sda_tf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacked Denoising Autoencoder (SDA)\n",
    "Uses the Tensoflow machine learning library to train the Stacked Denoising Autoencoder (SDA) to reconstruct \n",
    "compressed \n",
    "***MNIST*** images with Poisson noise.\n",
    "\n",
    "**Author:** Fabian Santiago  \n",
    "**Update:** August 21, 2024\n",
    "\n",
    "**Implementation:** SDA architecture discussed in:\n",
    "**[IEEE publication](https://ieeexplore.ieee.org/abstract/document/8682767)**\n",
    "\n",
    "***NOTE:*** *Code is setup to reconstruct 7x7 compressed noisy images. Batch size and learning rates may need to be adjusted for 4x4, 14x14, and 28x28 cases.*\n",
    "\n",
    "Python Version: 3.11.5  \n",
    "Jupyer Notebook Version: 6.5.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Modules and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-25 08:47:29.250822: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, regularizers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import os\n",
    "from skimage.util.shape import view_as_windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set Values: Seed, Compression, and Output Dimension "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the seed for TensorFlow and Keras\n",
    "tf.random.set_seed(101)\n",
    "\n",
    "# Dimension of the compressed/noisy images (width=height)  \n",
    "# cmp_dim = 4:(for 4x4), 7:(7x7), 14:(14x14), or 28:(28x284)\n",
    "cmp_dim = 7\n",
    "\n",
    "# Dimension of output, original are 28 x 28\n",
    "out_dim  = 28 \n",
    "\n",
    "# Number of training epochs\n",
    "num_epochs = 25\n",
    "\n",
    "# Size of training batch sizes\n",
    "batch_size = 250"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper Function Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define sliding window compression\n",
    "def median_downsampling(in_image, cmp_dim):\n",
    "    if cmp_dim < 15:\n",
    "        # Compression dimension to window size\n",
    "        window_size = 28//cmp_dim\n",
    "        # Create sliding windows\n",
    "        windows = view_as_windows(in_image, (window_size, window_size), step=window_size)\n",
    "        # Calculate the median over each window\n",
    "        return np.median(windows, axis=(2, 3))\n",
    "    else:\n",
    "        return in_image\n",
    "\n",
    "# Define compression of entries in an array\n",
    "def down_sample_list(in_array,cmp_dim):\n",
    "    out_array = np.empty((len(in_array), cmp_dim, cmp_dim))\n",
    "    \n",
    "    # Iterate over array\n",
    "    for idx, image in enumerate(in_array):\n",
    "        out_array[idx] = median_downsampling(image,cmp_dim)\n",
    "        \n",
    "    # Return array containing compressed entries \n",
    "    return out_array \n",
    "\n",
    "# Split dataset into two\n",
    "def split_dataset_rnd(in_images):\n",
    "\n",
    "    # Total number of images\n",
    "    num_images = in_images.shape[0]\n",
    "    \n",
    "    # Random permutation of indices\n",
    "    shuffled_indices = np.random.permutation(num_images)\n",
    "    \n",
    "    # Calculate the split index\n",
    "    split_index = num_images // 2\n",
    "    \n",
    "    # Split the shuffled indices\n",
    "    idx1 = shuffled_indices[:split_index]\n",
    "    idx2 = shuffled_indices[split_index:]\n",
    "    \n",
    "    # Use the indices to create two random datasets\n",
    "    dat1 = in_images[idx1]\n",
    "    dat2 = in_images[idx2]\n",
    "    \n",
    "    return dat1, dat2\n",
    "\n",
    "# Create compressed noisy training data\n",
    "def create_training_test_data(clean_train, clean_test, cmp_dim, out_dim):\n",
    "    \n",
    "    # Compress images using median in sliding window\n",
    "    cmp_train = down_sample_list(clean_train, cmp_dim)\n",
    "    cmp_test  = down_sample_list(clean_test, cmp_dim)\n",
    "\n",
    "    # Add Poisson noise to compressed images\n",
    "    noisy_train_ = np.random.poisson(lam=cmp_train)\n",
    "    noisy_test_  = np.random.poisson(lam=cmp_test)\n",
    "    \n",
    "    # Scale to [0,255]\n",
    "    noisy_train  = np.clip(noisy_train_,0,255)\n",
    "    noisy_test   = np.clip(noisy_test_,0,255)\n",
    "    \n",
    "    # Reshape Compressed/Noisy Arrays and scale to [0,1]\n",
    "    noisy_train = np.array([matrix.reshape(cmp_dim**2,) for matrix in noisy_train/255])\n",
    "    noisy_test  = np.array([matrix.reshape(cmp_dim**2,) for matrix in noisy_test/255])\n",
    "    \n",
    "    # Return training data\n",
    "    return noisy_train, noisy_test\n",
    "\n",
    "# Stacked Denoising Autoencoder (SDA) Builder\n",
    "def build_SDA(in_dim, out_dim, noise_factor=0.2):\n",
    "    model = models.Sequential()\n",
    "\n",
    "    # Define the input layer\n",
    "    model.add(layers.Input(shape=(in_dim**2,)))\n",
    "\n",
    "    # Add noise to input using GaussianNoise layer to stimulate \n",
    "    # denoising, thereby ignoring small perturbations in input\n",
    "    model.add(layers.GaussianNoise(noise_factor))\n",
    "\n",
    "    # Encoder\n",
    "    model.add(layers.Dense(in_dim**2, activation='sigmoid', kernel_initializer='he_normal'))\n",
    "\n",
    "    # Add Gaussian noise after the encoder\n",
    "    model.add(layers.GaussianNoise(noise_factor))\n",
    "\n",
    "    # Decoder\n",
    "    model.add(layers.Dense(out_dim**2, activation='sigmoid', kernel_initializer='he_normal'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load/Create Training Data\n",
    "Load traning data or load MNIST data and create training data if it has not already been created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check compression dimention variable:\n",
    "if not (cmp_dim in [4, 7, 14, 28]):\n",
    "    raise ValueError(f'cmp_dim = {cmp_dim}, not handled. The cmp_dim value must be 4, 7, 14, or 28.')\n",
    "\n",
    "directory = 'training_data'\n",
    "\n",
    "# If the directory does not exist, create it\n",
    "if not os.path.exists(directory):\n",
    "    os.makedirs(directory)\n",
    "\n",
    "# Load the MNIST Dataset\n",
    "dat_file = f'{directory}/mnist_{cmp_dim}x{cmp_dim}_train.h5'\n",
    "    \n",
    "# Load MNIST\n",
    "(clean_train, _), (clean_test, _) = tf.keras.datasets.mnist.load_data()\n",
    "    \n",
    "# If the compressed/noisy data does not exist, create it\n",
    "if not os.path.exists(dat_file):\n",
    "    # Create compressed/noisy training/test data\n",
    "    noisy_train, noisy_test  = create_training_test_data(clean_train, clean_test, cmp_dim, out_dim)\n",
    "        \n",
    "    # Save compressed/noisy training/test data\n",
    "    with h5py.File(dat_file, 'w') as f:\n",
    "        # Save compressed noisy images\n",
    "        f.create_dataset('noisy_train', data=noisy_train, compression='gzip')\n",
    "        f.create_dataset('noisy_test', data=noisy_test, compression='gzip')\n",
    "        \n",
    "else: \n",
    "    # Load compressed/noisy training data\n",
    "    with h5py.File(dat_file, 'r') as dat_file:\n",
    "        # Load compressed noisy training/test images\n",
    "        noisy_train = dat_file['noisy_train'][:]\n",
    "        noisy_test  = dat_file['noisy_test'][:]\n",
    "        \n",
    "# Prepare original mnist data for model training\n",
    "clean_train = np.array([matrix.reshape(out_dim**2,) for matrix in clean_train/255])\n",
    "clean_test  = np.array([matrix.reshape(out_dim**2,) for matrix in clean_test/255])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Training Dataset Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 2 by 4 subplot handle\n",
    "fig, axes = plt.subplots(2, 4, figsize=(9, 4.5))\n",
    "axes      = axes.flatten()\n",
    "\n",
    "# Add title\n",
    "fig.suptitle('Training Set Examples', fontsize=20, fontweight='bold', fontfamily='serif')\n",
    "\n",
    "# Shift window through training dataset \n",
    "sft_idx = 0\n",
    "\n",
    "# Plot noisy and decompressed realizations in each subplot\n",
    "for idx in range(4):\n",
    "    # Plot noisy\n",
    "    axes[idx].imshow(noisy_train[idx+sft_idx].reshape(cmp_dim,cmp_dim),cmap='gray')\n",
    "    axes[idx].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx].set_yticks([]) # Remove yticklabels\n",
    "    \n",
    "    # Plot original\n",
    "    axes[idx+4].imshow(clean_train[idx+sft_idx].reshape(out_dim,out_dim),cmap='gray')\n",
    "    axes[idx+4].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx+4].set_yticks([]) # Remove yticklabels\n",
    "    \n",
    "# Set ylables \n",
    "axes[0].set_ylabel(f'Input\\n{cmp_dim} x {cmp_dim}\\nCompressed & Noisy',fontdict={'fontsize': 12, 'fontfamily': 'serif', 'color':'blue'})\n",
    "axes[4].set_ylabel('Original\\n28 x 28\\nMNIST',fontdict={'fontsize': 12, 'fontfamily': 'serif'})\n",
    "\n",
    "# Adjust layout to decrease padding between subplots\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n",
    "\n",
    "# Display the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile & Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0821 - val_loss: 0.0423\n",
      "Epoch 2/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0464 - val_loss: 0.0373\n",
      "Epoch 3/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0437 - val_loss: 0.0358\n",
      "Epoch 4/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0427 - val_loss: 0.0350\n",
      "Epoch 5/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0422 - val_loss: 0.0346\n",
      "Epoch 6/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0419 - val_loss: 0.0344\n",
      "Epoch 7/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0416 - val_loss: 0.0341\n",
      "Epoch 8/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0415 - val_loss: 0.0339\n",
      "Epoch 9/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0413 - val_loss: 0.0338\n",
      "Epoch 10/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0412 - val_loss: 0.0338\n",
      "Epoch 11/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0410 - val_loss: 0.0337\n",
      "Epoch 12/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0410 - val_loss: 0.0336\n",
      "Epoch 13/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0409 - val_loss: 0.0335\n",
      "Epoch 14/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0408 - val_loss: 0.0335\n",
      "Epoch 15/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0407 - val_loss: 0.0335\n",
      "Epoch 16/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0407 - val_loss: 0.0335\n",
      "Epoch 17/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0406 - val_loss: 0.0334\n",
      "Epoch 18/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0406 - val_loss: 0.0335\n",
      "Epoch 19/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0405 - val_loss: 0.0334\n",
      "Epoch 20/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0404 - val_loss: 0.0335\n",
      "Epoch 21/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0405 - val_loss: 0.0335\n",
      "Epoch 22/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0403 - val_loss: 0.0334\n",
      "Epoch 23/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0403 - val_loss: 0.0335\n",
      "Epoch 24/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0403 - val_loss: 0.0334\n",
      "Epoch 25/25\n",
      "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0403 - val_loss: 0.0335\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "SDA = build_SDA(in_dim = cmp_dim, out_dim = out_dim)\n",
    "\n",
    "# Create an optimizer with a specific learning rate\n",
    "tf_opt = tf.keras.optimizers.Adam(learning_rate=0.01)\n",
    "\n",
    "# Compile the model\n",
    "SDA.compile(optimizer=tf_opt, loss='mean_squared_error')\n",
    "\n",
    "# Train model\n",
    "fit_history = SDA.fit(noisy_train, clean_train, epochs=num_epochs, batch_size=batch_size, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ gaussian_noise (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussianNoise</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ 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\">49</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,450</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ gaussian_noise_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">49</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussianNoise</span>)                 │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<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\">784</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">39,200</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ gaussian_noise (\u001b[38;5;33mGaussianNoise\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m)             │         \u001b[38;5;34m2,450\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ gaussian_noise_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m49\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGaussianNoise\u001b[0m)                 │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │        \u001b[38;5;34m39,200\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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\">124,952</span> (488.10 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m124,952\u001b[0m (488.10 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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\">41,650</span> (162.70 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m41,650\u001b[0m (162.70 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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\">83,302</span> (325.40 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m83,302\u001b[0m (325.40 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SDA.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply SDA to Training Set and Display Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save output\n",
    "imdir = 'method_results'\n",
    "\n",
    "# If the save directory does not exist, create it\n",
    "if not os.path.exists(imdir):\n",
    "    os.makedirs(imdir)\n",
    "\n",
    "# Save file name\n",
    "imfile = f'mnist_reconstruct'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n"
     ]
    }
   ],
   "source": [
    "# Apply SDA to all training inputs\n",
    "pred_train_out = SDA.predict(noisy_train[0:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 3 by 4 subplot handle\n",
    "fig, axes = plt.subplots(3, 4, figsize=(9, 8))\n",
    "axes      = axes.flatten()\n",
    "\n",
    "# Add title\n",
    "fig.suptitle('Application of SDA Architecture\\n(MNIST Training Set)',fontsize=20,fontweight='bold', fontfamily='serif')\n",
    "\n",
    "# Shift window through training dataset \n",
    "sft_idx = 0\n",
    "\n",
    "# Plot noisy and decompressed realizations in each subplot\n",
    "for idx in range(4):\n",
    "    # Plot noisy\n",
    "    axes[idx].imshow(noisy_train[idx+sft_idx].reshape(cmp_dim,cmp_dim),cmap='gray')\n",
    "    axes[idx].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx].set_yticks([]) # Remove yticklabels\n",
    "    axes[idx].set_xlabel('⇩',fontdict={'fontsize': 25, 'fontweight': 'bold', 'fontfamily': 'serif', 'color':'blue'})\n",
    "    \n",
    "    # Plot decompressed with SDA\n",
    "    axes[idx+4].imshow(pred_train_out[idx+sft_idx].reshape(28,28),cmap='gray')\n",
    "    axes[idx+4].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx+4].set_yticks([]) # Remove yticklabels\n",
    "    \n",
    "    # Plot original\n",
    "    axes[idx+8].imshow(clean_train[idx+sft_idx].reshape(28,28),cmap='gray')\n",
    "    axes[idx+8].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx+8].set_yticks([]) # Remove yticklabels\n",
    "    \n",
    "# Set ylables \n",
    "# axes[0].set_ylabel(f'{cmp_dim} x {cmp_dim}\\n(Pre-Input)\\nCompressed',fontdict={'fontsize': 12, 'fontfamily': 'serif'})\n",
    "axes[0].set_ylabel(f'Input\\n{cmp_dim} x {cmp_dim}\\nCompressed & Noisy',fontdict={'fontsize': 12, 'fontfamily': 'serif', 'color':'blue'})\n",
    "axes[4].set_ylabel(f'Output\\n{out_dim} x {out_dim}\\nSDA Reconstruction',fontdict={'fontsize': 12, 'fontfamily': 'serif', 'color':'blue'})\n",
    "axes[8].set_ylabel(f'Original\\n{out_dim} x {out_dim}\\nMNIST',fontdict={'fontsize': 12, 'fontfamily': 'serif'})\n",
    "\n",
    "# Adjust layout to decrease padding between subplots\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0)\n",
    "\n",
    "# Save results \n",
    "plt.savefig(f'{imdir}/{imfile}_{cmp_dim}x{cmp_dim}_train_SDA.png')\n",
    "\n",
    "# Display the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply SDA to Test Set and Display Example Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
     ]
    }
   ],
   "source": [
    "pred_test_out = SDA.predict(noisy_test[0:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 3 by 4 subplot handle\n",
    "fig, axes = plt.subplots(3, 4, figsize=(9, 8))\n",
    "axes      = axes.flatten()\n",
    "\n",
    "# Add title\n",
    "fig.suptitle('Application of SDA Architecture\\n(MNIST Test Set)',fontsize=20,fontweight='bold', fontfamily='serif')\n",
    "\n",
    "# Shift window through test dataset \n",
    "sft_idx = 0\n",
    "\n",
    "# Plot noisy and decompressed realizations in each subplot\n",
    "for idx in range(4):\n",
    "    # Plot noisy\n",
    "    axes[idx].imshow(noisy_test[idx+sft_idx].reshape(cmp_dim,cmp_dim),cmap='gray')\n",
    "    axes[idx].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx].set_yticks([]) # Remove yticklabels\n",
    "    axes[idx].set_xlabel('⇩',fontdict={'fontsize': 25, 'fontweight': 'bold', 'fontfamily': 'serif', 'color':'blue'})\n",
    "    \n",
    "    # Plot decompressed with SDA\n",
    "    axes[idx+4].imshow(pred_test_out[idx+sft_idx].reshape(28,28),cmap='gray')\n",
    "    axes[idx+4].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx+4].set_yticks([]) # Remove yticklabels\n",
    "    \n",
    "    # Plot original\n",
    "    axes[idx+8].imshow(clean_test[idx+sft_idx].reshape(28,28),cmap='gray')\n",
    "    axes[idx+8].set_xticks([]) # Remove xticklabels\n",
    "    axes[idx+8].set_yticks([]) # Remove yticklabels\n",
    "    \n",
    "# Set ylables \n",
    "# axes[0].set_ylabel(f'{cmp_dim} x {cmp_dim}\\n(Pre-Input)\\nCompressed',fontdict={'fontsize': 12, 'fontfamily': 'serif'})\n",
    "axes[0].set_ylabel(f'{cmp_dim} x {cmp_dim}\\nInput\\nCompressed & Noisy',fontdict={'fontsize': 12, 'fontfamily': 'serif', 'color':'blue'})\n",
    "axes[4].set_ylabel(f'{out_dim} x {out_dim}\\nOutput\\nSDA Reconstruction',fontdict={'fontsize': 12, 'fontfamily': 'serif', 'color':'blue'})\n",
    "axes[8].set_ylabel(f'{out_dim} x {out_dim}\\nOriginal\\nMNIST',fontdict={'fontsize': 12, 'fontfamily': 'serif'})\n",
    "\n",
    "# Adjust layout to decrease padding between subplots\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0)\n",
    "\n",
    "# Save results\n",
    "plt.savefig(f'{imdir}/{imfile}_{cmp_dim}x{cmp_dim}_test_SDA.png')\n",
    "\n",
    "# Display the figure\n",
    "plt.show()"
   ]
  }
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