diff --git a/First_Date_with_TensorFlow.ipynb b/First_Date_with_TensorFlow.ipynb new file mode 100644 index 0000000..b26d034 --- /dev/null +++ b/First_Date_with_TensorFlow.ipynb @@ -0,0 +1,1133 @@ +{ + "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": "C4sGcsglnWRS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "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": "c1RIduyvnfN7", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "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", + "outputId": "af6ef332-6289-4f89-ed30-4b2c5f0683f7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + } + }, + "cell_type": "code", + "source": [ + "# Let's print them out!\n", + "print (t1)\n", + "print (t2)\n", + "print (t3)" + ], + "execution_count": 0, + "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", + "outputId": "85c01d87-bc2a-4751-ad7b-99d0960b97aa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 208 + } + }, + "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": 0, + "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": "OPPEmDLIn_rE", + "colab_type": "code", + "outputId": "5de7fd75-bb31-4daf-d3cb-ba878b1fbcb8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 194 + } + }, + "cell_type": "code", + "source": [ + "s = tf.Session()\n", + "print(s.run(t1))\n", + "print(s.run(t1))\n", + "print(s.run(t2))\n", + "print(s.run(t3))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "2.0\n", + "2.0\n", + "[1. 2.]\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", + "outputId": "63a0742a-fd87-44b6-d342-472ec473a85f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + } + }, + "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": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Comp Graph 1 : 7663\n", + "Comp Graph 1 Alt: 7663\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "hVRvZxrnokUw", + "colab_type": "code", + "outputId": "cfbffea2-424a-4ddb-d4ad-4d39f3f894db", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + } + }, + "cell_type": "code", + "source": [ + "comp_graph_1 = tf.multiply(tf.add(78, 19), 79)\n", + "comp_graph_1_alt = (tf.constant(78) + tf.constant(19)) * tf.constant(79)\n", + "comp_graph_1_mix = (tf.constant(78) + 19) * 79\n", + "raw_ints = 78 + 10 * 79\n", + "int_string = \"78 + 10 * 79\"\n", + "s = tf.Session()\n", + "print('Comp Graph 1 :', s.run(comp_graph_1))\n", + "print('Comp Graph 1 Alt:', s.run(comp_graph_1_alt))\n", + "print('Comp Graph 1 mix:', s.run(comp_graph_1_mix))\n", + "#print('Comp Graph 1 raw:', s.run(raw_ints))\n", + "#print('Comp Graph 1 str:', s.run(int_string))\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Comp Graph 1 : 7663\n", + "Comp Graph 1 Alt: 7663\n", + "Comp Graph 1 mix: 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", + "outputId": "988227d2-bc1a-4f91-9bc3-7808b2b06f0a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + } + }, + "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": 0, + "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", + "outputId": "58f51b22-04c8-41ba-d54c-cc0c60bebc36", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + } + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "A = tf.constant([9, 10], dtype=tf.float32)\n", + "B = tf.constant([7, 8.65], dtype=tf.float32)\n", + "C = tf.constant(5.6)\n", + "D = tf.constant([7.65, 9], dtype=tf.float32)\n", + "E = tf.constant([13.5, 7.18], dtype=tf.float32)\n", + "\n", + "mul = tf.multiply(A,B)\n", + "div = tf.divide(mul, C)\n", + "add = tf.add(D,E)\n", + "min_ = tf.math.minimum(div,add)\n", + "\n", + "\n", + "# Execute \n", + "sess = tf.Session()\n", + "mul_res, div_res, add_res, min_res = sess.run([mul, div, add, min_])\n", + "print(\"mul : \", mul_res)\n", + "print(\"div: \", div_res)\n", + "print(\"add: \", add_res)\n", + "print(\"min: \", min_res)\n", + "sess.close()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "mul : [63. 86.5]\n", + "div: [11.25 15.446429]\n", + "add: [21.15 16.18]\n", + "min: [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", + "outputId": "d3cef926-d9d9-46d4-9cda-2398fbebd08b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 156 + } + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "A = tf.constant([\n", + " [1.2, 3.4],\n", + " [7.5, 8.6]\n", + "])\n", + "B = tf.constant([\n", + " [7, 9],\n", + " [8,6]\n", + "], dtype=tf.float32)\n", + "C = tf.constant([\n", + " [2.79, 3.81, 5.6],\n", + " [7.3, 5.67, 8.9]\n", + "])\n", + "D = tf.constant([\n", + " [2.6, 18.1],\n", + " [7.86, 9.81],\n", + " [9.36, 10.11]\n", + "])\n", + "mean = tf.math.reduce_mean(A, axis=1)\n", + "mul_1 = tf.multiply(mean,B)\n", + "mul_2 = tf.multiply(C, tf.transpose(D))\n", + "sum_all = tf.math.reduce_sum(mul_2)\n", + "add = tf.add(mul_1,sum_all)\n", + "\n", + "# Execute \n", + "sess = tf.Session()\n", + "mean_res, mul_1_res, mul_2_res, sum_all_res, add_res = sess.run([mean, mul_1, mul_2, sum_all, add])\n", + "print(\"Mean :\", mean_res)\n", + "print(\"Mul 1 :\", mul_1_res)\n", + "print(\"Mul 2 :\", mul_2_res)\n", + "print(\"Sum all:\", sum_all_res)\n", + "print(\"Add :\", add_res)\n", + "sess.close()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean : [2.3000002 8.05 ]\n", + "Mul 1 : [[16.100002 72.450005]\n", + " [18.400002 48.300003]]\n", + "Mul 2 : [[ 7.2539997 29.9466 52.415997 ]\n", + " [132.13 55.622704 89.979 ]]\n", + "Sum all: 367.3483\n", + "Add : [[383.4483 439.7983 ]\n", + " [385.7483 415.64832]]\n" + ], + "name": "stdout" + } + ] + }, + { + "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", + "outputId": "73aa40c9-c9bc-481e-f0af-5d8130d9700e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 173 + } + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "A = tf.constant(7.0)\n", + "B = tf.constant([\n", + " [7.36, 8.91, 10.41],\n", + " [5.31, 9.38, 7.99]\n", + "])\n", + "C = tf.constant([\n", + " [7.99, 10.36],\n", + " [5.36, 7.98],\n", + " [8.91, 5.67]\n", + "])\n", + "D = tf.constant([\n", + " [1, 5.6, 6.1, 8],\n", + " [0, 0, 7.98, 9],\n", + " [0, 0, 7.6, 7],\n", + " [0, 0, 0, 8.98]\n", + " ])\n", + "mul = tf.multiply(B, tf.transpose(C))\n", + "sum_all = tf.math.reduce_sum(mul)\n", + "add = tf.add(A, sum_all)\n", + "divide_1 = tf.divide(add, tf.constant(19.6))\n", + "divide_2 = tf.divide(divide_1, D)\n", + "\n", + "\n", + "# Execute \n", + "sess = tf.Session()\n", + "mul_res, sum_all_res, add_res, divide_1_res, divide_2_res = sess.run([\n", + " mul, sum_all, add, divide_1, divide_2\n", + "])\n", + "print(mul_res)\n", + "print(sum_all_res)\n", + "print(add_res)\n", + "print(divide_1_res)\n", + "print(divide_2_res)\n", + "sess.close()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[58.8064 47.7576 92.7531 ]\n", + " [55.011597 74.8524 45.3033 ]]\n", + "374.48438\n", + "381.48438\n", + "19.463488\n", + "[[19.463488 3.475623 3.1907358 2.432936 ]\n", + " [ inf inf 2.4390335 2.1626098]\n", + " [ inf inf 2.5609853 2.7804983]\n", + " [ inf inf inf 2.1674263]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "J3T5oBzlP1ik", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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": "fbaa9304-7a6e-452e-87a4-e127834f8766", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351 + } + }, + "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": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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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": {} + }, + "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": 0, + "outputs": [] + }, + { + "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": {} + }, + "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": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ttI7ZT-ozAm1", + "colab_type": "code", + "outputId": "e4f2ee17-a8bf-4d4c-8564-c8128dcf5365", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + } + }, + "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": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.25383937\n", + "Loss after epoch 20 is 0.25364405\n", + "Loss after epoch 40 is 0.2534488\n", + "Now testing the model in the test set\n", + "The final loss is: 0.021730803\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+/djtdpYsWUL//v1p3LgxAwcOZOrUqYwbNw6AIUOG0KJFC1q0aEFcXBxDhw7F\nYrEwZcqUSm9EREQEYPXBVfxl5WR+PrQam8XGXXHDGddtAlFBUWaXdsEsxoXcEK4CZl8KqY6XYyqS\nr/cHvt+j+vNuvt4fmN/j9pwUpq2awpe7Pgfg+pa/Y9KVU2hVN6ZCju81l7VFRETMll6Qzow10/lg\ny99xGS6ubNCTP/f8C93qX2l2aZdM4SwiIl7rs9RPePTbsRwpOkJMndY81fNprms+BIvFYnZpl0Xh\nLCIiXie/JJ8//zSR95LfoZa9Fs9d/SJ3xg3HbvWNWPONLkREpMbYnLmRkV/fw44j24mr1543By6g\njaOt2WVVKO1KJSIiXsFtuHljwxyu+3d/dhzZzsiOY/jyxm98LphBZ84iIuIF0vMPMXbZSL7bt5yI\nWpHMGTCX/k0Hml1WpVE4i4hItWUYBl/u/oJHl48luzCbhKaDmNV/LhFB51910tspnEVEpNoxDIOv\n93zJK7+8yC/pawmwBTD9qhkMbz/S62diXwiFs4iIVBsut4vPdv6XV395iaTszQBc1+J6nuw+mXb1\nYk2uruoonEVExHQlrhL+vf2fzF73MjuObMdqsXJDzI082GUcsfXiyj+Aj1E4i4iIKY4W5bLn6G7W\nHFrF3A2vsffYHvysftzW7g4e6PwwLeu0MrtE0yicRUSk0pS4Slh9aCW7cneyJ3c3e47uZs/RXew5\nupucopyycYG2QIa3H8GYTg/ROLSJiRVXDwpnERGpFC63i1s/v5Hv9i33+HyALYCmoc24IqobzWo3\np2XtaH7f6o9EBkWaVGn1o3AWEZFK8fyaZ/hu33KuanQNN7W5heZhLWgW1pyo4PpYLVoD63wUziIi\nUuH+t+tzZq17ieZhLXjnuvepHVDH7JK8iv7XRUREKtTO3FTGLhtJLXst3rluoYL5EujMWUREKkxB\nSQF3/28Yx4qP8tqAecSFx5tdklfSmbOIiFQIwzB47LuH2HI4ibvj7+WmNreYXZLXUjiLiEiFmLt2\nLh+lLOKKqK78pfdzZpfj1RTOIiJy2dYeWsPDXz5MvcB6vD3o7wTYAswuyavpnrOIiJxVev4hFm37\ngHqB4fRo2JOWtVudddOJrONZ3LvkTlyGi3mD3qFRaGMTqvUtCmcREfGQfTyb19a/yl8T3+K483jZ\n5yNqRdKjYS96NOhJjwa9iK0Xj4HByK/u5kD+fqb3n841jfuaV7gPUTiLiAhwYq3ruRtfY97GN8gr\nOUbD4EZM6fUoAKsPrGDlwRV8mvoxn6Z+DECofxhNQ5uRlL2Z65oPYfxV48nOyjezBZ+hcBYRqeHy\nS/JZsHker61/lSNFRwivFcEW3VvnAAAgAElEQVSTVz7F7bF3E2gPBOCe+PswDIM9R3ez6uAKVh1Y\nwaqDK0jK3kzL2tHMGfCmVv2qQApnEZEawDAMCpwF5JfkU1CST37pr/UZa5m17mWyjmdSJ6AOT/WY\nyvD2Iwn2Cz7jGBaLhea1W9C8dguGtr0NgMyCTGr51SLEL6SqW/JpCmcRER90rPgoT/04gWV7vy4L\nZAPjrGOD/UIY13U893ccS1hA7Yv6OhFBERVRrvyKwllExMesT/+FEV/fzZ6ju2kQ3JDoOq0I9gsm\nyB5EsF8IwX7BpR8HEx4Uzp9a30K9WvXMLltOo3AWEfERbsPN3A2v8ezqqbjcLh7qMo4nuk3Ez+Zn\ndmlykRTOIiI+IKMggweWjWR52jIig6J4I2G+HmvyYgpnEREv923aN4xZOoLM4xkMaDqQ2f3f1L1g\nL6dwFhHxUiWuEl5Y8yxz1r+C3Wpnaq9nGdVxjB5p8gEKZxERL7Q7dxf3L72XX9J/pnlYC94a9A6d\nIruYXZZUEIWziIgXMQyDf277kAk/PEZ+SR43xPyJF/u8Qqh/mNmlSQVSOIuIeIkjhTk88f0jfLxj\nMaH+YbyRMJ8bW99sdllSCRTOIiJe4Kf9PzB22Uj25+2je/0evJ7wFs3CmptdllQShbOISDVW7Cpm\nxprpzFn/ClaLlfHdJ/FQl3HYrfrx7cv03RURqaZ25Gzn/qX3sjFzPc3CmjM34W261u9udllSBRTO\nIiLVjNtw87ekBfxlxWQKnAUMbXsb06+aQYh/qNmlSRVROIuIVCNpx/by8PKx/LDvW+oE1GFW/zf4\nfasbzC5LqpjCWUSkGjAMgw+2vMfkn54kr+QYg5pdx0t9ZxMVXN/s0sQECmcREZMdyj/Io8sfYOne\nrwj1D2N2/7nc3OZWLBaL2aWJSS4onKdPn87GjRuxWCxMnDiRDh06lL22dOlS5s6di7+/P9dffz3D\nhg1j9erVPPTQQ8TExADQunVrJk+eXDkdiIh4KcMw+ChlERN/fILcoiP0adyPV/u9TqPQxmaXJiYr\nN5zXrFnDnj17WLRoEampqUycOJFFixYB4Ha7mTZtGv/5z3+oU6cO9913HwkJCQB0796d2bNnV271\nIiJe6mDeASb88Bj/2/UZQfZgXuzzKnfE3q2zZQEuIJxXrlxZFrjR0dHk5uaSl5dHSEgIOTk5hIWF\n4XA4AOjRowcrVqygUaNGlVu1iIiX2ncsjTnrX+GDLe9R5CqiV8OrmNX/DS0oIh7K3bokKyuLunXr\nln3scDjIzMws+3N+fj67d++mpKSE1atXk5WVBcCOHTsYNWoUt9xyCz/99FMllS8i4h125uxk3LcP\ncuXCTryT+Db1gxvwar/XWfz7zxTMcoaLnhBmGEbZny0WC88//zwTJ04kNDSUxo1P3Cdp3rw5Y8eO\nZfDgwaSlpXHHHXfw1Vdf4e/vf87j1q0bhN1uu4QWKk5EhG8/Q+jr/YHv96j+vE9KdgrTf5jO+5ve\nx2W4aF2vNZOunsSt7W/1yVW+fPF7eLqq6q/c/zIiIyPLzoYBMjIyiIg4tYl39+7d+eCDDwB46aWX\naNSoEVFRUQwZMgSApk2bEh4eTnp6Ok2aNDnn18nJKbjkJipCREQomZnHTK2hMvl6f+D7Pao/71Hi\nKmFT1gbmb3qTj3f8G7fhJjYilgc7jeP30Tdgs9rIyT5udpkVzpe+h2dT0f2dL+jLvazdu3dvlixZ\nAkBSUhKRkZGEhISUvX7vvfeSnZ1NQUEBy5cvp2fPnnzyyScsWLAAgMzMTLKzs4mKirrcPkREqqVj\nxUdZvncZz695hj/+97fELGjC4H8PYPH2f9HWEcuCa//O5vs3c0PMn7BZzb1CKN6h3DPnLl26EBcX\nx9ChQ7FYLEyZMoXFixcTGhrKwIEDuemmm7jnnnuwWCyMGDECh8NB//79eeyxx1i2bBklJSVMnTr1\nvJe0RUS8idtw8+WuL/hh/7esPriK5OxE3Ia77PW2jnZ0r9+TQc2vJaHZtVgtVqyWcs+FRMpYjNNv\nIpvI7Eshuhzj/Xy9R/VXPWzIWMeE78exLuMXAAJsAXSOvIIrG/Ske/0r6Vq/O3UDHWe8z1v6uxy+\n3mNVXtb2vdkIIiKV4HBhNtNXTeO95HcwMPi/Vn9kePtRdIzsRIAtwOzyxMconEVEzsNtuFm45e88\ns3IKOUU5tKnblueumclVja4xuzTxYQpnEZFzWJ/+CxN+GMf6jHWE+IXydK/p3Nt+JH42P7NLEx+n\ncBYROY3bcJOUtZm/JS3g/eR3MTC4IeZPTO31DPWDG5hdntQQCmcRqfH2HUvju7TlfLfvG37Y9x3Z\nhdkAtHPE8tzVM+nV6CqTK5SaRuEsIjVOXkke36d9y3f7vuG7tOXszE0te61BcEOGtr2Nfk0G8JuW\nv9clbDGFwllEaoyMggwWbH6Tvya+TW7REQBC/EK5tvlg+jTuR58m/WlVJ0Y7Q4npFM4i4vN2HtnB\nGxteY9G2hRS5igivFc7DXR5jQLNBdIm8QmfHUu0onEXEZ61LX8tr62fx+c5PMDBoFtac0Z0eZGjb\n26hlr2V2eSLnpHAWEZ/iNtx8s/drXl8/m58O/ABAx4jOPND5Ya5v+TutbS1eQeEsIj6h0FnIRymL\neHPja6TkbAOgX5MBjO38MFc1ukb3kcWrKJxFxKtlHc/incT5vJM4n6zjWditdv7UeiijOo2lfXgH\ns8sTuSQKZxHxSttzUnhz4+v8a9uHFLoKqR1Qhwc7P8rw9iNoENLQ7PJELovCWUS8ytbDW3hm5RS+\n2vMlAM3CmjOyw2iGthtGiF9IOe8W8Q4KZxHxCnklebz08wvM2/Q6TreTbvWv5P6ODzC4xfWa5CU+\nR+EsItWaYRh8vvNTnvpxPAfy99M0tBnTr57BoOaDzS5NpNIonEWk2tqdu4uJPzzO0r1f4Wf149Er\nHufBLuMI8gsyuzSRSqVwFpFqp8hVxGvrX2XWLy9R6Crk6sZ9eeHql2hVN8bs0kSqhMJZRKqVVQdW\n8PDyMezMTSUqqD6zez/H71vdoOeUpUZROItIteByu3h57Qxm/DwdgBEd7md890mE+oeZXJlI1VM4\ni4jpMgoyuPXLUSzduZSGwY2YN+gdrmzQw+yyREyjcBYRU/2w7zvuX3ovGQXpDGp2HbMHzMURWM/s\nskRMpXAWEVO43C5eWvsCL619AZvVxsyBM7m91X26tyyCwllETJCef4hRXw/npwM/0CS0KfMG/pXB\n7QeQmXnM7NJEqgWFs4hUmf3H9vHj/u95euVkso5nMrjFb5jV73XqBNY1uzSRakXhLCKVotBZyOas\njaw99DNr09ew9tAaDuYfAMDP6sezV73Ave1H6TK2yFkonEXkkjjdTrKOZ5JRkE5mQQYZBRlkFKRz\nMP8AGzPXszlzE8Xu4rLxEbUiGdziN3St351rmw2mtaONidWLVG8KZxG5YP/b9TkvrX2BA3n7yD6e\njYFx1nF2q534eu3pWr/7iV9R3WkS2lRnySIXSOEsIuVyuV3M+PlZXvllJn5WP5qFNSembhsia0UR\nGRRJZFBU6a8Tf46uE6P1r0Uug8JZRM7rcGE29399L8vTltEsrDl/u+4D4sLjzS5LxKcpnEXknDZn\nbuTuL4ex99geEpoO4o2E+ZpZLVIFrGYXICLV0z+3fcj1iwey99geHus6gfev/6eCWaSK6MxZRDwU\nu4qZsmIiCza/RZh/bd6+9l0GNR9sdlkiNYrCWUTK7MrdyQPLRrHm0CraOWJ5Z/BCWtaONrsskRpH\n4SxSg7kNNxsz1rNk9xd8uft/JGcnAvB/rf7Iy/1eI9gv2OQKRWomhbNIDXPceZwf933Hl7v/x1e7\n/0d6wSEA/K3+DGg6kBti/sSNrW/WM8kiJlI4i9QQW7KTefWXF1my+38UOAsAcAQ6uLnNrVzbfAh9\nm/QjxD/U5CpFBBTOIj5vR852Xvx5Oh/vWIyBQXSdVgxu8RsGNR9Mt6ju2Kw2s0sUkV9ROIv4qF25\nO3lp7Qt8lLIIt+GmQ0QnxnebSEKza3XJWqSaUziL+Ji0Y3t5Ze2LfLj1fVyGi3aOWJ7oPokhLX6j\nUBbxEgpnER+QV5LHmoOr+GLnZ3y49T1K3CXE1GnN492e5Het/g+rResNiXgThbOIF8ovyWfNwVWs\nOPAjP+3/gQ2Z63C6nQC0qN2Sx7pO4IaYP+l+soiXuqBwnj59Ohs3bsRisTBx4kQ6dOhQ9trSpUuZ\nO3cu/v7+XH/99QwbNqzc94jIxdubu5dXVs3hpwM/sD7jl7IwtllsdIrsQu+GV9Or0VVc3agPfjY/\nk6sVkctRbjivWbOGPXv2sGjRIlJTU5k4cSKLFi0CwO12M23aNP7zn/9Qp04d7rvvPhISEti7d+85\n3yMiF2/x9n/xxPePcLToaGkYd6ZXw6vp3egqutfvoUegRHxMueG8cuVKEhISAIiOjiY3N5e8vDxC\nQkLIyckhLCwMh8MBQI8ePVixYgVpaWnnfI+IXLhjxUcZ//04PkpZRLBfMDP7zOL/Yv5IqH+Y2aWJ\nSCUqN5yzsrKIi4sr+9jhcJCZmUlISAgOh4P8/Hx2795No0aNWL16Nd27dz/ve86lbt0g7HZz749F\nRPj22Yev9we+1eOKtBUMWzyMXUd20a1hNz744we0crQyu6xK5Uvfv7Px9f7A93usqv4uekKYYRhl\nf7ZYLDz//PNMnDiR0NBQGjduXO57ziUnp+BiS6lQERGhZGYeM7WGyuTr/YHv9Oh0O3nllxd5ee0M\n3IabR654jMe6PklDh8Mn+jsXX/n+nYuv9we+32NF93e+oC83nCMjI8nKyir7OCMjg4iIiLKPu3fv\nzgcffADASy+9RKNGjSgqKjrve0Tk7PYe3cPopfex5tAqGoU05o2E+fRs2NvsskSkipX78GPv3r1Z\nsmQJAElJSURGRnpcnr733nvJzs6moKCA5cuX07Nnz3LfIyKnGIbBjpztvLFhDv3+2Zs1h1bx++gb\nWH7TTwpmkRqq3DPnLl26EBcXx9ChQ7FYLEyZMoXFixcTGhrKwIEDuemmm7jnnnuwWCyMGDECh8OB\nw+E44z0ickrW8Sy+37ec79KW8/2+b9mftw+AYL8QZvefy81tbtVqXiI1mMW4kBvCVcDs+xS6V+L9\nqnuPqw6uZMmuL/hu33ISszaVfb5uQF2ubtyXPk36MajZdUQF1z/r+6t7f5dL/Xk/X++xWt1zFpHL\nk1+Sz+QfJ/D+lneBE/smX92oD32a9KNP4360j+io5TVFxIPCWaQSbc7axKiv7mH7kRTiwzvwVI8p\n9GjQmyC/ILNLE5FqTOEsUgkMw+CtTW8wbeUUit3FjOw4hqd6TCXAFmB2aSLiBRTOIhUssyCTB78Z\nxbK9XxNeK5w5/d9kQLNBZpclIl5E4SxSgZbvXcbYZSPJPJ5B3yb9mTNgHlFBUWaXJSJeRuEscpmc\nbiepR3bwwZb3mLtxDn5WP6b2epZRHcdoopeIXBKFs8gFMgyDQ/kH2XI4ieTsZLZkJ5GcncT2nG0U\nu4sBaFk7mnkD/0rHyM4mVysi3kzhLFKOzIJMZvw8nU92LCanKMfjtVr2WsTWi6NdvTjah3dgaLth\nhPhpNTwRuTwKZ5FzKHYV8/bmeby09gWOFR+lYXAjejW6+kQYO+KIrRdLs7AW2Kzm7qYmIr5H4Szy\nK4Zh8NWeL5ny00R25qZSN6Auz139InfGDcdu1T8ZEal8+kkjcpqth7cw+ccJfLdvOTaLjXvbj+Tx\nbk9SN9BhdmkiUoMonEWAw4XZzFgznXeT/orLcNGvyQD+0vs52jjaml2aiNRACmep0dIL0nlr4xu8\nk/g2eSXHiK7Tir/0mk5Cs2u1K5SImEbhLDXSnqO7eX39LD7c+j5FriIiakUyvvtE7o6/D3+bv9nl\niUgNp3CWGmXr4S3MXvcy/9n+ES7DRdOw5ozt9BBD295GoD3Q7PJERACFs9QAhmHwS/rPzF7/Cl/u\n+hyAdo5YHujyCH9o9UfNwBaRakc/lcQnGYbBlsPJfLJjMf9N/Q+pR3YAcEVUNx7qMo5Bza/T0poi\nUm0pnMWnpBzexsc7/s0nqf8hJWcbAEH2IH4ffQN3xt9D74ZXa6KXiFR7CmfxehkFGbyf/Dc+2/0x\niRmJAATaArm+5e/4Q6sbSGh2LcF+wSZXKSJy4RTO4rUKnYW8tWkur/4yk7ySY/jb/LmuxfX8Pvr/\nuLb5YEL8Q80uUUTkkiicxesYhsHnOz9l6sqn2Ht0N45AB9OvnMHo3iMoPqb7yCLi/RTO4lU2Z25k\n8k9PsuLAj9itdkZ2HMO4K56gTmBdageGknnsmNkliohcNoWzeIWMggyeXz2NhVv+joHBoGbX8XTv\nZ4muE2N2aSIiFU7hLNWaYRi8vflNnlv9DHklx2jraMfTvabTr+kAs0sTEak0CmeptvKKj/Hw8rF8\nkvofHIEOXuj5MrfH3qVFQ0TE5+mnnFRLO3K2c9eXt5KSs40rG/Tk7UHvEhVc3+yyRESqhKa2SrXz\nWeonDPqoLyk52xjR4X4W/+4zBbOI1Cg6c5Zqw+l28tzqacxZ/wpB9iDeHLiAG2L+ZHZZIiJVTuEs\n1ULW8SxGfn0PP+z7lha1W/LOdQuJrRdndlkiIqZQOEulyi06wqep/8VluAi0BRJoDyTQXosAWwCB\n9loE2gI4XHiYcd8+yP68fVzbfDCvDZhH7YA6ZpcuImIahbNUCsMwWLTtA/6ycjJZx7PKHW/BwpPd\nJ/PQFeO0W5SI1HgKZ6lwydlJjP/+UVYfXEmQPYgnuk2kRe2WFDoLKXQVUugspKj090JXISXuYq5v\n+TuuanSN2aWLiFQLCmepMHnFx5jx83PM3zQXl+FiSIvf8sxVz9M4tInZpYmIeBWFs1w2wzD4eMe/\nmbJiEofyD9I8rAXPXf0iA5oNMrs0ERGvpHCWy7IjZzvjfxjHD/u+JcAWwOPdnuSBzo8QaA80uzQR\nEa+lcJZLctx5nFd/eZHX1s+ixF1CQtNBPHv1DFrUbml2aSIiXk/hLBdt6Z4lTPjhcfYe3U3D4EY8\ne/UMhrT4DRaLxezSRER8gsJZLtj+Y/t46qcJfL7zE+xWO2M6PcS4buMJ8QsxuzQREZ+icJZylbhK\nmL/5TWasmU6BM58rG/RkxjWv0K5erNmliYj4JIWznNeK/T/y5A+Ps+VwEo5AB89d/SI3t71VC4WI\niFQihbOcweV28b9dn/PGhtmsTV8DwLB2d/JUz6k4AuuZXJ2IiO+7oHCePn06GzduxGKxMHHiRDp0\n6FD22sKFC/nkk0+wWq3Ex8czadIkFi9ezKxZs2jatCkAvXr14v7776+cDqTCFJQUsGjbB7y58TV2\n5e4EYFCz63j4isfoWr+7ydWJiNQc5YbzmjVr2LNnD4sWLSI1NZWJEyeyaNEiAPLy8liwYAFfffUV\ndrude+65hw0bNgAwZMgQxo8fX7nVS4XILMjkr4lv8U7ifA4XHsbf6s+wdncyquNYWjvamF2eiEiN\nU244r1y5koSEBACio6PJzc0lLy+PkJAQ/Pz88PPzo6CggKCgII4fP07t2rUrvWipGAfzDvDS2hks\n2raQIlcRdQPq8ugVj3N3+xFEBUWZXZ6ISI1VbjhnZWURF3dqX12Hw0FmZiYhISEEBAQwZswYEhIS\nCAgI4Prrr6dFixasX7+eNWvWMHz4cJxOJ+PHjyc29vwze+vWDcJut11+R5chIiLU1K9f2U72l1ec\nx4s/vciLK17kuPM4Leu25NEej3JXp7sI9g82ucrLU1O+h75K/Xk/X++xqvq76AlhhmGU/TkvL495\n8+bx5ZdfEhISwp133snWrVvp2LEjDoeDvn37sn79esaPH8+nn3563uPm5BRcfPUVKCIilMzMY6bW\nUJkiIkI5lH6ERds+4LnV00gvOERkUBTTr3qRoW1vw2a1UZDrpgDv/TuoCd9D9ee9fL0/8P0eK7q/\n8wV9ueEcGRlJVtap/XgzMjKIiIgAIDU1lSZNmuBwOADo2rUriYmJ3HjjjURHRwPQuXNnDh8+jMvl\nwmYz98y4Jlu6cykPffEIydmJ1LLXYlzX8Yzp/JAWEBERqYbKfVi1d+/eLFmyBICkpCQiIyMJCTnx\nA71Ro0akpqZSWFgIQGJiIs2bN2f+/Pl89tlnAKSkpOBwOBTMJtl2eCu3fnYjA98byJbsJG5ucysr\nb13H+O6TFMwiItVUuWfOXbp0IS4ujqFDh2KxWJgyZQqLFy8mNDSUgQMHMnz4cO644w5sNhudO3em\na9euNG7cmMcff5x//OMfOJ1Onn322aroRU5T6Cxkxs/TmbthDi7DRd/mfXmq21/oENHJ7NJERKQc\nFuP0m8gmMvs+hS/dK1mf/gsPfDOKlJx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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", + " x = tf.placeholder(tf.float32, name='x')\n", + " y = tf.placeholder(tf.float32, name='y')\n", + " W = tf.Variable(np.random.random_sample(), name='weight_1')\n", + " b = tf.Variable(np.random.random_sample(), name='bias_1')\n", + " pred_y = (W*x) + b\n", + " loss = tf.reduce_mean(tf.square(y - pred_y))\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)\n", + " with tf.Session() as sess:\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", + " print ('The final loss is: ', final_loss)\n", + "\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()\n", + " pass" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "A6MaclhK4rc6", + "colab_type": "code", + "outputId": "792b2c58-b191-4c89-eaa6-c43a020d7f78", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 555 + } + }, + "cell_type": "code", + "source": [ + "# Okay! Now let's tweak!\n", + "linear_regression(learning_rate=0.000034, n_epochs=500)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.19373068\n", + "Loss after epoch 50 is 0.19265419\n", + "Loss after epoch 100 is 0.19158578\n", + "Loss after epoch 150 is 0.19052546\n", + "Loss after epoch 200 is 0.1894731\n", + "Loss after epoch 250 is 0.18842869\n", + "Loss after epoch 300 is 0.1873921\n", + "Loss after epoch 350 is 0.18636337\n", + "Loss after epoch 400 is 0.18534237\n", + "Loss after epoch 450 is 0.18432906\n", + "Testing the Model\n", + "The final loss is: 0.7989045\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "peoHmV2M40uU", + "colab_type": "code", + "outputId": "aa2dfc59-6aa2-4bad-8fc7-4c6872854f9b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 729 + } + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.0000006, n_epochs=1000)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.19373068\n", + "Loss after epoch 50 is 0.19371162\n", + "Loss after epoch 100 is 0.19369255\n", + "Loss after epoch 150 is 0.19367348\n", + "Loss after epoch 200 is 0.19365443\n", + "Loss after epoch 250 is 0.19363537\n", + "Loss after epoch 300 is 0.19361633\n", + "Loss after epoch 350 is 0.19359727\n", + "Loss after epoch 400 is 0.19357823\n", + "Loss after epoch 450 is 0.19355917\n", + "Loss after epoch 500 is 0.19354014\n", + "Loss after epoch 550 is 0.19352108\n", + "Loss after epoch 600 is 0.19350204\n", + "Loss after epoch 650 is 0.19348301\n", + "Loss after epoch 700 is 0.19346401\n", + "Loss after epoch 750 is 0.19344495\n", + "Loss after epoch 800 is 0.19342592\n", + "Loss after epoch 850 is 0.19340691\n", + "Loss after epoch 900 is 0.19338787\n", + "Loss after epoch 950 is 0.19336885\n", + "Testing the Model\n", + "The final loss is: 0.82940775\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "KjY_KnlE5ClG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Drive the loss to a minimum." + ] + }, + { + "metadata": { + "id": "JKiHjGN15HPX", + "colab_type": "code", + "outputId": "5d36ee4a-7956-435f-a8ad-1f5f4e94ae99", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 555 + } + }, + "cell_type": "code", + "source": [ + "linear_regression(learning_rate=0.006, n_epochs=100000, interval=10000)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.19373068\n", + "Loss after epoch 10000 is 4.036964e-06\n", + "Loss after epoch 20000 is 4.017399e-06\n", + "Loss after epoch 30000 is 4.017399e-06\n", + "Loss after epoch 40000 is 4.017399e-06\n", + "Loss after epoch 50000 is 4.017399e-06\n", + "Loss after epoch 60000 is 4.017399e-06\n", + "Loss after epoch 70000 is 4.017399e-06\n", + "Loss after epoch 80000 is 4.017399e-06\n", + "Loss after epoch 90000 is 4.017399e-06\n", + "Testing the Model\n", + "The final loss is: 3.4938046e-06\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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bQ9FOGIsy6S4ZzVqgn7cIS/kK6AqoqwZINibTfWdXDl37hbZVXmfla2uwU9kV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\nkfWfvYSiKCgoHP3mMAAOKgeCvEcwxj+UMT3HEuo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