flatten = self.conv_block(inputs)
@@ -359,17 +389,21 @@ def train_step(self, data):
         with tf.GradientTape() as tape:
             y_pred = self(x, training=True)
             classification_loss = self.compute_loss(x, y, y_pred)
-            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
-            total_loss = classification_loss + moe_loss
+            if self.moe_loss_considered:
+                moe_loss = self.moe_classifier.compute_total_loss(
+                    load_balance_coef=0.01
+                )
+                total_loss = classification_loss + moe_loss
+            else:
+                total_loss = classification_loss
 
         # Compute gradients
         gradients = tape.gradient(total_loss, self.trainable_variables)
 
         # Update weights
-        self.optimizer.apply_gradients(
-            zip(gradients, self.trainable_variables)
-        )  # Update metrics (e.g., accuracy)
-        self.compiled_metrics.update_state(y, y_pred)
+        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
+        for metric in self.metrics:
+            metric.update_state(y, y_pred)
         # Return a dict of metrics for monitoring
         return {
             "loss": total_loss,
@@ -384,7 +418,8 @@ def test_step(self, data):
         moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
         total_loss = classification_loss + moe_loss
 
-        self.compiled_metrics.update_state(y, y_pred)
+        for metric in self.metrics:
+            metric.update_state(y, y_pred)
         return {
             "loss": total_loss,
             "moe_loss": moe_loss,
@@ -394,9 +429,7 @@ def test_step(self, data):
 
 # Instantiate and compile the model
 inputs = keras.Input(shape=input_shape)
-model = MoEModel(
-    input_shape=input_shape, num_classes=num_classes, num_experts=6, top_k=4
-)
+model = MoEModel(num_classes=num_classes, num_experts=5, top_k=3)
 
 model.compile(
     optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),
@@ -413,6 +446,7 @@ def test_step(self, data):
     batch_size=BATCH_SIZE,
     epochs=NUM_EPOCHS,
     validation_data=(x_test, y_test),
+    verbose=0,
 )
 
 """
@@ -422,3 +456,25 @@ def test_step(self, data):
 score = model.evaluate(x_test, y_test, verbose=0)
 print("Test loss:", score[0])
 print("Test accuracy:", score[1])
+
+"""
+# Conclusion
+
+This example demonstrated how Mixture of Experts (MoE) can be used to increase model capacity without a proportional increase in computation cost. The key benefits are:
+
+1. Conditional Computation: Only a subset of experts (TOP_K=3 out of NUM_EXPERTS=5) process each input,
+   making the model more computationally efficient than a model that uses all parameters for every input.
+
+2. Specialized Processing: Each expert learns to handle different aspects of the input space,
+   allowing for more sophisticated processing without requiring a larger dense network.
+
+In our implementation, we:
+1. Created a basic MoE layer using dense networks as experts
+2. Implemented three types of load balancing losses to prevent routing collapse
+3. Applied the MoE architecture to MNIST classification by replacing the final dense layer
+4. Achieved comparable accuracy to the baseline model while using experts conditionally
+
+This approach is particularly valuable for large-scale models where computational efficiency
+is crucial. The same principles demonstrated here are used in much larger language models
+and other applications where model capacity needs to scale efficiently
+"""

From 542f560fdd3ae0fd4a333f91f31e09e25c68c0c4 Mon Sep 17 00:00:00 2001
From: Damoon <damoon.shah3@gmail.com>
Date: Mon, 3 Mar 2025 14:44:15 +0300
Subject: [PATCH 4/5] Converted to keras 3 with torch backend

---
 examples/audio/vocal_track_separation.py | 673 ++++++++++++++++++++++
 examples/vision/ipynb/mnist_moe.ipynb    | 685 -----------------------
 examples/vision/md/mnist_moe.md          | 585 -------------------
 examples/vision/mnist_moe.py             | 279 +++++----
 4 files changed, 831 insertions(+), 1391 deletions(-)
 create mode 100644 examples/audio/vocal_track_separation.py
 delete mode 100644 examples/vision/ipynb/mnist_moe.ipynb
 delete mode 100644 examples/vision/md/mnist_moe.md

diff --git a/examples/audio/vocal_track_separation.py b/examples/audio/vocal_track_separation.py
new file mode 100644
index 0000000000..ca16c35ab1
--- /dev/null
+++ b/examples/audio/vocal_track_separation.py
@@ -0,0 +1,673 @@
+"""
+Title: Vocal Track Separation with Encoder-Decoder Architecture
+Author: [Joaquin Jimenez](https://github.com/johacks/)
+Date created: 2024/12/10
+Last modified: 2024/12/10
+Description: Train a model to separate vocal tracks from music mixtures.
+Accelerator: GPU
+"""
+
+"""
+## Introduction
+
+In this tutorial, we build a vocal track separation model using an encoder-decoder
+architecture in Keras 3.
+
+We train the model on the [MUSDB18 dataset](https://doi.org/10.5281/zenodo.1117372),
+which provides music mixtures and isolated tracks for drums, bass, other, and vocals.
+
+Key concepts covered:
+
+- Audio data preprocessing using the Short-Time Fourier Transform (STFT).
+- Audio data augmentation techniques.
+- Implementing custom encoders and decoders specialized for audio data.
+- Defining appropriate loss functions and metrics for audio source separation tasks.
+
+The model architecture is derived from the TFC_TDF_Net model described in:
+
+W. Choi, M. Kim, J. Chung, D. Lee, and S. Jung, “Investigating U-Nets with various
+intermediate blocks for spectrogram-based singing voice separation,” in the 21st
+International Society for Music Information Retrieval Conference, 2020.
+
+For reference code, see:
+[GitHub: ws-choi/ISMIR2020_U_Nets_SVS](https://github.com/ws-choi/ISMIR2020_U_Nets_SVS).
+
+The data processing and model training routines are partly derived from:
+[ZFTurbo/Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training/tree/main).
+"""
+
+"""
+## Setup
+
+Import and install all the required dependencies.
+"""
+
+"""shell
+pip install -qq audiomentations soundfile ffmpeg-binaries
+pip install -qq "keras==3.7.0"
+sudo -n apt-get install -y graphviz >/dev/null 2>&1  # Required for plotting the model
+"""
+
+import glob
+import os
+
+os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
+
+import random
+import subprocess
+import tempfile
+import typing
+from os import path
+
+import audiomentations as aug
+import ffmpeg
+import keras
+import numpy as np
+import soundfile as sf
+from IPython import display
+from keras import callbacks, layers, ops, saving
+from matplotlib import pyplot as plt
+
+"""
+## Configuration
+
+The following constants define configuration parameters for audio processing
+and model training, including dataset paths, audio chunk sizes, Short-Time Fourier
+Transform (STFT) parameters, and training hyperparameters.
+"""
+
+# MUSDB18 dataset configuration
+MUSDB_STREAMS = {"mixture": 0, "drums": 1, "bass": 2, "other": 3, "vocals": 4}
+TARGET_INSTRUMENTS = {track: MUSDB_STREAMS[track] for track in ("vocals",)}
+N_INSTRUMENTS = len(TARGET_INSTRUMENTS)
+SOURCE_INSTRUMENTS = tuple(k for k in MUSDB_STREAMS if k != "mixture")
+
+# Audio preprocessing parameters for Short-Time Fourier Transform (STFT)
+N_SUBBANDS = 4  # Number of subbands into which frequencies are split
+CHUNK_SIZE = 65024  # Number of amplitude samples per audio chunk (~4 seconds)
+STFT_N_FFT = 2048  # FFT points used in STFT
+STFT_HOP_LENGTH = 512  # Hop length for STFT
+
+# Training hyperparameters
+N_CHANNELS = 64  # Base channel count for the model
+BATCH_SIZE = 3
+ACCUMULATION_STEPS = 2
+EFFECTIVE_BATCH_SIZE = BATCH_SIZE * (ACCUMULATION_STEPS or 1)
+
+# Paths
+TMP_DIR = path.expanduser("~/.keras/tmp")
+DATASET_DIR = path.expanduser("~/.keras/datasets")
+MODEL_PATH = path.join(TMP_DIR, f"model_{keras.backend.backend()}.keras")
+CSV_LOG_PATH = path.join(TMP_DIR, f"training_{keras.backend.backend()}.csv")
+os.makedirs(DATASET_DIR, exist_ok=True)
+os.makedirs(TMP_DIR, exist_ok=True)
+
+# Set random seed for reproducibility
+keras.utils.set_random_seed(21)
+
+"""
+## MUSDB18 Dataset
+
+The MUSDB18 dataset is a standard benchmark for music source separation, containing
+150 full-length music tracks along with isolated drums, bass, other, and vocals.
+The dataset is stored in .mp4 format, and each .mp4 file includes multiple audio
+streams (mixture and individual tracks).
+
+### Download and Conversion
+
+The following utility function downloads MUSDB18 and converts its .mp4 files to
+.wav files for each instrument track, resampled to 16 kHz.
+"""
+
+
+def download_musdb18(out_dir=None):
+    """Download and extract the MUSDB18 dataset, then convert .mp4 files to .wav files.
+
+    MUSDB18 reference:
+    Rafii, Z., Liutkus, A., Stöter, F.-R., Mimilakis, S. I., & Bittner, R. (2017).
+    MUSDB18 - a corpus for music separation (1.0.0) [Data set]. Zenodo.
+    """
+    ffmpeg.init()
+    from ffmpeg import FFMPEG_PATH
+
+    # Create output directories
+    os.makedirs((base := out_dir or tempfile.mkdtemp()), exist_ok=True)
+    if path.exists((out_dir := path.join(base, "musdb18_wav"))):
+        print("MUSDB18 dataset already downloaded")
+        return out_dir
+
+    # Download and extract the dataset
+    download_dir = keras.utils.get_file(
+        fname="musdb18",
+        origin="https://zenodo.org/records/1117372/files/musdb18.zip",
+        extract=True,
+    )
+
+    # ffmpeg command template: input, stream index, output
+    ffmpeg_args = str(FFMPEG_PATH) + " -v error -i {} -map 0:{} -vn -ar 16000 {}"
+
+    # Convert each mp4 file to multiple .wav files for each track
+    for split in ("train", "test"):
+        songs = os.listdir(path.join(download_dir, split))
+        for i, song in enumerate(songs):
+            if i % 10 == 0:
+                print(f"{split.capitalize()}: {i}/{len(songs)} songs processed")
+
+            mp4_path_orig = path.join(download_dir, split, song)
+            mp4_path = path.join(tempfile.mkdtemp(), split, song.replace(" ", "_"))
+            os.makedirs(path.dirname(mp4_path), exist_ok=True)
+            os.rename(mp4_path_orig, mp4_path)
+
+            wav_dir = path.join(out_dir, split, path.basename(mp4_path).split(".")[0])
+            os.makedirs(wav_dir, exist_ok=True)
+
+            for track in SOURCE_INSTRUMENTS:
+                out_path = path.join(wav_dir, f"{track}.wav")
+                stream_index = MUSDB_STREAMS[track]
+                args = ffmpeg_args.format(mp4_path, stream_index, out_path).split()
+                assert subprocess.run(args).returncode == 0, "ffmpeg conversion failed"
+    return out_dir
+
+
+# Download and prepare the MUSDB18 dataset
+songs = download_musdb18(out_dir=DATASET_DIR)
+
+"""
+### Custom Dataset
+
+We define a custom dataset class to generate random audio chunks and their corresponding
+labels. The dataset does the following:
+
+1. Selects a random chunk from a random song and instrument.
+2. Applies optional data augmentations.
+3. Combines isolated tracks to form new synthetic mixtures.
+4. Prepares features (mixtures) and labels (vocals) for training.
+
+This approach allows creating an effectively infinite variety of training examples
+through randomization and augmentation.
+"""
+
+
+class Dataset(keras.utils.PyDataset):
+    def __init__(
+        self,
+        songs,
+        batch_size=BATCH_SIZE,
+        chunk_size=CHUNK_SIZE,
+        batches_per_epoch=1000 * ACCUMULATION_STEPS,
+        augmentation=True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.augmentation = augmentation
+        self.vocals_augmentations = [
+            aug.PitchShift(min_semitones=-5, max_semitones=5, p=0.1),
+            aug.SevenBandParametricEQ(-9, 9, p=0.25),
+            aug.TanhDistortion(0.1, 0.7, p=0.1),
+        ]
+        self.other_augmentations = [
+            aug.PitchShift(p=0.1),
+            aug.AddGaussianNoise(p=0.1),
+        ]
+        self.songs = songs
+        self.sizes = {song: self.get_track_set_size(song) for song in self.songs}
+        self.batch_size = batch_size
+        self.chunk_size = chunk_size
+        self.batches_per_epoch = batches_per_epoch
+
+    def get_track_set_size(self, song: str):
+        """Return the smallest track length in the given song directory."""
+        sizes = [len(sf.read(p)[0]) for p in glob.glob(path.join(song, "*.wav"))]
+        if max(sizes) != min(sizes):
+            print(f"Warning: {song} has different track lengths")
+        return min(sizes)
+
+    def random_chunk_of_instrument_type(self, instrument: str):
+        """Extract a random chunk for the specified instrument from a random song."""
+        song, size = random.choice(list(self.sizes.items()))
+        track = path.join(song, f"{instrument}.wav")
+
+        if self.chunk_size <= size:
+            start = np.random.randint(size - self.chunk_size + 1)
+            audio = sf.read(track, self.chunk_size, start, dtype="float32")[0]
+            audio_mono = np.mean(audio, axis=1)
+        else:
+            # If the track is shorter than chunk_size, pad the signal
+            audio_mono = np.mean(sf.read(track, dtype="float32")[0], axis=1)
+            audio_mono = np.pad(audio_mono, ((0, self.chunk_size - size),))
+
+        # If the chunk is almost silent, retry
+        if np.mean(np.abs(audio_mono)) < 0.01:
+            return self.random_chunk_of_instrument_type(instrument)
+
+        return self.data_augmentation(audio_mono, instrument)
+
+    def data_augmentation(self, audio: np.ndarray, instrument: str):
+        """Apply data augmentation to the audio chunk, if enabled."""
+
+        def coin_flip(x, probability: float, fn: typing.Callable):
+            return fn(x) if random.uniform(0, 1) < probability else x
+
+        if self.augmentation:
+            augmentations = (
+                self.vocals_augmentations
+                if instrument == "vocals"
+                else self.other_augmentations
+            )
+            # Loudness augmentation
+            audio *= np.random.uniform(0.5, 1.5, (len(audio),)).astype("float32")
+            # Random reverse
+            audio = coin_flip(audio, 0.1, lambda x: np.flip(x))
+            # Random polarity inversion
+            audio = coin_flip(audio, 0.5, lambda x: -x)
+            # Apply selected augmentations
+            for aug_ in augmentations:
+                aug_.randomize_parameters(audio, sample_rate=16000)
+                audio = aug_(audio, sample_rate=16000)
+        return audio
+
+    def random_mix_of_tracks(self) -> dict:
+        """Create a random mix of instruments by summing their individual chunks."""
+        tracks = {}
+        for instrument in SOURCE_INSTRUMENTS:
+            # Start with a single random chunk
+            mixup = [self.random_chunk_of_instrument_type(instrument)]
+
+            # Randomly add more chunks of the same instrument (mixup augmentation)
+            if self.augmentation:
+                for p in (0.2, 0.02):
+                    if random.uniform(0, 1) < p:
+                        mixup.append(self.random_chunk_of_instrument_type(instrument))
+
+            tracks[instrument] = np.mean(mixup, axis=0, dtype="float32")
+        return tracks
+
+    def __len__(self):
+        return self.batches_per_epoch
+
+    def __getitem__(self, idx):
+        # Generate a batch of random mixtures
+        batch = [self.random_mix_of_tracks() for _ in range(self.batch_size)]
+
+        # Features: sum of all tracks
+        batch_x = ops.sum(
+            np.array([list(track_set.values()) for track_set in batch]), axis=1
+        )
+
+        # Labels: isolated target instruments (e.g., vocals)
+        batch_y = np.array(
+            [[track_set[t] for t in TARGET_INSTRUMENTS] for track_set in batch]
+        )
+
+        return batch_x, ops.convert_to_tensor(batch_y)
+
+
+# Create train and validation datasets
+train_ds = Dataset(glob.glob(path.join(songs, "train", "*")))
+val_ds = Dataset(
+    glob.glob(path.join(songs, "test", "*")),
+    batches_per_epoch=int(0.1 * train_ds.batches_per_epoch),
+    augmentation=False,
+)
+
+"""
+### Visualize a Sample
+
+Let's visualize a random mixed audio chunk and its corresponding isolated vocals.
+This helps to understand the nature of the preprocessed input data.
+"""
+
+
+def visualize_audio_np(audio: np.ndarray, rate=16000, name="mixup"):
+    """Plot and display an audio waveform and also produce an Audio widget."""
+    plt.figure(figsize=(10, 6))
+    plt.plot(audio)
+    plt.title(f"Waveform: {name}")
+    plt.xlim(0, len(audio))
+    plt.ylabel("Amplitude")
+    plt.show()
+    # plt.savefig(f"tmp/{name}.png")
+
+    # Normalize and display audio
+    audio_norm = (audio - np.min(audio)) / (np.max(audio) - np.min(audio) + 1e-8)
+    audio_norm = (audio_norm * 2 - 1) * 0.6
+    display.display(display.Audio(audio_norm, rate=rate))
+    # sf.write(f"tmp/{name}.wav", audio_norm, rate)
+
+
+sample_batch_x, sample_batch_y = val_ds[None]  # Random batch
+visualize_audio_np(ops.convert_to_numpy(sample_batch_x[0]))
+visualize_audio_np(ops.convert_to_numpy(sample_batch_y[0, 0]), name="vocals")
+
+"""
+## Model
+
+### Preprocessing
+
+The model operates on STFT representations rather than raw audio. We define a
+preprocessing model to compute STFT and a corresponding inverse transform (iSTFT).
+"""
+
+
+def stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
+    """Compute the STFT for the input audio and return the real and imaginary parts."""
+    real_x, imag_x = ops.stft(inputs, fft_size, sequence_stride, fft_size)
+    real_x, imag_x = ops.expand_dims(real_x, -1), ops.expand_dims(imag_x, -1)
+    x = ops.concatenate((real_x, imag_x), axis=-1)
+
+    # Drop last freq sample for convenience
+    return ops.split(x, [x.shape[2] - 1], axis=2)[0]
+
+
+def inverse_stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
+    """Compute the inverse STFT for the given STFT input."""
+    x = inputs
+
+    # Pad back dropped freq sample if using torch backend
+    if keras.backend.backend() == "torch":
+        x = ops.pad(x, ((0, 0), (0, 0), (0, 1), (0, 0)))
+
+    real_x, imag_x = ops.split(x, 2, axis=-1)
+    real_x = ops.squeeze(real_x, axis=-1)
+    imag_x = ops.squeeze(imag_x, axis=-1)
+
+    return ops.istft((real_x, imag_x), fft_size, sequence_stride, fft_size)
+
+
+"""
+### Model Architecture
+
+The model uses a custom encoder-decoder architecture with Time-Frequency Convolution
+(TFC) and Time-Distributed Fully Connected (TDF) blocks. They are grouped into a
+`TimeFrequencyTransformBlock`, i.e. "TFC_TDF" in the original paper by Choi et al.
+
+We then define an encoder-decoder network with multiple scales. Each encoder scale
+applies TFC_TDF blocks followed by downsampling, while decoder scales apply TFC_TDF
+blocks over the concatenation of upsampled features and associated encoder outputs.
+"""
+
+
+@saving.register_keras_serializable()
+class TimeDistributedDenseBlock(layers.Layer):
+    """Time-Distributed Fully Connected layer block.
+
+    Applies frequency-wise dense transformations across time frames with instance
+    normalization and GELU activation.
+    """
+
+    def __init__(self, bottleneck_factor, fft_dim, **kwargs):
+        super().__init__(**kwargs)
+        self.fft_dim = fft_dim
+        self.hidden_dim = fft_dim // bottleneck_factor
+
+    def build(self, *_):
+        self.group_norm_1 = layers.GroupNormalization(groups=-1)
+        self.group_norm_2 = layers.GroupNormalization(groups=-1)
+        self.dense_1 = layers.Dense(self.hidden_dim, use_bias=False)
+        self.dense_2 = layers.Dense(self.fft_dim, use_bias=False)
+
+    def call(self, x):
+        # Apply normalization and dense layers frequency-wise
+        x = ops.gelu(self.group_norm_1(x))
+        x = ops.swapaxes(x, -1, -2)
+        x = self.dense_1(x)
+
+        x = ops.gelu(self.group_norm_2(ops.swapaxes(x, -1, -2)))
+        x = ops.swapaxes(x, -1, -2)
+        x = self.dense_2(x)
+        return ops.swapaxes(x, -1, -2)
+
+
+@saving.register_keras_serializable()
+class TimeFrequencyConvolution(layers.Layer):
+    """Time-Frequency Convolutional layer.
+
+    Applies a 2D convolution over time-frequency representations and applies instance
+    normalization and GELU activation.
+    """
+
+    def __init__(self, channels, **kwargs):
+        super().__init__(**kwargs)
+        self.channels = channels
+
+    def build(self, *_):
+        self.group_norm = layers.GroupNormalization(groups=-1)
+        self.conv = layers.Conv2D(self.channels, 3, padding="same", use_bias=False)
+
+    def call(self, x):
+        return self.conv(ops.gelu(self.group_norm(x)))
+
+
+@saving.register_keras_serializable()
+class TimeFrequencyTransformBlock(layers.Layer):
+    """Implements TFC_TDF block for encoder-decoder architecture.
+
+    Repeatedly apply Time-Frequency Convolution and Time-Distributed Dense blocks as
+    many times as specified by the `length` parameter.
+    """
+
+    def __init__(
+        self, channels, length, fft_dim, bottleneck_factor, in_channels=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.channels = channels
+        self.length = length
+        self.fft_dim = fft_dim
+        self.bottleneck_factor = bottleneck_factor
+        self.in_channels = in_channels or channels
+
+    def build(self, *_):
+        self.blocks = []
+        # Add blocks in a flat list to avoid nested structures
+        for i in range(self.length):
+            in_channels = self.channels if i > 0 else self.in_channels
+            self.blocks.append(TimeFrequencyConvolution(in_channels))
+            self.blocks.append(
+                TimeDistributedDenseBlock(self.bottleneck_factor, self.fft_dim)
+            )
+            self.blocks.append(TimeFrequencyConvolution(self.channels))
+            # Residual connection
+            self.blocks.append(layers.Conv2D(self.channels, 1, 1, use_bias=False))
+
+    def call(self, inputs):
+        x = inputs
+        # Each block consists of 4 layers:
+        # 1. Time-Frequency Convolution
+        # 2. Time-Distributed Dense
+        # 3. Time-Frequency Convolution
+        # 4. Residual connection
+        for i in range(0, len(self.blocks), 4):
+            tfc_1 = self.blocks[i](x)
+            tdf = self.blocks[i + 1](x)
+            tfc_2 = self.blocks[i + 2](tfc_1 + tdf)
+            x = tfc_2 + self.blocks[i + 3](x)  # Residual connection
+        return x
+
+
+@saving.register_keras_serializable()
+class Downscale(layers.Layer):
+    """Downscale time-frequency dimensions using a convolution."""
+
+    conv_cls = layers.Conv2D
+
+    def __init__(self, channels, scale, **kwargs):
+        super().__init__(**kwargs)
+        self.channels = channels
+        self.scale = scale
+
+    def build(self, *_):
+        self.conv = self.conv_cls(self.channels, self.scale, self.scale, use_bias=False)
+        self.norm = layers.GroupNormalization(groups=-1)
+
+    def call(self, inputs):
+        return self.norm(ops.gelu(self.conv(inputs)))
+
+
+@saving.register_keras_serializable()
+class Upscale(Downscale):
+    """Upscale time-frequency dimensions using a transposed convolution."""
+
+    conv_cls = layers.Conv2DTranspose
+
+
+def build_model(
+    inputs,
+    n_instruments=N_INSTRUMENTS,
+    n_subbands=N_SUBBANDS,
+    channels=N_CHANNELS,
+    fft_dim=(STFT_N_FFT // 2) // N_SUBBANDS,
+    n_scales=4,
+    scale=(2, 2),
+    block_size=2,
+    growth=128,
+    bottleneck_factor=2,
+    **kwargs,
+):
+    """Build the TFC_TDF encoder-decoder model for source separation."""
+    # Compute STFT
+    x = stft(inputs)
+
+    # Split mixture into subbands as separate channels
+    mix = ops.reshape(x, (-1, x.shape[1], x.shape[2] // n_subbands, 2 * n_subbands))
+    first_conv_out = layers.Conv2D(channels, 1, 1, use_bias=False)(mix)
+    x = first_conv_out
+
+    # Encoder path
+    encoder_outs = []
+    for _ in range(n_scales):
+        x = TimeFrequencyTransformBlock(
+            channels, block_size, fft_dim, bottleneck_factor
+        )(x)
+        encoder_outs.append(x)
+        fft_dim, channels = fft_dim // scale[0], channels + growth
+        x = Downscale(channels, scale)(x)
+
+    # Bottleneck
+    x = TimeFrequencyTransformBlock(channels, block_size, fft_dim, bottleneck_factor)(x)
+
+    # Decoder path
+    for _ in range(n_scales):
+        fft_dim, channels = fft_dim * scale[0], channels - growth
+        x = ops.concatenate([Upscale(channels, scale)(x), encoder_outs.pop()], axis=-1)
+        x = TimeFrequencyTransformBlock(
+            channels, block_size, fft_dim, bottleneck_factor, in_channels=x.shape[-1]
+        )(x)
+
+    # Residual connection and final convolutions
+    x = ops.concatenate([mix, x * first_conv_out], axis=-1)
+    x = layers.Conv2D(channels, 1, 1, use_bias=False, activation="gelu")(x)
+    x = layers.Conv2D(n_instruments * n_subbands * 2, 1, 1, use_bias=False)(x)
+
+    # Reshape back to instrument-wise STFT
+    x = ops.reshape(x, (-1, x.shape[1], x.shape[2] * n_subbands, n_instruments, 2))
+    x = ops.transpose(x, (0, 3, 1, 2, 4))
+    x = ops.reshape(x, (-1, n_instruments, x.shape[2], x.shape[3] * 2))
+
+    return keras.Model(inputs=inputs, outputs=x, **kwargs)
+
+
+"""
+## Loss and Metrics
+
+We define:
+
+- `spectral_loss`: Mean absolute error in STFT domain.
+- `sdr`: Signal-to-Distortion Ratio, a common source separation metric.
+"""
+
+
+def prediction_to_wave(x, n_instruments=N_INSTRUMENTS):
+    """Convert STFT predictions back to waveform."""
+    x = ops.reshape(x, (-1, x.shape[2], x.shape[3] // 2, 2))
+    x = inverse_stft(x)
+    return ops.reshape(x, (-1, n_instruments, x.shape[1]))
+
+
+def target_to_stft(y):
+    """Convert target waveforms to their STFT representations."""
+    y = ops.reshape(y, (-1, CHUNK_SIZE))
+    y_real, y_imag = ops.stft(y, STFT_N_FFT, STFT_HOP_LENGTH, STFT_N_FFT)
+    y_real, y_imag = y_real[..., :-1], y_imag[..., :-1]
+    y = ops.stack([y_real, y_imag], axis=-1)
+    return ops.reshape(y, (-1, N_INSTRUMENTS, y.shape[1], y.shape[2] * 2))
+
+
+@saving.register_keras_serializable()
+def sdr(y_true, y_pred):
+    """Signal-to-Distortion Ratio metric."""
+    y_pred = prediction_to_wave(y_pred)
+    # Add epsilon for numerical stability
+    num = ops.sum(ops.square(y_true), axis=-1) + 1e-8
+    den = ops.sum(ops.square(y_true - y_pred), axis=-1) + 1e-8
+    return 10 * ops.log10(num / den)
+
+
+@saving.register_keras_serializable()
+def spectral_loss(y_true, y_pred):
+    """Mean absolute error in the STFT domain."""
+    y_true = target_to_stft(y_true)
+    return ops.mean(ops.absolute(y_true - y_pred))
+
+
+"""
+## Training
+
+### Visualize Model Architecture
+"""
+
+# Load or create the model
+if path.exists(MODEL_PATH):
+    model = saving.load_model(MODEL_PATH)
+else:
+    model = build_model(keras.Input(sample_batch_x.shape[1:]), name="tfc_tdf_net")
+
+# Display the model architecture
+model.summary()
+img = keras.utils.plot_model(model, path.join(TMP_DIR, "model.png"), show_shapes=True)
+display.display(img)
+
+"""
+### Compile and Train the Model
+"""
+
+# Compile the model
+optimizer = keras.optimizers.Adam(5e-05, gradient_accumulation_steps=ACCUMULATION_STEPS)
+model.compile(optimizer=optimizer, loss=spectral_loss, metrics=[sdr])
+
+# Define callbacks
+cbs = [
+    callbacks.ModelCheckpoint(MODEL_PATH, "val_sdr", save_best_only=True, mode="max"),
+    callbacks.ReduceLROnPlateau(factor=0.95, patience=2),
+    callbacks.CSVLogger(CSV_LOG_PATH),
+]
+
+if not path.exists(MODEL_PATH):
+    model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=cbs, shuffle=False)
+else:
+    # Demonstration of a single epoch of training when model already exists
+    model.fit(train_ds, validation_data=val_ds, epochs=1, shuffle=False, verbose=2)
+
+"""
+## Evaluation
+
+Evaluate the model on the validation dataset and visualize predicted vocals.
+"""
+
+model.evaluate(val_ds, verbose=2)
+y_pred = model.predict(sample_batch_x, verbose=2)
+y_pred = prediction_to_wave(y_pred)
+visualize_audio_np(ops.convert_to_numpy(y_pred[0, 0]), name="vocals_pred")
+
+"""
+## Conclusion
+
+We built and trained a vocal track separation model using an encoder-decoder
+architecture with custom blocks applied to the MUSDB18 dataset. We demonstrated
+STFT-based preprocessing, data augmentation, and a source separation metric (SDR).
+
+**Next steps:**
+
+- Train for more epochs and refine hyperparameters.
+- Separate multiple instruments simultaneously.
+- Enhance the model to handle instruments not present in the mixture.
+"""
\ No newline at end of file
diff --git a/examples/vision/ipynb/mnist_moe.ipynb b/examples/vision/ipynb/mnist_moe.ipynb
deleted file mode 100644
index 931cb764c6..0000000000
--- a/examples/vision/ipynb/mnist_moe.ipynb
+++ /dev/null
@@ -1,685 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "# MoE for MNIST\n",
-    "\n",
-    "**Author:** [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)<br>\n",
-    "**Date created:** 2015/06/19<br>\n",
-    "**Last modified:** 2020/04/21<br>\n",
-    "**Description:** Simple MoE implementation for MNIST classification."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "# Introduction\n",
-    "\n",
-    "In this example, we implement an adaptation of the Mixture of Experts (MoE) architecture\n",
-    "([Shazeer et al.](https://arxiv.org/abs/1701.06538)).\n",
-    "The idea is to use conditional computation to increases model capacity without increasing computation.\n",
-    "Experts are identical blocks within a layer where each are trained to specialize in different parts of the input space.\n",
-    "At each forward pass, a gating network selects a subset of experts to apply to the input.\n",
-    "\n",
-    "The components to implement are:\n",
-    "\n",
-    "- Gating network: A dense layer that outputs a probability distribution over the experts.\n",
-    "- MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.\n",
-    "- Model: A simple model that uses the MoE layer."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import keras\n",
-    "from keras import layers, models\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras import backend as K"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "### Data Prepration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "# Model / data parameters\n",
-    "num_classes = 10\n",
-    "input_shape = (28, 28, 1)\n",
-    "\n",
-    "# Load the data and split it between train and test sets\n",
-    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
-    "\n",
-    "# Scale images to the [0, 1] range\n",
-    "x_train = x_train.astype(\"float32\") / 255\n",
-    "x_test = x_test.astype(\"float32\") / 255\n",
-    "# Make sure images have shape (28, 28, 1)\n",
-    "x_train = np.expand_dims(x_train, -1)\n",
-    "x_test = np.expand_dims(x_test, -1)\n",
-    "print(\"x_train shape:\", x_train.shape)\n",
-    "print(x_train.shape[0], \"train samples\")\n",
-    "print(x_test.shape[0], \"test samples\")\n",
-    "\n",
-    "\n",
-    "# convert class vectors to binary class matrices\n",
-    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
-    "y_test = keras.utils.to_categorical(y_test, num_classes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## Constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "NUM_EXPERTS = 5\n",
-    "TOP_K = 3\n",
-    "BATCH_SIZE = 128\n",
-    "NUM_EPOCHS = 12\n",
-    "LEARNING_RATE = 0.001\n",
-    ""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## Base architecture\n",
-    "\n",
-    "The most basic [MNIST classifier](https://keras.io/examples/vision/mnist_convnet/) consists of a stack of convolutional layers followed by a dense layer. In this tutorial, we will first replace the dense layer with a MoE layer. Then do the same for convolutional layers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "model = keras.Sequential(\n",
-    "    [\n",
-    "        keras.Input(shape=input_shape),\n",
-    "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
-    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
-    "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
-    "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
-    "        layers.Flatten(),\n",
-    "        layers.Dropout(0.5),\n",
-    "        layers.Dense(num_classes, activation=\"softmax\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "# Linear MoE using Dense layers\n",
-    "\n",
-    "For this layer, we will create multiple dense layers that will be used as experts. Then a simple gating network will select at each step which exerts should be utilized for the current input. We will keep track of the number of times each expert is used. Then the selected experts will be combined using a weighted sum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "class LinearMoE(layers.Layer):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        hidden_size,\n",
-    "        num_experts=NUM_EXPERTS,\n",
-    "        top_k=TOP_K,\n",
-    "    ):\n",
-    "        super(LinearMoE, self).__init__()\n",
-    "\n",
-    "        # Initialize experts\n",
-    "        self.experts = [\n",
-    "            layers.Dense(\n",
-    "                hidden_size,\n",
-    "                kernel_initializer=tf.keras.initializers.RandomNormal(\n",
-    "                    mean=0.0, stddev=0.001\n",
-    "                ),\n",
-    "                bias_initializer=\"zeros\",\n",
-    "            )\n",
-    "            for _ in range(num_experts)\n",
-    "        ]\n",
-    "        # Initialize gating network\n",
-    "        self.gating_network = layers.Dense(\n",
-    "            NUM_EXPERTS,\n",
-    "            kernel_initializer=tf.keras.initializers.RandomNormal(\n",
-    "                mean=0.0, stddev=0.001\n",
-    "            ),\n",
-    "            bias_initializer=\"zeros\",\n",
-    "            activation=\"softmax\",\n",
-    "        )\n",
-    "\n",
-    "        self.num_experts = num_experts\n",
-    "        self.top_k = top_k\n",
-    "        # Keep track of how many times each expert is used\n",
-    "        self.expert_usage_count = tf.Variable(\n",
-    "            tf.zeros((num_experts,), dtype=tf.float32)\n",
-    "        )\n",
-    "\n",
-    "    def get_top_outputs(self, x, top_k_indices, top_k_weights):\n",
-    "        batch_size = tf.shape(x)[0]\n",
-    "        flat_indices = tf.reshape(top_k_indices, [-1])\n",
-    "        repeated_x = tf.repeat(x, repeats=self.top_k, axis=0)\n",
-    "\n",
-    "        # Compute outputs for unique experts\n",
-    "        unique_expert_ids = tf.unique(flat_indices)[0]  # Get unique expert indices\n",
-    "        expert_outputs_dict = {}\n",
-    "        for idx in unique_expert_ids:\n",
-    "            mask = tf.equal(flat_indices, idx)\n",
-    "            selected_inputs = tf.boolean_mask(repeated_x, mask)\n",
-    "            expert_outputs_dict[idx.numpy()] = self.experts[idx](selected_inputs)\n",
-    "\n",
-    "        # Gather outputs back into the correct shape\n",
-    "        output_size = self.experts[0].compute_output_shape(input_shape=(None, 10))[-1]\n",
-    "        flat_outputs = tf.zeros(\n",
-    "            [batch_size * self.top_k, output_size], dtype=tf.float32\n",
-    "        )\n",
-    "        for idx in unique_expert_ids:\n",
-    "            mask = tf.equal(flat_indices, idx)\n",
-    "            indices = tf.where(mask)\n",
-    "            flat_outputs = tf.tensor_scatter_nd_update(\n",
-    "                flat_outputs, indices, expert_outputs_dict[idx.numpy()]\n",
-    "            )\n",
-    "        top_k_expert_outputs = tf.reshape(\n",
-    "            flat_outputs, [batch_size, self.top_k, output_size]\n",
-    "        )\n",
-    "\n",
-    "        # Combine outputs using top-k weights\n",
-    "        return tf.einsum(\"ijk,ij->ik\", top_k_expert_outputs, top_k_weights)\n",
-    "\n",
-    "    def update_usage_counts(self, indices):\n",
-    "        updates = tf.ones_like(tf.reshape(indices, [-1]), dtype=tf.float32)\n",
-    "        self.expert_usage_count.assign(\n",
-    "            tf.tensor_scatter_nd_add(\n",
-    "                self.expert_usage_count, tf.reshape(indices, [-1, 1]), updates\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "    def call(self, x):\n",
-    "        gating_weights = self.gating_network(x)\n",
-    "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
-    "        combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)\n",
-    "        self.update_usage_counts(top_k_indices)\n",
-    "\n",
-    "        return combined_output\n",
-    ""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "Output of the top 3 experts out of 10 for one layer of MoE:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "sample_data = tf.random.uniform((1, 10))\n",
-    "linear_mode = LinearMoE(32, 10, 3)\n",
-    "linear_mode(sample_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## Routing Collapse\n",
-    "\n",
-    "One common challenge with MoE architectures is \"routing collapse\". The \"route\" refers to the selection process of which expert to use for a given input where the model falls into a pattern of only using a small subset of experts. This happens because:\n",
-    "\n",
-    "1. Early in training, some experts may perform slightly better by chance\n",
-    "2. These better-performing experts get selected more frequently\n",
-    "3. With more practice, these experts improve further, creating a feedback loop\n",
-    "4. Other experts become neglected and never improve\n",
-    "\n",
-    "Code below demonstrates the randomness of expert selection:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "def check_expert_usage(runs):\n",
-    "    # Running the later multiple times to show randomness of expert selection\n",
-    "    for i in range(runs):\n",
-    "        sample_data = tf.random.uniform((1, 10))\n",
-    "        linear_mode = LinearMoE(10, 5)\n",
-    "        _ = linear_mode(sample_data)\n",
-    "        print(f\"Run {i}, Expert usage: {linear_mode.expert_usage_count.numpy()}\")\n",
-    "\n",
-    "\n",
-    "check_expert_usage(4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "### Load Balancing Solutions\n",
-    "\n",
-    "To prevent routing collapse, we implement three types of losses that were introduced in various MoE research:\n",
-    "\n",
-    "1. Diversity Loss: Encourages the gating network to use all experts by maximizing the entropy\n",
-    "   of expert selection probabilities\n",
-    "   [Shazeer et al., \"Outrageously Large Neural Networks\" (2017)](https://arxiv.org/abs/1701.06538)\n",
-    "\n",
-    "2. Importance Loss: Ensures each expert handles a similar total amount of input across the batch\n",
-    "   by penalizing deviations from the mean usage\n",
-    "   [Lepikhin et al., \"GShard: Scaling Giant Models with Conditional Computation\" (2020)](https://arxiv.org/abs/2006.16668)\n",
-    "\n",
-    "3. Overflow Loss: Prevents individual experts from being overloaded by penalizing usage above\n",
-    "   a specified capacity threshold\n",
-    "   [Fedus et al., \"Switch Transformers\" (2021)](https://arxiv.org/abs/2101.03961)\n",
-    "\n",
-    "These losses are combined with the main classification loss during training to ensure balanced expert utilization.\n",
-    "The combination of these techniques has proven effective in large-scale models like GShard and Switch Transformers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "class LinearMoE(layers.Layer):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        hidden_size,\n",
-    "        num_experts=NUM_EXPERTS,\n",
-    "        top_k=TOP_K,\n",
-    "    ):\n",
-    "        super(LinearMoE, self).__init__()\n",
-    "\n",
-    "        # Initialize experts\n",
-    "        self.experts = [\n",
-    "            layers.Dense(\n",
-    "                hidden_size,\n",
-    "                kernel_initializer=tf.keras.initializers.RandomNormal(\n",
-    "                    mean=0.0, stddev=0.001\n",
-    "                ),\n",
-    "                bias_initializer=\"zeros\",\n",
-    "            )\n",
-    "            for _ in range(num_experts)\n",
-    "        ]\n",
-    "        # Initialize gating network\n",
-    "        self.gating_network = layers.Dense(\n",
-    "            num_experts,  # Match output to num_experts\n",
-    "            kernel_initializer=tf.keras.initializers.RandomNormal(\n",
-    "                mean=0.0, stddev=0.001\n",
-    "            ),\n",
-    "            bias_initializer=\"zeros\",\n",
-    "            activation=\"softmax\",\n",
-    "        )\n",
-    "\n",
-    "        self.num_experts = num_experts\n",
-    "        self.top_k = top_k\n",
-    "        # Keep track of how many times each expert is used as a layer weight\n",
-    "        self.expert_usage_count = tf.Variable(\n",
-    "            tf.zeros((num_experts,), dtype=tf.float32)\n",
-    "        )\n",
-    "\n",
-    "        self.batch_capacity = BATCH_SIZE // num_experts\n",
-    "\n",
-    "    def _diversity_loss(self, weights):\n",
-    "        entropy = -K.sum(weights * K.log(weights + 1e-10), axis=1)\n",
-    "        self.diversity_loss = -K.mean(entropy)\n",
-    "\n",
-    "    def _importance_loss(self, gating_weights):\n",
-    "        batch_importance_sum = K.sum(gating_weights, axis=0)\n",
-    "        mean_importance = K.mean(batch_importance_sum)\n",
-    "        self.importance_loss = K.mean(\n",
-    "            K.square(\n",
-    "                batch_importance_sum\n",
-    "                - mean_importance * tf.ones_like(batch_importance_sum)\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "    # Replace the current get_top_outputs method with this vectorized version\n",
-    "    def get_top_outputs(\n",
-    "        self, x, gating_weights\n",
-    "    ):  # Changed to take gating_weights directly\n",
-    "        \"\"\"Compute outputs from top-k experts.\"\"\"\n",
-    "        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)\n",
-    "\n",
-    "        # Store indices and updates for usage count\n",
-    "        self.indices = tf.reshape(top_k_indices, [-1, 1])\n",
-    "        self.updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)\n",
-    "\n",
-    "        # Compute expert outputs symbolically\n",
-    "        expert_outputs = tf.stack([expert(x) for expert in self.experts], axis=1)\n",
-    "        batch_size = tf.shape(x)[0]\n",
-    "        batch_indices = tf.tile(tf.range(batch_size)[:, tf.newaxis], [1, self.top_k])\n",
-    "        gather_indices = tf.stack([batch_indices, top_k_indices], axis=-1)\n",
-    "        top_k_expert_outputs = tf.gather_nd(expert_outputs, gather_indices)\n",
-    "\n",
-    "        combined_output = tf.reduce_sum(\n",
-    "            top_k_expert_outputs * top_k_weights[:, :, tf.newaxis], axis=1\n",
-    "        )\n",
-    "        return combined_output\n",
-    "\n",
-    "    def update_usage_counts(self):\n",
-    "        updates = tf.ones_like(tf.reshape(self.indices, [-1]), dtype=tf.float32)\n",
-    "        self.expert_usage_count.assign(\n",
-    "            tf.tensor_scatter_nd_add(\n",
-    "                self.expert_usage_count, tf.reshape(self.indices, [-1, 1]), updates\n",
-    "            )\n",
-    "        )\n",
-    "\n",
-    "    def call(self, x):\n",
-    "        # Get gating weights and normalize\n",
-    "        gating_weights = self.gating_network(x)\n",
-    "        # top_k_weights, top_k_indices = tf.nn.top_k(gating_weights, k=self.top_k)\n",
-    "        combined_output = self.get_top_outputs(x, gating_weights)\n",
-    "        self.update_usage_counts()\n",
-    "        self._diversity_loss(gating_weights)\n",
-    "        self._importance_loss(gating_weights)\n",
-    "\n",
-    "        return combined_output\n",
-    "\n",
-    "    def compute_total_loss(self, load_balance_coef=0.01):\n",
-    "        self.batch_overflow_sum = K.sum(\n",
-    "            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)\n",
-    "        )\n",
-    "        return load_balance_coef * (\n",
-    "            self.diversity_loss + self.batch_overflow_sum + self.importance_loss\n",
-    "        )\n",
-    ""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "## MNIST classification with MoE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "class MoEModel(keras.Model):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        num_classes,\n",
-    "        num_experts=NUM_EXPERTS,\n",
-    "        top_k=TOP_K,\n",
-    "        moe_loss_considered=True,\n",
-    "    ):\n",
-    "        super(MoEModel, self).__init__()\n",
-    "\n",
-    "        # Define the convolutional block\n",
-    "        self.conv_block = keras.Sequential(\n",
-    "            [\n",
-    "                layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
-    "                layers.MaxPooling2D(pool_size=(2, 2)),\n",
-    "                layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
-    "                layers.MaxPooling2D(pool_size=(2, 2)),\n",
-    "                layers.Flatten(),\n",
-    "                layers.Dropout(0.5),\n",
-    "            ]\n",
-    "        )\n",
-    "\n",
-    "        # MoE classifier\n",
-    "        self.moe_classifier = LinearMoE(\n",
-    "            hidden_size=num_classes, num_experts=num_experts, top_k=top_k\n",
-    "        )\n",
-    "\n",
-    "        # Softmax layer\n",
-    "        self.softmax = layers.Softmax()\n",
-    "        self.moe_loss_considered = moe_loss_considered\n",
-    "\n",
-    "    def call(self, inputs, training=False):\n",
-    "        conv_flatten = self.conv_block(inputs)\n",
-    "        moe_output = self.moe_classifier(conv_flatten)\n",
-    "        outputs = self.softmax(moe_output)\n",
-    "        return outputs\n",
-    "\n",
-    "    def train_step(self, data):\n",
-    "        x, y = data  # Unpack input data and labels\n",
-    "\n",
-    "        with tf.GradientTape() as tape:\n",
-    "            y_pred = self(x, training=True)\n",
-    "            classification_loss = self.compute_loss(x, y, y_pred)\n",
-    "            if self.moe_loss_considered:\n",
-    "                moe_loss = self.moe_classifier.compute_total_loss(\n",
-    "                    load_balance_coef=0.01\n",
-    "                )\n",
-    "                total_loss = classification_loss + moe_loss\n",
-    "            else:\n",
-    "                total_loss = classification_loss\n",
-    "\n",
-    "        # Compute gradients\n",
-    "        gradients = tape.gradient(total_loss, self.trainable_variables)\n",
-    "\n",
-    "        # Update weights\n",
-    "        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n",
-    "        for metric in self.metrics:\n",
-    "            metric.update_state(y, y_pred)\n",
-    "        # Return a dict of metrics for monitoring\n",
-    "        return {\n",
-    "            \"loss\": total_loss,\n",
-    "            \"moe_loss\": moe_loss,\n",
-    "            **{m.name: m.result() for m in self.metrics},\n",
-    "        }\n",
-    "\n",
-    "    def test_step(self, data):\n",
-    "        x, y = data\n",
-    "        y_pred = self(x, training=False)\n",
-    "        classification_loss = self.compute_loss(x, y, y_pred)\n",
-    "        moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)\n",
-    "        total_loss = classification_loss + moe_loss\n",
-    "\n",
-    "        for metric in self.metrics:\n",
-    "            metric.update_state(y, y_pred)\n",
-    "        return {\n",
-    "            \"loss\": total_loss,\n",
-    "            \"moe_loss\": moe_loss,\n",
-    "            **{m.name: m.result() for m in self.metrics},\n",
-    "        }\n",
-    "\n",
-    "\n",
-    "# Instantiate and compile the model\n",
-    "inputs = keras.Input(shape=input_shape)\n",
-    "model = MoEModel(num_classes=num_classes, num_experts=5, top_k=3)\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),\n",
-    "    loss=keras.losses.CategoricalCrossentropy(),  # Assumes one-hot encoded labels\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "###  Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "history = model.fit(\n",
-    "    x_train,\n",
-    "    y_train,\n",
-    "    batch_size=BATCH_SIZE,\n",
-    "    epochs=NUM_EPOCHS,\n",
-    "    validation_data=(x_test, y_test),\n",
-    "    verbose=0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "### Evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 0,
-   "metadata": {
-    "colab_type": "code"
-   },
-   "outputs": [],
-   "source": [
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print(\"Test loss:\", score[0])\n",
-    "print(\"Test accuracy:\", score[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text"
-   },
-   "source": [
-    "# Conclusion\n",
-    "\n",
-    "This example demonstrated how Mixture of Experts (MoE) can be used to increase model capacity without a proportional increase in computation cost. The key benefits are:\n",
-    "\n",
-    "1. Conditional Computation: Only a subset of experts (TOP_K=3 out of NUM_EXPERTS=5) process each input,\n",
-    "   making the model more computationally efficient than a model that uses all parameters for every input.\n",
-    "\n",
-    "2. Specialized Processing: Each expert learns to handle different aspects of the input space,\n",
-    "   allowing for more sophisticated processing without requiring a larger dense network.\n",
-    "\n",
-    "In our implementation, we:\n",
-    "1. Created a basic MoE layer using dense networks as experts\n",
-    "2. Implemented three types of load balancing losses to prevent routing collapse\n",
-    "3. Applied the MoE architecture to MNIST classification by replacing the final dense layer\n",
-    "4. Achieved comparable accuracy to the baseline model while using experts conditionally\n",
-    "\n",
-    "This approach is particularly valuable for large-scale models where computational efficiency\n",
-    "is crucial. The same principles demonstrated here are used in much larger language models\n",
-    "and other applications where model capacity needs to scale efficiently"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "name": "mnist_moe",
-   "private_outputs": false,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/examples/vision/md/mnist_moe.md b/examples/vision/md/mnist_moe.md
deleted file mode 100644
index 2696176d0c..0000000000
--- a/examples/vision/md/mnist_moe.md
+++ /dev/null
@@ -1,585 +0,0 @@
-# MoE for MNIST
-
-**Author:** [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)<br>
-**Date created:** 2015/06/19<br>
-**Last modified:** 2020/04/21<br>
-**Description:** Simple MoE implementation for MNIST classification.
-
-
-<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/mnist_moe.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/vision/mnist_moe.py)
-
-
-
-# Introduction
-
-In this example, we implement an adaptation of the Mixture of Experts (MoE) architecture
-([Shazeer et al.](https://arxiv.org/abs/1701.06538)).
-The idea is to use conditional computation to increases model capacity without increasing computation.
-Experts are identical blocks within a layer where each are trained to specialize in different parts of the input space.
-At each forward pass, a gating network selects a subset of experts to apply to the input.
-
-The components to implement are:
-
-- Gating network: A dense layer that outputs a probability distribution over the experts.
-- MoE layer: A layer that applies a different expert to each input in the batch. And a loss function that ensures specialization among the experts.
-- Model: A simple model that uses the MoE layer.
-
----
-## Imports
-
-
-```python
-import numpy as np
-import keras
-from keras import layers, models
-import tensorflow as tf
-from tensorflow.keras import backend as K
-```
-
-### Data Prepration
-
-
-```python
-# Model / data parameters
-num_classes = 10
-input_shape = (28, 28, 1)
-
-# Load the data and split it between train and test sets
-(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
-
-# Scale images to the [0, 1] range
-x_train = x_train.astype("float32") / 255
-x_test = x_test.astype("float32") / 255
-# Make sure images have shape (28, 28, 1)
-x_train = np.expand_dims(x_train, -1)
-x_test = np.expand_dims(x_test, -1)
-print("x_train shape:", x_train.shape)
-print(x_train.shape[0], "train samples")
-print(x_test.shape[0], "test samples")
-
-
-# convert class vectors to binary class matrices
-y_train = keras.utils.to_categorical(y_train, num_classes)
-y_test = keras.utils.to_categorical(y_test, num_classes)
-```
-
-<div class="k-default-codeblock">
-```
-x_train shape: (60000, 28, 28, 1)
-60000 train samples
-10000 test samples
-
-```
-</div>
----
-## Constants
-
-
-```python
-NUM_EXPERTS = 5
-TOP_K = 3
-BATCH_SIZE = 128
-NUM_EPOCHS = 12
-LEARNING_RATE = 0.001
-
-```
-
----
-## Base architecture
-
-The most basic [MNIST classifier](https://keras.io/examples/vision/mnist_convnet/) consists of a stack of convolutional layers followed by a dense layer. In this tutorial, we will first replace the dense layer with a MoE layer. Then do the same for convolutional layers.
-
-
-```python
-model = keras.Sequential(
-    [
-        keras.Input(shape=input_shape),
-        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
-        layers.MaxPooling2D(pool_size=(2, 2)),
-        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
-        layers.MaxPooling2D(pool_size=(2, 2)),
-        layers.Flatten(),
-        layers.Dropout(0.5),
-        layers.Dense(num_classes, activation="softmax"),
-    ]
-)
-
-model.summary()
-```
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "sequential"</span>
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
-┃<span style="font-weight: bold"> Layer (type)                    </span>┃<span style="font-weight: bold"> Output Shape           </span>┃<span style="font-weight: bold">       Param # </span>┃
-┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
-│ conv2d (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)                 │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">26</span>, <span style="color: #00af00; text-decoration-color: #00af00">26</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>)     │           <span style="color: #00af00; text-decoration-color: #00af00">320</span> │
-├─────────────────────────────────┼────────────────────────┼───────────────┤
-│ max_pooling2d (<span style="color: #0087ff; text-decoration-color: #0087ff">MaxPooling2D</span>)    │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">13</span>, <span style="color: #00af00; text-decoration-color: #00af00">13</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>)     │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
-├─────────────────────────────────┼────────────────────────┼───────────────┤
-│ conv2d_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)               │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">11</span>, <span style="color: #00af00; text-decoration-color: #00af00">11</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)     │        <span style="color: #00af00; text-decoration-color: #00af00">18,496</span> │
-├─────────────────────────────────┼────────────────────────┼───────────────┤
-│ max_pooling2d_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">MaxPooling2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">5</span>, <span style="color: #00af00; text-decoration-color: #00af00">5</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>)       │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
-├─────────────────────────────────┼────────────────────────┼───────────────┤
-│ flatten (<span style="color: #0087ff; text-decoration-color: #0087ff">Flatten</span>)               │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1600</span>)           │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
-├─────────────────────────────────┼────────────────────────┼───────────────┤
-│ dropout (<span style="color: #0087ff; text-decoration-color: #0087ff">Dropout</span>)               │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1600</span>)           │             <span style="color: #00af00; text-decoration-color: #00af00">0</span> │
-├─────────────────────────────────┼────────────────────────┼───────────────┤
-│ dense (<span style="color: #0087ff; text-decoration-color: #0087ff">Dense</span>)                   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">10</span>)             │        <span style="color: #00af00; text-decoration-color: #00af00">16,010</span> │
-└─────────────────────────────────┴────────────────────────┴───────────────┘
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">34,826</span> (136.04 KB)
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">34,826</span> (136.04 KB)
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">0</span> (0.00 B)
-</pre>
-
-
-
-# Linear MoE using Dense layers
-
-For this layer, we will create multiple dense layers that will be used as experts. Then a simple gating network will select at each step which exerts should be utilized for the current input. We will keep track of the number of times each expert is used. Then the selected experts will be combined using a weighted sum.
-
-
-```python
-
-class LinearMoE(layers.Layer):
-    def __init__(
-        self,
-        hidden_size,
-        num_experts=NUM_EXPERTS,
-        top_k=TOP_K,
-    ):
-        super(LinearMoE, self).__init__()
-
-        # Initialize experts
-        self.experts = [
-            layers.Dense(
-                hidden_size,
-                kernel_initializer=tf.keras.initializers.RandomNormal(
-                    mean=0.0, stddev=0.001
-                ),
-                bias_initializer="zeros",
-            )
-            for _ in range(num_experts)
-        ]
-        # Initialize gating network
-        self.gating_network = layers.Dense(
-            NUM_EXPERTS,
-            kernel_initializer=tf.keras.initializers.RandomNormal(
-                mean=0.0, stddev=0.001
-            ),
-            bias_initializer="zeros",
-            activation="softmax",
-        )
-
-        self.num_experts = num_experts
-        self.top_k = top_k
-        # Keep track of how many times each expert is used
-        self.expert_usage_count = tf.Variable(
-            tf.zeros((num_experts,), dtype=tf.float32)
-        )
-
-    def get_top_outputs(self, x, top_k_indices, top_k_weights):
-        batch_size = tf.shape(x)[0]
-        flat_indices = tf.reshape(top_k_indices, [-1])
-        repeated_x = tf.repeat(x, repeats=self.top_k, axis=0)
-
-        # Compute outputs for unique experts
-        unique_expert_ids = tf.unique(flat_indices)[0]  # Get unique expert indices
-        expert_outputs_dict = {}
-        for idx in unique_expert_ids:
-            mask = tf.equal(flat_indices, idx)
-            selected_inputs = tf.boolean_mask(repeated_x, mask)
-            expert_outputs_dict[idx.numpy()] = self.experts[idx](selected_inputs)
-
-        # Gather outputs back into the correct shape
-        output_size = self.experts[0].compute_output_shape(input_shape=(None, 10))[-1]
-        flat_outputs = tf.zeros(
-            [batch_size * self.top_k, output_size], dtype=tf.float32
-        )
-        for idx in unique_expert_ids:
-            mask = tf.equal(flat_indices, idx)
-            indices = tf.where(mask)
-            flat_outputs = tf.tensor_scatter_nd_update(
-                flat_outputs, indices, expert_outputs_dict[idx.numpy()]
-            )
-        top_k_expert_outputs = tf.reshape(
-            flat_outputs, [batch_size, self.top_k, output_size]
-        )
-
-        # Combine outputs using top-k weights
-        return tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
-
-    def update_usage_counts(self, indices):
-        updates = tf.ones_like(tf.reshape(indices, [-1]), dtype=tf.float32)
-        self.expert_usage_count.assign(
-            tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(indices, [-1, 1]), updates
-            )
-        )
-
-    def call(self, x):
-        gating_weights = self.gating_network(x)
-        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
-        combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)
-        self.update_usage_counts(top_k_indices)
-
-        return combined_output
-
-```
-
-Output of the top 3 experts out of 10 for one layer of MoE:
-
-
-```python
-sample_data = tf.random.uniform((1, 10))
-linear_mode = LinearMoE(32, 10, 3)
-linear_mode(sample_data)
-```
-
-
-
-
-<div class="k-default-codeblock">
-```
-<tf.Tensor: shape=(1, 32), dtype=float32, numpy=
-array([[ 4.5012074e-04,  3.6236792e-04, -3.6404550e-04, -1.9282849e-04,
-         1.1771411e-03, -4.8739690e-05,  4.8100296e-04,  2.9840387e-04,
-         1.3504212e-03,  4.3620504e-04,  4.2805035e-04,  6.2110950e-04,
-         8.0633757e-04,  3.1539859e-04, -6.5098109e-04,  9.3904360e-05,
-         2.4753943e-04, -3.7851423e-05, -8.6873386e-04,  1.2532169e-04,
-        -5.0137896e-04, -9.8181982e-04, -2.3387320e-04,  1.7193856e-04,
-        -7.3027433e-05, -1.1662465e-03,  3.3941280e-04,  8.5291412e-04,
-        -3.9347171e-04,  6.5931707e-04,  3.8440802e-04,  4.5856956e-04]],
-      dtype=float32)>
-
-```
-</div>
----
-## Routing Collapse
-
-One common challenge with MoE architectures is "routing collapse". The "route" refers to the selection process of which expert to use for a given input where the model falls into a pattern of only using a small subset of experts. This happens because:
-
-1. Early in training, some experts may perform slightly better by chance
-2. These better-performing experts get selected more frequently
-3. With more practice, these experts improve further, creating a feedback loop
-4. Other experts become neglected and never improve
-
-Code below demonstrates the randomness of expert selection:
-
-
-```python
-
-def check_expert_usage(runs):
-    # Running the later multiple times to show randomness of expert selection
-    for i in range(runs):
-        sample_data = tf.random.uniform((1, 10))
-        linear_mode = LinearMoE(10, 5)
-        _ = linear_mode(sample_data)
-        print(f"Run {i}, Expert usage: {linear_mode.expert_usage_count.numpy()}")
-
-
-check_expert_usage(4)
-```
-
-<div class="k-default-codeblock">
-```
-Run 0, Expert usage: [1. 1. 0. 0. 1.]
-Run 1, Expert usage: [1. 1. 1. 0. 0.]
-Run 2, Expert usage: [0. 1. 1. 0. 1.]
-Run 3, Expert usage: [0. 1. 1. 1. 0.]
-
-```
-</div>
-### Load Balancing Solutions
-
-To prevent routing collapse, we implement three types of losses that were introduced in various MoE research:
-
-1. Diversity Loss: Encourages the gating network to use all experts by maximizing the entropy
-   of expert selection probabilities
-   [Shazeer et al., "Outrageously Large Neural Networks" (2017)](https://arxiv.org/abs/1701.06538)
-
-2. Importance Loss: Ensures each expert handles a similar total amount of input across the batch
-   by penalizing deviations from the mean usage
-   [Lepikhin et al., "GShard: Scaling Giant Models with Conditional Computation" (2020)](https://arxiv.org/abs/2006.16668)
-
-3. Overflow Loss: Prevents individual experts from being overloaded by penalizing usage above
-   a specified capacity threshold
-   [Fedus et al., "Switch Transformers" (2021)](https://arxiv.org/abs/2101.03961)
-
-These losses are combined with the main classification loss during training to ensure balanced expert utilization.
-The combination of these techniques has proven effective in large-scale models like GShard and Switch Transformers.
-
-
-```python
-
-class LinearMoE(layers.Layer):
-    def __init__(
-        self,
-        hidden_size,
-        num_experts=NUM_EXPERTS,
-        top_k=TOP_K,
-    ):
-        super(LinearMoE, self).__init__()
-
-        # Initialize experts
-        self.experts = [
-            layers.Dense(
-                hidden_size,
-                kernel_initializer=tf.keras.initializers.RandomNormal(
-                    mean=0.0, stddev=0.001
-                ),
-                bias_initializer="zeros",
-            )
-            for _ in range(num_experts)
-        ]
-        # Initialize gating network
-        self.gating_network = layers.Dense(
-            num_experts,  # Match output to num_experts
-            kernel_initializer=tf.keras.initializers.RandomNormal(
-                mean=0.0, stddev=0.001
-            ),
-            bias_initializer="zeros",
-            activation="softmax",
-        )
-
-        self.num_experts = num_experts
-        self.top_k = top_k
-        # Keep track of how many times each expert is used as a layer weight
-        self.expert_usage_count = tf.Variable(
-            tf.zeros((num_experts,), dtype=tf.float32)
-        )
-
-        self.batch_capacity = BATCH_SIZE // num_experts
-
-    def _diversity_loss(self, weights):
-        entropy = -K.sum(weights * K.log(weights + 1e-10), axis=1)
-        self.diversity_loss = -K.mean(entropy)
-
-    def _importance_loss(self, gating_weights):
-        batch_importance_sum = K.sum(gating_weights, axis=0)
-        mean_importance = K.mean(batch_importance_sum)
-        self.importance_loss = K.mean(
-            K.square(
-                batch_importance_sum
-                - mean_importance * tf.ones_like(batch_importance_sum)
-            )
-        )
-
-    # Replace the current get_top_outputs method with this vectorized version
-    def get_top_outputs(
-        self, x, gating_weights
-    ):  # Changed to take gating_weights directly
-        """Compute outputs from top-k experts."""
-        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
-
-        # Store indices and updates for usage count
-        self.indices = tf.reshape(top_k_indices, [-1, 1])
-        self.updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
-
-        # Compute expert outputs symbolically
-        expert_outputs = tf.stack([expert(x) for expert in self.experts], axis=1)
-        batch_size = tf.shape(x)[0]
-        batch_indices = tf.tile(tf.range(batch_size)[:, tf.newaxis], [1, self.top_k])
-        gather_indices = tf.stack([batch_indices, top_k_indices], axis=-1)
-        top_k_expert_outputs = tf.gather_nd(expert_outputs, gather_indices)
-
-        combined_output = tf.reduce_sum(
-            top_k_expert_outputs * top_k_weights[:, :, tf.newaxis], axis=1
-        )
-        return combined_output
-
-    def update_usage_counts(self):
-        updates = tf.ones_like(tf.reshape(self.indices, [-1]), dtype=tf.float32)
-        self.expert_usage_count.assign(
-            tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(self.indices, [-1, 1]), updates
-            )
-        )
-
-    def call(self, x):
-        # Get gating weights and normalize
-        gating_weights = self.gating_network(x)
-        # top_k_weights, top_k_indices = tf.nn.top_k(gating_weights, k=self.top_k)
-        combined_output = self.get_top_outputs(x, gating_weights)
-        self.update_usage_counts()
-        self._diversity_loss(gating_weights)
-        self._importance_loss(gating_weights)
-
-        return combined_output
-
-    def compute_total_loss(self, load_balance_coef=0.01):
-        self.batch_overflow_sum = K.sum(
-            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
-        )
-        return load_balance_coef * (
-            self.diversity_loss + self.batch_overflow_sum + self.importance_loss
-        )
-
-```
-
----
-## MNIST classification with MoE
-
-
-```python
-
-class MoEModel(keras.Model):
-    def __init__(
-        self,
-        num_classes,
-        num_experts=NUM_EXPERTS,
-        top_k=TOP_K,
-        moe_loss_considered=True,
-    ):
-        super(MoEModel, self).__init__()
-
-        # Define the convolutional block
-        self.conv_block = keras.Sequential(
-            [
-                layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
-                layers.MaxPooling2D(pool_size=(2, 2)),
-                layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
-                layers.MaxPooling2D(pool_size=(2, 2)),
-                layers.Flatten(),
-                layers.Dropout(0.5),
-            ]
-        )
-
-        # MoE classifier
-        self.moe_classifier = LinearMoE(
-            hidden_size=num_classes, num_experts=num_experts, top_k=top_k
-        )
-
-        # Softmax layer
-        self.softmax = layers.Softmax()
-        self.moe_loss_considered = moe_loss_considered
-
-    def call(self, inputs, training=False):
-        conv_flatten = self.conv_block(inputs)
-        moe_output = self.moe_classifier(conv_flatten)
-        outputs = self.softmax(moe_output)
-        return outputs
-
-    def train_step(self, data):
-        x, y = data  # Unpack input data and labels
-
-        with tf.GradientTape() as tape:
-            y_pred = self(x, training=True)
-            classification_loss = self.compute_loss(x, y, y_pred)
-            if self.moe_loss_considered:
-                moe_loss = self.moe_classifier.compute_total_loss(
-                    load_balance_coef=0.01
-                )
-                total_loss = classification_loss + moe_loss
-            else:
-                total_loss = classification_loss
-
-        # Compute gradients
-        gradients = tape.gradient(total_loss, self.trainable_variables)
-
-        # Update weights
-        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
-        for metric in self.metrics:
-            metric.update_state(y, y_pred)
-        # Return a dict of metrics for monitoring
-        return {
-            "loss": total_loss,
-            "moe_loss": moe_loss,
-            **{m.name: m.result() for m in self.metrics},
-        }
-
-    def test_step(self, data):
-        x, y = data
-        y_pred = self(x, training=False)
-        classification_loss = self.compute_loss(x, y, y_pred)
-        moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
-        total_loss = classification_loss + moe_loss
-
-        for metric in self.metrics:
-            metric.update_state(y, y_pred)
-        return {
-            "loss": total_loss,
-            "moe_loss": moe_loss,
-            **{m.name: m.result() for m in self.metrics},
-        }
-
-
-# Instantiate and compile the model
-inputs = keras.Input(shape=input_shape)
-model = MoEModel(num_classes=num_classes, num_experts=5, top_k=3)
-
-model.compile(
-    optimizer=keras.optimizers.Adam(learning_rate=LEARNING_RATE),
-    loss=keras.losses.CategoricalCrossentropy(),  # Assumes one-hot encoded labels
-    metrics=["accuracy"],
-)
-```
-
-###  Training
-
-
-```python
-history = model.fit(
-    x_train,
-    y_train,
-    batch_size=BATCH_SIZE,
-    epochs=NUM_EPOCHS,
-    validation_data=(x_test, y_test),
-    verbose=0,
-)
-```
-
-### Evaluation
-
-
-```python
-score = model.evaluate(x_test, y_test, verbose=0)
-print("Test loss:", score[0])
-print("Test accuracy:", score[1])
-```
-
-<div class="k-default-codeblock">
-```
-Test loss: tf.Tensor(0.9811909, shape=(), dtype=float32)
-Test accuracy: {'accuracy': <tf.Tensor: shape=(), dtype=float32, numpy=0.9919000267982483>}
-
-```
-</div>
-# Conclusion
-
-This example demonstrated how Mixture of Experts (MoE) can be used to increase model capacity without a proportional increase in computation cost. The key benefits are:
-
-1. Conditional Computation: Only a subset of experts (TOP_K=3 out of NUM_EXPERTS=5) process each input,
-   making the model more computationally efficient than a model that uses all parameters for every input.
-
-2. Specialized Processing: Each expert learns to handle different aspects of the input space,
-   allowing for more sophisticated processing without requiring a larger dense network.
-
-In our implementation, we:
-1. Created a basic MoE layer using dense networks as experts
-2. Implemented three types of load balancing losses to prevent routing collapse
-3. Applied the MoE architecture to MNIST classification by replacing the final dense layer
-4. Achieved comparable accuracy to the baseline model while using experts conditionally
-
-This approach is particularly valuable for large-scale models where computational efficiency
-is crucial. The same principles demonstrated here are used in much larger language models
-and other applications where model capacity needs to scale efficiently
diff --git a/examples/vision/mnist_moe.py b/examples/vision/mnist_moe.py
index bcec4edf87..0b47e29edf 100644
--- a/examples/vision/mnist_moe.py
+++ b/examples/vision/mnist_moe.py
@@ -1,8 +1,8 @@
 """
 Title: MoE for MNIST
 Author: [Damoon Shahhosseini](https://www.linkedin.com/in/damoonsh/)
-Date created: 2015/06/19
-Last modified: 2020/04/21
+Date created: 2025/02/28
+Last modified: 2050/02/28
 Description: Simple MoE implementation for MNIST classification.
 Accelerator: GPU
 """
@@ -27,11 +27,14 @@
 ## Imports
 """
 
+import os
+
+os.environ["KERAS_BACKEND"] = "torch"
+
 import numpy as np
 import keras
-from keras import layers, models
-import tensorflow as tf
-from tensorflow.keras import backend as K
+from keras import layers
+import torch
 
 """
 ### Data Prepration
@@ -66,7 +69,7 @@
 NUM_EXPERTS = 5
 TOP_K = 3
 BATCH_SIZE = 128
-NUM_EPOCHS = 12
+NUM_EPOCHS = 1
 LEARNING_RATE = 0.001
 
 
@@ -106,12 +109,12 @@ def __init__(
         top_k=TOP_K,
     ):
         super(LinearMoE, self).__init__()
-
+        self.expert_output_size = hidden_size
         # Initialize experts
         self.experts = [
             layers.Dense(
                 hidden_size,
-                kernel_initializer=tf.keras.initializers.RandomNormal(
+                kernel_initializer=keras.initializers.RandomNormal(
                     mean=0.0, stddev=0.001
                 ),
                 bias_initializer="zeros",
@@ -121,9 +124,7 @@ def __init__(
         # Initialize gating network
         self.gating_network = layers.Dense(
             NUM_EXPERTS,
-            kernel_initializer=tf.keras.initializers.RandomNormal(
-                mean=0.0, stddev=0.001
-            ),
+            kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.001),
             bias_initializer="zeros",
             activation="softmax",
         )
@@ -131,62 +132,79 @@ def __init__(
         self.num_experts = num_experts
         self.top_k = top_k
         # Keep track of how many times each expert is used
-        self.expert_usage_count = tf.Variable(
-            tf.zeros((num_experts,), dtype=tf.float32)
+        self.expert_usage_count = self.add_weight(
+            shape=(self.num_experts,),
+            initializer="zeros",
+            trainable=False,
+            name="expert_usage_count",
         )
+        self.batch_capacity = BATCH_SIZE / num_experts
+
+    def build(self, input_shape):
+        super(LinearMoE, self).build(input_shape)
+        self.built = True
 
     def get_top_outputs(self, x, top_k_indices, top_k_weights):
-        batch_size = tf.shape(x)[0]
-        flat_indices = tf.reshape(top_k_indices, [-1])
-        repeated_x = tf.repeat(x, repeats=self.top_k, axis=0)
+        batch_size = x.size(0)
+        output_size = self.expert_output_size
 
-        # Compute outputs for unique experts
-        unique_expert_ids = tf.unique(flat_indices)[0]  # Get unique expert indices
+        # Get outputs from top-k experts
+        top_k_expert_outputs = torch.zeros(
+            batch_size, self.top_k, output_size, device=x.device
+        )
+        flat_indices = top_k_indices.view(-1).to(x.device)
+        repeated_x = x.repeat_interleave(self.top_k, dim=0)
+
+        unique_expert_ids = flat_indices.unique(sorted=True)
         expert_outputs_dict = {}
         for idx in unique_expert_ids:
-            mask = tf.equal(flat_indices, idx)
-            selected_inputs = tf.boolean_mask(repeated_x, mask)
-            expert_outputs_dict[idx.numpy()] = self.experts[idx](selected_inputs)
-
-        # Gather outputs back into the correct shape
-        output_size = self.experts[0].compute_output_shape(input_shape=(None, 10))[-1]
-        flat_outputs = tf.zeros(
-            [batch_size * self.top_k, output_size], dtype=tf.float32
+            mask = (flat_indices == idx).to(x.device)
+            selected_inputs = repeated_x[mask]
+            expert_outputs_dict[idx.item()] = self.experts[idx](selected_inputs)
+
+        flat_outputs = torch.zeros(
+            batch_size * self.top_k, output_size, device=x.device
         )
         for idx in unique_expert_ids:
-            mask = tf.equal(flat_indices, idx)
-            indices = tf.where(mask)
-            flat_outputs = tf.tensor_scatter_nd_update(
-                flat_outputs, indices, expert_outputs_dict[idx.numpy()]
-            )
-        top_k_expert_outputs = tf.reshape(
-            flat_outputs, [batch_size, self.top_k, output_size]
-        )
+            mask = (flat_indices == idx).to(x.device)
+            flat_outputs[mask] = expert_outputs_dict[idx.item()].to(flat_outputs.device)
 
-        # Combine outputs using top-k weights
-        return tf.einsum("ijk,ij->ik", top_k_expert_outputs, top_k_weights)
+        top_k_expert_outputs = flat_outputs.view(batch_size, self.top_k, output_size)
 
-    def update_usage_counts(self, indices):
-        updates = tf.ones_like(tf.reshape(indices, [-1]), dtype=tf.float32)
-        self.expert_usage_count.assign(
-            tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(indices, [-1, 1]), updates
-            )
+        return torch.einsum(
+            "ijk,ij->ik", top_k_expert_outputs.cpu(), top_k_weights.cpu()
         )
 
+    def update_usage_counts(self, indices):
+        flat_indices = keras.ops.reshape(indices, [-1])
+        one_hot = keras.ops.one_hot(flat_indices, self.num_experts)
+        updates = keras.ops.sum(one_hot, axis=0)
+        self.batch_usage_count = updates
+        self.expert_usage_count.assign_add(updates)
+
     def call(self, x):
         gating_weights = self.gating_network(x)
-        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
+        top_k_weights, top_k_indices = torch.topk(gating_weights, self.top_k, dim=-1)
         combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)
         self.update_usage_counts(top_k_indices)
-
+        self.batch_overflow_sum = torch.relu(
+            self.batch_usage_count - self.batch_capacity
+        ).sum()
         return combined_output
 
+    def compute_total_loss(self, load_balance_coef=0.01):
+        self.batch_overflow_sum = torch.sum(
+            torch.relu(self.expert_usage_count - self.batch_capacity)
+        )
+        return load_balance_coef * (
+            self.diversity_loss + self.batch_overflow_sum + self.importance_loss
+        )
+
 
 """
 Output of the top 3 experts out of 10 for one layer of MoE:
 """
-sample_data = tf.random.uniform((1, 10))
+sample_data = torch.randn((1, 10))
 linear_mode = LinearMoE(32, 10, 3)
 linear_mode(sample_data)
 
@@ -207,7 +225,7 @@ def call(self, x):
 def check_expert_usage(runs):
     # Running the later multiple times to show randomness of expert selection
     for i in range(runs):
-        sample_data = tf.random.uniform((1, 10))
+        sample_data = torch.randn((1, 10))
         linear_mode = LinearMoE(10, 5)
         _ = linear_mode(sample_data)
         print(f"Run {i}, Expert usage: {linear_mode.expert_usage_count.numpy()}")
@@ -245,12 +263,12 @@ def __init__(
         top_k=TOP_K,
     ):
         super(LinearMoE, self).__init__()
-
+        self.expert_output_size = hidden_size
         # Initialize experts
         self.experts = [
             layers.Dense(
                 hidden_size,
-                kernel_initializer=tf.keras.initializers.RandomNormal(
+                kernel_initializer=keras.initializers.RandomNormal(
                     mean=0.0, stddev=0.001
                 ),
                 bias_initializer="zeros",
@@ -259,82 +277,97 @@ def __init__(
         ]
         # Initialize gating network
         self.gating_network = layers.Dense(
-            num_experts,  # Match output to num_experts
-            kernel_initializer=tf.keras.initializers.RandomNormal(
-                mean=0.0, stddev=0.001
-            ),
+            NUM_EXPERTS,
+            kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.001),
             bias_initializer="zeros",
             activation="softmax",
         )
 
         self.num_experts = num_experts
         self.top_k = top_k
-        # Keep track of how many times each expert is used as a layer weight
-        self.expert_usage_count = tf.Variable(
-            tf.zeros((num_experts,), dtype=tf.float32)
+        # Keep track of how many times each expert is used
+        self.expert_usage_count = self.add_weight(
+            shape=(self.num_experts,),
+            initializer="zeros",
+            trainable=False,
+            name="expert_usage_count",
         )
+        self.batch_capacity = BATCH_SIZE / num_experts
 
-        self.batch_capacity = BATCH_SIZE // num_experts
+    def build(self, input_shape):
+        for expert in self.experts:
+            if not expert.built:
+                expert.build(input_shape)
 
-    def _diversity_loss(self, weights):
-        entropy = -K.sum(weights * K.log(weights + 1e-10), axis=1)
-        self.diversity_loss = -K.mean(entropy)
-
-    def _importance_loss(self, gating_weights):
-        batch_importance_sum = K.sum(gating_weights, axis=0)
-        mean_importance = K.mean(batch_importance_sum)
-        self.importance_loss = K.mean(
-            K.square(
-                batch_importance_sum
-                - mean_importance * tf.ones_like(batch_importance_sum)
-            )
+        if not self.gating_network.built:
+            self.gating_network.build(input_shape)
+        self.built = True
+
+    def get_top_outputs(self, x, top_k_indices, top_k_weights):
+        batch_size = x.size(0)
+        output_size = self.expert_output_size
+
+        # Get outputs from top-k experts
+        top_k_expert_outputs = torch.zeros(
+            batch_size, self.top_k, output_size, device=x.device
         )
+        flat_indices = top_k_indices.view(-1).to(x.device)
+        repeated_x = x.repeat_interleave(self.top_k, dim=0)
+
+        unique_expert_ids = flat_indices.unique(sorted=True)
+        expert_outputs_dict = {}
+        for idx in unique_expert_ids:
+            mask = (flat_indices == idx).to(x.device)
+            selected_inputs = repeated_x[mask]
+            expert_outputs_dict[idx.item()] = self.experts[idx](selected_inputs)
 
-    # Replace the current get_top_outputs method with this vectorized version
-    def get_top_outputs(
-        self, x, gating_weights
-    ):  # Changed to take gating_weights directly
-        """Compute outputs from top-k experts."""
-        top_k_weights, top_k_indices = tf.math.top_k(gating_weights, k=self.top_k)
-
-        # Store indices and updates for usage count
-        self.indices = tf.reshape(top_k_indices, [-1, 1])
-        self.updates = tf.ones_like(tf.reshape(top_k_indices, [-1]), dtype=tf.float32)
-
-        # Compute expert outputs symbolically
-        expert_outputs = tf.stack([expert(x) for expert in self.experts], axis=1)
-        batch_size = tf.shape(x)[0]
-        batch_indices = tf.tile(tf.range(batch_size)[:, tf.newaxis], [1, self.top_k])
-        gather_indices = tf.stack([batch_indices, top_k_indices], axis=-1)
-        top_k_expert_outputs = tf.gather_nd(expert_outputs, gather_indices)
-
-        combined_output = tf.reduce_sum(
-            top_k_expert_outputs * top_k_weights[:, :, tf.newaxis], axis=1
+        flat_outputs = torch.zeros(
+            batch_size * self.top_k, output_size, device=x.device
         )
-        return combined_output
+        for idx in unique_expert_ids:
+            mask = (flat_indices == idx).to(x.device)
+            flat_outputs[mask] = expert_outputs_dict[idx.item()].to(flat_outputs.device)
+
+        top_k_expert_outputs = flat_outputs.view(batch_size, self.top_k, output_size)
 
-    def update_usage_counts(self):
-        updates = tf.ones_like(tf.reshape(self.indices, [-1]), dtype=tf.float32)
-        self.expert_usage_count.assign(
-            tf.tensor_scatter_nd_add(
-                self.expert_usage_count, tf.reshape(self.indices, [-1, 1]), updates
+        return torch.einsum(
+            "ijk,ij->ik", top_k_expert_outputs.cpu(), top_k_weights.cpu()
+        )
+
+    def update_usage_counts(self, indices):
+        flat_indices = keras.ops.reshape(indices, [-1])
+        one_hot = keras.ops.one_hot(flat_indices, self.num_experts)
+        updates = keras.ops.sum(one_hot, axis=0)
+        self.batch_usage_count = updates
+        self.expert_usage_count.assign_add(updates)
+
+    def _importance_loss(self, weights):
+        batch_importance_sum = torch.sum(weights, dim=0)
+        mean_importance = torch.mean(batch_importance_sum)
+        self.importance_loss = torch.mean(
+            torch.square(
+                batch_importance_sum
+                - mean_importance * torch.ones_like(batch_importance_sum)
             )
         )
 
+    def _diversity_loss(self, weights):
+        entropy = -torch.sum(weights * torch.log(weights + 1e-10), dim=1)
+        self.diversity_loss = torch.mean(entropy)
+
     def call(self, x):
-        # Get gating weights and normalize
         gating_weights = self.gating_network(x)
-        # top_k_weights, top_k_indices = tf.nn.top_k(gating_weights, k=self.top_k)
-        combined_output = self.get_top_outputs(x, gating_weights)
-        self.update_usage_counts()
-        self._diversity_loss(gating_weights)
+        top_k_weights, top_k_indices = torch.topk(gating_weights, self.top_k, dim=-1)
+        combined_output = self.get_top_outputs(x, top_k_indices, top_k_weights)
+        self.update_usage_counts(top_k_indices)
         self._importance_loss(gating_weights)
+        self._diversity_loss(gating_weights)
 
         return combined_output
 
     def compute_total_loss(self, load_balance_coef=0.01):
-        self.batch_overflow_sum = K.sum(
-            K.relu(tf.convert_to_tensor(self.expert_usage_count) - self.batch_capacity)
+        self.batch_overflow_sum = torch.sum(
+            torch.relu(self.expert_usage_count - self.batch_capacity)
         )
         return load_balance_coef * (
             self.diversity_loss + self.batch_overflow_sum + self.importance_loss
@@ -384,30 +417,33 @@ def call(self, inputs, training=False):
         return outputs
 
     def train_step(self, data):
-        x, y = data  # Unpack input data and labels
-
-        with tf.GradientTape() as tape:
-            y_pred = self(x, training=True)
-            classification_loss = self.compute_loss(x, y, y_pred)
-            if self.moe_loss_considered:
-                moe_loss = self.moe_classifier.compute_total_loss(
-                    load_balance_coef=0.01
-                )
-                total_loss = classification_loss + moe_loss
-            else:
-                total_loss = classification_loss
-
-        # Compute gradients
-        gradients = tape.gradient(total_loss, self.trainable_variables)
-
-        # Update weights
-        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
+        x, y = data
+        self.zero_grad()
+
+        y_pred = self(x)
+
+        if self.moe_loss_considered:
+            moe_loss = self.moe_classifier.compute_total_loss(load_balance_coef=0.01)
+            total_loss = self.compute_loss(x, y, y_pred) + moe_loss
+        else:
+            total_loss = self.compute_loss(x, y, y_pred)
+
+        total_loss.backward()
+
+        trainable_weights = [v for v in self.trainable_weights]
+        gradients = [v.value.grad for v in trainable_weights]
+
+        with torch.no_grad():
+            self.optimizer.apply(gradients, trainable_weights)
+
+        # Update metrics
         for metric in self.metrics:
             metric.update_state(y, y_pred)
+
         # Return a dict of metrics for monitoring
         return {
-            "loss": total_loss,
-            "moe_loss": moe_loss,
+            "loss": total_loss.item(),  # Convert to Python number
+            "moe_loss": moe_loss.item() if self.moe_loss_considered else 0.0,
             **{m.name: m.result() for m in self.metrics},
         }
 
@@ -469,6 +505,7 @@ def test_step(self, data):
    allowing for more sophisticated processing without requiring a larger dense network.
 
 In our implementation, we:
+
 1. Created a basic MoE layer using dense networks as experts
 2. Implemented three types of load balancing losses to prevent routing collapse
 3. Applied the MoE architecture to MNIST classification by replacing the final dense layer

From 76de5f49e010198a527b9874fb19e3a4e20e9810 Mon Sep 17 00:00:00 2001
From: Damoon <damoon.shah3@gmail.com>
Date: Wed, 5 Mar 2025 23:56:05 +0300
Subject: [PATCH 5/5] Revert changes to match upstream

---
 examples/audio/vocal_track_separation.py      |   2 +-
 .../examples/audio/vocal_track_separation.md  | 921 ------------------
 2 files changed, 1 insertion(+), 922 deletions(-)
 delete mode 100644 templates/examples/audio/vocal_track_separation.md

diff --git a/examples/audio/vocal_track_separation.py b/examples/audio/vocal_track_separation.py
index ca16c35ab1..25574e0ab8 100644
--- a/examples/audio/vocal_track_separation.py
+++ b/examples/audio/vocal_track_separation.py
@@ -670,4 +670,4 @@ def spectral_loss(y_true, y_pred):
 - Train for more epochs and refine hyperparameters.
 - Separate multiple instruments simultaneously.
 - Enhance the model to handle instruments not present in the mixture.
-"""
\ No newline at end of file
+"""
diff --git a/templates/examples/audio/vocal_track_separation.md b/templates/examples/audio/vocal_track_separation.md
deleted file mode 100644
index af44162d78..0000000000
--- a/templates/examples/audio/vocal_track_separation.md
+++ /dev/null
@@ -1,921 +0,0 @@
-# Vocal Track Separation with Encoder-Decoder Architecture
-
-**Author:** [Joaquin Jimenez](https://github.com/johacks/)<br>
-**Date created:** 2024/12/10<br>
-**Last modified:** 2024/12/10<br>
-**Description:** Train a model to separate vocal tracks from music mixtures.
-
-
-<div class='example_version_banner keras_3'>ⓘ This example uses Keras 3</div>
-<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/audio/ipynb/vocal_track_separation.ipynb)  <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/audio/vocal_track_separation.py)
-
-
-
----
-## Introduction
-
-In this tutorial, we build a vocal track separation model using an encoder-decoder
-architecture in Keras 3.
-
-We train the model on the [MUSDB18 dataset](https://doi.org/10.5281/zenodo.1117372),
-which provides music mixtures and isolated tracks for drums, bass, other, and vocals.
-
-Key concepts covered:
-
-- Audio data preprocessing using the Short-Time Fourier Transform (STFT).
-- Audio data augmentation techniques.
-- Implementing custom encoders and decoders specialized for audio data.
-- Defining appropriate loss functions and metrics for audio source separation tasks.
-
-The model architecture is derived from the TFC_TDF_Net model described in:
-
-W. Choi, M. Kim, J. Chung, D. Lee, and S. Jung, “Investigating U-Nets with various
-intermediate blocks for spectrogram-based singing voice separation,” in the 21st
-International Society for Music Information Retrieval Conference, 2020.
-
-For reference code, see:
-[GitHub: ws-choi/ISMIR2020_U_Nets_SVS](https://github.com/ws-choi/ISMIR2020_U_Nets_SVS).
-
-The data processing and model training routines are partly derived from:
-[ZFTurbo/Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training/tree/main).
-
----
-## Setup
-
-Import and install all the required dependencies.
-
-
-```python
-!pip install -qq audiomentations soundfile ffmpeg-binaries
-!pip install -qq "keras==3.7.0"
-!sudo -n apt-get install -y graphviz >/dev/null 2>&1  # Required for plotting the model
-```
-
-
-```python
-import glob
-import os
-
-os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
-
-import random
-import subprocess
-import tempfile
-import typing
-from os import path
-
-import audiomentations as aug
-import ffmpeg
-import keras
-import numpy as np
-import soundfile as sf
-from IPython import display
-from keras import callbacks, layers, ops, saving
-from matplotlib import pyplot as plt
-```
-
----
-## Configuration
-
-The following constants define configuration parameters for audio processing
-and model training, including dataset paths, audio chunk sizes, Short-Time Fourier
-Transform (STFT) parameters, and training hyperparameters.
-
-
-```python
-# MUSDB18 dataset configuration
-MUSDB_STREAMS = {"mixture": 0, "drums": 1, "bass": 2, "other": 3, "vocals": 4}
-TARGET_INSTRUMENTS = {track: MUSDB_STREAMS[track] for track in ("vocals",)}
-N_INSTRUMENTS = len(TARGET_INSTRUMENTS)
-SOURCE_INSTRUMENTS = tuple(k for k in MUSDB_STREAMS if k != "mixture")
-
-# Audio preprocessing parameters for Short-Time Fourier Transform (STFT)
-N_SUBBANDS = 4  # Number of subbands into which frequencies are split
-CHUNK_SIZE = 65024  # Number of amplitude samples per audio chunk (~4 seconds)
-STFT_N_FFT = 2048  # FFT points used in STFT
-STFT_HOP_LENGTH = 512  # Hop length for STFT
-
-# Training hyperparameters
-N_CHANNELS = 64  # Base channel count for the model
-BATCH_SIZE = 3
-ACCUMULATION_STEPS = 2
-EFFECTIVE_BATCH_SIZE = BATCH_SIZE * (ACCUMULATION_STEPS or 1)
-
-# Paths
-TMP_DIR = path.expanduser("~/.keras/tmp")
-DATASET_DIR = path.expanduser("~/.keras/datasets")
-MODEL_PATH = path.join(TMP_DIR, f"model_{keras.backend.backend()}.keras")
-CSV_LOG_PATH = path.join(TMP_DIR, f"training_{keras.backend.backend()}.csv")
-os.makedirs(DATASET_DIR, exist_ok=True)
-os.makedirs(TMP_DIR, exist_ok=True)
-
-# Set random seed for reproducibility
-keras.utils.set_random_seed(21)
-```
-
-<div class="k-default-codeblock">
-```
-WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
-E0000 00:00:1734318393.806217   81028 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
-E0000 00:00:1734318393.809885   81028 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
-
-```
-</div>
----
-## MUSDB18 Dataset
-
-The MUSDB18 dataset is a standard benchmark for music source separation, containing
-150 full-length music tracks along with isolated drums, bass, other, and vocals.
-The dataset is stored in .mp4 format, and each .mp4 file includes multiple audio
-streams (mixture and individual tracks).
-
-### Download and Conversion
-
-The following utility function downloads MUSDB18 and converts its .mp4 files to
-.wav files for each instrument track, resampled to 16 kHz.
-
-
-```python
-
-def download_musdb18(out_dir=None):
-    """Download and extract the MUSDB18 dataset, then convert .mp4 files to .wav files.
-
-    MUSDB18 reference:
-    Rafii, Z., Liutkus, A., Stöter, F.-R., Mimilakis, S. I., & Bittner, R. (2017).
-    MUSDB18 - a corpus for music separation (1.0.0) [Data set]. Zenodo.
-    """
-    ffmpeg.init()
-    from ffmpeg import FFMPEG_PATH
-
-    # Create output directories
-    os.makedirs((base := out_dir or tempfile.mkdtemp()), exist_ok=True)
-    if path.exists((out_dir := path.join(base, "musdb18_wav"))):
-        print("MUSDB18 dataset already downloaded")
-        return out_dir
-
-    # Download and extract the dataset
-    download_dir = keras.utils.get_file(
-        fname="musdb18",
-        origin="https://zenodo.org/records/1117372/files/musdb18.zip",
-        extract=True,
-    )
-
-    # ffmpeg command template: input, stream index, output
-    ffmpeg_args = str(FFMPEG_PATH) + " -v error -i {} -map 0:{} -vn -ar 16000 {}"
-
-    # Convert each mp4 file to multiple .wav files for each track
-    for split in ("train", "test"):
-        songs = os.listdir(path.join(download_dir, split))
-        for i, song in enumerate(songs):
-            if i % 10 == 0:
-                print(f"{split.capitalize()}: {i}/{len(songs)} songs processed")
-
-            mp4_path_orig = path.join(download_dir, split, song)
-            mp4_path = path.join(tempfile.mkdtemp(), split, song.replace(" ", "_"))
-            os.makedirs(path.dirname(mp4_path), exist_ok=True)
-            os.rename(mp4_path_orig, mp4_path)
-
-            wav_dir = path.join(out_dir, split, path.basename(mp4_path).split(".")[0])
-            os.makedirs(wav_dir, exist_ok=True)
-
-            for track in SOURCE_INSTRUMENTS:
-                out_path = path.join(wav_dir, f"{track}.wav")
-                stream_index = MUSDB_STREAMS[track]
-                args = ffmpeg_args.format(mp4_path, stream_index, out_path).split()
-                assert subprocess.run(args).returncode == 0, "ffmpeg conversion failed"
-    return out_dir
-
-
-# Download and prepare the MUSDB18 dataset
-songs = download_musdb18(out_dir=DATASET_DIR)
-```
-
-<div class="k-default-codeblock">
-```
-MUSDB18 dataset already downloaded
-
-```
-</div>
-### Custom Dataset
-
-We define a custom dataset class to generate random audio chunks and their corresponding
-labels. The dataset does the following:
-
-1. Selects a random chunk from a random song and instrument.
-2. Applies optional data augmentations.
-3. Combines isolated tracks to form new synthetic mixtures.
-4. Prepares features (mixtures) and labels (vocals) for training.
-
-This approach allows creating an effectively infinite variety of training examples
-through randomization and augmentation.
-
-
-```python
-
-class Dataset(keras.utils.PyDataset):
-    def __init__(
-        self,
-        songs,
-        batch_size=BATCH_SIZE,
-        chunk_size=CHUNK_SIZE,
-        batches_per_epoch=1000 * ACCUMULATION_STEPS,
-        augmentation=True,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.augmentation = augmentation
-        self.vocals_augmentations = [
-            aug.PitchShift(min_semitones=-5, max_semitones=5, p=0.1),
-            aug.SevenBandParametricEQ(-9, 9, p=0.25),
-            aug.TanhDistortion(0.1, 0.7, p=0.1),
-        ]
-        self.other_augmentations = [
-            aug.PitchShift(p=0.1),
-            aug.AddGaussianNoise(p=0.1),
-        ]
-        self.songs = songs
-        self.sizes = {song: self.get_track_set_size(song) for song in self.songs}
-        self.batch_size = batch_size
-        self.chunk_size = chunk_size
-        self.batches_per_epoch = batches_per_epoch
-
-    def get_track_set_size(self, song: str):
-        """Return the smallest track length in the given song directory."""
-        sizes = [len(sf.read(p)[0]) for p in glob.glob(path.join(song, "*.wav"))]
-        if max(sizes) != min(sizes):
-            print(f"Warning: {song} has different track lengths")
-        return min(sizes)
-
-    def random_chunk_of_instrument_type(self, instrument: str):
-        """Extract a random chunk for the specified instrument from a random song."""
-        song, size = random.choice(list(self.sizes.items()))
-        track = path.join(song, f"{instrument}.wav")
-
-        if self.chunk_size <= size:
-            start = np.random.randint(size - self.chunk_size + 1)
-            audio = sf.read(track, self.chunk_size, start, dtype="float32")[0]
-            audio_mono = np.mean(audio, axis=1)
-        else:
-            # If the track is shorter than chunk_size, pad the signal
-            audio_mono = np.mean(sf.read(track, dtype="float32")[0], axis=1)
-            audio_mono = np.pad(audio_mono, ((0, self.chunk_size - size),))
-
-        # If the chunk is almost silent, retry
-        if np.mean(np.abs(audio_mono)) < 0.01:
-            return self.random_chunk_of_instrument_type(instrument)
-
-        return self.data_augmentation(audio_mono, instrument)
-
-    def data_augmentation(self, audio: np.ndarray, instrument: str):
-        """Apply data augmentation to the audio chunk, if enabled."""
-
-        def coin_flip(x, probability: float, fn: typing.Callable):
-            return fn(x) if random.uniform(0, 1) < probability else x
-
-        if self.augmentation:
-            augmentations = (
-                self.vocals_augmentations
-                if instrument == "vocals"
-                else self.other_augmentations
-            )
-            # Loudness augmentation
-            audio *= np.random.uniform(0.5, 1.5, (len(audio),)).astype("float32")
-            # Random reverse
-            audio = coin_flip(audio, 0.1, lambda x: np.flip(x))
-            # Random polarity inversion
-            audio = coin_flip(audio, 0.5, lambda x: -x)
-            # Apply selected augmentations
-            for aug_ in augmentations:
-                aug_.randomize_parameters(audio, sample_rate=16000)
-                audio = aug_(audio, sample_rate=16000)
-        return audio
-
-    def random_mix_of_tracks(self) -> dict:
-        """Create a random mix of instruments by summing their individual chunks."""
-        tracks = {}
-        for instrument in SOURCE_INSTRUMENTS:
-            # Start with a single random chunk
-            mixup = [self.random_chunk_of_instrument_type(instrument)]
-
-            # Randomly add more chunks of the same instrument (mixup augmentation)
-            if self.augmentation:
-                for p in (0.2, 0.02):
-                    if random.uniform(0, 1) < p:
-                        mixup.append(self.random_chunk_of_instrument_type(instrument))
-
-            tracks[instrument] = np.mean(mixup, axis=0, dtype="float32")
-        return tracks
-
-    def __len__(self):
-        return self.batches_per_epoch
-
-    def __getitem__(self, idx):
-        # Generate a batch of random mixtures
-        batch = [self.random_mix_of_tracks() for _ in range(self.batch_size)]
-
-        # Features: sum of all tracks
-        batch_x = ops.sum(
-            np.array([list(track_set.values()) for track_set in batch]), axis=1
-        )
-
-        # Labels: isolated target instruments (e.g., vocals)
-        batch_y = np.array(
-            [[track_set[t] for t in TARGET_INSTRUMENTS] for track_set in batch]
-        )
-
-        return batch_x, ops.convert_to_tensor(batch_y)
-
-
-# Create train and validation datasets
-train_ds = Dataset(glob.glob(path.join(songs, "train", "*")))
-val_ds = Dataset(
-    glob.glob(path.join(songs, "test", "*")),
-    batches_per_epoch=int(0.1 * train_ds.batches_per_epoch),
-    augmentation=False,
-)
-```
-
-### Visualize a Sample
-
-Let's visualize a random mixed audio chunk and its corresponding isolated vocals.
-This helps to understand the nature of the preprocessed input data.
-
-
-```python
-
-def visualize_audio_np(audio: np.ndarray, rate=16000, name="mixup"):
-    """Plot and display an audio waveform and also produce an Audio widget."""
-    plt.figure(figsize=(10, 6))
-    plt.plot(audio)
-    plt.title(f"Waveform: {name}")
-    plt.xlim(0, len(audio))
-    plt.ylabel("Amplitude")
-    plt.show()
-    # plt.savefig(f"tmp/{name}.png")
-
-    # Normalize and display audio
-    audio_norm = (audio - np.min(audio)) / (np.max(audio) - np.min(audio) + 1e-8)
-    audio_norm = (audio_norm * 2 - 1) * 0.6
-    display.display(display.Audio(audio_norm, rate=rate))
-    # sf.write(f"tmp/{name}.wav", audio_norm, rate)
-
-
-sample_batch_x, sample_batch_y = val_ds[None]  # Random batch
-visualize_audio_np(ops.convert_to_numpy(sample_batch_x[0]))
-visualize_audio_np(ops.convert_to_numpy(sample_batch_y[0, 0]), name="vocals")
-```
-
-
-    
-![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_12_0.png)
-    
-
-
-
-
-<audio  controls="controls" >
-    <source 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" type="audio/wav" />
-    Your browser does not support the audio element.
-</audio>
-
-
-
----
-## Model
-
-### Preprocessing
-
-The model operates on STFT representations rather than raw audio. We define a
-preprocessing model to compute STFT and a corresponding inverse transform (iSTFT).
-
-
-```python
-
-def stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
-    """Compute the STFT for the input audio and return the real and imaginary parts."""
-    real_x, imag_x = ops.stft(inputs, fft_size, sequence_stride, fft_size)
-    real_x, imag_x = ops.expand_dims(real_x, -1), ops.expand_dims(imag_x, -1)
-    x = ops.concatenate((real_x, imag_x), axis=-1)
-
-    # Drop last freq sample for convenience
-    return ops.split(x, [x.shape[2] - 1], axis=2)[0]
-
-
-def inverse_stft(inputs, fft_size=STFT_N_FFT, sequence_stride=STFT_HOP_LENGTH):
-    """Compute the inverse STFT for the given STFT input."""
-    x = inputs
-
-    # Pad back dropped freq sample if using torch backend
-    if keras.backend.backend() == "torch":
-        x = ops.pad(x, ((0, 0), (0, 0), (0, 1), (0, 0)))
-
-    real_x, imag_x = ops.split(x, 2, axis=-1)
-    real_x = ops.squeeze(real_x, axis=-1)
-    imag_x = ops.squeeze(imag_x, axis=-1)
-
-    return ops.istft((real_x, imag_x), fft_size, sequence_stride, fft_size)
-
-```
-
-### Model Architecture
-
-The model uses a custom encoder-decoder architecture with Time-Frequency Convolution
-(TFC) and Time-Distributed Fully Connected (TDF) blocks. They are grouped into a
-`TimeFrequencyTransformBlock`, i.e. "TFC_TDF" in the original paper by Choi et al.
-
-We then define an encoder-decoder network with multiple scales. Each encoder scale
-applies TFC_TDF blocks followed by downsampling, while decoder scales apply TFC_TDF
-blocks over the concatenation of upsampled features and associated encoder outputs.
-
-
-```python
-
-@saving.register_keras_serializable()
-class TimeDistributedDenseBlock(layers.Layer):
-    """Time-Distributed Fully Connected layer block.
-
-    Applies frequency-wise dense transformations across time frames with instance
-    normalization and GELU activation.
-    """
-
-    def __init__(self, bottleneck_factor, fft_dim, **kwargs):
-        super().__init__(**kwargs)
-        self.fft_dim = fft_dim
-        self.hidden_dim = fft_dim // bottleneck_factor
-
-    def build(self, *_):
-        self.group_norm_1 = layers.GroupNormalization(groups=-1)
-        self.group_norm_2 = layers.GroupNormalization(groups=-1)
-        self.dense_1 = layers.Dense(self.hidden_dim, use_bias=False)
-        self.dense_2 = layers.Dense(self.fft_dim, use_bias=False)
-
-    def call(self, x):
-        # Apply normalization and dense layers frequency-wise
-        x = ops.gelu(self.group_norm_1(x))
-        x = ops.swapaxes(x, -1, -2)
-        x = self.dense_1(x)
-
-        x = ops.gelu(self.group_norm_2(ops.swapaxes(x, -1, -2)))
-        x = ops.swapaxes(x, -1, -2)
-        x = self.dense_2(x)
-        return ops.swapaxes(x, -1, -2)
-
-
-@saving.register_keras_serializable()
-class TimeFrequencyConvolution(layers.Layer):
-    """Time-Frequency Convolutional layer.
-
-    Applies a 2D convolution over time-frequency representations and applies instance
-    normalization and GELU activation.
-    """
-
-    def __init__(self, channels, **kwargs):
-        super().__init__(**kwargs)
-        self.channels = channels
-
-    def build(self, *_):
-        self.group_norm = layers.GroupNormalization(groups=-1)
-        self.conv = layers.Conv2D(self.channels, 3, padding="same", use_bias=False)
-
-    def call(self, x):
-        return self.conv(ops.gelu(self.group_norm(x)))
-
-
-@saving.register_keras_serializable()
-class TimeFrequencyTransformBlock(layers.Layer):
-    """Implements TFC_TDF block for encoder-decoder architecture.
-
-    Repeatedly apply Time-Frequency Convolution and Time-Distributed Dense blocks as
-    many times as specified by the `length` parameter.
-    """
-
-    def __init__(
-        self, channels, length, fft_dim, bottleneck_factor, in_channels=None, **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.channels = channels
-        self.length = length
-        self.fft_dim = fft_dim
-        self.bottleneck_factor = bottleneck_factor
-        self.in_channels = in_channels or channels
-        self.blocks = []
-
-    def build(self, *_):
-        # Add blocks in a flat list to avoid nested structures
-        for i in range(self.length):
-            in_channels = self.channels if i > 0 else self.in_channels
-            self.blocks.append(TimeFrequencyConvolution(in_channels))
-            self.blocks.append(
-                TimeDistributedDenseBlock(self.bottleneck_factor, self.fft_dim)
-            )
-            self.blocks.append(TimeFrequencyConvolution(self.channels))
-            # Residual connection
-            self.blocks.append(layers.Conv2D(self.channels, 1, 1, use_bias=False))
-
-    def call(self, inputs):
-        x = inputs
-        # Each block consists of 4 layers:
-        # 1. Time-Frequency Convolution
-        # 2. Time-Distributed Dense
-        # 3. Time-Frequency Convolution
-        # 4. Residual connection
-        for i in range(0, len(self.blocks), 4):
-            tfc_1 = self.blocks[i](x)
-            tdf = self.blocks[i + 1](x)
-            tfc_2 = self.blocks[i + 2](tfc_1 + tdf)
-            x = tfc_2 + self.blocks[i + 3](x)  # Residual connection
-        return x
-
-
-@saving.register_keras_serializable()
-class Downscale(layers.Layer):
-    """Downscale time-frequency dimensions using a convolution."""
-
-    conv_cls = layers.Conv2D
-
-    def __init__(self, channels, scale, **kwargs):
-        super().__init__(**kwargs)
-        self.channels = channels
-        self.scale = scale
-
-    def build(self, *_):
-        self.conv = self.conv_cls(self.channels, self.scale, self.scale, use_bias=False)
-        self.norm = layers.GroupNormalization(groups=-1)
-
-    def call(self, inputs):
-        return self.norm(ops.gelu(self.conv(inputs)))
-
-
-@saving.register_keras_serializable()
-class Upscale(Downscale):
-    """Upscale time-frequency dimensions using a transposed convolution."""
-
-    conv_cls = layers.Conv2DTranspose
-
-
-def build_model(
-    inputs,
-    n_instruments=N_INSTRUMENTS,
-    n_subbands=N_SUBBANDS,
-    channels=N_CHANNELS,
-    fft_dim=(STFT_N_FFT // 2) // N_SUBBANDS,
-    n_scales=4,
-    scale=(2, 2),
-    block_size=2,
-    growth=128,
-    bottleneck_factor=2,
-    **kwargs,
-):
-    """Build the TFC_TDF encoder-decoder model for source separation."""
-    # Compute STFT
-    x = stft(inputs)
-
-    # Split mixture into subbands as separate channels
-    mix = ops.reshape(x, (-1, x.shape[1], x.shape[2] // n_subbands, 2 * n_subbands))
-    first_conv_out = layers.Conv2D(channels, 1, 1, use_bias=False)(mix)
-    x = first_conv_out
-
-    # Encoder path
-    encoder_outs = []
-    for _ in range(n_scales):
-        x = TimeFrequencyTransformBlock(
-            channels, block_size, fft_dim, bottleneck_factor
-        )(x)
-        encoder_outs.append(x)
-        fft_dim, channels = fft_dim // scale[0], channels + growth
-        x = Downscale(channels, scale)(x)
-
-    # Bottleneck
-    x = TimeFrequencyTransformBlock(channels, block_size, fft_dim, bottleneck_factor)(x)
-
-    # Decoder path
-    for _ in range(n_scales):
-        fft_dim, channels = fft_dim * scale[0], channels - growth
-        x = ops.concatenate([Upscale(channels, scale)(x), encoder_outs.pop()], axis=-1)
-        x = TimeFrequencyTransformBlock(
-            channels, block_size, fft_dim, bottleneck_factor, in_channels=x.shape[-1]
-        )(x)
-
-    # Residual connection and final convolutions
-    x = ops.concatenate([mix, x * first_conv_out], axis=-1)
-    x = layers.Conv2D(channels, 1, 1, use_bias=False, activation="gelu")(x)
-    x = layers.Conv2D(n_instruments * n_subbands * 2, 1, 1, use_bias=False)(x)
-
-    # Reshape back to instrument-wise STFT
-    x = ops.reshape(x, (-1, x.shape[1], x.shape[2] * n_subbands, n_instruments, 2))
-    x = ops.transpose(x, (0, 3, 1, 2, 4))
-    x = ops.reshape(x, (-1, n_instruments, x.shape[2], x.shape[3] * 2))
-
-    return keras.Model(inputs=inputs, outputs=x, **kwargs)
-
-```
-
----
-## Loss and Metrics
-
-We define:
-
-- `spectral_loss`: Mean absolute error in STFT domain.
-- `sdr`: Signal-to-Distortion Ratio, a common source separation metric.
-
-
-```python
-
-def prediction_to_wave(x, n_instruments=N_INSTRUMENTS):
-    """Convert STFT predictions back to waveform."""
-    x = ops.reshape(x, (-1, x.shape[2], x.shape[3] // 2, 2))
-    x = inverse_stft(x)
-    return ops.reshape(x, (-1, n_instruments, x.shape[1]))
-
-
-def target_to_stft(y):
-    """Convert target waveforms to their STFT representations."""
-    y = ops.reshape(y, (-1, CHUNK_SIZE))
-    y_real, y_imag = ops.stft(y, STFT_N_FFT, STFT_HOP_LENGTH, STFT_N_FFT)
-    y_real, y_imag = y_real[..., :-1], y_imag[..., :-1]
-    y = ops.stack([y_real, y_imag], axis=-1)
-    return ops.reshape(y, (-1, N_INSTRUMENTS, y.shape[1], y.shape[2] * 2))
-
-
-@saving.register_keras_serializable()
-def sdr(y_true, y_pred):
-    """Signal-to-Distortion Ratio metric."""
-    y_pred = prediction_to_wave(y_pred)
-    # Add epsilon for numerical stability
-    num = ops.sum(ops.square(y_true), axis=-1) + 1e-8
-    den = ops.sum(ops.square(y_true - y_pred), axis=-1) + 1e-8
-    return 10 * ops.log10(num / den)
-
-
-@saving.register_keras_serializable()
-def spectral_loss(y_true, y_pred):
-    """Mean absolute error in the STFT domain."""
-    y_true = target_to_stft(y_true)
-    return ops.mean(ops.absolute(y_true - y_pred))
-
-```
-
----
-## Training
-
-### Visualize Model Architecture
-
-
-```python
-# Load or create the model
-if path.exists(MODEL_PATH):
-    model = saving.load_model(MODEL_PATH)
-else:
-    model = build_model(keras.Input(sample_batch_x.shape[1:]), name="tfc_tdf_net")
-
-# Display the model architecture
-model.summary()
-img = keras.utils.plot_model(model, path.join(TMP_DIR, "model.png"), show_shapes=True)
-display.display(img)
-```
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "tfc_tdf_net"</span>
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
-┃<span style="font-weight: bold"> Layer (type)        </span>┃<span style="font-weight: bold"> Output Shape      </span>┃<span style="font-weight: bold">    Param # </span>┃<span style="font-weight: bold"> Connected to      </span>┃
-┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
-│ input_layer         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">65024</span>)     │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ -                 │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">InputLayer</span>)        │                   │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ stft (<span style="color: #0087ff; text-decoration-color: #0087ff">STFT</span>)         │ [(<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,      │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ input_layer[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">1025</span>), (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>,     │            │                   │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>)]       │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ expand_dims         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ stft[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]        │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">ExpandDims</span>)        │ <span style="color: #00af00; text-decoration-color: #00af00">1</span>)                │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ expand_dims_1       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ stft[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">1</span>]        │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">ExpandDims</span>)        │ <span style="color: #00af00; text-decoration-color: #00af00">1</span>)                │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ concatenate         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1025</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ expand_dims[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">2</span>)                │            │ expand_dims_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ split (<span style="color: #0087ff; text-decoration-color: #0087ff">Split</span>)       │ [(<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,      │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ concatenate[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">1024</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>), (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>,  │            │                   │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)]       │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ reshape (<span style="color: #0087ff; text-decoration-color: #0087ff">Reshape</span>)   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ split[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]       │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">8</span>)                │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ conv2d (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)     │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │        <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ reshape[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]     │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │    <span style="color: #00af00; text-decoration-color: #00af00">287,744</span> │ conv2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ downscale           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │     <span style="color: #00af00; text-decoration-color: #00af00">49,536</span> │ time_frequency_t… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │  <span style="color: #00af00; text-decoration-color: #00af00">1,436,672</span> │ downscale[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ downscale_1         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │    <span style="color: #00af00; text-decoration-color: #00af00">246,400</span> │ time_frequency_t… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">3,904,512</span> │ downscale_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ downscale_2         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │    <span style="color: #00af00; text-decoration-color: #00af00">574,336</span> │ time_frequency_t… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">7,635,968</span> │ downscale_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ downscale_3         │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">8</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>,     │  <span style="color: #00af00; text-decoration-color: #00af00">1,033,344</span> │ time_frequency_t… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Downscale</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">576</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">8</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>,     │ <span style="color: #00af00; text-decoration-color: #00af00">12,617,216</span> │ downscale_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>] │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">576</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ upscale (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>)   │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">1,033,088</span> │ time_frequency_t… │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ concatenate_1       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">896</span>)              │            │ time_frequency_t… │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">16</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>,    │ <span style="color: #00af00; text-decoration-color: #00af00">15,065,600</span> │ concatenate_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">448</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ upscale_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │    <span style="color: #00af00; text-decoration-color: #00af00">574,080</span> │ time_frequency_t… │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ concatenate_2       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],  │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">640</span>)              │            │ time_frequency_t… │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">32</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>,    │  <span style="color: #00af00; text-decoration-color: #00af00">7,695,872</span> │ concatenate_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">320</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ upscale_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │    <span style="color: #00af00; text-decoration-color: #00af00">246,144</span> │ time_frequency_t… │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ concatenate_3       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale_2[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],  │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">384</span>)              │            │ time_frequency_t… │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">64</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,   │  <span style="color: #00af00; text-decoration-color: #00af00">2,802,176</span> │ concatenate_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">192</span>)              │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ upscale_3 (<span style="color: #0087ff; text-decoration-color: #0087ff">Upscale</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │     <span style="color: #00af00; text-decoration-color: #00af00">49,280</span> │ time_frequency_t… │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ concatenate_4       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ upscale_3[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],  │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">128</span>)              │            │ time_frequency_t… │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ time_frequency_tra… │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │    <span style="color: #00af00; text-decoration-color: #00af00">439,808</span> │ concatenate_4[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">TimeFrequencyTran…</span> │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ multiply (<span style="color: #0087ff; text-decoration-color: #0087ff">Multiply</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ time_frequency_t… │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │ conv2d[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]      │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ concatenate_5       │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ reshape[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>],    │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Concatenate</span>)       │ <span style="color: #00af00; text-decoration-color: #00af00">72</span>)               │            │ multiply[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]    │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ conv2d_59 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │      <span style="color: #00af00; text-decoration-color: #00af00">4,608</span> │ concatenate_5[<span style="color: #00af00; text-decoration-color: #00af00">0</span>]… │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">64</span>)               │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ conv2d_60 (<span style="color: #0087ff; text-decoration-color: #0087ff">Conv2D</span>)  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">256</span>,  │        <span style="color: #00af00; text-decoration-color: #00af00">512</span> │ conv2d_59[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">8</span>)                │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ reshape_1 (<span style="color: #0087ff; text-decoration-color: #0087ff">Reshape</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>, <span style="color: #00af00; text-decoration-color: #00af00">1024</span>, │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ conv2d_60[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)             │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ transpose           │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ reshape_1[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
-│ (<span style="color: #0087ff; text-decoration-color: #0087ff">Transpose</span>)         │ <span style="color: #00af00; text-decoration-color: #00af00">1024</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)          │            │                   │
-├─────────────────────┼───────────────────┼────────────┼───────────────────┤
-│ reshape_2 (<span style="color: #0087ff; text-decoration-color: #0087ff">Reshape</span>) │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>, <span style="color: #00af00; text-decoration-color: #00af00">128</span>,    │          <span style="color: #00af00; text-decoration-color: #00af00">0</span> │ transpose[<span style="color: #00af00; text-decoration-color: #00af00">0</span>][<span style="color: #00af00; text-decoration-color: #00af00">0</span>]   │
-│                     │ <span style="color: #00af00; text-decoration-color: #00af00">2048</span>)             │            │                   │
-└─────────────────────┴───────────────────┴────────────┴───────────────────┘
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">222,789,634</span> (849.88 MB)
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">55,697,408</span> (212.47 MB)
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">0</span> (0.00 B)
-</pre>
-
-
-
-
-<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Optimizer params: </span><span style="color: #00af00; text-decoration-color: #00af00">167,092,226</span> (637.41 MB)
-</pre>
-
-
-
-
-    
-![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_20_6.png)
-    
-
-
-### Compile and Train the Model
-
-
-```python
-# Compile the model
-optimizer = keras.optimizers.Adam(5e-05, gradient_accumulation_steps=ACCUMULATION_STEPS)
-model.compile(optimizer=optimizer, loss=spectral_loss, metrics=[sdr])
-
-# Define callbacks
-cbs = [
-    callbacks.ModelCheckpoint(MODEL_PATH, "val_sdr", save_best_only=True, mode="max"),
-    callbacks.ReduceLROnPlateau(factor=0.95, patience=2),
-    callbacks.CSVLogger(CSV_LOG_PATH),
-]
-
-if not path.exists(MODEL_PATH):
-    model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=cbs, shuffle=False)
-else:
-    # Demonstration of a single epoch of training when model already exists
-    model.fit(train_ds, validation_data=val_ds, epochs=1, shuffle=False, verbose=2)
-```
-
-<div class="k-default-codeblock">
-```
-2000/2000 - 490s - 245ms/step - loss: 0.2977 - sdr: 5.6497 - val_loss: 0.1720 - val_sdr: 6.0508
-
-```
-</div>
----
-## Evaluation
-
-Evaluate the model on the validation dataset and visualize predicted vocals.
-
-
-```python
-model.evaluate(val_ds, verbose=2)
-y_pred = model.predict(sample_batch_x, verbose=2)
-y_pred = prediction_to_wave(y_pred)
-visualize_audio_np(ops.convert_to_numpy(y_pred[0, 0]), name="vocals_pred")
-```
-
-<div class="k-default-codeblock">
-```
-200/200 - 8s - 41ms/step - loss: 0.1747 - sdr: 5.9374
-
-1/1 - 4s - 4s/step
-
-```
-</div>
-    
-![png](/img/examples/audio/vocal_track_separation/vocal_track_separation_24_2.png)
-    
-
-
-
-
-<audio  controls="controls" >
-    <source 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5z7ePtX+2/7iPtb+1n7ZPtK+zr7S/ty+6T7kfuS+7L7uPuz+7j7uvuq+577ffuA+3T7XftJ+0P7ZPuP+6H7jPtz+3/7rvun+6n7s/uc+5T7hvuI+6j7mvuK+3b7gvuh+637zPvW+8D7pPuj+6n7vvu2+5n7iPuB+3n7h/u6+6P7hvuC+2z7gPuf+5L7gPtw+2T7Zvt6+2z7dPub+4j7aPuO+7r7jful+837uvuf+4D7dPth+4D7gPtp+5j7l/t/+4f7ivug+7L7mfuH+5L7pfue+6D7wvuN+4r7pfuW+6v7xvvS+8f7zvvz++j70/vm+wv8C/zw+9T7y/vH+6b7pfu1+5v7jfuP+477jfuN+4P7f/uA+3j7bfty+4P7e/uB+4X7fPuJ+5b7l/un+7H7nvt6+3L7dftj+zj7S/t2+5D7jPuC+477pfuh+477j/uQ+4f7gPuN+5n7gftX+0b7PPtM+2f7avtg+zH7Hvs3+1D7U/t0+3H7UftX+4X7h/uP+6X7hPtj+2v7e/uI+5D7dftz+3r7c/uK+5n7j/uH+4b7eft0+5L7rPvB+877wvvD+7X7pft++1n7ZPtz+3j7kfuD+5L7n/u5+9n74vvc+9D72Pvr++771fvq+9f7nvuJ+5b7fvty+4L7o/uV+5f7tPvC+9f72/vJ+7L7jvuO+7b7wPu4+5z7hPtg+137evuB+4z7lfuR+4H7ovvD+7z70PvI+6L7rfuo+6/7u/uh+2z7WPta+0f7Lfsu+zb7Mfs6+yj7Mfs5+zr7SvtU+2D7Zvtg+0j7Sftt+3j7XPth+2P7Svs6+2L7hfum+7771Pu9+8f74Pvq+/L76fvP+8D7xvvM++z76PvZ+9X77vvS+8z77fsB/Oj71/vj++v72/vb+9X7vvur+537svul+6z7tvuQ+2v7X/tq+2T7S/te+3H7P/sd+zf7QPtQ+4/7t/ur+7D7svu++8z7/PsC/OP79vvo+8b7wfvg+937wfux+7f7w/vh++v75vvp+937vPup+7D7uvur+4/7b/t7+2v7OvtL+3L7WvtI+1D7Qvs2+0D7Vvtd+2z7fftz+4D7pvus+8T7y/up+5v7o/ue+5f7n/ub+4P7a/to+3H7gPu2+8f7ofub+437j/ui+6H7k/uY+4f7b/tX+zL7Ovtb+1b7VPt7+3j7aPuI+6n7n/uH+577ivuD+6v7mvte+2H7a/tK+0f7evt5+2P7dvt9+3X7aPuJ+477iPun+6T7lPuz+8D7sPuo+7n7zPu2+6r7rPvU+9H7q/u9+7L7kvuW+6r7qvvB+8b7jfuA+637uPuu+6z7svud+4L7e/tr+3v7n/uc+4L7fvuk+6v7tPvX+9772PvD+8n70/ux+5L7nfuq+2L7WvuS+2L7TPt6+2r7Wfth+2j7Y/t0+4H7b/t3+3D7efu3+637efug+7j7j/tp+3/7lvuB+477i/tr+2T7ePt0+337q/uo+337ivut+7f7wfu7+7f7pfuR+3L7b/uH+4P7kvux+7D7xPvX+9777fsN/A/88fvc+8P7yPvX+7j7oPuD+3D7f/uI+6D7pvug+6n7tvuv+7H7wPvO+6f7mfuT+3T7a/t8+177TPtl+2H7Wfti+1n7Wftf+z/7QPto+2z7ZPt/+3X7afty+1D7XPuo+777ovu7+8T7lPuH+5L7cPtY+3D7efuJ+4r7ofuy+5/7vfvX++T73PvV+9f71Pu0+6j7pvu4+5j7oPup+5n7j/un+6T7cfty+4D7d/tt+3T7Z/tq+1r7Wvtk+2X7ePuN+4j7o/u0+5D7f/uQ+6b7ovue+6z7n/uL+6T7ufub+4P7o/us+5H7nfur+2/7R/tN+1T7QvtO+1/7afuN+6X7m/us+7j7wvvG+5/7lPuq+4D7Rftq+4T7a/tZ+3f7avtd+3L7i/uc+6r7uvvf+wD89PvF+7P7pvt++4T7mfuK+4L7kfuK+4b7ofuh+5v7p/u3+7L7rPuX+4b7lfun+6D7sfvL+737vPvK+9n74fvW+6r7pPu8+6/7tPu4+4r7dvt6+3v7l/vH+7r7fPuA+4j7jvuQ+4b7mvuS+2T7Z/to+037OPtj+3r7dPuL+4T7ePuY+5X7pPum+4P7Z/ts+2j7XfuW+577dvto+3T7ePuA+5f7pvuV+4r7dft5+4z7jvt0+3f7kPt6+2L7ivue+5z7q/uh+4v7jvuj+5r7n/ua+5D7ovuq+7f7xvul+5j7pvvN+8L7qPvN+9H7xPvP+7T7tvu7+6X7p/uY+5r7oPuW+5v7rPuR+5/7ofuW+5b7m/uV+5z7pfuG+2P7cvuC+4P7l/ul+8773vvJ+7D7w/vD+6H7ovup+4v7jPur+4f7lPu3+537gvuc+6D7h/uc+8n7uvuk+8b71vvf+9r7v/uo+5z7hvtr+177bvtg+2D7YPs++1j7ZPtf+1n7l/vJ+6j7oPvF+637qPvD+7z7ofub+5H7YfuE+5n7aPtf+3z7kvue+437efuZ+7H7c/t6+7f7qfuY+6/7t/uj+6f7wvu9+8j7zPvJ+7P7fvuG+4T7cftp+3P7YftL+4L7nPt9+5j7pvuh+5P7lvuz+6L7mvui+4n7YftH+0r7O/s8+3b7c/t6+4T7lvvF+8j7nvuh+6j7dPtK+1j7VvtB+137XPtM+1z7aPt3+5/7ufu5+9L76/vj++v73/vE+6j7pPu5+6L7e/t5+277e/tu+2H7kfuW+5n7r/vf++n71vva+9X7tfuS+4n7k/tW+2b7jvty+3j7dPth+1X7afuH+4T7rfue+2r7qfu0+577qvuk+4n7hvt/+2P7dfuP+3b7lfvF+5n7kPts+1v7nfut+4D7h/uz+4T7gfvS+8v7tvvA+5b7gfu0+8H7r/uu+5D7Yvtl+3r7ZfuH+5L7ivuw+7T7vfu9+637xfvI+6r7kfu7+7f7e/uU+6/7ivts+3T7kfuC+2b7mPu1+6H7xPvO+8T74vvl+6X7i/vF+977vPu3+9L7qPt7+4L7gft/+4L7gPuA+5X7rvua+3z7fPuA+3/7bvtM+0v7VPtd+4r7oft7+237jvud+6n7mfti+4D7bPtx+6/7wPuk+7D7oftZ+1r7ift3+2f7mPum+4b7lPuo+5v7pfuI+2v7j/uI+2j7c/uZ+777s/t7+2H7i/uq+2T7j/vA+4P7jPuf+7H7uvup+9P7/fvq+/D72vuu+677vvum+437sPuL+z37ifuG+1D7e/s8+zL7Z/tV+1L7XvtP+1X7h/uh+5D7uPvZ+6L70PsP/Ov7wvux+7f7xPuW+437zPvL+2j7Yvua+5n7h/uc+4n7f/uQ+377qPvJ+4D7Z/ul+8z7sPuw+6r7hfua+4T7b/uV+4z7h/t5+1b7Z/tU+2/7dPt7+7r7hft6+6/7q/u7+7X7vvvQ+5L7nPuQ+137Yvtt+2r7U/tk+337Wft/++D7r/t9+4n7pftv+2f7hvt6+5/7qPtg+4v7mfuX++/76vu9++L7x/uv+/H76/uz+6v7yvuG+2v7bftZ+3r7lft2+3X7kvui+9374vuz+8X7/fuj+4X7kvts+zj7P/sU+yf7FPve+g77ZPt6+277sPuh+6/74vsZ/M/7zPvb+8L7pPus+8n7u/uI+6T7vvtm+1f7lPu/+4H7kfup+5r7j/uc+2P7P/tn+2z7Xfts+3n7ffuf+4r7vfu3+6r7rPuT+3v7b/tq+137efuM+z/7MvuV+437Yvti+537lPuN+7P7nPuR+6j7ovuh+5b7fPtt+0P7VvuJ+6n7a/uJ+6/7kPuu+937x/ud+5r7m/ui+5r7i/ub+7D7qPuv+6f7s/up+5X7h/u3+8b7ovvC++f7n/uC+6v7j/uB+237b/uJ+4b7hPuL+437gPun+7L7iPtw+4D7f/ta+277cPtb+2P7SPtn+8376fuh+477vvvH+5r7yPv3+6P7mPuq+6b7nfuG+4D7gfua+5r7bvuX+537XftX+337jPuE+6n7ovtz+4j7ovuR+5H7hfuS+8n7uvux+6n7xPum+537x/uz+477ufuk+2r7kfu7+6r7lfu9+7/7b/ul+w78xPua+7P7xvtx+0z7vvu0+3f7dfuH+4T7bvuN+7v7c/uC+5T7jPty+1n7m/uR+3L7h/t6+2r7TvtS+4T7YPt7+1r7Wvt7+1r7ZPum+8z7xPuZ+5P7nvtW+0P7a/u1+8P7wPva+8f7kPug+5v7dvuY+7L7gvtg+377a/th+3b7tfui+5j7hfut+9j7zPvR+9v7vfuQ+6H7p/t9+3v7yvuQ+1X7d/t8+2f7cvvt+yL8p/up+yD81vtr+5n7A/yo+1v7s/vb+4v7dvu7+9b7kvt2+8b7rfuI+4/7qft1+3j71/uf+zH7c/uM+1r7d/u9+9/7n/uL+9X71fu3+5b7kvud+2H7Z/uA+2D7Vvt9+4r7bfuY+4v7SPtU+5z7uvuk+4r7xvu4+0T7Qfue+4v7Mvs5+3L7Svtp+6j7pfu/++v7r/ub+wL8xvuD+8v74vtt+0f7rPuU+0H7V/uA+177RPt8+5D7QPt4+637aPtJ+1b7XPtl+3P7e/td+1L7ePuP+7v7qPvQ+/T7xfvL+xz8+Puk+/H7+/uF+5z74fug+2f7ivuo+5L7iPux+9D7x/uh+7L7vPt1+5P7xfuO+z/7Xfup+637ePtk+4P7e/tW+3j7jvty+1/7ifvG+4b7afuj+8r7qPuv+7n7dPtb+5T7kft9+6X7tPuP+zz7Qvtu+3P7jvtz+1P7RftM+0j7Qfts+5r7jvuR+6n7mvuG+7f7xfuQ+5f7qPuR+3j7r/ve+7H7dfuD+1b7MfuX+9P7nPuQ+7D7vvu++9X7yPuk+6P7yvvV+737kvuE+3f7Qft7+8b7y/uF+7D75fuO+2X74fvL+1X7n/v6+4f7Xfva+7H7a/vB+wH8lfuA+7D7q/tp+6H73vul+1b7WvuC+2v7e/u4+5L7R/t0+6f7hPuS+8L7w/ul+6r7h/uo+8L7qvuX+2T7SfuE+5b7Zvts+5D7kPuc+8X7rvuK+3r7lPuO+477hPtk+4b7rvtw+1D7p/vO+5X7kfvX+677dPt2+4n7jful+7D7gPuV+6z7X/tu+7D7h/tw+7r7w/t8+4b7wfuz+7D73PvF+4/7q/vu+8n7n/ug+5/7mfuE+437s/u3+7T7pPue+6P7n/vS+8n7f/t0+4n7p/uk+5n7ovuZ+3r7oPud+7/75Pu++4n7pfvg+6r7iPu/+8D7f/uA+3H7RPtM+6L7rfuS+4P7T/tU+3T7f/us+5n7dvuq+677b/tq+5X7Vfs7+2D7Yft5+6z7pvuA+6r7z/uZ+3P7f/tu+0v7gPuo+177L/tT+377V/sx+4/7l/tD+2z7jPt3+4X7mPuZ+477pfur+4L7iPty+1v7iPup+6n7m/uL+4j7ivuK+2v7rPvY+2v7Q/un+6D7eft5+4n7gvt4+6X7v/u8+6r7qvur+477dvt++3/7vPvf+5L7Qfs++3L7j/tR+0X7lPuv+4X7m/vl+7r7jvvU++f7v/vT+8P7iPuc+8T7gPtR+5H7svt6+2b7qfv5+777i/u3+9X7mPud++371Puu+7/7nfue+6b7r/u7+5D7cvuV+4z7dfuC+4P7YPuD+6f7W/tE+377Yvsl+5f71ftj+1b7ufuQ+4D7zfsE/LX7bvuW+6/7c/tj+5H7aPsQ+y77j/uB+1r7jfvG+3v7gPu5+4/7YfuV+937rftH+3X7b/sc+1D7q/uc+0/7ovvL+3/7r/vy+7/7rfvR+9r7jPuL+977pvty+6374/uz+4v7p/uz+6v7w/vR+937yPu5+777yPvL+3/7Tvt1+4b7ePuM+4n7kPuR+7b7z/vK+7r7f/ti+3v7lPuK+1L7KPtY+277jfu0+6L7ePub+6b7bvt+++H7w/uQ+8L7vPt2+3b7qPuW+3v7j/uR+277f/t/+1r7ZPud+5z7dft2+6P7n/uP+5n7uvuR+3L7uvvv+8/7zPvb+5L7evuw+6/7e/uT+677mftt+2/7gvtz+1H7lPu1+3T7a/vJ++f7yvvY+8L7gvuq++n7mftR+4/7rvtc+3j7uvun+5n7qPu2+6f7bvth+3v7hfuf+8D7lft3+6z7gftE+2L7rvuK+2L7wPv++777oPu2+7/7fftm+5v7avtF+2D7OfsP+277rvtw+1P7eftR+1z7mfu/+8v7kft3+5j7ovuJ+7P74fuk+2f7c/tZ+1n7ivt5+1v7c/ud+5f7cft8+3z7b/uG+7D70ftl+1f76Pv/+7v7xPvJ+3n7Tfuw+8T7qfuu+4X7nvu6+4v7fftv+2L7ovve+5P7VPup+6T7aPvJ+/j7jfuF+8P7tPt++6f77/vn+5z7j/vP+7v7dfuu+6/7PPsy+4n7ivtt+5P7ePtI+4j7gfs8+xj7IPtB+1f7mPvY+7z7ifuZ+9P7yfvJ+/b7svt6+8H71Ptv+1v7p/tf+wD7afu9+2X7KPub+7b7IvtG++z76Pup+9X78ft4+0f7bPtA+yT7bvtm+wv7Hvtt+237R/t3+9D7tvtb+3v71Pud+2n7z/sC/Lj7o/u++4P7VPuq+6j7Yvuq+9H7evt8+8v71PuC+237tPuy+277ffu6+7n7yPvy+7/7hPuN+2z7XPuX+6D7dPuA+4L7aftv+6j7mPul+7z7dftR+3/7tfu0+8f7tft8+477fvtq+5D7lfup+7z7qful+7L7rPuo++b7/Pu8+5L7svuT+5r7xPux+5j7sPuL+2b7mft6+4D7n/vI+9H7j/uB+7X7x/up+8D78Puu+3f7n/uS+0b7Yfur+3f7RvuG+6v7Yfte+5z7h/tG+2L7rPuK+1f7hfvU+8L7rvvO+6D7WPuM+7T7e/uY+737fvt++8b70/uP+3L7ovuU+1D7WvuJ+2T7TPt/+477ZPtt+5j7mPu++7r7nvuX+4/7kvuF+5v7sPur+537cPtt+4j7gvuL+6f7p/uR+7370/u6+437aPte+2T7VvuD+7r7o/uM+4/7c/tm+4v7evt8+6b7dPth+5X7kftc+3L7hPtC+2D7fvtX+5T7wPtt+4f7+/sM/M775PsJ/Nj7h/th+2b7Zfsr+xf7PftR+1H7WPt7+3b7b/sy+yb7n/v1+677kPvw+/j7h/uB+9D7ufuT+5z7ufuH+2f7qvvD+6/78fsF/L/7tPsH/Mv7Z/vR+wL8iPtT+5/7n/t5+6D7o/ti+1b7Xftm+3n7ufuz+3D7ivvW+8b7fPt6+7b7iPtX+5/7xPt4+2v7r/u++7f7uPug+4T7avuG+5T7b/uU+9v7sPuK+7f7qvtX+4D7uft++3v7pfuK+3P7hft7+2L7VPtk+5D7mvuH+8L75PvD+8372Pui+5/7xPuP+0b7dfuB+zb7XPvH+777nPug+6T7qvu6+6b7yPsP/PD7wfvd+/b7z/uy+5b7n/ui+1v7RfuV+477bPu++8b7i/uM+5X7b/uh+7P7Z/tb+4r7QvtB+5n7uPvU++P7lvto+3P7Svtc+4L7VftV+4z7Qvsq+5T7h/st+377wfts+0T7ZPto+177WPt5+5b7cvtT+3j7VvtN+7b7q/tt+7r7xvtj+2371fug+1/7jPtw+zT7VPtk+1j7Z/uS+5r7avtz+7X7zPu0++f7RPwB/Kr73PvM+3f7cPub+4P7T/tM+0D7RvuD+5L7kPuP+5D7jfuD+3v7mfvA+8/73/v9++X7xvvs+9f7l/u6+8f7YftY+5z7rvuI+3j7hvts+037SPti+1T7F/sD+zP7J/tF+2P7UPtk+4f7PPtC+4r7Wfsj+3T7mvto+5n7tPu9+8r7zPud+5T7fft8+4j7hft3+477dftJ+5T7tPuB+5H7l/tr+8T73vue+6P7AvzQ+3H7ufv5+8D7f/t/+6D7nPuF++j7FPzN+537wvu8+877C/wS/PL7E/z++7H7vfve+737lvvB+8X7avtN+6P7xfu1+8H74fuo+1j7lPvo+7f7f/vH+777Ovt1+/f7k/tZ+8H7wftv+4X7p/uK+5D7o/uT+3z7fPut+6/7c/ua+8P7ZvtN+8z7oPtG+7X7z/ti+6f71vtP+yT7cftY+037bvuU+8z7wfue+/b7KfzG+8b7EPzr+8n73PuK+6D73Ptz+3P73/um+2z7zfvs+637xPvU+7X75fvM+7D7B/zy+5P7mvt++zn7gPvR+777w/u6+4L7lPuo+6n74vsH/Nz76PsS/MP7Z/ue+837nvuN+377bvuI+4P7iPuq+4L7NftZ+7r7lPt3+8L7tvuL+5v7jPt4+677s/t6+3f7Zvst+zf7SPs8+0v7a/uP+6j7nPuM+7f7rPuC+9z79PtX+2P70/uW+y/7VPth+0L7ePt9+0z7SPtX+1j7d/ua+6j7evtj+5r7zfuf+4P7u/u4+4r7rvue+3L7t/vM+6n7tfvm+9T7rvvG+937wfuC+2P7iPto+xX7O/tk+zb7OPtd+1P7SPtR+177UftV+1H7S/tf+3b7kvuB+3b7qPu4+6D7dPtN+077PftQ+y77CftF+3D7a/tk+4P7YvsZ+1L7gPt4+5T7ZftY+3b7YPtB+2j7i/tu+1v7TPtc+3f7R/s6+5X7nvuI+8j76Pum+6z75fup+6P7zvu8+6X7rPtw+0j7dfuI+1r7bPuC+2L7V/tn+4f7fvtk+337rvuT+5T78fsF/KD7rvvS+7n7w/vP+8j77vv3+8/70fvW+8v7v/vD+9H73Pu5+4/7l/uW+2L7YvuF+3P7VftN+zH7MPtr+377S/s3+3n7jvti+1b7gvvD+8H7rfsA/CX80/uy+9X7pPtz+5z7lvt7+4P7dvtg+2z7cPtR+1b7hPuL+3D7Y/u2+937hvuK+8P7h/tT+537sfta+3/70PuI+1v7nPvK+8H77vsn/Bj89/vy+wT8FPzm+8X76vvg+7L7ePtO+1n7evtM+0T7cvtT+yP7Tvtl+0/7bfuM+4X7mvuP+3n7gPtY+0D7a/uO+2P7e/vU+837mvup+8r7xvul+4j7qPum+3r7hfu9+5/7Pfs3+1b7W/t2+377h/ui+5D7mfve++T7xPv6+xD8x/u/++v7sfu6+/P75fvq+//7rPt3+8H7yPuQ+6T75/vW+437kfvN+777pPvG+/f7v/uF+8v78vua+5n70fus+6f72/vZ+577o/u7+6z7p/uo+477hfux+6L7bPuB+6D7g/te+2v7hPtj+zH7QvtU+0b7QftW+3T7UPtT+377RfsW+z/7c/tl+1/7o/u7+437ePuF+4z7aftM+0b7U/t1+3P7e/uz+7f7c/tg+3X7ZPta+3f7ofu3+5b7ffuL+3T7U/td+1L7B/sM+1j7ePtb+3n7yPve+6n7WPuQ+9j7nfuG+9j76Pud+7f7+/vK+6f7kPtt+2b7N/sk+2L7eft1+337dPtr+3j7lft4+4z7uPt9+3X7rvt8+1P7ifuO+1/7ePuy+5P7RvtO+237fvun++D7/vu8+6b7x/u/+2H7V/uX+4r7R/so+z/7Z/tv+2r7dvuU+6P7gPuD+4f7XPtQ+2/7nfuo+337gfuR+5r7gvuA+6L7ifuH+8n7/fv++y78RvwO/Nb70Pvh+8v7zfsE/Bb8v/uN+5/7k/uX+5f7Z/ti+4L7ifuo+/L7/vvu+xL8FPze+8n7xfvM+8L7kvt5+6T7tPuT+377YPs1+0n7cPt4+2z7W/tc+4b7o/t2+6b72vuD+0z7cvtk+zv7Z/uD+3P7hPuQ+5z7sPuo+6L72vvA+2T7YPta+0n7bft0+1D7Z/uV+2/7Vvtx+2X7YPt/+2P7c/u9+9b7xfut+8z71PvZ+8r7qvuw+2H7Pfu1++P7tvub+8D7z/vB+6X7lvuz+7X7l/uj+9D75vv1+9v7rvuf+5r7efuG+9j7zvuk+7P7kPtf+4H7gfs0+0z7qPuP+2H7ifun+5D7c/uL+7v7sfuL+6j7xfuB+1b7tPvZ+8f7uPt1+037TPs4+zX7YPtH+zH7e/ua+2r7bfuP+5f7p/uX+2X7cft/+3L7mvuw+4P7r/v0+9f7xPvH+4r7ivup+4L7lPvB+437mPvs+9j7h/ul+8v7nfuG+2b7XPtr+2r7g/u1+577ePuz+9T7lftz+4f7iPuN+577gPt9+277ZPuV+6v7a/tl+6L7wPu++7j7xPvq+wH8zfu5+/f70fuO+9v7BPy1+3/7q/ul+3r7W/s1+yf7QftC+0r7Wvto+4H7bPtP+2z7bPtF+177oftw+yD7P/tP+2D7nPtk+x/7ffvF+4n7d/uf+4H7b/ub+7X71/va+5v7pPul+1b7QPtm+277ivuU+1r7XPuv+6P7l/vu++L7sPvA+5f7Yftq+3/7e/t7+4L7f/uF+3/7jvvB+4z7W/ub+7L7uvvJ+7P7hPt5+4X7lfvE+6X7X/uh+9j7kft2+4n7g/ui+6f7dvuT+7P7jvuh+9j7sPt4+6L7tfuI+3T7cPt/+6D7m/uB+4D7ffuF+4n7Zfs8+zv7SftI+2D7X/tR+4P7gftW+5D70/uO+3f72fvT+3b7gPuk+5j7qvut+5f7o/ux+637xPu6+4L7hvux+437iPux+6H7qvvI+4L7d/u7+6v7t/sK/Nf7avuZ+/r78vu9+5r7sfvb+4/7evvK+6T7Z/uj+6b7Qftc+7D7mPuk+5v7ZPuS+7L7ivu8+wf8vPuS+9T7xPud+6b7rPvg+/f7tvuu+8T7evtQ+6H7oPti+2f7gfuK+177PPuI+7b7lfu4+/b7tft7+6f7pfuC+2T7Nvta+6b7V/sN+1D7cvs8+0z7c/tT+1P7gfuQ+3b7bft/+537pvuq+577gPto+2P7gvud+5n7gPut+8X7q/uh+637v/ub+237gPu6+7L7hPvG+9P7U/tR+8D7yPuq+6H7g/t/+4X7k/vB+7f7ifvK+/T7n/uN+5r7Y/tm+3T7JvsY+1L7JvtD+737hftG+6v70fu1+8/7v/vD++X7q/uN+8j7k/tS+7P7uvtM+1T7fPtj+4P7nvuE+5X7jfuH+6v7hPtO+2f7gPtz+3n7XftA+5f7tPtu+4L7pft7+737DfzL+4D7kPut+6j7kfta+277m/t7+3L7lvup+6/7uvvr++P7pPu1+/r7AfzU+9P7uvuS+8r72Puj+6b7ofuC+5r7nftu+3f7hPty+2v7RvsZ+yT7UvuU+7j7l/uN+8v7zfuf+537YftM+5r7f/te+6L7pftv+7/70/tQ+0b7m/uV+5f7cvsr+1f7k/uR+6f7n/to+5r7yPuC+4T7tPuO+6/74fvC+9X74fui+6f7nPtU+1z7a/tg+6T7h/sU+1X7tvt3+5/79vua+3P7w/ux+2j7ZPtN+3T7y/u3+4f7jvuN+5X7sPuW+4L7nfus+7z7vft0+2b7mfui+6n7mftu+2v7m/t/+2z7h/tm+377rPtr+3D71PvP+5X7xPvZ+577fPt8+4P7YPsf+x37Yft++6L7rPuo+8L71fvN+9v7y/uV+7b79fvW+7n7q/t3+3P7cfsS+9X6y/rM+gz7OPsM+w77a/t/+4P7iPtv+4v7nvtt+1b7dPti+2X7svur+6/73fvO++j74/uN+6r7yft/+6v7E/zG+4b77/vq+5b7yfvQ+6X7xvu5+4n7rPup+4f7yvva+6L7jvuk+7n7uPt5+1r7qPu6+4b7pvuO+yD7TfuV+3j7bvtZ+2b7yvvZ+5j7v/vH+1n7bfvA+4f7d/vW+8z7h/uJ+4P7d/uj+6n7gPt6+3z7Yftt+7j74PvC+8n77Pu9+3X7evua+337Tft9+6b7oPuP+6n73vun+1r7U/tq+2j7U/tS+2D7ZvuS+9T78vvG+8D7+/vS+3X7ZftV+1z7a/tk+4j7sPtw+2v71/vD+1T7bfug+177Wft3+3b7pPuV+2z7tfu4+377qPvw+8T7i/uG+4z7uvvD+5X7ofub+0L7aPuz+3T7Vvtx+2L7aPuV+3v7jfvH+6v7rPvt+637gPv4+wH8n/uj+637svu9+577rfsV/BT8x/sI/B78vPuZ+9L7wvt0+zD7Oft4+4L7avuf+9X7t/u8++z70fuY+477hvt++3D7Z/uN+9X74PvN+977uvtf+1z7nvuG+zX7Lftn+3j7S/tQ+4j7qftv+1T7mPt5+zT7cPuz+4T7ZPuJ+8P72vu4+5f7lvub+0/7Wvu4+437YPuE+4T7Ufs0+1f7lPuo+3r7Xvtu+1X7T/ud+777fft1+6T7u/uW+2/7pvvN+3L7Pft/+3/7KftS+6/7sPuR+477r/sJ/A78vfu0+6n7aftB+3v7f/tV+1n7gvuu+4z7d/u1+9X7mPt2+4b7afs6+2b7i/tm+137c/uu+9D7ofuU+9r7svtS+2z7pPuA+3n7x/vd+6L7ePuF+7j7qvtY+2P7nvt5+2j72PsT/Nz70vvu+8n7iPtM+0D7ePuK+1L7a/uR+2j7l/vu+7/7X/tP+2r7Z/tI+zD7d/uY+0r7WPun+4H7cfvu+xn82/vN+7/7pPvK+7T7efui+7D7dPuY+877rvvI+/z70PuR+4P7j/uX+5b7Zftv+5D7Yvtf+8n73/t9+2f7sfvE+577evuP+7X7kvuR+5/7jftY+1v7ift/+2L7TftS+4H7mvuS+7X7wfuy+7/71Pu1+2z7dPt3+1D7Mvs7+2z7nfuk+6P7uPu7+637xvvY+6D7Vftf+2/7VvtO+237qPuh+5L7svvH+6v7f/uq+8X7lft4+1/7kPvL+7/7qfuu+5/7p/vL+9L7uvvW++j7vvu4+6r7lvuo+677lvuH+3D7QPts+6j7gPtb+3/7ovuu+6X7bfuk+8/7kvt/+5X7YPs4+3j7n/uW+7f7vvvW+yz8Kfzw+/j7vfuM+6f7hfta+3r7j/t5+537qftw+477rPuQ+7X7uft5+2/7nPuQ+2H7Zvts+3f7kft/+5z7t/uj+5f7kvt1+1P7cPuq+6L7bvtE+2L7j/t2+1T7O/s8+zv7RPte+4n7nvuf+7f7vfuh+4T7dft9+3/7a/tz+177V/us+9z7uvuG+577hPtX+2X7aPtZ+0T7Ivs2+2n7Svsq+4T7s/t3+2L7a/uE+5T7hPtn+2v7W/s8+337p/uL+6r7wvuP+2r7TftL+5v7r/uA+4X7g/tY+5T7zPuR+4T7mPuB+3L7Yfs7+337q/tu+4L7sPtz+137tvvW+6j7iftq+3r7pfux+9L70fuN+2v7rvu++5j7q/um+6L7q/uP+5X7y/u3+7b75Puy+337qvvt+937r/ux+7v7ufud+3X7nvup+5L7sPvp+8/7mPuy+/L7xfuB+4v7i/uT+5b7pfu7+7j7lfuO+6f7mvt3+577w/ue+5X7ifty+437lvuu+9P7svuM+7v79Puu+3v7i/th+137dvuL+777w/uP+577rvta+2n7y/uk+0b7UPti+6f79fvl+/77Efy4+3X7rPuO+0/7iPuc+377gPuW+7D7z/u5+5z7oft4+0n7cvuW+5j7kft4+2b7dvtK+yv7i/uK+0T7efuk+2T7aPu4+9H76/ve+5r7ovvL+7/7yvvJ+377gfvG+7r7mfvE+8/7t/ui+2f7W/uD+5D7g/uC+2b7NvtP+537nftb+2r7ofuq+477kPvI+9j7r/ux+9n7wvt7+4n7nfuK+337gfut+837zvu++7X7jPs9+z77d/ti+1v7gfuN+6X7tvuq+6v70PvV+6T7nvtc+z/7v/vt+6f7ofvP+9D7vvuP+1z7XPtS+z37Zft5+1D7Zfuo+6b7Yfsd+x77Sftk+2/7ovvI+6X7r/vW+9P7zfvW+8L7kPuY+4z7pfvL+5H7lPvX+7b7dvug+877m/t4+4b7hfum+6b7oPu1+437afuC+6T7sfuV+8/7+vvQ+4v7a/uu+737mfuR+3n7UftI+2L7hPt3+3T7hfuP+4b7RftW+3T7Rfs3+037Uvta+5r7y/u5+7/71vuw+437gft3+3P7c/tX+1H7fPuJ+1j7Xvt/+2f7OvsN+x/7Xvt7+5n7uvvC+8D7vfu1+4r7evuF+377i/uM+3X7qfvd++r70fuh+377cPuF+237OPs7+0b7YfuC+2z7d/u3+7j7ffuS+7j7n/ub+7j7kvtX+0v7Uvtn+1P7Pvtz+4n7bvuI+7774Pvo++X76vvT+8H7l/uP+4n7cvtw+2j7Xftb+4j7qPuD+3/7bvtr+5n7iPt++6L7o/uL+6P7mftJ+0n7mvuq+4/7evt/+8b76PvX+9773fuy+3r7e/tn+z77O/s/+1T7b/uO+6T7sPvE+7j7lftp+0P7PPt++437cPt/+5z7tfug+5L7lfuo+5v7oPvD+5f7d/ud+7z7xvue+237e/uW+4L7evuP+4n7mPvD++P73fvF+8f7zvux+4r7lvu5+7v7jvuC+4T7Zftf+5f7pft7+4b7jPud+6T7mvuy+7T7ift7+6b7wvup+8D7+fv0+9z7qfu2+wD8+PvY+8j7e/sk+zT7kPuL+3P7g/t9+477hvt3+5H7nvuY+677oPtZ+0v7o/vT+7D7tfvF+9T7u/uH+577q/uO+4H7ovu++8D74Pvx+/z74PuP+3X7jfub+6n70PvB+5z7mvu4+6f7l/uK+3v7fftT+zH7NvtZ+2P7ivvU+7n7ifud+7P7nftl+1r7efuL+377aPt4+1r7J/s2+2b7UftN+6H75PvK+2D7UfuQ+8P7lvty+4f7XPtH+2z7gvuM+4j7k/uj+537n/uw+8P7xPvA+9D7rPuB+5f7tvuy+2D7IPsx+1P7WPtd+6v7wvuW+5b7tPu0+5T7sfvu+wv8wvtK+1/7nvuh+6n7wPuO+237mfud+4z7i/uO+8P7yvt5+4z72/vv+9b74fva+4v7Wvtv+3b7W/tY+2P7iftz+2X7i/uv+537YPtq+2j7LvtF+3n7iPt3+0v7bfuB+3r7l/u4+7b7e/t1+7n7w/uk+5j7l/uY+1f7PvtS+337n/uV+4b7Qfsd+1f7o/uV+2D7cfup+7n7oPuZ+7f7p/tq+1D7Y/tW+1b7e/uY+577Zvtg+6L7vPul+437h/te+0X7a/uS+3v7dPuU+6P7j/tc+3b7sPup+5L7tfvD+2r7Z/vc+w78zvuK+5T7pPuI+2z7evuF+4r7nPuq+4n7bvuz++378PvQ+7f7ufur+4/7ffuR+5r7Zvtg+2f7Wftd+4z7tPuR+1z7Qvta+2/7efuV+677f/tg+4r7nvuA+3v7sfvD+7v7pPup+7H7jft7+4X7i/tq+zz7XfuS+5j7Z/sk+1P7aPtQ+1D7Rfs7+0n7evuY+5L7rfu7+4D7UftP+4f7rPu8+9r75/vc+8H71vsR/NT7YPt7+5X7hvuP+337k/uw+6z7sfu5+8v7vvu6+9v76/vR+6T7lfuo+6b7vfvI+5r7m/vJ+9X7zPu/+8376Pve++T73fvV+777tPvD+7L7jPuN+4n7c/tm+1f7d/ts+4H7hfuL+5D7cft/+5r7fftj+2b7nfup+2j7Svt2+6v7p/vD++X7zvu4+9P70/vN+7f7rPu/+477UvtV+0H7LftN+2T7W/sj+yT7Uvtc+2z7U/s/+2n7f/uO+4r7jvt/+537pftf+0/7Xftw+5v7k/ti+3v7ovuY+3X7fft1+2T7b/t2+277aPtn+637t/uF+4f7lvuj+6X7u/uS+1D7Svtn+377l/uf+6X7tfvB+8v7v/u9+4/7hfuh+4H7cPtu+4j71/vT+5v7jPvR+/P72vsT/A780fvJ+9f73fvD+4v7ift++1P7PPtD+4P7fPuA+777i/tv+6j73vvi+6b7kvuw+7r7mfs9+zf7Sfsx+1n7bPtV+1r7b/u8+8f7nvuT+4D7i/t++6H7n/tG+zj7OPtd+3n7WfuH+9X77fvb+8r7zvu0+7P7zPvJ+7j7mfuT+5z7Xfsq+0/7hft2+2f7g/uj+4f7hPvi+/H70vvh++z78fvn+9X7wPu2+5L7X/tz+3j7T/tq+5X7bPtG+1/7fvuE+2z7h/us+537rfvL+/T74vu5+/L7NPwm/N77ovuv+5D7fPuY+537bvs/+277kPte+177fPup+6P7Ufs9+2j7dPti+3L7ePtP+2T7nvu++8v7x/vZ+/X74/vO++H7D/wR/PX7x/t0+xr7FPtM+1b7PvtG+2r7bvt0+4/7l/uL+477l/uj+1v7Ovtc+3j7f/tO+yf7VPt0+5X70Pvd+8P73PsV/Cv8EPze+877Afzl+5v7r/u/+6b7qfvC+6b7Uvte+5f7w/vY+7z7vvvl++77pfty+1f7O/tG+zv7G/sb+xL7Qfuh+5n7iPu2+/r7KvxP/Eb86vu4+877uvup+237Z/ut+7D7dPtc+3H7Zvt6+6v7yPuT+1z7gfvP+8r7f/s9+1D7bvtQ+yL7Pfs0+y/7Z/tc+zr7Zvt8+437jfto+2X7hftP+zT7Tfsn+w77FPtH+3H7JPsW+4j71vvO+5H7mful+4T7kvu1+9T7qvtp+4L7p/uR+2T7fvuy+5f7cPuP+1/7Oftc+1z7Zftw+177Zft9+4b7YPty+2X7Ivs/+1v7NvtQ+5L7w/uY+2v7qfvl+8v7uPvN++H7xvvJ++D77PvK+5D7o/u0+4H7Wft9+777xvuc+5z7lvtt+3P7gftu+1f7QPtm+5D7dPtN+0r7VftX+4f7ovt4+2n7qvvP+7f7k/uQ+6f7oft8+4r7p/uj+5H7mfuk+4X7jvud+5T7eftH+1r7cvuD+4D7W/t2+3n7pvv2++v71/vO+9P7zfud+2P7UPtw+2r7h/vQ+8P7nfuk+8H7yfuf+237TPto+177Qvs/+037XftU+1r7a/tX+4n7n/uw+/v7A/wM/CH8M/wz/A789PvL+9H7qPsr+yL7R/uA+8P7sfuo+8v70fvE+/v7/vvc+8X7q/uc+137Bfse+5j7p/uS+7b7nPus+xT8GPwO/Ov7rfuz+6n7hft8+1r7b/t0+4T7iPsw+zX7fvul+4b7YfuA+4n7f/un+6P7o/u9+6D7s/vB+7L7xfu/+8j7yfuI+2/7kvvV+8r7h/uT+6L7hftu+2/7U/s5+1L7fftc+z77L/sk+xX7DPsz+yz7APtG+7v7vfuh+9v79/v/+xL88/vw+/v70/uo+6/7jPtk+037Wvtx+2P7SPtn+8T7zfuh+8j75vvT+9n7zvtY+xb7MPsz+1/7XPuM+8n77/v0+/f76vu7+7P73vvn+8D7avtJ+2z7lvty+z77TPtp+1/7Vvt3+z/7Cfs/+537oft3+5H74/vc+7v7yvvv+/77CPw6/EH86PvX+wD89/uX+1f7jftp+3n7ivtu+3P7Wftw+7H7pvt0+2v7jfuO+zz7FvsD+zb7cftk+4f7mPuN+8P7/vsy/Cv8PPyI/GX8Jfzs+9X7p/t3+3f7Uvsk+x37/voi+077PvtZ+3X7evsx+y77dPtc+2z7mPuU+577i/uF+6z71Pui+5D7vfuo+7j79Pvc+8T7rvtl+2z7ePtL+zv7XPtx+1P7T/tE+zX7e/uw+6r7gftn+8P75ful+6P73fu/+477yPsF/MX7xvvW+7j7tPt++2P7j/ul+1r7F/st+2H7m/ug+0X7Nvtw+2H7PftH+0r7P/tx+8D7xPu0+/T7+fsz/Iz8UPz2+9j7/vsH/Kf7Pvse+0P7S/sd+3D7iPsu+0z7cPtm+0j7Zfut+6r7tfuy+5j7tPup+4/7gvt/+4/7qfuy+7z7lPuH+5H7h/uc+4v7Z/tB+yX7R/s3++r60foc+2P7RPtn+337j/uc+6H7pPuu+5H7h/vM+/v71/ut+7D7vvve+9L7x/vd+wH8Dvzq+8n7l/tW+0T7Pfst+wr7H/tB+0X7WftD+0f7Tft0+6j7qfua+4/7+fvw+477lPur+7T7sPvI+9770fvF+9v71vuo+2n7SPs++237cPso+w37L/tS+0z7QftI+zT7Zfuk+6z7u/vX+9z7mvuq+8n7nfuY+6L7pvuq+5j7pPvI+6X7h/uU+2/7lvvR+9b7Y/sg+zv7IfsZ+zf7RPs++0z7oPuy+3j7s/vl+9z76fvs++r75fsn/GX8R/wC/Kn7wPvf+5b7Z/uG+6r7nfuN+1f7KPso+y/7U/t8+3P7QvtX+5r7d/uq+5X7SfuB+8v7ufus+8D71/vo+8D7f/uE+7D7l/vh+w/8yfu9++X7/vsH/Nn7svt3+2r7bPtp+zH7A/tp+5r7o/vJ+8n7l/uq+/H72PuA+3j7evtT+1z7lPuK+1L7RvuR+5P7Vvt5+7/7i/tc+4j7iftu+277gftt+1D7T/uc+9f7tPtg+2/7j/uh+9j7Jfwc/AP8PfxQ/PX7dfsj+wP7/vrQ+vT6UPtY+2D7ufvH+3b7a/uf+1z7Q/ty+137UftD+yX7J/s3+0P7bvut+8X7nvvF+xn8Avy/+9P7yfup+4z7mPu3+6n7jfvD+8D7gvt/+7z7lvtY+9T7l/s7+xf7NvuB+2D7S/t1+4/7wfvh+9P7wfvI+9r7uvu+++775vuo+6/75vu6+037ffuv+4f7X/t3+7f71fvk+8X7uPuN+2L7hvth+xL7BvsL+xb7Wvuy++X70vvm+yH8CfzW+677jfup+6b7ePuF+5v7sPvc++n7y/uk+5H7iPtk+1H7QPtS+3/7hPtx+zv7KPtQ+4j7jvt1+7X7+PvX+wj8UfwM/N770fvj+x78/vux+8779vuG+3v7j/s5+0j7o/um+2P7evuW+5H7i/t4+177Yvtz+337sfvL+6L7wvsY/CL8v/t1+5X7uPu4+6H7o/t1+4H7wPvS+537ovvU+8L71vvQ+437cfuQ+4L7RPtZ+4n7Z/t1+5X7pPuO+6n7DPwU/Oz7C/wC/P378fut+6j7h/t4+5X7i/tn+2H7V/s0+z77Vfsf+//6Lvtv+5n7tPt8+0D7Z/uW+5D7ffut+737ufvt+wH8xfvt+xz8Kfzn+7f72Pue+6X7w/t2+1H7UPtY+2n7YPs3+xr7UPt2+1v7Nfsr+0b7hvuL+3X7dPuB+8X7uvur+8D70fvU+9779fu3+7H7wfuT+1v7f/uC+zX7ZPvI+577VPta+4n7dPtw+1j7S/tV+yz7LvtG+yT7C/tf+5P7mvuf+8r7p/ul+/j75fvF+8v7Gvw0/M37xPu8+6L7fPs5+1L7SPss+zX7lful+3b7nPuT+3j7oftq+wf7TPuw++r7Bvwk/CH8KPxk/Df86vvF+437mvuh+5r7e/tw+537iPtb+3D7TPsP+zP7SPtD+wL7DvtW+yz7GftX+8H7A/zj+zT8Z/wd/B/8dvw//Or7//vp+9n76vvN+5v7dvtT+077OvsE+836G/s8+xL7M/uZ+5D7Mftf+7f7kvtN+677APyY+6L7F/z/+wP8LPw4/PL7xfsD/OD7tPta+xD7U/tH+2j7dPtK+0b7+/pY+z371vr9+iD7bPtR+0770fvU+8T7TPyd/CL8wfvf+6H7Vfta+zj77PoV+1z7lPuV+6D78/vA+5L71vvQ+zH7//p4+137Lvth+1X7bfun+8n7nvus+9z7zvv2+637t/sV/NH77PvG+zz7RPtG+xn7Rft3+/P6J/vl+4z7jfvE+337c/uN+3D7Cvsw+3T7bfus+5X75vsJ/Jn71vve+4z7WvuT+8f7hvvW+xv84/sM/Pj7zPt1+zX7Svsn++X6q/oN+277P/to+977qvus+x78KfzU+8D7t/uF+9D7wvuo+/b7w/uR+/X7Afw4+zj7EPyn+0j72fvi+5r7xfvD+4P7RvtG+w77APtv+5D77fso/Gn88Pxy/BD8APy2+2X7D/tv+zb76vpo+4b7bPvQ+7r7ZPtX+1v7aPsn+9r68fpX+0n7Y/tK/H/8HPyG/G/8K/xv/BL8ifuP+1T7BPtp+7/7evuS+/X7ovuW+7L7fPto+8P7U/zm+zz7CvsI+wz7m/rT+hf7D/uY+w383PvR+wP8DPx4/IP81vtu+677nftB+0L7Hfu8+lX7rftc+3379PsD/Hr7k/vL+137rvsl/Mb7mPt6+537s/vK+8j7b/tA+1f7x/sY/Kv7Yftt+zr76fol+1H7Ufvq+9f7nvsT/Cz8bvtF+w38dvv6+vL7Afzd+3f8Mfww/Jf8jfx9/Fv87vt/+zP7JPv5+qr6Hfrs+bL6qfoH+6L7Tvuq+//70Pu8+7H7n/tB+5r73fuc+9D7vvvp+xb89/vX+/n7pvwe/FX7pfuu+0X7+Po8+0/75vpE+2j7fvu3+3P7h/sN/Dj8kvtF+2n7EPvU+vr6CPtI+wj8lfyQ/KP8tfyi/JH8DPze+877Lfsl+/r6j/rT+jX7W/sC+4P7f/ur+mD7B/zS+6T7o/sb/NT7UftN+yj7AfsE+4j7uPsh+6n7c/w2/Df8NPxS/JP7QfvC+2H7Nvs6+1D7oPum+3X7UvuA+1T7evpw+nX6FvoT+vP61ft5/An9Hv1Q/Uz9qfxY/Av8YPsC++361/o/+zn8Afy4+w/8sfvw+u/6Pfun+tP6aPs9+977i/xi/A78F/wo/G/7CvvT+tz65/oY+2X8qfyi++T7Gfww+yv7evvz+pX6Tvse/D78UPyR/KH8GvyM+hb62/kf+ZT5MvoK+0L8+Pw8/Zz9zf2W/M/74/sE+2v6LPq1+QL6f/oY+5T7Afx+/Gv8kPw+/NT7EPw6+836pvsC/Jn79/sJ/Xn8lPs0++H6oPrr+m37Kfvc+978oPwl/DX8nvsj+sv5Tvqd+n/7Uvxy/Ij92P2g/GX8Pvz2+vH5hPlC+VP68vpL+n/7NP3e/YD9bf4W/8n81ftr+8z6ovob+oP6Dftf+477R/sF+7D6ffp6+pP6yPvk+1n7qfzz/PP75vtz+z/6zfl6+lX6+fn8+j/8JP2e/MD8AP0t/B38Bf2//Cv7LPui+xb7Tvu++3z7pPuo+9n6SvoE+sf5w/mC+j77hfu2/Mn9X/4u/mP9Mfz4+mn6Evqx+nX6gvre+0f8c/wa/Ir7HfvI+l77Hvv5+jf78vp6+k/6sfqX+gX62fpM/Lr8cf3P/Xv+jv6Z/aD9of1d/GL7pvuF+uD4c/k5+QT5TPoA/ID8t/zQ/Vb9bvzL+x762vg4+LL3ovgq+v/7/PxG/Q7+Yv0J/Ej8M/yF+836W/vD/H38e/w4/dj8gvss+9H73Psl+yj7Efzf/MX78/of+9r5IPlL+fP4cPkD+wn8Mfx//ZP+if6U/iD+PP2R+6D5v/h/+DP4NPiQ+ZP7p/13/y0ArP9T/vT79PkP+dn3Jfcz95L4Tfps+9f7KPxh/LP7ZPx4/V/92P3L/lL/3/5g/lb+Cf2L+hf55fg1+G73Cvit+XD62fqt/HX+TP4X/fr8rPxQ+hT4Vvks+8/7pPx1/ar9pv3C/Uv8ifrB+b/4pvj++dj7Nv1R/i//HP9G/hP95Pqq+Nf22vVt9pz3T/kj+5j87v11/8T/C/7l+wX7z/rX+qb6H/tq/BT8SvyK/aX90PrX+Af6J/pZ+rH7aP2P/p/+Iv7i/Z39Y/zL+rv5oPjT9yr4Vvl7+hj7l/tK/SH+Fv0J/cD9Mvz4+cf6dftX+ub65P3Y/gD99/y3/Ab7TPmm+Nj4hfjo+Ff63fsf/eH9yf2F/Sb9MPwj+zH7YPsL+1H8s/3j/Cf8svyI/Hv7//rg+v35Q/mD+bP6L/sE+1n7Bvyg/JD8wvxt/T/9J/w4/N/8hPzT+5b7jPto+gj6ofpj+Wj5HPsw+xr7/vsw/Vn9YPzk+xr8bvyC+7P6gPop+a73DPeK9xv6wvtz/Lv9iv+n/x/+uP1d/Qr8S/vq+qf6lvq/+sT6Wfo++u35FfqX+tr6bvsU/fv8WfvP+4z+i/4J/IP8Cf22+038Nv9M/7P8rPpu+rX5W/hk+Gf4x/cf9/v3hfrl+y78/vuD/Iz9v/2g/vD9GPwc+gr50PlA+Vj4DPrx+4v8Lf5FAAwAU/4Y/5X+c/tp+b75EPwL/AL7svq4+ir5d/ZM90r4vPfN+fD8c/2l/eb+IP6L/Jv8a/u/+Xb5iviA+Gr7hvzC/Cb+G/+x/eL7F/w8+6H5F/hA+Gz6Wvt7+tj65/tP+2z7yPu++pn6vPvF/AP9Zv1p/dr81vwJ/Kf7jfv7+hf5evfL97L5x/qN+lb+dwLLAdr/JQBL/rD4Xfak97L34Pd0+RD84vxM+wz7Xfol+Fn4OfoY+4z8fgBJBPsE1QNaAgv/Rvql9oT0rvLq8vr0dPeb+fL8uf/N/2f+O/2K/Aj8FPwg/Fj8dfsY++/7tPsH+sb4Ovl8+bP3KvmW+qf5U/vs/ogAQf85/iz+4vyG+6b6HPnS96v2zvm9/O37k/zI/tz/r/1Y/df9R/vI+Jb4Mvns+oD9KP5T/r0AAwCy+/v5vfmz9870nPRQ91/6rv2g/xMBPAKtADT+Gfww+qD53fZG9Jr0Zfhk+SH5MPxP+pj3Jvkt/Ij+fAB2AlcBX//X/cD7x/wB/6/9cPuQ+J729PUT9m/3jfjC+oD/PgPmAwkCJAGR/k35Q/g6+EX11fM6+O/8vvws/Pz88vuT+EP3HPmM/PP/9wEzAWsA8/9+/fb6Hvh39QH0+vS/9mf52/tZ+g37ov2y/fL9yQBHARH/yf6O/5j8Cfrp+Xz5dfZm9cP3XPch9XD31f0PAVwAzQFNBCgCWAGwA+//p/tw+nT32PQ/9Aby4vC69ED52f2MAzYFAgPsAK3+6/pg+DT5RPkn+RX7Xf50/uv8wPn99ur1ZPWZ9Lz4aP3x/tP/iQBh/57/X/5y+gP5T/mH+CP7nwGKAMT+sAB0/038JPsw92vxUfDz8IDzevVK+D74/v/cBswFvAijCpkDrf26/on0wu1L8U3wI++U+ZL/o//cA88HxQfbCNUGOQD0+kT6p/ZH9Pr2Y/Su8Enx4fBK78XxPPVp/aIHkAwtDwIQ0QzfAGj6RvGr54Do3PAK9M32sQKHBab/owJcAPD4ZPSw9Rb0g/S49zv4iPo8/rH+L/3VBxsMqgoSCCkFTvrS7wDsy+bb5aPrqvomAvwNORS9FBcMAAnyAjr3kvfa96jvXu7W7gfodeKW5Lrmueio/BIMvROlIcUinhJzDEcEcOu64QTqj+Wu5AzzFPia+xYCpQilBJQErgr3CpsG5QXq/8Dztum64pfkPOhq7wL6HAC6A6cI7AEi/CP6H/mm+Gj/oQk1DEMJNgy5DTYC8/vT/JD3CfJU8C3pL+Ou3WreaeO96g/3xgHuD+EZoSArIkAaSg6kAqrzQOfw42jlUeTg5v/szfHf95v9OgTsDOMTXBeAFgATvQtn/nDxaeSe3+bck+F25pbqF+549GH+1QqdFogYBxYHDdcJWAMK+YLvAu9M8fbzp/qgAiADofx8/YL5HPKk8B/y9uu47K70NPuq+3EB/ArNDIwMPwqJBk3/1Pat71rpvOKG4t/n6/HR/aYKEA+bEGkWzxMnDK8MjgmX/7H5R/XV6ajeZdElyxjLp9ud8PUJ9Bw6JCEo3CPJGGkKCAI09Uzv//FL9cLv//N898nwhOqa7qr0lPPk/tQL3Q3KCNsGWQIj+931lPED7rTxMfgr/Hv6rvpE9jTyWvJL+sACNwaiCjwSPhCEBE8AL/lW8ADnt+uy82H63AG7A8D46+vp6armhOmM+NEHVxLGHEUazRLGB2X6d/Ah6XTpO+h67DP3mf1R+fT6Tvto+Rb6dwJzCnsMZA3DD+sJbPzg9YHy8u5q7p7xivIr9kL96P7Q9xb2hffN9Drx6fUP9xX39fwcB7gLUQvQB0sHWweE/CL6fP1g987uaPTj99TuVOoR8xX5WP2gBH0GfgZY//P9G/+L+U314fcr95X4LgBVBHACYABW/lf7Jfwo/br5nfyGBjMGuQUoBZL96u8T6XbfL9xc2iLgmu5VBzUdOiDFIYQgvBCS8pju8+bO307mTfrBB3sJlAhUBKf7yu1D6E3sW/GC9YYDMwpvBR0HZwm5/cz0JPps+gDx4/d9AwcBDwDoAogAh/zi/O8CxQOG/8b7J/s/62PZidAl0bLVcOyRGvQ3ZUXaQzE6vxg18trQT8EnvDfPbe95AoEFxAnWDLn5qOhq5X3w4vG7+oURgxttEicM5Ajn92znaOda747t0PnzCxEL2QEyAUf/mfk5/i0JrQ2uC2kGePh+4EjHdb9evnjVrww1M9dEW1VFR7MaefLS1Lazvqd2zIP0OgeiIZwuUx0vA731Ou1k37XhQfRQAckIGwvgCAv/m+r55OHsye4P9agJoByMGyEa4g9O+/PvlvKy9Mz6tAbpAeHs4tqtzXu76riK0HkGojSzSyhnqmaTOJoHDOM1uUqVbKJBy3vgyveEHI8g2A/vASn+eQb1/fD7hxihJ5IVBg6iD0vyO8uGyxTdwN+r67sJwRJZDWkIFAaw/x37oQS9FHgWeBCuB+3nJsxmuauzWbZP7ZcX4DukWLJbrzxfFQj44sykrQuuDswa3mz6kA65ID4WRgf2ATT8/vKR8ukFjw2eBZIDpwwx9VrksOrL8Q733QibFQ0WYQvaAav2V+t57Sj0tvunACD7eem/2ejLbsWGwSnb+wchMt5Z52MWXpc9hxre67nIjKy4qWyviMfk4KnwyQK9DmMQWRUkIBUZ6Bt6I/MhZRAw/z3u791V1tLXuN476qr6FP4mBw8L8QMAAZwGagcnEsgZbhYhBNrvS9eaxXmzzLEu1N/+8CmBR1dXnklcLKIV6vzE2ebBZsBEyWXVDtoK+AEEvvxAAm0MBBHyD7cIDQ1qDUX79vV99H3u1Ofq7ML9fwsECzAUyxJcAr7xWPPk/Az71wAlBj351OHU2S/SBMBduWrTP/rfJqJOklwKWvhD2ihYCbPzZdmzvk21RMP4yGfKrNeD8dX+C/46EsAqWSjuIGYkmR2QBzfzsuoe4xDcguD99CsExPrS/rT+TfDI6976xwvNFYsjUyhoGBP73eB2xteuyaM4v3fysSPrPO5NjVDmNbMbMgcV7KvSIs6J1Wrhrt0v1iPgmfGR8hAAKw9KGBchuSDfGUIQI/wv7DLq6eNd55H21AYNCokG5AeY+hHn9Oib9pn9VwozHSAjWAZv46vUx8VIs/Ox29ltCjw0PUxEXcFRzC7cEHz6nOZiz1jJV8sv2mfRV8tf0mHkW+0Z/BcfcivLJRYrci8pGnAAcu+P7Bbk6OIy+r8MIgXr+zn8U/X16NHk+foiCzITth1JIawKZudiyQm/KbNErSLX7gnaL7Q/e1SZS4UtFA8KAMfpHtkg08nXVNghzNDIQ84c5OnuWQg5JfgymTUMLdAiBBL69lrpf+bT4FnmlPcCBfgHVgFG8xvqO+M852sA5xK8IZ8rnB+o/gXeP8W0rlmftqxC3GsTb0AOTvtVDEtvMaEXtQrE9uffwdbX03jQ/sDnur21MsHj2/j3sxs9OsNDhEcoPBIghwiT9Cvruenv7Kj2YP2n+MbzLOki3ZvRd9YM+dARmyUuOT824hKe7G3QVbiUnE+ijc9wAkIv60ezUVdJUTAQGugSGAOk70bo6eyE6ubMPLiJtrOyNL9953oUTCYNK1Y/FD+3JWoMrP0V8HfjN+vX/4IIWQN2+lb4c+Z3x7HJI/IKDQwZ1TSAPI8bYPAq1Ku+QKCQlQW5UvPPIzxE81BfVTQ7ix/AFAgKc/z48MbqTexQ3bLHirdmseCuUsB96dUV8CxlPdVHgTOYGOgIDgCh8rDoL/dqAqL93fT68erlHtgP1I3gxQGmGoEn+zH4MjELh+Glyaay36SEqiHRLwjcO2lH+EwyQusoCAljAA3/kPrj+twA2vxj3/jIXrYorjmyOsfS6Okc/DbPPThGVDr2GU4FF/xJ7pXfk+aC+Uv+efc69KrqIuHE4n/twRGkLY8xgCflHRT9stMTtranyZq2rPDeORP4OldGEkCDL/EdPAZ9AnT/UwQ0Cb4J+vob3erBI7OHrhO14cYZ5lIUtzKaQl5FzT+3LPUPGvtX8+PoD+Rb8Dv44uut4mHbB9rB4svz3xIuML45fTa8JKf+m9youEmo4KR1veDnAhYDNZw/0TXIIIQTjwXkAlX0GgAzDhEJi/XH6NXQAMGFtiS20sl44yUFLyTRPIBHY0TNKyMeUgoW8+vtduxC48blK+bW3x7PC8x03l7yoQwkLaFFJzulHAT7V+Qlz3OyW6OZvSflXwWHLStFUkIzIY4PLAcg/TXvp/yyCwUNogCf81jfMMhTuOSx6b0p0w/n8wdIOsFHKj90OCkxFh1KBvP9cflD57rfy97R2VbQX8t61VzoYwc8Ke1AzDm6I64AGtmGw4G4rq/gvZfsoxO7MrdAmUOBK0YSKAKy93LvI/GNAyYYEBQJ/AztI9YLvwy0NLr6xuzZwvH/G486gzuvNRM27iikC3b6oPwS9aXhPuDK4FTSxsx81+HoRwdVIJMvQjAwHbf5z91pzfzA5Ljlwd/soRNWN3VHmkSXMpAXzPq67lLjgt0887MLgRj0BSv1HeIoyzy/PMNJzOPcb+qfBN0s4zl+NNIyairkGh0RMwxpAGfqedwz1snUpcq50vDklvrpGK4xMyktFxcIauxh0La69bWRtZrUygc4MVRCzkK8PTwpDgu67lrryugv7Xr5Xw/+Bsb3m+5M4xjNlb8dw7nXDugS6RMIdyy4MQwx1jdOLTwhJBjjCzb3j+Ut0tDK9cqQ1dnlxvVOEqMqXTDLJVMM+ubJyqLBq7rJthDGo/gvHTc/c0dQPucmzQ3I9mzqWuic6if0DwJfEugIevsC8NflZdCOyNTLPNgD5Qb18RASKSAt5ys4LLggRBLrBVj9ZPH14nTdzOUo5bfkhOtKAuobRSUeHTAOm+/p1N7L08DsulDB4N0gELhBlE6gPjUwyRro/XXtLeiS5J3uVAFrGqEh7g3M7kfehNBAwIa8MspU2OXrDgjFHkMrGiaMHywhCSCbFawKNP938kPiROO74EbeHuo2+nEHIA9OEfIR9QKU6O3WH80oxc7G8tgGAyQtPkW9N28rOxszBgPt5eeQ5gHrUPo8EHUdyxTP//zoROH82FnPY9HM3nPdfeUM+AgN5hbkGFcd8iKOHz0aeBHbBm35Suu434vX8Njt4VTyrAfjHtggixK3+VTp19XgyDa+6MpK87Al30X0SYE+pylxDe3vddvsykrOvduA+lcVURopEi4NywJn8yHuGO/m4a7L2Mwy3T3ysAcTEOESSR6oI5sl2COFFez39uJy3KvaodeL3Az18wzHIE8r0Bqn9GfbdsdXusq3zs6c72cXtj9eUSVJoSzqBv3lst9J1MnOjtws9x8HGxEVFtsROQM178LlkeO96HXgwNsy6V33QQDhES4e8CLFJJcmdh0nEmECUOvp2gXOx8mZ0cftnglNICUnGhvr+DPeRsyTwim9tMWH+RcnA0d+UfBNJiihA5fo/eTx1bHNF9uQ9RMIOQmdBCkDEgCP6NrjceqZ7h3pyO1V+JQA5v+ZA1gHyBPqImcrVjABIuYBKe2k3WDJA80A4VH07wUsG6MiJxCi7UHRncLovBa1xNi8ECgyI0JHTQNBkh2pAs7rEt3DzhTVx+bnAIsIywUYAzUBx/Ot7GzwyfCS7+rtT+8E8TzuMPDlBu4XGRgJH14xOi0IHssKLPTY0lS+9Mr94XX1TAayF0QZbv2J4inV6snlvsbJV/YOH6FD31SSUNkuYxMa+Djsztfv0KPcMO6X+zMEsgOw/sD1/+qX73Tw1Ouv5ATo8esj5tzdtPKyDNYewS7HQyBGYjw7KkUM1eALvx64e8Tm3K/4lQZICt4A3+sE2OvCw7ovv7Lk/xq7TbVc72GzSz4nMv255z/T78ii2Wztz/rU+VTxQu639d3wK/PI+3b+APGp8kr6kfCQ11jbSvZODEobUi/APYA5Mh6EBxn0zNbzwrXLV+ZhAa8Pew4FBbfsMsuYw7/C1Mh17Q8gvUnaVVtThDcnE5vt6d0Szk7QP9z69XIDlPx09nb3z/Bk51Lytf3AA70C0AN2/+HzQtpuz87jQwK7FNUqsUEYQhYnVA2A+VHhlc2v10buTv3pAPwA6e5V2CXIh8ZzvPjIXvlpL5VK0VUYV5UznQR25EvdBstG0mHnpf8zA5ECN/xK/NvzM+ht7cD93QUyA7cHJwAj5BLHjs5H5Pz3DBZIOzlLTz5nJzEWLPqr2fPVmuUl78D05QIvAR/mw8fLvT+0eru55/EVUz+KVBtdr0UrG8LsjNyBy2rKTdMf6+v9E/wP+ij9Ifqj8dz1tAM6EqkQgg/8Cgb+GuB0zHHTp+lSBPclgUHNQxkzkRvn+xPf6srny9bbcOoG+Jf47Oi52BrMFbsbyBjw6yC1SAdjBWxpTBAeAPMT14e9cr6WyLnhT/Kv+vr9wPo280nuPvOGBUgULRjYHe0X0wMM6zjUJswy0onrBhrJOCw/bzQiH68G4OUq0X/VLtyi4+j4wvoT7G/a7sx+vbfOTfFmHN89U1J/VEQ+aCOA/QvdDsU8xybNUeTe9mMD/f6M+Fj45/pS+0kLihqDGkgVdQSS8C/aYcgMzYrcQvZCH1w5Pz6AL88YvwSu68vdb+CP283eL+py4FrZKNr9yVzVJ/QSGKUxvkPPST4y3hPrAsLw4tw+4jXqCfji9VTvKe0W8L/nResq+GwLqBPoFWgWBg0f843bD8w3zKHepPZmGPwvmzURLWseoweN99fyY/KD4JPZsd0o1YrF7cj3zrjbNveWHcdCfkX6QkwzphiS9aLgMNTn25zmHv48B638WfUS8e3qvOob9BMINxP9DpgMbQnH9TXc3tFS0xvdpuxrFBMxHzIJIJgVIwmr98rwyffa9CPts+SN0HPGJ8Ajs43RYQJIMutOe2HnWnk8mw6g7mbRncK5y67WOvI3+A3xvu/J8YXpcu6S+csIDg4NHywoJRbq/lXse9xO3rzrCfuJCN4M1BifG38PqPmO+Pz2rfAB6vTsmOWL0+HIcLzQ19T0hRYXOv9NeEeUMtgV0/5U5/XRJdYY13rpsvDR9XT6R/vg7I7shfiBCIwP4BhxF60I9/bS6A3j294j3rzxYgjMFPccbBgmECf9LvXg+sX7mPVs9DbrfeFW17rGTeHt9FkK1Ck6OZgvzR4LDeL5m+kd2Lrg0OTY81bztvAx84Tv5+oq+kAG8w2nEd8X+BaTDAH9kPHa6ZzcEd+j+0MNEhKBHekXBwzS+ovx9OjS3eTTDtpM3A/hu+d07db89gSDKQk2GTDDHwEYsv0X7IPZr9ye4aTp7Pd6/dACI/2A95v6BP+5/bEA4APnDfcJ/gPU/MXtj+Dl3bPhCfZ/ASAW0iiZJi4VyQ6MAoXlz9h/1M3KTsOczprkvwbmEcYjsjAyLvAggAylANj0Ot4j2DDlxuQd7ZXumv3ZBJ339PTzBMADcAasF5ogfRJ6Bi4Cz+6v35rjZeNg6dH6VQXUGs0iwxhUFasTY/nf2e7FabpjsrSzaNFX/jsTii90RhhBRCwOE+n/F+0r2e3Zpelv80QFhwo5Cqn8kuUC4L7u3PSxAUITNhxoFQMFnv4v8u7g4d8B46bnovgxCMETUh85GnsO3gSq+eDh8tFFyNrBqsE02s4Adw7lKuI5zjp7KSgZPwJu7ILQcdTs3g/pzflqAzMP0A32BAQEyABd9UX1KP0MCSoJ+gtkB6779e2i45bfiuaW6iQBTg8iE2EZ1xJT/+7pENPByFnN1sTs3kEMNBTiH5g19i2SGc0B9fiz68/W5twx8NvzM/sr/0IGfQhwAND/AwdUB9UI0wpEEjcYag5HBSj32eW93PXYx9sz6E/tevk+AJ4FmhFCDaj56+nk25XQd81U0QD34hUGJJo/GjyjJZMP5PLP3U3Xhc/A3wLqxP9mDUELSg4qCfj9MfqS8GH2pAlODiIRMhKHEyAG+vX/7H7r3uLz4e7offH69WX5Wv7yCEv/HOkB4V/V4dBM1k3pLwqDIUc/UEsVOnEf5ASo3DvIOMAkzz/hXu9ACFcYYx5VFtkIngTc/cXtOfSbBaATuAwwCSwQ+v+x6kLoh+nQ56fjoeh86tbtyfbt+or/gvlr6Qbhodv702DpuQT6Fbw05EPfOT8jqQiz6wPXS8mV1q3n9PQbA10F4g20DXICJQOfBMz+TAZDCTwStg8fBOX96PNK8aHxce5i75btHOx/8DPsOPIR+mvsH+mm5yHjWOSG45f2OhHLE60ndS8SIbgRAPym6VLe6s6I0zXl2PEHBbgP0CCUJhsaDBAKDTcCjvxC+nUB/QGc+TD5+fo280Lt5+ua6/7oXetQ8X3wFvFQ+UH5Jutz7LvkJORZ5ID7Nw71GsoouSe1IC0QGALR6DDd2NQi27DdivBN/gUG0g1tEHcOkAwYBWsJRhSoEXMOKgRP/fv03u1n7brz2/W99bjyGPLl74bo3uWT5dHo2+eP4+Pvm/gx9xEMfh1qJJkoRh1RD+38rOjl3KjRI81A4IfsMgCRDfgN4BEKD1wGUwjsCa4MqBCVEIYYTg//AjP4m/NB7rvkkuTX7PPq1efY6wvs++oy46fimuPV4ort//YgBTUibiZoKL88siosFW75nuLXzqXA5b3d0vfd0vEvBvQWZiLEG9AVyRPmC2wIXQnACTMMVAQT++z1YvVD9gj4z/re/SXygu4G6uDiktxJ2cvaxN8V7D3ur/fgA64jOzC0NGI4KyoQElDx5N1qySrA97W4yfLXhO1b/wQQPRuyH0YgUB1+FHYN7A0XDhULxwPJ/Hr03e+s9kAAAgNvAyX7RO1c3LHXC9Bwy/jKAtAD3+7z/QCrG2s/o0RsTNFECzFwDP/jHcqovweyuLY8xT7X0PPl/loQZxsBHaMbIhhjHEIhyxW4DqUEOPW97E/q/u/l9NH6Cwj8B/QCifvw6xTgMtThyD3B5b1myvHh7vfeI29J101wWyRVeDlbF+/zuNUyvP6k4at1vcjPg+go+BkNtRTID3wWiSCzIAQZ6BIBF40MSAD2/M/4OvDD7jP3QADvAFAAmvlg8PXkGte+zXzEOrzozSHoxfaEHDpFlkwKVO5FpivKEHvzos/+u4WyfLb2uXHRd+/C+fEJOhm8HqYhgSR8JNQj/RZxCtX/C/cD8Gvt6u7O8bj1WgApAhQHxQTe9X/pONiexoy2ObY2wp7g+PnyKBJNaWWDapxVpTiRCYbgo7yRq0aj27RqwmvbuO8o/UIIuxHNGPIbcRuZHlwalxNSE08K6Py38hzwh++I7AH0UwRUCKgLwAijAIjqN9KkxKO5WLULxF7abvdHJsBCxF45Z+9SrjMDConnoMussYmqdLdQvPfTX+MR8ngFzQ9fFoshSyfZK9gmPCD9G4cN7P5s9ZvwFOxo55/uKPtH/4sBsf4Q+MHm/NKaxRq5Z7nayq/Zg/kTKdtA6l60Z89X2DuYEH3hg8lysHWlFbM8wnnbTuyJ+rkEpwv+DjYQBha+IPwfDSDQH4EZxQq++rvxye0t8fL3cPuOAJwCq/6xAc3yOd/awoSxiqviuN7LYOyGJFpDH1nOX35dADdaEbPsQdb0u8CterPTwcLYauXb86YDIQ4zDBARohqLH0AayBtlHFUWSA0BBoz/Tfiv7Q3xDfN09bD7ogWnCAf5uuZ60Fe3rqpItfTEYeSMC1Evg1fhXfxRpjs/HUH9COF6xqLAfsG4x5XZkuMl8Iv0nfk7A6UHKw5GHDoZSxwaIvcXFAtZBcb/pvni+/0E0wJ6/44EOP/w/F30w+Py0C28u7Amua/CGdaM/XIcM0HWT6hQaEASJcgCzeu41SXLTMvUzU7cGd6t5/n3Mvrx+8YGAAwcEo8XfSIEKFocWwrIAu//Vfdm880FwwyHA7UBUQWWARLvpOO71cfCdbjKuMLBBdNP9iMO1SYsPhNFsDLeIekXgATg7YnZBdUx1UvahNwC5YbuWPZH+OP85gfXDScOmxbsH8kh2RmYDO4CrvrE9Qr7wQEUB0UQ7QzcAFXzbOlN3f3ISb48xL3KaM+67VECRxbPGDkkBSxlJ7gejxSdAEHp3uAv1/Pfnt9O4iLqnPZE+/D+Iv/pBzsKtwrZFiMbIRbPDtsOkg8BCXoHCwlzASb9MPyc+ET0qece3vzUJ84AzbbRvdON4e74bwjiF3gSTBLgFTsVAgsgDCsClvmE7/7wx/TT7Rroq+ro7drzKfzSAhsJBg6bDiMOOg7JCjYLvgseCRoHVg6uClv+aP1j/xTzTeQG26TUuMoCzH3gsOSc5R3+uAm/E0kWORXZCssA5AAv/3j17/Mc91b1j/5494T2svrG9UPve/AK9hv8Uv38BqATnRWvEBYMMA/7EdkGPAphFLgONAaW/tL1luf/1RPLrsSPw7/P9uAa73kIBxciFtsaSBQaCLn0bOlP5EDmtOnHAJYJCRQ+GI0P0Ap4A9Hy0ulh5rrpoPaGABcIBQ/NCTkASf/WAyUFGQe8EWkUPxMtEKcH3Pjy5bfTAcQ3usm9rNFB4Kz3qxvUKIMuxCQzHEYDGuvS3LvYz9PE45n2mQh8HpEc5xciEcQBQ/AZ5W3mRe7y8PIBDBJmDTUHDgTs/Yj4W/dBChsaCh0CG6YRTQYA8tvXmMhKv9+6u8Mm1cfhmwIpHLYugjYmL98YX/zz4trTGs+Uy1TjivlcEEMhKCqGJAYZdQmL9NPpfusR7cvxOwDaBMD9l/jC+n/0r+m49NIBKwciFE4deBiIEZH/DOkE1hrEV7+vyb/OsedfEXgmLjuzQUg2+haN+KLfV89rvlvBMs7y6LgG2hCKIpQ0TSk+GfINv//U9nHmf+Vo97L7DvSC7l7x0/E57gP4xQHRBXoNZQ29Dk8OrgEd8CHYCshgyXnRuNM98xcQJiY4MSA3jjLVHR4Bieq63FLN7ccoyWLbo+rdAIwZeScrLPgqWh+nEggFS/zF9PPsqeSs38Pimug95m7vyvoz+pL8hAlUDc8IlfzX9UrvPOGK4f7myOlA9hYQrRiKKp4nLSF3FTANj/xG73ncS9LPzunS39+R4fXvvwWdDtwWMyOzJvYhGRYlDiMI0/aR483d1t8o4ezmRvAd9bX+8gOyAZX+Vf3D797g6ttq3G3l1/CuBKIe5ShJMwYxPiEHFuwI1vRB6LfeM9m516LbaOgy6Fzmdue67bT9Ag3iF5YlwyYvIbsbIA/+AUXybd4q1jXXU+AE7yL3VACrBav+9fX57I7mzNtz2GXhau1s9owWGS1gQhFMoT74Ju8Mivcq5SLajtLv1pvYmual6iHrTu6470vpQufl7jX+XgxcHIwvzTEeIycREgPf9BPh9Njf2ijgq+3f+nH/nfya8Lfjm9rRzujXzuaD650MIS5TQRBPrUuCOq8hdAIl6FrWlcd6z0rYN+q09oX0XPLO9cvym+ox4vLii+4s+tQUdydAKJIj/hzdEu8EG/z57YfjIOag7xLvJ/BZ5vPantCnxwjJONWq4hEAPSz8PG1V1Vk3TGkr8BT096rex8qmxQPL79d+7//2PAJ0BRP9UfLb6hLjAOXx5WnwQARiD5IVzxfPGN4QpAxeDgIJDwLQ/kP4J/Cl6U3cUsy/wv6//sUI1/7pQRRiMVdQElU3UN85hCBpBVnu+taIyETN0c764DHoZ/QvAEILAQbM/Rb6g/PI6Frof/Ek7lXvQ/f9BSsSaRrWIpMmCh8vF28LKvmx60DY6Mj5vXy39MI308TekAELGyEwY0Q6R2Y/bzATFE34SOhn23zZ1dbn5Rrlk+Id9IwCpgRCBC4ENwIL/c38MP8J9I7kntkg3xnp+fObDAAjGy2DMOAsKCBkC1jr7dadx5O5s7MWv5jR5foxEtwm+jzhOeArNRneDoj82O9c3urigOIf6FXpH/Bw+iT5efVQ/K0J/BDuEycShhEJAyXx/+j04N/S586B4vr3Tw4MJUUzsjTHLSIVjP0V226+K7NmvGHHT+bQAFMb7TCUM5UtURVsA2TxoeZp1pfb9t/q8Gf5eAFTCzYJkAA9+jT3jPt9BDIJORRpFxkPxwAF9TTpcNhpzxzWhefi9wkPTyFZLhEsjh41AanrsdlgxWjETdyi9RgH8x7rJd0mdBG7BcfxAOAjzKbJKtCB5gD7xgRFEaMZpRSPCn4GSgQTAMj/sA2tE+4T5xO3C8b9LO0i6MHiVdqw19npLfjzARgMzhFoCKT6mvP87Bzh0OTi9jcDVBdyGxMaMgn0+xLryN/rzpjQytPR4j7vMPcACOwXghscF3YYchSYDIYIIg/KC5MIkAghB2wA7faO85XzqPLZ7FXrc+Nf5ZLjXOL446TsHfrOANr9CwgrInAi4iUeIQsTEvbT6efXg84KyC7KvtCw3SDyZvb1/fkLqhKaFr8bGhuoHrMdJyAyHvcQWAdf/wP7z/Zw+SkDSwWD+4L6y/Pz5ibS1b8Muyq6UMLb2Jb0+RVUOU5EMFaLSbAo/wH87AbVV8PasYG6FMZT1czuJvnYBJ4KVgdHCYoWaR0CIK4ijy/TLoIkPBkzCL76wOtc5RTvmf31+mv7SP6++7/mJ85ntcKjt6UutffJjPkDMaBK3GIQZOFc1jUgCk3kecZ2rTipO693xM/dUetRA40MYAqGCtYPAA+GGGcfNCiZMSsw7yg7GJEN7v7l8knrnfAD8bTyBvwYAcLwLdjFwdaqeKDZoVuzH86jA3It4FTFYtdoVk8KMzUO5urbyRqw2KzktI7JT9lg8K77SAmAErYVQBS/FYAYoh5gK6AreyU1G7cUKAcGAUD+3vb28kj2tPUT8bjwFNoBw2O0IKj5pZeu68Cg7nkO5zjVVa5dcVPmRB8l8A1v6lXOTMZ7ukXFZM8i3PDw+v7N/p8JmBN9FzAZwSRmMvsp1B0RFLIQYgUJ/cL6kv+5/sz8Bv/Z+vXrzdj8zc+5ha0+qOusErdW2+v7riKePF5KEUuBP6My8Rc+BPfrOd6n0t7VYNZI5CHs2e0k9AL5c/9VCOQTQhyZJX4nfSTwGscUIAiRAawBqfrM+ZH6SQA6/V76zOnU1qC+gbDApiKsRq/FzyXwCRCMLyg9aEOsPeMyLyC3Dirx1euy5NzrBupk6anzZ/WC8tv4nvu2/OUFdQmKEpAZmRyaFgQQkQ9wBGr6OPm4/oz9IQQYCk4JIvJp3vDLW7oErXSsLK2FxVrktQIcIRMyRTccL3MzahsdCOr00PNR9Zn6zgCHB40FbAQtApj1bu446NzsQPscChUPjxS7D80HngeM/7v6efqM/9b/3gQ4DJwMVfsG7Q/bwcPbs3O0MLMex4fjT/iDFo4kaC5qJjIjxBagCnv6ovpK9nYArAq1Cm0ZDxtyDEX94fS28TTul+s395j9pP6w+0z3W/o09wH1fffT/m4Flwm7Cl0TnQ47+Wfj1tvJz2e+VrvPzu3nmfRuEyYjZyLgFc8NegJS/NHv/O+29tf5dgoGFV4bEx4tGOgL6ALS/AD8t/i7+mMAkP5h/qP1yelr4CblJeqB77X6ywrnEXoUnAsl/S320OWz007JvcsEzm7k5fn3EGEaXiC9FiUPpQM/83jr/ujK71b7uAkZDngWZRzDHrQWfwljB7IFL/vO9yr+o/298N7n3ew95IjbiN774nzpiPT0AuwO1hTcCkT/BvGV5SXdQdvk4v76hwQsF5Ai5Sm2G7wEZPvC6PjWzdaA3uTjvfkVBy0aSyQkHF4PlwhOCd0D8wDWBjEHSf+iAbn+pPzc7gbd1djs3U7eOOks/gUJ6wpCAzoAne8q54zf1+Ot6CH7JgMpFKInCyq3JZIWEgZj67Pb3MuPztrOeOCI+RwPGhh4GaEc+hCcA6oE6QMJ/YwGLg0sDo4I1f7X97TpuN8G2jfdlNyF46vyRgOOAe790Pri88fn0+Ju5Bn8sBD2G8EsajJFLL0U4Q2r+23lEc1fyUDJPdiQ5JH3DQ14Ej0SgBEkDk8FD/zn+MMC6QabCiUN5AqTCJ/4I/AZ7mPqht9w4oDqCfX68zj32viC9Lbrwu047lr6CQj8F0UtEzKcMZIgRBRj+Q/lSdI0yKDCn82/3PHt4vvoBBcMjgmrBB4EFwLH+RX6VwUXEO4RKQ/9DE8H5QHe9rfxYe5B64Xq0fNE9+vv+uj/5oHlXOgR63D1pw1DHNEt6DQkNW4jLRP6+mfnXNVvylbIDNSv4Z3okPcrBX4JUQfpBHT/U/fO8D72HAW6DvAQ3RELFj4OLwS8+tLy7uky6XLw0/nw+3z5uPSd7m7mmd5T3+Pl0ADjEkgtNzbkOrou7BtYA+/qUdmXyd3Cs8cA16PihfSQ/7gH3AmPBKL99vd/9BXyQPvNBV8PkhTxF3EV7g9XBH76CfUy8YX1Evnh+L/yb/Bp66Ho1OA95fnlmPrQD+8iizImNe8tkhqTEDz6Neqx0R/Fdr6Jzovdyemu+tkHqwLa+CL5ywBX/L316gALCgAK5wvaDpsTPg0TBiMAEv0F9/v19/V4/Qb8wfnn71vnw+Gh4cXlAvG1CWwZwSxYLY4vjyLIFJH79OO60CPImscVzgXbreoS99f4Gf+W/8v51/h0+Hf3jwEdDJAM6hL7GcAdnhW/EJ4HX/lj8dHtge3I8JP0d/HZ7fbrROwo5o7kpON79cMFbBpuK+E3FTZ4Jh8WGf2V6JDVCcrNxRbOmtjk5iPybP4yAjr6nfbq97L2mvmCAEcFLg7XF9Ie9yLzHK0UEwlHASLzAe276c7r6urO7Xvul+nt52DmUuvg7/H/qQd0HoEriC+4JTgesgtm9rTjkdRgzK7KTc4f1UrluvGR+Yv90PthAG//HfkX/ssHww2DFg8eoSX6I3Mf/RS+CE/9vuzh4RbibeR04V3jduBi5Unqp+mg4uT2PQb4En4eSS/zLYEe6ReJCqn6IeIE0WzPo9Y02qHkxuwA8Aj0q/Wb9U712vZP+1sFthAxGq4iRSr1JScbnRldDMH+LPJ47nrnJuYB48XhhN/q2CTaYt/S5LHl4Pi9CswgXir+NmwvkSZEE1z+cuXn15nS59By1UveEOr98lX2e/b88o/wke4n8/L9fQ4yGVMk+CpTMKoovx7+D6IE/flA7Bfm3+Ta6aHlOuK83hDh+NoA2MzV6+aN9jkIjR+oMMM5gjExKkMW9gCw6AvZ+s9X0azTqdeZ5RnzYfqN+hn4BvbF9Kn4ZgFJD20epiewMKQ2JCo9GUAKoQFR8Qbo4t7N2Ujb5eLN5bfiM9sz1y7WSdfd58j2SBQ4JGMuETGhNccqKRPjAQb0t+DX0JXTpdXC2b3l++8p9Zb0ovaI9nL5sP+xCW4SCxwPIpAs5y3yJVgZuwof+3TsweTe3/Xeit/r4YLgAt7T213a/9Yn2lfoSPfaDA0aXi6YNes1hCp4HewJufPO3yfVWNFD0FbYjeHm7Nv16PlM/uD8lf9pA5kJ2BJzHb0kqindJlIjKBX+A7/17+zL5K/cQNwh3bPe4dxo2djUE9xD3MHjqvIRAl4MVxhlMB47wz7TK2IVXQBg8SHlIt3508TPftZw2w7jUOne89T55vlNBT0PwRM9GxQh4iScKRMjNxuaE9UMJP9e8IXlvdxR3OLd0t2e2+na38+IyXTKcd7m7cT9sBUdKOE1zzrGPsowPyC0CfnzC+Cu1jLQ+M/S1pves+hk7Bnyd/gbAdUE7wf1EBIaYx6CITMl8yciIugUfQcF9wXs1uO73NHYqNgf14rSFdBu0F7R3s7j3mnuXgrIHCEssDrARmVGpDRTJXcRiPWx337VI8gTydLObNZI4PLmifLy9/7+FgX0CKcMjBFtGTIkgCnRKLwkJR16EvgEqvfC5/3c39UF0lDPZM99ywHH/cEpyEfeBvEaCZsamTMxPzxIIEVTOmUpURUp/jHuiuBL0FvK+ciZ047aYuKx6g/w8PY1/CwDowyRFNsYoSCSI5sl/CJdItEcABB4BF70++Hn03fPhMn1wfm9xcBwwazMjd/98akMDyA/NHY/r0gTRCg5ZylkF0oB2+8G4BLVUdGwzfLRkNSp3XfnVuy18ar5GQJ/BVoKXhQDGwgejiRHKtct+yTOFncLRftp6C7b4dIeymrCmbyyvNa/KszZ3njwzgmeHKExEj18QyZA7jdVKBAZBwg++TbnT9s01ZDOvc/B0HDYn+GT52jrU/E++Dn9ygX6EYMYkx54KEMrvCxkKvkh6REkAzLzauKD1gXMesHFvda/AsEDyAXXxuqDADAUcSrxNmc/9j/IOgAvBCC6DGb9fe1S4QXYbNO419jcJOCX31bksumh6mXrSfFp+B0APgvqG9op2i8yMhovOSdkGMUGuvME5ePbD9JxyjrI88dFyGDO79wH6mH7LAuDGSkl4DCSNd426i5mH0gOKgC+8sPoYuMf3STaStrP2uzbEeGS5NDmbOti8bTzHfvBBy4VhiPSLY81SzW6Li8kzBOKAvvwHeAb1LLKisSGxLvF7s0q233ntPVQBFsTuB7jJ5Yt7iwSKKcfZRNVB+f5e+5W5/HhqN5S3lfgY+Pr5MzjjuKs5NzoqO5Z9yoDEBAsG8UliC21LrAt3ShIHAIPVQDx8Mniftd/zgDIHcaWxyfSPd+y7oX80QtGGM4h4CJEII8eCxsND14EV/zt8Mvq8ubr44Pnde2h7QHqF+tY7HfoTupq7wr15PyBBcIQzRwfJeQnWyj2JbgcURGbBY/2U+e02RXQX81bzEXNytST3gjudPrlBrUTkR4KIIceyh7vGUoNfQLb/Pn0x+3u6J7oVOuG6rjq9e157pbrVezu7W7v1fJf+ugDDA5qGKkfwyNdJTgjEB20E8MHMPoe7YTh2ti109HOFc7z1QTgEuuu9lQEMRDqFOoW5Bf7EQQL9wQW/nv1DvCy79TuefKW9wn5/PqZ+0X6JfiF9Hzuxe028O30i/z8B9gSThrZH9wiByHuGIIPmAK59STs0+Ft2K7Sy8ugzJLXpeOZ8q7/VAskE90YaxuRGg4UNgxmAff2YO606kjskev97zz3Evlb/FgAO/60+mT4Mfbk8OjvvPVU+/8BEg3+FpkaVB0eHmkathE5BK/3He3S4CrWdNKw0A3SSdc54jjw4/3iCfETXByHHnIa/xKFCPn9xfVa7NjocerI6yXxUPbZ+t0A7AN7Aor/3vwF+Zv1PvWW93H7yf/7BRcOGhd1GiwYzBTvDsoDSPeR7LDiCdrE0qLPOtOH2W/j5PGWAdwLkxU8HugdZxcuEz4N0ADf9YDuqeoR6pLsQvM2+WT+vgDdAF4BWQGZ+572QPbV9Ujz7/Q5/QEILQ1WEoMYGxhvEn0K7ADv9IDoSttI0+7QCdOn1x/gLu8X+ukEYg/gF9UbexyVGIYSPQu1A1r5H/CM7DfsBe9F8kf2V/t9/5z/GAB5/+L6dPYG83PzyvZg+p3+0QamDeIR9RTkFnMSagmb//r1fOpg39LZZNRk1LfX1t/86eH2AQRbDXgV4BkeGnoYABR+C20CDviX8pXvoO207z30mvgk/WMBKQLb/oj69PhP9hbzfPKE9Bz4ifynAIYHnA7hERURhA19B3795/P86ofj6d262eLZRd7H5F7tPvgDAoEL5hQBG2ob+xfYE04O2gUw/bf3F/Ma8OjupPGz9br3J/lz+0T9Lvvk9ov08/PO8sbzu/iS/bwB2gc9DQEPLg7iCoUFJf4h9Xzstubp4pPeo96N4hvpJPB1+B0CrQliD9YSQxUiFekS7g1gCLwAOPr39Szz2/A68B/yjvWe9/n4T/oX+bb3Zvbj9Rz1tfTL9qr8PgJXBfIHzwnDCCcGngHs+c719vIH7VroM+ih6KTo2ew98+X36/2ZBE0LHw+bEZMSqxCKDJoHsgHL/Hf2e/Ga8KbxuvDj8Zn21viy96n4lPpX+Vb3Pvda+DP6V/2CANoE2wcsB/UEwwIAAD36L/Ww8BTtpu0N77zuOe9k8Dryq/Wc+54B2gV7C8AObhCZEA8Nxwi8A/z8z/cz9HfxI/AT8GbwBfDd8e3zDPVQ9tb24Pf6+QT9FADYApAESQRbBKIEaAIXALf91/r7+J/3gvbC9UH2Q/V68mTxEfLM88X2JPpcAK8FqQimC9oKnwqmCUMFEgAL+5z3RfPS7R7uf++g7s7uufA/8xL0d/X6+Sz/8gGTA/AGkQpeCH8DYAD//oP8P/kl+aL4xvW69JX2WPfC9qn28vVE9av0GfZ7+P77EQB1BQEKpAsRDJ8J8gUlAtr+N/yC+CD0C/El7rzrSeqp6srtl/GJ9TX5Iv5NA4sGIwhCCcsHZgWCAq/+Jfun+TH5sfhb+e/5i/lo+QX55Pfy9oL2UPUj9dn2tvm8+23+dQGBBJ4FlwRaBkMHKwQzAhP/Jfkd86ju7eum6I/oxerz65nwz/ggAJ4F1wjJCwsOkAs4CN4EHwD2+4D5OPcK9r/1HPYn9/n4NPro+X74rvdh90b3qfbR9qP5Qfxb/Vj+wv+XAC4AhgFeAzUDs//3/CP8S/kt9EzwIu4u7LDq1e5Z9CT5GQBjBuEKEQ0mDUELBQdjAjH+c/oc+OT1LvS788Lz6vPQ9f34uPqY+r/6Tv3x/tj+7/5a/0P9G/tR+uv57vjQ+ab8Q/5R/zj/D/5S/Nj5pvZO8zbwWu+r8P/xVPUU+or+TwI3BQoHiwfYB38HhgS1ARv/5fuj+cP3YPaX9B7yNvIF9Ff10fay+Xz8qf2l/5ABtAFzARcAff1W+9L5+fcc95T3Tvji+Ir5uvnW+ZT6jfoI+4L7BvuM+xP8Jvzv/RMAQAGRAa4BTAHy/wD/Z/7U/fr8OPsj+T74TPff9PzyUPNK9Cr1fPbH+NP66PwAAOUCEAS6A6cBkf9t/f/6R/jG9T70x/T69Zv2BfgH+Wv61/ul/ab/1gDtAAQBvADR//b9MvzA+lz5Sfkz+e/4ofmY+ub6w/vI/L/8yPvn+ob43vZo92v4Mvhh+Rv7+PsH/l4AdQCe/7z+av5t/Xz7R/rW+I72j/VC9nT2Fvfz+Hb6J/w3/8UB2gKnA7UDLAPDASX/8fv/+K71mPNM9AX2cPYM99T5gvtj+0j9gf9I/2b+SP7T/dT77fqJ+pX5Jfkk+U35LvkR+fv4kfmA+hb76/pD++v63fp0++L7uvun+wv8ZP2o/oP/cwAxAV4Bzf/m/uH9gvvp+Jv2hPQx85fy4/OO9QL2rPct+93+KwAPAssDgAPLAqACnAFx/3v85Pk3+Mr2qfWx9dL3A/j99uz3Nfq9+nf6EP3J/j3+tv6w/wz/Hv7q/Yj9F/y2+qr50/h6+MT5//pI+vn5Yvle+HP3Tfc4+BL5y/nR+6X9ZP9gAacBjwBC/0D/Yv/K/f36iPlL+PT1FfWI9rP2CfVA9Dz1KfYA+Az7/f1uAS8EbQV9BtYGOAQYAZf+Evxu+YP3DvZK9MXzlvQJ9pn32PiJ+Mj4wPlB+4/94P5f/yUA+f+8/0H/Av7L/Cj85vtC+9f7dvyn+8j6Qvq/+BH2S/Tm85/0CfaA96z5dPxY/rD/wwHKA1QEkAS5A1UAnPwg+j34hvVr9If0x/LR89f2+/jb+Uj75f0P/8IB6gNNAx8CHwEU/3X8afqT+Jz2+vVr9iH4z/mk+cH4XPjr9wT4RvjR92D4VPnc+lT8d/5gAEj/ef40/9r+Mv6y/0kA+f0D/br84vh29bv0P/TG8wX17fiz++H8yf5QAfICjQJzAcIAzf7W/PH7WftV+p34xvc2+Lb4Rflv+fr5IPpU+SP52/np+cb46/jy+sf8Xv2n/Wj/lv9Y/jT+HP47/dr7Tvue+ln5WfjR99X37/dM+HT5rfrp+8X9dP/s/87/W//K/TL8Rfv++T75D/p7+lb6ivsG/SD95PuD+xT7gfmn+BD5j/m/+aH5Kvpx+zb8FP2o/uz/7v+m/5H/wv7T/BP75/lA+Mb2W/aF9xn5wPq7/J7+DwCwAHIA6v+8/lP8R/lj99b27vZK90v45/q+/OX90f6D/7L/dP5X/HX6//jF9w33lPYv9l72v/bk92H6WP2Y/9IAMwIAA4sBUP6l+4z6avmZ99r3a/kP+iD6+vvw/gAAVf8u/uD8kvvK+Sn4w/fs93j3qffH+RD8qv2D/a79Ov8HACv/qP62/zD+Qfux+fH3k/Vg9Hv0YfWk9qX4TPuw/a7/owCyAJv/0f1b/GH7gvpd+hT7f/zX/er+cv8Y/lX87Pqv+T74nvYQ92b4Tfka+pr6+vsU/N/7NP0J/+P/8v93AHcAJAAQ//78Efvp+NL1RPSV9Cf1bPUU9m73TfmI+6j8bfxR/K37/vqV+xD9V/6s/3EBeQH9AMoAvP+t/SX7G/lm9xj2QfX49Zr3gvhG+c/6KPx6/Yv+P/9H/z//u/9//4z+of2Z/E/7XPon+oD5MvoS++X5Tfle+hn7YPnS+KT5QfhC96b3DPn0+jj8Df6AAPwBkQCQ/1H/bf3o+7D6KfkS+I/3NveZ9zD5N/rn+lT8wf2e/mH/KP+t/fv89fzH+5D6Y/pG+vX4V/eQ97D4rfl3+zr9wP23/NP7m/rJ+fP5KPll+LX5b/vQ/J7+mAAwAY4Ac/+f/r/9+PtI+tz57fl1+Q/5Ovmh+Vv6P/uD+0L8Ef2D/bH9SP00/J76APoE+l/5jPnV+WH5pvlu+sX7MPwg/AH82fts/Dv8AvwU/Jr7Avss+6/7RvwX/QX9vvu++jz6KPpw+YL5wPko+Yv5R/kY+lP8B/6H/j//SwFhAbIAQQHLAMT+jvzd+an30PV/9C30AfVz9nj3Mvg5+q761fnA+cv6dvy//Bf+cP8QAHEAAAC+AHIBNQBS/rr8pfsm+p74G/hS+D33uvXM9W32z/Zt9+T5Y/zP/bP/QwERAocCVwLYAUIAWP4W/d76xfge9+H2rPbS9Sn2nPY39+b3F/mH+g/7+/sg/Yn+vP8OACUB/wG3AS0BcQAp//v8L/sk+vX4C/gr95P28vXp9FH0kvRk9cj1mfYr+d381P8UAtcEZgZSBmsFIwSKAtX/2PwW+hn4kfbU9BT0APRX9Bv1LfbI95z4Rvmy+mv8Mv7Q/+8AcAH7AaAClgJoApYC6gGlAE//R/1++tP3DfUN8mvw2u9Y8LnxG/T89kL51PuD/qUBOQSdBPIEBgVFBDgC7wD/APD+wfxg+7f5+/eW9V/z4PJp8vXxR/Ip9DH2xves+gv9Xf9TAssERgfVB6cG7QRhAhIAP/3U+jb4OvXG8wfz4vGP8YPyYfOM9An2CPi2+iv98/4iAdcDdwXzBqoIAQjcBXQDMQEH//37ivmr9pXz6fGo8Ijv5O5b7wvxZ/O99nH68/2TAU8FOwhOCVkJWAjJBekCXQAh/rr74fjg9q71v/TR8vbwbfA88Gjw7vH49Fj3Lfnm/C4B+gRNCAYLFgzRCtEIjgZgA6n/XfwO+vT2IfNW8dDvGe7V7e/u4vAa85b1efjb/C0BNANiBC4GRAbuBAQEXAPdAbz/WP0w+2f5Dfdv9ZX0gvN68vjxNvKW86L1D/g2+1L/1QI0BaUH8AjSCAkI+gWDAjf/4fuM+N71UvTH863zifNf8pbxSfJo9G/2+fgH/En9jf4gAfUCsQOaA50DgAN6AgYBK/+5/ZH7/fi39x724fOU8kfyk/Ji81n1xPeX+fn7if7xAGIDYwU6BvsFvwSAAq7/qPyD+SH3jfVu9A30W/T49In1APZ+9lf3yvgA+hD7ePw1/sH/rgAhAoQDBgPtAX8BQAD4/Qz8kvrj+Cb34vUT9eX0KfWt9Yz2h/dE+Sn7L/13/7YB+QKYAjMCWgF5/8f9/PsM+lP4JvdB9rH1cfZc97z3EPjB+IL5Xvk7+Sr6Yvt5/Dn+5gAKA54CLAGSADgAwP6F/Jf7kPrc+O33HPcO9yP4dfgC+GT43/nV+YD6V/3T/k3/yf+N/yj/Pv7M/IP71voT+kL5hfnq+bz6gPvD+hL62vra+gr5A/id+AL55PlE/K3+SAAUATYBxgArAIX/HP7//Mj7Cfra+GH4gviL+MX4Uvn5+cH6Cftr+2z8wfx6/JP87fxS/LH7FPwy/DD8ivyX/M78yfx9/Jv8/Pzq/P77eftX+gP5o/gn+DT4mfmf+wz9Pv5L/5v/YP8O/zP+wvw4+3z5Hfit9x74uvj9+dn6dfp7+o77rvx0/HX8z/xq/F78JPyX+477RPsn+9D7Av0+/dP8H/3//Ir8GfxG++r63fpG+sj5Ifqw+m/6O/pU+qT6g/um/OD97f4+/6f+g/1O/CP75/ny+Gr4mvhS+Rj6dvqN+rz6EPsl+0j7kvtT+xr7T/sE/Kn8Sf0l/qT+kP7L/an8Mfyd+zb61vgq+M33bPcS+Cr56PmG+vX6y/tU/Cv8dfxX/VX+nf5g/h7+X/00/KP60Plz+nv6D/o4+pf6Zfqz+fH5Lvqf+cL5Gfp7+jL7VPzS/V7/AwAd/7/+1/70/RL8PPt++vH4SvgJ+Fv3/fa395j4B/lk+fP56/qi+5L8L/7d/pT+dv5//qb9efwR/P/7ovsN+4z6Y/of+hL5wvf/9pf3FfhK+FD5hvp5+9D8JP8TAZ4BcAERAaUAgP9t/ab74Pr0+WH41/dj91r2APZK9sb2qffD+Mr5r/ow/AL+Kf90/x//fv8q//z93/0b/r/92/wC/Ej7P/pm+XD4UvjB+Gv4H/jh+FT6cvt6/OT9cP9RAIwAhwBiAL3/5/4V/ir94/tV+vT49Pcq9zL3sfd+90X3a/ib+rD7X/zd/Z3+j/7z/lf/gP9D/6j+XP50/v/9iPx++6/6Tflo+BL4OPiW+K744/in+d361/vB/NL9nf4Q/6L/EwABABz/8f0U/d37Efp3+O33svd99873Svhu+J/4qfkb+wL8hPxc/Wz+Of+Q/3b/NP/E/gP+XP3N/Ff8qfsa+676Lvqb+fv4i/hy+Kv4+/iE+ZD6BPxR/Uj+1/7V/sr+nf5U/gf+Df25+4b6x/kr+UP4iPdu99D3Xvjs+KD5NPqw+oX7kvyg/WL+rv7O/un+kP4S/lz9lvwl/DT8LvyX+xj7YPpt+ST5ufhv+Gf5X/pa+ln6dPs3/Fn8U/0P/gX+v/1K/Qn9vPzN+5H66vmo+Rv5KPn6+a767vr1+iL7wPs0/A38Dfxx/GH8S/xA/SL+2/19/V79w/zj+3/7Vfu5+nL62Pou++n6cvpx+qz63foU+3T7OPzf/Gj93v3X/Z796/xy/Jv8aPzL+1z7NvsK+xX7XfuY++P7HfwO/AD84fu8+7T7GPxX/En8rvxV/Zn9Xf1Y/Qz9H/xt+/r6SPqU+UL5T/l++eX5oPpO+1f7Pvvq+w79xf34/S7+Av5d/eb8tvxR/Mz7Q/sY+4D7OPx1/ED8bPwe/Cj7qvqG+ob6rvru+ln7zPsF/Ab8JfwR/N37BvxT/C38/fvG+2X7Hvve+ob6Avp4+R35N/nD+SD6jPp7+yv8VPxt/Jf8vvy1/Ln83vzI/Kf8vPwG/db8M/y/+0v7x/pK+ov54viy+Lj4Zfk0+sP6Hft2+8f7uPua+1/7Mvtl+5T7WfsC+8H6g/py+m36APrx+Sb6Nfp2+gn7OfvM+pn6h/qr+hb7Vvu/+1n8zPz1/Bf9zPz7+2/7Z/tn++/6lfob+lD5/fjK+JH4uPg++Sb61vpm+6f71Pv6+8z7yfug+zr7HvuL+6v7d/uT+7D7O/sB+//6qfpo+ln6g/rm+lT7rPsm/J783PwP/UT9W/1G/Tz9D/3V/Mf8evzd+876XPl8+GL4JfjW92X4DfmE+Tf6IfvM+/D7GPwc/Fj8cfxk/HT8rfyy/Jz8lPx1/DL85fua+3L7pvu5+9L7APz6+7T7rvvb+0D8ofyw/LX8y/xh/PT86fsh+7T7O/vW+l/6wfp0+mf6xfk0+uv63/oT++36yfuY/Jz8Bv2F/aD9N/0Z/bP88/tp+3P6GPsA+0/7Ufs5+0L8I/vu+u37ovsW+5n7ifzk/AD9X/0y/sj+u/5e/vT88fsv+oD4Zvgv+Fz4Y/g4+RL6Rfqw+t36NPs7+zj7Uvyr/WD+h/7i/rH+1/1O/Eb76/p5+hn6HPoa+hv6W/pD+o35UflQ+QL5ePlW+hX74/vo/Jv9Ef5s/lj+uP3z/A78SvvE+l765vmG+Rf5K/kr+Rz5Mvns+bz6FvvG/MD+mP+Q/7b/0P6r/Uv9Jf0N/ab8bfwj/Ef7Dvoi+X34/veF9433u/cD+Hj48/ij+dv6nPvn+3b8Bf0L/Sr9cv13/UT96PyO/E/89Ptn+xL73frS+qv6t/rx+jD7Rvt7+9j7I/w7/DL8JPw//Eb8YfyK/K/8efwE/Gz72fpr+vH5t/nn+VT61fpk+9v76vvQ++n7FPwN/Br8WPxk/FT8dPy2/LD8f/yM/K38uvzi/PD8zvyU/Gj8GPzI+7r71fvr+w38GPz4+6/7W/sk+wz79frd+tL6o/pc+gb6ufmb+Zb5qfnU+Un62vph+wv81fyB/dv95P33/Sj+Cf6Y/UH95/xd/Az8Bvz3+7z7X/sO+/v6GPtJ+3777/s8/HT80PwT/R39C/3R/Gj8yvv++mv6+/mb+WH5JfnN+Hz4cPif+Pb4VPm3+Sj6vPpx+zT8CP3E/S7+Wv5N/hv+yf1w/Ur9Pf0W/ev8rPws/Hv70vp9+lT6Wvql+tX6qfqU+nf6Vvpw+uv6ePvw+078i/yE/Dn8uvst+3n6ovnp+IL4QvhK+Hv4xfhP+fn5d/rG+lj78ftp/BP9vf0L/iD+L/49/h7+3/1x/Qz9v/xg/A/82/tn+9z6Wfra+Vv5+Pju+Bj5Tfm9+Tv6ufpN+8z77fuq+0z78fpr+hv6Ivox+j/6jfog+8X7N/yk/OT8Bf0j/Sr9Hf0I/dT8v/yZ/HP8Q/zZ+3z7K/sQ+wr7Dvs7+4D7lPuY+7n7xftt+wj7qfo1+rb5VPk/+WX5n/nZ+Qb6Efod+h36GvoM+vH56/nS+er5dvpK+xL81fyW/Sz+eP6g/vP+Kf/9/rz+iv4n/qj9E/1J/GP7ePpt+X346Pej96P39veN+Dn53vl3+hD7pvsL/FX8pPzc/Ob82vyl/D787/uy+yf7lfpY+jX6Cvr6+TT6iPrP+i/7qPsc/E78b/y5/Cf9nP0E/oP+2v7a/qX+M/6Y/dn8Avwu+1j6hPmx+Pj3fPcq9wT3Kfey94j4kfmv+s773Pys/SP+Vv5X/hn+5P3z/Rj+F/7g/YT9CP2N/DH8+Puu+1f7Dfu8+lX6Mvpi+nr6nvr2+ir7JPs/+3/7x/sP/Er8Y/xU/Bb8uftH+8H6Nvq++Y35nvnT+UP63fpq+837Bfw1/Ez8PfxL/Ff8ZfyR/MP89Pw8/X79wv32/Qf+GP4V/s39U/3V/Ef8fvuk+g36X/mg+CT4+/f09xn4i/jv+DH5dvnF+Sn6iPrt+kL7ifvf+0T82Pxu/ff9bf66/sX+pv6o/r3+o/5Q/tX9C/0D/Pb6C/oX+UX42Pf696n4xvkJ+zP8If3Z/Tr+Rf46/jj+DP6M/dX8t/s/+tD4yfdC9+32AveE9yb44fjz+SD7Lvwi/QH+uf5O/9H/NwBhAC4A3/+L/yT/jf7g/Rr9Lfwr+0n6lfkP+ZT4Hfjf97L3cPdC91T3jvfV91v4HfkT+hr7I/xA/Sn+n/6a/mX+Ev58/fD8kPwN/GX77/qW+if66/kV+nb67vqS+3r8f/1l/h//zf9AAFoAXQAiAGP/Rv4J/Y/7+/m/+OL3DvdZ9hX26fWv9aP12fVj9h/3C/gy+V/6ffuV/I39Vv76/nL/pv+u/5//hv9N/+z+nP6S/o/+dv5j/jf+sf3M/LH7kPqQ+ef4kvh0+Jr4Hvny+dL6kvtG/Mn85vzx/Ab9r/wA/Er7Yvpg+Y743/dX90X3qPdo+HP5pPqp+2v8Lf3D/S3+a/5X/iT+8v2+/ej9Tf6Q/uj+If8F/8D+jP7q/bT8mfvc+h/6RPmy+P338/ZP9jf2SvbI9u73MPks+gP75Pt5/Lv8Y/0q/l3+I/4O/hD+7P31/Xn+Cf9u/9P/GgD7/7b/Uf9k/gP9ZvuL+ar3YvYJ9ob2Y/d9+KX5kfoy+6L7Cvxo/Kb8xvyR/Nn7v/pv+SX4EPdB9u71OfYC91f40/nk+n37yPu8+6X78/uo/GP9/P2t/oP/GABnAMYA1gBZAI3/0v7+/R/9VvyN+8P6+flG+XX4kvfX9kf29fUI9mv2KfdH+KH5HvuW/Lf9af6j/mH+BP6K/bP81/tz+zn7pPo5+h/6s/lT+XH53PmF+lv7CvzT/JP92/29/cH94P2o/UX98Px+/Pj7gftJ+x773vrJ+sL6ffrr+VT5q/gQ+LD3qvfo95v4p/n++mv8qf2s/o3/VQDQABYBOgEMAV8AZP9S/kH9F/wV+1X6w/lH+ef4mfh4+D/49fcr+Nb4LPlu+T/6l/pG+kn6p/q++h77yPsW/CP8+vs/+0D6e/kF+eb4T/m6+cX54flG+tz6cfvZ+zH8uvw9/eD9gP7l/kX/tf8iAEAAIwAEALj/Qv+w/tH9Nfwe+qH4sPdw9gP1FfQh82PykfJY89f0pvcs+nH7Svw8/aH98f3j/ir/ff7a/W39Wf2B/ab9pf1k/dL8JfzD+1371vpQ+m35JPj09j/2F/a89v73fPk0+9z8ZP6i/zEAYwDDANgAWgCb/8z+pv05/N76PPmr98L2DPZi9WX17/Vt9tT2ZPdK+Dn5hPpt/H3+wwBUA2AFxgacB5MHBAZUA40AQ/1Z+cL1KPP58LLu+u0A7yPwIfGH8i30gvUu90b5D/u7/Gb+u/+wALMBfwJdAoACTgNEA5ICoQLQAZL/cf0o/DD6Dfc/9ZrzovEK8e/xXvN69Q75Ff0+AKECPAVcB+MHUwjiCEEIrAb5BJUBCfzI9qTy1u4x617pwOgS6Xnr8u7D8Uv0rvf2+ov9pv81AVsCNQMSBNYE4wQYBHEDqAIoAcL/x/4V/or98/wu/Dn7Vfpw+Un4DvbZ8kHweO9H7yjvIvHZ9Ob4gP24AVEFEggZCYAJlQmKCIEFUgHs/V37kPiz9aTz/fHw8KTxO/N/9NH1Svd3+D36kfy+/Qr+n/4//6r/UgD/AGgB2wEhAuYBNgIIA4IBcP72+y36nvdH9OfxQe+Z7Ibr9+t27XjwV/Qf+Dj7Vv6uAaMD6wTLBtAH5wdQCOYI3AjUB/8FEwT+AUz/1vxf+/D5o/fo9Bbyue+h7pjuP+/C8FHzvvac+pD9SP/UAH0CrQMbBOUDTwM7AtoA9P6G/PH5NPgk9771nPR9827yLfK98j7zxPOo9A32HfiS+8v/JQPcBQsIsAmPCjkLhQuhCVYG/gK4/0T9bfuv+fD3Q/Zj9O3ykfLA8vzyrfO69On1t/cF+hr8sv34/g0ANgHOAh8E4ARxBVYFIANBANj+Bv6G+6X5BPlN9wn2OPbp9cb0jfOH82L0M/Wq9Sb3tPlo+6L9qwBqAgMDrQPBA4sCiwCt/uH8Dvu++aP5SPq8+t364/pZ+s75CvpK+iX6E/vb/Lv9q/5w/zT/fP9mAEwAEgAPAeYBPQIcA2oDMALiAJj/P/12+r/3W/Rb8G7tN+zX64Ls3O809Kj38PpN/lUA6AHgA7IFdwYnBkEFzQMoAaf9X/t7+Qr39fWN9uz2WPfJ+D36Wft6/On8cvw6/Gv8bvxh/dj+2/8oAUcCdQO0BQwIiwefA33/1/y0+o/3IvVP803wd+5q7jzwr/M/96v5QPv9/eX//ABZA00ELgNKAtoB5wH8AUoBNwAf/1X9Yfoo+F/2pPQD9e/1gPW/9b72qfg1+0D9cP4//rb89PoL+Uj3iPcS+Z76OfxG/n0B/QOaBsQIrAgiCKEHXwXcAYD9pPiY9BjyUvK+9ET2IPfX+B/7jf3JAIAEvgUeBbkD9gEoAH/8AvjK9N7x3u+F8bv1/feL+O/6Ff1Y/WT9nv25/NX6Wvim9tv1NvQs8nzyQvTZ9uP5FP3OAOcElggrCQsIAAiyBzQFhQJ4ALr8T/kc+EX35/Yt9nL2L/qP/Tn+G/7h/34BhAJQA7ABWv9E/if7FPWG8kfzOPI78l31n/bA9aP3uP2rA1YEgAKaA54Crv7J/Mv7T/mR9033YvZS9ij4FPlN+Vr63vsu/cn+nAHCBF4GlQW+BKMDCAGm/MX1Ne4O6ifpdOnQ7YT1U/yCAcAIPQ8ZESwRNQ9YC/0GeQCz+Xn29vNB8vTzHPYt+J38gQAMAq4CxQLlAPP+n/0++Zv0iPEy7KDpcepD7LPwsvUG/BcDSgq1EUsTjxKOE1QS+QsVBVv+nfMi6Xfk5uBX3RjdFOFX6JTvTfRW+pkC6QijDl0SZBJcEpUSbBGXENAOswvaCA8G5gJl/Xv52vUu8ofxS/Dp7hLuI+0r623omedi6abrHu+N9bb8hQKSBgAKPgzYDTwQmRAzEZkR/Q5XCj4EZP9n+SDyGu0T6YrjUt9d3NzbAd143zDnWvFE+ykFfQzIEBcWnRh1GA4XCxN1EB0QJQxdCOUBbvoH9dPvIO107FLstuwB7sXwavFd8QHzvvRT9fb25vjs/CMCjwVqBY0FFwniDTEQvQ/cEGsNKAfCA/IAr/v29nXxQurJ5GziKOCu2nzZJ91g4bTngPDT+EQC7whBEF8YeRpJGnMVrBL5DgYHIQPQ/7f8/vsK+rX4sPdj9dXzDvFo8WXwde6W79jw3PIu9Xz10vUk94n6WP0KARMIFQwLDz4ToBXTFsUWQhKJCu0E4vws87nsqOfQ4tndndth2v/cp9984Crkfuq376n4qwFFCZQTqxjGHF0gzB4AGwAVdA8MCcMBEP4w+Yn13/I67+HtwOo45i7kht9x4b3i9OLh5gfnoPBL++kBMA23E98Xsxw8G7Ic5BlGFyoaCBhbFi8OoQRG/qHwmuaA3ArSiM5iyWTILs3P0EXb5eXg8IP+QgRFD5UY5h1/JQImSSOiIR4cCBnvEc4K2gG79aHvIurW47LivN/g3s3f09/u4QDhNuBE4vDlT+us8Ib4iAFoCpQULh67J1MwtjaPOIs2IzARJgsYtwdo9Z/kBNZzxxy8drLpsTW6p8HuzL7bfuvZ+DcELA/bFiYaOR39Hqkc5RqkF98XvxoGHG8fYyAaHfAVjQqs+h3p4NS7xaa0jaKhm46bjaRAtoPRV/DLCowkVj5RUGRdJWCEWktVgEq8NtUfFwoG9Qzdjcrfwd+8VrtZwezIw9Cq1qPW7dkO4TDk/uen73j3pwTNEcEa1CI0K8kxNzVUOsY8bjRdKWQdLQsJ+unolde7yaa+0bbNsLuuBbGvtg+/NM734P7yDwSsDmAdESzMNmo+kEApQmM/+zxCOukuXx9jD7T9Suvk1n7GNr3isZisO61Hrhq5OcjC2LbqTfxyDjQdlCeYL8wzGjUsNHUvvSn2IC0Z0RNIC0MEngL+/bz2JvEo5k3UrcM/uvGxaqr7qhasaLHFvWfOIeN9/44huUF1V45qKHfsdshx/WPDS4oqNgYJ6FHMpbFeoqibopqpnaulXbX/yNbd1O6S+vsCjAyEFKcZ/B2sIM0kdyiHK5IsIi4GLysq5yIoG68TkQek+d/sL99Wzmq92bCHpCWaBZq1o+Ox78dZ4N/4fxMQK9hCzVhrX1hfglwNVU9LTDeSHaoDcOs12ZHHlbg6sACrgq90uGfCttH93MflmvAB9yv+Mwa9CgwRzBhwIfwpVi7bMtc2dTlJOsk3YTD+H/kJEfgJ6FHRpcL/umauS6TsopKkZKrMsYC+mdOX6qkINRyTM+BHKE8vWeRbuFbUT2Q+DyoUFgoAi/BX4F/OrsMAuVi4Ybr0uFbAgcdjzgjXkOBs7jf7RgcEFnwgeCaULgo1MjtCPXE7zzkVNIMqgyDxFIQFPvOn4MnSzsPIsrCnU6KJnyaicKt3ufDOCuNa+/oREiZHO9tNl18ZZjtem1RGSlI4piPxDPj0aODEy+e7LbKOqz6tXrKAusHG1NGC3UboH/NU/o0GJw4oFrseTykhNsU9Gj8JOz45JTimL2whFRZqCPj0uuUm14fHlrejq8Sp/KqvrWKyab3N0PDif/rIE8Ek8DrCTQNXolriVg9NnEMnNp4ksA7I8undm85AxXq8w7YCt1i1abdCvQLLVdtt5kPuovriBrgO/xYGHvcmVi2EM7Q6yT7pPss7XDd9LlEiExIXArzu8tiyxyi5qq98qe6ma6uYtSXBtM2X4cj2HRD3IXku7zyIRYZPe1NKTwRHBjmAKQQZmQQi8M3fINGgyNfA+Liitvy1srpfw1HKGNN/3U/qNvqtB2gWnCibOiBEykJXPxM/HTvxM6EwzCgHFyAGvfsY74Dde83cwe66qLqLuDe2BLdnueLE3Nfg6V/9EBbFJuc0CEGQSZFPME9ZTKxHOztnKAYX2ANx85fmYtZnxDm1FbGWq0qtA7uawvrM7djJ4ifynAAWCV8TRh/jJ6YthzJMOTo+sUDBP543GS6WI/0SNgOo83PhfNOOyu/AtLkItGqxXbTEvcLIdtFk23vrKwNOFpwrsUQQUjtWd1YRU5hLqj+qMpIhPAu298zlu9YFzMa/NLc1r/araLCKuMC/bcdW1hboZ/gkAyIKFBXiIqUq0Da/RJNFC0djTO5J4j6rM74m5xIJ+Yrhi9bFzMS78Kt2oJqfy6dCsdW428Mc2FzsHAG7EaEgzi8QPH9KxFeOXT9baVIBRCQyrh7LDab/vu723tnNRMPcuwiwkqhWpSSmka7mt2HCPNNA4TX0ZQy1ISU0NUKITT9XIFqhVgxR7kYiO8UrKBmwBuDytuL20hbBWbC2pBif85+Xo9ynhLAPv8TOk+M++0wQkCrVPnVLj1jLW8xZA1zYWERP9EGCL/0c5gbi8SLflsyTv+O0uam6nEeVOJmUnjul8LSPxs3Ww+j4A3Qiljh5Sr9bBWpdbqpktlRyUFVMPDn4HNT9e+XS1EDIcblnqcGcSJlQoMOonq8StEa/Sc8X5mD+lw8EJHY1q0OtTsJVFlsyXIdZSFStSzE6JiZ3D0b7zurN1wbFeLA4ouuaGpYpk4OUUJofp5S7+M4S5R74yw8ILtNFYViCZ1tykneGdt9sEFyBQickUQhw8PHa+sTStC2md5kDlOuT0JeGn/SodrZcyXLa1ezk/SETvCryO01Milu+ZfVonmZjY4pcc0irL8obWQdh85fdI8vQtBid8JQnlDGUmZSLl2Skdbevx3jToepGCvwj/D96VMpbCWa5cNxy9W1DYTBNkTfFIhALe+7jzHWrYpypmzGZzpfql9yZ6p9zq7/AsdZ75xz53AmZH602B0T9TZlXHF24YN9cyFRyTbA+OitkGFcD6u/h3DvKmLeLpvuZIJPPkymXkZt7piS0c8ad3033ChFtJwk30UiWWmtnymwubMZnTVz1SeA1wBsh/hHjQcwqum+tUKQlm+OXlJiim4GlULCCvobTA+cQ+7UMuhxaM/tDQEdTS21So1nBVCZMZUkuPL8pvRwXCrHzh+MU1orDY6qCmbSVBJh6oFWrvLMGvRnNmuOM+1wOGhzLIvAvXUtTWxJdX1xWU5xGUz0BNJwpbhjr+jjfXc51vmev96kyqPimCajiqgi0Vb99yazUa+NY+CIFhhGhJrs3PkPrSlJOmVJcUqBKM0EONA8i3hH5Ag71guWZ1YXHSLxpt/u02K7Mptqkxq5HvofLj93n7XT42QoEHZEr0jt7SfhRyVhvXOtWkEzKPZksGxh+/vflmdAfvmSwJ6tyqlerd67Urk+vD7Zawh/OhNsN8h0CCwvkH8UzTjqCQs5K81AfVTVXKFUiRIYrMhYOBGH1I+j02VzK/bd4q9+nZaihpzunJKxztijEJtSo5b71iwf9FwYoTj0XTw5aY2GLYsRdVlWTSv47UiUjCt/wfNmXxl+2qKnBopWg1Z9SonCpa64UsXG5jsum3eDxAwQeFVUraULcVXherF3hXKRdvl2MVTxB+SZ1Dhr4q+fT17fFtrLZoJKaRqBjpuqnzKoWtMm+UMlw2/Xs6f3oFNApyj/dUjReWGZjbGluh2rmXnRGuyU6B9fxJNpuw6+2V6eymDmWTpf3k9OZhqWLsGy5N8j6287qZgGbF2crAkHjUJxdNGhgb7VwwmfzVehA6CpOEvT+9eyn1WHAY7TUqa2dj5jSmCmbsZ/xqGe1k8Mm14vqEgE1GPkqE0JkWbdn1G3xcHZtOmUFV99CiC5kFXr5DOHUzvm5OKXNliCQfpJbmD+gAaa+qcCzQMSj2ivta/mhC+EfyTI7SWBdD2gJa65p72kHZdRT6Ts/IZ0HMO3r07zDnrWWpqiZ6pJhk6iXup16qNK2DcHzz0PoRgR3GioqB0OOWYNiGmnabihtqmJiUKs9JCzAF08Ftvc+5VPKbLTrpCuYupXEmuifo6EupaOu275hz8ra4ugT+1IPPB/GNCxRXGW9bShu6mrsZkxeVk6gN6cbP/3r4qvOMb17qYyZc5J0j1OR1pjUpMqwoL140TfnzP3lD5gcAC+AQ3hUi2L1aUFosGJrWtxOkj4NKVYVbgI373TcqsjGsdmdAZPMkRKUdJhwnvOjuK3wvxjRuuN7/p8U7icuOPVGeFemYyVq5mnBY8FbcFB9PC4kaAqb8r/h7sjyrWSe9piRk+2N3ZPvnKuiwbJ8xGjOiee4B4AigzdzSDFZ2GVmchZ623cWbIpaXUPfLbUXKPq43b7JwLnlo5GXLpatkwaUxphhns2jQ7PWyFnadOvJAA4VCiSKN8BIKVSOXlBnnmqBZq1dIlKbQmcsDBDW8kHXdL92qHeWVYveh3yKN40ck7OfRbdHz3DoWgJPFx8xxkorXmxty3dgd8FrqFpoSjo6AyRxEdH+sehV1ZbHwbvXsr+ukKzlq5+rWq9vs5+6DcUyz7PYJeOF8oIE2BpULydDlFPZWNhYYVsOXRBVJkSdNpcmjAtH7aPPG7PUo9GdjprulxaX+JpMoWC16M926xIGShXoJqs6MUesUDZVVVIcSmJElj+nMFEf5hQeBRv1Texv44XYSs6bylTKkcmUx9/ECsMAw0/HiM0P0inZj+Rt8bn/dhNlJ0U3N0MdRZtHWkZGPhM4jC4wHwEORQEn8w7g689jxW68RrTPsA+wZ7RgwFnK9tvP8MP/pg87Gysk3y3xMvQxpzDgMHwyejGGK+okkRnMCyICIf0R9wTsseFg2pHSDMzGzAfP2tI30vbM1M2W1Z3ei+P879r9GQRZDWoYQR5VIHIq5jbQNyYxIypmJKkgch3yFmII+vJk3TzNQcDMutS7yLVCsnK3wMLD1wHyegbWEegdOyn1L6MysTGSLp8qSCvBKjElJxwsEXYFtPu59WbyUO9n57bfLdyN2XDXHtiE163UxtX/1YrYkd7c4zHpH/KV/d0GARFSGs8mQTXxPYNB+z7RN80xmCaiFPT9cuVg0Ka8n7JPsDyw7LUKvtrMV97j768DERSCHdYjICYiJ7wl/x27GIoVGBTRFP8VfxF7Cj8KXw71DWEIswIP+dLufuff3KnRy8p2xGTHVtQ73HHbXN0l5NXpv/gkCW8WByLcJYUq/zIgOLw1aS5cKModmwlF+Jvrh90D0WDFwcMYxALIItRt3SbnwfNX/wAJ8hBLEm0ReA6fCL8BrP2X/78GQhGhGMIeYyNbJbwjUR8CGGoKwfqd7lXk3dtF1gPQycpNxwjQd92a4Q3mk+ty7nH4nASUDfMT/xPhGFwfvSHXIbIcBxccEl0Ivf9k/Mvyc+4m6P/gIuWe5M3lIOoe7anvvfBA8QLyR+/o7RD35fnI9AT2HAPBDksT9R1HKdMmgCEhIJAc/B1YGiEJ9/ON50zgH9xu46TrL+mt4kbfUeDJ4iDmV+rn6xntnfPJ9vD1GQGnCmELHA8KEDQOeBP7GcIYihVFFJwR+g1/C08GO/1N8HDkRN722QLW8tFQzyLQW9LS14rgoemW9LEDgxJDIJor6DFnNFwxtSvKJEEclhUOEKIIpgFD+pv1PvI/7X3lkdps0ZXM5cWqwIbGh8y/0y3fEuyv+IgDGA8BGHMh6S3ZL7gyzTKhLCMu/ykfHOYQGAQF9anl49gX02rKP8Dnu3S7kb1uwOrFWs6r2XPnn/1VF0MnNy55MlE7wD/JPiVCT0HhN6kqthttDysDTvNn5BHc8dL1w8q2ia0JqbepXqzjuPfIXNlX77QFtRXYI+sz+T9OTB1Pmk3/TQdEXDJ4H8sNq/008CLiPNX6xmO667MGsG+upKwkr+C5KMdf1EDi0fSSA5cRAyikPg1M2VAKVY9X2lKNTNZDbTYgIgIIwO+p2G/BZ6+9pMKerJePlt6iiLDOvZ3LY99A+PQJyiFwObpH5lILVX1ZmlpYT3RCgjekJb4Nd/ji5xnb6cSYsemqJKFXlhSbWKDjrlm+g8+K3kLt6/6hEvklED54VH9cgV97XSFVgk2nRCQ4vyV+Dij1F9nFw9yt7Z58lxCQI4+Zo92qeb3nxiTfovJODXcqXjhmTulR7V9fXcJas1D4Qro4jh/eCQEAl+Vi0mi9nqrhp1meW6Oto6WoNa74t53S2tw37Vn98Ru4MZQ/ik/zWQ9ilFyRV5ZGAjzHIB0KZPpL49TL6cMwsfioH6Zbm7+c06dqtLe9tM6r5gX/vx4YMwlKg1REXs1v52AhVspDqzJPKsMVrwAA8UvIFLiXpzKdV6DQmpOqV7UCxOvGqNPN6Br2qgUXJPM0Kju3SkpQ51BWT6tDg0YKNZgmzQ5++VTfd8DAs0ebIpZ6lUqcKKX+sVnDTssq4kT4JRcYLVI+dVRfUH1XD10cVmdJCT8yMc8aOQPm9K3gcsNusxWoCaS4npOje6disFS7Y8yw4t35NAz0HQEzo0K6S/VZcGelYqhaRUHrMzMlTgMx69fX9MTIsI2mSZtNlGmOQZBNpKS1UMZN3rP0Pgf9JeY7M1SeYexkVGqtaXdiJVeBPzohWgp78ubY+MHTrM+Y/Y2jjhyUM6QWsbq0tb7r0k7qhABKIJ09nlIrZGBt7HAscfVnX1YeRREt7hUW/XbghcVWrmuYZoV4gtiAAYA3iwSgX7MbzGrpygSvJU4+A1VVaYJwUHatdOVrXlw/SiI1Ux6gAS3q6c6zsjWe6o1GhdaGE4nwkvqeUKwBxnTeXv0LGYY03ErXXrptZ3pef/9/anPlWQRD9Cb0CpznvMsptYmeBY5HhpODgYC+i5SYEal3vY7SG+3vCsQnLEHNVoVnum+gd6lvRWzJYnFLXywfCgDvvNVRu1KlWplHjTeHX4k9j6GaGqkRvHHYZ/R8DVMoTD0mUBBhnGwwdE5wLmgaWg1I8TF0FQ78nd7nw5Kr95rGkS6NSJBFlWmeNq+EwNjVufHQEa0nYj4xUChZRWTFY+9aAFWfSns2vB+fCqT0wdsTx6K1qaotoKKVU5PHlgygaac/wKnfnfvXFtMpcz8gUCZXSGCxavlh11E8Qt0tTB47B9fxZNxGw1auQJ/EmlyboqTsrpG6vsV21pztcgOIHtgyLz6TSOVLEVBVVslVIFHORLYtLg6x7pPW5MUqsRWneJ86mAuYy51Kqce6zdLC6RwCbhrXLcQ4RUeaUvpWXlasUdlIEzueKZ8VHgJ37azZjsOtsduiHJt0m++kArXAv4/MEd938EEE0RwINXtA3E53WLVW81qwVQ5KwTmyJOgNTvZI4jvLhbZJpw2dMJZYl4qb/KcRtznFEd64+LETuyuBP8lOqVgQWkhVH03RRCU6uC+OHzcKI+4q0VzA6bTzqwqlN55hnvejt69Ww8XT4+jsAB4UbCe8OnxJ3VTtWY9deV8NVihHWjUlIloMufM63e7Gq64UnUeU5Y+IknuZA6eSug/UmO/mC2UiHzeGR6hTy1hTWodU4kltQKovQSKcEbP87ubzz+3Atrg8q/eklqF5n8qpg7rFzJThmO9f/IMNPiAhNR5BWE57U5NSb1RpTRpEbjMcHu4KOPPE167ArbChpCKedp4wpDGtw7ohy+3e1fQkCv0drjLlQXNJilFNTPtFREAxNB0lRhW2BpH4JOUUz7bD37lHsqmxErc1vTvCqcxt2Y3nYPS//7oOfiIBKxMw0DZDN105sT1LORMzuyfWGeMKNfd73qzEMLXprdOpQao2ssi5AsZT3I32TA8RIhktOTeYPiE+4DzDOHYvlyPLFT4I5vzt89rptOC/2YjTAMtKxXHJhM+b0rPZROnF8mb5CQBUBOkN/REUFMAa8iFWKEMtJi44KSEhThUmBDb1wudb1x3Irryjuvu9D8D4yOzVHuUb9yMIhRr3K8I1bTkhOhU4FzLBI+oVhgiI+4zx6ulF60Xnkt6G0q3Lesv20mjgd+a38Fz4KPodAbIEXAXkCSQQaRWbFToXsBZsE+YUAxa2FH4OVwKV99XtUOOV3OXU4MnpwsfGOs+02JHoo/dWA1gShCRRMKIx+y71KTckrxkqEVkIkvlS72LjR97h3tncP9lm2+vgxOf47g32b/7mAnsFtweDC/AOkwwiCAQG9wXTBpkGBwlnDaoOKgyQCbAEd/sl8pTk8trb04fPMc4U09/b3uMl7zb+Vwt6FsEiUiufKsYjRxynEyEIkvtS9tXyHeqV5PvjleJ43svfrOeJ7i/1tfr3BG4OXhASEywSHg0HCrMAdPyE/Qf83f0J/Bz6Yvli+WL+mgBRAMb+gfd976HrQekx6AjqTOvI6wvvbffk/8kGJw4pEDYVCB97IPkYERBYB0n3xOcT5N/iYOGt4PreOdxJ32TkxOvw+YEE7g3vGf0hHCHWGGIOowkJBcn+PPti9mPyD/Ek8GH0D/UZ9tn3kPn2++T6KPmw9sTw8ejQ6K/p0u9q+WIFZQ7KD0QRHxSIGCkY/BRtEBEI8/wt8FHjttiO05jReNP82BndAuXP8o7+lgvbEbcWLRyoGtkYwBfVFA0OBAfe/PTyxe3X6aDqruon6kvtb+43773yIPhf/fX/7v05/f35TPUq9AL2zP16Bb0MBhOSFaEXGhcHEYwKp/8a8e/sYe3L5qTf1d0I3pfbnd3P4zHo3vAv+s8GyA/4FRIdmSAxIYYelhi0EaQHB/zo7c3iyN0L2tbZGtzu4mvqpO8s9QL7jv17ANsCzgSFCPYI5gc9BPUClAhVDfkRBxSZEjUNNAWN/kX4jfEo6aPiNtvQ1uTW9tT/2F3emuHD63/5Pgm4Gf8kaCzLL64v4ScLG/4S4AzWAE3zNem/3MPTXM64zBHQLNOa3P3k7eoL8pr2xv5kBToLvBHhFRAXUBhLGqMdFx2fGPQU0Q9KDNUFWvvX8gnmVdbIzDHLfczJ0BnVj9d13x3imewzADcL/BgJI+8oYy8yMTcwQCuQKKkluBeq/+Trkt0g04DLlMS0xWzCGsPHzybbkef38EH2pP9zC7kP9BN1GisfuyL/JQ8n3iP7HyYbLBRID6IELPV46XPeh9JjyuPGGMX+yOHM38/G1lTesu3q/e4OBR8TK6M3WEAMQoM8fTNYJhsVkAN7973qQ9oI1JzOhsfJx/DIT8/41fDdDenS8vT5EQBEBgMK3Q6TE4EZ3x6mIYYjPiJmIqwf/haaCjT+GPX/6QPeCdPnx5K/5cGhzDrS0NYA4CXri/kDCpcYTiR2Kw8yYThPNjwvHSbuG/MOy/888x7mU9km0/HMP8vqy1bODtgM4Kjnn/MX/l0EIgkvDXkRghOqFBIYZBx0Hysa/BGdDeMKSwgcAcj48u/840rZndO10MrOGNEh1srZnd6D5MbwFwHwEdIiNi1gMzQ4wDi6MsQouh1hDtr8su6Y4arU/c12yh3Iqsydz3TVvtyJ6LP3ggL/Ct8RcxX8FosYYxrOGukYWxcmFnYVERTaEdEPPgrY+RPpct751TLLcMViwgbB+sjp1dTije3n+pUIsBOCJPEw3zS+N8s3WTPTLMkj9RUTBs71Hubl16/KOMThvqy/7scT05/fSuaS7xT7EQT3CzwUnxhNHLogZyBTIPgedh3FG+IabRfPDKECbPfB7eDlaNzH0wfNXMdSxHbG8Mn8zxLaWebO9RcHDBoTLMU3Uz1qPkU8FDYQLQQiRRKdAXfw3+E30cfEjL8NvfTCPctx1jngKOk/9Kj9jwZkDZoSyxVkGgccvxyLHDkc9RwSHIIashTmDb0GzvwU9P7qbN7T0p7LcMU2wcO/hMKSy5nZie4xBLMTNyNOMdo4zz7nPpc3py6mJFIYfgi0+FvphtlhzrjJXcMixFjJsM+J25Hk+u2h+IX/QgQXCLoL/Q8RE9cVjRkWHS8gTyENI0ci9Rn+DzkFcfoH8VnmktzS0VzIxsJPwknDXMjM0jfeku3P/jcOAB7ELH83Cj6wPt45Ty+0I94WmQYw9m3p6Nt/01HKzsR/yjDNm9T53bTm0e6f9ZH6yv1UAWUF2wgQDO8RrRRPFnkZpBv5HSAg2R2TGQsTeg2iASju89ww0XvHg8CSv4rCYcNjyhDdzPHgAfYOOB0QJpAvAzbENRkwBye/G+4PZQQO+nruWuQj4cbdWty812rV19zX45DozfHZ9yL2U/Zl+HH44/n+/ZsAlwQZCjsP5BesHyQjXiSPInoZzw5vBHL0M+R82F7QZcyzyZPKys3b1Hfe++t/+zkILBRDH8Un1yq6KFYjchzTEugKzwKv+h/zhezN6GXl1+OT4Drj+edO61rxRfbk99r25vRj86jzBPTA9TD61P8MBQ0KNxHBF4Mc7x7JHSoZ4hAyCO77MOtI3aTWadNrz+bP3tMo1xbfT+9x/j0KChFeGKce7x9RIFsetBZwDTgFfP2X9ZLvj+5B7AnukPAj8nP0APZS+Jr4J/e79Gjz2e9o7K/qA+o37rryjPm1AtoLAhMNGAQdHx7LGvIUuw37A+f6h/FT6OPgutpz13XW4NlF4PDpuPMe/CgFxgsaEbsTtBLnEW4OcglRA5L+hPzc99/2HPco+KH7Pvok+1/8gfym+8n57feJ9InxPfAZ7vXoVucS6E3rdfIx+7wEigtEEaQWkRo4HC0aBhUHD2gG2vZA6pjjnN+Z2u3cZuNk5Y7pV/Hx+rABHQZ0B00IUgiIBC0DpwLk/YD7XPlN9/75Yfu9AGoDAAbHCQII4wjqBdb/QPt+9FXtqucq45XgTN+v4X7oku98+KAD2Q3NFE8ZTxtLGpwW3Q//B/7/JvjN8PLqDOca5QrlVedj7CvwNvQf+Kn7xP+BAuECkwJo/7b7mfjO9cD0VPP69/P85QPcCxEQWBVAFt4SPA0CBnT91fP+60bnY+IN3pfdqN7+4Ajk9eqI9KH8ygWODjIWRxstH/8fdhsoE1MJTgGB+dHy0exF6TzoQunm6+HuJfHt8Q7yf/Rc+FT5yPan9KP1EfPW9Gn4pPu3/8AEDQ0nElIYKBs+GooY8RLIC+YBSPW66eDgutjr0lvQWc+g0ZTXEOHW6wz4cwTUD2QamCKnJg4lLCJkHCgXzxAzCOYAQPfh7yXsB+u9687pkuiE5yHka+OM5O7kIeYX6PrqOe+M84v5CAF0CN0R/RovIycpTCyHKkIkdx3IE5IGg/fR6Fnanc6Gx0bD/8IMxt7LutVH4djsj/jdA7sOuRg4ISkoMS0BL2stBCrlIj0WSQnjAWn65O9M6uzlRN2J1+nWg9Yv1ijVXdUf1jrYQN7i5fHwH/3XCkgZ7SNEL183sjtYPN83RjG/I7IRzwCh8Rbi/tLzyKrC8LzPu8S+ssOdy8TUeeBa7sf8Kgo1FwYkHyvqMPYzNTH2LdQoaiA9F9YP9wfz/YfzZeo54enYiNHkysHH4cbXx7TMM9LK2pvnsvRaAygQFh3vKBUypjqLPWg8MDXxKO8daBDlAV3zm+Uj2QXOosYCwoPA58JoyFDP1tfT4gru3fffAhQPsBlHI9UqhzBMMm0ukCj/IRgbmRCFBAv8gPBV5UDeyte30wfP8Mu1y1fOhtIH2X7jpO7F+Y8FXxF+G+8l2CxeMEExMS8eKcIflBQ2CRX+wfHS50/fGNjy0SHOIsx9zO7OY9SZ3AjmefCx+iUFgQ6MFroeXiUTKdQppCb3IWUaaRKgClQBq/id75nnuN+f2PzTbNBmz6LR9NTH2orjZO5Z+REDOg3UFVMc4R+HIgMlCSQkIK4bUhV4DS4E4fpD8iXqoOID3EPX9NMx047UWNhN3Rbj4OnV8aT76wVpD/QXvR4XJacnXCXNIT0eERnbDsUFif1F8snnauCr2oHWvdRY00vTltet3OriiOz49Wn9gwU1DfoThRmuHLQeuh9uH1EcVxdYEYcJZAGb+Yfxr+rk5O3fA9zy2cbYi9gC28DfjObP7Qn1Ffy4ApIJqBCGFyMdkSDhIREgJRseFWoNjAW2/pP3ge+z58Lge9pD1yzW+tVR2Grd2OMD7Ab1fv35BDwMaBIdFm0Z2hraGWoXwRQHEXgLjgY2AjD8Jfbc8GTsvugh5DXgkt5C3rLeOOI25//rVvKW+b8A6AdeDa4PDBLNFhwajRnUGNAWOxE8CzAFev1A9VfsbuRg4PneI93s27neW+IF5nzsg/NO+Xz/8wSbCSkOQhHtEq8TyxN+EZANRQnUA/f+3vnY9X3zMPHm72Pulezv6X7nxOZ05/Tp7e2k8sn3xvzmAdgG4QpRDZcO4Q/KD24P7Q0xDPIJqAUJAM75BvTt7ZfoUuUS46/h0OI35Zjpru7E9HX7sAHjBqsKKQ4tDnENEQ3KCz4JDgYdBCcB5fzf+TP3UvXf83Xxq+927j3tL+wk7R7vs/Aj85/2Sfow/t0AJAQiBzsHqQcNCiwMkwoNCtoJSQXmAHP+wPqq9afwaewz6T7nu+Z56FTrvO4c8yr4Gf3sASIGqQjUCrwMow1WDM4J+wUmAUj9sfmp9gz1TfTU84vyrvAT8DnxOfIw82H1Ffbh9fn2c/gP+tL8FQDLAi4EFgWOBSQFZgXdBWcFBgS2ATf/R/zM+Gz1JfLt7p/sb+tw6+vslO/l8zT4Of1xAlUGQQlcC44MZAt3CHoESgAb/HX4evbd83zxJPE28eLwx/Ch8bnyJvSv9jL5G/sB/dD+7AA9A50E5QTGAy8Bp/4h/Tz86Pr9+iP8wvt++6z7W/qL94v18vPo8RHw0O+l8EPy+PTg+Nf82P+uAnkFegfaCBkKWgqYCVUHUwNr/o35lPQz8LTsu+o+6mTqE+yT7iLxxvT7+K38cgCDA9EF+wfRCOoHywZaBcQCEwD1/af7Hfnx9nz1C/U59Sb10fTa9Gn09vI48pPypfIy8tHzCPdD+Yf8kgB7BIwHTAltCk0LNQtKCb4GVgPk/lj6A/bh8frtcOvx6d3ox+nE607uw/He9S/7oP/rAlIG6gj9CZIJkgg9CCYGpwHm/c37hfrk9jv1RPZK9OTytvNz87LzAPXC9U/1O/Ue9s/1zfax+Nf6d/1HANwDfAb6BzAJ6gnaCdIHUQRxABz82/dY85bvXuwM6rLpQOpH7E/v9fIG+O38NQFCBf0HJwnjCL4IPAgeBU0BH/95/Hr4g/Z29pX0u/Jo8/D0ovVb9mr3v/ct+Dj4f/fr9zD44/fP+N76jP0YAMkCNQRDBZUGagYdBgcF3wE1/8X8GfkW9kfzGu4D62jsM+wL7d/w3PQE+W/9hgIQBaYF2AeiCW4KlwkKBn4BwP39+675V/fp9KfxdfB48dvzo/ev+Qf6SPps+k77O/wX/Nr53Pj7+fD6k/x//m7/jv9QABoCLQKPASsBLADs/3D+mfsk+CX0c/Fh8OHveu8G8ILyd/XL+A39PADSAt8FgQi8CUwJYgfJBC4CHP/S+974wfWy8+bySPPI81v0L/bJ9/v4d/qv+6385fxF/R392vxD/Xz8IPxb/DH9jf4V/wIAlgAwAe0AGf/V/T39Afx4+eD3svaQ9Cr0N/V99W/1GPbZ9yr6U/0jAJoB9wKKA+kCuwKcAqIBiP8c/JT4/Pbn9on1avX49oL2LfbJ9+H5r/oF/CD+4/5v/5X/h/68/a38F/yy+1P7JPwI/UT9Pv3O/f79uPzY+5j74PpU+pf5SPiO91n3Qffc9wL5AfoJ+z/85P2c/z8ADQA+ACQBsgAb/6/+yv0k+4z53/iU9yL3UPdJ98f3tvi9+ab6NPx1/Sj+C/8x//f+L/7n/F/8/vsz/Hr8yvx6/ZD9E/1g/Aj88/pX+fT4V/k0+Yb4jfgG+OH2M/dR+H/5wfqv+0T9q/56/2AA6wAqAc8AbgARAOT+uP01+/D3O/b69Zn1ufQw9l33n/cv+bb6nPv1+7/8Sf4b/zD/Uv4l/kT+6v12/rj+XP5l/vr9V/0X/YX8o/uE+pX5pvir9472ufXT9bD1yPW59hf4fPnm+u784f6ZAF4B1gHvAhoDZwJmAd3/ef3k+sL41fZM9W30QvRX9OL0C/ZF90P4J/kr+kX7C/xp/Ar9qP3l/ZD+qv98AM0AygAsAakA8f6x/Sb9Z/yc+lP5avh09nb1IvUO9JzzR/Ri9aj27ffm+ZX80P4LAT4DZgRqBOkDggM1Ao0Aw/6s/bH8kvl89lr1YfVJ8zbytfNp82vzvvS49e33qvqP+x78IP4B/6X+YQAkApkCMQPaAqYC8QLTARwAE/+s/bv6s/d89WvzF/JL8X/wf/AC8nLzqPSQ9qL6qv1r/7MCGAXfBhcHBQaiBWsFhgJQ/vL9Y/wu9yD2U/cM9KjxkPIN8RjvEPL080zzmfZh+T35FPvT/Xz+wv8jAT4CjwTcBAoEwgS3BfYDl/8G/rb99fhX8wfzAvMY7yfuIfBO8CvyefR69WL50PwY/hcC2wUUB1MJrwrsCFsIJAglAz7/xv0a+2/46vVU9Fz0XPIV8PbyvvK78Kr02vTr8pr4OfqN+Ir+DgGc/kACJwZ5BDoE5AYbBh8ChABLACz8vPbb9pP1d+8P75jw2O2S7lDxLvHE89z3OPnV/aoCVgKfBZcKngjbB5sKtAjMAwwCBwJu/Vz4yPad9kf0oO8N8QH1zfFQ77PzdfaE87j0X/pD/Kf83v+LA/4ElwSsBaIHTQUSBEcF/QJ//SL+sPz089Xw9fAr76HrTOzY8DjyefFq81j4v/oK+5D+HwO+BWkFOQWKCO0IuwUkBWUElALE/xr7Vvf89gL3GPJC8Rvzx/HF8F3w5vIf9uv36fiU+37/QwBLAXsDagX5BmsGEgaVBhIG4AIWAMT++vp+9nnzJPAY7x/uZutw7LLv4fCt8MX0T/mZ+kj+dgIIBKUEVwXeBSMGVAYPBQQEtQJDAAb+EvqB9mT1SPSe8kXyIPOd88fzpfSu9l748fiY+rb90P97AcYDhQXQBkIHOAfkBtkF4wNxAV7/Sv0A+uL1VfIN8I3uaO1T7QHutO+B8mD0e/Yb+sP8lf4kAX0DMARDBQcHjwZ1BYoEbAKg/5r8tPnu9rf1zvRA89vyPfIF8l3yM/L08jP16fcV+qn87v/pAU8D3gUUCFwIygdOCAQI2wTzAUYAy/zq+P/2KvVx8unwI/BO70fvVPC28RHzk/WF+FD7lf4cAUQD9gThBaMG1wY9BoMEawKYAN79ifrG96T1W/NH8VXwb/B28PXwPvKV89T0s/Yq+Zb75v0xALsC5gTkBpEITAkgCTIIIwYKAw//k/tJ+Q33tvTk85PyqvC9763vhfBn8oD0fvah+Zr8l/5HAQEEMwVCBvwGJQdvBhcFUgO7AVwAH/1z+ez2NPW58jbwze8D79nt2u4z8OrxL/Vt+cz8Xf/oAdgD1AQ2BhIIvAj+B5AHgQbFA9EAsP11+oT3UPQG8vjv7+4T75vvYPGD82z1KPcZ+Sz7Nv0eALwCPAUWCGYJRgmoCPEGwAObAH/+6/sj+Sz3GPWN8xbyhfAW7z7uGe4Z71fxL/S794f7vv7KAVcEHQZJB1EIxwj9B50GxgTKAv0AZ/4r/O75Z/cR9P7vmu2y7L/ryOxM8JbzMfZN+cH8Sf+kAcMDkwVFB1oIDQnWCJsHiQYoBGoAvfwI+j73k/MT8i3xPO+F7u3ulO+n8FnyQvSS9j35r/vt/sQCcQX8BmwJtgp6CW0IEwcqBEkBrv6R+0z4CfZd9CfyYfDX7xfw2e/k733xgfNS9Sv4mvur/o0BnwRTB3kJBAspC0cK9wcUBXoCz/4C+3L4b/aa8wrxZ/BQ78DtbO7K7/bwBvJ685r2LvoM/XYAWAQ9B0MJxgppC20KtAjjBbwCMgCK/d76yfgh9krzVfGg76jtieya7Sjvw/Fq9KH2mvnv+0/9pP+YATID1gR6Bl4HkAeNBwQGygPRAU7+5/k796n16PMW8/zyUPJh89j0NPVx9U/3RPm/+u381P64AGYD7QagCOUI1gdfBgAFdAHq/a/8O/oY9q/z1PIO8XnwCPOo9fL2tvhE+SD5ifmc+Xj7r/0xAKMBwAKtAAUAOwKp/nj5hvdb+PT4lfMS8db1efRR8bDyFfbm9Nf1OvyYAvQA/wKgBl0E4QDjAHUBSwD+A5EByQH6BEsFWwTPAqH/C/1O+VHwNuzw7EzudvFc900BSwBp/wMFUgJyAtgE8gFQ/VgB2gXoAr33kvzBAL8Acv+p90vznuyp5hjg1uVP54DigOiU8yz6lvlz+zUC7QLrAZQEMwl/Db0MygtfDeII/wOW/D32cfnX9xX5zfZR9Hf6ZvX57mPx3vKN8H3z8vU6/gIKjAiRCYIUWBDSEBQTFgflBd0MnAaK/ooAJfzk+lL8NfZ97UfoPOtn5DjdTOT05MvoROxJ6dru5+7m9dMBRQOWBCEI6A0PDdAHPASd/in9zP7p/2cAvP2d+FvxFvFQ78Tqou7d7JfwCvh7+mP/1v3q+XH9+gZMCsQP1BT3GKsYOA4/Co8J5gp/CFEFBgsnCbgC+/uK8yvnp94b2pLZBeHz5F3tUfLi+UL7pfJa8KDxgfXk/JMIFxC2FR4W8g2aAur1Vu0Q6lfum/Oj9qr6Ffkp9BHtYeXh3U/dbODB6BH1LP0/Be8EOgddDLkNnQ6RESoUzBYYGb0UBxXFFG8RDhBQDycJ4gD9+yzwK+dD3CLV8dTp2KXgpO5XAU4F7wrDDPgFUPwj9Dz0R/bm++sD3gvxCIcDmPt+653n/uYy6Y3pNvBX+az18+4R5ufjO+BF4i3qrPv/DjkaUiFRId0SZvyD8Xzpk+uL9ScAww4SFasTrQ0/B2oBIPr++fb4NvhM+VD7Cv1E+on6tPwO/XP9tgEgCHgKGAjeBKL9lvWu8SPyB/OM9MX3vvZh88ju5OlD6YPoCOqS8FbzX/PT8fvsmOrY6lDshve8BtQUKSHqHuoW/Qi28Erie9yP3F3pPPmjBmoO8QupAyv2oe3R6ZDwnv+4CRASGhFXD58L5AwkDOUHXQft/8oCOAqyD0gOQQh2+m/l5tf00KPOv9Qc38rpqvTJ+E/4Sfrq+ID4GPuA/W0BPQE0BMsHtwpoCb8J5gySCp4El/lV8szraefj4oLg5eHx5I/phe069Gb49/rF+yT9Zf8lASEF2Ay1EZUV9BapFSYUORLQDcwLegmEBBYAkPx593Pww+t65zrmtuae6NPqMeyh7eXw9fQC9Un5Efk9+U362PsNAM8BMAYYC/kKqgjPB5MAmviI70Pn9eU540blGem/7ELvuu517XTsAutL6FX3+wAmBicQVRQpGjAX2xKqEEoOrhA2ExERBwxBBi3/6PSE61bjbdqP107cxuTC7RH6NAcZD9cNDwaV/lz49/Wx+R0AdgWVDEMO7gkEA+b7vvK76//spe5m7Ajpi+JC3l7i3OZX8JD7fQPXAwgEcgF//7r4xPUz82LyTPfDBRQLRQ5uE1kMkQrUAKv4hvMO95vyH/Dj7mXueO8D8ofzLvkqA0AKVxI8F2kWJhEOCuT9RvU+7hjsUvPG/ewI1w1RDKUHHPq/6gLgP9lG2cfhGO599lb8+Py09bLua+0f9lQDdw8JHFgg2BikBjrx0uDZ1LbS7dw+7Z7+7A1xFEEO0gM78rbjbdtE23zn3PRfBOoP5A7oBGz6YfkO/YwGURQWGRsVeAdSAaj4l/B591H/zAkaEL0Q+wcI/H7wPeql6wnlSOXW7HnykvXA+bT7uvoG+wT/AgIcCG8JZQZdBdEANvwr9Tb07PRm9P/1xfTV81Xy9u1W60noPuc/5/rk++e06gLxLPjt/x0IywyoDL8D4QCB+kT8GwIEBx4L4gqDBwT++fcv9Uz2Lvz7AmUBHwNQBHf/oPu2+bX56Pj7+WT/2gHnAloFJAAA/Yv6PvIc70nzpvm++W76wf+N/skBhQNFBdYHOwjKAV35c/NE55fj7OXs6LXsue/h8Lrvq+0v6UXj4uM46cLqoe26+hgDVArbFd8VDBZHFicJXvsj8onqGey58nkA2g3oFQEYOxyiHYESPAcO+UzuAOly6wj0mfsj+X/t9eRV3T7h9eqv99ELhxWbF/4TbgsNBtUBvAD0AqwJ+A2iEEUP5glz+zTrDuBT1MTIZ8XAywDX/9/k5O/sMuvI5PjfM+Nb79X/YRIPGvoahRsIFr0PQQ5zD6EPeg6zD5QOOwyVDFkMfAliAif0Luaw3gXXwdGG1/7fmeUB7TH5NgLUCoMRthPOCpwCUQEVAJoMPBweLKw3czi5KuAXB/445XncZ9U/ymrGWcawxVzHCsgZzkvVluKa7cvvAu4Q6/7s3PvuEnglCC5nK4skKSPvIAIdeB7zF4EL0wnLBYT7S/bk8ProAeRC3OfVItQn0x/XP9165LjrgvO5AnoJyg6UF2MYDhaTGZIhdyHHJ600dzLBKl4ewQUt9M7m/tzy14nVo9WtzLHDNLrZs/KyI7lVxePPkuT69EIFVBriI94qbixTMVswZy5JMQYunSoEHe4YFRD4/sjxiuTf3y/Twcryza/I9so50JPSSNU/4pjp0+og/V8JPRr/Jgkz4TpGNbAwBS0YKzsmMCc2Ksok2BeRCX3vqda0x5i8b7XwsGiyKbJErRqvda19uNjMs9+a+KMPMCD6LRA9iELOScpMXEpMRzo+SDLyHk4OvPul67jdq9GnyRHHTsegx9LKHcWBxEPDuce22VvpMAAqE1ogji+WOH079zrHPVQ7ozbvMA0neiGBGKUOqQFx9gLu8OFC0cLBfbj+qLmdkKEuolOnsLNBwajZRPcnDQ0iVjICOVc9pDofOaA3VzdcNms3yDXgLVQjKBHX9kTebMkps0mna6JNrV67/rzUxUPS4t257Hj9mg1dI/IuNjTwPkZIVUqlRRpAtTCXI40SNP6h7tTgkNZM1HzaX+GH4g/fpNw90DzDSbW1qrmtDrcFysniNAinIE0uMzY4Mf4pth75HYIfmSD7JfYkWxk0D8wHEPxn7dnhrdJlzpPO3dZY5+7uW/Ea8rXwCeYw4Yrdj99r+YoVFydZMs4zTSlZG5gMggLFAoz+dQMQFB4TIRMhGLUNNwdn+8znEN711K3I2LsSt0+3FLTFtBm+K9Yt8KcHiBt0IIwsIyuXKCUu+CyDMKQxSC0oJ9Qa1QQF8qLjZtWK0nvWGdP+16Hf6+AH6SPvIutk7d3rDecx60r0hwjNGnMpjjBaN90wlRo3DFcAMQOQA+H+uQT6Dk0MsAMK/2/5e+7a0rrC8L6+vRy+SsGLyObRQ+D+8BL8PgncGxoc9h1aJzImuivgLn0uUSvFIHUSvvn/4GXOCsaxwQbGUdpG8Lf+cP/sBBAFd/7vANwArwRSB8sQBBQ7Ed4G8vn2+575ovhv88LtBfB86/HuCP4rCP0T6SA8KF0lbxrQDTb7xebX0oXF58Oauru4N8KiyrPY/eaC9jsDwA8yGJ0pkDd5LyoveTBMKEMfcAsr/2rz8OHX2krT9tGb1PLay+IF8Hn9IQtkDlETthzJGSsbcRxOF4IJ6vjr6gPfVNHwz3XYoOLx6yPyywH/Di8b1yQ1K/oniiepJogUzgSe9Bjbi8GasbulUZ3DqQXIn+Uw/PkQWBjUHhMkoiXCKNIr7inzH5EY4Qmv/HjuH92O2ebcStzv3t3lT+ss9KL2HAB5EZ0U5RkUGxgeQB2hGiURrgLF8V3dqtCmw+LBbsWW0QPjuPiBDeUluzuUP9k2+S4bKI8QofkO7AndYMopx83KbL+krZO4FsyM3m38PwktGTgouSsNMqUwpyWvIEsaVBIPBgzzkt8+zlTHtsNuxSTLx9cz6af4AhCHHq8kYy0sMQcuaix2L7ApmSKlDLH1J+iozHm376tqqVK2Xshx3Qf2bQz5HWsxK0BHPcEtix5MGKEIXvM75Z3c6tiD0AXJQc4S24neQfE6AX0E/BRGHQsgfBgADG8EVP91/dj1t++L69Ll/txj3H3be9tZ4r3tV/thCgYjEzLEOpU5PjusNycgXhHF+EzmD9/h2CHVwcZ/wxW7S7pNz8fnfP8+EUYo4S+SJ7UovyHaFKMNcwJT91ztRuT53a3We8tYxUzSE+nW+uwNWRW1H24liiLtHdsHufYe63ncIdwz3mLaDd2E397equEQ8+UC0wvjGRAozSkpLEsv0in0HmEVqAfi9ZXlJtNZyUPGSczo0bvYg+WB8sv9FgvbJFwuci6uMuUlXRYJADzlCM+6w7/DL9Md3S3k1+8R8nX1ePc0/asIRBWFJBUxpTYUMU8f7Ah56mDT2MZ6wxHLo8/+0cbb1OOu6Db18gY1GvIqtjJnN5441DBnHuoQOASO803nEdSuyYbEIMShzKLZqPBuCHIUchXKIdItlDDdLnUorif0GZX/yOxt1T66sqqTqs6nr6/XxnDf/fjoCcUVWCARLTQ3MUXkR8tFITbEJrYUqe01yxmxUZoXlnqlLrd0yxTjAfspCTsPUh3vMWk+ekJrRtRGzzf8G+EC9OfZ0d/EmL4swMvEftH64XfwUf5zC0gVpSDgKWouyDn9OwYyfifkEeH4V+BxyXK7xbWosuu2TMPFzM7SXN9s8YsA2w0uJZ40mUJlULRM+UHDJ7II8uOFwtar7aLHpQ+wVMKp1PTmRPFv+qcKGxycL0M9lk1mVCFNhjz5HfH+yN/ivk+3orqUwazMwNVB5H3qmfKS/cAEBRThK/g5O0FwRso+mCmBFPb8cO2/3ZnPLNRa0kXZQ9kl1L3RB8yvy8fIVtV55Uz0DhhPQBBWOlRCSew2UxXc8wLXR8M1ui27VcT50XHh2+Rq4ePok/VSAX4ZRS4dOKxGT0ODNKIi/AQ/7MHV9MVfwiy/g8Sq2JHpGfJDAfgNnhEvGBscwR4rKAUtpyi0KZEoIRl5B6L01OVV2zjKkscKyNjP4dqK1sLWBdhD073Twt6O7IIDCyA1OpNIb0+0RWIrxhOy/M/f7cXkwsPDHshh1m3ce90a5Gzmq+pj9nQOMinxMCI5gDtfM8QZo/8y7ynf6NmS1pHcuume9lH1LvP6+CX6A/mN/8MH0hGxH7QeWhnTFhgXNhTGDbsLyAc2/Q3w++Lg1dzMEs2Uz6DVEN212abW69WU2kTksffOD7EiHTxPQ7M4XSiJET35wt+A08zSeNxe4kLp+fET9h34dfQJ94v2fPhpA/4U9R/rG2wWZw6B/THuXehZ7IvrPupr8y76aPjy/EEFeAFgA0gIyAt5FO0WxxLTEsYSgw+IDlQLuP1y9xnvw+I63OLQjdAO1gbWg9gp3dziG+kw42HpBQCpCHsSZyW+LM0pYCy6JggasQeM9P7k3tjA2RzZ2dXh2Nje3duq3gntLPS0/ugNxxt3JLwmpiEBIgoZSQquAygA+fsP7XPm4uUN6DnqUe339V354vxMAtEDBgugDgMODxJkD44OWwgK/aPzm+6n5gvfOeN+7mT4Kvyf9lTwve6K53DiOt6o5UTvwf9pGN4cVx7bEyQBuPyS9tzw++/Y9Lr95vd68aDu/uSO4dPgiuQ88Lz7cAgaFXYgHyhaIjgWNQ69A1r7e/Eu8Yv4Yvlp+mn36fPr66noCuP732blOvDH/ogIBRSqGkwcviDRGcYT4hNlD5AGrfs+8XHkRttq1oDX69hL22XhmOBO4injjePF8KwB6RhtKBUvWi7vH7wU0fzN6azgtNuE4V7mhO/+8/rz8/Jz73/s5POTABIHOw8lGVocQxW1Dc8A2fKs76nrxepd8uz6yfw8+4L5WvL77lbyhPXz904BhgpbD8UUShPgDzkJPQQ8ArwC8AN1AgL/aPQ07ZPgW9/a4wPjluy89Yj3pvOI883pLeKA6oL2PBDvJcQtgSxvHmcNUvDm3rjT8M1D3BDm1/Qw/Af/zfy/8l/xqfNx/WQGSA0lF80ZwhJYCIX6VOnP4MjdzuOY7hf3iAHQCYMMtggBBqUEBATZCPoP5hUYFN0Lqv8g8q/oOejO6yXtK/a1/Hb81ADNA8H/Xf3Y9x/5mfwi/hUAjfrt8K3ibdgd2UzmYvqNENYe+CXvIWwVagOr78vfVNME1r/amOLv7BHw1fQp9ezxRPdb/vAC6w1vGVkcFxnJFUYKrP7I9Fjr1uh0653vt/FG+R7/IP0F/gAEhwQRBA8IAAppBTQCZgPP/i/6afhT+Ez50/vm/1j9kPyc+V33DvtU/S4FDwnbCXECSPeM7D3d1tMS0Wzb5e+eAn0QNBkjGf4R5AdJ/NzvwekK60DqI+m57pPw++9y8bDxWfBY9dL+2QN6DZMWEBflFW4S8Aug/tPx7OlS44rlYekR7Yn0Vfkp+6n8zP18/8AGEg+JF2MgQiCgFdcGpv+U93ns6e0L8XjuA/K1+PH2hfDF7Wfs6O/299QCwAggCWICf/Me6OjeweS88ksAuAu1D88RzQrKATT2Qutf5/Dp0/AC9379K/xV9PzuTOac4zTo5vOZAMALyBncG+wXqBCNBdr7E/O78FHyTfpJ/8D/ivl39DXwcubG4RvnXfH9/D0LkBS0GEETDw57CwcIvguLDfcP7QzLAQj02ePJ2OLQ5tDz1t3fjeyy9NP3SPao86T0yPyeDBEbtiSnJ5EhyBcPBfnuXeL72SXbr+Hu52ntBOwy6/fpiOkW7Yfzw/5+CRsUzRvyHdIchBKPCK4BqPdE9fDzG/Rd9Cf2cPUX727okONc4kHmt+6o+pEFIQ0iE4gUbBUFFeUS7xSeFCMR2AfJ+xjuWd5v1VrS1tE014rjFe9t8GPv++yh6mTzSwBOGkEu+jYFOJgpWRpDAGvqd9gN0rjTjdgd4W/iLeIu4Unlpe90/LAKzBePIOMk5x55GEAQ6gKm/AH1gu5P6irqsew07w/25PgM94z2ofOq8ALy9PSo+d39JQTkBqMHzQp7DDcMLw8yEWYPdQpSADDzyee44CndQd4L4VfktOSU4czcCdyW5mD4qQyMI64xiDY6MrAlvQ8C97DkgdeF1v7Y0d/N5QXq3uq+6jbvkPW+/pQOJR4tJ6ApuiWgHOQJ4/QO43bYrdSU2PHi0PBI+xv+1P3W+bP2W/au+q4AnwdOD0QTMA5GBv38XPeW9Xf4c/sH/vj+FPk+9EHu5+mB51jpmu/m8w71r/Me7p3nR+rl9XkIahrKJigqnx8JEkb/1uy54UnbTd/c4hzqX+2f7SvtuO9v9ob9DgtqFtAfBSZ8KKwkWhfyBmT0u+Gs1V7RINDw1u7ef+hY9SEAmwelCPILGw4tD94Q/hCAEOIKzAZhAP74vvMt8F7seOhs593n8OVM5+LrPu9k8lX3kPv6+Qb0p+wn643y4wP6FOMgzyYVIzMY1Aa69Rfkddlp1InW0N985/HwSPVN+aP93wMiDp0YViXxLWkxFi3gHzYIse6O2QrJNscTzunaCehq8IHzOvCT72XsPvNNALAPeSESLOwtqyFxFMcDoveM75XqtejJ5eXnMOfE4V3dt92U347nqPSV+y781PkC91X85wcmGHcnOyhaIDoVrgba9QfiRNZvz6DRiOE98OX3LPgn9xf2fffJADsMVhciIVwnCSUKHSEUnwVr9xftuuPa33zfI+KM5t7qHfA880/2GvNp7/bzYvvUBCIPfReYGqIbQxrcE4wLvQM9+wTy++ov5CDd19iK11PatOD16aHvt+8J6p3jEuTF8S0IDSO6N8c9VzfVJmIOLfSs4GbVx9VC3oXqxvIm9obzW+yR6crrLfMyANIOPB37JlYolSM9GXMJ1vjF6qPgct3h3pzlmuuQ8FPyhfPO9QD4ivz2/9cDXQNzCOQKYgzZDpgQ4xTiEO0NAwQ++QHurOFy3X/dY+GM4rvn8elh50Liqts92R/i6PW4DuMnbzYtPZM0CSSMDkr11+Xu2ezaN95W5bDntuaR5pvkbOix7pX5nAY/Ev8cgiNlJu4jTRsnEPYBI/UM7Znnoek+7W3y+vN99ezzLu9B7vfu/fV2+xwB7QPzBAcH2AbaB50HNAqhC/4LlwnQASX3hetZ5NThGOQa6HTmAOJm21XU59MD4Xj0qA9jJwQ4wD0WNYskoAtB9cHlOtwH3rPgHOaz5enki+Q748nkRelv92YIgBiCIyEouCZNIEkWlQie+7bwQ+pI50rrX/E686TzVPQv9JLyqfSe+2oDzQkhDsUO9gnGAYb6W/b69vb74gExBj4IPgjEAhv77fHZ7Fjq5eqj66/nxd/E1gvTe9zY75cE8RXBIhQmiyP2F7oIUvk07MvmRedi7HLvPe/S6iXmh+XG5qPt4PkpCLYWiyE5KsMpnyOIF4oJVvpi73HpDug57ILuBfCn72zvcOth7a31LP8JCD8OUBL4DqgJGQLu9invFe6Z9CD8XwRiCQsIKQOW+knyfO3Q7gn0YPa/8xvuQOQ43bvclOAI7ar/kBBFH0gjTiAbFxwJ8fth8tzw1vBR8sH0s/Ba6s7j/t9H47rpDvdzBPwPNxyEIcUfzRfJDCD/UvWv81/01vYe+Uf5JvVq8iLyafH98tL2evvJ/7EESQdCBzUE5ABK/yL/qv+BAh0G2QbnBn4BRfl98RHqoOMu5cXsWPT6+jP8vPlx7aLdms9Cz7bf8PbFD6Mkyyo+KVQg+xKrBHf6YPZi9Sz57fo497ruVuQA3fvaC+Et7Xz9cQ77HMsk3CPyHW4SjwWK+Xfy8PFh8iXxAe8x7V7oF+hb7CHxq/faALMJWA1dDnsKXwKd+hj0DPHc8db47f9qB4QLtQrgBtT8WfLx6g3qpu6I+EUBkwPhAGr2+OXw17bRgdEt3870uQyAI+YtDi/aJ4ob9xGEBs0BafnP8tjrAOFl2zvTV84wzxDXnuUC+HENXx8xKk8skyacG7APAwfrAWP/NP3x+JjyWumu4KPZBdbL2lfnDvicBzITmBd7E2UKqgD9+g76H/z9/hAAWf+8/Cn3fe/g6ZXp/+0v9qP9DgQFBVABEv2w+fD3AfXt8mrzXvbk96z66AAcBlsMPxIoFtQW3w8bBm/7lPVT8vnrrueC4fXd9ts73JrfdeYZ8r//kQ+TH/0nUSnuI84Y8guI/2T26e/f6czkHd8F25LZkdse4w7vZv5iDn0ZcR7jHGcWmg+dBpMBQwA9AEwAzvu99D7sVuSv3o7e8OOQ6zr09f0oB64LMgx9CvsIAghRBWcC2/4O+ozzVe646uvss/KM/LkHeA++EzIPbwep/AjyxOxg6xbu5PFu9Fzyye1m7Pnri+7y9rEDSxFJHXkllSG2E8ECLfPp5x7iduMr5+vrlPA48jXz9vKh8sv0nvgnAWMK4hGUGOEXRxIwC7ADaPxh9g7zZO9l7LLoJ+bX5TTnAOsw8sf6oAO5DfUUvRYhFQARlA3lCfMFTwJ1/Gv0aegz21/Pa8oVzZbYROoE/2ARRBs8HfsY6hLAC5kGtQOjAf7+kPlG8TfoM+Hf3HPejedI9nUGRBRYHfEcTRejD9MILgTL/6T8P/eV8CznXd4e2TrYO+Ds7bz+lQ8GG6IdCBnDEQ8J/AG8/av8BPvC99LyIes75ljl+ecm7Fv26gFZCgcNOA5WC7gCrPqb9l75sP6xAC/+Mvdg7YvhrNo23MzkrvQ3BN4TrR+IIQIa/g57A0v4r/Oa8h3zJvKw7kLpceEY4XnjUenv9M0DaROHHTIk8iAfGYwN0gBs9hHt9Oc55D/l3uZ96qDup/H++AkB6QmVENIUbxWTEjMOBwe//oX3R/DP6gvo0+bL5AHjTOP55tnw2PtYBpkMLxCGD7UM4QcPApz/5P0A/Zr8GfyX+QH0p+9d7wHw5O+28MLw7+9i8W71tPwZBbMNHhIPFBwUPxBHCBcBd/d87e/o8+kV7HnsgO9O8QLyjfdM/+8GwgunD9cPNww/CKgDb/5u9wjzv/DL7snvC/DD8Krx4vQW+jz/TgMcBcUDLAKeAvkB6f8H/rj88frs+DD2ePLt73PxKPXX+u0A/gNVBIgE/AIQAE791/k59tj0Yvee+nL8qfqb9ovybe9S7uLwSvjjALAHWgtBDEQKxgdJBT0EJQUDBOL/dvqo9dXu4ejw5KPj5uUL6g7wk/Vl+nj/YAXlDfAUcxbxEwANtQNa+5H0ie5N61PrCO3f8On0+feQ+OT3zPjM/MEDOgjGCR8LWQrjBX3/ufpF9Q7xlu3K7DbuTO/F8Ov0Z/pp/c4AgQNrA5oCZAEz/zb+CAC3AdMDsQe4CjMKxQapAN/4V/BI53LgQ98s48jsXPbF/U8BWgG7AJgAJgS4B0sL0RBLE1ER0geD+ontGeIs3Zrd4eNv7uH4cQGeByENVQ68DKwKzQajA0b+hPgS8hHq9+O+4SLm5OsE80r7BAIqCKcLHg0rDmcOLA/dDoULRAUr/VP1Ue+T6/npvOkE6/Psde4z8oH3rv1GBfALAg8jD5IM6QWp/An1A/LR8i31Avm0+gH42vOu7lzrN+zQ8tn9fwltEvoW0hWrEHsJXQLt/Df40PRt8AXs1+Y34encMNsM3zfpdPiJCF4Vgx15H4IegRmlEhAMVgP0+yf0iu2q5lPg5d1L3VPh0uhn8on8HQUYDFAQrxGDEEQOxgtuB8YCI/0m983x6+xF6RDnNOhh7T/0BPwZA0wJGQ0UDfUJXgW2AucBEADy/Nn5zPfO9XbzWPG078/vN/LO9oH6EvxZ/Fz7K/tH+vP6G/63AToGvQfrBUwCI/4p+sH28Paf9yH24vTs9Bj1zfOE9FX2gPkI/6cFMgl3ChcKvwU1/5L5U/X67xHtc+w87sfxrfZs+wj/MgJxA4QDfATcBUYHuwc4BpACQfzK9UvwouzK6+frs+2D8L7zkvf5/OwDBwnQDC4O0AuHByIDvP76+dH3evgy+pX64PeM8tLtX+v06hruq/Rr/N8CxAW2BP8Agv6h/Tv+YQFtBUAHIwYNArj6SfFm6iTmpebs7O71Yf3bAPIBxP9T/nz+/f+8BBgJ7wsNCiUFnv659rXvROyU7AzwLPVH+an7Uvxs/EP9xv1C/noAoQN8BfcDagAM/If3efUG9j/3mPgL+nX64/og/Jf8Uf2d/vz/mf9X/gv92vs0+976Wvt0+0P7/vhM9hP1+PRc9gz4ofoX/Rr+lP6N/V78pfyA/Rz/tgBzAQ0AWP1E++74ifak9ZT1KffE+AX6u/kw+Jb36PfE+XX9dAI4B9AIygcjA+X75PQp8PvuH/Gd9ZX6d/1I/xD+zvuH+6r8bv5TACcEMQZ8BM4BXv1v+GH0SPPn8w70hPQ89BX1Kvf4+hD/uACVAcIBHwIvAn8B8AC//9z+Hv2k+uX4jvYS9O7yrfQ09kL3vfqG/ST/Y/6x/FX6b/lk+uD7pv5EAewBcQBe/i77Tfg99rz1hvYD+aH7Hf3N/Yn8xvq4+bn77f5aAjwEtgR1AygAQfwt9w3zdPAG7zfv8/BV83/12/jm+6f+0QE7BWIHbQhOCJAGYQPtACL/4vv/+In1V/Ge7+3twO7C8TD1g/nV/KP/HwA+ADsBEwN/BSsHqAbjAj7+Hvgb8+7vX/AQ8yL5vP/WAP/+Pfyc9/T0X/iq+2kAEgSuBXgEKwEV+7/0M/JZ8bX0w/ah+eb5u/cT9m30wPUZ+qn9NQPSBnQH6QaKA7QB5f7e/Xb7//rT+lD3VvPq7pbuqO+P8/L4Rf4rAvQCtgGZ//H+gP4NAKgDlwNrARz/Hfij8+Lxsu/+8S/3Jv2p/2AAQQBu/qv+S/5TAMACnwNYA4T+ZvkI8xft8u3u8MH1hPuc/Gj+Ifxa+ID3SPjj/S4ESQodDBMJGQNc+pzyf/BH8VD12Pqs/M37+/c98/PxQPLO9n3+vAZ1C+sKkgcAAC75h/SG8jzz4vVS+OX4afqK+qX7R/sl/bMA+AEpA0ECNAFb/kz7xvl9+EX3R/dN9xb3Dvhn+Bn4kfjB+nH8+v9IAhQDWwIRAWb/n/23/K360/id9+323fUK9hz2wfcx+lP7zfw7/Vr9YP0F/ob+nf54/tX/Y/9U/Qv7efnt+IH3r/c49wj4r/m4+cD5z/pI/SYAZQJQAxsDdQGP/or8m/rm+VT5V/g89wf2HPTC8lHzFfUB+OT89AEYBRkHXAYsBPcASv7R+/b5wPkU+Lz2JPUZ88zxsPEe89v1pfnl/UoBEARzBkUHpQevBhEEugGv/vX63PbM80XxZvDM77zwKvMC9Dj2Yfjw+5H/MQMCB4AJtwmyB00EFACu+3D4J/YP9NXzlvIF8qfyOfTF9hz7uP+JA1cFgQQxA+8A/f/F/sv+sP7Y/Gz60faw8j/v8+4g8Rj0pvdq+1wAoAP9BL8DTgL/AToA+/63/Pv7ZPtN+hP56/bE9Rj16PVE93/5Mftu/R3/TP8V/2f+Iv2K/Ez84Ps9/LT7gPvg+h76Q/kf+PH3hPjA+nT94f7x/0z/qv51/sD9Zf2h/ML7iPrD+ZT4wfad9YH1bPZY+CH7lP3+/nn/HP8o/1b/wf4p/gr+W/3p/AX82vrH+B/3Mfb49RX3nfjr+ij9Pv9AACsAaP+c/YL8zPsN+9b6M/qq+P/3Afhf94H30/eL+Qz8+v6RAYwCVwJoAKX+Vf3j/J/7wfko+Tn4Tfiu9xb2YvW19eP22/gP/Mn+vgAQAnsBxgCU/+j+Af5K/ev8fftD+mb4Cfap9HL0k/XJ9zH6tvyZ/vr/YgByADMA0/94/6v+Dv6I/Hf6hPh198j2rfb+9r33jfii+ev6wfvs/Eb+uP8MATMCrAIuAp8A//1g+3/5Wvcy9oH1j/Uj9sL23vht+iz8VP2R/kr/gv/K/6r/9P+//+//E//1/N75afad89/ysvNK9rf5t/yt/k3/iv/H/iP/BwA5AeQCKgOwAhAAePzm9yz0EvMT83D1+vfI+jr8iPz9/JX82fyF/an+2P+EAOj/BP6e+yX59PY09j/2Evff+Ef66ft3/LX8NP3M/XX+f/96APsAkgCt/2X9Nvsd+Wb3mvZe9mH3NvjW+Pb5Dfs//Kf9dv6Z/9j/5P6x/u39m/z/+h/5e/cq9uj1zfZd+P75O/wA/jT/S/8M/5r+of73/kn+3f2l/Pz6K/n394L2WvY49/r3Mvki+hD8tfx5/bH+Ev9c/2v/u/7T/dv8Ufv7+bv4+vec97j3W/jX+Hz5j/o0+/f7cvzY/Cz94f0f/zr/kf4//dn7Hfsy+rD52/nf+UL6vvrn+tL6w/oj+yj8Kv0u/tb+4f5U/kb9FPzd+o/55vhz+DL4hfi7+Db5I/op+0X8Kf0L/kn+O/7y/XD9K/2N/Mr7JfuY+vL5s/k8+eT47Pgm+Q/6Z/u+/Jf9Af4E/rv9av1S/Wf9b/0w/aH8afsU+pz4KPeg9kv3k/hi+sP79fxG/Wf9qv3L/cn+fv/e/6H/Y/8h/jP8hvoP+T34Efgr+J/4UPkm+sv6gvtn/CX9Kf5H/2AAzACKAIj/5/24+/T5tvjj93j3Hfeu93r4ePlD+hb7Lvyb/br+ZP91/xn/Kf4z/fv7pfrG+cD4gvg6+Eb4Y/jw+LT51Ppo/Mb98v6H/5X/B/9l/nf9w/wc/Cr7efp++Yz4qfeY9wD4yfgs+rP7L/00/qz+j/51/i7+3P2u/SX9afwe+y769vjW92b3ive6+PT5lfvg/Mz9X/6S/t/+t/6g/mL+7f0k/cH7UPoU+VX43Pef9+f3evin+b76iPtz/OL8fv0S/k7+d/4Q/lX9V/wy+/H5H/nM+PH4d/k8+nb6M/tL+1r7U/zQ/Or9If4Y/s39U/2j/Gz7VfqJ+Vv5dfnC+dL5nvml+ej5Wvol+1n8M/1E/jD/gf9Q/4L+TP04/Gv7qfo8+ub5Pfmv+Dr4Ovii+En5TPqY+6T8ff0R/nf+iP6c/oL+uf5l/kf9Qfz9+oD5m/j29/f3jPgY+SD6svqC+8f7M/x+/ET9xf0a/h/+X/1//E77wPrz+dn5Hvpl+pj6vPrl+qD6xPom+yr86Pzv/Yv+uv5f/sX9Af2N+xL7TfqI+mb6NPrh+UX5IfnZ+LL5iPrM+8z82f2R/rj+xP4N/sj9b/0t/QH9cPxU+1D6Bfko+O73+vfd+Of5bPt6/An9V/1D/ZX91P3k/U7+6/07/SX8//rj+QL52vjj+LH5HvrQ+gj7gPsm/K38Sv2E/cj9df0v/Xj8z/sy+836ePpd+gn6pfkA+jv64vqB+0n8zvwT/VH9hv2r/Tv9Hv3S/Kf8WfzW+2X7mfo9+gD6Uvq7+jj70fuF/JD8cPyW/Hj8l/zY/P380/x+/Bf8nPvv+rn6cPpf+l76nPrm+hL7cvt2+yH8W/ys/Lf8kfyI/CL83vuD+1f7DPuN+nv6Uvp++vL6PPuR+9H7S/xd/IP8kfxX/Fr8N/w4/Bn8kvvB+xH7NvsK+9n6B/ss+5X7xPsh/Db8Nvwz/O/7Cfz1+9z7/vur+3/7XvtF+wD7xvrI+uP6NvuR+9D7FPxH/GX8X/xX/Ez8L/wL/OT72/u7+577Uvv8+sL6t/r9+jj7w/v/+xj8KvzW+8D7SPtJ+1j7t/sb/Ej8Ofy7+5b7wPqC+m76wvo6+6P7Afwy/DX80vu++6D7p/ud+977/vsK/ND7iPtY+/z6tvqO+pv6xPoe+zr7cft1+3X7nfu2+xH8afyo/Kj8jfxI/Mv7m/sg+8r6zPqh+un6/Pof+1z7Wft5+3z76fsL/I389vwY/dn8ivxG/IL7F/vg+v369/o6+zH7YPub+5X72vv0+0/8Gvy8/NT82vzI/F386fsm+7L6dvpb+jj6U/o2+l36ZvqL+q/6+Ppu+/T7i/y5/A/9+fwA/a38j/wf/Nr70Psx+1L7//oB+9r6CPse+1f7j/uz+9H71/sL/BP8EPzx+/L7evtv+yv7FvsS+936/frk+jf7IPtj+537t/vk+x78X/yG/KH8SPwh/MD7ePs++0r7Nfsc+xz7Lvsv+yf7QftO+3372vse/Cb8cPxu/In8hfxh/Er8R/wR/LH7s/tn+wb7yvqc+rT6u/re+vj6BftO+1r7o/sJ/Pj7L/wU/BL8LfzI+7P7QfsV+yr7LPv9+jP7I/sj+y37MfuH+4H7vvuM+/D73vsP/Pr77fve+4n7dPvr+iH7z/rZ+s36M/tu+3T7rvvX+w38Cvxz/GD8avxl/Cj8CvzK+5z7nvtM+zP7Jfvu+hf75/o2+z37ZPuH+6z74/vE+wj8Ffwi/AT8zvta+2T73vrL+rz67PoO+y/7ivuH+/z72Pv0++f7D/wb/Ff8Xfww/Cb82fuk+1r7+/rd+r36ufr5+hX7Xft0+7z7KPw//HT83fyp/DP8b/tz+0D7mvqb+sz6BPsg+wz7Xftf+1f7ZfvM+zb8Kvxw/IH8sPyl/IX8P/wK/LX7m/tI+2D7ePut+637jfub+1T7dPtn+6z7wPvv++P7+vsn/AL8HfwR/Az82/uT+5v7jfuN+2r7SPs5+1f7aftr+4P7v/vo+/37Efwk/F78J/wq/BX8uftB+/r6j/pX+kP6bfqU+ur6Jftl+8D76/tW/IL8rPyL/JX8V/wv/Pr78/vJ+4n7YPtQ+3L7OftI+6n7s/uw+3T7lPuw+4f7vfv1+7H7iPuB+xz7W/tC+1L7L/t9+6H7gPur+4z7tPuy+9378/su/AH8zvuN+4r7Z/tP+zz7QPsf+xT7Hvvo+ln7lfvD+/P7Ovws/PX7mPul+7n7mPuj+9f74/uu+6r7Mvv8+un6+fol+2z7cPt1+4v7c/uk+477jvuL+8b7s/uX+577O/tu+wn7Ivs4+1n7pfud+9r7t/vm+6/7ufvN++j7Qvwt/FX8EPyJ+yH76Po2+676jvop+2L7HfuC+sb6Bfun+gX7c/vC+xL87vsU/Bf8D/zT+wb8+Pvg+7D7efuE+4X7mPt5+6376vvr+8P7evtW+zj7NPs++yL7W/su++D68foK+wT7Bvta+2X7ifu1++v7FfwP/Dj8jfzu/Bj9Mv3+/KH8N/zA+1z7+fr6+vj6u/qR+l/6J/oB+iD6bPoA+2T7d/vK+xP8/fsx/FX8W/wd/EH8Jvzv++X7o/uk+4X7ofvg+9775Pv9+/D76Pux+9X70fui+1z7pft0+w37wvqD+rP6cPrH+t/6SPth+yj7e/tg+1P7gPuj+9v7Dfz+++f7AfzO+8b7m/u3+5L7XPs++xH7H/vW+sH6ofqm+sT6C/sV+2j7fPup+6H77/sM/E78i/yu/Or8xvyr/Gr8MPzb+677Nvsf+wX78Pqk+tn6ofr0+hL7JPt5+zn7fPtu+6r7o/u3+7/7tPvY+7r74vvX+7r7zPu2+wT82fv1+637q/uE+3j7hvtx+1j7Nvsj+wn7RfsN+wH7uPq8+pH62/oF+1H7YvuD+9n7+vs3/E/8avxG/FP8KPwS/Bv8+vve+9X7k/tu+0D7GPsP+xD74frT+s76vfrW+u76+/pB+037Zfti+2z7RftA+zT7QPul+6H7DPwV/Cz8Dfz1+9f7xPvZ+7v75Pv3+9/77vvG+4z7bPsH+8D6ePpp+iH6MfpU+lD6hPrK+iT7aPuR+577yfvg+/f7CPwT/DX8HfwQ/Ar8tvut+6n7zfvJ+577ovuq+577svu6+937svum+277JfsF+/X69frz+gv7+/o1+0X7g/u2+/P7EPz3+wj86Pvb+8P74vuu+1j7Lvv4+gj7/voE+xr7PPtc+2/7o/va+/r7Gvwc/B78Rvwc/P377vvw+9j7wvuy+6X7oPtl+4n7ifuP+5T7kvvk++T7Efwe/Pz7+vvI+8P7kfuo+4P7UPtQ+y778vrL+hf7Qfu2++T7A/xF/B78TvxV/HX8rfyh/JX8cPxB/NP7S/sH+8T6l/qf+pX6qvqk+pL6lfrg+kX7gvuh+/77SfxA/Gf8XPw2/Az89vvm+8P7uPuP+2j7Uvsu+y77Bfsf+277i/uT+7/7pPtv+1v7NPtI+zz7JvsS+wv7C/sS+xf7J/sn+1L7gPug++D78/tG/Db8F/wP/Nv7o/uk+7j7mvur+6/7mPuJ+4z7avs2+yX7Cvsi+137sPtm+yv7aft3+7n7rPtq+3f7s/vY++z7Jfwr/N770vvy+/X7BfwC/BD89fvR+6v7ovtc+1v7Zftq+5r7pPum+4v7h/uF+3H7Y/uU+7P7pPvS+6n7Z/tN+y77P/uO+7P7w/ut+7T7lvuK+4z7dPtx+6b73Pvy+wD85Pu0+3T7bfte+1b7Yvt8+2H7ZPtD+z37V/tk+3r7hfuh+5/7uPul+7T7rfuj+3f7XPsz+xr7EfsY+zD7MPsm+y/7I/tk+577i/vd+/b7QvxW/Dn8E/ys+yz7ePp5+t76RvuJ+1X7Ivsq+zn7VvuE+0D7T/uY+9v7LPwg/Or7rPuP+4f7v/vT+4P7avtx+2D7Pvtt+3H7iPuA+3T7u/uO+6b7qfuW+8H7yPve+9/79fsB/NL7o/tT+yD75frr+kz7mPu1+677o/un+5v7q/uZ+7f7rPvN+wP85/vw+/L74vvd++P73vvv+8H7ovtz+137WPty+3n7ZPtx+zT7M/tC+zb7Tvty+4X7gvuf+4L7iPud+3P7gft1+1b7Zft6+3v7r/vH+9z72Pvh+8n7OPzs+3r7wPvV+zT8WfxS/N37Jvue+l36d/rB+tr6Jvtb+4P7Zvtp+2j7SPsn+1r7pPvY++r7vvux+477jPt/+7b72vvv+8X72PsB/Cn8YPxV/Cf88Pvk+7r71Pu6+6n7lPtr+4P7efuF+7T7u/uq+4/7kvt8+3/7j/u2+7j7kfuC+0n7/fo6+5D7h/uJ+2r7M/vv+if7XfuY++37yfva++T7t/up+8z7mvtV+5D7o/vJ+9r7svto+2H7j/t/+6r7t/vY+7X7oPuF+2D7Q/s++0r7l/v0+1D8W/wj/Mj7NPv0+gH7h/vn+yD8MvwU/K77mPv++/37H/ww/CD8E/wG/CP87/vD+3z7U/tG+037fvuJ+4/7bPt5+0r7ZfuB+4j7pPuj+3/7aPtK+0L7V/t7+5j7qPvN++H75fuj+6n7h/uH+6/7yfsK/Lv7aft3+3b7cvuG+5n7T/s9+zP7XvuV+6b7fPtd+5D7efuM+8L73vv7+/T77Pv++/P79/uq+6v7eftG+137T/uT+377mvuq+6H7dvs4+0f7c/vU++v7yvuM+3X7R/s9+y37Kvt3+6772PsC/AX8pvt9+5f7u/v6+xX8Kvyz+zn7Ifs++xv75Pq4+s/60PpN+8H73vuy+zH7Vvsw+3j7qfuq+8r7gPtS+wL7wvqs+qD6Kft/+8P7NfwP/An8K/w//CH8V/xH/CL8HvzO+7z7nfsD+9r6Bvtr+2j7e/uj+4/76/vf++j79fum+5v7qPuX+3r7Nfv8+vj6BPsY+0f7VPtO+yv7+/oz+4n7m/vM+/77+Psq/Cz8/vvt+8v72vu8+6H7oPu5+7v7YvtL+zz7GfvB+oz62/r1+if7f/uy+3L7XfuC+5L79vuw+3X7j/uV+0L78/p4+zX7z/o3+0H7bftQ+3H7xPvF+/z78fsd/Ev8Sfx4/FX8Xvwj/PT7J/z3+9X7mftY+2r7tPsM/Fv8NfwX/OX7+/sB/L/7vvuV+6L71vvu+xr8z/sE++L6p/rW+sT60Pox+wv7BftW+1j7UPtq+7X79/tA/IX8VPzl+//7evv7+t/6p/qs+gD7pfu++7n7evs4+377m/sG/KX8WvwO/Nv72/uf+0X7YPst+w/7S/uD+2L7Vvs6+y374vtk/Hn8Svxw/Cv80PsD/Hn7F/sH+936Iftn+2D7Vfsc+wz7G/uU+7T7v/v7+6D7dPt4+9j7x/t8+5b7ifsl+3D7h/uN+9r7P/xZ/Gj8EvxP+wD7jPuS+1j73PvX+8P71PuZ+z77vfrP+rX6lPqE+/L74PuR+0D7ivup+3/7wfsV/EX8pftL/JH80PsM/Jv7afud+7H7qfuK+8L7v/uq+7b7VftJ+377KPtq+9P7xPuD+6L7ffsW+1D7gftb+2378fvk+9r77/u8+5r7rPvb+yD8S/xB/FH8Xvxi/Bn8gfsM+1T7oPsY+zv75fpb+nT6Y/qD+hL7QPs++6L7Gfwy/N37yvtd+5L7sPt/++T7sPtH+zL7GPvy+vP6+foj+z77xfsj/D38QvwH/PD70Pus+6b7lPtO+x37cvuS+577y/va+5L7hvu0+8X7Afwd/D38Nfww/PT70/vc+4/7hPum+6z7r/tW+3f7bvtu+z37Q/uL+y/7lfuv+337Y/ue+8D7svsB/MP7H/sr+3/7K/sn+zP7B/v9+jH7ivvR+8f7g/uM+7772/sO/B38DvwB/Lz7qvuk+4z7N/sh+x37Mfuf+6X7kfvW+7/7vPvi+8v7mvvL++r7gfuu+9T7s/uv+9r72/tB+2r7P/vr+lX7qvuw+6H7qPtW+3f7hvuG+5/7hvuT+477r/up+477VPs8+5z76Pu0+3j7OPtY+6H7pvvv+/r7XPvg+gT7KPs0+4f7rPt/+3L7hft9+4b7iPud+3n7hvv2+z/8MPyz+877hfs7+2j7avtc+2r7ffto+1X7XftJ+3j7uPux+9H7/Pv/+6n7N/sM++/6w/oD+4H7tfuI+2L7wfut+6X7CPzT+6X7APwY/Ob7DfwZ/Hn7DvtB+1P7i/ux+5z7VvsQ+1H7QftI+637+fvw++374vud+5L7r/t0+9D7F/yc+wH8TfwS/PL7+/u3+5P77Puu+2D7pvuv+z37LPvZ+or6IPss+3/7wfuc+237BPtS+3P7O/tl+zX7hfv4+8D7x/uu+6v7xvv7+/77xPsA/Ar8Ifyg/Gr8yvt0+y77gfti+yP7Avsb+zn7A/t6+3X7HPtl+5/7evvI+yf8C/y8+3/7a/tB+/j6N/th+2f7Yvt8+6T7kvuh+837C/ze+2/7HvtD+4X7xfsF/JL7YPsr+wb7Xftb+3/7xfue+7r76PvS+8D7vvv3+/P76Pvj+577t/tm+3X7yfvK+977q/uU+8X7gPtG+1D7Y/s/+wz7cPtk+w37M/ty+5T7bvv2+wP8a/uJ++b7t/tQ+5H7l/tO+177R/tl+237APsa+3X7g/t3+7X73fvr+/D7uvvz+8f7X/tT+2L7rPs5+1X73fts+3f7l/uT+9D7kvtY+7v7Mfzh+0r7pPv/+2z7kfv7+/n79PuP+1j7QvtX+3v7a/uA+837Evy1+z77Rfu/+7P7qPvj+8D7Nvs2++r7CfwD/KT76Pr9+jn7CPtC+5H72/sC/Oj7uvt2+6L7KPsn+8f7tvvF+9H7sft4+zP7HfsL+yL7o/vb++z7rvud+7j7WvuG+7j7vPuu+6f7qfuH+3v7bftL+0f7hPtt+z/7LfsX+077ifu4+6f7bPuG+6T7rvvj+zT8PPza+7P7sPvL+9H7iPtN+0D7a/vH+5/7r/v4+6P7cftW+537ifsp+0f7e/ug+5X7mPum+2n78/oM+yf7VfvC+9D71PsN/ED8G/wG/E/8PPz0++j76fvq+3n7/PoY+x/7K/tG+8b77fui+9v7g/s7+3D7SPtR+9X7Hvz9+977Dvze+6376/uy+8H7uvuJ+9j7Lfwf/LH7VPu2+wD8rftL+9v6O/to+wj7m/vp+277Q/uK+/X7tvt8+5T7ZPtp+4X7wvsN/IX7H/sX+yT7Jfsr+4f7cfta+5/7pPvq+7z7evs1+/r6X/uR++n73/vx+/f7tPup+7b7t/uQ+0b7L/tb+1/7W/tn+2r7mPt8+2b7o/up+4j7mvu++8n7lPur++L75/vE+4D7mvvI+6T7jvuk+6P7pPu4+637zPvi+//7C/zu+4f7kvtx+wH7cfvf+7L7u/vo+6n7b/tf+177U/tw+3L7t/vk+6T7dvsD+9r6K/sh+wv7Mfsi+xv7G/tl+6/7Wfud+7n7c/uZ+8T77PsP/Hr7a/uj+8v71/vT+0n87/tL+yj7C/tu+5T7afum+9f7svtp+zP7N/tR+5n7p/tH+2v77Pui+2z75vux+wr7Jfti+5j7u/uC+6f74Pu6+5z7s/ve+4/7mPuv+6X7xvue+3T7k/uj+3/7fPtj+5z7Vftz++f75vuv+277hvt0+4f70fub+137UPtC+zn7efue+5v7p/uN+1b7TPsa+wz7W/tT+1X7lfvM+3X7U/ur+5P70vtG/AH8KvzB/Kj8WvwO/PL7L/w2/Ob7CPze+xP7APuu+5n7GPtD+yv7zvoN+3H7V/uB+2r7YPtj+4P7nfuD+5P7wvvn+yj8Gfwh/ED8L/wh/C38H/z/++X7uPuG+1X7CPsI+976A/sd+xX7tvuZ+/r6MPuD+2j7ePu2+4z7EPti+9b71Pvk+2n7WPub+5P7+fsK/LH7xPvl+yb88Pt0+zr7JPtl+zj7JPta+x/76foU+0z7a/tS+137ovuu+8b7xfvT+/L79/sv/E78XvwO/Mf7y/vH+7r7u/vR+5H7cfuc+5/7Z/tv+637t/u1+8n78vu5+xf7CvuL+2f7XPuG+5v7P/s0++77r/uM+yL8pPuI+x38PPwk/P777fue+7r73/uH+0z79Prk+iD7J/s2+2/7PvtE+3n7z/v9+9n7ufvE+wL8JPzn+/L78vuS+5L7uvuJ+237gvt4+2z7rvuX+4n7ifuj+7H7oPuA+4L7ivtF+2v7W/s8+1L7cvt8+2b7vPvF+1P7qfvV++77Q/x1/Gf85fv8+x387fuj+8f7xfsC+4D6Qvvf+zr7Kvu3+5D7Dfsq+1b7lfu5+3D7LPua++P7lPu1+zX8L/wi/BP8EfwL/MH7kvuj+/37BPyx+6L7YPsp+2H7jPuK+3/7s/u6+wn8QvzM+8r7y/uJ+6b7Jvzn+2X7XfuS+6b71fuP+0/7zPvI+5T7nvum+5j7kvuf+6P7tPuW+2z7d/uL+yT7Wvtk+yL7lfv1+7r7mvvm+8v7h/vO++H7jPv7+9j7LvtD+2n7a/sq+337tPtt+3j7qPvT+937aftR+3n7UvtQ+zj7N/v6+tD60Prq+ib7LvsN++36Fftk+2P7bfvk++P7ufu2+6D7rPvQ+/L7BPzU+377P/sy+zX7QPvi++z7VvuC+6r7svu2+6r7m/tk+yf7IvuL+7f7WvsI+4T7mfuu+6D7a/vu+w783vsB/FX8L/zu+w/8Jfz8+/j7xPun+9H7vPug+6T7rfur+5b7kvvM+8D7uPua+2P7Tft/+9D7kPuy+7D7ifuF+6L7wPvC++T7lfty+7X71vvO+7H7ifuj+7v7qPvG+zT8Dfxq+4H73fvd+4v7evtd+2j7WvsZ+7T7yvtv+6z7sfu4+8T7yvv/+wX8J/zB+6n7+/ue+2D7bvue+5P7RPs9+2H7Yftk+5H7s/uz+4z7/PtL/Cb8NPz++1z8AvxZ+9n72/ud+2r7qvuv+9X7sfuK+wX8uPsp+8/6Lfut+3v7ivuc+5H7jvvE+5D7dfuL+yH7avuA+7L7rPvB+2389PtA/Hv8gftN++b7//um+5H7zPuH+wj72/tM/JL7C/ug+un65Ppj+q/6nfrP+h37P/uN+9n7ofvC+gX77/sE/K/7w/uz+4v7bftA+8H73/u3+/P7yvsw+zn7k/tM+x/7jPuX+7L7l/sG+wD7Ofvr+sb6OPsM+4X6+PqU+4b7m/vf+537X/uG+7v7/fsG/Nv72vvD+637ePtk+xT7y/re+gr7VvtB+9v6u/ry+jT7UPt/+3r7ZvuF+577L/xT/Mr7yvsf/B787vsu/Nz7Pvt5+6n7xPv/+6b7Xfvk+9z7ofsV/NT7YPtd+4H7XPtj+4j7kPvS+/P75/vO+9f7g/tA+1b7iPuW+yj7oPsb/Br89fvf+wv8t/vO+y/83Ptu+177/vrL+lH7yPu8+z/8MPww+yz7f/tz+8v72/uW+8P7q/ur+xj8CPym++b7NPwU/Db8PfyC+zb7cPsL+7r62PrX+rn6E/sf+wv7gPth+2D7rvtz+zf7c/u6+977H/xW/ET8DPy++0z71/sz/Nz7tvus+9f7ivt1+7D7fvsa+wL7T/s6+yH7I/vT+rP6T/uI+xf7APs3+5v7kPvU+wz81vt3+277f/ue+7L7pPuN+437v/uU+4P7hPuV+1X7zvrI+ir7N/sU+zb7SvtD+1v7Lfsj+zL7I/sF+0D7Ffzv+7/76vvi+9D7ovu6+977vfvE+/n7Cfzu+8r70fuY+2D7Lfvq+hb7H/tK+5r7bPtH+0f7YfsM+z/7lvt6++X7+/uZ+xH8i/xQ/AH89vtY/Eb83/u7+/H79vuB+137Bfyk++r6W/vY+8f7Q/sY+3L7ZPvp+8H7Afx9/Ln7a/ut+7f7j/t2+6D7wftv+3b7f/us+8T7Evw1/Pz7Zvwl/Mf7DPw0/J37j/vc+6/7nfsO/Fr8R/y6+6373fuf+4779PvP+4D7ufs3/Cv8NfxO/Nn7qPuu+1/7Pfuf+7j7xfu7+5n7l/uM+0b7d/va+7r7nPsH/ND7Pvtn+3D7P/t4+9L7y/vj+xT8yvuf+6D7TvtX++f7C/yX+5j7zfun+537pfvJ+3X7Avsq+4b7evuI+237Ofst+yz7FvtM+6L7nvuQ+677n/uX+6H7nPuM+6r7tfu1+7T7fvtJ+0z7Dvvz+kD7bftr+wj7fvug+yz7Wvtp+4v7r/uf+3j73/sD/LP71/v0+5T7M/vO+hD7rvuI+/76D/vD++T7Qfst+0H7EPut+nn6QPus+6v7pftk+8n77/vE++X7u/tw+yf76Po7+4L7nfvY+7v7sfv2+/X7wvu0+xv8Nfz/+wP8vfug+9X7T/uK+837Nfs3+5f73Ptq+4/70vuT+8v71PuF+6/7Gvw2/Bb8Gfw1/MH7h/uU+5X7mvus++r7/Pvx+8/7k/t8+6D7uPue+0/7evuJ+zT7XfuK+2n7oPsd/B/8sPt4+6r7+Puj+4v75ftV/ND7tvte/OP78/s6/OD7/fvi+6z7ovvI+8D7UftN+1b7TPtm+0b7YvtF+1n75Ps5/OX7tvsm/B384Psa/Fz8MPzn+8X7z/vj+6D7Yvst+2f7Xfui+x78yPud+7/7gftl+xL7GvvO+8T7VftG+437R/tG+5T7eftD+0f7Tfts+6n70Pvf+9n7jPtv+5X75fsA/OD7FfxP/DD88ft5+3v7sfsy+xf7d/sj+xr7B/tM+8j7pvuH+zT7lft++x77uvv/+577Q/vN+yX85Psd/N/7n/vD+4/7XfuL+9H7mvtz+7r7qfuz++77pvt/+2/7DPsn+3b7kPuY+2z7qPuu+7773PuD+3T7l/uP+2T7sfup+3n7q/vX+6D76fve+0b7XfuX+1z7Qvsr+1v7i/tm+3b7c/uy++n72vvq+wP80fth+377xfux+9n7I/w6/CD88fsS/Bf8iftA+3372fuy+zb7afuv+1D7IPtW+6T7Xfuy+gj7ovu8+5/73/t1/AL8vvvk+xv86vtQ+3b7Hfz6+8v7TfxF/KL7Xvum+6T7Pft6+5b75fsX/H37fPvB+037LPtm+3v7YftT+177iPu6+4n7VfvG+0D8d/s4+2n7tfu/+3H7oPvr+9r7mPsQ+5n7x/sO+537Z/wq/EX7R/sx+/r6IvtP+xX7afte+5T73/uT+9b7vPtj+4n7tPur+4j74vvo+3X79vvC+2H7fPub+5P7Vftk++z7Fvy4+1n7ivub+zb7C/uN++P7xPtw+wn7W/tZ+2j7wftG/BP8jfsk/D/8v/s9/Pv7p/vH+177NfuD+5z7Ffvz+gf79vov+0/7BvsW+yH7QvtO+1f7hPs8+y77uPu0+7P77fvh+/r7Hvzv+6b7+Psi/Av8hfu++zT81/uS+3/7XfuD+4r7mPtE+2n7gfsw+zf7Rvty+zD7HvtF+1n7Bfvx+ov74ftw+1L7gvub+7j7+ftV/GT8ZPzi+3n7zfvk+0D7ufv4+zf7zfpB+1n7GftP+yT7Ovvj+3r7EvvH+8T7avuU+w/8vvuA+6/7tfux+/j7pvub+9H7sPt5+8P7EvzZ+3T7XPti+4v7k/s4+z77E/sc+6T7hvty+437zPvt+4L7YPu9+9v7RPsd+9X7uvtS+zr7hvvb+8X7dfvG+877OfsV+7v7+vsj+wn7Wfs3+zD7NftB+2X7Yftu+5r7rfts+2X7nvvD+9f7D/wy/BH84/uv+9r7Evyg+1j7u/v8+9v7ivuc+4L7UvtR+1n7o/vA+3T7evvm++37svvN+z382fvQ+977F/zp+2X7U/vf+wH8b/tR+7D7UftU+3T7Tvvg+9X7WPtK+9T7qfth+577tPtu+277XPt++9j7pvtg+9n7KPyE+4H7ovuP+1P7avvG+9v7rvup++P7H/yQ+zj7r/sP/Iz7Vfto+3/7FPvm+kP7M/s/+9T6vfqL+9r7lfuN+9n70/t2+377uvv1+837XvtZ+3z7Yfty+2b7Svtu+3z7aPt4++f7v/uP+2P7Kvuk+9f7aPsf+1X7n/ub+7H70fuQ+8T75vsK/En8u/tu++H7p/uD+8f7pPtx+5P7rft6+2D7TPv2+jr7Pfvp+hf7Qfst+xb75for+1f7OvtX+5L7vvvV+9772fsd/A/8pfvt+yz8H/ys+/T7BPyQ+7f7hvuU+/H70vuS+5D7YPvq+uf6P/tS+xz7DPsM+0b7dvtP+0z7uPvD+337ivux+/H77/vI+/X7BPwY/Of7oft3+077jfv7+wD8fvsy+037O/sY+4/7ovtc+0j7Yful+777l/t7+7P7mftN+wL8pvzy+2L71/sC/Kz7nvus+8P7Efzm+8/7NPz5+2T7VPtl+2X7P/tK+3P7hPue+6r75/sc/Bv8+fuY+2/7rfvx+7r70fva+537mfuR+2j7mPvi+5P7Wvur+/n7pfuG+5j7uvuH++T6y/oY+zb7CPtX+6f7mfuM+2X7aPvN+9r7tfuv+xb8MfwQ/B389PvO+637hfuS+3P7dPuA+zH7I/sw+4L7dfs1+1H7XPtm+5z7aft0+7r71ful+/r7Zvz6+9P72PsF/Nv7gftM+437jfsU+wr7bfuq+7L7qvuG+6v7uvtI+077mvuB+0n7BfsE+yL7WvtY+1X7aftM+z/7wfvZ+4H7c/tr+3f7lfui+7L7uPuu+4L7o/vb+6X7Zvtw+0j7JPuU+8f7gfto+6P7mvs0+1z7q/uZ+3z7XPuX+9f7qvuK+5/70Pup+2D7kvug+1X7MPto+7X7fvtO+3r7v/t7+0n7mPui+0j7/vrz+iz7KfsM+0j7l/vC+4X7rfsL/MP7nPvC++j7tvsm+0H7u/ur+1f7UfuV++P7lvs8+0H7e/uI+yH7Tfu2+5P7uvvT+9r70Pt4+0z7gPvd+8n7kvvL++H7wfsD/Mj7fvuJ+7T7qfty+8P7nPtK+5P7dfuw+xv8j/sV+yH7bPsu+zz7nPuw+277Lvsz+4n7jvtO+3f7z/va+3T7cvvY+737jfvG++v74/uC+2j7bvu5++37wfur+4n7UfsF+5H7v/tD+yr7MPt3+9z7ufuK+6P7z/vg+9n7zfve+9P76/vo+6374Pva+8n7zPvs+wn8A/zi+7n7jPt0+z37OPtO+z/7R/uJ+7T71vvI+9v7Ivzy+5L7cvu1+/X75vv0+wz88/vl+7D7c/ux+9r7o/uC+6b7vPtt+2X72fu0+2v7Rvtb+3z7N/vq+jT72Pu6+3v7yPtA/Dr8+/sJ/D/8D/zh+xD8Pfw8/Az8/fvm+7T7j/uF+5b7c/sk+xT7XPsc+wz7ZPtz+0f7Qvtb+4v7lfuB+537F/wN/IL7HPyK/Cr8CPz7+wP82/vJ+/j78PvS+5j7XPut+977tPtt+2H7oPt/+3P7h/sy+yL7TPtg+0z7W/uE+5j7qvu8+8/7OPxI/N376vs5/Bz8n/vL+9z7hfu++9X7VPtS+1b7P/sh+077Ufv4+lf7lPt7+3b7WPtI+2L7ivuJ+2/7wvux+5/79vvj+7v74fvQ+4/7gvuT+5L7l/t4+0X7YPt8+1b7Oft6+3z7MftG+5n7c/sU+/j6Z/uu+537cPuY+9/7tvvA+/z7/Pvo+8b72fvV+5T7ufsO/Ar8k/uJ+5/7d/sx+zP7bftn+zb74/oS+3z7g/tJ+2j7Z/ss+4L70/vq+zP8Kfzt++L7vvuy++j7yvt4+5T7m/s0+4X78/uA+2H7evtg+4n7l/sx+0H7p/uA+2L7pvvl+/L7rvt6+437/fsn/N773fsN/BP81vvK+6f7fft1+z37TvtG+0n7aftu+7T7qftm+zv7Uft2+w77Q/uM+0T7UftN+z/7kPuq+5D7b/uq+/n7AfxU/CL8wPs0/Bb8nvuv+7v7ifts+3j7Tfs9+077Ivv6+jL7OPso+2b7Z/tZ+4T7mPvC+7n7dvtj+5375/uq++H7E/z6+wf8GfwQ/Af8BPwZ/Aj81Pud+237hPtX+yD7JPuC+4T7ZPtG+2n7Zfve+sD6Nfuk+4T7c/uv+9D7xfvN++D7D/zk+8X76vv2+9D7xvvR+6/7mPuI+1X7KPs8+y/7PPtB+//63/ot+zz7IftS+4n7g/un+8371/vK+6b7nvvL+9v7p/ue+8T7tvt8+4n7rfu4+4f7ffuX+3v7XPtZ+1z7fft2+0b7NPsg+yr7Jvsz+1D7Uvst+zv7p/sG/Of7vPu6+wT8TPzz+7L7w/us+0L7efuj+3X7WfsR++D6N/uG+z/7SPuk+4n7mvvW+5P7efu4+837rfsM/HL8E/z6+yb8J/z6+2D76/oU+1n7avuJ+3r7Ofsq+1z7YftN+0/7B/v++jj7N/sO+zr7OPse+1T7o/vw+877kPuf+wP85/uf+9T7A/zW+777+Psi/K77Yvuf+//7vvse++n6B/tC+z37PPtb+x/7LPuH+6v7i/tU+377fPuc++z7EvwL/Mb7x/sN/B782fuG++X7+/uY+4b7svuc+1/7nPvy+wH8nvs1+wf7a/t++/368voV+/H6/fov+3j7ZfsU+0f7m/sH/Nb7rfu7+6b7yPsF/Ov74vuu+3f7rvvu+xr8p/tl+4X7Mfsz+4v7p/uC+xP7S/uf+7H7c/sj+0/7cfth+2r7fPui+9f73Pvi+1D8Qvzn+xn8Ffy9+6/76fu6+237c/tZ+yf7PftA+1L7jvuV+337Zvtc+2n7uPvB+2n7Qvtx+6D7zPvb+8z7zPvQ+7v7lPvj+/P7zfut+8D79/vB+3z7r/u1+2z7NPtm+737XPsV+037rvur+z/7aPuv+8z7x/ue+6r7kPuE+7n7z/vU++L7svuI+2H7TPtd+0T7H/sE+yD7APvn+hj7NPtK+3r7Uvts+6n7qfuV+3z7QPs0+2T7dvui+7j7rvt++5L71vvK+7v7wPt6+3r7nPvQ+9r7pfuZ+3/7pPu4+3/7V/tc+1j7hfuJ+4D7PvsU+zX7l/vd+8v7uvvf++37Jvxo/EX8Gfzc+wL89vv9+xf8xPue+5v7lPuq+2v7I/v1+lL7pvuh+7n7i/uE+4r7LvtT+4b7FvsD+2j7bvtW+4n7hvtv+7T75/vg+wj84/uT+6r71vvr+637kfuC+337T/se+0T7T/sL++v6Hftl+2r7Vvs5+4f7yvuk+6b7wvui+6770vvQ+5r7OvtT+8f73fuU+337gftH+077q/tn+1v7WPs4+437tvuc+3n7r/uE+1/74/v5+5z7mPut++n72/t3+2L7wvvV+6P71/vu+6X7hvvL+9b74fut+0T7EPuO+837hPuq+8X7lPuq+7v7kPtl+z/7Tfu1+937k/uJ+6j7vPvG+7b7tvvs+6P7YPuU+9/7t/ts+8/70Puo+637hPtO+1/7M/t0+5H7bPtR+1v7Wfsz+0L7T/sl+4b7lvuI+5b7UfvX+wb8WPwL/N37ZPz5++r7Vvwl/OX7sfvE+5/7T/tq+w37A/sh+xn7e/ut+3L7Zftg+5f7lvu6+/D7q/vg+9X7xvvL+737v/v2+/f7r/u2+777e/uc+8v7qft6+2z7YPtJ+0b7jPue+6/7Xvsx+4r7k/uG+2P7uPvC+5n70fvf++b74vu9+837vvuP+577mPt++3X7s/uu+2H7mPum+2X7Svsn+177mvuV+4f7nPvL+7D7tPvA+6f7tfuu+1z7hfuk+6v7n/t6+7j7xvuq+8X7kPuw+7j7rfu4+2L7U/t2+3b7T/tK+437kPtx+5P7Wvsk+9r6BfuS+6r7rPvT+yb8D/yR+4b7sfvO+8T7vvvk+6T7pft7+yn7Wfum+7z7k/t0+277nfuo+8P7o/t9+6T7sPuX+2f7b/t5+0T7ift1+yH7UPtJ+237o/t++6H73vun+2L7ZPuh+6v7ofvD+6/7j/t4+1L7Rfsx+wL7E/tV+4r7e/tw+5H7ifuU+7T7hfub+9374fsg/DL8/vvL+6r7xfuG+yr7G/tP+yH72voY+z77XPuA+137S/u1+7j7bvvG+/f7svvZ+yj8CPzL+4z7g/se/FP8G/ze+7H7wvut+7f7vvuk+637Svv7+mP7hPsd+w77Svts+4T7sfvK+6H7n/ti+4z72vuw+4n7Wfs6+2z7sPuK+4D7mfu7+8D7rvvM++X74/vK++v7Jvzx+5T7cfuE+6X7Xftw+6D7b/tj+2/7Vvsw+y/7RPtp+6P7lvte+277fvtO+4P75/sS/N374/sR/Oj7uft6+4r7sPuh+7T7x/t/+2f7e/t++377jfuc+6L7i/tq+2n7rfvD+5v7wvuc+0/7Xfuf+8r72Puy+4v7ufvF+8n74vvE+6n70fuL+yn7X/ui+0r7Evub+9f7vPu2+7f7xvu8+7X77fs4/BT8wfvf+xf85/vR+937wfu0+5z7WftH+2r7YPtj+4z7eftJ+2v7f/tm+3z7qPus+8371vvP+8r7sPuh+6b7tvvZ+/n71fuK+077p/vD+477nfuw+/D7xvtv+7b7zfuu+1/7R/uA+3H7ffuE+1r7Z/tf+0j7Wfuo+7P7evud+7D7rvvC+6v7hvtZ+2T7pvuy+8b7gftD+2D7Ofsd+xj7JfsM+z37rfuV+4b7k/u1+9b7svuU+9b77fuv+0/7b/uB+5D7EfzF+0j7fftl+zT7Y/uy+7j7jfuJ+2D7iPui+2j7vvu6+1L7OPtS+1r7I/sJ+0X7gvuv+5/7kfsA/Jb7OPvC++77tvub+/f7Dfy3++P79/vS+4X7JPsd+xH7+PrQ+gb7CvvE+hj7QPs/+zL7YPua+4373PsC/OT78/uX+2T7ZvtA+2r7X/ux+6H7O/uz+6n7i/vB+8T76vvu+/77tftj+3j7YftK+5D7jPtQ+377r/uD+3H7hvvG+/D71ft/+4r7r/ue+8b70vuh+5v7h/s4+1f7j/tZ+137vPvu+937ofte+0f7TPsd+2/71fuE+0L7b/tv+0z7iPtl+yn7fvuY+4j7qvuv+3f7bvsw++D6oPtX/Nz7ePvY+8T7nfvA+4f7ePuJ+3n7KPtI+3/7K/sX+077Z/uc+477YPt7+7f72fvA+/L75PuP+3f7rPvZ+wH8y/t++7n7i/tc+6r7vvuT+637y/vd+7X7qPuj+6f7lPse+2f7yfut+437lfu4+4X7WvtU+3/7yPuC+2v7fvtY+2z7tfuz+z/7GPtv+2f7XPuc+337L/sf+1j7lfuM+2f7l/vc+6L7svsR/OP7l/t8+6T7x/uv+3T7rvv0+237QfuT+8D7kPuA+3b7yPvZ+5n7U/sY+wr7F/sO+wn7RPtK+x37/vpS+3X7M/tB+5f7gvt6+4r7yvva+3b7TvuH+/37kvsz+6j7xvub+1v7TftR+xj7LPtT+1j7hvtn+2b7gftn+2v7Zvt6+6L7gvtd+3z7lPui+3/7a/uV+5H7ZPt7+7v7rvu9+8r70vvN+8z7wvvD+9n7tfuJ+3f7UftD+1r7LPtf+6n7yPui+7L7tfty+5f7nfuE+/z7Jvy6+6X7xfuh+6r7Efz4+1/7+vo++7P7hPse+xj7V/tj+0D7X/uH+5n7cft5+9T73fvF+7f7tvuQ+0f7Yfuc+7r7z/tI+3L7sfuG+777tfuz++H7CfzA+1T7ifuF+0D7ZfuJ+3j7U/tn+1L7UfuY+4b7dfu2+9P7rvu1+5z7bPtG+1f7Vvs8+037WPt2+5D71vvw+7H7l/uR+1v7KvtG+2z7N/tL+0L7LPtX+1f7Mvtj+7X7mvtY+5D75Pup+3z7xvu8+2r7UfuP+4r7N/tQ+7776/uw+7H7Gvz2+6b7xPus+5/7kftp+3n7fvt/+5376fuw+1X7b/uo+5v7pPvR+677afuD+6v7y/vL+0D7RPt0+1L7h/uu+4j7hfuI+3P7c/vq+9/7Uvtq+6D7tPuD+x77avuw+7z7nPvG+/j7jvtM+zH7ZvvM+7b7r/vb+6z7kPt6+4n7e/uC+2z7OvuK+5P7YvuO+5n7XPty+5P7b/te+6/7ovuv+8/7ofum+7n7b/tt+7H7hvt8+7P7ovuh+/D72/vE++X7tvun+7z7hvtM+1H7LvsV+0z7TftQ+3/7ePtv+3T7YftO+237l/vA+9v75fvS+8v7nfua+/X77fum+5P7vvvV+6P7a/tX+3X7iftV+5H7ovt1+4b7eft/+6X7n/uS+6b70vvO+9D77/uj+5r7kvtg+5L7nvt0+7r75fvC+7v7r/ux+8v7zfu7+5H7mPuE+3T7ivtg+5X7zPu6+8r7yvvm+6P7ePu7+8376fuy+4374vu++3n7lPvD+/f7lvtM+1P7fvtw+y37jvtx+x37W/tV+0n7rPuU+0370vso/KH7nvvP+1v7K/tQ+1n7sPsI/Kj7VvuX+437evvF+8b71PsW/Pz7kPuZ+3H7Mvst+xb7IvtN+yP7Hftk+3D7J/to+xr8Lvz/++v76/vo+7j7bftS+y/7CPv3+hr7gPuP+3v7Y/tU+2f7kft6+zj7+fop+3H7X/uH+1D73vr/+mH7mvuz+9f7uftp+1b7e/uG+4b7S/sh+yz7LPtl+0L7K/to+6D7r/uc+/z79PvD+xD8wPuA+6r7m/t9+3f7gPta+3H7gPtF+1D7gPt6+3z7rPvG+8f7wvvw+977wfue+6j7vPuR+5X7s/u1+8z7s/uT+4n7avtT+137jvt5+4n7vfuY+3f7fPuo+9L7z/ul+6X7sfuB+3n7eftk+5T7j/uL+5v7gPtb+1r7ufvB+6H7q/t++2n7f/uf+9j7oft0+4z7l/uy+9P76vvN+677xfuh+4r7u/ub+4X7mPuR+5r7v/vN+4/7u/vr+6n7ovuu+5z7ofug+5H7gPtQ+0X7VPtQ+0X7YPuE+237gvuJ+5H70fvj++37Ivzy+7j7uvuu+677rvvV+8f7evtt+2v7lPu/+5T7h/uf+3b7mPvR+7v7dftn+3n7Vvte+3/7h/vE+9H70fvd+5/7hvuU+6j7s/uT+6n7rPuB+2P7cPuf+537qPuF+2L7ZPtg+1r7dPt8+4X7u/u++4z7bPtx+4f7vfvI+6j7tPvw++L75vsF/AT8A/ze+5H7efuH+5n7bvs++y37Efte+4P7Y/tp+0/7cvub+5/7xfvl+9r7hftI+y377foo+5z7ofuV+2v7Xfte+1n7bPuV+9376/u++3n7N/sw+2X7Wvtk+4r7cPtM+3X7d/t5+4n7jvuZ++L7C/zH+/n7F/zD+6H7nvtd+1z7Qfs/+2H7hvum+6v7vPuR+5v7ivsy+xb7Q/th+y77IPtG+2z7evuO+4P7t/vC+4T7lPu5+6n7cft/+877+Pv2+977sftl+zr7V/ul+8X7z/vr+8D7wvvi+8X7rvuL+377lfuw+8X7xPvQ+5L7b/ui+6L7ivtl+4H7kvuK+477sfun+6j7Sfsx+277b/tk+3X7mvuj+5P7dft7+6b7wvu8+6j7qfur+6D7w/vl+wb87Puy+777Efzw+4v7ffu2+437bvuG+4j7uft0+2f7m/vE+7P7m/uV+5L7XPtX+2/7kvua+3z7b/uE+4j7cvtI+2D7kfui+5f7b/uc+6b7l/ui++r7pfuy+3z7s/ut+7H7uPuc++X7rfvc+6r7wPuc+3H7rfuD+3L7f/sl+1P7hPtk+5D7dfvL+6r7q/vT+9/7G/y2+6v7pvvR+5v7bft3+7X7lPtv+7z7q/v7+5f7dft5+zf7Ifsq+x77fftk+yH7RvtM+037PPuM+777jfum+2/7h/uH+3v78fvC++b74Pua+6H7lfue+577OPu5+6z7a/ul+0n7p/u/+8f7b/tt+5f7W/tZ+6r7nfuU+6v7Yfuc++L7rPvf+5X7ovtZ+y/7I/v3+ln7C/sv+wP7CPtg+437qft5+5n7kvvd+/T7zfvb++j73fu4+7H7tfvt+xr89vvs+wr88Pu2+7/7pvvR+8f7q/tc+2j7YvtV+0T7OftA+0f7Zvtj+1P7W/t8+3n75fvl++b7sPuq+/T76PvS+6X7q/uO+5b7ofvS+9r7rvts+4v7s/t++1v75Prr+jX7Ovsb+0j7bvtx+5b7dPt9+4H7cfuC+5v7rvuj+6P7iPt6+4r7jPuY+6b7cPtV+6v7zfut+9D77fvQ+/X7BPyJ+3v7cPtz+4r7mPur+7n74/ue+5L7o/uE+3b7ift6+4T7kfuF+4b7cvtg+z/7YPtx+0P7Uvt1+6f7zfvr+6n7yvvb+5X7i/vb+8D7kPt5+5r7x/vN+9b7y/vk+4b7dfu1+9/7xvun+3n7k/vC+7n7rPtv+0j7CvtK+3f7nfuu+237bvuL+5n7hfvE+7X7bfto+0v7F/sU+z37aPuf+/L7xPvC+9f7yPul+6X7rPug+777oft5+4v7lftn+1z7i/u++8v7xPuq+4f7n/uI+4v7nfuE+437mfu1+6/7q/ul+5H72/sE/PL7u/vF+937xvuw+7T7pvt3+0n7JvtK+4r7jfu5+977xvu3+8D7wvul+5b7lfuY+6D7oPtm+2n7h/uw+7D7jvt6+2r7oPuA+4j7pfvB+837bfs/+3T7jvuN+6r7o/uK+537fPt++7r7/Pv+++j7Efz7+9v78PsC/N37w/uZ+7f7CfwO/Nz76vvS+637rftj+yH7SPsm+8T68vol+yz7VfuR+2L7efu6+3T7T/uf+2n7S/uX+4r7k/uT+637uPvc++j7s/u1+7r7tfu1+5v7jfu5+477lfuE+177L/sp+z/7I/tI+0r7U/t7+1r7SfuQ+8D7mft++5f7d/tv+3z7gft9+7P7sfuV+/D70Pu6+8n71fvR+8j7qvt8+1j7PPtP+2L7g/uC+4L7vPv2+9z7/PsY/PH77PsD/Nj7sfux+5j7iPtu+zn7MftX+1z7VPtw+7r7wfuI+5X7evtg+5z7rfty+zn7O/s/+zT7dPuF+4r7nvuo+477hPuL+5f7uft/+0z7Qftz+6n7nft5+6n7tfuZ+5n7yfut+537svuR+8D7t/ui+5L7m/ue+3P7j/va+5r7lfuF+4D7tPuo+4H7WPtk+4/7lPt9+7D71PsE/OL7rfvE+8f7m/tX+5H7ufuM+6P7rPuW+4L7eftz+5D7mvuO+3n7fvuI+6D73fvy+8v7vfvj+7r7sfvP+9n7xfu8+4n7f/uR+2L7Xvt4+5L7kfui+4z7svvV+4v7evum+7j7x/um+4T7fvtc+0v7a/ux+7T7zvuy+5n7yPv0++D7s/vT+8r7xfuK+y77Cvsz+0/7ZvvG++n7sPub+7D7y/v1+8v7qPvB+7L7cft2+537iPu2+777jPuV+6X7pvub+9r73vuu+6H7lvuj+5j7ifuQ+3f7P/st+zj7a/uW+6T7rvu9+/L78/uk+5/7pvuD+3D7dPt4+5r7xfum+6D7pfvS+7P7QvtG+3P7LvsB+0/7bvuF+7v7xvue+7/7ZvsU+5j72PtR+1P7sfvW+7v7gfud+9z71vt6+4375fu3+4n7r/vG+8b7pPuA+677//vY+0r7Svt6+2/7ZfuO+6H7dvtv+3D7ovvC+6v7Q/sn+4r7mvuV+7H7wvu1+7z7xvu0++H70/ua+6z72PvB+5v7PPvv+g77OfsU+wP7XvuU+737yPt4+8T7u/uO++j72/uO+3D7vfvX+737+/u6+7L72vuX+1n7e/u5+7v7rPuU+3v7kfus+2z7j/ul+3/7bfto+2b7VfuM+637kPuR+7r7pPtr+4n7j/tM+2b7uvvs++z7zvvv+/v70Pvf+9b7o/uk+4b7hPue+577hvus+9z7r/t++5z7s/ub+4v7ofuY+3r7gfto+2D7ivtg+zj7hvuu+4H7qvu7+2/7iPub+2L7bPuH+0z7TfuT+3X7UftT+1f7YvuH+3n7W/ty+3n7e/uZ+777ovt7+4z7r/ut+9j7v/ui+9b7+PvS+4f7bft1+1n7Z/tn+3L7kPt5+6P7dfuE+537dfts+4P7qvum+5P7evuD+3L7Uvt6+8P7t/ui+8f7yfvV+737k/ur+/b76/uF+137b/uD+2n7tPvY+6z7wfsH/PD7zfu0+4b7bPuo+5n7QvtN+177Cvvp+jf7kftt+xP7FPtL+7X7oft9+177aPt0+0/7Zftx+y37NPtD+1/7xPvc+8r79fsZ/Of7tfvz+xr84vut+6P76fsb/O378vsd/A789vv6+8j7c/tZ+137P/tr+4L7Uvtq+7T7o/uj+9D77vsA/L/7bfuh+wj80ftN+1v7p/uR+6X7xfvh+8b7cvuZ+8/7sPuo+6X7tPvM++b7pvsy+yn7SPsk+yv7dPvd+8v7bPtX+7z7A/yU+y77gvut+1v7ZPuP+4v7j/uZ+4P7zPsC/LT7p/v2+/n75vvL+yn78/r2+s764vpN+6D7l/uu+7L7t/uO+1v7nvvi+5/7UPtb+7v7sPuw+5r7WvuO+/n70PuA+4L7ufu/+7b7qPuu+6X7ffud+8z7gPtl+1L7ZvuB+3H7sPvF+9v7t/uR+8j7nPuH+zD7vfoU+4X7jvt4+4f7rvuP+7P71Pu0+6P7mvuH+5j7l/tt+zr7c/ub+3z7tPu9+6L7i/uV+7D7pfub+1n7M/uG+6T7gfuR+7P7r/vX+yr80vu4+/X7/vvs+w387vug+4j7dftT+1T7Y/th+2z7cftx+6z7sfuH+5b72fse/Bb81fuu+7X7vvu8+9P7/fsW/PX7qvuG+4v7VvtI+5H7ivtk+337m/uT+4/7ifth+3X70/vL+5b7rvvN+6D7cPt/+437V/s4+0n7cvuC+6P7qfuT+7j7svvy+wb8xvt5+3b7qPuJ+7j74vu0+6b7vPvm+/T7v/un+537r/uu+7r7y/ug+177Pftd+5f7oPuO+6T7l/u++9b7v/u6+9n77Pus+4j7cfsc+yL7L/ss+1n7kPus+6H7lPuR+437mPus+5L7lPua+4z7hftz+4D7fPuW+/H72ftn+0n7YvtT+xL7P/t7+3L7sPu1+yz7Vfv5+9D7ePuU++H7qPvG+x/8pfus+9j7CPw6/Nv7q/tG+077ofud+6L7nfuI+4r7cPuz+537L/sR+x37Ifv/+k/7hvsy+1H7ivu1+9/7z/uC+2X7pvuv+7j7tfuk+8f7svun+/n77PvE+9X72Pvh+zH8/vtU+0H7TPsb+wz7WfuD+4/7WPtT+5v7kPuZ+6r7v/vF+877kvuj+837rft0+3b7gfte+1r7dvtT+zX7c/vP+5v7dfuI+1z7OftO+077afuI+2X7Yvun+9j7YvtR+7D76/uW+1j7gfuU+0D7SfvB+wb80fuf++f72fu/++X72Pu3+/D7//u3+5b7kPs5+0r7h/uH+377g/uS+5/7wPu9+6j7vfux+3b7X/tl+1v7T/tD+2r7sfuu+3j7mfus+5b7l/vF+9D7r/t9+2r7U/tQ+0n7ZfuP+6H7g/th+4b7i/uy+wj8EvzS+7b71/u1+577ovub++r7OPwx/On73/u1+5j7qfum+7n7zPuX+3r7oPuI+1T7QPtr+1/7kPuq+4T7mPvC+8/7xfvK+837wfuV+537o/vY+7T7nvuU+7H73/u5+3j7hPuW+4P7Z/um+6H7U/uU+7j7vfu7+7T7e/tf+4D7pPuA+1/7Qvs5+zb7JPtC+2P7Y/td+4v7xfvp+677gvuN++n7x/uN+4H7YPsq+wP7EPsr+2n7jvtx+4H7p/td+177o/vF+6/7p/ul+6T7jftn+2n7pPv9+yP8HPyx+4D7uvue+3v7u/vO+6X7hvuH+6L7dvtP+1P7cPt5+5n7yPt2+3r7xvvM+xf8/vvF+8X7gvtz+4D7r/uk+5P7p/ub+8L75Pt/+1D7Wvtm+1H7P/tz+zb7Fvso+0r7xfvO+1f7NfuT+277OvuX+677jfue+6X7j/uk+5v7evuE+8P72vu7+6X7hftY+0r7Y/tg+6f75/uc+1f7pfuQ+137i/u6+7H73fv0+6T7nPuz+4r7dPvW++r7wvus+5j7ifuM+5T7tvvl+9P7v/vc+9T7pfun+5D7oPv8+xD88vv3++v7rPuD+7H7vvun+7z7n/t3+3T7k/u9+377X/un+7f7oPuw+637e/t++6P7g/t2+4P7T/tA+1f7VvtW+2r7e/ud+9r7//vu+8L7mPuH+7H7uvux+7j7qvuW+4H7e/t2+3T7c/uf+8X7zPvK+677pPt8+3f7g/uR+4L7aPtu+4D7Yfs9+2X7kvuv+6D7t/vL+777kPuT+5L7wfv7+9j7nvu/+9f7f/tV+2f7b/tO+0j7evuh+3b7PvtZ+3v7bftW+337hPuK+6z7o/vJ+/z77fuu+8X75/vE+8D72fvZ+7b7mvuX+8r7xvuZ+4D7iPtr+1D7gfuh+6n7l/tn+3f7oft/+337gvuE+3/7oPt8+0H7T/tv+1b7Yvtx+2v7Zfto+5/73PsE/Jv7h/vT++T7sfvM+9L7sPue+6776vv6+8/7cvuU+4f7XfuL+777vPvR+8f7nvul+577aftb+7L76Pvj+6/7k/uy+777lvu4+9P7aftM+4D7hPtq+4f7d/s/+137cftP+0v7YPtj+2r7r/u2+537rvuL+3f7pfuK+237mPux+4r7fPup+8L7v/uQ+3r7efts+077fvuD+0D7Kvty+6f7pvuE+3X7cvtc+5L7u/un+6v7y/u1+5T7r/ui+2D7dvu3+6z7xPvI+4b7Wvt6+4z7ePvQ++n7jvuM+577bfue+8r7mfuy+/T73vu9+8L7vPui+6X7y/sB/NX7ift1+3n7Yvtj+5/7v/us+5f7o/ud+6n7qPuO+2r7h/ue+8r7zPuK+2n7dfuB+0r7VfuE+2/7Yfte+3r7nft2+0r7avuL+3z7avts+1z7bfuR+4P7kfuN+3L7W/t5+6P7gvtT+2L7c/ui+8771/vU+8P7tfun+8n78vv0+837u/v9+wD8rvuq+8X7mPtw+6X7l/tQ+0v7UvtH+2D7hvt5+5H7lftg+0z7X/t7+4f7cftl+6370/uU+5L7yvuo+877IPzi+7b7zfuW+3v72fv3+537f/t7+0T7JPs1+0P7Ofse+0f7fft/+2z7ZvtO+zv7ivuy+4b7dPt7+4P7lPuZ+6f7nvui+5X7xvsG/OH7gftb+3D7lPuU+7T76vu2+4T7m/uq+2z7Sftn+2r7evu3+7n7oPts+1v7dPt1+3z7l/uW+4f7q/vV+9H7yPu4+5v7nPug+4b7gvuM+237WPuD+6r7z/vT+577nfu6+5n7ivuf+7H7u/u7+777tvul+1j7P/tq+3j7gft8+2z7YfuV+6/7kPuI+3f7efuB+337fPt/+1z7RPt5+577oPut+6j7sfv8++H7p/vf++/7k/uh++/71vux+5z7h/t7+6b7svvC+8j7ifuH+7j7sfuA+437kPt5+2z7W/uD+5f7Svsx+5f7rPuI+7H70vvH+8L7xvuw+5v7hvuT+6X7jPuC+8P7lvtQ+3v7jPtM+2b7yPui+4r7qvuU+5X7k/tc+3H7ufvP+8X7vfuu+8b72vuf+6f73Pud+2r7lvuE+1X7RPtX+3z7rvu++8H7yvul+3r7nPui+5L7jvu5+8P7l/tw+2X7fPtr+3n7ePuB+577m/u4+wL88fuy+5X70PuV+3f7vvvO+737t/u4+6v7nft6+4H7rPuw+3/7lPt5+1n7ffuf+5r7rvvF+7H7sfvU+9j7tPuf+6n7uvuF+4L7q/uj+3L7bPuP+2b7QPs6+0H7hfu0+5H7jvuG+2L7Vvtm+1v7V/tm+2P7lvvH+5X7gvul+5X7nvv0+9r7l/ug+3f7P/ti+3L7RPuF+6b7i/uT+6X7b/tr+3/7bftU+3L7iPt4+2z7Uvtg+3T7ePue+8D7p/us+8b73PsA/OX7sfu6+837pfuN+3r7L/sN+2L7h/u2+9H7nvt3+437iPtT+137Y/ti+1z7a/uE+4r7WPs5+4D7ePt6+8/7zfuN+637BfwN/MD7R/s9+4H7hPs4+1z7bvsT+wT7Wvuq+9v7l/tL+4r7n/te+3L7tfuu+6j7q/uz+9H7wftp+2z7rvvM+7D7dftl+4j7tvuI+6r77vvR+6r70PvI+8j75PvJ+6X7sfvU+8r7qvvP+/v76/uK+4H79fvK+477l/uL+5v7ovt2+2H7Wfst+zD70vva+1j7avvc+wL8CvwZ/AP84Pu++/77S/xD/L77ifuX+5f74fvr+4L7hvvO+5X7Yfuy+737b/to+4P7k/s2+8L62/ox+x77+fpC+2r7bfvD+7D7j/u8+377Xvuv+8/71/sM/N37rfvT+xL8x/sB/Dn8ufuf+7X7ofuF+2D7KftG+5f7jPtq+6z77fv2++r73vvy+xL89/vm+wL8y/un+8b7z/um+777zPs9+yb7tPvg++H72fvp++b7k/ub+4D7J/sZ+zn7Wvtj+1/7WftU+4H7mvuP+4r7cvua+9D73fvZ+777tPu7+8n7lfuJ+8/7svuR+8H7uPtu+1v7gftr+3X7cPsW+zv7gPuO+7r7w/ux+7/7vft/+2D7gvuk+8L7tPuY+5v7pPuO+4r7svvV+/77//u4+8L7/vut+1n7dPtW+yb7K/ti+6L7xfua+3z7w/uc+1T7gPuV+5X7rvu9+7r7wvvW+9r73vu++7375Puw+9L7QvwZ/Lj7sPvB+9T7w/st++P6PvuW+137o/vq+4T7iPvm+9T7sPuc+4H7b/s++yL7Qft7+0D7FPt++4j7gPua+337d/ux+7n7xfu7+5L7KPtP+5D7dPug+5r7ePtn+0z7ufsL/Lv7mfvt+8f7Y/uq+8r7ofvJ+8n7gfud+837y/va+/H7DPzu+477XPty+177Dfsw+3T7ZfuE+3v7V/uQ+4r7UPuh++v7lfsR+1v7/PsD/Mj7jPv3++T7cfvJ++j7hfuP+6r7jvuj+7b7g/uL+8n7kvtj+5r7rfvb+9773Psf/M77W/vd+z38C/xZ+0D7QPsU+z/7Z/ui++/7k/tG+577sPtv+4r7tPt9+7n7m/v5+g77fvsI+876OfuQ+8r74PuF+7z74PtX+0X7lft2+0D7qfu2+6f7qvuu+6D7n/vN+577k/ui+5j7nvun+3/7Uftg+5P7gfty+6776Pvj+wb8DfwS/BP8QPwU/IT7qPvW+477Dfsu+5H7KvtZ++L76/vk+6j7EfwJ/NT7CPzc+6D7lPtT+1T7j/ul+4f7dvuh+6X7uvum+3b7q/vM+9/72/up+2r7gPuS+3f7pvvQ+4j7jPuU+y77LPuw+4P7Kfuz+6n75frU+jT7gvud+6D7ufuV+237e/uD+1/7Lftm+6j7qPtX+0H7P/tc+5f7s/vK+5b7kvv1+wz8rfuP+9D7X/sW+1T7O/vu+iL7dPuE+3L7ePuY+3T7Vfuh+9v7rfuB+5371/vL+8H7r/uT+4v70PsJ/Mj7r/v2+8X7XPuV++P7YPvQ+gL7dPuK+4b7rPuJ+437tPvY+/f74vu3+7b7pPug+477dftA+xH7Xftz+1T7lvvL+7X7tvve+yX8zPtN+4n7//uS+/f6M/tV+yj7Y/up+9/7yftJ+0P7vvvE+3X7zvv3+437aPt8+7T7yfvZ+7n7uvvl+/b7IPwL/Mf7nfut+3r7Tvvm+7D7DvtB+8n7mPsu+6v7zftO+4/7s/uJ+9P7Bfzo+577yfvC+6H77vuY+3T7uvtq+4H7x/vP+7D7ifu8+7b7MfsH+0/7yvuP+5b7IPyV+0n76vsA/M/7yvvo+5/7cfu3+6j7rPvK+6z72fun+6H7/vva+4n70vv5+0D77Pq2+6L7w/rf+uv6Pfvr+xT8e/ty+4D7MPth+5b7dfuG+5T7lvt5+1P7OPtv+4D7TPuT+4v7XPuh+577ZPuA+537UPsd+z77ePtv+1v7aPto+2370Puv+7n7r/th+0r7dvvM++P7svst+5f6Hvuy+wr8EvyE+837nftj++f74fuN+177PftV+077bvti+5b72fuH+237hvuE+537hvuT+9773PuH+2n7cvtU+1T7rfv4++r7h/s1+y/7pfvf+7H7yfud+4r70Pu2+2b7c/vj++D72/u++3P7X/t9+5L79vsN/KL7f/vh+7v7XPvJ++r7l/uF+8/7I/wO/K/7tPu4+6P7yvvI+5D7aftZ+0j7T/vQ+wv8hfuD+7X70vvh+9P77vv5+7f7r/vz+9f7kfum+7v7u/sl/Az8dPtp+9j70Pvz+7r7Z/tn+5H7e/uW+yj8yftj+9f72/vM+/z7qfuW+9z7rvs7+337ifso+xD7J/v2+nz7tPuB+4f7k/t5+437mftv+6/7IPyi+4z7c/sm+0r7fPuV++b77fs++1n7zvvF+xj8R/yn+4n7yfuS+4/7+fsF/M37t/u8+xf8PvzY+3j7jPus+6f75PvQ+xL7IPuc+0f7sPsP/KP7jPvK+0T7b/sN/Or7t/vS+/77vftb+zj7evuu+4P7Wft++5n7uPu/+4/71Pv7+7X7dvuF+7v7uPvB+9f7fvuL+9X7wvvL+5L7mfuh+5P71vv0+/D7y/tV+2X7ofvO+/375fs8/Gb80/td+2778vui+zD7V/st+3v7z/u6+7v7Cfyd+/v6QfvQ+5H7hfv0+z38MPwh/Kv7pPvP+5f7q/vC+8T7nvtP+1T72fsQ/Hn7CvuU+9T7e/t++8z7wPuP+7n7Jfw3/Nn7ofu5++n7m/t++4P7avtS+xr7Hvu0++37tPv8+/77nful+4L7bvtc+5r7xvvW+/z7p/vF+9v7uPuz+6v7lfuV+8f7K/we/Ab8IPwb/CX82Pv3++n7gvu2+w/8AvzW+9r7o/uf+/L7mftd+6b7l/tV+3z7ePtn+8X7A/z9+wP8svt2+8L7+PuV+4D7vfuy++H7Dvyx+1z7P/ts+5j7iPsV+yP7SftC+0L7Sft1+5D7W/sA+wL7O/su+2r7n/uG+3P70/vV+7z7+vsM/NX7s/up+7f7+vvn+3n7rvs3/PP7kfuL+7r7K/wr/Nf7+vtD/C/8/vti/CH8vPvJ+7T7s/vs+w78+fvv++H71/vc+wr8JvzK+6v7mftw+3H7x/vf+/j7/vvd+1n7Ufu6+8T7V/uw+9v75Pu/+7T7S/x0/Nb7Ffta+537WfvM+8n7jfvj+wP8l/uy+637d/uE+137FPtr+7X7Ivsn+0n7bfuj+5n7u/qM+o/7hftY+4r7UPtS+wX8Qvzw+1b7J/tD+8n7r/v/+lX7YvsZ+z77x/vo+637OPv8+lb73fvM+/j7ZfxL/P/7l/t0+9T7kPuz+7v7YftW+3H7pvt7+9T7v/v8+lL76/vi+6X7zfuu+5X7Yvt2+/77QvzY+0r7Vfuu+zv89/t9+7b7afvG+2X8HPyl+8D7pPuM+/L70Ptq+1z7t/v5+//7zftd+z77uPv2++n71PvH+yP8Wvw9/GD8FPx7+3f7Jvx6/Ej8+/t2+9L75/u/+9j7GfwH/B38WfzE+1n7K/xs/O77A/yd+0z7Pvuy+6b6Qvsm/Qv8ifqr+iP7hfzz+yf6svud+7b6F/vF/Gv7d/oj/KX9z/o6+jj8cfua+1z73Ptg/Cj8J/oS+gn+6Pzo+hP7Cfze+9T7Wvq++jz9iPza+mb7af3t+5z6dPte+n77E/3g+5H5nPsj/mv92/oO+Uf7pfyY+176lPtG/V/7a/oB/tH94fou+yr8kPzy+y38FfsW/GL8s/ll/G79Xfsx+Tb7jPyw/IL7KPqU/D39C/yY+gn7Q/5d/AD6eft3/Jb7dPvK+Rj73v2T+7b5Ivyu+iD7FPzA+bv6//wU/EL6jvsQ+578nvxG+y77SPsE/b36qPkR/Pb7JPwB/JL6KPtq+ub7uv2l/KX6HPpG+2L8zPzg/Db8YPrx+cP6KvyI/Ir7l/lJ+wz8UPvw/OX6wvpx+5b7yvy2++L5NvrH/I37XvsK/IX8g/xN+hv7VPyy/M/7ZPp8/Oz81Ptn++H7yvuh+3j7BfvX+1P7HPxZ+7T81fzY+hP7YvsR+zj7m/xk+zr7a/wb/Hn7D/tG+/n6iPvC+/n6BPxo+0z71vsj/P/7wfvb+yL7+fsN/OT76fup+x77Ofxf/O778fpo+/v7q/qq+1r7yPtQ+7f7Uvvz+sH7l/rG+j/7e/tO+0j7gfqM+mj7bvsa+9z7FPu0+tz6jPoc/GH71Pok+8T7EPwq/DD7mvuy+wn9YPyg+lL73/k/+177yvvo+lf65PoP++b6/Ppq+2P7B/wW+0P8CfwW+zP8tPvt+0r75Poy+7f6QPyH+078R/wO/GT7s/vP+7n6Q/t1+9j7PfvU+6P8nfuX+kn7Evyg+zT7j/s0+4n7/Pvp+yP8MvzT+9r7cPtN+9/7dPvS+t/6Lvvl+w78NPtA+5X7evtl+3v7Q/vv+4j7rvtS/O37+vvA+ur6X/sN++b6OPtO+yH7DPum+0f71fuG/KP7avs7/A78WPtJ/Jn7dPv8++z6Gfvl+377MPsB+yr7qPpQ+xb7n/uv+0D7m/oy+3r7g/vm+0T7pftS+oj7A/z/+/b7q/rB+tf6lfv4+on74fvV+lr6Avum+0n8IvwX+6/7Jfxd/Cj8jvtc+6T7Cfzl+x37Bvsr+xb7hPuy+wj7//oi+wr7WPt5+8r6ZPrk+hb76/r6+vX67/pz+9D75fuk+xz7sfum+677I/s4++37Nvtz+/37s/v4+oL7dPsn+zX7Z/vx+2r7mPtY+yz7ufsw/LX74Ptb+5j68vuT+8L6gfv8+tD7nPs3+8X62Ppa+7H7zfuL+3r7mPvA+xP8mPwE/Cv7S/sT/Hn7Tfvc+xb8p/uW+0T7NPv4+/374/qR+5P7mftk+x/7SPt/+277Dftc+0r7d/t7+yL7LPtU+3v7tfvY+zH8JfxK++D7evtT+wf7d/qN++b7/ftN+5b7cfvK+/H77/pL+1n7ofrG+oP78fuJ+1v6ZPmy+nr7u/wC+6T7ivo2++n7I/tQ+hH7avu/+hv7Yvv++xb88fvT+k37BPzX+6X7Nvvm+lz7RPuT+yL7iPpN+1b8YfyN+4T7Cfz2+7376/pZ+mX6Ffqz+rn72Pt3+xv7Hvuy+5n7xvsC/C379Psw/Oz7Xvyx+0v7QvvX+4L79PqL+7n7D/wu+5H7yfuh++z7j/v++9v75vuK+8/7xfr5+hn7/PoW+1H80vy2+w/7EPt3+9P7VvxV/B78jPvA+tv65/tL/Hj71voK+9v7t/w4/JX70/vP+/j71vtA+yr7HPta+yD7X/ts+zL7/foS+5b73Pq/+zf7Rvui+2b7D/ss+1D8Y/vP+jD7x/ud+7j7Cftn+yn82/v7+9D7evsy+y371fsO/If7l/t/+3f7r/vj+jb7nvvM+wf7y/q3+w37M/zn+/76lvsL/Ej7FPvA+3f7kPvQ+1n7EPvo+6n7W/sW/Pv7Vfu9+4/8Lvzo+q77k/sS+xf8TPt6+3H8JvvF+nr7q/sj/HX7wvso/Gb7UftH+8z6+vry+h/7OvvO+s/6Fvsj/K37kftg+4v6cfsg+zH7n/zQ+6z7cfwf/Kj7BfyE+1j6Avte+zX7fvuM+7762/t2/UX9svz2+wT7nfuT/E/8X/w4/BP8q/u++4P8ZPwT/MX7uvu2+4P7UPtZ+x78kfuF+zn8o/uD+4H7M/u8+/T7afvH+1f8Svwb/Ob77PsJ/Cn7ePsC/B/8V/xG/P36CvvB+077p/vJ+vn6QvxJ/JX7WfsH+4X6d/tB/FP7U/vS+9D7UfxA/PD7wvoF+0H7Z/ri+i/7v/qe+7b7+/ou+yD7X/tE/H78tPtA++f7BftC+1f8+vu0+277yPsX/D/8YPtZ+5n70vqw+iz89PtJ+xf8Z/sl+/r78Psw+4f7A/zz+vX6hfzy+8L73Pz++1X8Jfzu+wL8j/us+976IPuS+3v7wftz+2T7xvq9+yv8lvsk/Kf79fql+m37H/sk+0H8nfvk+wn7mPv7+yD7pPpY+tj7+/pc+nj7qPoH+9H7Rvur+hr88Pt5+u37x/se/DD8bfyt/Jv7vPsa/OH8XPwg/Kv84vxr/AP8lvtb+677DfxZ+0b7jft7+3P7M/zf+/f7NvyJ+zT7R/uD+7T7k/yd+2T7Pvs/+5X7JfuU+/76Cft5+xX8RvxC+9b7/PxT+5H7fPwD/KH8CvzP+2T82/tY/Iz79PrT+8j7Z/sW+6z7M/um+9b7Lvtm+3372PsC/BT8kPz7+/r6LPvP+1b7xPpR++/6yPsz/Mf7Afya+9P6b/vw+wv70/uM/Oj7UvuI+zP7S/vs+7D77PvE/F38VfwX/Sf8ivs9+9j7mvyk+wb7avuE/Ej8p/uj+6D8/PsW+yv7SvuV+2z7xftJ+9b7Fvx8+6373/u++9n7P/xC/ID7G/vq+9n74Ps7+wT74vux+xX7XfoQ++L6EfrG+hP7gftN+7X7B/yC+5z7Bvyt+6n7JfxL/Hf8RPzH+5z7G/ss+x/8B/xy+8n7hfx2+zb7uvsm+mj66/ve+2779PvA+x77f/rm+ir8ZvvJ+xz9v/sp+xr8GvuI++H78Pq9+4P86Pso+3H7OPvG+4X7Xvop+zD7Lvt0+5D6l/vb+mr6xftE+xT7O/u5+hr7G/wa/Hv7Bvu6+jH7P/zd+uP6Wfwl/C/8E/wJ+yH78fp8+tz60vrs+8/7MPs5+g/7wfsb+zP77voZ/Pv7svrY+pr7QfuX+sj6evsm/C37I/vb+6b8RPwg+4/6Q/vu+5/7CPuK+8X8Cfy5+yj8lvve+/b7hvvl+8n7pPu0+5f7xfu3/Mz7s/qd+/f7Jvxb+577Zvyz+6X73fu/+xH7vfvU+/37HPwp+7b7vPtZ+6T7zvtz/Gz8+fvH/If8mftx+1n7S/tH+837r/vd+0T8nvv6+1v7Mfs3/I/7lftW+4j7avyR/IX78foK+1v6d/u0+4j7V/w++zr7lvsy+0/7WfsJ/Dn8afzc+877qPsq/ET8jfsa/Jf7s/s//Mn7MPwr/NH7ovsP+8r71PuY+3f7MPuq+4n7Mfst+6P7lvt/+1r7VPu+++z7i/sh+xz89/s3+1/80Pwi/DT7/vvR+1z78ftV+5n7svxT/On7sPwQ/MD7ZPsT+2v73fvQ+wr7bPue++f73Puf+yz8lvt3+4P6f/rM+zT8vvsD/Gn85/sx/En7wvrh+vr7EPzy+wj82fvt+4b7mfqK+un7Nvts+3f8KPwX+8b6wvsL+4D6lPu7+5X7S/uT+rb6RPto+3L7Ufz2+5n7kvvb+tb6x/qf+h77iftu+1j78/ob+yv7uvq3+yX7vvqp+4n7Rvvh+r37h/tP/PD7BftF/Kj7tfvf+3j7lftO+y77xfvZ+1D7UPuz+wP8i/tS+yL8y/s5+0X7r/sZ/F/7Gfv/+yH8NPvk+0j8SvuU+yH8gvs3+6n7CPyS/Dn85/tZ+1f7QPzR+//7bvsU+zL74/v1+1r78vuP+7D7J/ti+0b7v/p8+yf7KPt5+/P7/PtN+xv8Kvx6+7j7cvtw+wX7sfun+4f7w/yB+x37kfsd+0z7UPsf+4j7yfsk+xb7nPsQ/MP7Zvtd+537pvtz+5H7ZvsW+137Y/v2+1P8Lvt++2n8mfur+tL6TPuw+n36qvtj+0X7CPz1+5b7qPt9+1v7Yvsx+/T7+Pup+/j77/un+7v7e/tZ+1v8CvyC+wn8p/vp+5v7ZvsM/Oj7U/sS/LT7HPvf+937ovsQ/O77pPvN+2L7Z/tw+yP7RPvG++D7+fuP+2D7qftQ+zb7+vtv/Bj8APzg+2z7SvsX+9b6gvvi+177vvvH+5r7u/sK+837Y/wQ/H/7cvu0+yv85fv++mL7qPsU/L/7cfvZ+637CPth+777O/tY++P7JPwj/DX88vu8+x37K/uA+0T7Uvu0+5v7Fvwb++L6wPs3+2/79PtR+yb7BfzN+2X7g/v++yj8hvuh+yL8sPvB+gT71PsT/Lv7sfuk+3b7OPv1+lD7dfsK/CP8wvvu+xb87/sq/An8M/v2+yn80/sx/Bv8Rvy8+/D7Kfz2+937rfuK+yz7HvuL+8b7oPsv+7X77Put+7P7k/uf+3P7v/uV+0L7SfuF+5L7YvvC+7z78vvN+1P7gvtw+0j7JPvk+9D7U/uZ+1374vta/Br76vpl+1j77/rc+nD7W/tT+5P7avuB+w38l/tq+/372/ut+4T7BPtk+zD7WftH/Ob7APzc+6f7aftM+5n7Lfty++v7YfsH+7b72/uE+5L7z/t3+/T6D/t4+6b7evtR+1z73fv8+7n7CvxP/BT8Yvwe/Ir7vfs0+/T6v/tM+3X7Tvz3+9f7ffsp+9H78vsc+zv75/u5+5r7Gvv3+tb7sfsG+577BPxg+5H7mfuy+z38Wftx+zX7VPux+7r6V/sn/H37Vvva+577fPuz+4f7UPtm+3T7F/sq+w/8EPyR+5770fuv+4H7gvuR+/P7hvth+0r75fu0/KX7JvuC+zD8mvvw+qz7+fsi/NH7o/sL/FP86Pu1+/n7aPwE/Bb7Xvu6+wT85PtI+937O/wE/Jr7tvst/C38KPwi/Nf7f/uA+/L6H/uC+yL7J/uZ+477A/vW+sf6E/sQ/BT8fvum+2H7UPvJ+zr7Wvvi+6D7n/vz+yD8jPtU+1v7Dvti+6X7rPvX+8f7wPuy+1/76vq0+n37N/uq+oH7evuW+gf7dftF+5j7YftT++D7qfv++qv7EfyZ+//7D/xQ+8P72PtA+4L7DPtc++X7YvuD+3T7SPvG+2T7rfsO/IX7jPsI/Nr7Z/t/+2n7/PoN+7T79vvf+xz87fuH+437Ffyy+//7tPzb+5f7r/tv+5z7n/sj+5373PtT+1P74/sD/EL7KvtW+4f7HvsU+6T7ZPuN+9T7i/u/+8b7bvvG+4n7wvvl+4b7afvf+xf84/uh+/D7GvyS+wz7G/uX+4/7nfuH+6j7Lvvf+rD7+vvr+6H7Xfva++H7nPs3+z773/vD+5X7//sR/PH7yvuD+937FPx5+yD7gvvM+4L7UPux+9j7mPv5+tv6sfuo+zX7IPs2+0D7nfu7+7b7ovvd+9r7wPsU/AD8Lvwn/Cf7FPsI/Ov7cvvj+/T7f/uk+3T7f/u2+2D7TvuM+7H7WPta+8X7TvxX/Ar8yfuT+0v7DfsH+xL7aPuo+//70Ps4+xX78PpA+9j7ofvV+2n8J/y7+3P7c/uL+6P74Pu8+xn8yfuC+6f7nvu3+wT8/vvm+wT8JPzI+y77o/vt+9P7uftY+2D7w/vX++H7x/tg+1j7bvta+7j7yvvY++D7avuB+3z74vrP+nn7dvuE+5T7RvuG+5b7fft0+9T7Rfyg/GL8ePsi+6L7kfuI+/H7DfzH+2b7Vvsm+x372frw+uz7bfyZ+xT7NfvK+ib7tvul+7L7pvuK+4H7r/uf+5f7rfvR+8778vv2+7X7FPw0/EH8Bfzi+8b7Oft3+3j7F/ta+377ovvl+2f7cPul+077jPvC+2v7qfse/Kn7gfv4+437qPvh+8f7c/tL+2L7jfvD+/P70fvZ+1T88fvy+7D7MPug+9j7e/t9+yv72/rv+hj77/qU+yP8ofty+wH8Cvx4+9n7lPyj/BT87Pto+2b7nPti+wL74Poh+3H7mvtD+3/7TfvA+hb7YvsY+/D6KvuW+/j7ifw+/Lr7gPtu+/D7IPwP/Kn7l/vM+6j7q/vv++f7yPvX++X7DPw1/B788vuE+/j61Pom+z37gPu6+5T7fPsD+736uPq0+gP7fPvI+6n75/tn/DT8Hfyy+6D7ePsG/O/73ft7+wH79frL+gP7Bvs6+9z6s/r2+k77CPtL+9D77Psz/Df8EfyN/FD8ufuw+6P79/tX/HL8hPuW+vL6K/sl+2D7zfuA+1f7zvvB+137LvsP+3z7kfx7/Pr7IvtK+2T7e/sg+wT76vuL/B38Ivtv+4X7Pvvd+rD6s/s//Ib8Mvw2/IT79PoA+zb7lfuc+/f7qfyN+zv7ivro+Yr6q/qS+5n7qfsz/Fb8O/xt+6r6D/v3+yb9mPxe/Dr8WvtI+/D6R/uA+1f7+fr/+oL7Ufsr+xb7ivv1+2n8cPzz+637cfsv+4H72Puy/Kj8DfzV+4v7v/ve+9P72vtt+/L7Wfyz+9z7ifuX+7P7HPyA/Cz8sfv/+tf6z/pX+yL7L/uy+tb65vk5+fr5Z/rr++/80PwW/fD8Sf1I/Yz8l/wV/Mv8iPwZ/GP7ufo2+l35hflH+qv6NPoi+v/61vvq+337d/v5+1T9Av3//Cb9tvtG+6T7v/xq/K37m/uy+5v7C/uI+nP6l/pM+5v7HfyK++P77/vM+3v8xfxI/ev8mvxk/Bj87/rj+Zj5/fkB+i/6F/uH+2r7gvpz+9/74vvs/ID93f2g/fH8X/xO+2H6+vmx+jf7bvoQ+U/51fpP+7z6kPom+/T7ofx4/Gf9hv1U/ZH8mPxz/If84/tm+zj7dfpF+nT5CvoR+mb6m/pB+0z7EPvf+o37jPyL/HL8Av3U/Fr8V/z3/Kn8nPta+yT7HvtL+rb5gfks+sX6yvqs+hj7Ffys/Ov8eP1J/UH9b/1J/bf8W/ym+0P7j/sY+zD6TfnB+cr5aflt+XD5WPpE+wr8Vvyn/Pz8zfwB/eT8p/y+/CT9KP2E/In6+/js+DX5Yfn2+iD7vfoD+4X7Lfut+jv7Cvxj/Xr+Kf53/Vb9L/wp++/7Wft9+2L7BPtk+vf5fPm8+SL6ifon+2L80f1//bL95fxp/CL9/f0G/tn8f/xT/CT6F/rD+Q35y/ix+Nv5fPqS+8372vt7/OT8sP2Y/fn92P2v/c79eP36/Lr7kvoy+qf6OvqI+fT4v/i4+Gf5o/qz+lL7x/tR/cf80/zX/F/9i/1y/af90P2r/WD8I/pd+JL4J/oR+8z7ZPqz+VH6CPqR+nf58fr/+43+0/+Q/5X9j/sH+4z7Uvz/+7D77vr5+R76u/pB+e733vcj+fv6SfxP/XP9z/xh/OD8wf0j/Xb8O/3s/Pr8Bfzj+rf5bfg1+c/54/qq+l36Svqe+dr6J/x1/N38zf1w/pP9ovxW/Hr8Nvzi+8/7SfzH+036r/lY+fv5LvqW+rL6nfvB/Jf8aPwx+0j7hvzV/bH+U/6x/Cv6c/nE+iX72vqZ+fX4Ufpm+xb7n/r0+nr77/x7/Vb96fx6/TH9WPyQ/Bf9cf07/PP62vo+++36I/rL+fT5qfn0+ab5vfkd+tn6i/tN/LP93P2//db8lfz4/AT91fx2+3b7jPsf/Mv7bvo7+mb5C/rL+fr5n/qa+j773vtf/W39Uf2a/D78PvwJ/NL7lPt4+4b7R/uX+uv5QvqT+qj6V/ow+1X7O/u8+8T77/ux+wP89PvH+x78pvw+/Z384/pH+a74fvmo+/b7r/tU+hv6dfpY+yv7N/tM+8j8HP1q/mP+9vst+5X6IPze+9383PuV+rL5pfpu+kf6Wvmy+Zz6TvvL+1v8VPy6+/H7ufxr/Rf92vxH/HP8rPxH/Kz7Hvte+nn6w/qg+iv6PfqU+vX54PoE/Bn87/q2+yD9V/2r/Uv9mfzC+5X7y/ve+7T7gfuC+/f6Hfpb+sL65/rn+oP7Ifyw/Bf9kfyc+wj7VPvF/Gj90vyI+4T6DPoy+gb7mfti+ij5Lfor+9v7Vvs5+jH7vPwM/vv9kf1O/YP8Qvxz/JX8u/y/+4X6vfrH+or6xvlw+QH65/qw+9f7DvuV+tT6Tvyf/T/9//sn+5X6m/oY/Jz8kPs5+lv6Ofsa/Kf70Ppa+rD6ZPvL+/L7tvrK+t36SPtn/F38GPyr+zX8Bv2C/Ab89/od+m366fpn+5/6KPo2+lT6MvpY+iD7zPsC/PH7UPwP/D/7Kvv5+7D80/x//D37Ovrc+ZL6aPu1+8T6E/r5+tz8lv2V/G/6mPq/+xn92vzf/Mf7YfrO+sT74vv1+w78DPtm+1P8hPvY+df5d/oG+8v7VPzF+6D7lvuC+/f7Dvy7+/T7l/wJ/Z38mvtm+kX6DvsR/NP7zPvb+rj6pPrp+tz6O/uY+737H/yD/Kv8aPy8+4/7l/sN/JH7I/uI+rb6CvtL+4j7QftT+3z7evuZ+5H7oPsw+7L7Lvzg/Hf8Q/tU+6P7K/yr+8z6qvlP+Yv6jftT+9b6wPoP+9H7Uvwo/N/7+vpP+w/87PwO/Gf7/voR+9z7dftx+zr63PqP+n76cfsd+yj8N/tz+5f7jfvB+1v6g/sA/In8sPwk/Hn73vo3+7P7N/z1+8/63fqU+537WvsG+3X69Ppu+5v7kPt2+wr71Pqt+0H8E/zX+3D6ePo4+/r7hPvA+9X7A/tr+xr8uvsJ+zz7ufs+/I/81PtO+3L7OfvG+x78TfyQ+3r7ZPsn+077Afv5+oX75vtm/Fr8vftU+1n74vuA+wz84PyJ/If7Xfuc+2j7IvvE+hT7cfuO+4n7WPuJ+zL7SPvO+yn8BvxM+zr7n/vR+yL8Jvyi+/r6Q/sw/An8LvzX+7v7WfwL/Dz88Pqh+2v7bvuP+7r7I/vx+W/6Ivta+5D7dPtk+4v7m/uV+4z7DPzY+/b7Ofx5/Dr8evso+4L7S/xQ/BL8//vF+7X7mPub+6j7kPth+0j7dPtj+zT7Hvvj+rL6kvsm/Ub9ovzT+2X7Mful+zP8zfs4/H/8Tvxg/I77qPtl+6z7FvxU/Oj8e/yT/AX8Pvtm+xz7tvql+tH7gvvW+m/7cftw+8v7Mvzn+2r7w/tR++H73fvj+/r7nfsu+7P6ifvr+3f7Jvui+yD8D/xk+1X7dfsL+0j7OftQ+2D7ZPtY+zT7mvtI+zr7XvuW+w/8EfwF/D/8rfy7/Gn8CPx8+0T78vp4+uv6JvvY+hf7SPu7+5n7lPt++wr7eftz+1D7pPvn+238Afzo+8P7Avxl++P65PvL+7T7Vvu1+0z7iPsh/Nj7u/vi+937tfu2+4z7dftV+0P7UPtb+177uvpV+4P7i/sF+5D6C/v++tz7ovye/Gv7aPsu/B/8fftO/EP8hft8+8775vu6+yX7zPpg+1b7PvsP+6n7AvsG+4/7IPuF+4/7gPum+wL8N/xs/J38KP04/Sn8TfsJ+2T73foG+0j83/so+1j7lPs4+0v7cPuL+837xPvd+8r7fPtH+5T7Y/tl+9b7hfs7+xH7cfun+8r7QPzw++z7G/yJ+5n7vfuf++L7cvt0+2f7VPtM+8X6tfrd+mv7jPtl+8j7ufs5+5z7//v1+xj8EPz5+zX8dfxl/Df8NPyP+4T7lfs++3v7mvuQ+2n7b/uZ+0777Ppk+u36S/sJ+0f7Y/st+936VfvC+gz7d/vK+777Gfxc/Mv7CPy1+7j7IvwG/AX89fuN+9f72fsY/L37kvs1+w37svuM+7v7l/y0+3D72/rV+zL7Evt2+2n7gPt6+yX9LvxY/G77bvvJ+sv63fs+/EX8VvtH++f6Sfs3+737z/r0+iD8rftA/Jr7ePsJ+8H7oPvi+qj7dPuZ+6/7y/sv/BT8PPz6+x780Pts+6X7vvtn/If7dvvG+qj6Zfpa+x/8qvsB/Lz75PuA+4P80fuk+1n8bvtr/J/72Pum+3f79Pv9+lf8yvuE+0L7nvsE/JL7hPwl/Gv8svtQ+9D6D/sB+7X6s/pq+/76AfxQ+5H7gvtd+1j8fPtY+977d/zX+zf8/vs9/Oz7R/zO/Iz8+PtB/Ev7pfu++9v7T/tc+2T7L/vb+4f79/ui+4b7gPuJ+9L7cvv/+pT6Rfv2+wD8pvvi+gr7dfts+5H7MPww/Kn7svvX+xb8t/tX+0T8ofsi+4D7Vvuv+zr7YPtu+w38KvsA+0r7JPt7+7/7O/zQ+xH8wPuX+/n73vxb+338Wvzx/Kb8e/ve+nT7w/ug+Qf7xfus+577rfqK+7T7bvv9+nP7Sfw7+/f7V/sn/Bj76vsh+1r7u/uf+0z7Xvqn/I36gvwt+1P71fv/+hP8uvrb/Mb6ofvO+q37avvg+rH78Prc++L7V/z++7f7vvtw+wH8Q/xX/O/8uPx+/K78Jfzo+1P6ifoX+/T7L/wF+7P6t/p4+zr7rvsZ+yn6jfvp+lH7Lvxt+1r7pvqb/DX7jfvy+0/7afuA+/b7j/tm/IX7oPuH+7n7Y/vl+hL8Ufth/FL7dPuY++b6Bfy2+lD7KPv5+5v7J/zR+2X7GfxS+iv8OPtQ/ET7svr0+/T7IfxW/Jv7YPvF+w376fy6+nj88PuK+yz8wfsh/Nv6KvyB+777cvyX+8f7yvrM+1j7w/sL/Lf6lfvK+kT7svt0/Av8WPwi/N76Mfzz+m/8Gfzg+yX9ZPvZ+2/6A/yk++n5aPvc+9b7H/yE+8X7p/uj+2D8i/uk+8/7+fvA/Eb6kPzK+yr7sfuj+xT9C/si/MT7jfze+1f8Hvyj+uz6+PqB++P6IPvd+8b7Xvt7+4P8i/vd+0b7R/3u+5H75ftt+5L8rvoS/S37Dv0L/cj6JP6a+rr7h/yQ+j/9uPrQ/IP62Pmd/f/4tfyH+hv9lfsM+9r8WvsZ/J/7qfyc+qH9bfoz/Q/7TvtP/MP6N/2n+lb82fun+8n8gvqN/sH6Q/zA+3j6rf3H+F39zfnn+2/9Kfpq/YD64/1z+mj7SfyO+lz8tfkx/cX7BfsW/Oj6zPxa+9H8xPtA+7r7ZPpQ/E37evse+/j74PuC+wr8xvpo/KH6bfwX+zD8xPxA+xb9Ufvn/L/63/tM+5b7lPzi+nv8BfwU/HD7YPzt+336ZfxF+6n8Cvpc/Nn7VPmJ/KT5z/sM+1D7SPxI+rD8IfoX/Zb6hPuB/Kj60fxi+sD8Tvtm+0f8zPvO/OH6vfyF+7n7Dvww+4T8Y/rf+yD7p/tj+/H6AfyU+pD8ZvuC+237+PqQ/M/63/va+477jvvK+1T83/tq+676/vpt+5P7VPyx+yP8HPvU+0j6x/oT+xz7vPuj+nX8u/pk+zL7vftP+2T7Dfyo+9D7Gvx0+5f7KPws/GD7wPuc+038CPtQ/Mf8tvp4/Gn7cvwy/PP7dPv7+9b7Nfy1/Fj6FvyF+zv8rfr8/Cj7nfoc+4n7EvzY+wL9WPtN+3r7+fpq/Lb7uPv3+mH7z/uT+t78NPqL/AH7bPvh+1r7OvxR+z/89/qS/Gf7UPuP+zL7WPs1/L37lvsR/D/7CvyL+0j8Tfyr++77Rvxp/Jz8Hfwi+7v7J/xL+737BPuU+yb7evsw+x78evun+zP7O/vK+6r61Pyb+oX77vpE+zX8KPsk/O37hfvl/DH71Pyy+yj7+fsY+3X86PoQ/N/6mPup+//6LPzN+hr8Bvt7+x38T/sp/Nb7Lfv++x775/vx+/n7yvt5+3f7ePuK+5b76vsF/Gj7Ifym+5n69/sj+gP8k/vi+uD8KvqR/Er7JPuk/AL6vfzQ+tn7v/zl+nr86fpV/Ef84/uE/Sz7z/s2+5P72fue+yD81frm+/f7XfsP/JT7c/tI+8f7QfwG/GP88fvr+yr8D/uy+2H7zvtW+936z/vy+hn7xfvh+gj8XPtH+3775vpu+zj7aPuk+736/vqS++X6SPwO+0X8svsM+2T8zvqk/JH7jvsH/AL7vvvo+k38YPud+wP8Sfuw+0b8UvtD/Hr7B/za+6H7MPyJ+in8sPqM+xD8Cvuq+zv77fuE++X7l/tU+477afvA+wv7R/zA+vj7dvtK+5P74PvI+0D7YvyP+wD82/uT+zn8e/t0+8v7r/to/KL7mvq/+5b7vvuk+//7AfsO/F77pvty/Gr7PPvF+7z78vsP+wD8hftO+3H9kfqh/Mf6Q/wp+xD8ifyT+ov9fPrV++T65/uw+t/73Po1+yn76Ppi/Mr68/wQ+/H7UvvB++r7Ffx4/Gf7rvvQ+jT7dfvA+337qvvz+1n7iPvM+6j7nfve+/X7svsU/NL7PPws+8P7GvuP++b7aPuF/M368vs2++T77/tD+8/8Qvsm/F37Hvv3+oX6vvtO+t/7c/tf+8X7Afv2+2H7sfte/Cv7Kvyl+4L75Pub+zn7tfvM+gb8SPs9/Bv8fPvJ/Nz60PzE+4H7Qfu3+4v7IPxS+7r7L/sN+wb8avsG/HP7W/yu+pL7dfvT+1L7r/sn++v7Uvtt/Mf6wfs/++76Hf3V+lX9wfrN/Nf7mvt2/Fb7hPue+kr8svtH+/D7Cfv5+5X65/sf/KX6JPwR+hb8Rft8+xz8FftO/Bz7u/w0+8v66ft6+wL86PuL/Pz7p/sK/IL7rfxS+9P7ivuy+hb8CvuM/LP7QPtX/BH7Efxt+5/7gfso+0v73/sX+4z7lPtd+4b7oPvK+1j7evtc+y37ePuC+1z7g/uE+/T6B/zl+8D73Ptl+wj8TfvT+737LP19+/H7vvtY+3H7aPtJ+zH76ftu++j72PvK+9L7Kfti+477Lvsh/O76+Psf+0b7sfvE+hv7Ovvw+7b7SPzq+3L7jvus+6z7x/s//Iv78fuG+4b7dfyI+8/74PvJ++D7yfvH+xP8ivuo++/7RvtR+wr7dfsv+6j7aft5+z77R/to++D7BvyM+7r7VfuN+/v6XvtE+2z7LPvu+iP7mvs8+0f7QvsA+477zfud+8D7H/sz+1P7N/th+w77ffuR+iD70vuz+0D8evsM/P76o/sG/Pf7G/w9+2v7nfqp+wv7YfsX++36D/xq+3v7NvtN+/P6Z/v0+zD75/uW+477k/un+yP8IPu9+9T70vvh+8D70ftx+2X7kPv6+0P8QPzQ+yf7nfoy+2L79vpl+zr7Vftl+8T7R/t4+2X7Gfu6+1v7qfsu+zH7YPsO+4/7TPvP+3z7SfvA+9P7I/zQ+w/81ftP/Hn89/v6+7774/u8+4j7ePsg+1P7Kvum+5T78Pot+8n6E/ss+2P7Tvs4+4z78frr+qb65/rn+gr7nftN/OH73fvW+8/7tvuf+7/7dvuE+4/7oPsi/A78cPuL+0n7w/u6+wr8uvsm+7r7NPvw+9T78/vt+z77wfsT+1T7NvuP+7v7XfvQ+2X7N/st+y/7mPtN+yn7yfo4+6H7r/sD/A/8xvvy+yj8Q/xR/Cj89Pus+777kPuc+yj7Y/s5+3P7kvvZ+gP72/r5+uL67vpD+wP7oPrW+vL6zfqW+k77jvuY+zH8gfsO/JP79fvY+5b7M/yY++z79Pua+wX8ZvtQ+yb7Fvt1+2b7gvsn+xr7Evsv+5/7EPtI+/L62/ow+8P6e/sL+037evvQ+3D7+/pZ+1T7x/uR+yH7V/sm+3X71/vs+xj80/v/+yr8N/xC/Lb7CPzY+4T7Mvsg+7b7TPtT+w/7s/rt+vX6g/vX++/75ftP+0D7yPpx+1H7uPoQ+037//vB+/r7mvsD/Mr7Fvur+7r7WPzF+2r7aPud+xb7Pvug+un6NPtw+wj8QvtC/LT7pfsE/Cb8WfwD/BD8DPzK+9f7OPsa+zz7RfsN/GX7y/u++6L7R/w1+wT8H/tD+7H7hfsY/Cv7I/xo+9f8evxr/EL8R/ue/LT7YPzf+6f7BPyk+wb8wPsh+6v6m/qN+pj6Nfss+2r6OPq8+hP7IvsC+y36Kfta+7772/uz+wH8hvur++D7YvwY/HT7hfvH++L7MPyF+w77Yfsu+wH8dvvg+2b7F/sX+0v7ovti+0T7BvuX+9f7DPw2+3H7I/tH+8L7c/tk/M/7WvsE+yj7sfv3+lb7Mvti+0f7oPvW+5P7DvwN+0z7qftf+5L7Fftp+zP7YPvU+277wfsz+3z7n/vC+6j7P/vH+/H7yfzn/P77P/vu+jT77/uw+5r73PuN+y/8t/sD/FD7FPsi+576KPvx+gL8LPuV+xH7iftq+//6evu0+rD7/frD+1r7a/tf+8H6cvtF++n7e/uB+6P7mvvz+wj8DPyU+277lvuC+7v7bPs6+y/7FfuK+4P7svue+z/7BvtQ+2X7k/u++7L7wftK++v7efu3+2L7D/uL+2v7w/un+x37Qvux+5f7uvt7+/T7N/tY+3z7M/tR+9f6pvuo+/n7rfuf+8b7vvur+6H7yftz+577r/vT+9j7yvuu+5L7gfuk+/b7mPsh+zv7Lvst+zP7Cvsh++n6iPoz+2r7+PtO/P37E/zO+/L7A/zs+6r7cvtQ+037p/us+437s/tx+6X7l/vD+3z7Hfvs+kn7fvs3+/H6Dvtj+yP7C/yG+7H7pftN+577gvvO+4b70vt1+2L7XfuC+0X7mvqe+q/6+fpX+5D7qvvZ+2z75ftv+1z7Zvse+0b7SvvI+7j78fvQ+//79/s5/N/7i/vD+4P7Ivy8+wD8r/vM+8n7J/sJ++b6NPvB+vP6fPtm+zX7mPs5+4v7Uftq+377JvuT+8L6Nvst+z37UPtg+9f74/tx+4n7ePv/+jn7aPuU+6n7kvuD+7/74Pv3+7n7Q/s1+1T7Mvuf+2L7Iftg+0L7mvt5+137Tftb+5z7f/te/L77RPs8+1z7Rfve+o/7svsi+2H7Sfth+277YvuL++z6dvvf+6X7Mfyt+4z7KftU+337aPt7+4v7m/u7++r7s/vU+3j76/s6+6v7aPts+3r7K/vt+w77YPsH+z37n/vZ+zz89vtN/Lf7TvuY+sX6FPtC+xj8Nvws/Gf79PqY+0b7xvso/Pr7HfxX+0z73PrA+h77M/uQ+xT7FfuT+3P7Uvu7+0z7Ufs/++37u/uk+4j7xvse/Pb7pPzj+937SvuC+8X78fvg+7f7rvuA+3P7evtD+3L7M/vk+gf7+PpD+1v7Vfu/+7f7SvuF+4/73vuG+1z7APvf+jb7cfsB/Bv8Kfy7+xv7GfsP+7n7HPzM+9j7Qvt2+2z7ZvuR+5L7WPuK+6b7ofv7+gL7FPsz+wX7Hftp+4r6vPr0+of7pPud+/X7mfuT+4n78fsL/KT7f/vc+t767PpF+277T/sx+2372vvP+5L78/qx+gL7kftc+3/7cftZ+9z7z/ue+9r7qPt/+337Kvwc/Bf8JPzk+6/7LvtB+/z6G/ui+tj6FPsp+4v7rvt8+zT7YftT+2f7dfuE+077V/uH+377zvuU+x/8Jvwh/GX84vsH/Er7FPvQ+tL6Bfsj+8D7Rvs5++76ofo0+or6Mvtt+7n7+/uf+2H7WftZ+0T7cPu7+2P7k/uK+077Q/ve+wD82Pt8+4n7ZPsQ+xn7P/th+4/72vuP+8b7zPuP+7v7z/s2+/76Efss+4v7aftl++36QPtp+4P7vftr+6/7mvv1+4P7ZPuL+7D7cPsw+6z7EPtG+y/79/pf++n6L/sp+4f7F/wE/DD8yPsE/Fb7wvo4+2/7uvvQ+/f7Afyo+8X72fvO+/774fst/OX7Bfz0+877+fvc+zb8tvuH+2L76/ri+hP7WfuO+7/7tfts+4v7ZvtY+4v7yPve+/D73Pvl+9T7sfvE+8X76fvR+5r7NPvd+uP6CPsx+yT7bft++zz7fPtl+z775vpY++b7UPx0/AX81vtX+137ovvf+/T7yvul+6v71vvc+6b7yfvH+8z7rvtV+2z7GfsS+1z7gfuQ+3z7p/vf+4r7bPtn+837fPvH+4P7a/vT+1T7y/t0+9v7g/ub+wL8Rvue+4z7k/u0+3D7OftG+2X7i/vP+5/7wvun+w78vftv+7D71/vm+2j7BPzH+0f7Y/tY+8n6ofrb+kv7m/sO/BH8H/yn+337UvsQ+zz7cfux+7r7qPtX+0j7Sfua+2z7sfvd+iX7L/sH+0/7H/ue+yP7Lftj+8H71PvF+wD86ft2/Fr8Svzv+1P7hft/+7/7VPt1+1D7M/tp+9r7z/u2+9f71vsU/Kz78Pu/+777jfu2+7/7x/ui+4j7hvti+4r7l/t6+zL7p/vn+0f8YfwD/IT7Kvvk+lT7bPsV+1f7pvt7+5/7xfty+4D7bftP+6L7kfuS+4H7rftu+4T7rfvu+4L88PtS/Cr8BPxk+wT76Prg+nX7mvvm+9D7W/ta+w37JPt7+0X7fPuh++v7uPue+3P7JPtP+5X74/sM/GT8PfyR+yf7Xfvc+6z7b/t8+2X7iPuV+4/7dPt4+1P7G/tO+8r79vsZ/Oz7C/wA/N37+/vh+7T7uPvR++n78PvG+/H7tPvv++371/u3++T7Rfz8+8T7T/tA+xz7Kvt2+0n7Qfsh+3f7gvu2+8D7gfuN+577x/vo++z71PsX/Lj70vv8+8j71fvL+8j7sfsD/Mf7h/tM+z/7YPtk+//7y/u5+837pvuF+xT7/fq9+hT7ZPu/+/r7B/z9+6/7y/uQ+8L7p/uz+w/84PsP/Lr7p/u3+2X7iPur+5/7d/tr+4b7i/tF+1D7ZvuO+577pvvq+wP87vvv+6X7cvtg+077qfu++9X7A/wo/Cv85fu8+2b7Wvul+4z7rPty+277ift2+277o/vy++L7mPtB+1n7dfv++uH6Ofvp+vb6WPvN+/X70Pux+7T70fu4+wn8/Pv4+xn8+Pu9+4b7avsQ+xz7V/uJ+6n76vsE/K37dvsc+xz7Jftz+7r7Dvz++2D7ZPtK+037R/tb+8T76fst/H78gfxJ/Nv7Y/so+zP7XvtG+277pPvG+1H7Mftj+yn7U/uU+9H79fv++8v7k/tQ+2H7bvtn+6r7x/vI+/X75/u7+6n7cfu4+/z7EfwC/Mz7VPsM+0/7ePth+3P7YPtw+6T7vvvI+6/7hftb+5b7pvvh+9X7vvvH+3b7b/uo+977xPu/+wj8DPzx++37v/uX+3/7fvt1+1P7YPtf+0L7bvty+3j7Tvvi+lP7QftW+0z7TPuG+1T7rPuj+xn8I/yW+4b7q/sI/Nr7qPup+377K/ta+1H7Efs4+wf7zvoL+437Y/uG+5n7ovuk+3P7pvto+277cvuK+5H7WPu3+2v7k/vY+7n73vud+9b75/vw+/f78vv5+8f7y/u5+6z7ovt1+5H7uPvG+9D77PvX+4D7vPu8+7P7s/uL+6b7nfuG+5n7hfs0+1P7a/uN+3L7Uvsk+wb76PrW+iv7EPsl+0j7RPtp+5D7k/tk+1v7Oftm+5j7gvu4+7z7b/uO+4D7ePtz+1H7aPtw+4D7hvu3+4r7XPuD+1z7X/t5+4X7mPtj+1L7bftZ+xP7/vpH+2b7jvvD++r75fvK+6H7oPt5+2P7tvvN+yv8TPw2/OX7kvti+3P7Fvs8+2L7W/tx+3/70vvL+777Efvw+h37Sftg+4b7mvse+xT7ZfvE+/T7w/us+4X7tPvE+9b7/PvS+9b7lvt/+477SvsY+xH7NftO+0n7nPvV+6r7hvs0+x/7+Prm+lT7Z/sF+/T6YfuV+5n7nvuq+3P7Zfuz++X7EPzq+yX88fuP+0n7Kvsc+z/7avty+2f7XfuF+3j7ifue+7373PsX/Cn8GfzX+7f7t/ud+5P7Xvtu+4L7j/u++6v7mvtJ+zf7Zfu0+4f7TPse+/P6J/v/+kD7Mvsq+y/7O/tJ+2f7fftx+4z7mvuf+5r7pfuR+4H7Yfuh+wP8OfzS+9f7Fvyp+7r71PvA+4/7Xvt9+4b7e/t6+3b7H/tK+2f7avuG+3X7pvtf+1H7afuJ+7P7pvvV+yz8Efy1+zf7dPvK+4f7y/sU/Pz7nfvy+9D7k/t8+0X79fry+nf7tvvM+7T73vul+3/7yfvt+7v7jfuF+4T7mPuM+7D7pvvP+/P7JPwy/ND7gft4+3v7VfuX+3T7Zfs2+4/7nvtk+3v7K/tI+yP7Y/ux+6H7rPus++779/vZ+wL8jvtq+2T7WvuS+5X7vPvI+837mfuq+5P7Vfsx+zL7avty+337p/vG++b7zfu++4z7GPs/+0X7r/v++/P7Avy9+4/7i/ue+4X7dPt0+2/7jPuR+7b7uft7+477lft3+1/7bftN+1X7dPvC++T72vvQ+8z7tPvD+/D70/uH+8T7y/vo+yj8HPw+/CD8V/xR/FD8L/zK+8j7y/vR+8r76PsM/N/7Dfxo/En8evsT+xT7Mvt1+5L7/fvA+y77bft8+3z7X/sq+3r7ZfvR+w78GfwK/NX7u/t/+4r7t/t4+0z7kfuS+7P7q/v9+//7t/uM+7b70Puz+2z7pPvi+8f79/sY/DT8+Pvm+wD8wfvu+zf8Y/yW/L38h/wa/I/7lPvP+7L72fvW+8j7c/th+6b7u/vJ+7X7vvvY+9D72fvT+6n7nvuZ+9T73vu/+7D7zPsD/Aj8K/zl+9L7xfsn/Gf89vvR+5b7mft7+877JPzp++D72fvc+8/78/s7/GT8LfxI/Ab8xPuQ+4T7KfsY+6j7zPvw+wv8SPwk/Ov7H/wX/MP7kPti+337hPuq+5j7ivuJ+377pvvK+7z7S/sx+1v7ivu+++T7D/zy+xz8VPz/+1X7SPuP+4P7wPtr/I/8Gfw0/P770/uB+2H7KPsI+3z7fvsM/O378vv6+7X7w/sD/PL70/uA+3P7lvt1+6T7jfvX+7T7pvvl+8H71vuX+637svu7+477f/tz+0f7YPtH+0L7Ivss+zf7X/u5+/b7BvwQ/On7Evwv/O37Kfzr+8v7s/sC/Ff8U/xk/BD8xPuY+8b7tfuD+1v7v/vd+8j76vu0+3z7G/s8+y37OfsM+4L7xPvW+977qPu4+zL7+Prq+qL70/sC/PH7mftO+yX7hvvr++r7qftK+3z7cPtM+1H7OftT++n6Qfs8+0z7XPsf+z/7f/vj+3D7aftF+0b7KPtL+5D7tvsJ/AL8M/z3+6L7P/sV+137zfva+9X72/vb+xb8fPs2+wn7ZfuH+1X70/uL+y/7VvvC+737efuO+7X7rPsg+zz7g/ur+9D7qPuD+3b7n/uy+7T7j/uS+5f74fsI/B38iPs5+7b7l/uk+0n7QPtU+z37wvvU+wn8//vj+9v7uvvq+9v7HfwA/O/74fvn++v7xPuS+3L7X/tZ+2T7Nfs9+yj7Zft1+2j7k/uR+677z/vS+/H7y/vU++T76/ui+6j74fu/+177i/vL+wD80/sV/CX8pvt5+5z7gfsz+2/7dPuw+5r71/t2+z/7q/sM/BX8FvzF+8/7zvu3+y/8kvvi+4X7xPv0+xL8Zvwg/Cj88/vL+7X7pPtS+3X7g/vE+8z7iPs2+x37S/t2+3z7YPuk+477fPu3+8P7/vvL+w38KfwD/F37aPsd/Pr79/s9/Cz8I/xS/Nz7yvt2+5T7Y/sq+1T7NPvu+8T7vfvS+5b7ZfuW+577d/u4+4v7iPtA+0H7bfum+577gfvt++z7G/zI+5r7XPsp+y37pfvO+6f7e/sx+037Bfv0+sv6z/ps+9T7E/wf/AP8qvs++037W/t5+7b72/v3++j7t/tZ+yr7efuh+7j7tfvw+9n7ift8+0j7pvvV+677lvuf+6P7Wvsu+1z7rvvA+9b7CPwB/Lv7ZPsq+wD7Ovs5+zj7c/t++3r7M/t9+0P7V/ta+1j7Z/t6+5T7Vvtj+1D7kvvR+8r76vut+3b7n/uA+5H7iPuG+9P7x/v5+7b7iPuV+5D71fsD/P77uPs9++368Pr/+kr7o/u1+9v7YfvQ+jf7MPut+sz6NPs7+yT72/sc/KP7Ovvt+gb7EvtO+4r7cft9+4b7Yvs1+9/6Cvu4+sb6CPsh+zv7SPt2+y/77Pq6+vj6+fqQ+9z7w/uO+2/7Yfs4+4b7TPtS+1L7pvsf/EP8R/yi+0j7PftT+377gvvN++X7i/s2+237YPv/+tf6Afsp+3z7jPu8+6v7hPuq+8z7Sfwj/AH8w/uF+2D7E/tH+3H7u/vN++r74vvh+2f72voL+9T7DfyW+5D7ifuR+2f7J/tx+8n7vvuy+8v7PPwp/Lb7gvuC+4H7m/u++9L78fsQ/AD8oftx+1H7pPsI/Bb8OvwJ/L/7lvso+yr7g/uM+5b7W/vD+/n74/vC+yv7Qfs8+5f7FvxB/LH8Wfx7+/76Rvtd+zv7VfuT+7D7vPub+4T7ZvsM+7/6nPoE+z/7xPvM+4X7zvub+577qvvH+8L74/s7/Bn8HvzJ+6j7e/uj+6P7sPsJ/Cj8ffwT/IT7dPpo+n/66PqE+4v7pvtA+/z6nvqk+tv6CftB+3b7g/uT+8P7u/uG+4z7L/tk+8z7Cfw//Mn7JfuO+lz6rvoC+3b7vvuv+4D7OfsL+8L6qvrN+gj7ePtZ+4n7S/sv+2b7X/tV+177l/vl+238Tvyv+2n7S/tO+0P7tPvH+0/7S/tX+xz7DPsU+yj72vrX+u/6PfuN+4n7ivsq+wX7Kfsp+1D7e/t8+137V/uG+6T7lvuO++P73vuY+537gftl+4f7Nvtf+9D74fsD/PH74vvb+8v7qvuv+/P72/vD+7H7j/uT++37Svyf/Hz8d/wM/N77G/z8+9r77vsC/NP7L/wM/L77fftA+1n7g/ud+4z7Zftr+z/7dPuW+1/7kfuh+5z7mPvi+xX84/vN+8f7wvsB/M376PsA/AT8svu2+7P7vvvP+8f7vfuZ+6r7Y/tY+3z7y/vk+8T7zvsS/MX7t/vF+w/8IfxE/HH8Tvw5/Af87fve+9/7qvvK+3r7X/tH+2b7xvux+477LvsE+xL7VPuk+5L7ePuH+4P7ofup+/D7yPuz+9b7DPw+/Fr8Pvy4+xn7tPqD+hX7Zvu++9f7hvtY+xf7QftK+3L7n/uS+4L7UvtV+0P7M/tu+1v7iftp+137cPuU+3n7pPvf++P7CPw8/A/8ePtd+4D7mft9+8P7xvtO+0f7NftC+2X7R/tj+2P7XPtj+277W/tV+7H7nftv+9z71/t4+2L7jPu2+9X78Pul+5D7dPt/+5n76/s0/AL8xfti+1z7tfuv+5z7ofuf+3P7X/t6+1v7UPs6+yD7WvuL+6n7ufu5+9z75fv8++v73fvQ+6f7gvtt+zv7Lfsl+wb7GftE+3z7n/u5+9P7o/tj+1H7L/sz+3D7o/uW+4P7WPtH+2n7k/um+5P7fvt4+5n7fPuA+6r7f/tH+0f7Y/tv+277fPtK+zP7TPtN+377uvvS+6X7oPu9+637u/uN+5z7pPuk+777tPu0+4n7nPuX+7L7svtt+0f7OPtN+zz7SPtc+1X7Vftr+477nvu3++L78/vz+777hfuG+0T7Sftt+3X7gPup+7L7mfvL+8D7y/v9+xL8Bfz8+wL87vvp+/j77vsF/Af88vvz+yD8Nvw7/Hj8hvyo/In8Uvwc/BL8JfwZ/F78nfza/Pn8wvyK/DD8Pvxa/KL80/zP/Lr8kfxe/GD8Vvw6/En8WPxT/GX8ivx3/Gn8QfxB/Db8TPxl/Gf8fPx6/JH8ofzV/N382Pzx/PH85vzL/LP8cPxy/JL8o/yy/Jv8oPxw/E78Qfxa/Gb8Z/yB/Lb8o/yU/J38ovyS/If8l/yk/J38c/yb/K/8qvyi/KL8lvyV/LD8tvyk/KH8sfyR/Jz8wfy2/Kj8kvyB/Hf8g/yU/IT8dPxM/ET8W/xf/FP8Rfwr/DD8Qfxm/JX8tPxs/E78gvyl/Jj8jPzH/Jn8jfzC/Mn8vfyt/H/8bPx9/KX8sPy+/Iz8ePyC/Fn8e/yG/F78SPxA/Dv8OPxL/FX8a/xR/GX8WvxK/Ff8QPw0/Cz8PfxT/G78c/xd/Ez8IPww/DT8NPwj/Bb8/fvu+wn8Kvw+/FD8SPwl/Br8KPwu/Cv8Tfwq/Av89vv2+9b7uvvG+6j7q/vQ+/D7KPwJ/Pv71fuz+6X7wfvS+9j7yfu7+7X7m/ul+6L7pvvF+9D73fvV+6r7iPuq+7P7wfv1+w/81PvY++b7vfuh+237UPsc+xn7KPtK+4n7k/uC+4D7avtT+2D7aft8+6X7qvu1+8T7xPvM+7v7rPuL+4f7dvtY+3n7YPtx+2/7i/ve+5f7sfvK+437XftJ+3P7h/t5+0v7fPtj+0X7YPtU+4D7T/tK+177Xftx+2b7dvss+0T7Q/s5+2r7O/sn+//6HPtX+xP7PPti+yj7DvtM+zf7DPv0+uz6TPuH+9r70/uK+1f70Pq2+rz6zPrb+vL6Q/s1+zf7NfsM+/n6y/rX+hz7b/uI+3T7TfsR+/T6yfof+0T7aftb+zH7I/sM+xz7L/tH+2P7Vvtj+1H7Pvsg+/367/rs+un6BfsA+wn76/rQ+t/6/voD+yn7OPst+1D7aPua+4z7qPul+3v7sPsk/Fb8TvwU/Kb7Ifsa+zb7Zvtt+2X7LvsK+wn7Cfsw+zT7JfsW+yL7M/st+1L7Qvs1+0/7ZPt1+477qvuX+5j7p/uz+7v70/vB+4/7cvt6+3v7fft3+3D7QPsn+yz7LPsR+/r6Afvc+t/6F/s7+0v7cPuF+5L7kfuq+8f70PvL+9X73/vv+//7Fvwb/BD8Bvzf+7j7qvuT+4n7jfux+9X75fv4++37yPul+5X7jfuc+6j7n/um+777z/vP+9T71PvJ+737zvvn+/H73vvD+5v7i/t6+5H7nPuj+5b7dftt+3v7k/ua+6z7nfuj+6r7uvvJ+9r72vvG+7P7svud+6r7t/vJ+8f7x/vB+6v7jPtz+3H7hvuS+6b7rvuj+5r7pvus+6T7mPug+6j7sfvJ+/f7Ifw5/D78SPw8/Cn8Lvwa/C78R/xR/Fz8U/xg/Fj8V/xT/E78N/wy/Br8EPwN/AP8BPwD/BT8Kvws/DH8KfwE/PT79/sP/B/8T/xg/F78Wvxb/Dz8OPwl/C38IPwm/DD8Nvwx/Dr8RvxD/DT8JvwY/Bz8JPwo/BX8Cfz5+/P76/sA/BD8G/w4/DD8Nvw5/Ef8T/xW/E38Sfw9/DP8M/wy/Db8L/wu/Cb8C/wG/AP8BPwJ/AT8+fv1+wv8JPwn/Dn8Qfwx/DH8PfxY/Gf8cfx9/Gj8Yvxc/FL8TfxG/C/8Ifwa/Bv8F/wK/Af88fvd+8370/vK+8370/vW+9T75/v3+xf8H/wo/Dj8S/xU/FL8MPwK/Of74PvM+7f7rvuP+2f7VftM+0j7Wvt6+477gvuM+4r7kvuR+5X7oPuP+5T7k/uN+5P7nPuk+6n7rvuk+5j7g/uS+4b7ePtd+zX7Bvvy+gD7Jfs/+037NvsZ+wX7B/sm+xr7DPsG+/P68PoA+xL7F/sa+yP7Ifsw+0L7UvtO+0X7Pvs2+zD7Lvse+wn7Fvsf+x/7IPsh+yX7Gfsm+yv7IPsx+yn7KPsw+y77Jvsh+y77MPsj+yb7Nfsq+z/7PftH+0D7N/sk+wr76/rZ+rz6tPqw+q76ufrK+uf63vre+tz60/ra+uj6Bfsd+yD7CvsA+/z6BfsR+xz7JPsm+yf7Dvv7+g77EvsQ+wT77vrC+qT6tfrB+tb69vr8+vP67fr2+gL7EPsQ+/n65fre+tz64/r6+hD7Ivso+yv7IfsJ+wT7B/sX+zr7R/tO+zj7LPsx+yv7P/tJ+1H7VftK+0f7SftK+1L7TvtH+zr7LvsX+wb7D/sV+yf7QPtu+3T7ifuU+6b7nfuq+6X7lPuD+4r7lfui+6v7tfu6+9D75vv9+wT88/v1+/P73fvh+937v/uq+6j7qPut+8T72fvR+9T75/vb+9X70PvX+9P74fvc+9T7xPvL+937B/wY/CH8Ifwh/Cj8Mvw2/Df8Mfwg/Aj8/PsE/Pr77vv4+/X7//sO/B38Lfwq/CT8G/wL/Pr76vvV+7b7sPu1+677uPvG+7v7s/u2+7v7s/uk+6T7o/ui+7P7ufvK+7/7uPvF+9v74vva+9r72fvT+9X7yvu6+6L7fPtu+1/7V/tk+4P7rfvQ++P78fvp++H75/vz+/P76Pvg+9X7tvvA+9H73vv5+/H72Pu8+7/7uPu7+8z7wvud+3/7bft4+4z7q/vH+8r70fvH+8X70vvf+877yvu6+7j7ufu5+7r7sful+5n7nvu/+7/7xfu2+6T7nvuP+4b7cPti+1H7QPs7+zP7K/s7+z37O/s6+zf7Nfsr+yr7K/sc+yj7Ifse+xf7Efsc+yP7OPs4+zL7Q/tY+2X7cPtf+zv79vqp+qX6sfqu+r76y/rI+rv6w/rQ+sr61frQ+sr6vPq8+tL6vfrD+tr6z/rM+sz64vrp+sr6uPq2+qj6nfqs+qn6efpE+kr6TPpU+n36pfqp+pT6k/qn+rf62PrZ+tT6v/qi+qb6wvr1+gX7A/sX+xD7/Pod+y/7PPtB+0n7MPsY+xP7IfsK+w77C/v/+v/69/oJ+wD7/PoJ+w/7GfsU+//60fqh+rT60Prl+gj7H/su+0H7RPtN+2D7d/te+1H7UftG+zr7RPtT+0T7LPsY+yj7O/tt+4v7k/uG+2z7Wfs1+xz7K/st+xn7Kvs3+x/7Jvs4+yn7BPvv+gH7+/oI+wn7Avv0+ur67/r7+hL7FPsi+zH7Ofs8+z77SftQ+0b7Qfsv+yT7NPs2+zj7KPsZ+xD7Ifs5+1v7ZPtc+0v7Pvsu+yb7Efv8+uT62vrL+uH67Pr9+gb7//ru+uH61PrA+q/6wfrh+ub69PoK+x77Fvsb+yX7OftF+z77Sfth+1r7VPto+0P7HvsF+/j6+foO+zr7TPto+4r7ifuM+5T7qPus+6H7nPuj+5/7ivuO+5H7p/uy+7T7pPuY+5j7iPuo+7/7kfts+1n7bftc+2D7iPty+3j7pvuZ+5D7ifuF+2z7bPuK+7771fvW++T75vu9+577s/uy+8H7zPvP+8j7wPup+5b7nful+5j7hvty+2r7cvts+2P7cPtS+zr7QftR+1L7Qvs++yv7NPtH+zn7O/tP+2H7bvuH+577i/uA+4X7l/uE+0r7JPsg+yX7Ifs9+0r7O/s5+0D7O/tG+1b7b/uG+4j7ffte+1f7afuL+4T7i/uf+5r7o/uh+6L7jPt9+2r7ZvtY+zj7Lvsj+xz7A/v2+gz7Gfsu+zP7E/sI+yn7TPt0+6T7oPtn+0H7R/s/+yf7JvtO+2v7bPtw+1j7Yfts+4L7ofuW+5j7iPtp+1z7X/tp+2/7f/ug+5v7nPuL+5X7ofuc+4v7a/vW++j7fPtA+6T70fuu+wT8Kvwc/Jr7kvuV+5n70/vg+9r7j/t/+137Tvtm+5z7l/uh+8j75/vO+5/7nvu3+/r7EPzk+8P7kfuR+8/7DPwd/NX7mPuD+4D7fvts+2r7efuB+6D7lPur+6H7nvvf+5n7pfvX++j78fvQ+937k/t1+7L7xvvF+5j7nvtm+2P7SvtV+7T71vu2+4f7avtm+1X7b/uF+4L7kPt++5b7qftg+wL75/pY+6X7nftw+zX7Yftt+4X7rPuv+8r7qvuY+5z7rPvc+677B/wV/Lr7U/s8+6n7Zfsf+w77E/tB+0T7S/t8+1v7ePt6+7L7wvux+5X7jPt2+277e/t3+4n7avt0+z/7XfuK+6L7qvvM+9D7evtK+3v7z/vK+8f7rft2+1T7Vfth+6L7q/ub+437lvuX+5j7o/vN+/b7r/tr+337eftu+4j7z/vZ+8/7u/uk+5X7q/u3+6/7nPuO+5f7Wvto+477r/t9+4n7nvtw+1T7V/ux+777u/sw+w/7uvrp+n77Gvxm/Mj7qPuG+/X7IvwE/N77qvuG+077mPvA++z7svuT+1D7dPuI+6D7s/t++3v7QvtY+2j7sful+3n7qvsY/NX7tvvT+6X7OPsp+7n7wfvC+577hftC+2H7efuo+5D7i/uy+4b7Vftc+5j7iPum+7n7d/tq+9b75vvx+9X7z/vL+5n7sPvh++v75/u6++T7v/u8++n70vvN+2j7b/tw+2n7V/sm+yz7NPtU+3P7UPtJ+1X7KPtR+4j7vvuS+3f7cPuH+7z7/fvz++n7BvzF+477S/tu+4H7gvtk+1v7Lvv7+hf7VPuy+9372Pup+177iPui+5r7ivtp+5D7Zvty+4v7dPt/+zv7PvtC+3H7p/uf+6r7d/t9+3v73fvi+8T70vun+8/7kvu1+7D7qvu++7772fu7+7/7tfuO+3X7nfty+4b7bPui+6b75Pv2+9f7/vvC+/777vvW+3H7mPvG+6L7p/vz+7b7ivuq+7L7n/uI+7v7pfu5+8j75Pvd+9b7y/sl/AX8SPxD/Ob7l/to+4v7hPvv+6b7gftI+0H7i/vj+/v77Pu++3f77foC+/b6Ivtq+5P7qPuR+3j7TPt9+9H7+PsG/Pn73vvi+8r76vsN/Bb83vvC+3v7Pftm+277tvt/+5n7cvtC+zj7EPsu+237pvvE+4T7gPuH+7n7p/vM+w/8wPup+5P72Pvw+/37DPzo+/L77fsk/Av84vvv+3/7efub+637j/ty+2D7Tft/+6T72/vr+/r7B/zc++b7CPzn++j74vsE/PT77fsE/Ar8HPzC+9L7rfu++9P7Bfzv+8D7r/tm+zP7MPuG++n7LPwB/P37EfwZ/Cj8Pfw0/PP7vvuS+5f7uPsP/AL8/fsF/AD85PvJ+8H74vvy+8n7hfvC++v7tfv7+xf8IvwU/AH8vvus+8X7AfzH+/z76Pso/DL87Psf/Jb71vua+yv8Qfxl/F78+/vr+6H79PsY/Bn8+fu8+2z7cfuG+7j7xfvd+6H7d/tt+9/71fsJ/AD85/vw+4r7rfud+yD8Ffwq/B78Cvz0+8374vv4+yD8Afzq+7v7kvuU+3T7Xftu+4P7wfvm+5D7X/sU+wj7KvuV+/D77/sg/LX7jvt++877EPxI/GD8XPxD/P771PuM+7X7bPuU+577gfu0+4/74PuO+7f75vub+9P7tvv5+7P71fua+4T70/vI+/77B/z9++L7E/zn+wL80vsI/LL72fvk+9v7Qfyw++L7Rftb+3z7TfuV+277zPuB+8D7u/u++9/7qvv/++377vvs+9v79vvj+wP8wfui+9H7lfvx+9/78PuB+3/7hPtU+6T7X/uv+5r7q/u3+6X77vvH+wz8D/wW/E387/vn+5f7p/uN+4b7ufuv+9b7Ffxw/CP86/uC+4P7ZvuQ++z79fvc+737u/ud+4L7cPth+7L79Pvv+xb8EPze+3n7YvtD+1L7i/t8+4b7YvuS+3n7hvvd+3r7mPtr+3f7fft2+7P7afvP+6/7xPut+5j7o/t9+8P7zfvA+237Tvv++jX7G/to+9P7zPsW/Mr70Pul+6r7jfty++v7wPvD+537ovu9+5v7+/vH+8D70fvu+/z7/vv8+5/7mft5+3D7l/uy+5L7dvtf+1z7Qfsx+xf78/op+4j7vfuV+5D7mfu7+4373PsF/Mb7nfu0++X75vsR/Kn7Y/tj+xX7VPu0++r7Lvzo+4b7YPtz+4L7bvuB+0v7X/uP+5n71vvL+8z7g/uF+5z7lfui+7b7f/tn+2X7KftD+yr7Zvt3+7z7l/uh+6D7Yftr+yf7OPsm+2n7ofvB+8P7sPun+7r7z/vJ+8z7pvtm+5P7m/ui+2f7RPv/+gr7bPtW+9H7Vftn+y77Kvti+1b7mftF+2H7RPuD+6z7nvt8+yb7NPuA+7n77vvU+8D7l/t7+4f7nPuM+2f7dfuB+7z78fsH/N/7uvtS+zf7IvsK+0n7Mvtp+5X72fv9+yL8MPzo+8v7pvvE+7D7tvuz+3H7lvts+5376fvC+7n7Y/tH+1v7h/u9+7b71/uX+4b7gPup+8r70/vm+6X7rPuZ+977ufuy+5L7Yft4+2T7dvtN+zD7TPt5+6f7u/vX+4P7OPsr+xT7IftM+3H7dvt9+5T7rvvl++n7y/t8+3L7ePuR+7P7sPu++2P7ZPs7+5T7g/uD+5L7GftT+yT7evtS+0r7Qftc+7/7rfvV+9X73fui+5H7q/u8+9H7yfuh+6r7ePtq+3P7gfuQ+5L7svuM+7z7jvuE+2j7TfuC+2T7dfs1+zr7B/sR+3T7p/v++/T7A/zJ+7z7u/u1++b71PvT+5/7vvvF+5771/vC++P7wvub+6L7cPtS+xr7Ovs9+2r7pPvI+/f73Pvz+/b73Pv5++374/ve+9f7x/u9+8T7s/uu+7X7t/u7+9H7vPty+2H7c/t6+1n7gPuB+1T7fftm+2j7a/t8+6D7w/vw++f79Pvb+9r71vvS+737rPur+4L7qvvL++v76fvv+9X7z/vS+6n7hPth+1/7QPtB+zH7OvtR+277hft8+5L7q/u2+7H7tPu1+8T7uvu5+8T7wfu++8372Pvb+/T73/vI+637mPuc+6H7tvuv+637rfuq+7H7y/vx+9b7vvu8+9f79PsJ/BP8+fvf+9373fvS+9b7zvu1+7r7y/vJ+8D7w/vC+8f7w/vI+7X7nvuI+3v7jfug+7D7vPvQ+9n74/vr+//7GfwJ/Oz75vve+8z7zvvn+/f79Pv9+/z77/vP+9r74vvd++L7z/vD+7f7s/un+5j7jPuG+4r7l/ux+9L71PvK+9T78fvt+9D7xvu8+8P74fv/+/L7z/ut+6H7wfvW++P7zfu2+5L7e/ty+4X7ufu6+5f7f/uO+437gvua+5j7qPu4+9H70/u5+5H7dvuE+4f7kfuW+4j7jfun+8v75/vY+7T7lfuO+4D7hfuW+4b7c/tv+3X7ZPtZ+1r7jfvT+937uvvA+8L7qfvB++L7yvvP+8/7x/vI+9X70/u7+8L7uvvD+9v78fva+6z7ivuR+7v7zPvS+7r7kft6+4r7pvu8+8D7wPvA+9X71/vL+6/7qPuv+7772fvf+9n7zPu++5j7lPuv+677lfuG+477gPto+3f7fPtv+3b7lPuS+2n7SvtO+2b7ivu5+9L7z/vK+8b7svut+6T7ift1+4b7ofvB+8f7kftl+1r7i/t/+4f7pPuA+2z7ifuG+3b7fft0+2f7Z/ty+1v7U/tR+0/7bPuK+5P7gftr+2b7YvuA+4/7d/tn+1H7RPtN+1r7U/tX+137ZPty+3f7cPtv+3n7h/ua+7z7x/vG+8374vvv+9772vvO+8b7z/vo++/7zvum+5v7kfuM+4f7fftw+2X7c/uA+4j7gPt++3z7hfuU+5P7lPuc+6f7r/vY+/v7+fsB/P77+Pv1++X7yfus+5f7i/ue+6f7nPuA+2f7TPs9+0b7TvtX+2n7ffuR+6b7q/uU+4f7lPuw+8z70fu/+7P7tPut+6r7vPvB+7n7s/u0+7D7nvuZ+477gft4+3j7c/ts+2j7WPtX+2H7ZftW+1f7X/th+3P7cvtv+3f7i/uM+5f7rvuo+6/7vfu7+7L7pfuT+5D7ofuq+6P7m/uG+3X7YPtd+437vvux+5L7n/ua+4n7pPuv+5j7j/uO+5L7pvuv+6H7pPuq+6H7mPuT+4/7l/uq+7771vvN+637hvts+1b7RvtO+zf7L/s8+0b7Vvtf+4L7n/uj+5P7mfuM+1z7bvuD+377kPun+5n7gPuB+3L7Xvt4+3z7bft4+4f7hfty+2/7cvt3+337hfuR+5D7hPt6+3f7gvuX+5v7oPuY+4z7iPuG+3z7avtm+1z7c/uI+477gvth+2H7hPuo+6r7r/ut+5D7ffuL+5D7ffty+3/7mvuh+5z7ofub+5D7n/uw+7P7r/uQ+277ZPti+1n7TftN+0P7Qvta+0r7Lfsi+z/7QPtF+3j7cPtS+2T7a/tf+2P7aPtZ+1D7WPtZ+2D7a/tp+2z7jvur+6z7nPuY+4/7iPue+577ofue+4n7hfuL+5X7jvuL+4X7evt3+3b7eft9+377b/tv+3j7eft3+3X7dvt2+377j/uT+5T7jfuM+4P7c/tq+1r7VvtS+077UPtM+0L7O/s++0f7SPtS+1r7UPtK+1r7cvt3+3H7eft8+3X7fPuJ+4r7fvt4+3z7fPt++3z7dvtx+3b7dft8+3z7eft3+4L7kPuC+3n7aftn+3L7i/uf+5/7l/uJ+4j7g/t4+3X7Z/tc+2n7gfuA+2n7Zvtg+0/7W/ty+377fPt/+3/7ifuU+477gPtu+2/7aPtu+3P7cftr+177ZPt2+4L7dPtv+4H7h/ui+7X7p/uV+337dPuE+5T7iPtx+2v7Zftn+2b7Wftt+4f7hPtz+4j7cftK+1f7Z/tx+3f7d/tn+2f7bfts+3f7gPtr+1v7Xvtr+4H7i/uH+5D7kPuJ+4n7kvuR+4b7h/t8+3P7e/t2+3n7lfuz+8D7q/uY+5r7m/ui+7b7vfuY+4D7f/uJ+5b7mPuZ+4v7gft1+3T7dvt3+277UvtP+077TPtG+zf7PvtA+0H7N/sz+zf7Lvs7+0r7Wvtb+1z7X/ti+2/7evt7+4T7k/ub+5D7gPt7+477nPup+7z7t/uX+3r7d/tu+2D7Xfte+2f7V/tS+2H7XPtY+1n7TvtD+0b7TftL+0/7WftX+1/7bvto+2/7gftp+2D7avuE+3n7dvul+6j7r/vF+777pPt5+1P7NPsx+zD7Lvs7+z77K/sp+zT7Ovs++0H7Vfth+177Yvti+2D7bftx+3L7aPts+3f7evuS+537mfuQ+4v7n/up+6r7pfuY+5b7lPuS+5j7kvuZ+5v7o/u4+7X7pvuS+4T7evt2+3f7Z/tO+1T7Y/tm+2r7aftj+137bPt3+237Yfto+3H7c/uG+5f7nvuN+4T7f/tu+2P7U/tS+0f7Rfta+177VvtM+0H7PftH+1P7YPtz+3z7hPuT+5r7nfue+6L7qPu++8/7v/uu+6b7oPuc+5r7pfuj+5v7k/uJ+4X7c/te+2L7aPtt+4D7gPuB+4T7gvuO+6L7r/uq+6P7pvuo+7j7rfuN+4L7gPt9+3/7lPuV+4f7jvul+6v7tPue+5P7lfuQ+4f7h/uB+3P7cft6+5P7svu2+5D7kvuj+5f7n/uw+6P7mfuX+477iPuL+3b7ZPtv+3X7cPt3+3n7ivuc+577ovuW+3L7S/tM+2L7Wvtj+2z7TPtH+1D7Vftu+4j7k/uO+5v7o/uW+5H7gPuH+5H7kPuE+4b7eft1+3v7lfun+6D7pvuk+5j7gvtY+zH7DvsJ+x77QPty+4n7g/tr+1f7TftO+2f7fPuJ+537rvuz+637qfuj+8L74/v2+/376fu7+5z7tPvN+8372vvc+8b7s/uq+5z7gPtu+2r7f/uM+3f7dvtx+1r7Uftc+1T7Ofsz+zD7O/tJ+177a/t7+377gPum+577bftW+3P7gft7+7j70/u++9X77fv5+wH88vvZ+8b7s/uX+4/7j/tx+3r7mfuw+6L7lPt9+2f7Vvts+4P7ifuI+4j7d/tj+1r7WftT+1T7Y/tj+2r7Y/tu+237dfuL+5r7p/um+537mfuP+577nfum+6v7sPu0+7f7uvus+6P7lPuP+4r7hPuH+377gvuJ+4P7iPuD+4X7b/tx+4j7kvuT+5r7qfu++8n71vvR+7X7pPua+5/7n/us+6n7pfuU+4n7eft8+3D7bvt9+4v7k/ua+5r7ovuj+7b7xPvG+8L7yPvE+7z7uvvD+7P7pvut+777yPu2+6f7lft++2f7aftg+1D7S/tL+0H7Q/tH+0D7OPtD+0f7TPtb+2v7efuB+4P7i/uZ+5X7gfuZ+7n7tfvK+9P7w/ut+6L7p/um+7b7u/vA+8P7sPuU+2j7PvtL+3P7c/t9+6H7kvtw+3j7gvt2+4P7ifuT+5T7nvuJ+377bPtc+2L7b/uJ+6P7o/ue+5H7fPti+2n7hPuJ+5P7pvuU+4z7gvt++4b7ovuu+677u/u4+7f7ufu0+7P7pPuU+4v7mvud+6L7rPus+6r7rvuv+7D7ufuv+637ovuh+5X7kPt1+3b7i/uX+5j7pful+4/7avta+0b7Nfs/+1n7cPuB+4f7ivt++377f/uQ+3z7cvuU+5v7lPue+7T7ofuP+5z7lvub+5n7lful+5/7nPus+7n7r/uv+6T7jft1+3T7bfto+2X7Zvtu+4H7l/uW+7P7rPuJ+237dPuA+2v7ffug+4/7rfu9+8v7u/u9+7n7q/ur+6P7ofum+5n7nPui+6P7m/uL+5L7hPt3+3j7jftz+277g/tv+3z7fvt0+3D7dft3+137YvtY+2T7cft++4n7ifuF+4H7kfuW+5j7lPuf+5/7s/u/+8L7wPvB+777vfu5+7P7sfup+6n7tvvC+8r7uPvE+8b7zfvP+9H73PvK+8n7vvu3+577i/t++237bvtw+2j7avtt+2r7cPtw+4D7hft/+4L7jPuI+4X7i/uV+5T7nvux+7T7t/u6+7L7vfvI+8v7sPux+6D7kfuh+5b7mvuZ+6b7pPu6+7T7mPuQ+5H7j/uS+4/7fft2+277Xftk+3f7Z/tV+1v7Xvts+4n7gvuH+6P7mvub+6T7sfun+5j7svur+7n7ufur+7j7rvu8+7f7v/u9+7X7s/uc+6X7vvud+4H7lfuo+6P7uvu5+6T7jfud+577pvus+6T7nfud+5j7l/uB+3r7iPud+7f7xfu4+6H7jfuw+5P7nfuT+4X7h/uD+577sPu6+7D7r/up+7z7q/vW+7/7rfvV+4r7u/uZ+677mPu4+9n70Pvc+677zPvR+7f7rPuL+4X7dfuC+3v7ivud+7b7lPuT+5P7rvuP+4P70ftv+837ZPvM+5v7ifuz+2P7zftj+9v7aPuQ+8r7m/vd+6r7mPvU+1L7wvu/+zn7xvtM+9z7qPuw+/P7lvsx/L37yvuT+6b7jvtW+4b7cfuP+2n7nPt4+9T7ifuK+5/7V/sq+6n7PPtV+8T7N/uQ+1r7rftk+1D77Psm+wL8Pvuv+3r7S/uy+zT70ftx+177p/t0+1P7qftH+577oftN+737QPuT+3/7cvt++1z7cPtv+yX7zPtj+4/7rftr+8j7mPui+8/7t/vH+5X75fud+2z71Ptb+677fvuU+3b7ifuv+0/7lftg+637bft6+5f7YPuS+1v7n/tn+5T7m/uF+3L7p/uC+2r7i/tt+6v7Ufuz+4z7pPu3+3L7mftd+3v7dPtu+3D7bfti+6f7Nvuq+wz7ivuS+zT7Bvw3+9P7afuy+1L7bPus+yT7pvuC+7D7k/uz+037kPtu+5r7k/uW+4b7nPuO+377nPtW+3f7YPt4+4j7jvuj+5j7tfuV+7n7t/uX+877ifuu+5b7mvuL+2P7Zvti+0r7mfuS+3j7kvt4+3f7aPtd+z37KvtA+6f7QvuH+2/7KftY+zT7mPsw+5T7aPuR+6j7q/u6+4T7j/uK+2/7qfuj+8P7tPut+9X7ovun+5T7lPt8+4X7qPuQ+3j7n/uh+6D7v/vE+6r7sft6+8n7fvu7+6L7a/uc+0r7qPt7+7T7tvuU+677Svuu+zX7X/tX+xX7e/tR+337ePtL+3/7NPu0+077m/ty+4n7xPuX+9X7lvud+4r7g/uF+6H7pvul+6f7oPuW+3v7fvud+5v7gPvA+337ePuN+3f7gft/+377jfuZ+6L7hfuW+3v7gvt/+3D7TfuX+5X7ffuj+5L7ivtp+4b7Wft/+6b7gPtG+5L7U/ul+3r7X/tr+yL7YvsY+2P7YPuD+4P7dPuN+4D7cPuO+1n7jPuo+7z73vt2+6z7fPtt+2j7fPth+1b7cvtH+437cfuL+5r7W/uI+1z7V/ti+2b7bPtV+2z7SPtz+7X7i/v1+7D7zfuf+5L7h/tC+2r7UPtn+1r7qPtv+5n7lvtS+2X7U/tw+1/7bvuF+4n7j/t/+3/7g/tw+2f7Tftx+5T7ovvI+6X7ufun+6j7mvuh+6D7hvuZ+4f7lfuG+6X7aftq+4H7Z/uJ+3T7fPtq+2f7a/sx+2/7n/uq+8T7q/ui+2v7uPt4+037j/uG+3P7dvt9+3b7qftv+4H7Y/ts+1f7YPtY+1n7YPtw+177ZPtt+2j7fvuB+537jfub+437hPuV+4b7hPuW+4b7g/uJ+4z7g/uM+5D7hfuD+2j7dft3+3/7cftV+2n7bPuR+2D7PPuR+1v7kvuK+4/7kPuL+5/7dvul+4L7jvt0+437k/ub+6T7kvu6+6r7tfui+5/7kftz+6H7e/tx+6D7TvuD+5L7hvum+8b7uvul+5n7nfuV+4n7n/uB+5T7nfuO+6D7gvuk+4T7cfuM+3/7rPuP+4v7fvt9+3D7V/td+0z7Yfto+5P7mvuM+4j7c/tn+2n7ifuZ+6f7qvup+7z7tfvB+8X7sPvI+737xfuG+4v7ofus+6j7mvur+4P7ePt0+1b7SPs9+1H7bftz+4b7j/uf+4n7o/uO+4D7f/td+2f7XvuD+3T7f/t++3T7lPuX+5b7h/s1+yf7VPst+0b7avtw+177ePt8+2H7fPtn+237Zft++3f7efuS+2v7sfu6+8f7svuu+7D7gvu7+6T7ovu0+577n/uh+5b7f/uO+5j7ePuJ+337dfuV+5j7kvuy+437lfuI+4f7lfue+6/7vPvO+8X7yfuo+7D7m/uJ+4z7d/ub+2X7f/tv+1z7Y/tO+2P7UftY+z37L/s9+0P7ZvtU+3H7evt++537nfuw+7H7rfuw+8H7v/uy+8T7ufu1+5T7ift2+3f7g/uI+6X7pvvJ+6j7ufu0+6P7vvun+837sfu++7f7pvu8+5j7wPuf+6j7vvuh+6b7jfuY+5v7lvuf+5b7l/ub+437jvuL+337ePtw+4H7kfuX+677uPut+6/7nPuS+3v7mvuY+5j7p/uz+6b7rfur+6b7kPuB+4D7Yftq+1n7X/ts+2v7b/ta+1D7f/ui+4H7lfu1+5L7l/u1+6T7nvub+4n7g/t++2f7evt1+2/7f/ty+4L7fvuH+4n7cvt5+277PvtI+z37TPtn+3n7jPuh+6L7lPuv+7f7u/vH+9z73Pvn+977z/vd+8r7xvu0+7r7ufu6+7j7vPvI+8f7wfu1+5f7iPto+1f7Vvs++yj7O/tf+437pPud+6D7fPty+237kvuc+7D7wPvA+7771PvN+8n7y/vC+8H7qfuD+037evto+2T7dPt3+2L7SPtS+zn7QftS+3/7jvuh+7j7zfvJ++D7/Pvj++H71PvS+8j71fvX++b75/vO++j72/vq+9v7svtm+2f7b/tI+0/7aftV+0/7Y/tM+0z7Qvs++1T7Tvto+1/7cfuC+3f7lvuj+8771Pu4+9T7q/u0+7P7o/ub+3/7cft4+4X7gPuE+4H7WftR+0L7R/tR+2b7a/ts+3T7cfuN+537nvuz+777xPvT+9f70fvW+8H7vfuz+7r7wPu3+8X7qfuj+537mfuR+337lPuA+4H7a/tk+2/7Wft7+3r7k/uk+7T71PvT+/j77fv5+/T74fvR+677pfuN+377kvue+6n7o/uO+4v7gfuB+437mPuf+5f7mfuU+5D7rvuc+577lfuG+5f7l/uX+5D7rfub+5v7l/uj+6P7nvuv+6P7o/uU+5X7mfup+7H7uvu5+6n7vPu8+737zvu4+8v7xfvJ+6r7wPu3+6j7tfuk+737qfu2+6b7cvt/+2L7bftg+3T7afto+3H7Yfto+2H7jPuv+6j7rPuy+677nfuz+7X7vfuq+5z7mfuU+5b7ovuk+5P7j/uh+5D7mfuf+5b7fPt5+2P7Nvsy+zP7NftG+1X7b/tc+1z7Xvto+3f7nPus+6r7tfup+6j7o/ud+6j7pfuy+7j7v/uz+7P7vvvJ++n72vvT+7f7qvuQ+337gPtl+177WvuJ+8v72fvd+8z7v/uc+5n7vvvN+9374fvy++j76Pv2++D77fvi+9/71Pug+4/7l/ua+5v7ofuw+6D7d/t2+1z7Nfsr+yX7QPtA+2j7kfur+7/71Pu2+5L7lfuR+5L7pfu7+8j71fvf+9/72vvS++n75/un+4T7ifuE+2H7aPt0+1b7avtx+2n7avt/+3n7ffuH+4/7mvut+7/7svvS+/L7/fvw+9P70vuu+7P7xfur+5z7fPt5+3j7hvuB+4f7e/tT+0v7U/t5+437sPu0+6P7sPuy+8z72Pvi++773vvm+8370PvC+7r7sPuu+7r7xPvB+8T7t/u7+6X7rfui+6X7rfuu+7b7ovuV+3/7cPuL+4/7qfuy+7D7qPui+5z7n/ub+5z7svu/+8P7wfup+477d/tr+3T7cft7+4n7nfus+6H7sfua+5r7mvut+9L71fv2+/P76/vM+7b7tvu9+6z7vvvI+7H7v/ut+6L7kvuE+4H7hfuM+4b7jPuH+3z7efts+1z7cfts+437qPuw+777rPuZ+3/7bPt9+4b7j/uM+5D7hfuK+3H7ePtm+2X7dPt0+4n7h/uT+4j7hfuC+2z7l/vJ+9v73fvr++b70vvL++b78fv2++v75vvq+9n72Pvn++j71PvS++D77vsM/AH87vux+2D7GPvs+uD65foT+0H7Vftw+3b7hftu+2j7Y/tn+4D7mvu8+9X76/vq+9f7zPvE+7P7wvvQ+9v78fsG/Bb8DvwG/OX7uvup+6z7svur+7X7u/u0+9n77PvO+8373/uu+6P7vvu6+7L7pvuk+4T7gvtu+2P7dvtY+1T7Y/s0++/6+vr/+vP6/vof+x37Bvso+zL7Lvti+4X7hft4+4z7r/uv+/n7IPwn/D78Sfw9/Db8Mfw6/Cf8SPxh/G38evxr/GH8OfwG/Ln7ofut+4z7dvuN+4D7Yfta+1H7QvtW+077Uvtg+2P7cPtV+2L7Tftc+5T7wPvH+5n7nPtj+z/7T/tT+1n7Wftl+1L7Xfta+1D7Wvtd+1/7V/t0+437qPvX++T7+/sH/Df8PvxD/F38VvxO/Db8O/w0/Cj8F/wL/P/73vvr+8T7tPuU+3X7ZPtK+0D7GPsU+/363vrJ+sD61vre+gr7Gvs2+0D7Q/tt+3f7j/uw+9H73fvl+/f77Pv2+/T7//vv+/T7APzy+wD89PsB/Af8AfwA/P77DPwG/BT8J/wy/D78Mfwc/A38+Pvp++772PvK+8H7tPum+437gPte+1r7UftN+0H7KvsX+/T66vra+uj65frz+hf7Hvs0+z77Ovs2+y37Q/tL+2f7mPuz+8f70vvV+8D7wvvb+/z7GPw0/Ev8P/wx/BT8Bvzv++L7Bfwm/Cj8CfwG/Pb7wfu/+8T7vvu9+8L7o/uZ+5L7i/tz+2b7aftN+1L7Y/uB+6L7qPuc+2j7Rvs1+xr7DPsX+x77Cfsi+0b7a/uC+5b7j/uZ+6f7ovus+8j72fv2+xP8H/wW/AX8BPwC/Bj8NvxI/GX8aPxX/B38APzq+8r7vPuX+5T7b/tz+6T7pPuN+2/7WPsr+xr7HPsZ+yf7H/sQ+/765vrq+vn6B/sJ+yT7NPsL+9764vr8+vP6Afsj+yP7E/sp+zj7Rft/+7D7wPup+9f77Pvl+yD8QPxE/Df8Qfw4/CP8Hfwk/CX8Nfwz/Db8T/xD/EP8Pfwi/OP7rPug+5b7hvth+2L7SPsQ+xf7Gvsj+y37Nvs7+yL7H/sT+xf7DPso+3b7oPuT+4X7lftt+0z7hfuZ+6j7w/vQ++X75/v4++j7E/w+/Dz8PvxD/FX8U/xB/Bj8DfwS/A38MPxS/F/8b/xo/Ej8KfwD/Nb7wPul+577qfuz+6H7kPuB+1r7Pvsu+x376vrT+sb6yvrC+sb6z/rj+uf66voF+xT7Hfsw+1L7avuI+6b7xPvL+8f7x/u2+6v7rvux+7373vvw+/j7C/wF/P37AvwN/Af8BfwH/Pz7A/wZ/C38JvwI/BH8CfwR/BH86PvL+6r7nvuH+3X7Vfs4+zb7Mvs3+xb78vrI+qv6j/qI+p36qfrF+s/61vrm+vv6CfsX+yD7J/sy+1j7jvu6+9P74fvq+/L79fsA/Bz8L/xD/En8SPwe/Pf74Pu8+7/78vsH/Nv7sfu1+4/7W/te+1D7Nfs/+zf7RPs3+xz78frG+sT6t/qo+qn6svrG+uD6yfqh+nb6cfpm+nL6lvqk+pr6pvrN+tf66foO+y77U/tw+6b7xvvV++j7C/wz/C/8Uvxo/Gr8hvyk/Lr8yvzl/OH8qPxX/EX8Rvw2/Cb8JfwD/LP7s/vq+wT8y/ul+5L7U/s9+z/7QvtG+0X7MPsM+wH7APsH+xH7MftV+3L7O/vY+tb6BfsI+/X6HvsM+8T66voY+0P7hvvh+/T7CPxI/DP8Ovxd/IT8dPx+/J/8qPyj/JT8k/yZ/Jr8ePxp/Gn8X/xi/Gj8Zvwo/K37c/tx+3H7Q/sk+xb7v/qo+rX6qfqs+qH6m/qT+pz6mvqP+mz6kPrp+h37B/sX+yz78fry+jj7Q/tR+3X7mPu3++H79vvn++T75vv/+x78QvxX/HD8dvx1/H78fvxl/En8Ovwx/C/8KvwV/PT7zvuh+477e/tm+1r7PvsW+//69frU+rL6ofp8+mz6ZPpY+kP6H/oU+iL6NPpE+mX6f/qU+sf6Afsv+077Z/t5+5H7vPvS+8j7p/us+9D76PsB/CP8Nfwx/C78L/w3/E/8U/xJ/Fv8XPxa/DX8Evz9++H76vv1++T7yfui+3H7QPsi+wb76PrP+sH6r/qU+of6Zfo8+hr6Cfr4+eP55vnl+eb5+/kq+k36W/qG+r369vo2+137dvuA+7b7+vtK/H38o/zD/MH81fzh/Pv8FP3+/Or80vy+/IH8V/xt/HT8aPxF/Dj8EfzP+7z7sfuP+3T7Z/tk+1f7XftP+yb7Avvh+rn6o/qo+q76mvph+jr6Gfog+jz6Vfpt+pb6wPrd+u/6+PoE+xH7N/uC+9j7Dvw4/F78h/y4/OT8Ev1B/V79bP2c/cn9zf2+/bD9jv0+/f384vy//IL8Z/xc/B38v/ua+7T7pvtq+0n7NfvV+nz6ZvpW+j36N/on+gf6+/n8+QX6HfpG+nT6kvpT+vv5FfpY+l76f/r9+iT7Cfsd+zT7i/sL/Gn8mvzy/En9Ov1h/Zj9i/2F/W/9SP1F/U39M/0k/T79E/3P/LT8rfya/Hr8WPxK/BL8hPsd+/D61/qW+n/6f/oj+vH59fnd+cD5qvmO+Yj5h/mC+WD5dvnh+Tz6TPov+m36ifpv+p36z/r++kX7gvuz+9z7GPw//Gv8sPzP/On85vzz/BP9LP0t/TL9Pf0j/Q/9B/0J/fr8yfyS/F/8MPz2+7X7bvsa+9T6vvqb+mT6QPoN+tf5ofmB+Vv5VPky+Qn5Dvkn+SL5BPkI+Sv5X/mU+dT5FPpA+pD6APtH+4H7xvv5+w/8PPyC/Kn8rfyw/Mj84/wD/Tj9aP1j/Vn9fv2L/XH9Wv02/QD96vzm/Ln8e/xB/CL8Evz4+9f7nftX+wX7sfqB+nb6Yfo0+gL63Pnj+fL5xvmP+U35Ofla+ZP5n/mm+bT5wfkN+m36sfrZ+g/7UPuZ+wP8Z/y1/OH8/vxG/YH9p/3O/df9zv3T/er98/3I/Xj9RP1M/WD9Pv0W/fD8qvxt/Gb8MPzQ+5v7e/tz+3r7ZvsK+7H6e/pt+n/6hfps+kP6/vnS+dj54Pnc+d753fni+Rv6c/qI+on6mfqz+tT6+Pod+1P7h/vO+zb8jPzC/Nf83vz1/ET9s/30/QD+7v3t/ev95P16/fb8pfyh/IP8Uvw7/OX7oPul+8771fuk+2j7Qfvm+rH62frB+mL6JPr1+e75KfpZ+lb6Svpj+pn6z/qC+gz68fko+iP6cPr6+hb71vrm+gL7Vfsq/E78YPyX/Pv8Jv1Q/Zf9Sf0s/Tz9F/0+/Tn90/y1/Ov8Mv3q/Mf8mPyO/IH8lvzn/LD80/vR+uH6Dfvi+tX63vpU+vX5KPob+jb6Ivrv+dL55fnf+dD5CPo2+p76t/o3+0P7Dvsj+yP7MPuE+9b72vsh/F/8n/zB/M78uPyV/L/8Cv11/an9nf1v/fj8Bf3x/Nj89vzt/Jr8Xfxs/FD8Mvzz+5/7VPsx+0z71Pqg+oL6iPqQ+or6b/oy+iX6L/oz+jn6UPpX+lj6QvqT+ub6/frt+hX7V/uB+7r7E/xb/Fv8q/wM/T/9g/1L/UP9Uf2c/fD93P2f/Uz9Mv1Y/Yf92P3C/V79Rv3+/Ov8ffwW/O37gvue+zX7cvs0+yH7Mfre+fP5vPn3+nz5ePmq+Vn6FPpZ+db5gfq6+n76Uvq/+gP71/pn+8z7Vvzz+1H8YPxc/BD9/fxM/Wf9k/2S/e79I/5k/kr+bv1L/dX9wP3R/SH9U/xa/Pz7APzi++D6k/o5+pH6hvoa+mj6ofmd+Sn5PPkX+Vn57PnT+b75P/pt+hD69vpK+vT6d/tu+7n7RPug+7D7KPwQ/Rf93vwD/Wf9jP09/vr90/0d/iP+p/5U/kX+9P0j/t39w/2E/Vv9T/3S/FP8s/sU/Pv7W/sJ+qv5C/p7+Ur59fh++KX4bvjG+Cb5ZflJ+Yb53vng+Xr6dPqD+i372vte+6j7xPsv/Pf8D/1q/d79Q/5z/aj8UP34/ST+U/5H/or+GP5j/jP+b/4//j/9ovxI/MD8Vv3a/Lr77Ppj+5D7vfqG+cX4evk6+tn5+vgl+Pz4lvkY+av41/jk+Un6ifno+DL57vn++Vj6rPpg+0j8+/t7/AH8aPxO/Zz9Av5O/rf+zv4n/0b/l/+X/1r/kP8P//b+mP4v/gr+dv1s/QT9dfx2+2L7z/oy+if6sPkl+pX54PhK+An4Bvhc9xf3h/d/9wb49vfx97P3RPjc+CD55/l0+rn6Fvtw+3r8//zk/Lf9n/2J/jX/tf8SAEH/MwCHAIMA3/9UAH0A4//j/8D/2P8x/9z+Z/5N/bH9tf2q/Oz7k/uZ+2v6f/g190H30Pft9qz1Kvbr9aP2DPYO9Sf1uPUV98j2lfY+9zj4gfmD+eT5o/nh+j782Pwh/YL9xf4i/2//w/8FACIBAQGxARQCpgH2AcsBnQHIAYIBqAAIAMH/Bv9T/on9E/3b/J78mPv/+Un5KPl2+Ib3DPeS9gr2k/Vz9Vb0y/Rh9Lz01vRK9Bf1L/UY9n72Z/fj99P3f/hj+W37zfvA/Fb9yf3P/m7/TQD0AHUBMwJzArwCBwMhBMYDMQMfA0UCTAK1ApUCbQE8ADr/uP4e/rr9cP1a/Lz7rPob+P31TvVP9W71kfTD83DzxvN18xz0QPP58uPzAPTk9O301PWE9pf3uPfB9yD4S/n0+sf7ZPyN/Tr+hv9tAIEB+QHaAcoCcgOlA6YDXQT4A7UDSgPVArYCRgItAlABVQAPABf/h/1j/NP7t/r8+RH5bvdl9qP1mvXD9HL0cPP78qDyRfJc80jzSvPs85TzwfOf8wL1c/XW9eL36vf2+Hr5xfuV/Cz9nv7L/mYAFAFLAogCggIUAykDzAPSA84EywS8AzoEzgIXA5QCQAJBAokAHQD5/r/+1/zp++36Ofnb+O73bvZ89bf01vTs8wHzUvKm8FvxbfFZ8yLzA/PB8ybzPPT383j1Xval94z5Bvo/+p76+ftG/en9w/8JAAwBYAEsAiMDmwNsBPoDbQRaBIgEZAVXBMoD9QLiAVcBtQCwAJj/Hf64/Hb7kPpS+nv5Tvgu9/L1OfVd9N3zmPOE88vyBfKp8ZjxQ/Gb8pTzwfOM9JfzUvRq9V732vg2+Uj6fPpN+6r84f2B/0AABAH0ALYBagJgA4sEawRmBJIE2ATQBKoE5ARcA3sCHQI/ATMBdgB3/z39l/tU+pv5Nfmw+I74NfcV9lf1zvRZ9LjzYvOV8yXzAvMF8gXycvJR9K31vPTh9HP0lPW/9gv5jfmM+vL6ZPs3/GD9Ef/j/0YBTQEFAqgC9QKAA4oDNwSXA/EDagRbA+MDbwP5AR8BrgAaANn+s/7u/NL7i/ql+er4RPiV+Jr3i/Z69XX0ePTk9N/0BvRm88vyIPMZ9Pjzt/R89HP1OvbD9g/3jPeB+D75BPuU+0/85/w4/YX+//4W/zsAagF9AesBaQL2ASEC3QLhAo0DEgMYA5sCJAGBAIL/XP82//T+9v1q/ET7lvq2+j36yvmx+In3p/bx9YP16/Sg9Bj0YPOn86bznPNh9BX1e/U89aH17vUM+Mb4Sfki+jX6S/ok+wn9fP5j/2P/Pf8j/x8AVwFHAukBLAK4AcoBjwEDAiUD9wGcAXYA3f/b/nD/Z/9I/ef8/Ptu+1X7P/vK+oz5uvlJ+If3V/cI9173RfZv9nn12/Xy9Cj1jPYO9nr23vWP9pT2i/bW9173V/ng+RP6N/pJ+oz8iPxf/kj++P5+/13/rADSAIEBNgGVAl8CIQL7AQwAewBUABwAyP/O/ib/I/65/QX94/wf/Mf7Cfwg+gD65PhE+Aj4Hvie9/P1Ffdb9i32DPa99U33E/a39iH2K/Z+90b4mfhr+NP5Fvnf+fD6kvtc/Gv8xPyu/JL9j/6C/uv+XP89/9L+xP9yAHb/UACH/3r/OP8Y/33/r/6u/hf+6v0T/az83/yA/CX8pvrj+bn5uvm9+r35vvje9wz4B/iR99L3JPZW93D3gPfq94H39ffJ9xb5/fgF+Uj6Jvo/+kr6EvvV+wP8nP0W/aD9xf2l/WH+XP7f/rr+4/4a/nT/4v9H/2T/yv89AOT/8v7C/qz9S/4u/Yr9CPwD/C3+DfwY+vT5Z/tU/FX4J/eC+fD5tvea9q736fTp9Kz3aPkb9mL2kfmF+F74s/jI+R76Kvv9+GH5Rf/O/sj9U/5E/Hn9FP0S+w77Zf3Z/gn+HAHpAj8APv4PAFv+Kf+v/i38OPkw/jYEzwHC+Rn58vny+9r+7fxI/SL6a/aQ9KX6sPon9iX2VPe8+bP4QfmM+0P7z/dx91L5CPqq+Mn4jvuI+pj5fvhW9+D62vve/PT7zPqj/9/8iPpr/bMAtv9q/qL7o/11AkP/mvwKADb+Vf6H/677kv0TAnz/hvoy+6L6Tv5g/xX7ffhD+ID7Mvkf99P61/ow+xr7fPbq9w333Pj4+yb4Ofe593b7cvtf+Rv59fY1+EL6Yft8/Oz7afsI+QL8Ff42/fb9vPsQ/Xf/n//t/57+hvzw/IoA0v4U/jf+6f/DAGf8MvwK/kv/2/sm+Zv9MP1F/EX7vvpy+TD5fPm3+Wf7IPnH+cb3nvpm/OH5lPhr+Mn3J/jP+vT4J/sP+5D4s/dt90X4Evr0+u/7sPy0/ef7lfvR/AT9uvsM+6n6cvxgAWcB0P9b/Lr7rvo6/rsAuv/d/qD+Lf8Q/ND8bP6W/Oj6rvxw/Lr8zP26/C/9R/vn+I/4P/k8+bj5qPpX9nf2yvj4+PL4MPh5+Er31fhv+Fr63PrK+Xj5kviK+jL8B/7u+lb57fr++4/+iP2J/nb+3/2W/WT9Yf4N//j+rP8U/8L8xf3x/Zf/tP/g/Kz82PyN/Zb+Kf5B/SL71vs7/NH7pfsy+gz5tPey+Hv7jfuu+hT6ePh49ij2a/qs+xT73fhu9jL2svf7+538Ovtu+Mf2GvnT+Rr7Cf4Z/ab7Jfw1/P/8Yf5n/bL9n/yV+/79FP51/9//ZPwG/Jr8R/3Z/nj+7v7q/fL8dvyd/Q3+Gv2S/GP7xfqp+yH8ZfuT+pv6afmv+Nj6DPlm+Rz6D/n694j40vjR91P4q/fz94L4Pvnh+dD5WPlE+PH58vmP+pz8e/yr/EL77/uz/BP+wf1a/T7+NP7J/nj+8/9RADMAhv6h/Cr9k/5k/3D+Bf6f/QP9evyn/Nz8kfuz+qj6xfp5+qL7Nft0+4P6Hvd39yz4IPhD+PD3q/fE91/4Xfh8+Jj4Lfl7+Y754Pkj+pH6VPq/++n7wfuK/BP8gPt9+ev5O/z3/GL+3f9H/2P+n/6m/nz93Pyh/6cAu//a/Tj9kP3a/0j/mPwW/Av70vtd/AX8Z/vM+3r7Evsr+mv5JPmG+C74k/gn+H347vj2+SL54fig+cz5Evl9+CH5u/nq+rn67/kl+v/5C/qg+tz8ivw6/Jf89ftR/Gv9X/7m/nP+Wv1v/aL9Wf5S/tn+7/4N/tX9Yf0//a39ZP9s/nb+9PzY+7f7oP0e/Zz6zfoX+WT76/vl+gP5lPmH9y32K/cg+En5j/lS+EL5afqC+vX5HPrK+W34XPrJ+qf8Yvuj+X/4FPlB/C3+CP9S/vP8MvuK/Iv9zv3f/sf/fP4X//3/Qv7Z/EH8Mv2C/ZH8Z/xM/bX9pf07/JP7cvt/+6n8qP3U/N772PoV+pX6vvpR+tL5U/ii+DD5M/pk+uX56/jO93f4kvgv+Qv6kPqn+nH5cPo4+yD7fvx0+4X7Dvtj/FH86v3M/Uf9+fyf+zX8/P0r/8/+8f3z/ET8K/xn/Q7+FP9R/hj8J/sz+1L87vyo/f39+vsh+5b6Ifvr+7f7NPvG+bz5BfrZ+UP6jvmV+Rz5WvmG+cb6A/uW+r77e/o6+vv6U/uc++L70/um+pj6yfou/K385Pwa+1X7K/zc+/L7a/zv/Hn8pPyI/XH+GP7a/eH7e/uQ/NL6VvsN/RD+N/zr+qT8w/wv/Q/7rfn7+fH7k/xn+277E/sB+4b7NfrT+hH8sPwq/DD7HfrY+Ef54fpE+y/67fvr+5D67fve+2T8Af2O+rz5XvkP+nj7jPts/GH76/p5+vv7l/yR/NT7+/os+1z7dfyU/az+Av05+1f6+Pm5+4z8d/wj/Sn7n/mW+qr70/vp+0L76Pla+rz7If1F/QP8APoe+Xj6hPzd/DH9Vfz4+nT6O/pQ/Gb9Nfwy+pX5Gvog/ML9Of0i/Kj7Qvnb97/4gfrl/Gb8rPuI+Zn4DPqQ+6b9lf3/+3T5+Pmr+un7kvza+6f6zPqX/KX9bv4c/6D+XPzL+9H8/fzN/fD8kfsf+877gfyw/Rz9w/u2+/P6HPq3+nD82/tW+zH5GfkU+lv79vyz+9D6TPlT+MX52PuZ+0P6HPmi9xX46/mR++r7E/tc+Dr4Mvmv+hD8yfzM+676RPqV+tX8if64/oL9D/yf+4/7+v2j/mr+//1P/L37xfzQ/mr+dP0Q/cb7/vva/Z39kv0r/Az70vrw+x7+hP3u/Cv7/flG+Wz6z/zc+9n6Xvf89aX4Dfpi+xb6QPn09jT2a/hS+lv8ovqP+uX4nfhl+gX8hv4s/R37+vm4+Jf6K/0z/tD9qv3j/HD7v/0L/x4Asf6i/Lf74foE/Zv//ABk/3L86fql+iv9sf/9/hT86/gc+C75Hfu7/Fj8L/oq+Zv4d/vI/Zv9Xfs++MX2gvYY+sf6Lfv7+aD3FPiC+NH7Pvzk+875tvjp+Tb77v2z/lv9Rfu1+mH6V/xO/v795f56/HL7nPtJ/TAADQDb/iH8evsu/BL+c/+l/v/8vPo5+iv75fxm/jH9pPpn+Sf7mfvD+5X8Lfrg9yH3Tfc2+l38P/va+M33W/fq+BL7Iftk+v/3p/bS9+n64/64/pT9iflY+ED62ftq/0f+FP7y+pD5aPp8/R0BNQAd/8j7U/z3/K39uf+4/lr9Tfub+0H88v0o/x79tfsl+pP63/uN/Yb92PsI+t750vll+jD7L/ro+ZX4vvhC+af7wvvx+Uv4Ofdi+Fn6kfte+yv5qPeY+ND4/Ps3/cL82fqL+Rn6sfu+/ZX9Ff7z+037OfzW/Sv/g/8U/3L74Pvn/Nf9l/+N//T98fvy+n/72f1Y/nr9v/1S+0n69vuB/J786PuA+qT5TPqt+nb6bfrr+a348Pfq+KD6n/s0+lH5NPes+VP76/q7+wH62Phb+cP5b/xZ/jL9xvuP+Rz6jvtR/x7+Zv2A/FH6Tvs//Ij9Qv///iP8bPrL+o78Vv5l/ib9O/1y/AP7lvvM/KT+zv0q+/75zPp/+7D7wfx0/GX7yviA+cf6UPzy+zT6v/rO+nP8kPsl+sf4xfm/+HP48fmW+Zj5L/mH+vT6/vzX+/n7gfp2+qL7iPt+/P/85f3z/Iv7FPzS/Mv9hv0T/cr7ivrn+yT90/5x/rf91PxV/KD7p/zB/Pv7Kfug+fn6h/vZ++/87fud++L5F/rN+nb7wfst+u37ovpQ+xL8v/rH+rj5ZfmK+UP65/nO+pX6OPo7+in8hvz1/C/7xflg+pv5uvsf/FP9cfx6+2/6dPov/C39EP5d/fD8hvsd+/H70fuL/PD7J/xo/DH8+/xF/Ur9XPyY+6j7Vvux+5r7w/v5+rb6z/qH+cH5vPon+536sPvd+m364fpC/Cj87/pv+3f6G/sx+oT7DPqY+0/7bfnQ+175vftH+YH6ifs5/Cz9DPtS/Lf6If33+5b8Iv49/On9GPuO/Kn8af3e/C788/xs+7T8gfvZ/Kv87fx2/An7WPvJ+nL8JPwX+976rfr5+VH6Zfsv/GH75/lD+0f6ifqq+837q/sI+iP6QfmU+936Kvv6+/35LPuK+bT7r/pq+8D6n/ov/H76RPzU+hn7+fsD+3n9rPu9/Fr8QfyS/N37r/01/ND9Tfz8+k/8zvu9/NP87PyG/Cf8Zfvk+gT8NPtR/Db7afrz+Wj5K/xA+yH77vky+uj6sfqw+6j76/tN+nL77Ptp+zb8NPyS+wP7dPuZ+6X85/zD++L6ePnQ+q76BPyM+7v7lPsm+1T7wvqn++36vvuJ+2v83vwd/O/8L/uL+xb8Fv0O/nb8o/0P/PT6rvvc++D83Pvk+1j7RPsS/Dz8Bfy5+5n7yvoB+zD65vph+1b6lvpG+Yz7APoO+x377fnr+4/5Pvzd+hf8tvug+537mfq+++/6Mf2g+8D6MPuY+hj8SPtv/T77CfuY+wL7Av20/NX9/vtN/H/7Uvw0/AT8ZPyx+kj8avtq/Oz7fPyN/CL6rvx9+pf8rPtA+537TPph+1/6WPz++fT7ZPpi+rP7LfnZ/Hf59/ua+hv7lftK+tz8X/pY/KP6avvy+2/7Vfxy+3r94vrd++X8ufvM/PD68vsZ+2L8S/sP/EL8KfuD/Hn6vfzM+0j8MPxU+k78ovs+/WD7FvzH/AD7ifz5+l797fzM+y78+vr5++b5tvwC+wf6X/uj+A38Sfkh+7/78/mY++L5jvum+jf7dPvT+0v88Pqo/DL62/q1+2D7e/xj+/z9U/yb+8z7rPvL/CT70/xP/Nf8xPtp+4H7Ffs0/F375/xT++r78/vV+wj8BPyG/Jj6efu6+V/7SPu++gr9Zvpt/FD6UPvB+qH6kftm+gj8JPoq/I76Ffy8+1L6Wfwn+qX7ovp9+5D7b/sP/Gr7XfxY+1T8lPud+6P86Pvl/N77ffwg/F783/wE/Jf8q/s0/ET7Zfzo+378lfs/+1v8EPuF/AX7uPt8/PL5NvwI+qL74/v4+o38q/ld/Pf5xfuj+/H64vya+cT8dPq6+wv86PrK+z371PvR+vj7Vfo4+636bfuX/Mf6pfsB/Bb85/us+4v8tPtx++D7VPu7/CL8uvtR+2f8K/tK/KP8fPug/Of7HvyK+yH7cfvL+/v7dvuV+zj6kvqx+/P6IvwK+3r8nvmf+776o/rW/lz47P5k+R/82vvS+HL+Ivi2/Ub50Ptg/Of5u/3V99D9GPoq+mz9R/kr/nv58Pz++v/6lf0s+b/8nvsn/GT7tPut/D37pftO/O363fsk/PX6m/uF+5/8lfsu+/b72fwt+6b7n/v7+jb8tvnC+076W/zz+nf7b/zQ+qX89fkt/Oz5DP2J+3r6wvv/+kL7zvtT++n8W/tf/F/7n/vg+/76wfzq+pn9U/rI/U/8A/xX/Ob6//xs9zv+7fil/Nr7CPoH/tP4qf2X+BT9E/vt+fP8xvkC/bP6x/uS+9T6yPtg++r7/vpz+5770/vo+537Ovy7+xf7ivte/dz6Kfzm/Cv6z/za+XL8Yfsg/EL7p/on/LP6ovsI/A/7WPt6/Kb61f29+Qj9Xvwo+3j8Ofqz/Dz6ifxU+v/8F/wL+gf+Mfr4/Nz66/yZ/DX6Lf70+aH8+fvs+j79jPnZ/FT6n/tB+w37JvyH+hT9mfo+/QL6Jvwk+8D6dPvV+TL+g/me/Jb60/wL+276iv1I+Zz86fpV+1/8hPpP/Zn5B/4g+tL8PPwW+kb9Mfll/lD3V/+J+aD9F/v3+jz9nflA/sL4lP/p+BP+EvrJ/G38Zfng/kv3GgCU+K383PvK+dH/Cfbu/vT53fud/FT6i/0a+ur7wvtv+ur8avzP+gz9Vfpz/aL5b/wd/JL6WP1T+vn7XvtU/I77zPqr+437tfo0/Lz76Ptx+137RPv8+ij7Lvyh+on7rftI+wv8sPvr+6/7V/uS/N36cvvi/eD47P63+WL9FPrU+/r8tPiq/8D3v/4j+bv9NfvA+ij91fhq/fD32f6r9279XvwU+lj+oPjC/t73oP3z+WH7wPyu+oz83vpN/E37HvvY+yb7c/qp+zz8Yfrg+5r8qvkI/iX5/fzx+jP7Hfxx+Uf9nvjt/N358PtV+2j7wPxb+X7+wvh1/S77nvrC/LL4kP689xj9e/yc+W3+Xfc7/wX3GP04+sf5fP06+WX+V/gL/jb5I/14+h/6+/08+a7+pPla/cP7KPsj/vr4Bv5W+037W/yu+1381vuJ+0X8wPrf+/L7IPsv/Lz7R/wN+nD9Pvly/Mb77fkw/3D3VwAt+LT8gvza91sAc/Zm/6P4Xv0E/JD4n/9x95X+hvhQ/ir6wfvk/F350v2r+ZH8RPkh/hf5Gvp0/cD5FfxB+9/7sfwZ+hn9w/tj+qP9y/jf/W36h/uO/On5Nf5s+Jb9iPvN+uj8Zvp7/Vz6ovs+/Kj5PP1c+pD7mvzg+az9rPio/df6vfpP/bv5YP1Y+t77aPzt+sj8Yvqu/dL5oPwg/R/5/P1x+r/8pvod/d36Nf1Z+8j7Xfzz+jT8xfoX/hD5Jf5X+wv8jPzs+t39P/tG/dn7dPvf/F77GPod/TL64PsT+x36Y/wt+pL77Plp/P360vks/eD5q/xw+o78E/rX+8X7nvph/JT5K//V97f+rPpn+qT9Rvln/oj4ov8c+F/9A/0a+OT+dPfy/0T2yv5j+u/6Kf55+S39Z/q7/b34Af0p+3j8c/qB/FL8iPoO/Zf6E/yR+0/7+/xL+XD+1Poo+0v+OvhZ/o74z/8u9/D91Pyk+CYAF/dz/9T57v0C/Kr6f//m+Pr96fpU/Ov7XfvS/Zb6+Pyw+5379ftS/Br7HvzJ/Kf5sf26+q/84frE+uT9WPfR/hD5+fyh+zz68f1i+C7/Cfmr/K/8FfqP+5j8ffsn+ln8avwc+TD8ovyp+Rb9WvpU/Qf50/2o+hf7Nf3k+e/9nfjQ/074svz+/OD5AP1V+oD+Gvpo/Mn8cfxU+zb8KfsZ/a/68fsN/f751f09+uf8Rfui+lD9APmN/jH4rv2K/Kj59/vC+mz+FPes/m/6S/1N+Tj8ivxe+X38o/ni/Yr56Pwh+6n67v3s+E38e/1m+QT9E/qm/cf5w/ut/Ur4Sv+w90/+BPu0+bP+evfNAK72Y/4E/Tn42P9X9wAB+/eP/Of8SvmR/rX3qf8M+AT8If7A9YsA4ffB/S/6A/ud/HX4RP5W9+f92/rY+0P7dfkW/8z4XPud/Pr4Nf+q9+/+A/m8/Lf+DfeJAHj35v9Q+UH70f25+Nz/h/cB/hz7wfvR+hv9S/sR++L7mPvc+RX97fv9+F3+Y/q4/Kr65/sd+jH98Pnx+5v6l/yg+mn6t/2795X+iPdT/0z2N/53+hX7+fyK9gADJ/R9AKj5mPyq/Hv3rwGY9mP9bPtN+tP+nvcK/0f60/qy/YX3NgHN9XH+qvqr+uD8Kvfv/8n2W/5e+Cv/Zvj3/BL8mPmz/ob4iP6C+Pn9mflh/Rb5vP2Y+Rf8ufxJ93cA/fZr/yD49/05+xz4xwDu9W39+PmG+3/8hfhi/9n3B/6n+hL8jfqx+939c/mt/cr6K/0p+n37zv1n+Jf+DvpB/LL7nPtm/QL60fwG/DL7ov15+Yr7+PzB+mD5Bftq/1z3+/yR+mf+0viq+6T+QfYAALL4Wf4B+bj7i/5P9qkA8vZ+/2/5nfxH+vf7V/6i9LoBIvnb/cj4IP1S/j33IABc+Er/9vk1+wz/a/eo/6f59fwG/Q36aP5v+tz9lfuu+l3+gPqj+z79lPt0/GD6Ufzg+xr7zfpE/Hf6NP3D+LP81fuV+wv8EPv6/Pj5uPzz+t/7/fvJ+vf83vo6/E36n/zd+6z6CvyE/A37evvd/KH5E/57+dn8V/vz+hX8VPpD/TX5N/yO+pv66P26+Mf9yvtY+ij+a/kf/Z76dfyo+3H5UwHh9DYBd/c9/ED+dvZ6AFr3of66+XP7qvwi+cv7CvvV+W38Ofp0/OL5t/tk/Cr4DAC09RL/LfkF/IT60voh/Vf6Jv3w+RD9Qfpv/Nb5F/wQ+g/8G/z3+C/+t/pR+4j7/fkO/i/4Tf9z+TX7sfxR+yj6E/xy/TX5m/4U+g38ZPsZ/Ez66/sA+6H8W/i1/WD5NPuC+yv64PtP+b388/lH+/r6yfwr+lH6q/14+bH8APoQ/dL4c/z0+zf4dv/2+NX8OfnQ/Sb7ovhFAY/1iQD492/8w/or+rz8E/iB/7D31v2m+7H6z/uH+r79pfnM+878zvi7/tn4Y/tQ/Pr7MvuM+vr77vtH+bT7Yvvl+rb7TfmK/p732/wx+ZL6Lv7y9lr/dPi6/Sz4Lv14+9H5Of7t+Gz9pfuS/Lz3cgEl9if99PrW+7r7svhmANf2G/9E+oH7qv6I9r3/KPnF+4j8jvVfAXD1S/8U+4P43wDF9YP/8Pj2+/D8Xvd3AJ32/f4p+T77LP0j+Dn/vfYhABr6hvhsAJT1AQGe9FkC0vgc+ZoB9/Uv/735VvwR+338aftZ+176wADA96r+KfyO+yL95vcAAeT2RwEI+fX8q/o4+3j9wfm//e/6/ftn+vv9yvnB+uz8WvmE/cT5Nf5X+fX8svyg+u79k/aKALr4uvzc+h/81vws+uD7UPvB/NP7OfoQ/Ef+9/gh/Vn5EgAl9xL98v0t+ZH89/kc/Xz6Tfp2/a77Ufoi/tT5uv7B+CUAHfe//w38E/oY/rT5bf/09SkA1vkZ+lX9tvpu/IH6g/0R+kf7gv2N+Rb6s/tB/Jv4Uf1u++r6aftG/Gn7hPun/HT6Efwl/cT5Ffz2/sP35f0/+4L8+/ol+9H7CfyE+hb95/o7+3P9nvmi/n34mP0K+b36rfsS/IP9zPhS/DP8tvsO/N/8OPnO/uH4xfss/Az5Df1A+Pr+4/dB+uv+w/ZQ/532Pv7v+Rz6kv4M98gA1fUG/zX6tPuH+lb8Ffz9+c393Plu/AT7ffvf/J35PP67+K38y/x2+iX7e/sO+7z65/hH/Jb7avqb/Hb59vxQ+5n6u/uI+tX7T/zj+Zn+//d2/l/61Pr//dX3yP+1+Ab8cfxt+fj9tvgy/Q779PrM+8D5Cv71+Gz8bvpt/k/3E//8+Rz9svlH/eb9lPgXAOb51P1h+zL9rfrx/DD4vvnc+lf7IP9n94X7ev13+tr7bP6R9uj+QfgN/bv5Ivqn/e30QgE/+Wb+o/VqAFn6oftz+lP9Vvvi+SH+Cvli/+z26v6H+0v5JP59+bz+Ivmc/aj6//vu/Ff4t/9s+PX9SvtN/FT7Kfw3+jv9MfqW+0b9p/lz/cf6Af4x+Zz+8/m3/Yf6pvse/WT4ZP5x+0/71voe/IP9ufif+yj9Kvnq/6z0lAEJ+SX8H/3Q+J8Ah/a0/xf6Uf0++l76e/9h+Tj8dPya+7j7svy3++H8JvsI/1f4QgFG+ij5gP4P+fL/0feO//b2aPvH/Eb8NvoU+2f8DPrJ/z34kP/v+Wf84P0++VD/nfctAP/4b/s//SX5ov3G+q365f2y+6364vkUAIz6ePho/Yr7uvz694kACPec/Yb7s/u7/M/6Wv8a99kAJvjL/rH5rPwI/K348AG79gL/+fmA/C37x/vM/cX3Dv7d+Yn8e/sd+WH/AfhC/Zj6R/t9/sL1EAIV90D/L/sT+7n///bIANb2cf5f+RT8KPwq+aP+iPgIANj3Mf1H+y39y/lA/OT9lvrF/HH7IgCa+XD8Jfoy/B35qP6I+2z7sPpe+3n8cPpx+wL9R/cr/fL70vr1+yj54/7O9Y/+kfkz+W/9lPj+/fP5SPuz/OL3BQBI+JD8g/zG9hsCZvXNAnD1bP7p/cH2cQLY9CICB/Ym/sb7wvmq/WH6UfxA/Zv4o/3U+dj5P/2i+CoARfc0/x342v27/Ab6jf3p+jT9hfuf+nb7/f1K+AcA5fiHAE34Qfxq/8/3RADA97UBRvTJAcj3Tfq4/P736/5y9SkBTvUvAR73gv1i+1P5fv7W9+f9ifu1+or8Ovy/+8v53fhE/+33//4t+Yv8CvzU9zcAsfbTACT2XQCR+DD9gv669l8BOvV4Alb1/gAZ+hj7iv6++Z4ApfZ6Ajr3zP6L+4T7Lv37+UH+vPgx/VL+0/je/Hb7Efw9+6D5LP8D9+/+w/vb+aD9ifri/dL4E/2X/bj34f0a+i38Z/nO/RT5KP3p+hP8QPlo/jb5Lv5f+o/6Ov2b+o79WflX/5r39f4h+Oz+TPir/On6NfsQ/JD7Wf1d+on+Ofni/9P5k/1R+8D8mPzo+AMAuvdK/pr5Xv4I+qD70v9g+Qv8uPrj/LX67Pln/PT5G/ty+7X7i/ps+gn9rPl+/qr6LfsT/AD8y/rU+lb+fPoD+/r9C/uC/Xf5nP1o/Nn5B/79+bT9Bfq2/RD5/f1x+ZX7XfrW+036wPqa/Pv2BADO99b/jve+/ZL77foC/Pv5vfoj+k74kPzR+Sr7g/uf+HX/6fQsAnn3BPsq/sz2CAKi9nAA0vjJ/QL+u/j0ANX6pvzv/NX8Bv4a+jUAVPrA+uX+xPoa/WX8W/tK/aL5nv95+K/7r/0e+KD8y/p9/EP6eftu+877G/pf/ab2PPpv+in7yfly/CH7qvc0/IX4n/zk99P9fPfY97P+pfUl/hL44fyr+SD6Gv1O9///mPgK/MP8cvwE/eP64/0N+7P9lfqd/7j7Bf4G/H/7vP/C+p0A/vhT/xH8bvxP/rj6N/+R+gD+WfvH/dT4iv2a+A37CfvZ+qb83PZE/7v2i/32+HD6I/p7+eL53PoD+qz6s/k/+Ib91vVgAL70l/5I9+z8WPnR+UP9W/ci/tn4G/62+/f6UPvi/X76Jf1n/Ff84/yc+/v8Jv9B+vMA3fn1AHn78f6v/Zf6O/9B+o8A5fiB/8D3wP2Q+rL72P3O+Mn6lvkD/W347vsx/Kr5Fvn1/DH5XvwO+kX6FPvX+Lj7pPmX+iH9OvhW/Qj5Av6p+Oj7Yv0o+OX+qfa+AeX1xAC4+Qz6oQBd9gj9svtj+uD8gPqP/Z77RPtH/uv5i/7I+gL9TvzW/ev74vqX/hT4Hf8V+eH9BPto/Eb8E/oA/1T5xP3H+hv98vqm+sT9/fdF/Tb6mfwH+zz6NQDY93f9Q/zm++L6jvsu/733Gv6F/MP50P63+CD/ZPmU/rn4if2n+9v5nv9f9ogB9/V4/mL4hvtV/OL2XP2B99L7wvoF/Ob5//uC+Qr9E/gA/Zr4AvqT+2f4v/yM+MX9GPjS/C75yvzs+tr6k/tE+ij9jfliAHr3hwOK9g8AoPy1/HD/Y/nEAlv4lgDM/KX8f//m+cD+Jfs6/Of+z/mG/mz8Ovuk/iv4RP+U9jz92vqr+ZH9M/ZU/mT2Hf2g94v6a/kb+Jf7gPZN/fT19vz19kv6Vfr297v7a/di+0n3Kfzy+A77M/ge/lX36v09+dr86fvv+IgC2PXjAy733QGU+//85QFl+a8ENPqEAYH+ov6I/879/P6G/cP+v/1W/Rn/5vtq/v/8Hvws/632CQE89Uj/2fht+Ez+avSr/0n0j/7F9Qz8K/fi+NT7D/Ww+6z3/fpK94f6HvhJ+e348fkv97T73vnB+Kv8Bvmi/OP3Uf2D+Dn9Rft7+wP87/rX/Zr7mP4i+jr/rvqh/oz7SP+U/oH8D/+g/ej9Uv7q/Mz/MfkhAf36qvyp/Yf7pv1C+4L9TfmK/eH6Q/qI+ZH9Pfj0/KX48fzt+GD6O/qz+aX6LviJ/J34ffu8+br7xvf/+0z65/lP/Cr5rPx6+T/7gvvz+cD82Pnr+1j6tvmy+zT5efxd+YP7ZfzE+cz+G/l+/q/6Ivus/Qf5tv759+X8d/0L+VX/7fZSAd71e/3t/Kn4VwBs9jwBdfb0/nD7bfiaAGz2dgAZ+uT7rP/T993/nPqf/kv8dPuRAJ35YAHf928C2vnN/z39KPqMAOv5qABM9hAB3PrD+qT/DfaTALH0ef64+ff3uP3y9VD7mvmE+1z52PmD+gf4pPmo+BX3MvuK9Wv76fVq+7X2wfnX+Q34w/vN9ZD9Zfiy+YL8ePcg/WT9dfh1AML4AAAA/F7+uwCU+WkDy/oxAar9HgDE/rr9fQLl+6wCJv1pAZr+jfxuAnD7FwLc+WwAGvw6/d79KvkA/6j4gv2d+Nf6lvrV97H8SfRS/Uf14/eF+gP24fhv95T36vZY9FP5V/SZ9+v1bPix98b2kvrT88z71vXF+/H3D/r3+575AP6I+Z/+Bvvb/k79JP3C/5P9NAH7/P0Br//a/04BDADEAJkAVQGmATn/pgJA/17+fgGe+6gBHfs+AGz8Bv1//Pj7wPxq+f79Nfdl/F/4AfnO+Hr5kPcE9wn61fPH+cT0Zvd29lr2svjL9N74M/fZ9WH3IfhN9kv4Lfiy+Kb4Yfr6+E76MvvB+bj7OPt//Mn7bP4u/er9Tf+H/UH/lP54/wr/5P7p/5H+/f8YAIP+zgDD/qD/8P5j/5D+aP7E/tP9ev7B/uv9wv1H/TD9Cf0L+7b8I/sk/LD6CvvN+GH7pfi9+X35IvjV+JP4Efg++Bv54Pfo+OP3B/lL9332RPgs9jL4Tfdj+Hv3cPgQ+hf3E/vS+FH5aPtB+bv78vnm+5T7nPq3/az6Uf3w+8z8CfyY/fP9CP6D/iD+qP4a/jP/Av6v/wP+tf/j/hb/4/+i/8v+HwAh/7j+Pf8S/sr+rv3g/mT9t/5J/ZD9C/wJ/IX7bftm+yf6rfpL+aX6nPfg+br3I/jn9w/3BvgW95739/bq9kj3N/dx9s72mfU79sH14/aF9xP3Ffdu94f3Q/j6+Ab60/ic+mH75vqh+wP8tfxW/br+xf5RADgA+P8LAZsBIgL4AtYBngL9AScC7gK5AVsCNAHgAQ8BegJtAaUACQG5/qr/Yv8h/4X9z/zK/LL7n/vv+lj4A/m+9oH40/bh9rL1EvQS9rLzXfX882300vKM8ujx5fKh8orySfNv82P0EPRB9fDzP/bE9fH3qvh/+ZP6Gvpy/Jv8TP7F/gb/UgCUABICrwLPAtEDpQNvBEUFqgUfBUUF6gUABoYGnAUgBVkE6wP5AqgCmgJBAYkBh//b/9L+5P05/ej63Puc+Yj5oPj69mD27vTz9K/zKPNr8hXxlfHQ8HHw7+/r79bwAfE48MjwUfC28K3xgPL78lf0rfRE9Tv3RPgt+Yv5L/tT+4n9hv4r/+QAEQFZAkQDuANeBMQFWQVgBwwHoQekBnMHCgePBxYIRQfBBjUFvAVsBOMEXQNdAi0C6wBNAXL/4f1P/Sz7Jfu2+U74UPfg9Sf0BPTn8m/xqfDn7QDv2e0c7i7vFu1f7uPsXe7i7RruIu/E7U/xNfA78gvzJvNG9ez1ofj5+Aj7q/tv/LX+j/60AYoBkgOXBIgERAbeBacHnwaTCG4IkwnXCj0KtApHCXUJ/AdhCC8IygaPB8EFFgXlA9gCeAFZAO3/0v03/dP7Avmk+J/3L/bL9Nryu/Hs8Ebv9u5X7k/tW+xA6/jrAeuV7Cnsc+w77cbtTu6G7gfwu/DT8aDzhPQd9zX3pPgo+Wf6SP33/Nv/Vf+1Aj8DXQRjBngGWAjdCLMJuwq9CncKvgqfC/gLgQzfC00LAgvrCowJHQk1CGwGsQXJBEMDZwGc/w390Pvi+Xj4K/bQ8/vy5PEU8dvwrO9z7rPt0+sy7NXqJupj6a7oA+ox6uLriuvA61rrbuxH7qbvvfI789H0ZPZw9kT4sPhJ+wj9cv+UAXEBlwMDBH8FwQcgCDoKDApUCygMxgyVDVINYA3pDCENggx/DLYLMQvoCUYJKwjMBnEFkwOKAg8Btf9M/R/73vie9t/1JPNa8inxUe+07p7s1utZ6rjr9Ooa60PqBemh6ELoO+kb6l7rvuyQ7CTvMfCx8N7yePMf9sT23fmb+hn9X/+pAA0BBQLxAtsE6wWaB3gJ1Ql9CkUKBQsqC28LPAw0DHsMrQtiC3EKYwpHCfcHtwebBUgF2AI3AUv/4v0P/eX6G/ne9+r2ePTR8/LyfPA+8NDuf+0Z7h3tlewr7FPsh+to6/vr7et27XDtc+8S8A3x4PEE8hz0gvUP99T3zvl9+8v8o/6l/7kA+AHkAksEtgV3BiYHLAcrCIUIqAlaCc8IpwliCQoJoQiHCDMI/wdnB8cFewMvAT//Jf5q/df9KPwW+9746fbm9Un1efTL9Bj0PfKv8QvwV+9w7nTvxu6E74jvX+/v7h3w2e+88aLy5/Io9Cn1FPeZ92D5bvoL+478V/33/Vb/x/+fAAwCvgLXA6UEugR4BegFvQacBYQGzQUjBnYGYgaXBsoFCwVaA8cCDgJqAasAO/87/iz8Z/vT+jj5NPl493P2vvTs89byE/F48bPxyfHK8XbxOfAO8HbwwvAv8eXwlvEv8qvyT/SF9If1iPXl9hX4ifmA+8H7YP3w/rb/OwF6ArQCwQOmBHwFpwbRBlEH/AZQB4EHYQcvB8gG0gb1BY4FigSyA1UDLwJoAcv/dP/P/e389Pz3+i36efin91/2JfX2813ztPKq8ULxWvBm8IzvwO987/rvUO/77gDvk+5u8NjvgvFn8YHzMfTV9K/20/ay+KX5Afsj/Cz9lv4Z/3MASgLrAm4E6wQHB/QHEgmpCb0Jogp4Cr8KWwvbClwKbglFCNkHKwfTBSoFugR3A4ECBgHg/3/+Pf1c+435GPh09g31I/M68yXyj/Cp7wzvru5D7Vrs+erp6xrrduuj6+jqG+v+6urqpOyP7ubvG/Gc8V/zivOy9RP2Mfg0+rP7tf1L/7EApwGUAgcE4AWkB20JYAprC7UL8AtMDBMNUw2XDawN0Q0WDXwMWAupCu0JtglhCKQHzwUXBBMC0/9E/ir8u/rG+Oz3gvbm9J3zevFs8H3vde497aHsnesE6u/op+dC5wrom+fP56/ok+if6ZfpWOvM63btku4o8PLxGfSJ9fj2Yfle+x793P4bAK8BuQNdBTMH8AgrCqMLXQwTDrkOEQ/PD04PohD5EB4RIBFIENkPtw2VDFkLiwp/CYQHuwYVBL4CBgDD/Qj8Y/pC+Z73vfXR8xLyjvD17gzuJ+3w65Lq++j85+jmx+XE5a3kNeU05XHluuYk52zozOhs6eTq1utQ7t/v6fG99AL2rfjI+RT8bv3s/7QCUgQ1BzYIygk0Cz4MyA3EDk0QPhDyEHoRiBEDEtcR3hFVEckQXhBsDq4NtAtqCi8JvAc/BpYDoAEU/1n9Ufsz+bj31fWz9MjyNvGQ7mjsy+qJ6fPoOehk53XmnOWY5Trl7OSE5aTkveZ155joHep86u/r5ewM7/3vPfJx9NT1fPic+o38m/48ABICLATmBr8IAAqwC1gMOw0WDlEP2g97ECsRFREXESARcxB0EKQPgg+yDqsNEQxPCh4JOAcSBsoErwJyAHP+Evz1+T/4UPZW9OPyFPF+7zzuFuyy6vPpSuiw54nmKeY55QPkUeUH5WzmUOZ856XnwuhA6sfqf+2P7X3wtvE29Ob17Pe3+TH71P2r/08BAwPLBHoGQQgwCvYLgAzmDYIOEg+0DysQgRD3ELYQwhA/EHsPgg66DcsMEwwbC34Jtwf5BbUDwQGQ/879k/wX+wP6SvjX9uz02PI98WPvG+6b7H3rQ+qd6fromedc5xTnCucm6PLnK+iJ6MzobOnT6v3rguxQ7nrvLfHI8iz0O/Vc9zX5YvuH/YH/WwGnAjQExwXJByMJygmFChYLWwzzDL0NWQ7LDkYP/A4cD6IOvQ10DMIL/gpSCrgJswisB+cFiASpAnoAIf9+/ff7gvos+VT3N/W38+nxhPAh79XtzOwj7EfrWuo16qDpJerV6SzqZOpc6nTqYuqy69Prf+177lHvj/DM8YDzfvQK91H4mvmu+gn8R/2E/nQA3wEuBKYFSQc5CDoJMQrbCiwMgwylDf4N9A23DSINSw1mDCcMeQuGCsIJYAjEBzwGpgVGBNcC2gEeAMz+p/xX+6L5ZPgU95b1SPRh8ojxCPAm743t1Oy061br5+ox6Xrpn+gv6ZrpEuqK6hrrJexv7OLt5+6y7wHx5vGx8+v1SPcR+Sb6OvuN/PT9yP8uATAD8AOOBfgG4gdZCBcJBgrMCr4LPQyfC54LnAs7Cy4L5gqGCnkJBAk6CCcHSAYdBZcEoAPEApoBVQDz/uD8MfuC+Q/4ova79W70L/P/8Rfxk+/e7snty+y77HXspew/7NHrAetM69vrB+yc7IDtTe4g7xbwP/FI8g/zJfTf9CH3i/h8+pz7ovxh/hH/hgBtASsDAAQbBa8GJwfsBwkIUgjvCFcJUQpTCmoKNQrKCaMJUQnkCC0IkQflBhQGQgUiBDQDAQLeAHz/Hv6j/Cf7zvmT+PT2GfYC9aDzYfPc8VjxqPC17w7vlO5b7r3tBO7q7YDt9O267UruBe+976/wE/E18nLyjPNa9I31PfaM98j5PPos/Lr8gv3y/fv+EADgAPQCYANVBEUFwgWLBuAG/ga2BsIHGAgQCHYIEQjfBzYHRwfMBnEGuwWYBOMDPwMRA/QBUAE7AEH/Av7w/BP8rPpH+sX4EPgP9xv2CfUD9Kfz5PJx8hPySfEj8bvwYPAe8CfwqfCX8FnxaPH38QzyXPKU8iTz7/N39Dv1x/Wb93f3GPmL+Xz6hvsK/Mv8X/0k/3j/GgHNAVMCCwNYAyYEggR5Bc4FwQV3BiYGSAbwBRYGwAXTBawFrQQuBEYDmQLKAb4BswBSAGb/Qf5x/TT8WvsH+mr5uvio92X3nfaq9V71pPSk9Cj0FvQY86/yYfIs8iLyz/Ex8hjyNPIS8xrzs/PU89DzJ/Sd9Bn2IPYD+Bn4Gfk2+hP6DfuI+6/8G/0v/hX/Uv8lAE8A5QCEARsCvgL5ApEDDQP6AmUDgQOgA68DdwP6As8CJwIkAooBdQFCAa0ANABl//D+EP5H/bH8FPxo+2j6Cfo5+Un4DPgM9372Avac9S/1VfUG9dX02vRn9MzzXPNJ80bz0PP788b0rPT39LT16vUj9//2zPeB+Ab5GPp/+rr7Hfwh/ab91/2k/sD+Xf+h/4IAHAE+AeUBsAEHAkAC/QFLAnsCYQI5AvkBbgH0AMMATQAaAAwAe/8F/3z+pv0s/Sf8vfsz+7r6ifrp+U/5ufgn+Kz3dveF91f3MffE9ij2PvYF9ir2GvYk9k/22fUo9hr27fX69rr22vc9+IX4MPld+S36GvpM+0j7/Pu0/MT8dv15/Q3+Ov5A/rT+sf4I/z//bf/g/63/wv/W/gf/Of+V/tP+wv7D/nz+mf5i/rv9W/2N/M/7Fvz++7T7f/tv+yL7gvrc+Wb59Phr+Df4SPhg+Ej4" 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-
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-
----
-## Conclusion
-
-We built and trained a vocal track separation model using an encoder-decoder
-architecture with custom blocks applied to the MUSDB18 dataset. We demonstrated
-STFT-based preprocessing, data augmentation, and a source separation metric (SDR).
-
-**Next steps:**
-
-- Train for more epochs and refine hyperparameters.
-- Separate multiple instruments simultaneously.
-- Enhance the model to handle instruments not present in the mixture.
-