From 4d1dfa391f5cb997f995ef173c78d855dd47aa6a Mon Sep 17 00:00:00 2001 From: Arghadip Chakraborty Date: Sun, 14 Oct 2018 23:03:18 +0530 Subject: [PATCH 1/4] Created using Colaboratory --- Assignment-4.ipynb | 758 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 758 insertions(+) create mode 100644 Assignment-4.ipynb diff --git a/Assignment-4.ipynb b/Assignment-4.ipynb new file mode 100644 index 0000000..9177207 --- /dev/null +++ b/Assignment-4.ipynb @@ -0,0 +1,758 @@ +{ + "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": [ + "[View in Colaboratory](https://colab.research.google.com/github/arghac14/Assignment-4/blob/arghac14/Assignment-4.ipynb)" + ] + }, + { + "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]]])\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vmMcjzTxbWzw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 + }, + "outputId": "17f8183a-10a0-4ef1-fb8a-5531da9ff1b5" + }, + "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": 212 + }, + "outputId": "865d2a85-8743-46fa-dd90-086631e5a5b7" + }, + "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": 52 + }, + "outputId": "bab1b1e5-8d1c-4ed9-82a6-1ff53ba1bed4" + }, + "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": 70 + }, + "outputId": "6cdebb1c-d9e6-415f-d443-13edfea10b36" + }, + "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": 12, + "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": 70 + }, + "outputId": "7792ec49-b829-4141-a94f-758c75a33a0a" + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "cg_1a=tf.cast(tf.multiply(tf.constant(([9,10]),dtype=tf.float32),tf.constant(([7,8.65]),dtype=tf.float32)),dtype=tf.float32)\n", + "cg_1b=tf.cast(tf.divide(cg_1a,5.6),dtype=tf.float32)\n", + "\n", + "cg_2=tf.cast(tf.add(tf.constant([7.65,9]),tf.constant([13.5,7.18])),dtype=tf.float32)\n", + "cg_3=tf.cast(tf.minimum(cg_1b,cg_2),dtype=tf.float32)\n", + "# Execute \n", + "# YOUR CODE HERE\n", + "sess=tf.Session()\n", + "cg1,cg2,result=sess.run([cg_1b,cg_2,cg_3])\n", + "print(\"part 1\",cg1)\n", + "print(\"part 2\",cg2)\n", + "print(\"result\",result)\n", + "sess.close()" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "text": [ + "part 1 [11.25 15.446429]\n", + "part 2 [21.15 16.18]\n", + "result [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": {} + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE" + ], + "execution_count": 0, + "outputs": [] + }, + { + "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": {} + }, + "cell_type": "code", + "source": [ + "# Build the graph\n", + "# YOUR CODE HERE\n", + "\n", + "# Execute \n", + "# YOUR CODE HERE" + ], + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "outputId": "db725b26-80c0-46c2-c419-bb77ceee0ee2" + }, + "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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6fNGen5N306/2Eyxo/4EKW0RuSGfaIibYe/oXHo3tTmLqXwxr+AKRTV5xqE/c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\nsyOJiEghsOnyeExMDNOnTyc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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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 434 + }, + "outputId": "f84248cb-1b0e-4e65-868a-50ab60626e2f" + }, + "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 0.1934528\n", + "Loss after epoch 20 is 0.19332597\n", + "Loss after epoch 40 is 0.19319926\n", + "Now testing the model in the test set\n", + "The final loss is: 0.82814354\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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PPnbWoPbuAVSDEjmafoSlh77j+wPf8MvRn8nLzwOgbuV6DGg8iKuqNSAjNx3D\nM5f4lCTSc9JJz00jIzed9Jx0PD08uSGsM13Cu3FtcHO3Of91aVI4iwC2P34vrEFlZqoGJQIkZiUS\nuaw/64/+UvC164JbcEu927i53q1cHXjNWQdpBQcHkJCQZsaobkfhLBXXmRrU7Cg8t2wGVIMSOcMw\nDJ5e/STrj/5Cu5od+NdVd3Nz3d56GbqMKJylwrEe2I/v7NM1qORkDIuF7JtudtaguvdUDUoE53aM\n3x5YQruaHfjiX9/iYfUwe6QKReEsFUNeHl7Ll+I7a8ZZNajMJ0aR1T+C/DpXmDygiOs4nnGMZ38e\njZ/Nj7e6T1cwm0DhLG7NGn+8sAZ11FnfyGnXwVmDuvUO1aBE/odhGPx71WOkZKfwcufXqVflSrNH\nqpAUzuJ+iqpBVfIna9AQZw3qmsZmTyjishbsmsuKmOV0Ce9GRJNIs8epsBTO4j5SUvCd8eHZNajG\nTcmKiCT73j4Y/gEmDyji2mLTYnj+l2cJ8KrMm93eq1Cny3Q1Cmcp987UoFj8Kf5nalD39CErYgh5\nbdqqBiVyAfKNfJ78cQTpuWm83f19HZVtMoWzlE9F1KCoW5f0foOwP9hfNSiRizRr+0x+PrKam664\nmfsbPWj2OBWewlnKlSJrUD17YY+IpMr9d5N1MtPsEUXKnQMp+5i8/gWqeVfjta5v6+VsF6BwFteX\nl4fXD8ucNaiffgQgPyiIzMf/7axBXVHXeT0P1T1ELpYj38FjPz5KZl4mb3R7l9BKNcweSVA4iwuz\nxMfjO382PnNmFdSgctu2dx7gddu/wFu714hcrv9ue49fj2/kjvp3cedV95g9jpymcBbXYhh4rluL\nz6yZeH/3dWENKiLSWYNq3MTsCUXKjYTMBPan7iMzN52M3IyzPjLzMsjISSd6RxRBvsG83Pl1vZzt\nQhTO4hIsp1Lx/uRjfKOjsO35C4C8a5o4nyXfd79qUCIXIDM3kw3H1rE6dhWr41axM2l7sbexWW28\n3vUdqvtWL4MJ5UIpnMVUtj+3OXeD+vyTwt2g7r6PrEFDVYMSKYYj38GfidtYE/cTq2NXsfHYenLy\ncwDw9vCmU3hXWgS3JMArAD9PPyp5+lPJsxJ+Nuef/Tz9qFmplt5ndkEKZyl7druzBhUdheeWXwFw\n1LmCrAGDsD/QHyM42OQBRVzfpmMbGb5iCDFphwu+1jSoGV3Cu9Gldjfa1myPr83XxAnlciicpcxY\nDx4orEGdPFlYgxo0hJxuPXS0tcgFMAyD97e9y5QNE8g38unT6AG61+lBp7CuBPvpH7buQuEspetM\nDSp6Jl6rVgLnqEGJSLFS7MnKhmZLAAAgAElEQVQ8/uOjLD30HSF+oXzQ8yM6hnUyeywpBQpnKRUF\nNai50XgciQNUgxK5HFvjtzB0eQQxaYfpFNaF93tGEeIXYvZYUkoUzlJyDAPP9b84a1DfLjm7BjUw\nEkeTpmZPKFLuGIbBzD//y8R1z5GXn8fo1mMZ1foZ7bHs5hTOctkKalCzP8L2124A8q5pTFbEENWg\nRC7DqexUnlw1km8OfEWQbxDTe8yka+3uZo8lZUDhLJfM488/8I2OwufzRc4alKenswYVMYS8tu1U\ngxK5RIZhsPzwUp5fO5ZDpw7SvlZHPuj5ETUq1TR7NCkjCme5OHY73ku+wHfWzMIaVO06ZD01CPuD\nA1SDErkMhmGwOm4VL22czG8ntmDBwhMtR/FMm/HYrPp1XZHo0ZYLYj14AN85s/D5eG5hDarHTdgj\nIsm58SbVoEQu04Zj6/m/jf9h/dFfALi9/p08ff2zXB14jcmTiRkUznJuDkdhDerHFQDkV69O5mNP\nkTVgkGpQIiVga/wWXto0hVWxzqphzyt68Uyb8TQLvs7kycRMCmf5B0t8PL4L5jhrUHGxAOS2aees\nQd1+p2pQIiXgQOp+Jq17nu8PfgNAp/CujG0znutrtDV5MnEFFxTOU6dOZdu2bVgsFsaNG0ezZs0K\nLluxYgXvv/8+Xl5e3HrrrfTr14+NGzfyxBNP0KBBAwAaNmzI888/XzorkJJxpgYVPRPvb5w1KMOv\nElkDI8kaOBhH02vNnlDEbayKWcnDPwwiNTuF62u05dm2z3NDWGezxxIXUmw4b9q0icOHD7No0SL2\n79/PuHHjWLRoEQD5+flMnjyZL774gqpVqzJ06FB69OgBQJs2bXj77bdLd3q5bJZTqXh/utC5G9SZ\nGtTV1xTWoAIqmzyhiPswDIMZf7zPC+vGYbPYeLv7+9zf6EFt1Sj/UGw4r1+/viBw69evT2pqKunp\n6fj7+5OcnEzlypUJDAwEoF27dqxbt46wsLDSnVouW2EN6hMsmRnOGtRd92AfNJTctu1VgxIpYTmO\nHP7902PM3zWHYN8QZt+ygNY12pg9lrioYsM5MTGRJk0KN7gPDAwkISEBf39/AgMDycjI4NChQ4SF\nhbFx40batGlDWFgY+/btY9iwYaSmpjJy5Eg6dux43u9TrZofNpu5R/wGB7v3yTKCAzzhs89g+nRY\nv975xTp14JFxWCIj8QkNxcfcES+b2z+GWl+5dCLjBDfO6c3amLW0rNmSr/p+RXjlcLPHKhXu+hie\nUVbru+gDwgzDKPizxWLhpZdeYty4cQQEBBAe7vzLVrduXUaOHMktt9xCbGwsAwYMYPny5Xh5eZ3z\nfpOTMy9h/JITHBxAQkKaqTOUFuuhg1T/bD75M2cW1KBybuzp3A3q7zWocr5+d34MQesrr7Yn/smA\n7/oSlx7LnVfdzZvdpuOd7eeWa3XXx/CMkl7f+YK+2HAOCQkhMTGx4PMTJ04Q/LcTTbRp04YFCxYA\n8NprrxEWFkZoaCi9e/cGoE6dOgQFBREfH0/t2rUveRFykRwOvFYsx+dMDcowoHp1Mkc+6axB1a1n\n9oQibu/bA18zYsVQMvMymdxtMg9f/bjeX5YLYi3uCh07dmTZsmUA7Nixg5CQEPz9/QsuHzJkCElJ\nSWRmZrJq1Srat2/PkiVLiIqKAiAhIYGkpCRCQ0NLaQnyd5YTJ/B7cxqB1zejSv/78V75A3mt28Dc\nuSRt3UXGC/9RMIuUosSsRL478A1Pr36KQUsfAizMunk+z3V+TsEsF6zYZ84tW7akSZMm9O3bF4vF\nwoQJE1i8eDEBAQH07NmTPn36MHjwYCwWCw8//DCBgYF0796d0aNHs3LlSnJzc5k4ceJ5X9KWy2QY\neG5cj8+sGc4aVG6uswY1YDBZEZE4ml7rfPnEjV9uEjGDYRjsTd7DpuMbnB/HNnAgdX/B5bUD6jDn\nloU0CdKObHJxLMbf30Q2kdnvU5TH90osaafw/nQRvtEzse3eBZyuQQ2MJLtP37NqUOVxfRfL3deo\n9bkGR76DFTHL+XjXPNYfXUtydnLBZQFelWkdej1tarajTY12tK7RBl+bL1B+1nc53H2NLvWes7ge\njx3b8Y2OwvuzRVgz0gtrUBFDyG3XQTUokVJwPOMY83fNYd7O2RxJjwOgTsAVdK/TsyCMrw68Rvss\nS4lQOJcX2dl4f/2lczeoXzcC4AivTcbjT5H10ECMkBCTBxRxP/lGPj/HrWb2jo/4/uA3OAwHlTz9\nGdgkkoFNBtM0SGfOk9KhcHZx1sOHnLtBLZiDNSnJWYPq3oOsQUPJ6aHdoERKQ1ZeFrO2z2TOjo8K\n3kNuXL0pEU0iubdhH/y93LvLK+ZTOLsihwOvlcvxiY7Ca+UPWAyD/MBA1aBEykBazike+rYPG46t\nw9vDmz6NHiCiSSStQq/X0dZSZhTOLsSSkIDPgjn4zpmFR2wMALmt25A1aIhzNyif8n7+LhHXlpqd\nQt9v7mZL/GbuqH8Xr3Z5g2o+gWaPJRWQwtls56pB9R/krEFd26z4+xCRy5aUlUSfr+/kz8Rt3New\nL291n47Nql+RYg79zTNJkTWoRlc790y+ry9G5SomTyhScZzIPMF9S+5g18md9G8cwatd3sRqKfYc\nTSKlRuFcxv5Rg7LZsN95t7MG1b6jalAiZexY+lHuWXI7+1L2MvTaYUy54WW9tyymUziXhTM1qOgo\nPDdtAMARFu6sQT04AEOnNhUxRWxaDHd/dRuHTx3isRZP8Vy7iQpmcQkK51JUUIP6eC7W05uH5HS7\nsbAGZdOPX8QsB1L3c89Xt3MkPY6nr3+W0a3HKpjFZSgdSprDgdePP+Aza2ZhDapaNTKHP+6sQV1Z\n3+wJRSqsXEcucemx7E3+i1E/PUF85nGeazeJx1s+ZfZoImdROJeQImtQra531qDuuEs1KJEytC95\nL9sStnL41CFiTh12/jftMEfS48g38guuN6XjSzzcfLiJk4oUTeF8OQwD28YN+EbPwPvrr07XoPzI\n6h+BPSKSvGubmz2hSIUSnxnP1A2TWLh7PgZn7+lTs1It2tRoR53KV3BF5bq0q9mBTuFdTJpU5PwU\nzpfAkp5WWIPatROAvIaNnM+SVYMSKXP2PDsf/jGdN7ZMIyM3nWsCG9O/cQT1qlzJFZXrER5QGx+b\nXr2S8kPhfBE8du7AN3om3p/+rQb1r7uxD1INSsQMhmHwzYElTFr/PDGnDlHdpzoT2k+mX+OBOoGI\nlGv621uc7Gy8v/nKWYPauB44XYN67EnsDw0gP7SGyQOKVEx/Jv7B82vHsu7oWmxWG8Oaj2RU6zFU\n8a5q9mgil03hfA7WmMP4zo3GZ/5s1aBEXER6bjq/HtvIkv1fsGDXXAwMetW9hYkdplC/agOzxxMp\nMUqYv3M48Fq1wlmDWrFcNSgRk6XnpLHp+AbWHfmFX47+zLaEreTl5wFwdeA1TOowlW51bjR5SpGS\np3AGLImJ8NF0Aqe/j0fMYQByW7Uma2Ak2f+6G3x9TZ5QpOI4fOoQs3d8xLojP7Mt4XcchgMAD4sH\n14W0oEOtTnQMu4HO4d30vrK4rYr7N9swsG3aiO+sGXh/8xXk5GBVDUrEVD/GrOCRHwaTmp2CzWqj\nRUgrOoZ1on2tjrSp0RZ/rwCzRxQpExUunC3paXh/9gm+0VHYdm4HIK9BQ2yPjSTpljsxquhgEpGy\nZhgGr/zyCs+ufBabxcYrnd/gvkZ9qeRZyezRRExRYcK5yBrUHXc5a1AdbiA4pDJGQprZY4pUOBm5\nGTy1agRf7ltMzUq1mHXzPFqGtjZ7LBFTuXc4Z2fj/e0SfGfNLKxB1QpTDUrERRw+dYiB3z/IzqTt\ndKzdkf/eGE2on3ZpE3HLcLYkJOD3wXv4LJhTWIPq2t1Zg+rZSzUoERewJu4nhi4bSHJ2MgObRPLh\nXdNJPZlt9lgiLsEtU8p/3NP4fLXYWYN69DHsAwfhuPIqs8cSKfeyHdks3vMpCVkJZOVlkpWXRWZu\nBll5Wac/MrHn2aniXZUw/zDCAsIJ8y/8CPYLwYKFD/54j4nrnsPD4sFrXd+mf+MIvDy8AIWzCLhp\nOGeOHkv27f8ip+fNqkGJlKBxPz/N3J3Rl3x7T6sn1X2DOJ5xjBC/UD7qNY82NduW3IAibsItw9nR\n6Gocja42ewwRt7I1fgvzds7m6sBreKH9f/C1+eFn88PX0w9fmy9+tkr4evri4+HDSftJjqTFciT9\nCEfSz/w3jqPpccSlxdElvBvv3PhfalSqafayRFySW4aziJSsfCOfsT+PwsDgpU6v0SHshvNeP8Qv\nhBC/EFqEtiqjCUXci9XsAUTE9c3fNYetJ37j7gb3FhvMInL5FM4icl7J9pO8uGEilTz9mdjhRbPH\nEakQFM4icl5TN07mpP0ko1uP1XvEImVE4Swi57TtxFbm7PiIhtUa8XCzR80eR6TCUDiLSJH+fhDY\n/3WahqeHp9kjiVQYCmcRKdLC3fPZEr+Zf9W/m07hXcweR6RCUTiLVCD2PDuOfEex10uxJzN5/Qv4\n2SoxqaMOAhMpawpnkQpi8/FNtJzbhOvmXMNrm1/mROaJc173pU1TSLIn8e/WY6jlH1aGU4oIKJxF\nKoTvD37LPUtu56Q9iYzcDF7e9CIt5zRm+IqhbI3fctZ1/0z8g+gdUVxVtQHDmo8waWKRik3hLOLm\nPto+g0FLH8KChbm3LOSPgbv5v07TuKJyXT7bs4hen3fjls+789meRWQ7shm7ZhT5Rj5TO716ejMK\nESlrOn2niJvKN/J5ccMk3tn6BkG+wSy49VOuC2kJQOS1DzO46VBWx60i6s8PWH5oKVvihzJ2zWhO\n5aRye/076Vq7u8krEKm4FM4ibijbkc0TPw5n8d5PubJKfRbetpi6VeqddR2LxULX2t3pWrs7h1IP\n8tH2GSzYNZfKXlWYpDOBiZhK4SziZlKzUxi0tB9rj6yhdWgb5vZeRHXf6ue9Td0q9fhPx6mMbfMc\n2Q471XwCy2haESmKwlnEjRxJi+OBb+9h98ld9K53O+/3nImv7cL3NPfz9MPP068UJxSRC3FB4Tx1\n6lS2bduGxWJh3LhxNGvWrOCyFStW8P777+Pl5cWtt95Kv379ir2NiJScXEcum+M38WPMCj7ePY8T\nmfEMufYRJnd8CQ+rh9njicglKDacN23axOHDh1m0aBH79+9n3LhxLFq0CID8/HwmT57MF198QdWq\nVRk6dCg9evQgJibmnLcRkct3JC2OH2NX8GPMCtbE/URazikAvD28mdRhKsOaj8BisZg8pYhcqmLD\nef369fTo0QOA+vXrk5qaSnp6Ov7+/iQnJ1O5cmUCA53vT7Vr145169YRGxt7ztuIyKXZlbCLd355\nn1WxK9h9clfB16+oXJf7Gt7PjXV60iGsE5U8K5k4pYiUhGLDOTExkSZNmhR8HhgYSEJCAv7+/gQG\nBpKRkcGhQ4cICwtj48aNtGnT5ry3EZGLcyB1P9N+fYnP93yCgYGvzZcedW6ie50edL+iJ1dWqW/2\niCJSwi76gDDDMAr+bLFYeOmllxg3bhwBAQGEh4cXe5tzqVbND5vN3PfHgoMDTP3+pc3d1wfutcZD\nKYeYvHoys7fNxmE4aB7anBe6vEDvBr3xsfmYPV6pcKfHryjuvj5w/zWW1fqKDeeQkBASExMLPj9x\n4gTBwcEFn7dp04YFCxYA8NprrxEWFkZ2dvZ5b1OU5OTMix6+JAUHB5CQkGbqDKXJ3dcH7rPGY+lH\neWPLq8zfNYfc/FwaVmvEM23GE9H2IZISM0hLziWNXLPHLHHu8vidi7uvD9x/jSW9vvMFfbGn7+zY\nsSPLli0DYMeOHYSEhJz18vSQIUNISkoiMzOTVatW0b59+2JvIyL/dCLzBM+tfYY285sTvSOK2gF1\nmN5jBqvv38Dt9e/EatHZdkUqimKfObds2ZImTZrQt29fLBYLEyZMYPHixQQEBNCzZ0/69OnD4MGD\nsVgsPPzwwwQGBhIYGPiP24hI0fKNfObtnM2k9c+TlnOKOgFXMKr1M9zXqC82q05FIFIRWYwLeUO4\nDJj9Uohejin/yuMa9yXvZdTqx1l/9BcCvCozru0L9G8cUeSGE+VxfRdD6yv/3H2NZfmytv5ZLmKC\nXEcu7/3+Fq9tfplsRza9693OS52nUaNSTbNHExEXoHAWKWO/xW/mqVWPsevkDkL9avB/naZxW/07\nzB5LRFyIwlmkjGTkZvDSpinM+ON98o18+jeO4IX2/6GKd1WzRxMRF6NwFikl6bnp/HHid347sYWt\nJ7aw/ugvJGYlcGWV+rzW9W06hnUye0QRcVEKZ5ESYBgGO5N2sCX+V7ae2MJv8Vv4K3kX+UZ+wXWC\nfIN4suVonmr99EXtFCUiFY/CWeQybTi6jhc3TmLjsfUFX/Oz+dG2ZntahLSiZUgrWoS2Ity/tjaj\nEJELonAWuUR/Jmxj6sb/sDLmBwB61b2Fm+veSovQVjSs1kgdZRG5ZPrtIXKR9qfs5eVNL/LlvsUA\n3BDWmXFtX6B1jTYmTyYi7kLhLHKBjqYfYdqvL/Hx7nnOjSiCWzC+3QS6hHfTy9UiUqIUziLnkW/k\ns/HYej79ayGf7llItiObBlUbMrbt89x25R0KZREpFQpnkSLsS97Lp3s+5rM9nxCbFgNA7YA6jG49\nVue8FpFSp98wIqclZiXy5d7P+HTPQrae+A2ASp7+3N/oQe5r1JeOtTrhYTV3z3ERqRgUzlLhpeek\n8fTqp/hq/2Ly8vOwWqx0r9OD+xr25eZ6t1LJs5LZI4pIBaNwlgotLi2Wft/dz86k7VwT2JgHrunH\nXQ3uI9Qv1OzRRKQCUzhLhbU1fgv9v+/Licx4IppEMrXTq3ovWURcgn4TSYX0zf4ljFg5FHuenSkd\nX2Jos0d15LWIuAyFs1QohmHwztY3mbJhAn62SszpvZBedW8xeywRkbMonKXCyHHkMGb1UyzYPZda\nlcKYe+sirg1qZvZYIiL/oHCWCiHFnszgZf1Ze2QNzYNbMLf3QmpUqmn2WCIiRVI4i9sxDINjGUfZ\nm7yHfSl72Ju8hxUxPxBz6hC9693Oez0+VD1KRFyawlnKvf0pe1my70tisg6w/fhO9ibvITMv46zr\nWC1WHmvxFOPbTcBqsZo0qYjIhVE4S7n118ndvLHlVb7c9zn5Rj4A3h7eXFnlKhpUa1j4UbUhV1a9\nSs+WRaTcUDhLubMjcTtvbHmVr/d/iYFB4+pNGdniCXo17o5fTqBOsSki5Z7CWcqNPxJ+57XNr/D9\nwW8AaB7cgn+3HkOvurdgtVgJrhZAQkKayVOKiFw+hbO4vG0ntvLqr//H8sNLAWgV2ppRrZ/hxjo3\n6cQhIuKWFM7isg6mHmDqhv/w1f7FALSt2Z5RrZ+hS3g3hbKIuDWFs7ichMwEXt/yMrN3fERefh4t\nQlryXLtJ3BDWWaEsIhWCwllcRkZuBv/d9i7vbn2LjNx06laux/h2E7ij/l0KZRGpUBTOYrq8/Dzm\n75rDq7/+Hycy4wnyDeK5dhPo33gQXh5eZo8nIlLmFM5imozcDD7562M+2PYeB1L342fz49+txzDi\nuscJ8Kps9ngiIqZROEuZO5IWR9T2D5m7M5rU7BS8rF4MaDyYp68fS2ilGmaPJyJiOoWzlJlfj2/k\nw23v882Br3AYDoJ8gxjdeiwDm0YS6hdq9ngiIi5D4SylKt/I56t9i/nwj+lsid8MQOPqTRnWfAR3\nXnUPPjYfkycUEXE9CmcpNbmOXB77cRiL936KBQs31+3Nw82H07FWJx19LSJyHgpnKRX2PDsPL49g\n6aHvaB3ahnd7fMCVVeqbPZaISLmgcJYSl56TxoDvH2DtkTV0Ce9G9C0LtCOUiMhFUDhLiUq2n+TB\nb+9lS/xmete7nQ9u+ghvD2+zxxIRKVcUzlJi4jOO0+frO9l1cif3N3qQN7q9i82qv2IiIhfLavYA\n4h5iTh3m9i96sevkToZc+whvdZ+uYBYRuUT67SnnZRgGDsNx3qDdm7yHe5fcwbGMo/y79RieuX68\njsYWEbkMCmc5px9jVjBm9VPEpB0mwKsyVb2rUsW7KtW8qzn/61ONyl5VWLh7Hkn2JCZ2eJHh1z1m\n9tgiIuWewln+ITU7hRd+GcfHu+dhs9poV7MDaTlppGQnczD1ANtz08+6vgULr3V9m/6NI8wZWETE\nzSic5Sw/HFrKqNVPcDzjGE2DmvFW9+lcG9TsrOvkOHJIzU4lJTuZlOxkqvtU58qqV5k0sYiI+7mg\ncJ46dSrbtm3DYrEwbtw4mjUr/GU9f/58lixZgtVqpWnTpowfP57Fixfz1ltvUadOHQA6dOjAo48+\nWjorkBKRYk/muV/G8slfH+Np9eSZNuN5vMW/8fTw/Md1vTy8CPYLJtgv2IRJRUTcX7HhvGnTJg4f\nPsyiRYvYv38/48aNY9GiRQCkp6cTFRXF8uXLsdlsDB48mN9//x2A3r1788wzz5Tu9FIilh78jtGr\nn+BEZjzNg1vwVvfpNK7exOyxREQqrGLDef369fTo0QOA+vXrk5qaSnp6Ov7+/nh6euLp6UlmZiZ+\nfn5kZWVRpUqVUh9aSkay/STP/vw0i/d+ipfVi/FtJzCixROqQImImKzYnnNiYiLVqlUr+DwwMJCE\nhAQAvL29GTFiBD169KBbt240b96cevXqAc5n3JGRkQwcOJCdO3eW0vhyqX6K/ZEui9qzeO+ntAxp\nxco+a3mi1SgFs4iIC7jo38SGYRT8OT09nQ8++IClS5fi7+/PwIED2b17N82bNycwMJCuXbuydetW\nnnnmGb7++uvz3m+1an7YbB4Xv4ISFBwcYOr3L23BwQFk5mYydsVY3tn0DjarjRe7v8iYjmPcJpQr\nwmPozrS+8s/d11hW6yv2N3JISAiJiYkFn584cYLgYOeBQPv376d27doEBgYC0Lp1a7Zv3869995L\n/frOHYhatGjByZMncTgceHicO3yTkzMvayGXKzg4gISENFNnKE3BwQH8sGM1w1cMZV/KXhpVu5r3\nenxIs+DrSE7KMnu8ElERHkOtr/xy9/WB+6+xpNd3vqAv9mXtjh07smzZMgB27NhBSEgI/v7+AISF\nhbF//37sdjsA27dvp27dusyYMYNvvvkGgD179hAYGHjeYJbSlZefx39W/4fei3uwL2UvjzQbzvL7\nVtMs+DqzRxMRkSIU+8y5ZcuWNGnShL59+2KxWJgwYQKLFy8mICCAnj17EhkZyYABA/Dw8KBFixa0\nbt2a8PBwnn76aRYuXEheXh4vvvhiWaxFinAgZR8jVj7MlvjN1KoUxts3vk/n8K5mjyUiIudhMf7+\nJrKJzH4pxJ1ejonPjOfnuJ9YE/cTS/Z9QWZeJg9d+xAT2/wfVbyrmj1eqXGnx7AoWl/55u7rA/df\nY1m+rO0eRwFVcOk5aaw7upY1pwN598ldBZcF+4bwZrf3GNohwq3/pxERcScKZxeVlZfFs2tGszdl\nD55WT2xWT2xWj8I/W2zYrDZi0g6z9cQW8vLzAPC1+dKt9o10Cu9Kl/CuNAm6FqtFO4OKiJQnCmcX\nZBgGo356nM/2LMKCBYNzv/NgtVhpEdKKLuFd6RTeldY12uDt4V2G04qISElTOLugmX/+l8/2LKJV\naGu+vPN7PK2e5OXnnf7IJTc/jzwjjzxHLpW9KxPgVdnskUVEpAQpnF3MuiNreeGXcQT7hvBRr3kF\nz4K9PLzw8vAyeToRESkLejPShRxJi2PI8gFYLBaies2hpn8ts0cSERET6Jmzi7Dn2Rm8rB+JWYn8\nX6dXaVerg9kjiYiISfTM2QUYhsHYNaPYeuI37m/0IIObPmz2SCIiYiKFswuYveMjFuyeS7Pg63il\nyxtYLBazRxIRERMpnE228dgGxq8dQ3Wf6sy6eR6+Nl+zRxIREZMpnE10POMYkcv6k2/kM6PXbGoH\n1DF7JBERcQE6IMwk9jw7g5f250RmPJM6TOWGsM5mjyQiIi5Cz5xN8GfiH/T6rCub4zdxd4N7GdZ8\nhNkjiYiIC9Ez5zKUl5/Hu1vf5NVf/4/c/FwimkQyqeNUHQAmIiJnUTiXkQMp+xi5chib4zcR6leD\nt7pPp3udHmaPJSIiLkjhXMoMwyB6RxST1j1HZl4md111Dy91fo1qPoFmjyYiIi5K4VyKjqUf5clV\nI1gVu5Kq3lV5o9u73NXgXrPHEhERF6dwvgy5jlyS7IkkZiWSmJVA0un/JmYlkpSVyDcHviIlO4Vu\ntW/kzW7v6VzZIiJyQRTOF8ieZ+ePhG1sif+14ONIetx5b+Nn8+Plzq8T0SRSB32JiMgFUzifQ1xa\nLBuPrS8I4u2Jf5Kbn1tweZBvMB1rdSLYL5jqvkEE+QYT5BtMdZ8ggvyCCfYNokalWlTyrGTiKkRE\npDxSOP+PrLwsXto4hf9uexcDAwBPqyfXBjWjdY02tAq9nlah11M7oI6eDYuISKlQOP/NhmPrefLH\n4RxI3U/dyvUY1HQorWtcz7VBzfGx+Zg9noiIVBAKZyAjN4MXlz7P2xvfBuCR5iN4ts3z+Hn6mTyZ\niIhURBU+nNcf/YUnfhzOoVMHqV/1Kt7sNp22NduZPZaIiFRgbhnOq2JW8v3Bb6jlH0aYfzjhAbWp\n5R9GzUq18PLwAiA9N50XN0wk6s8PsVqsjG4/mseufVpbNoqIiOncMpyX7P+C+bvm/OPrFiyEVqpB\nmH848RnHiUuPpUHVhrzVfTq3XHsjCQlpJkwrIiJyNrcM52ld3uLhZsM5mh5HXHocR9LiOJLu/IhL\nj2NbwlYMw+CxFk/x9PXP6mAvERFxKW4Zzh5WD66p3phrqjcu8nJHvoPc/FyFsoiIuCS3DOfieFg9\n8LB6mD2GiIhIkaxmDyAiIiJnUziLiIi4GIWziIiIi1E4i4iIuBiFs4iIiItROIuIiLgYhbOIiIiL\nUTiLiIi4GIWziIiIiziJLZsAAAk+SURBVFE4i4iIuBiFs4iIiIuxGIZhmD2EiIiIFNIzZxERERej\ncBYREXExCmcREREXo3AWERFxMQpnERERF6NwFhERcTE2swcoTVOnTmXbtm1YLBbGjRtHs2bNCi6b\nP38+S5YswWq10rRpU8aPH09eXh7jx48nJiYGh8PBmDFjaN26Nf379yczMxM/Pz8AnnnmGZo2bWrW\nsgpc7PoWL17MW2+9RZ06dQDo0KEDjz76KLt372bixIkANGrUiEmTJpmxnH+42PW9//77rFu3DoD8\n/HwSExNZtmwZ3bt3p0aNGnh4eAAwbdo0QkNDTVnT/zrfGlesWMH777+Pl5cXt956K/369TvnbY4d\nO8aYMWNwOBwEBwfz6quv4uXlZdayClzK+l555RW2bNlCXl4ejzzyCDfddBNjx45lx44dVK1aFYDI\nyEi6du1qxpLOcrHr27hxI0888QQNGjQA4P/bu9OQqL4/juPvcU3LzLEUl6QUynoiQkVlapZGRVgR\nBJJopUSkBkVlRWpPQi2TFg1NC6TNcCGkJEU0iDRLkMKiTCEyy73FUcul83sgXjS3nD//cRzO69m9\n47ncj98559w5944uWbKEmJgYg6lfTk4OBQUFys/U1NRQXV2tt2MoQG1tLYcOHWLv3r3Ke3BIeXk5\nycnJGBsb4+PjQ0REBKCjPigMVGVlpThw4IAQQoi6ujqxe/du5bXOzk7h5+cn+vr6hBBC7Nu3T1RX\nV4vc3FwRFxcnhBCitrZW7Nq1SwghRHBwsHj//r1uA0xCm3x5eXkiISFh1LGCg4PFq1evhBBCHD16\nVDx58kQHCSamTb7h8vPzRUZGhhBCCD8/P6HRaHR05v9uoowDAwPCx8dHtLe3i4GBAbF//37x9evX\ncducPHlSFBYWCiGEuHjxorhz546O04ymTb6KigoRHh4uhBCio6ND+Pr6CiGEiI6OFqWlpTrPMBFt\n8j1//lxERUWNOpah1O/v9mfPnhVC6OcYKoQQXV1dIjg4WJw5c0bcunVr1OtbtmwRX758EQMDAyIo\nKEh8+PBBZ33QYJe1Kyoq8Pf3B8DNzY0fP36g0WgAMDU1xdTUlO7ubvr7++np6cHa2prAwEBOnToF\ngFqt5vv379N2/pPRJt9Yent7aWxsVK6I/fz8qKio0E2ICfwv+fr7+7l3796oq2B9M1HGb9++MXfu\nXNRqNUZGRqxevZry8vJx21RWVrJx40ZgZtRwvHwrV67k8uXLAMydO5eenh4GBgamLcNEtMk3HkOp\n33CpqakcOnRI5+c9FWZmZmRkZGBnZzfqtYaGBqytrXFwcMDIyAhfX18qKip01gcNdnJua2vDxsZG\n2Var1bS2tgJgbm5OREQE/v7++Pn54eHhweLFizE1NcXc3ByArKwstm3bprS/cuUKe/bsITY2ll+/\nfuk2zBi0yQfw4sULwsLCCA0N5e3bt0onG2Jra6scZzppmw+guLiYdevWMWvWLGVfXFwcQUFBJCUl\nIfTkj+JNlFGtVtPV1cXHjx/p6+ujsrKStra2cdv09PQoS2gzoYbj5TM2NlaWPnNzc/Hx8VFuR9y+\nfZuQkBCOHDlCR0eH7gP9RZt8AHV1dRw8eJCgoCCePXsGYDD1G/L69WscHBxYsGCBsk/fxlAAExOT\nEePEcK2trajVamV7KL+u+qBB33MebviArNFoSE9P5/Hjx8yZM4fQ0FDevXuHu7s7MHg/882bN6Sl\npQEQEhLC0qVLcXFxIS4ujjt37hAWFjYtOcbzL/k8PDxQq9WsX7+e6upqoqOjyczMHPc4+mQq9cvL\nyxtx3/zw4cN4e3tjbW1NREQERUVFbN68WecZJjM8o0qlIiEhgdOnT2NlZYWzs/OkbSbapw+mkq+k\npITc3Fxu3rwJwPbt25k3bx7Lli3j+vXrpKSkEBsbq9Pzn8y/5Fu0aBGRkZFs2bKFhoYGQkJCKC4u\nHvc4+mQq9cvNzWXnzp3K9kwYQ7X1/+qDBvvJ2c7ObsSVXEtLi3IVV19fz8KFC1Gr1ZiZmbFixQpq\namoAyMnJobS0lGvXrmFqagpAQECA8hDVhg0bqK2t1XGa0bTJ5+bmpjxE4+npSUdHBzY2NiOW75ub\nm8dc4tE1bevX3d1NU1PTiMFix44d2NraYmJigo+Pj17UDybOCLBq1Sru3r1Leno6VlZWODk5jdvG\n0tJS+TQyE2oIY+cDePr0KWlpaWRkZGBlZQXAmjVrWLZsGTAz+iCMnc/e3p6tW7eiUqlwcXFh/vz5\nNDc3G1T9YHCZ3tPTU9nWxzF0Mn/nH6qLrvqgwU7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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": {} + }, + "cell_type": "code", + "source": [ + "# YOUR CODE HERE" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From a2787f14ba573b92ca303186dba7ada7a63df0dc Mon Sep 17 00:00:00 2001 From: Arghadip Chakraborty Date: Mon, 15 Oct 2018 20:14:10 +0530 Subject: [PATCH 2/4] Created using Colaboratory --- Assignment-4.ipynb | 111 ++++++++++++++++++++++++++++++++------------- 1 file changed, 79 insertions(+), 32 deletions(-) diff --git a/Assignment-4.ipynb b/Assignment-4.ipynb index 9177207..e2c1e7d 100644 --- a/Assignment-4.ipynb +++ b/Assignment-4.ipynb @@ -3,7 +3,7 @@ "nbformat_minor": 0, "metadata": { "colab": { - "name": "First_Date_with_TensorFlow.ipynb", + "name": "Assignment-4.ipynb", "version": "0.3.2", "provenance": [], "include_colab_link": true @@ -103,7 +103,7 @@ "base_uri": "https://localhost:8080/", "height": 70 }, - "outputId": "17f8183a-10a0-4ef1-fb8a-5531da9ff1b5" + "outputId": "00185f55-ec8b-4e31-fb3f-ec0b460e73d7" }, "cell_type": "code", "source": [ @@ -147,7 +147,7 @@ "base_uri": "https://localhost:8080/", "height": 212 }, - "outputId": "865d2a85-8743-46fa-dd90-086631e5a5b7" + "outputId": "40dd0891-46c9-4aa9-bc7e-f6b62b93b111" }, "cell_type": "code", "source": [ @@ -228,7 +228,7 @@ "base_uri": "https://localhost:8080/", "height": 52 }, - "outputId": "bab1b1e5-8d1c-4ed9-82a6-1ff53ba1bed4" + "outputId": "792961b9-370a-43e3-ae8c-f11665076c58" }, "cell_type": "code", "source": [ @@ -274,9 +274,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 70 + "height": 68 }, - "outputId": "6cdebb1c-d9e6-415f-d443-13edfea10b36" + "outputId": "fc6bf7b7-96c4-4646-cedc-2e1f5756823e" }, "cell_type": "code", "source": [ @@ -295,7 +295,7 @@ "print ('Part 2 Result: ', part2_res)\n", "sess.close()" ], - "execution_count": 12, + "execution_count": 6, "outputs": [ { "output_type": "stream", @@ -335,7 +335,7 @@ "base_uri": "https://localhost:8080/", "height": 70 }, - "outputId": "7792ec49-b829-4141-a94f-758c75a33a0a" + "outputId": "58e118ac-94d4-4c6e-fca5-1bb4e629e292" }, "cell_type": "code", "source": [ @@ -355,7 +355,7 @@ "print(\"result\",result)\n", "sess.close()" ], - "execution_count": 34, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -387,18 +387,40 @@ "metadata": { "id": "0ZhYwAlLmEvB", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "outputId": "63a0ecbd-6cdc-4dc2-a418-3feeb1924d5e" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "cg_1a=tf.constant(([1.2,3.4],[7.5,8.6]),dtype=tf.float32)\n", + "cg_1b=tf.constant(([1.2,3.4],[7.5,8.6]),dtype=tf.float32)\n", + "cg_1=tf.reduce_mean(cg_1a, axis=1)*cg_1b\n", "\n", + "cg_2a=tf.transpose(tf.constant(([2.6,18.1],[7.86,9.81],[9.36,10.41]),dtype=tf.float32)) * tf.constant(([2.79,3.81,5.6],[7.3,5.6,8.9]))\n", + "cg_2=tf.reduce_sum(cg_2a)\n", + "cg=cg_1 +cg_2\n", "# Execute \n", - "# YOUR CODE HERE" + "# YOUR CODE HERE\n", + "sess=tf.Session()\n", + "print(sess.run(cg))\n", + "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[372.09158 396.70157]\n", + " [386.58157 438.56158]]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -416,18 +438,43 @@ "metadata": { "id": "GQWyCvsQmMcL", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87 + }, + "outputId": "af810ccf-fc7b-40b6-83a2-e92f12ba6b56" }, "cell_type": "code", "source": [ "# Build the graph\n", "# YOUR CODE HERE\n", + "cg1 = tf.constant(7.0, dtype = tf.float32)\n", + "cg2 = tf.constant([[7.36, 8.91, 10.41], [5.31, 9.38, 7.99]], dtype = tf.float32)\n", + "cg3 = tf.constant([[7.99, 10.36], [5.36, 7.98], [8.91, 5.67]], dtype = tf.float32)\n", + "cg4 = tf.constant([[1, 5.6, 6.1, 8], [0, 0, 7.98, 9], [0, 0, 7.6, 7], [0, 0, 0, 8.98]], dtype = tf.float32)\n", "\n", + "cg_1 = (cg1 + tf.reduce_sum(cg2 * tf.transpose(cg3))) / 19.6\n", + "\n", + "cg = cg_1 / cg4\n", "# Execute \n", - "# YOUR CODE HERE" + "# YOUR CODE HERE\n", + "sess=tf.Session()\n", + "print(sess.run(cg))\n", + "sess.close()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[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": { @@ -492,24 +539,24 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 350 + "height": 346 }, - "outputId": "db725b26-80c0-46c2-c419-bb77ceee0ee2" + "outputId": "bdec70e0-9c87-43d0-b652-86a54acf2a79" }, "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.plot(train_X[:10], train_Y[:10], 'r')\n", "plt.show()" ], - "execution_count": 0, + "execution_count": 21, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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6fNGen5N306/2Eyxo/4EKW0RuSGfaIibYe/oXHo3tTmLqXwxr+AKRTV5xqE/c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\nsyOJiEghsOnyeExMDNOnTyc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167Nnzx7Onz/PpUuX2LFjB40aNWL9+vVMmzYN+PsdmS1a\ntMAwDAYMGMD58+cB2Lx5M9WrVy/cgfLAnjM7y3EG2+euVKlS9lnM7t27qVSpEhcuXCAsLIz09HQA\ntm7dWqSO9bVmrlChAhcvXuSPP/4gMzOTtWvX0rx580KfKTe2zgywb98+qlSpkr2ssxxnsO/cznKs\nbVVkPuUrKyuL0aNHc/ToUby8vJg0aRLly5fn3XffpXHjxtSrVy+7lBITE6levTrPPfccTZo0ueZ6\nhw4dYsyYMVitVurXr89LL71k9ohXyW3mBg0aEB8fz6JFi7BYLPTt25euXbty5coVhg4dSkpKCqVK\nlWLq1Kn4+vqycuVKFi5cSIkSJQgKCmLChAmUKFHC7DFzsPfMznCcwfa5T506xcsvv8yVK1cAePnl\nl6lVqxaLFy/myy+/pFixYtSuXZtXXnnF4V7rtPfMW7du5e233wagffv2pr70cz22zgxk/6rTvy8T\nO8NxBvvP7QzH2lZFprRFRESKuiJzeVxERKSoU2mLiIg4CZW2iIiIk1Bpi4iIOAmVtoiIiJNQaYuI\niDgJlbaIiIiTUGmLiIg4if8DB9G3KJbizPkAAAAASUVORK5CYII=\n", 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vx+LxYDgc+Hv2+m9Qf3vCWY+eusQrDCnIRUQimWEQf/9SkubnEXC6cK9+As+on0CMfv1H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\n5eXlByy7Z8+e9m3oCATb87defPFFzjv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"text/plain": [ - "" + "" ] }, "metadata": { @@ -611,9 +658,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 434 + "height": 435 }, - "outputId": "f84248cb-1b0e-4e65-868a-50ab60626e2f" + "outputId": "acc28353-9115-473e-aef9-dd59d67201bc" }, "cell_type": "code", "source": [ @@ -648,25 +695,25 @@ " plt.legend()\n", " plt.show()" ], - "execution_count": 0, + "execution_count": 26, "outputs": [ { "output_type": "stream", "text": [ - "Loss after epoch 0 is 0.1934528\n", - "Loss after epoch 20 is 0.19332597\n", - "Loss after epoch 40 is 0.19319926\n", + "Loss after epoch 0 is 0.3600924\n", + "Loss after epoch 20 is 0.35979536\n", + "Loss after epoch 40 is 0.3594984\n", "Now testing the model in the test set\n", - "The final loss is: 0.82814354\n" + "The final loss is: 0.09196285\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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PPnbWoPbuAVSDEjmafoSlh77j+wPf8MvRn8nLzwOgbuV6DGg8iKuqNSAjNx3D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\np3AGLImJ8NF0Aqe/j0fMYQB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5vMW/8fTw/Md1vTy8CPYLJtgv2IRJRUTcX7HhvGnTJg4f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\nNDc3G1T9YHCZ3tPTU9nWxzF0Mn/nH6qLrvqgwU7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hIiIrUqOrwcYzn2H10TeRX5kHN1s3LB68DNN6PSr52rnUFEOYiMgK6PQ6fJWw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\n+kpZFhGR2WAIk1GnC+KxOO4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"text/plain": [ - "" + "" ] }, "metadata": { From 5f651150aabe26790074ac0bfcc7287d0470643e Mon Sep 17 00:00:00 2001 From: Arghadip Chakraborty Date: Mon, 15 Oct 2018 22:49:38 +0530 Subject: [PATCH 3/4] Assignment 4 done! --- Assignment-4.ipynb | 246 ++++++++++++++++++++++++++++++++++++--------- 1 file changed, 198 insertions(+), 48 deletions(-) diff --git a/Assignment-4.ipynb b/Assignment-4.ipynb index e2c1e7d..4be19cf 100644 --- a/Assignment-4.ipynb +++ b/Assignment-4.ipynb @@ -6,6 +6,7 @@ "name": "Assignment-4.ipynb", "version": "0.3.2", "provenance": [], + "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { @@ -101,9 +102,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 70 + "height": 68 }, - "outputId": "00185f55-ec8b-4e31-fb3f-ec0b460e73d7" + "outputId": "dabf3a64-06d3-4be4-f74c-b2a56683f1ba" }, "cell_type": "code", "source": [ @@ -112,14 +113,14 @@ "print (t2)\n", "print (t3)" ], - "execution_count": 3, + "execution_count": 109, "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" + "Tensor(\"Const_103:0\", shape=(), dtype=float32)\n", + "Tensor(\"Const_104:0\", shape=(2,), dtype=float32)\n", + "Tensor(\"Const_105:0\", shape=(2, 3, 2), dtype=float32)\n" ], "name": "stdout" } @@ -145,9 +146,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 212 + "height": 204 }, - "outputId": "40dd0891-46c9-4aa9-bc7e-f6b62b93b111" + "outputId": "efedfd5d-b8ea-48d4-e99f-c4ecd55f71af" }, "cell_type": "code", "source": [ @@ -159,7 +160,7 @@ "print (sess.run(t3))\n", "sess.close()" ], - "execution_count": 4, + "execution_count": 110, "outputs": [ { "output_type": "stream", @@ -226,9 +227,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 51 }, - "outputId": "792961b9-370a-43e3-ae8c-f11665076c58" + "outputId": "5fe81bb5-dee9-4b51-a93e-a4a950f6d49b" }, "cell_type": "code", "source": [ @@ -244,7 +245,7 @@ "print ('Comp Graph 1 Alt: ', sess.run(comp_graph_1_alt))\n", "sess.close()" ], - "execution_count": 5, + "execution_count": 111, "outputs": [ { "output_type": "stream", @@ -276,7 +277,7 @@ "base_uri": "https://localhost:8080/", "height": 68 }, - "outputId": "fc6bf7b7-96c4-4646-cedc-2e1f5756823e" + "outputId": "35496621-cd71-48bd-ea42-40ff66aa5b77" }, "cell_type": "code", "source": [ @@ -295,7 +296,7 @@ "print ('Part 2 Result: ', part2_res)\n", "sess.close()" ], - "execution_count": 6, + "execution_count": 112, "outputs": [ { "output_type": "stream", @@ -333,9 +334,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 70 + "height": 68 }, - "outputId": "58e118ac-94d4-4c6e-fca5-1bb4e629e292" + "outputId": "60b7e1a4-47ef-4d09-df03-ba9b890f325f" }, "cell_type": "code", "source": [ @@ -355,7 +356,7 @@ "print(\"result\",result)\n", "sess.close()" ], - "execution_count": 7, + "execution_count": 113, "outputs": [ { "output_type": "stream", @@ -389,9 +390,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 51 }, - "outputId": "63a0ecbd-6cdc-4dc2-a418-3feeb1924d5e" + "outputId": "fd7e7d35-92b9-416f-aa01-dc87dde85595" }, "cell_type": "code", "source": [ @@ -410,7 +411,7 @@ "print(sess.run(cg))\n", "sess.close()" ], - "execution_count": 14, + "execution_count": 114, "outputs": [ { "output_type": "stream", @@ -440,9 +441,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 87 + "height": 85 }, - "outputId": "af810ccf-fc7b-40b6-83a2-e92f12ba6b56" + "outputId": "9058981e-dbd3-4a01-a5d6-04f8e4b1699b" }, "cell_type": "code", "source": [ @@ -462,7 +463,7 @@ "print(sess.run(cg))\n", "sess.close()" ], - "execution_count": 17, + "execution_count": 115, "outputs": [ { "output_type": "stream", @@ -539,9 +540,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 346 + "height": 347 }, - "outputId": "bdec70e0-9c87-43d0-b652-86a54acf2a79" + "outputId": "23dd33e5-80b5-45a7-9f2c-33055d11a21b" }, "cell_type": "code", "source": [ @@ -549,14 +550,14 @@ "plt.plot(train_X[:10], train_Y[:10], 'r')\n", "plt.show()" ], - "execution_count": 21, + "execution_count": 118, "outputs": [ { "output_type": "display_data", "data": { - "image/png": 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vx+LxYDgc+Hv2+m9Qf3vCWY+eusQrDCnIRUQimWEQf/9SkubnEXC6cK9+As+on0CMfv1H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\n5eXlByy7Z8+e9m3oCATb87defPFFzjv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v38+QIUNwOBwsWLAAgLq6Ol555RWA1sv0AGPHjmXz5s1Hramy0t2B1o5NcnIc5eW1Ad9v\nd6aew0M49gzh2XdHenZs2kj8tVdhxMRS/dSfaDq5HwTxn1M4HecjfWHx69L6qFGj2LJlCwBbt25l\n5MiRbd4fPHgw7777LjU1NdTV1VFcXMzw4cPZtm1b60NxGzduZPTo0RiGweTJk6mpqQHgzTff5Iwz\ndIlHRCSgGhuJWvUY8df/DiPKSfWGZ2kaMszsqiQA/Hpqffz48Wzfvp3MzEwcDgf5+fkArFixghEj\nRjB06FBmzZpFVlYWFouFadOmERcXx8iRI1m7di0TJ04kISGBJUuWYLFYmDhxIpMnT8bpdNKrVy9u\nuummgDYpIhK2XC6ca57E+fCD2L7+CiM6hpp1RTSNGNn+thIULEZ7N7i7oc64jBJOl2e+pZ7DQzj2\nDOHZ93d7tlQexPn4CpyPPYy1shIjOob6yVnUT5mG78STTK40cMLpOB/p0rpfZ+QiItI9Wcu+xvnw\nMqJWr8Ja58KXmEjdbXOov+Z6jZseohTkIiIhwPrpJzDvYZKeeAJLYyPNvU7EdXsO9ZMmQ2ys2eVJ\nJ1KQi4gEMdueD4kuXELkn56B5mZ8fU/FfdMtNFxyGURGml2edAEFuYhIELIX7yJ6aQGRW/4MQNMP\nzsQ+9w4OjrkA7PqvPZzoaIuIBAvDIOL1vxO9tADHa68C4E0bjvvm2/CM+znJvRKC+jfh4h8FuYhI\nd+fz4XjpL0QXFhCxexcAnvPG4L55Ft5Ro8FiMblAMZOCXESku2pqIvL5Z4kuXIJ9z4cANI7/Fe4Z\nM2kammZycdJdKMhFRLqbhgainlpH9INLsX3+KYbNRkPGpbhvuoXmgT8wuzrpZhTkIiLdhMVVS9Tq\nJ1pGYdtfhhEZSf3vrsE9dTq+vqeaXZ50UwpyERGTWQ4ewLnyUZwrH8FaVYUvJhb3jTfjvn4aRq9e\nZpcn3ZyCXETEJNavv8L58DKcq1dhcdfhS0qibvYd1Gddh9Ej0ezyJEgoyEVEupj1k71EL3uAqKfW\nYvF4aD7pZOrnzKX+iskQE2N2eRJkFOQiIl3E9sH7LaOwPf8sFp+PptP6UX/TLTRkXKpR2MRvCnIR\nkU5m37WT6AcKiHzpLwA0nTkI982zaPzVxWCzmVydBDsFuYhIZzAMIv7+KtEPFOB4/e8AeEeMxH3z\nLDw/+7kGcZGAUZCLiASSz4fjL39uGYXt7WIAPGN+ivvmW/Gek64Al4BTkIuIBILXS+SfniH6wfux\nf7QHw2Kh8cKLWkZhGzzU7OokhCnIRUSOR0MDUev/SPTyB7B9/hmG3U7DpZe3jMJ2Rn+zq5MwoCAX\nEfGDxVVL1BOPE/3IMqzl+zGioqjPuq5lFLbefcwuT8KIglxE5FjU1+N8YiXRhQVYDx7EFxuHe/pM\n3NdNxUhJMbs6CUMKchGRjvB6Wy6hF9yN7atSfPEJLaOwXXM9RkIPs6uTMKYgFxE5Gp+PyD89Q8zd\ni7B9+gmG09lyBj5tOkZiktnViSjIRUQOyzBwvPQXYvLuwv7h+xgREdRffS3uW27D1+tEs6sTaaUg\nFxH5LxGvbSNm8QIidu/CsFppuOQy6m7N1lSi0i0pyEVEvmHf/RYxi+/C8dqrADReeBF1s++gecBA\ncwsTOQoFuYiEPduHHxCTdxeRW/4MtIzEVjfnTpqGDDO5MpH2KchFJGxZP9lLzL15RD77NBbDwHv2\nOdTlzMObfq7ZpYl0mIJcRMKO9atSopfcS9Ta1Viammj64VnU3TEPz0/P11joEnQU5CISNiwHDhD9\n4P04V63A0tBA0+nfx509t2U6UavV7PJE/KIgF5GQZ6mtwfnIcpwPL8PqqqX5e6fgvm0ODRMzwa7/\nBiW46W+wiISu/x5O9YQTcM2ZS/2k30FUlNnViQSEglxEQo/XC48+StL8Bdi+/qplONU5d+K+9gaI\njTW7OpGAUpCLSOhobm4ZTvWexfDpJ1g1nKqEAQW5iAS/1uFUf4/9ww8wIiLgxhs5eP10DacqIU9B\nLiJB7ZDhVC+9nLpbs+mZNghfea3Z5Yl0Or+C3Ov1kp2dTWlpKTabjby8PHr37t1mnerqambOnElM\nTAyFhYVH3W7Pnj3Mnz8fgAEDBrBgwYLj60pEQt5hh1PNnktz/wHmFibSxfz64eSmTZuIj49n/fr1\nTJkyhYKCgkPWyc3NJS0trUPbLVq0iJycHDZs2IDL5WLbtm3+lCUiYcD2wfvEX5lJ4gU/xfHaq3jG\n/JTKl1+lZtUahbiEJb+CfMeOHYwbNw6A9PR0iouLD1ln4cKFhwT54bbzeDzs27eP1NRUAMaMGcOO\nHTv8KUtEQpj1k73E3XANiWPSidzyZ7xnn0PV85upfupPGhNdwppfl9YrKipISmp5AtRqtWKxWPB4\nPDgcjtZ1Yg/zE4/DbVdRUUF8fHzrOj179qS8vNyfskQkBP33cKreQam4c+7UcKoi32g3yIuKiigq\nKmqzrKSkpM1rwzD8+vDDbdeRfSUmRmO32/z6zKNJTo4L+D67O/UcHoKy54oKuPtuWLYMGhqgf3+4\n6y4iJkwgoYPDqQZl38dJPYefdoM8IyODjIyMNsuys7MpLy9n4MCBeL1eDMNoczZ+JCkpKYdsl5yc\nTFVVVes6ZWVlpKSkHHU/lZXudj/rWCUnx1EeZk+4qufwEGw9tzuc6oG6Du0n2PoOBPUc2o70hcWv\ne+SjRo1iy5YtAGzdupWRI0f6vV1ERAT9+vVj165dALz88suMHj3an7JEJJjV1+N86EGSRqQSc28e\nREXiWnQ3B3cU03DZJI2JLnIEfv3LGD9+PNu3byczMxOHw0F+fj4AK1asYMSIEaSmpjJ58mRqamoo\nKytj0qRJTJ069Yjb5eTkMG/ePHw+H4MHDyY9PT1wHYpIt+f484vEzrlVw6mK+MFi+HuD20SdcRkl\nnC7PfEs9h4fu3rPtww9IHHce2GzUX3tDwIZT7e59dwb1HNqOdGld16pExDyNjcRPvRaLx0P1H5/C\nc/4FZlckEnT8ukcuIhIIMffmYX//XeqvuEohLuInBbmImML+5hs4ly2luc+p1P1+sdnliAQtBbmI\ndDmLq5b4G68DoGb5CozY8P4dsMjxUJCLSJeLyb0D22efUn/jzTSNPMfsckSCmoJcRLqU46W/4Fzz\nJE0/PIu623PMLkck6CnIRaTLWCoqiLvlRgyHg5qHHoMOjAgpIkenIBeRrmEYxN06A2tFOXU5uTT/\n4EyzKxIJCQpyEekSkU+tI3Lzi3jSz6V+yjSzyxEJGQpyEel01s8/IzbndnyxcdQ++Ah0cPYyEWmf\nRnYTkc7l8xE3/QasrlpqCh/G17uP2RWJhBR9LRaRTuV8ZDmO7a/TeMGFNF5ymdnliIQcBbmIdBrb\nhx8Qs3gBvhOSqS0oBIvF7JJEQo6CXEQ6x3cmRKldugzjhBPMrkgkJCnIRaRTaEIUka6hIBeRgNOE\nKCJdR0EuIgGlCVFEupaCXEQCShOiiHQtBbmIBIzjZU2IItLVFOQiEhCWigribtaEKCJdTUEuIsfv\nuxOizJmnCVFEupCCXESOmyZEETGPglxEjsshE6LYbGaXJBJWNGmKiPhPE6KImE5n5CLiN02IImI+\nBbmI+EUTooh0DwpyETl2Ho8mRBHpJhTkInLMNCGKSPehIBeRY2J/8w2cD96vCVFEugkFuYh0WOuE\nKIZBzbJHNSGKSDegIBeRDmudEOWmW2g650dmlyMiKMhFpIM0IYpI96QgF5F2aUIUke5LQS4iR6cJ\nUUS6NQW5iByVJkQR6d78Gmvd6/WSnZ1NaWkpNpuNvLw8evfu3Wad6upqZs6cSUxMDIWFhUfdbtKk\nSbjdbqKjowGYPXs2gwYNOs7WROR4aUIUke7PrzPyTZs2ER8fz/r165kyZQoFBQWHrJObm0taWlqH\nt8vLy2PNmjWsWbNGIS7SHXxnQhTX4ns0IYpIN+VXkO/YsYNx48YBkJ6eTnFx8SHrLFy48JAg78h2\nItI9aEIUkeDg16X1iooKkpKSALBarVgsFjweD47vPMkaGxvb4e0ACgsLqays5PTTTycnJ4eoqKgj\nfn5iYjR2e+Av8SUnh9/gFuo5PBxzz++9B4sXQEoKkatXkZwc3zmFdTId6/AQjj1/V7tBXlRURFFR\nUZtlJSUlbV4bhuHXh3+73ZVXXsmAAQPo06cPubm5rF27lqysrCNuV1np9uvzjiY5OY7y8tqA77c7\nU8/h4Zh79nhIvPQy7B4P1QUP4iEKgvDPTMc6PIRTz0f6wtJukGdkZJCRkdFmWXZ2NuXl5QwcOBCv\n14thGG3Oxo8kJSXlsNt9e7kdYOzYsWzevLndfYlI52gzIcrPNSGKSHfn1z3yUaNGsWXLFgC2bt3K\nyJEj/d7OMAwmT55MTU0NAG+++SZnnHGGP2WJyHHShCgiwceve+Tjx49n+/btZGZm4nA4yM/PB2DF\nihWMGDGC1NTU1nAuKytj0qRJTJ069bDbWSwWJk6cyOTJk3E6nfTq1YubbropoE2KSPs0IYpIcLIY\n/t7gNlFn3A8Jp/ss31LP4aGjPcfOmo5zzZO4p8+kbu78zi+sk+lYh4dw6vlI98g1spuIaEIUkSCm\nIBcJc5oQRSS4KchFwpkmRBEJegpykTCmCVFEgp+CXCRMWb/4XBOiiIQAv35+JiJBzucj7qYpWF21\n1BQ+rAlRRIKYzshFwpAmRBEJHQpykTBj+/ADYhYvwHdCMrUFhWCxmF2SiBwHBblIOPF4iJ96LRaP\nh9r7l2GccILZFYnIcVKQi4QRTYgiEnoU5CJhQhOiiIQmBblIGNCEKCKhS0EuEgZicu/A9tmn1N90\nC03n/MjsckQkgBT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L/Z8f4b7uBiq3/E0hLiIinUZn5AFi2b+f+OlTcPztf/GdkExN4UN4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\nTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": { @@ -658,9 +659,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 435 + "height": 432 }, - "outputId": "acc28353-9115-473e-aef9-dd59d67201bc" + "outputId": "b74563f6-3e06-40ce-bb90-67f7d7013447" }, "cell_type": "code", "source": [ @@ -695,25 +696,25 @@ " plt.legend()\n", " plt.show()" ], - "execution_count": 26, + "execution_count": 123, "outputs": [ { "output_type": "stream", "text": [ - "Loss after epoch 0 is 0.3600924\n", - "Loss after epoch 20 is 0.35979536\n", - "Loss after epoch 40 is 0.3594984\n", + "Loss after epoch 0 is 0.07239858\n", + "Loss after epoch 20 is 0.07236038\n", + "Loss after epoch 40 is 0.0723222\n", "Now testing the model in the test set\n", - "The final loss is: 0.09196285\n" + "The final loss is: 0.015346773\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { - "image/png": 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hIiIrUqOrwcYzn2H10TeRX5kHN1s3LB68DNN6PSr52rnUFEOYiMgK6PQ6fJWw\nFauOrEB6WRrsFQ545sb5mB3zBJxtXaQeHjWDIUxEZME0Og2+S9qBt/9ehYSi87CV22JWzBzM6/sM\nVPYqqYdHLWAIExFZoApNBb48+wU+PPEeMsszIBfkeDBqCp678QX4OwVIPTxqJYYwEZHENDoNtKK2\nVddsCysL8ck/H+HTfz5GUXUR7BR2mN5rJh6LmYtg55COHyy1K4YwEZFEqrRVWP/Px1hzbBVKqkvg\n7xiAMNeuCHMJQ7hrV4S7dkWYSzgCnYKRXZGFj06+hy/PbkSlthJutm549sYFmN5rFjztPKUuhdqI\nIUxEZGI6vQ7bE7Zh5eHlyCzPgIutKwb7DUVKSTL+yNyLPzL3Nnq9QqaAXtRDL+oR4BiIx/vMxQNR\nUzhXsxVgCBMRmYgoitibsQf/PrAYZwrjYSu3xew+8/Bkv2fg1sUdQO213pSSZCQXJyKpOBHJJUlI\nKk6EIAh4uOd0TOh6F2zkNhJXQu2FIUxEZAIn847j3wcX48/MfRAgYFLk/Vgw4CUEOgU1ep2DjQOi\nPXsh2rOXRCMlU2IIExFdB1EUkVKajJKqYpTWlKKspgxClgbZhXmGx8klSdiZ8iMAYExQLF4e9CpD\nlgAwhImI2iy9NA1P751rWO7vamJUfbFo8L8xPGCkCUZGloIhTER0jURRxOazX+CV/S+iQlOO4QGj\nEO3RC862znCycYKfhxdQrYST0glOSie42roixCUMMkEm9dDJzDCEiYiuQU55Np7Z9wR+Tf8FzkoX\nvDPmQ9wb+UCjBe5VKq63S63DECYiagVRFLE9YRsW/jUfJdXFGBU4Bm+Pfh9+jv5SD40sGEOYiKgF\neeo8PP/7U/g55QfYKxzw5si3MaXHI42OfonagiFMRGREaXUJ4gv+wd95R/HB8TUorCrEEL9hWDPm\nA04PSe2GIUxEnd6lqkKcyj+JU/kn8U/+SZwqOIGUkmTD83YKOywfthLTe81icxW1K4YwEVk9tUaN\nzLIMZJanI6MsAxml6Zc/LktHbkVOo9e72rpieMAo9PaMQW9VDAb7DYWPg69EoydrxhAmIqt0oSgB\nG06vx7eJO5Cnvmj0NXJBDj9Hf8QGjUNvVQx6qfqgtyoGAY6BvN5LJsEQJiKrodFp8HPKD/gs/hPs\nz/4TAOBp54kRAaMR6BSIQKcgBDgFIsgpGAFOgfBx8IVCxh+DJB3+7yMii5dZloGNZz7D5rMbDUe9\nw/xH4OGe03FL6G1c8IDMFkOYiCxSSXUx9mX8hu0J2/BL2i7oRT1cbF0xs/fjmNpzOiLcukk9RKIW\nMYSJyCKIooiEovP4JW0X9qS4kjmAAAAZ80lEQVTtwqGcA9CJOgBAH1VfPBw9AxO63gV7G3uJR0rU\negxhIjJbao0aB3P2Y3fqTuxJ2430sjQAgAABfb36ITb4X/hX6K3o5dlb4pEStQ1DmIgkJYoiciqy\nkVh8AYnFF5BUdMHwcWZZBkSIAAAnpTPuCP8/xAaPw01B46CyV0k8cqLrxxAmIpPT6rX4LnEH1v/z\nMc4UnoZaW9HkNV723hjsNxR9vPphbPC/MMBnEBusyOowhInIZGp0Nfj0+KdY9vtypJQkQy7IEeke\nha6uEejq2hXhrhHo6hqBcNeucLZ1kXq4RB2OIUxEHa5SW4kvz27Ee8ffRlZ5JpQyJab0mIYn+j3F\neZipU2MIE1GHKdeU44vTn+GDE+8gT30Rdgo7PDXwKTwS+Rh8Hf2kHh6R5BjCRNSuRFHE8by/8c2F\n7fgqYSsuVV2Cg40j5vV9BrNi5qBHcBgXvCeqwxAmonZx7tJZfHPhK3xz4WuklqYAANxs3fB8/xcx\no9csuHVxl3iEROaHIUxERmn1WtToaiCXySEXav9cuahBWmkqvr3wNXZc2I6zl04DAOwVDpgYcQ8m\nRtyNUYE3QSlXSjF8IovAECaiRjLK0vHJqbXYfPYLlNaUNHpOJsigEBR1waxAuab2tLJSpsQtobdh\nYsTdGBt8M2etImolhjARAQCO5B7C2pMf4Mfk76ETdfCy90Z/nwHQiTroRD10ei10og5avRZ6UQet\nXgeVvQp3hk/ErWG3wcXWVeoSiCwOQ5ioE9Pqtfgx+Xt8dPJ9/H3xCACgp0cvPBYzBxMi7oKt3Fbi\nERJZN4YwUSdTrinH+UtnEZe9H5/9sw6Z5RkQIOBfIbdgVswcDPUbzgXtiUyEIUxkgSq1lSitLoGN\n3AZKmRI2ciWUMmWj8NTpdUgrTcHpwtM4UxiPs4VncKYwHmmlqYb5mO0V9ngkegZm9n4c4a4RUpVD\n1GkxhIksxKWqQuxO3Ymfkv+HfRm/oUpX1eQ1NjIb2MiUUMptUK2rRqW2stHzbrZuGOI3DD08eqKn\nZy/cEjqetw4RSYghTGTGMssy8HPKD/gp+QcczIkzrJ8b6dYdUR49oNFrUaOrRo2uBhq9BjW6GtTo\na6DR1UAhs0F39yhEefREj7o/3vY+PNVMZEZaFcKvvfYaTp48CUEQsHDhQvTufXntzj179uDDDz+E\nUqnE+PHjMXny5A4bLFFnUFpdineOvY3/JX2Lk/nHDZ+/wbs/bgm9DePDbuOpYyIr0WIIHz58GGlp\nadi2bRuSkpKwcOFCbNu2DQCg1+uxdOlSfPPNN3B1dcWjjz6K2NhY+Pj4dPjAiayNRqfBF2c+w+q/\nVyJfnQ+FTIFRgWNwS+htuCV0PHwcfKUeIhG1sxZD+MCBA4iNjQUAhIeHo6SkBOXl5XB0dERRURGc\nnZ3h7l57TWnQoEGIi4vDxIkTO3bURFZEFEX8nPIjlh5chKTiRDgqHfHCgJcxvddM3ntLZOVkLb2g\noKAAbm5uhsfu7u7Iz883fFxRUYHU1FRoNBocOnQIBQUFHTdaIivz98UjuOPbm/HwzgeQWpKCh3tO\nR+ITiXjmxvkMYKJO4Jobs0RRNHwsCAJWrFiBhQsXwsnJCQEBAS1u7+ZmD4VCfq1ftl2pVE6Sfn1T\nsPYaLb2+5KJkvPjri/jv6f8CAO6MvBMrYlegu2f32hc4Sjg4E7D09681rL1G1tc+WgxhLy+vRke3\neXl5UKlUhscDBgzAl19+CQB466234O/vf9X9FRWp2zrWdqFSOVn9MmrWXqOl1pdakoJf03fj17Rf\n8HvmXmj0GvT16oclQ5ZjsN9QQATy88sstr7Wsvb6AOuvkfW1bZ/GtBjCQ4cOxbvvvov77rsPp0+f\nhpeXFxwdL/+aPmPGDKxcuRJ2dnbYu3cvHnnkkfYbNZEFq9ZV40D2fvyathu/pv+CxOILhuei3Hvi\nqRuexZ1dJ0ImtHhViIisVIsh3K9fP/Ts2RP33XcfBEHA4sWLsWPHDjg5OWHs2LGYNGkSpk2bBkEQ\nMHPmTEOTFlFnpBf1+CHpO3yVsBV/Zv4BtbYCQO3yfjeH3IqbgsfhpqCxCHAKlHikRGQOWnVN+Lnn\nnmv0uHv37oaPx40bh3HjxrXvqIgsjCiK2Jn6E1YeXo4zhfEAgAjXbobQHeQ3hIshEFETnDGLqAGt\nXouDOXEIcAxEsHNIi7NLiaKIvRm/YsWhpTiRfxwyQYZ7ut2Hp294Hl3dOKEGEV0dQ5iozj/5J/HU\n3rn4p+AkAMDPwR9D/IdhiN8wDPEbilCX8EahHJf1F14/vBSHcg4AAO4Mn4jn+7+Ibu6RkoyfiCwP\nQ5g6vUptJd488jo+PPEudKIOE7pOhFavw4Hsv7A9YRu2J9TOEOdt74Oh/sNwo/cA/Jz6E/7M3AcA\nuDnkVswf8BKiPXtJWAURWSKGMHVqf2X9gWf3zUNKSTKCnEPw1sg1GBk4GkDtqebzRecQl/0XDmTt\nx/7sP7HjwnbsuLAdADA68CYsGPAS+nnfKGUJRGTBGMLUKZVUF+PVuFew6ewGyAQZHouZiwUDXoKD\njYPhNYIgoLt7FLq7R2Fa9KMQRRGJxRdwNPcwwl0jMMB3oIQVEJE1YAhTp/ND0vd44c9nkae+iB4e\n0fjPqHfR1/uGFrcTBAERbt0Q4dbNBKMkos6AIUydQrWuGv9L+hafx6/H4dyDsJXbYuHARZjT50nY\nyG2kHh4RdVIMYbJqqSUp+OLMZ9hydiMKqwoBALFB4/Dvoa/zFiIikhxDmKyOTq/DL2m78PnpT7A3\n/VeIEOHexR1z+jyJKT0fQahLmNRDJCICwBAmM6cX9civzEd2WSayK7KRW5ENQalDcVk5NHoNNDoN\nNHoNtPrav2t0Nfgjcx8yyzMAAP19BuLhntNxe/gEdFF0kbgaIqLGGMJkNpJLkrD5zBfILEtHVnkW\nciqykVORDa1ee037cbBxxNSe0zG15zTeu0tEZo0hTJKr0lbh3eP/wTvHVqNaVw0AkAky+Nj7IkbV\nF/6OAfB19IOfgz98HXzhr/JCRZkGNjIb2MiUsJEpoJDb1D1WwMfBr9GtRkRE5oohTJLal/EbFvzx\nDFJKkuFt74PFQ5ZiiN8weNl7QyEz/t/T2tcyJaLOgyFMksityMErf72I75J2QCbIMKv3bMwfsBBO\nSmeph0ZEZDIMYTIprV6LT//5GCsOL0e5pgw3ePfHGyP/g16evaUeGhGRyTGEySQqtZX4I3MfVh5e\njviCU3C1dcWqkWswucdUyASZ1MMjIpIEQ5hapNFp8MWZT7Hh9Kfwc/THYN+hGOQ3FH28+l51ofrC\nykL8krYTO1N+wr6MX6HWqgEA90Y+gEWDl0JlrzJVCUREZokhTM0SRRG/pO3EkriXkVh8AUqZEucu\nncVv6XsAAF3kXXCDd38M8huCwX5DcYN3f1xU52JXys/YmfojDuUcgF7UAwDCXbvi5pDxmNB1ImK8\n+kpZFhGR2WAIk1GnC+KxOO4l/JG5FzJBhod7Tsf8AS9BL+pxKCcOB7L340B2HOKy/8L+7D8B1N5W\nVB+6AgTc6DMA/wq5FbeEjueiB0RERjCEqZGL6otYeWgZvjy3EXpRj5uCxmLxkGXo7h5leM3t4RNw\ne/gEAEBxVREO5x7Egew4HM49CPcu7rg5ZDzGhtwML3svqcogIrIIDGECAFyqKsQXpz/DmmOrUaEp\nR3f3KCwZshxjgmKvup1rFzeMC7kF40JuMdFIiYisB0O4k8pX5+Ngzn7EZf+FuKz9OHvpNADA084T\nS4Ysw4NRU5qdLIOIiNoHf8p2AqIoIqMsHX9fPIK47P04kP0XEorOG563U9hheMAojAwYhYd7Toez\nrYuEoyUi6jwYwlamsLIQ8SlHcTD5KM5eOoOzhWdw7tJZlGsuT/PoYOOI0YE3YYjfMAz2G4Y+Xn2h\nlCslHDURUefEELZwpdUl+CnlB3yf+A1O5p9AfmVeo+cVMgW6ukYgyr0Heqn6YIjfUPRW9eGpZiIi\nM8CfxBaoWleNPWm7sePCV9id+rNh5aEgp2CMC74ZNwT2RbBdV3R374GurhE8yiUiMlMMYQuh0+tw\nIGc/vk74L/6X9B1Ka0oAABGu3XBXt0mYGHEPQlxCAXCVISIiS8EQNmNFVZfwZ+bv2JfxG/ak70Zu\nRQ4AwNfBD5N7TMVdEfcg2rM3BEGQeKRERNQWDGEzUqOrwdHcw9iX8Rt+z/wNJ/KOQ4QIAHC1dcXk\nqKm4q9skDPIdArlMLvFoiYjoejGEJabVa/Ft4tf45sJ27M/6C2ptBYDahqpBfkMwKmAMRgaORoyq\nL4OXiMjKMIQlUqmtxJZzm/DB8XeQXpYGoPb67sjA0RgVOAZD/IbBUekk8SiJiKgjMYRNrLS6BJ+f\nXo+1Jz9AfmUebOW2eCR6BmbFzEGYS7jUwyMiIhNiCJtIvjofH5/6AJ/Gr0NZTSmclM6Y1/cZzIyZ\nzYUOiIg6KYbwdRBFEV8lbMX7x9dArVVDKVNCKbeFrVwJG3ntx0qZDWSCDH9m/o4qXRU87VSYN3Ax\nHomewekhiYg6OYZwG/1TcAov/vEcDucehK3cFh5dPFGhKUeNToMaXTWqddWGzmYACHQKwuw+8/BA\n1EOwU9hJOHIiIjIXDOFrVFxVhBWHl+Hz0+uhF/W4LexO/HvoawhwCmz0OlEUoRN1qNZVQ6OrgbOt\nC2SCTKJRExGROWIIt5Je1OPLsxux/OASFFYVoqtrBF4b/iZGBY4x+npBEKAQFLVzNNs4mHi0RERk\nCRjCrXAi7xhe+ONZHMv7G/YKBywavBQzez/OOZmJiOi6MISbkafOw88pP+B/Sd/hz8x9ECFiYsTd\nWDx4GXwd/aQeHhERWQGGcAPZ5Vn4Mfl7/JD8PQ5mxxkaq27w7o+XBy3BUP/hEo+QiIisSacIYVEU\nUaWrQpW2EjWlpcgsyUe1thpV2kpU6apw7OLf+CH5O/x98QgAQICAAb6DcFvYHRgfdkeTpisiIqL2\nYLUhXK2rxqYzn+O942uQVZ7Z4uvlghzD/UdifPgdGB96O7wdfEwwSiIi6sysLoS1ei3+e34LVh1Z\ngczyDNgrHDDYbyi6yLugi8IOrg5OELQKdFHUPu6i6IJgpxCMC7kFHnYeUg+fiIg6EasJYb2ox3eJ\nO/DGkdeQVJwIW7ktHouZi3n9noGnnafhdVzwnoiIzIXFh7AoitiV+jNWHF6GM4XxUMgUmNJjGp65\n8Xn4OfpLPTwiIqJmWXQI56nzMPXn+/D3xaMQIOCebvfh+f4vIsQlVOqhERERtciiQzi1JAUn8o7j\ntrA7sWDAS4h07y71kIiIiFrNokN4gO9AZMzKr50akoiIyMJY/IoCDGAiIrJUFh/CREREloohTERE\nJBGGMBERkUQYwkRERBJpVVfTa6+9hpMnT0IQBCxcuBC9e/c2PLd582Z8//33kMlkiI6OxksvvdRh\ngyUiIrImLR4JHz58GGlpadi2bRuWL1+O5cuXG54rLy/H+vXrsXnzZmzZsgVJSUk4ceJEhw6YiIjI\nWrQYwgcOHEBsbCwAIDw8HCUlJSgvLwcA2NjYwMbGBmq1GlqtFpWVlXBxcenYERMREVmJFk9HFxQU\noGfPnobH7u7uyM/Ph6OjI2xtbTFnzhzExsbC1tYW48ePR2jo1aeMdHOzh0Ihv/6RXweVyknSr28K\n1l4j67Ns1l4fYP01sr72cc0zXYiiaPi4vLwca9euxc6dO+Ho6IipU6fi3Llz6N69+ekji4rUbRtp\nO+kMqyhZe42sz7JZe32A9dfI+tq2T2NaPB3t5eWFgoICw+O8vDyoVCoAQFJSEgIDA+Hu7g6lUokb\nb7wR8fHx7TRkIiIi69ZiCA8dOhS7du0CAJw+fRpeXl5wdHQEAPj7+yMpKQlVVVUAgPj4eISEhHTc\naImIiKyIIDY8v9yMVatW4ejRoxAEAYsXL8aZM2fg5OSEsWPHYuvWrdixYwfkcjn69u2L+fPnm2Lc\nREREFq9VIUxERETtjzNmERERSYQhTEREJBGGMBERkUQYwkRERBJhCBMREUnkmmfMMkfXusqTVqvF\nSy+9hPT0dOh0OsyfPx833ngjHnroIajVatjb2wMAFixYgOjoaKnKMrjW+nbs2IE1a9YgKCgIADBk\nyBA8/vjjOHfuHJYsWQIAiIyMxKuvvipFOUZda40ffvgh4uLiAAB6vR4FBQXYtWsXxowZAx8fH8jl\ntVOjrlq1Ct7e3pLU1NDV6tuzZw8+/PBDKJVKjB8/HpMnT252m5ycHMyfPx86nQ4qlQpvvvkmlEql\nVGUZtKW+N954A3///Te0Wi1mzZqFcePG4YUXXsDp06fh6uoKAJg+fTpGjRolRUmNXGt9hw4dwpNP\nPomIiAgAQLdu3fDKK6+Y7fsHXHuNX331Fb7//nvDa+Lj43H8+HGz/TmakJCA2bNn4+GHHzb8H6wX\nFxeH1atXQy6XY8SIEZgzZw4AE30Pihbu0KFD4syZM0VRFMXExERx0qRJhufKysrE0aNHixqNRhRF\nUXzkkUfE48ePi9u3bxcXL14siqIoJiQkiHfddZcoiqI4efJk8fz586YtoAVtqe/rr78WV6xY0WRf\nkydPFk+ePCmKoig+88wz4r59+0xQQcvaUmNDO3bsENetWyeKoiiOHj1aLC8vN9HIW+dq9el0OnHE\niBFiYWGhqNPpxGnTpok5OTnNbvPCCy+IP/30kyiKovjWW2+JmzdvNnE1TbWlvgMHDogzZswQRVEU\nL126JI4cOVIURVFcsGCB+Ntvv5m8hqtpS30HDx4Un3jiiSb7Msf3TxTbVuOV2y9ZskQURfP8OVpR\nUSFOnjxZfPnll8WNGzc2ef6WW24Rs7OzRZ1OJ95///3ihQsXTPY9aPGno9uyytMdd9yBF198EUDt\nghTFxcWSjb8l7bWKVU1NDbKysgy/3Y4ePRoHDhwwTREtuJ4atVottmzZ0uQ3W3NytfqKiorg7OwM\nd3d3yGQyDBo0CHFxcc1uc+jQIdx0000AzOc9bEt9/fv3x5o1awAAzs7OqKyshE6nk6yGq2lLfc0x\nx/cPuP4a33//fcyePdvk424tpVKJdevWwcvLq8lzGRkZcHFxga+vL2QyGUaOHIkDBw6Y7HvQ4kO4\noKAAbm5uhsf1qzwBaLTK0+jRoxETE4PQ0FDY2NjA1tYWALBhwwbcdttthu3feecdPPjgg1i0aJFh\nOk4ptaU+oHYd6OnTp2Pq1Kk4c+aM4RupnoeHh2E/UmtrjQCwe/duDBs2DF26dDF8bvHixbj//vux\natWqRguOSOVq9bm7u6OiogKpqanQaDQ4dOgQCgoKmt2msrLScOrLXN7DttQnl8sNpyu3b9+OESNG\nGC4hbNq0CVOmTMHTTz+NS5cumb6gK7SlPgBITEzEY489hvvvvx/79+8HALN8/4C21wgAp06dgq+v\nr2FNAcD8fo4qFIpGPyMays/Ph7u7u+Fxfe2m+h60imvCDYnXsMrT5s2bcfr0aXz00UcAgClTpiAy\nMhJBQUFYvHgxNm/ejOnTp0tSR3NaU19MTAzc3d0xatQoHD9+HAsWLMAnn3zS7H7MzbW8h19//XWj\na9vz5s3D8OHD4eLigjlz5mDXrl24+eabTV7D1TSsTxAErFixAgsXLoSTkxMCAgJa3OZqnzMH11Lf\nnj17sH37dnz66acAgDvvvBOurq6IiorCxx9/jPfeew+LFi0y6fhb0pr6QkJCMHfuXNxyyy3IyMjA\nlClTsHv37mb3Y26u5T3cvn07/u///s/w2BJ+jrZFR30PWvyRcFtXefrqq6/w22+/4YMPPoCNjQ0A\nYOzYsYZmpjFjxiAhIcHE1TTVlvrCw8MNzSx9+/bFpUuX4Obm1ui0+8WLF42empFCW99DtVqN3Nzc\nRj8UJkyYAA8PDygUCowYMcLs30MAGDBgAL788kusXbsWTk5O8Pf3b3Ybe3t7w5GFubyHbakPAP78\n80989NFHWLduHZycapd5Gzx4MKKiogBYxvcgYLw+b29v3HrrrRAEAUFBQfD09MTFixfN8v0D2v4e\nArWn2Pv27Wt4bI4/R6/mytrr3xdTfQ9afAi3ZZWnjIwMbN26Fe+9957htLQoinj44YdRWloKoPY/\nVn1no5TaUt+6devwww8/AKjtCKwPsLCwMBw9ehRA7Wnc4cOHS1BRU21dqevcuXMICwsz7KesrAzT\np09HTU0NAODIkSNm/x4CwIwZM1BYWAi1Wo29e/di8ODBzW4zZMgQw+fN5T1sS31lZWV44403sHbt\nWkMnNAA88cQTyMjIAGAZ34OA8fq+//57rF+/HkDt6c7CwkJ4e3ub5fsHtK1GoDaEHBwcDKdnzfXn\n6NUEBASgvLwcmZmZ0Gq12Lt3L4YOHWq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w9/fnoosuYsSIERW+RkSqx/db53H/z/ewu2AXHcM68cLAV+ga2d3sskSkAhWG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\nInLGFM5StxkG1h2ZZbcupZQdmk7FmpN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"text/plain": [ - "" + "" ] }, "metadata": { @@ -744,9 +745,33 @@ }, "cell_type": "code", "source": [ + "\n", "def linear_regression(learning_rate=0.000005, n_epochs=100, interval=50):\n", " # YOUR CODE HERE\n", - " pass" + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)\n", + " \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", + "\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()\n", + " \n", + " " ], "execution_count": 0, "outputs": [] @@ -755,28 +780,108 @@ "metadata": { "id": "A6MaclhK4rc6", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "outputId": "29bc28d7-3c33-4b8d-bb54-0bf1f6631781" }, "cell_type": "code", "source": [ "# Okay! Now let's tweak!\n", "linear_regression(learning_rate=0.000034, n_epochs=500)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 125, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.07239858\n", + "Loss after epoch 50 is 0.07207565\n", + "Loss after epoch 100 is 0.07175508\n", + "Loss after epoch 150 is 0.071436904\n", + "Loss after epoch 200 is 0.07112112\n", + "Loss after epoch 250 is 0.07080759\n", + "Loss after epoch 300 is 0.07049641\n", + "Loss after epoch 350 is 0.070187554\n", + "Loss after epoch 400 is 0.06988095\n", + "Loss after epoch 450 is 0.069576606\n", + "Now testing the model in the test set\n", + "The final loss is: 0.01730872\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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8eDAjR44s8zUiUvXyLfnM3TibNzfOoai4iMGt/saMa15VKIvUAmWG8/r169m/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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { "id": "peoHmV2M40uU", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "outputId": "85652bcd-50f5-4f01-d3bb-53a9b8c433fb" }, "cell_type": "code", "source": [ "linear_regression(learning_rate=0.0000006, n_epochs=1000)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 126, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.07239858\n", + "Loss after epoch 50 is 0.0723927\n", + "Loss after epoch 100 is 0.072386816\n", + "Loss after epoch 150 is 0.07238092\n", + "Loss after epoch 200 is 0.072375044\n", + "Loss after epoch 250 is 0.07236916\n", + "Loss after epoch 300 is 0.07236328\n", + "Loss after epoch 350 is 0.072357394\n", + "Loss after epoch 400 is 0.072351515\n", + "Loss after epoch 450 is 0.072345644\n", + "Loss after epoch 500 is 0.07233974\n", + "Loss after epoch 550 is 0.07233387\n", + "Loss after epoch 600 is 0.07232799\n", + "Loss after epoch 650 is 0.07232211\n", + "Loss after epoch 700 is 0.07231623\n", + "Loss after epoch 750 is 0.07231036\n", + "Loss after epoch 800 is 0.072304465\n", + "Loss after epoch 850 is 0.07229859\n", + "Loss after epoch 900 is 0.07229271\n", + "Loss after epoch 950 is 0.07228684\n", + "Now testing the model in the test set\n", + "The final loss is: 0.015346416\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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zPrdw4UJee+01/P39GTlyJKNHj670NSJSM77btoD7f7yHPUW76RLalRcGv0JS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\nHT4SPnrXtDUvt8Iyd8tWlA6/oOKGDxE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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] }, { "metadata": { @@ -792,14 +897,59 @@ "metadata": { "id": "JKiHjGN15HPX", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "outputId": "1ac0165b-88d3-4306-da91-e1c6cdba4c75" }, "cell_type": "code", "source": [ - "# YOUR CODE HERE" + "linear_regression(learning_rate=0.0575,n_epochs=1000,)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 127, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loss after epoch 0 is 0.07239858\n", + "Loss after epoch 50 is 0.015752953\n", + "Loss after epoch 100 is 0.0077023986\n", + "Loss after epoch 150 is 0.0037670603\n", + "Loss after epoch 200 is 0.0018433541\n", + "Loss after epoch 250 is 0.00090298854\n", + "Loss after epoch 300 is 0.00044331065\n", + "Loss after epoch 350 is 0.00021860642\n", + "Loss after epoch 400 is 0.000108763714\n", + "Loss after epoch 450 is 5.506931e-05\n", + "Loss after epoch 500 is 2.8821923e-05\n", + "Loss after epoch 550 is 1.5991303e-05\n", + "Loss after epoch 600 is 9.719664e-06\n", + "Loss after epoch 650 is 6.6537114e-06\n", + "Loss after epoch 700 is 5.155091e-06\n", + "Loss after epoch 750 is 4.4224894e-06\n", + "Loss after epoch 800 is 4.064544e-06\n", + "Loss after epoch 850 is 3.88946e-06\n", + "Loss after epoch 900 is 3.8038895e-06\n", + "Loss after epoch 950 is 3.7620573e-06\n", + "Now testing the model in the test set\n", + "The final loss is: 3.226303e-06\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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B79690Wq1NG/enI8//pg9e/aQk5PDhAkTcHV1ZeDAgYwcOZKoqChKlChBu3bt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\nAejXuj9JT76PxWbENHsmJL+LTqulZNJUrsXOgX9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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] } ] } \ No newline at end of file From 34ceed6f1dfb0a2975eeb603b98f91c375487b0d Mon Sep 17 00:00:00 2001 From: Arghadip Chakraborty Date: Mon, 15 Oct 2018 22:56:34 +0530 Subject: [PATCH 4/4] Update Assignment-4.ipynb --- Assignment-4.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Assignment-4.ipynb b/Assignment-4.ipynb index 4be19cf..3bc43af 100644 --- a/Assignment-4.ipynb +++ b/Assignment-4.ipynb @@ -905,7 +905,7 @@ }, "cell_type": "code", "source": [ - "linear_regression(learning_rate=0.0575,n_epochs=1000,)" + "linear_regression(learning_rate=0.0575,n_epochs=1000)" ], "execution_count": 127, "outputs": [ @@ -952,4 +952,4 @@ ] } ] -} \ No newline at end of file +}