diff --git a/First_Date_with_TensorFlow.ipynb b/First_Date_with_TensorFlow.ipynb new file mode 100644 index 0000000..124585a --- /dev/null +++ b/First_Date_with_TensorFlow.ipynb @@ -0,0 +1,910 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "First_Date_with_TensorFlow.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "2XXfXed5YLbe", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# First Date with TensorFlow\n", + "\n", + "Hi all,
\n", + "\n", + "You know what's important for understanding Deep Learning / Machine Learning?
\n", + "Intuition. Period.\n", + "\n", + "And Intuition comes when you run the code multiple times.\n", + "\n", + "So, today I can write a couple of defination and say this is this, this is that.
\n", + "You Google half of the things up. You find answers which you need to Google further.
\n", + "In the process, you probably won't even remember what's the first thing you started out with!\n", + "\n", + "So?\n", + "\n", + "Hence on, I will execute cells with code.
\n", + "The neurons in your brain will optimize a function to get a hold of what each function is doing.
\n", + "**No Theory Just Code.**\n", + "\n", + "I will at max give a defination that extends for a line. That's it.
\n", + "Let's get started!\n", + "\n", + "
\n", + "\n", + "**RECOMMENDED!**
\n", + "Write the code in the cells using the signals sent by your brain to your fingers!
\n", + "Don't just `shift+enter` the cells.\n", + "\n", + "[Source](https://github.com/iArunava/TensorFlow-NoteBooks)" + ] + }, + { + "metadata": { + "id": "gYWUpE-bYKWP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Essential imports\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eKpz5NCIYMdi", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Let's define some tensors\n", + "t1 = tf.constant(2.0, dtype=tf.float32)\n", + "t2 = tf.constant([1.0, 2.0], dtype=tf.float32)\n", + "t3 = tf.constant([[[1.0, 9.0], [2.0, 3.0], [4.0, 5.0]], \n", + " [[1.0, 9.0], [2.0, 3.0], [4.0, 5.0]]])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vmMcjzTxbWzw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "52d83a28-040a-4725-9f6b-03d865e533de" + }, + "cell_type": "code", + "source": [ + "# Let's print them out!\n", + "print (t1)\n", + "print (t2)\n", + "print (t3)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Tensor(\"Const:0\", shape=(), dtype=float32)\n", + "Tensor(\"Const_1:0\", shape=(2,), dtype=float32)\n", + "Tensor(\"Const_2:0\", shape=(2, 3, 2), dtype=float32)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "10ahnfjYbcop", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Where's Waldo?
\n", + "I mean, the value?
\n", + "\n", + "So, the thing is you can't print the value of tensors directly.
\n", + "You have to use `session`, so let's do that!" + ] + }, + { + "metadata": { + "id": "ol6O5I7Tb2nb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "fcc84119-a778-4e9d-997a-ed89b7c60e5b" + }, + "cell_type": "code", + "source": [ + "sess = tf.Session()\n", + "print (sess.run(t1))\n", + "print (\"=======================\")\n", + "print (sess.run(t2))\n", + "print (\"=======================\")\n", + "print (sess.run(t3))\n", + "sess.close()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "2.0\n", + "=======================\n", + "[1. 2.]\n", + "=======================\n", + "[[[1. 9.]\n", + " [2. 3.]\n", + " [4. 5.]]\n", + "\n", + " [[1. 9.]\n", + " [2. 3.]\n", + " [4. 5.]]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "rXKfVs_zb-kU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Aaahaa!! Just printed those tensors!!!
\n", + "Feels good!
\n", + "\n", + "For some of you, who are like, dude you got \"No Theory Just Code\" in bold
\n", + "And you are still using the markdown cells for the theory ?!\n", + "\n", + "I am just gonna say I am a unreasonable man.
\n", + "\n", + "\n", + "So, you are programming with tf.
\n", + "What ever you do is broken down to 2 basic steps:\n", + "- Building the computational Graph!\n", + "- Execute that graph using `session`!\n", + "\n", + "That's all!\n", + "\n", + "
\n", + "\n", + "Let's compare this 2 steps with what we did above!
\n", + "So, I defined 3 `tensor`s and these 3 `tensor`s formed my computational Graph.
\n", + "And then I executed each tensor in this graph using a `session`.\n", + "\n", + "That simple!\n", + "\n", + "
\n", + "\n", + "Now, let's define a few more computational graphs and execute them with sessions.\n", + "\n", + "Okay, to start with let's build this computational graph!\n", + "\n", + "![Comp Graph 1](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_1.jpg)" + ] + }, + { + "metadata": { + "id": "FyVz0GNqgreZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "7500a357-f1fe-4894-c1eb-ea6a7f1ab4e3" + }, + "cell_type": "code", + "source": [ + "# Let's define the graph\n", + "comp_graph_1 = tf.multiply(tf.add(78, 19), 79)\n", + "\n", + "# Alternatively\n", + "comp_graph_1_alt = (tf.constant(78) + tf.constant(19)) * tf.constant(79)\n", + "\n", + "# Let's execute using session\n", + "sess = tf.Session()\n", + "print ('Comp Graph 1 : ', sess.run(comp_graph_1))\n", + "print ('Comp Graph 1 Alt: ', sess.run(comp_graph_1_alt))\n", + "sess.close()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Comp Graph 1 : 7663\n", + "Comp Graph 1 Alt: 7663\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "SVMMtuFYhaQB", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's define a sligtly more involved graph!\n", + "\n", + "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_2.jpg)" + ] + }, + { + "metadata": { + "id": "4856BTvRhiBb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "1faf1c5b-9c96-4ea0-9011-b75f5b75e42a" + }, + "cell_type": "code", + "source": [ + "# Let build the graph\n", + "# We need to cast cause the tensors operated on should be of the same type\n", + "comp_graph_part_1 = tf.cast(tf.subtract(tf.add(7, 8), tf.add(9, 10)), \n", + " dtype=tf.float32)\n", + "comp_graph_part_2 = tf.divide(tf.cast(tf.multiply(7, 10), dtype=tf.float32), tf.constant(19.5))\n", + "comp_graph_complete = tf.maximum(comp_graph_part_1, comp_graph_part_2)\n", + "\n", + "# Let's execute\n", + "sess = tf.Session()\n", + "part1_res, part2_res, total_res = sess.run([comp_graph_part_1, comp_graph_part_2, comp_graph_complete])\n", + "print ('Complete Result: ', total_res)\n", + "print ('Part 1 Result: ', part1_res)\n", + "print ('Part 2 Result: ', part2_res)\n", + "sess.close()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Complete Result: 3.5897436\n", + "Part 1 Result: -4.0\n", + "Part 2 Result: 3.5897436\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "B-_ZDtEbj4N0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Cool! Let's go! Build another graph and execute it with sessions.
\n", + "\n", + "But this time, it's all you!\n", + "\n", + "Build this graph and execute it with `session`!\n", + "\n", + "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_3.jpg)\n", + "\n", + "_Remember that `tensors` operated on should be of the same type!_
\n", + "_Search up errors and other help you need on Google_" + ] + }, + { + "metadata": { + "id": "-uHNe1BolJY0", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "c0200506-8746-42cd-dd00-7260420395a0" + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "comp_graph_3 = tf.constant([9 , 10], dtype = tf.float32 )\n", + "comp_graph_4 = tf.constant([7 , 8.65], dtype = tf.float32 )\n", + "comp_graph_5 = tf.constant(5.6 , dtype = tf.float32 )\n", + "comp_graph_6 = tf.constant([7.65 , 9], dtype = tf.float32 )\n", + "comp_graph_7 = tf.constant([11.5 , 7.18], dtype = tf.float32 )\n", + "comp_graph_8 = comp_graph_3 * comp_graph_4\n", + "comp_graph_9 = comp_graph_8 / comp_graph_5\n", + "comp_graph_10 = tf.add(comp_graph_6 , comp_graph_7)\n", + "comp_graph_total_1 = tf.minimum(comp_graph_9 , comp_graph_10)\n", + "\n", + "sess = tf.Session()\n", + "print(\"computational graph total :\" , sess.run(comp_graph_total_1))\n", + "sess.close()\n", + "\n", + "\n", + "\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "computational graph total : [11.25 15.446429]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "qmap38WelREN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's do another!
\n", + "It's fun! Isn't it?!\n", + "\n", + "Build and execute this one!\n", + "\n", + "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_4.jpg)" + ] + }, + { + "metadata": { + "id": "0ZhYwAlLmEvB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1492 + }, + "outputId": "69817b68-b3aa-4bf5-a8d8-f301d45fc939" + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "n1 = tf.constant([[1.2 , 3.4],\n", + " [7.5 , 8.6]] , dtype = tf.float32)\n", + "n2 = tf.constant([[7.0 , 9.0],\n", + " [8.0 , 6.0]] , dtype = tf.float32)\n", + "n3 = tf.constant([[2.79 , 3.81 , 5.6],\n", + " [7.3 , 5.57 , 8.9]] , dtype = tf.float32)\n", + "n4 = tf.constant([[7.6 , 18.1],\n", + " [7.86 , 9.81],\n", + " [9.36 , 10.11]] , dtype = tf.float32)\n", + "n4t = tf.transpose(n4)\n", + "n5 = tf.multiply(n3 , n4t)\n", + "\n", + "n6 = tf.reduce_sum(n5)\n", + "n7 = tf.reduce_mean(n7 , 1)\n", + "\n", + "n8 = tf.multiply(n3 , n2)\n", + "n9 = tf.add(n6 , n8)\n", + "sess = tf.Session()\n", + "print(\"n9:\" , sess.run(n9))\n", + "sess.close()\n", + "\n", + "\n", + "\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "error", + "ename": "ValueError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m 1658\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1659\u001b[0;31m \u001b[0mc_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_desc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1660\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: Invalid reduction dimension 1 for input with 1 dimensions. for 'Mean_2' (op: 'Mean') with input shapes: [2], [] and with computed input tensors: input[1] = <1>.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mn6\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mn7\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn7\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mn8\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn3\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mreduce_mean_v1\u001b[0;34m(input_tensor, axis, keepdims, name, reduction_indices, keep_dims)\u001b[0m\n\u001b[1;32m 1532\u001b[0m keepdims = deprecation.deprecated_argument_lookup(\"keepdims\", keepdims,\n\u001b[1;32m 1533\u001b[0m \"keep_dims\", keep_dims)\n\u001b[0;32m-> 1534\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 181\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mreduce_mean\u001b[0;34m(input_tensor, axis, keepdims, name)\u001b[0m\n\u001b[1;32m 1590\u001b[0m gen_math_ops.mean(\n\u001b[1;32m 1591\u001b[0m \u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ReductionDims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1592\u001b[0;31m name=name))\n\u001b[0m\u001b[1;32m 1593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(input, axis, keep_dims, name)\u001b[0m\n\u001b[1;32m 5569\u001b[0m _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[1;32m 5570\u001b[0m \u001b[0;34m\"Mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction_indices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_dims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5571\u001b[0;31m name=name)\n\u001b[0m\u001b[1;32m 5572\u001b[0m \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5573\u001b[0m \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + 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_add_deprecated_arg_notice_to_docstring(\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 3298\u001b[0m \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3299\u001b[0m \u001b[0moriginal_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_original_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3300\u001b[0;31m op_def=op_def)\n\u001b[0m\u001b[1;32m 3301\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_op_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m 1660\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1661\u001b[0m \u001b[0;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1662\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1664\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mc_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Invalid reduction dimension 1 for input with 1 dimensions. for 'Mean_2' (op: 'Mean') with input shapes: [2], [] and with computed input tensors: input[1] = <1>." + ] + } + ] + }, + { + "metadata": { + "id": "BnB0b6qCmGmg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "And a final one, before we move on to the next part!\n", + "\n", + "![alt text](https://raw.githubusercontent.com/iArunava/TensorFlow-NoteBooks/master/assets/comp_graph_5.jpg)" + ] + }, + { + "metadata": { + "id": "GQWyCvsQmMcL", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "4ec228e7-c52a-4372-9823-3272c95e1242" + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "t1 = tf.constant([[7.36 , 8.91 , 10.41],\n", + " [5.31 , 9.18 , 7.99]], dtype = tf.float32)\n", + "t2 = tf.constant([[7.99 , 10.36],\n", + " [5.36 , 7.98],\n", + " [8.91 , 5.67]], dtype = tf.float32)\n", + "t3 = tf.constant([[1 , 5.6 , 6.1 , 8],\n", + " [0 , 0 , 7.98 , 9],\n", + " [0 , 0 , 7.6 , 7],\n", + " [0 , 0 , 0 , 8.98]], dtype = tf.float32)\n", + "t_single_1 = tf.constant(7.0 ,dtype = tf.float32)\n", + "t_single_2 = tf.constant(19.6 , dtype = tf.float32)\n", + "\n", + "t2t = tf.transpose(t2)\n", + "t4 = tf.multiply(t1 , t2t)\n", + "t5 = tf.reduce_sum(t4)\n", + "t6 = tf.add(t5 , t_single_1)\n", + "t7 = t6 / t_single_2\n", + "t8 = t3 / t7\n", + "sess = tf.Session()\n", + "print(\"final result :\",sess.run(t8))\n", + "sess.close()\n", + "\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "final result : [[0.0515941 0.288927 0.31472403 0.41275284]\n", + " [0. 0. 0.41172096 0.46434695]\n", + " [0. 0. 0.3921152 0.36115873]\n", + " [0. 0. 0. 0.46331504]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "12NC7XTPsJw7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Linear Regression\n", + "\n", + "Okay, now we will create a dummy dataset and perform linear regression on this dataset!\n", + "\n", + "\n", + "To get you in the habit of looking up for the documentation, I am not providing what some of the following functions does, Google them up!" + ] + }, + { + "metadata": { + "id": "hW31RZkjtNwI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Create the dataset\n", + "X = np.linspace(-30.0, 300.0, 300)\n", + "Y = 2 * np.linspace(-30.0, 250.0, 300) + np.random.randn(*X.shape)\n", + "\n", + "# Normalize the dataset\n", + "X = X / np.max(X)\n", + "Y = Y / np.max(Y)\n", + "\n", + "# Divide it into train and test\n", + "train_X = X[:250]\n", + "train_Y = Y[:250]\n", + "\n", + "test_X = X[250:]\n", + "test_Y = Y[250:]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "LQKy6U33y4lt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Let's define the hyperparameters\n", + "learning_rate = 0.00001\n", + "n_epochs = 60\n", + "interval = 20" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1h1-D8K1uT48", + "colab_type": "code", + "outputId": "3afd1637-e58c-4673-ee5d-ebb91d0ec8b1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + } + }, + "cell_type": "code", + "source": [ + "# let's viz the first 10 datapoints of the dataset\n", + "plt.plot(train_X[:10], train_Y[:10], 'g')\n", + "plt.show()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Dw4PExN+/BGLBggW0b9+esLCw8nLOysqif//+DB069JzbWSwW+vbtS2xsLF5e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\nUpW4iIgDs+kdeU5ODoGBgQC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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jrsUps0nu8vj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "** Question **
\n", + "Why did I created a session to plot the graph?
\n", + "[Ans]" + ] + }, + { + "metadata": { + "id": "P3-iuxE4sjAf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Let's define the placeholders\n", + "\n", + "# Placeholders?\n", + "# The input to the model changes on iteration\n", + "# So we cannot have a constant in the input as we did before\n", + "# And thus we need placeholders which we can change on each \n", + "# iteration of the training\n", + "\n", + "x = tf.placeholder(tf.float32, name='x')\n", + "y = tf.placeholder(tf.float32, name='y')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "8hPRkaoxvRyV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88 + }, + "outputId": "638a4b59-74b3-472c-fd39-f169a5459f34" + }, + "cell_type": "code", + "source": [ + "# Let's define the linear regression model\n", + "\n", + "# tf.Variable?\n", + "# We define the model parameters as tf.Variables\n", + "# as they get updated throghout the training.\n", + "# And variables denotes something which changes overtime.\n", + "\n", + "W = tf.Variable(np.random.random_sample(), name='weight_1')\n", + "b = tf.Variable(np.random.random_sample(), name='bias_1')\n", + "\n", + "pred_y = (W*x) + b" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "cSw1P8bkv96r", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Let's define the loss function\n", + "# We are going to use the mean squared loss\n", + "loss = tf.reduce_mean(tf.square(y - pred_y))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "5G4uQqjsygNj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88 + }, + "outputId": "467f707a-cf84-4762-e50f-f9b83248fda6" + }, + "cell_type": "code", + "source": [ + "# Let's define the optimizer\n", + "# And specify the which value (i.e. loss) it has to minimize\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ttI7ZT-ozAm1", + "colab_type": "code", + "outputId": "6909cfaf-7ce3-4010-95f7-af88312bb007", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 432 + } + }, + "cell_type": "code", + "source": [ + "# So the graph is now built\n", + "# Now let's execute the graph using session\n", + "# i.e. lets train the model\n", + "\n", + "# What it is to train a model?\n", + "# To update the paramters in the graph (i.e. tf.Variables)\n", + "# So that the loss is minimized\n", + "\n", + "# Okay let's start!\n", + "with tf.Session() as sess:\n", + " # We need to initialize the variables in our graph\n", + " sess.run(tf.global_variables_initializer())\n", + " \n", + " for epoch in range(n_epochs):\n", + " _, curr_loss = sess.run([optimizer, loss], feed_dict={x:train_X, y:train_Y})\n", + " \n", + " if epoch % interval == 0:\n", + " print ('Loss after epoch', epoch, ' is ', curr_loss)\n", + " \n", + " print ('Now testing the model in the test set')\n", + " final_preds, final_loss = sess.run([pred_y, loss], feed_dict={x:test_X, y:test_Y})\n", + " \n", + " \n", + " print ('The final loss is: ', final_loss)\n", + " \n", + " # Plotting the final predictions against the true predictions\n", + " plt.plot(test_X, test_Y, 'g', label='True Function')\n", + " plt.plot(test_X, final_preds, 'r', label='Predicted Function')\n", + " plt.legend()\n", + " plt.show()" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.052007735\n", + "Loss after epoch 20 is 0.05197699\n", + "Loss after epoch 40 is 0.05194628\n", + "Now testing the model in the test set\n", + "The final loss is: 0.004058556\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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CiL/ZE7uT5UcXU0NXkzU9P8NB5WDtkoQAJLSFEOIOl9JimLJvEhoHDZ/3+RrP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\nw9GfAHA1bkzlYyOwJaZgv683mEwaFyqEELXrmkL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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "jgmH3wwt1src", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Okay, so we are doing good!
\n", + "\n", + "Now, let me just put everything here into one function so that you can tweak the hyperparameters easily!\n", + "\n", + "Or better, do it yourself!" + ] + }, + { + "metadata": { + "id": "OZ5TY7B_4E_v", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", + " # YOUR CODE HERE\n", + " pass" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "A6MaclhK4rc6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Okay! Now let's tweak!\n", + "linear_regression(learning_rate=0.000034, n_epochs=500)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "peoHmV2M40uU", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.0000006, n_epochs=1000)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KjY_KnlE5ClG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Drive the loss to a minimum." + ] + }, + { + "metadata": { + "id": "JKiHjGN15HPX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "9315862c-c515-42bf-e1ca-74c955bf7f62" + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE\n", + "tf.reduce_min(loss)" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "metadata": { + "id": "C4HRKCeZOaH9", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file