Machine Learning with TensorFlow/Chapter 7. A peek into autoencoders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 7. A peek into autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2. AUTOENCODERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5, 6],\n",
       "       [4, 5, 6, 7],\n",
       "       [5, 6, 7, 8],\n",
       "       [6, 7, 8, 9]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,1,1,1],[2,2,2,2],[3,3,3,3],[4,4,4,4]]) + np.array([2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Autoencoder:\n",
    "    \n",
    "    def __init__(self, input_dim, hidden_dim, epoch=250, learning_rate=0.001):  \n",
    "        \"\"\"\n",
    "        The constructor will set up all the TensorFlow variables, placeholders, optimizers, and operators. \n",
    "        Anything that doesn’t immediately need a session can go in the constructor.\n",
    "        \"\"\"\n",
    "        self.epoch = epoch               \n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "        x = tf.placeholder(dtype=tf.float32, shape=[None, input_dim])      \n",
    "\n",
    "        with tf.name_scope('encode'):                        \n",
    "            weights = tf.Variable(tf.random_normal([input_dim, hidden_dim], dtype=tf.float32), name='weights')\n",
    "            biases = tf.Variable(tf.zeros([hidden_dim]), name='biases')\n",
    "            encoded = tf.nn.tanh(tf.matmul(x, weights) + biases)\n",
    "            \n",
    "        with tf.name_scope('decode'):                     \n",
    "            weights = tf.Variable(tf.random_normal([hidden_dim, input_dim], dtype=tf.float32), name='weights')\n",
    "            biases = tf.Variable(tf.zeros([input_dim]), name='biases')\n",
    "            decoded = tf.matmul(encoded, weights) + biases\n",
    "\n",
    "        self.x = x                                             \n",
    "        self.encoded = encoded  \n",
    "        self.decoded = decoded\n",
    "\n",
    "        self.loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(self.x, self.decoded))))\n",
    "        \n",
    "        self.train_op = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)\n",
    "        self.saver = tf.train.Saver()\n",
    "\n",
    "    def train(self, data):                        \n",
    "        num_samples = len(data)\n",
    "        with tf.Session() as sess:                                \n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            \n",
    "            for i in range(self.epoch):                           \n",
    "                for j in range(num_samples):\n",
    "                    l, _ = sess.run([self.loss, self.train_op],feed_dict={self.x: data[j]})   \n",
    "                    \n",
    "                if i % 10 == 0:                                   \n",
    "                    print('epoch {0}: loss = {1}'.format(i, l))   \n",
    "                    self.saver.save(sess, './model.ckpt') \n",
    "                    \n",
    "            self.saver.save(sess, './model.ckpt')               \n",
    "\n",
    "    def test(self, data):     \n",
    "        \n",
    "        with tf.Session() as sess:\n",
    "            self.saver.restore(sess, './model.ckpt')\n",
    "            hidden, reconstructed = sess.run([self.encoded, self.decoded], feed_dict={self.x: data})\n",
    "            \n",
    "        print('input', data)\n",
    "        print('compressed', hidden)\n",
    "        print('reconstructed', reconstructed)\n",
    "        print('reconstructed shape', reconstructed.shape)\n",
    "        \n",
    "        return reconstructed\n",
    "    \n",
    "    def classify(self, data, labels):\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            self.saver.restore(sess, './model.ckpt')\n",
    "            hidden, reconstructed = sess.run([self.encoded, self.decoded], feed_dict={self.x: data})\n",
    "            reconstructed = reconstructed[0]\n",
    "            \n",
    "            # loss = sess.run(self.all_loss, feed_dict={self.x: data})\n",
    "            print('data', np.shape(data))\n",
    "            print('reconstructed', np.shape(reconstructed))\n",
    "            \n",
    "            loss = np.sqrt(np.mean(np.square(data - reconstructed), axis=1))\n",
    "            print('loss', np.shape(loss))\n",
    "            \n",
    "            horse_indices = np.where(labels == 7)[0]\n",
    "            not_horse_indices = np.where(labels != 7)[0]\n",
    "            horse_loss = np.mean(loss[horse_indices])\n",
    "            not_horse_loss = np.mean(loss[not_horse_indices])\n",
    "            \n",
    "            print('horse', horse_loss)\n",
    "            print('not horse', not_horse_loss)\n",
    "            return hidden\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: loss = 3.84677791595459\n",
      "epoch 10: loss = 1.6354140043258667\n",
      "epoch 20: loss = 0.7696962356567383\n",
      "epoch 30: loss = 0.40451157093048096\n",
      "epoch 40: loss = 0.3429800868034363\n",
      "epoch 50: loss = 0.33716776967048645\n",
      "epoch 60: loss = 0.3327878713607788\n",
      "epoch 70: loss = 0.326534628868103\n",
      "epoch 80: loss = 0.3179718255996704\n",
      "epoch 90: loss = 0.3068365454673767\n",
      "epoch 100: loss = 0.2929355502128601\n",
      "epoch 110: loss = 0.2763429880142212\n",
      "epoch 120: loss = 0.25743523240089417\n",
      "epoch 130: loss = 0.23601295053958893\n",
      "epoch 140: loss = 0.2228296399116516\n",
      "epoch 150: loss = 0.21785002946853638\n",
      "epoch 160: loss = 0.22063545882701874\n",
      "epoch 170: loss = 0.23126961290836334\n",
      "epoch 180: loss = 0.24841967225074768\n",
      "epoch 190: loss = 0.2673497200012207\n",
      "epoch 200: loss = 0.277983695268631\n",
      "epoch 210: loss = 0.27683186531066895\n",
      "epoch 220: loss = 0.271180123090744\n",
      "epoch 230: loss = 0.2662600576877594\n",
      "epoch 240: loss = 0.2630375325679779\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "input [[8, 4, 6, 2]]\n",
      "compressed [[-0.9763079]]\n",
      "reconstructed [[6.3771873 2.8754098 5.205508  1.8144557]]\n",
      "reconstructed shape (1, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[6.3771873, 2.8754098, 5.205508 , 1.8144557]], dtype=float32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "hidden_dim = 1\n",
    "data = datasets.load_iris().data\n",
    "\n",
    "input_dim = len(data[0])\n",
    "\n",
    "ae = Autoencoder(input_dim, hidden_dim)\n",
    "\n",
    "ae.train(data)\n",
    "\n",
    "ae.test([[8, 4, 6, 2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the train function will output debug info about how the loss decreases over the epochs. The test function shows info about the encoding and decoding process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[8, 4, 6, 2]]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3. BATCH TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    " def get_batch(X, size):\n",
    "    a = np.random.choice(len(X), size, replace=False)\n",
    "    return X[a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoencoderBatch(Autoencoder):\n",
    "    def __init__(self, input_dim, hidden_dim, epoch=250, learning_rate=0.001):\n",
    "        super().__init__(input_dim, hidden_dim, epoch, learning_rate)\n",
    "        \n",
    "    def train(self, data, batch_size=10):\n",
    "        with tf.Session() as sess:\n",
    "            sess.run(tf.global_variables_initializer())\n",
    "            \n",
    "            for i in range(self.epoch):\n",
    "                for j in range(500):                                  \n",
    "                    batch_data = get_batch(data, batch_size)     \n",
    "                    l, _ = sess.run([self.loss, self.train_op], feed_dict={self.x: batch_data})\n",
    "    \n",
    "                if i % 10 == 0:\n",
    "                    print('epoch {0}: loss = {1}'.format(i, l))\n",
    "                    self.saver.save(sess, './model.ckpt')\n",
    "                    \n",
    "            self.saver.save(sess, './model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: loss = 2.6363024711608887\n",
      "epoch 10: loss = 1.0925068855285645\n",
      "epoch 20: loss = 0.9361787438392639\n",
      "epoch 30: loss = 1.206675410270691\n",
      "epoch 40: loss = 0.871934711933136\n",
      "epoch 50: loss = 1.0045976638793945\n",
      "epoch 60: loss = 1.1220272779464722\n",
      "epoch 70: loss = 0.9350913763046265\n",
      "epoch 80: loss = 0.845188319683075\n",
      "epoch 90: loss = 0.9002195596694946\n",
      "epoch 100: loss = 0.857448160648346\n",
      "epoch 110: loss = 0.8786964416503906\n",
      "epoch 120: loss = 0.9661978483200073\n",
      "epoch 130: loss = 1.1443853378295898\n",
      "epoch 140: loss = 0.9914859533309937\n",
      "epoch 150: loss = 0.8054789900779724\n",
      "epoch 160: loss = 0.9900503754615784\n",
      "epoch 170: loss = 1.0313115119934082\n",
      "epoch 180: loss = 1.0719255208969116\n",
      "epoch 190: loss = 1.1349983215332031\n",
      "epoch 200: loss = 1.1112463474273682\n",
      "epoch 210: loss = 1.00417160987854\n",
      "epoch 220: loss = 1.1164103746414185\n",
      "epoch 230: loss = 1.0040432214736938\n",
      "epoch 240: loss = 0.9177000522613525\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "input [[8, 4, 6, 2]]\n",
      "compressed [[-1.]]\n",
      "reconstructed [[5.86216   3.0520132 3.789989  1.2100282]]\n",
      "reconstructed shape (1, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[5.86216  , 3.0520132, 3.789989 , 1.2100282]], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ae = AutoencoderBatch(input_dim, hidden_dim)\n",
    "\n",
    "ae.train(data)\n",
    "\n",
    "ae.test([[8, 4, 6, 2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "as the batch size increases, the algorithm speeds up but has a lower likelihood of successfully converging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: loss = 2.4599976539611816\n",
      "epoch 10: loss = 0.6239177584648132\n",
      "epoch 20: loss = 0.3012721538543701\n",
      "epoch 30: loss = 0.30549901723861694\n",
      "epoch 40: loss = 0.28878578543663025\n",
      "epoch 50: loss = 0.28523725271224976\n",
      "epoch 60: loss = 0.298778772354126\n",
      "epoch 70: loss = 0.302583783864975\n",
      "epoch 80: loss = 0.2945929169654846\n",
      "epoch 90: loss = 0.29243049025535583\n",
      "epoch 100: loss = 0.2950311303138733\n",
      "epoch 110: loss = 0.2885851263999939\n",
      "epoch 120: loss = 0.2971469461917877\n",
      "epoch 130: loss = 0.29389315843582153\n",
      "epoch 140: loss = 0.27550527453422546\n",
      "epoch 150: loss = 0.2931085228919983\n",
      "epoch 160: loss = 0.2846793830394745\n",
      "epoch 170: loss = 0.29560670256614685\n",
      "epoch 180: loss = 0.28842514753341675\n",
      "epoch 190: loss = 0.29833874106407166\n",
      "epoch 200: loss = 0.3010043203830719\n",
      "epoch 210: loss = 0.2871169149875641\n",
      "epoch 220: loss = 0.2933899164199829\n",
      "epoch 230: loss = 0.2899399399757385\n",
      "epoch 240: loss = 0.28994420170783997\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "input [[8, 4, 6, 2]]\n",
      "compressed [[-0.39960805]]\n",
      "reconstructed [[6.9019117 2.8041794 6.286553  2.257371 ]]\n",
      "reconstructed shape (1, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[6.9019117, 2.8041794, 6.286553 , 2.257371 ]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ae = AutoencoderBatch(input_dim, hidden_dim)\n",
    "\n",
    "ae.train(data, batch_size=128)\n",
    "\n",
    "ae.test([[8, 4, 6, 2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.4. WORKING WITH IMAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hakan/.pyenv/versions/3.6.4/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/hakan/.pyenv/versions/3.6.4/lib/python3.6/site-packages/ipykernel_launcher.py:4: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "gray_image = imread(\"images/ordinal.jpg\", True)   \n",
    "\n",
    "small_gray_image = imresize(gray_image, 1. / 8.)    \n",
    "\n",
    "x = small_gray_image.flatten()                      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1056,)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "def unpickle(file):                             \n",
    "    fo = open(file, 'rb')\n",
    "    dict = pickle.load(fo, encoding='latin1')\n",
    "    fo.close()\n",
    "    return dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = unpickle('cifar-10-batches-py/batches.meta')['label_names']\n",
    "data, labels = [], []\n",
    "\n",
    "for i in range(1, 6):                                              \n",
    "    filename = 'cifar-10-batches-py/data_batch_' + str(i)\n",
    "    batch_data = unpickle(filename) \n",
    "    \n",
    "    if len(data) > 0:\n",
    "        data = np.vstack((data, batch_data['data']))               \n",
    "        labels = np.hstack((labels, batch_data['labels']))         \n",
    "    else:\n",
    "        data = batch_data['data']\n",
    "        labels = batch_data['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grayscale(a):\n",
    "    return a.reshape(a.shape[0], 3, 32, 32).mean(1).reshape(a.shape[0], -1)\n",
    "\n",
    "data = grayscale(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1024)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000,)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 61.33333333,  44.66666667,  47.        , ..., 180.        ,\n",
       "       117.66666667,  95.66666667])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 1024)\n",
      "epoch 0: loss = 48.07082748413086\n",
      "epoch 10: loss = 49.289833068847656\n",
      "epoch 20: loss = 43.97731399536133\n",
      "epoch 30: loss = 50.267086029052734\n",
      "epoch 40: loss = 46.43141555786133\n",
      "epoch 50: loss = 45.77651596069336\n",
      "epoch 60: loss = 46.649662017822266\n",
      "epoch 70: loss = 45.08884811401367\n",
      "epoch 80: loss = 52.94432830810547\n",
      "epoch 90: loss = 51.30448532104492\n",
      "epoch 100: loss = 44.68873596191406\n",
      "epoch 110: loss = 45.59528350830078\n",
      "epoch 120: loss = 46.70559310913086\n",
      "epoch 130: loss = 47.49958801269531\n",
      "epoch 140: loss = 44.44028091430664\n",
      "epoch 150: loss = 46.77077865600586\n",
      "epoch 160: loss = 47.154842376708984\n",
      "epoch 170: loss = 50.5965461730957\n",
      "epoch 180: loss = 48.68315887451172\n",
      "epoch 190: loss = 46.19215774536133\n",
      "epoch 200: loss = 46.20065689086914\n",
      "epoch 210: loss = 46.13994216918945\n",
      "epoch 220: loss = 47.15237045288086\n",
      "epoch 230: loss = 46.429935455322266\n",
      "epoch 240: loss = 48.550071716308594\n"
     ]
    }
   ],
   "source": [
    "x = np.matrix(data)\n",
    "y = np.array(labels)\n",
    "\n",
    "horse_indices = np.where(y == 7)[0]\n",
    "\n",
    "horse_x = x[horse_indices]\n",
    "\n",
    "print(np.shape(horse_x))  # (5000, 3072)\n",
    "\n",
    "input_dim = np.shape(horse_x)[1]\n",
    "hidden_dim = 100\n",
    "\n",
    "ae = Autoencoder(input_dim, hidden_dim)\n",
    "ae.train(horse_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 1024)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1024)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horse_x[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "data (10000, 1024)\n",
      "reconstructed (1024,)\n",
      "loss (10000,)\n",
      "horse 59.87780100313134\n",
      "not horse 59.3969919238845\n"
     ]
    }
   ],
   "source": [
    "test_data = unpickle('cifar-10-batches-py/test_batch')\n",
    "test_x = grayscale(test_data['data'])\n",
    "test_labels = np.array(test_data['labels'])\n",
    "\n",
    "encodings = ae.classify(test_x, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode(ae, encoding):\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        ae.saver.restore(sess, './model.ckpt')\n",
    "        reconstructed = sess.run(ae.decoded, feed_dict={ae.encoded: encoding})\n",
    "    img = np.reshape(reconstructed, (32, 32))\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n",
      "INFO:tensorflow:Restoring parameters from ./model.ckpt\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 7200x7200 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (100, 100)\n",
    "plt.figure()\n",
    "for i in range(20):\n",
    "    plt.subplot(20, 2, i*2 + 1)\n",
    "    original_img = np.reshape(test_x[i, :], (32, 32))\n",
    "    plt.imshow(original_img, cmap='Greys_r')\n",
    "    \n",
    "    plt.subplot(20, 2, i*2 + 2)\n",
    "    reconstructed_img = decode(ae, [encodings[i]])\n",
    "    plt.imshow(reconstructed_img, cmap='Greys_r')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}