6. AutoEncoders and Embeddings/6.1. AutoEncoders and Embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoEncoders  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An autoencoder, is an artificial neural network used for learning efficient codings. \n",
    "\n",
    "The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/autoencoder.png\" width=\"25%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference\n",
    "\n",
    "Based on [https://blog.keras.io/building-autoencoders-in-keras.html](https://blog.keras.io/building-autoencoders-in-keras.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing _Keras Functional API_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Keras functional API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the Functional API relies on the fact that each `keras.Layer` object is a _callable_ object!\n",
    "\n",
    "See [8.2 Multi-Modal Networks](../8. Extra/8.2 Multi-Modal Networks.ipynb) for further details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "from keras.datasets import mnist\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this is the size of our encoded representations\n",
    "encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats\n",
    "\n",
    "# this is our input placeholder\n",
    "input_img = Input(shape=(784,))\n",
    "# \"encoded\" is the encoded representation of the input\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "\n",
    "# \"decoded\" is the lossy reconstruction of the input\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "# this model maps an input to its reconstruction\n",
    "autoencoder = Model(input_img, decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this model maps an input to its encoded representation\n",
    "encoder = Model(input_img, encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a placeholder for an encoded (32-dimensional) input\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "# retrieve the last layer of the autoencoder model\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "# create the decoder model\n",
    "decoder = Model(encoded_input, decoder_layer(encoded_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "(x_train, _), (x_test, _) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.3830 - val_loss: 0.2731\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2664 - val_loss: 0.2561\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2463 - val_loss: 0.2336\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2258 - val_loss: 0.2156\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.2105 - val_loss: 0.2030\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1997 - val_loss: 0.1936\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1914 - val_loss: 0.1863\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1846 - val_loss: 0.1800\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1789 - val_loss: 0.1749\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1740 - val_loss: 0.1702\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1697 - val_loss: 0.1660\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1657 - val_loss: 0.1622\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1620 - val_loss: 0.1587\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1586 - val_loss: 0.1554\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1554 - val_loss: 0.1524\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1525 - val_loss: 0.1495\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1497 - val_loss: 0.1468\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1470 - val_loss: 0.1441\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1444 - val_loss: 0.1415\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1419 - val_loss: 0.1391\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1395 - val_loss: 0.1367\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1371 - val_loss: 0.1345\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1349 - val_loss: 0.1323ss: 0.13\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1328 - val_loss: 0.1302\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1308 - val_loss: 0.1283\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1289 - val_loss: 0.1264\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1271 - val_loss: 0.1247\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1254 - val_loss: 0.1230\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1238 - val_loss: 0.1215\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1223 - val_loss: 0.1200\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1208 - val_loss: 0.1186\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1195 - val_loss: 0.1172\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1182 - val_loss: 0.1160\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1170 - val_loss: 0.1149\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1158 - val_loss: 0.1137\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1148 - val_loss: 0.1127\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1138 - val_loss: 0.1117\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1129 - val_loss: 0.1109\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1120 - val_loss: 0.1100\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1112 - val_loss: 0.1093\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1105 - val_loss: 0.1085\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1098 - val_loss: 0.1079\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1092 - val_loss: 0.1072\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1086 - val_loss: 0.1066\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1080 - val_loss: 0.1061\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1074 - val_loss: 0.1056\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1069 - val_loss: 0.1051\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1065 - val_loss: 0.1046\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1060 - val_loss: 0.1042\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 1s - loss: 0.1056 - val_loss: 0.1037\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd1ce5140f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#note: x_train, x_train :) \n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing the Autoencoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd228387c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)\n",
    "\n",
    "n = 10 \n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample generation with Autoencoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ke6Pkr6pP17fdM+WP/uHUQ25H+gd/8AfH/d6qqrvvvrsdWxbH+7PfH3300e46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\n21IS3pOpfq+++mp3HdOoL7r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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1ce6779e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoded_imgs = np.random.rand(10,32)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)\n",
    "\n",
    "n = 10 \n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # generation\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Convolutional AutoEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our inputs are images, it makes sense to use convolutional neural networks (`convnets`) as encoders and decoders. \n",
    "\n",
    "In practical settings, autoencoders applied to images are always convolutional autoencoders --they simply perform much better.\n",
    "\n",
    "The encoder will consist in a stack of `Conv2D` and `MaxPooling2D` layers (max pooling being used for spatial down-sampling), while the decoder will consist in a stack of `Conv2D` and `UpSampling2D` layers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "\n",
    "input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
    "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
    "\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(16, (3, 3), activation='relu')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "conv_autoencoder = Model(input_img, decoded)\n",
    "conv_autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "if K.image_data_format() == 'channels_last':\n",
    "    shape_ord = (28, 28, 1)\n",
    "else:\n",
    "    shape_ord = (1, 28, 28)\n",
    "    \n",
    "(x_train, _), (x_test, _) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "\n",
    "x_train = np.reshape(x_train, ((x_train.shape[0],) + shape_ord))  \n",
    "x_test = np.reshape(x_test, ((x_test.shape[0],) + shape_ord)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 8s - loss: 0.2327 - val_loss: 0.1740\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1645 - val_loss: 0.1551\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1501 - val_loss: 0.1442\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1404 - val_loss: 0.1375\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1342 - val_loss: 0.1316\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1300 - val_loss: 0.1298\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1272 - val_loss: 0.1301\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1243 - val_loss: 0.1221\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1222 - val_loss: 0.1196\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1207 - val_loss: 0.1184\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1188 - val_loss: 0.1162\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1175 - val_loss: 0.1160\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1167 - val_loss: 0.1164\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1154 - val_loss: 0.1160\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1145 - val_loss: 0.1159\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1132 - val_loss: 0.1110\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1127 - val_loss: 0.1108\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1118 - val_loss: 0.1099\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1113 - val_loss: 0.1106\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1108 - val_loss: 0.1120\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1104 - val_loss: 0.1064\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1094 - val_loss: 0.1075\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1088 - val_loss: 0.1088\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1085 - val_loss: 0.1071\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1081 - val_loss: 0.1060\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1075 - val_loss: 0.1062\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1074 - val_loss: 0.1062\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1065 - val_loss: 0.1045\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1062 - val_loss: 0.1043\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1057 - val_loss: 0.1038\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1053 - val_loss: 0.1040\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1048 - val_loss: 0.1041\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1045 - val_loss: 0.1057\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1041 - val_loss: 0.1026\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1041 - val_loss: 0.1042\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1035 - val_loss: 0.1053\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1032 - val_loss: 0.1006\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1030 - val_loss: 0.1011\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1028 - val_loss: 0.1013\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1027 - val_loss: 0.1018\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1025 - val_loss: 0.1019\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1024 - val_loss: 0.1025\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1020 - val_loss: 0.1015\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1020 - val_loss: 0.1018\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1015 - val_loss: 0.1011\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1013 - val_loss: 0.0999\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1010 - val_loss: 0.0995\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1008 - val_loss: 0.0996\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1008 - val_loss: 0.0990\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 7s - loss: 0.1006 - val_loss: 0.0995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd1bebacfd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size=128\n",
    "steps_per_epoch = np.int(np.floor(x_train.shape[0] / batch_size))\n",
    "conv_autoencoder.fit(x_train, x_train, epochs=50, batch_size=128,\n",
    "                     shuffle=True, validation_data=(x_test, x_test),\n",
    "                     callbacks=[TensorBoard(log_dir='./tf_autoencoder_logs')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1beaf56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decoded_imgs = conv_autoencoder.predict(x_test)\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + n + 1)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We coudl also have a look at the `128-`dimensional encoded middle representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AQBIjCQAAAEASIwkAAABAEiMJAAAAQJJksPLgqampbN++vXzIxRdf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1be13ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conv_encoder = Model(input_img, encoded)\n",
    "encoded_imgs = conv_encoder.predict(x_test)\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 8))\n",
    "for i in range(n):\n",
    "    ax = plt.subplot(1, n, i+1)\n",
    "    plt.imshow(encoded_imgs[i].reshape(4, 4 * 8).T)\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretraining encoders "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the powerful tools of auto-encoders is using the encoder to generate meaningful representation from the feature vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the encoder to pretrain a classifier "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application to Image Denoising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put our convolutional autoencoder to work on an image denoising problem. It's simple: we will train the autoencoder to map noisy digits images to clean digits images.\n",
    "\n",
    "Here's how we will generate synthetic noisy digits: we just apply a gaussian noise matrix and clip the images between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "\n",
    "(x_train, _), (x_test, _) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format\n",
    "x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "noise_factor = 0.5\n",
    "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
    "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
    "\n",
    "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
    "x_test_noisy = np.clip(x_test_noisy, 0., 1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how the noisy digits look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z2WaVYZtzINoR1pv2punSbHmt9o133313Xe/ZFHr06JE+k61IgZiOz/IpIKYi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb45b508630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 10\n",
    "plt.figure(figsize=(20, 2))\n",
    "for i in range(n):\n",
    "    ax = plt.subplot(1, n, i+1)\n",
    "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question\n",
    "\n",
    "If you squint you can still recognize them, but barely. \n",
    "\n",
    "**Can our autoencoder learn to recover the original digits? Let's find out.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the previous convolutional autoencoder, in order to improve the quality of the reconstructed, we'll use a slightly different model with more filters per layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "\n",
    "from keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "# at this point the representation is (7, 7, 32)\n",
    "\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train the AutoEncoder for `100` epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 9s - loss: 0.1901 - val_loss: 0.1255\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1214 - val_loss: 0.1142\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1135 - val_loss: 0.1085\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1094 - val_loss: 0.1074\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1071 - val_loss: 0.1052\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1053 - val_loss: 0.1046\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1040 - val_loss: 0.1020\n",
      "Epoch 8/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1031 - val_loss: 0.1028\n",
      "Epoch 9/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1023 - val_loss: 0.1009\n",
      "Epoch 10/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1017 - val_loss: 0.1005\n",
      "Epoch 11/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1009 - val_loss: 0.1003\n",
      "Epoch 12/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1007 - val_loss: 0.1010\n",
      "Epoch 13/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1002 - val_loss: 0.0989\n",
      "Epoch 14/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.1000 - val_loss: 0.0986\n",
      "Epoch 15/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0998 - val_loss: 0.0983\n",
      "Epoch 16/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0993 - val_loss: 0.0983\n",
      "Epoch 17/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0991 - val_loss: 0.0979\n",
      "Epoch 18/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0988 - val_loss: 0.0988\n",
      "Epoch 19/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0986 - val_loss: 0.0976\n",
      "Epoch 20/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0984 - val_loss: 0.0987\n",
      "Epoch 21/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0983 - val_loss: 0.0973\n",
      "Epoch 22/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0981 - val_loss: 0.0971\n",
      "Epoch 23/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0979 - val_loss: 0.0978\n",
      "Epoch 24/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0977 - val_loss: 0.0968\n",
      "Epoch 25/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0975 - val_loss: 0.0976\n",
      "Epoch 26/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0974 - val_loss: 0.0963\n",
      "Epoch 27/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0973 - val_loss: 0.0963\n",
      "Epoch 28/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0972 - val_loss: 0.0964\n",
      "Epoch 29/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0970 - val_loss: 0.0961\n",
      "Epoch 30/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0970 - val_loss: 0.0968\n",
      "Epoch 31/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0969 - val_loss: 0.0959\n",
      "Epoch 32/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0968 - val_loss: 0.0959\n",
      "Epoch 33/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0967 - val_loss: 0.0957\n",
      "Epoch 34/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0966 - val_loss: 0.0958\n",
      "Epoch 35/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0965 - val_loss: 0.0956\n",
      "Epoch 36/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0965 - val_loss: 0.0959\n",
      "Epoch 37/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0964 - val_loss: 0.0963\n",
      "Epoch 38/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0963 - val_loss: 0.0960\n",
      "Epoch 39/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0963 - val_loss: 0.0963\n",
      "Epoch 40/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0962 - val_loss: 0.0954\n",
      "Epoch 41/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0961 - val_loss: 0.0955\n",
      "Epoch 42/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0960 - val_loss: 0.0953\n",
      "Epoch 43/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0960 - val_loss: 0.0952\n",
      "Epoch 44/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0960 - val_loss: 0.0951\n",
      "Epoch 45/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0959 - val_loss: 0.0951\n",
      "Epoch 46/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0958 - val_loss: 0.0953\n",
      "Epoch 47/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0957 - val_loss: 0.0952\n",
      "Epoch 48/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0957 - val_loss: 0.0954\n",
      "Epoch 49/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0957 - val_loss: 0.0954\n",
      "Epoch 50/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0957 - val_loss: 0.0954\n",
      "Epoch 51/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0955 - val_loss: 0.0948\n",
      "Epoch 52/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0956 - val_loss: 0.0951\n",
      "Epoch 53/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0955 - val_loss: 0.0951\n",
      "Epoch 54/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0955 - val_loss: 0.0951\n",
      "Epoch 55/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0955 - val_loss: 0.0948\n",
      "Epoch 56/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0954 - val_loss: 0.0955\n",
      "Epoch 57/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0954 - val_loss: 0.0950\n",
      "Epoch 58/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0953 - val_loss: 0.0955\n",
      "Epoch 59/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0952 - val_loss: 0.0947\n",
      "Epoch 60/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0953 - val_loss: 0.0947\n",
      "Epoch 61/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0952 - val_loss: 0.0947\n",
      "Epoch 62/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0952 - val_loss: 0.0945\n",
      "Epoch 63/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0952 - val_loss: 0.0945\n",
      "Epoch 64/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0952 - val_loss: 0.0945\n",
      "Epoch 65/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0950 - val_loss: 0.0954\n",
      "Epoch 66/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0951 - val_loss: 0.0945\n",
      "Epoch 67/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0951 - val_loss: 0.0946\n",
      "Epoch 68/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0950 - val_loss: 0.0951\n",
      "Epoch 69/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0950 - val_loss: 0.0952\n",
      "Epoch 70/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0949 - val_loss: 0.0948\n",
      "Epoch 71/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0949 - val_loss: 0.0958\n",
      "Epoch 72/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0949 - val_loss: 0.0953\n",
      "Epoch 73/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0949 - val_loss: 0.0942\n",
      "Epoch 74/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0948 - val_loss: 0.0946\n",
      "Epoch 75/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0948 - val_loss: 0.0942\n",
      "Epoch 76/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0948 - val_loss: 0.0945\n",
      "Epoch 77/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0948 - val_loss: 0.0944\n",
      "Epoch 78/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0948 - val_loss: 0.0942\n",
      "Epoch 79/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0947 - val_loss: 0.0944\n",
      "Epoch 80/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0947 - val_loss: 0.0942\n",
      "Epoch 81/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0946 - val_loss: 0.0943\n",
      "Epoch 82/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0946 - val_loss: 0.0942\n",
      "Epoch 83/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0946 - val_loss: 0.0941\n",
      "Epoch 84/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0947 - val_loss: 0.0940\n",
      "Epoch 85/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 8s - loss: 0.0946 - val_loss: 0.0941\n",
      "Epoch 86/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0945 - val_loss: 0.0941\n",
      "Epoch 87/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0946 - val_loss: 0.0945\n",
      "Epoch 88/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0945 - val_loss: 0.0944\n",
      "Epoch 89/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0945 - val_loss: 0.0944\n",
      "Epoch 90/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0945 - val_loss: 0.0941\n",
      "Epoch 91/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0945 - val_loss: 0.0939\n",
      "Epoch 92/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0946\n",
      "Epoch 93/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0941\n",
      "Epoch 94/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0939\n",
      "Epoch 95/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0941\n",
      "Epoch 96/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0939\n",
      "Epoch 97/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0939\n",
      "Epoch 98/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0943 - val_loss: 0.0939\n",
      "Epoch 99/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0944 - val_loss: 0.0941\n",
      "Epoch 100/100\n",
      "60000/60000 [==============================] - 8s - loss: 0.0943 - val_loss: 0.0938\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb45ad95f28>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train_noisy, x_train,\n",
    "                epochs=100,\n",
    "                batch_size=128,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test_noisy, x_test),\n",
    "                callbacks=[TensorBoard(log_dir='/tmp/autoencoder_denoise', \n",
    "                                       histogram_freq=0, write_graph=False)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now Let's Take a look...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb459cdee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decoded_imgs = autoencoder.predict(x_test_noisy)\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + n + 1)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variational AutoEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_(Reference [https://blog.keras.io/building-autoencoders-in-keras.html](https://blog.keras.io/building-autoencoders-in-keras.html))_\n",
    "\n",
    "Variational autoencoders are a slightly more modern and interesting take on autoencoding.\n",
    "\n",
    "### What is a variational autoencoder ? \n",
    "\n",
    "It's a type of autoencoder with added constraints on the encoded representations being learned. \n",
    "\n",
    "More precisely, it is an autoencoder that learns a [latent variable model](https://en.wikipedia.org/wiki/Latent_variable_model) for its input data. \n",
    "\n",
    "So instead of letting your neural network learn an arbitrary function, you are learning the parameters of a probability distribution modeling your data. \n",
    "\n",
    "If you sample points from this distribution, you can generate new input data samples: \n",
    "a **VAE** is a **\"generative model\"**.\n",
    "\n",
    "### How does a variational autoencoder work?\n",
    "\n",
    "First, an encoder network turns the input samples $x$ into two parameters in a latent space, which we will note $z_{\\mu}$ and $z_{log_{\\sigma}}$. \n",
    "\n",
    "Then, we randomly sample similar points $z$ from the _latent normal distribution_ that is assumed to generate the data, via $z = z_{\\mu} + \\exp(z_{log_{\\sigma}}) * \\epsilon$, where $\\epsilon$ is a random normal tensor. \n",
    "\n",
    "Finally, a decoder network maps these latent space points back to the original input data.\n",
    "\n",
    "The parameters of the model are trained via two loss functions: \n",
    "\n",
    "* a **reconstruction loss** forcing the decoded samples to match the initial inputs (just like in our previous autoencoders);\n",
    "* and the **KL divergence** between the learned latent distribution and the prior distribution, acting as a regularization term. \n",
    "\n",
    "You could actually get rid of this latter term entirely, although it does help in learning well-formed latent spaces and reducing overfitting to the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "original_dim = 784\n",
    "latent_dim = 2\n",
    "intermediate_dim = 256\n",
    "epochs = 50\n",
    "epsilon_std = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = Input(batch_shape=(batch_size, original_dim))\n",
    "h = Dense(intermediate_dim, activation='relu')(x)\n",
    "z_mean = Dense(latent_dim)(h)\n",
    "z_log_sigma = Dense(latent_dim)(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use these parameters to sample new similar points from the latent space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers.core import Lambda\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sampling(args):\n",
    "    z_mean, z_log_sigma = args\n",
    "    epsilon = K.random_normal(shape=(batch_size, latent_dim),\n",
    "                              mean=0., stddev=epsilon_std)\n",
    "    return z_mean + K.exp(z_log_sigma) * epsilon\n",
    "\n",
    "# note that \"output_shape\" isn't necessary with the TensorFlow backend\n",
    "# so you could write `Lambda(sampling)([z_mean, z_log_sigma])`\n",
    "z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_sigma])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoder Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can map these sampled latent points back to reconstructed inputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "decoder_h = Dense(intermediate_dim, activation='relu')\n",
    "decoder_mean = Dense(original_dim, activation='sigmoid')\n",
    "h_decoded = decoder_h(z)\n",
    "x_decoded_mean = decoder_mean(h_decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done so far allows us to instantiate 3 models:\n",
    "\n",
    "- an end-to-end autoencoder mapping inputs to reconstructions\n",
    "- an encoder mapping inputs to the latent space\n",
    "- a generator that can take points on the latent space and will output the corresponding reconstructed samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# end-to-end autoencoder\n",
    "vae = Model(x, x_decoded_mean)\n",
    "\n",
    "# encoder, from inputs to latent space\n",
    "encoder = Model(x, z_mean)\n",
    "\n",
    "# generator, from latent space to reconstructed inputs\n",
    "decoder_input = Input(shape=(latent_dim,))\n",
    "_h_decoded = decoder_h(decoder_input)\n",
    "_x_decoded_mean = decoder_mean(_h_decoded)\n",
    "generator = Model(decoder_input, _x_decoded_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's Visualise the VAE Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"410pt\" viewBox=\"0.00 0.00 230.00 410.00\" width=\"230pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(vae).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Exercise: Let's Do the Same for `encoder` and `generator` Model(s)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAE on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train the model using the end-to-end model, with a custom loss function: the sum of a reconstruction term, and the KL divergence regularization term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.objectives import binary_crossentropy\n",
    "\n",
    "def vae_loss(x, x_decoded_mean):\n",
    "    xent_loss = binary_crossentropy(x, x_decoded_mean)\n",
    "    kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1)\n",
    "    return xent_loss + kl_loss\n",
    "\n",
    "vae.compile(optimizer='rmsprop', loss=vae_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traing on MNIST Digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2932 - val_loss: 0.2629\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2631 - val_loss: 0.2628\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2630 - val_loss: 0.2626\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2630 - val_loss: 0.2629\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2630 - val_loss: 0.2627\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2630 - val_loss: 0.2627\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2630 - val_loss: 0.2626\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2625\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2625\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2625\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2625\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2625\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2627\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2625\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2629 - val_loss: 0.2626\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb62fc26d30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "\n",
    "vae.fit(x_train, x_train,\n",
    "        shuffle=True,\n",
    "        epochs=epochs,\n",
    "        batch_size=batch_size,\n",
    "        validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our latent space is two-dimensional, there are a few cool visualizations that can be done at this point. \n",
    "\n",
    "One is to look at the neighborhoods of different classes on the latent 2D plane:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_test_encoded = encoder.predict(x_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1T17zrnc52JJ9xcYomfe+s2ioOb5XckBdHWePHV2BiCSKQq6HPsLMmjLK/OOu\nb5Yws+XADuApd38xVyxhE/oodz/yu+c2YFSWOmOBzN+xm4N93Z0/Kehu+Z2ZfShknFIBAxvqs+5P\nVFVRW5MoczQ9d/6kCXz5gvdTm0jQv7aG/rW1jOjfjx9deTlVWoRMoBirLe5y98aMsvC4r3BPuvs0\nYBwww8xyjsh3OyhqZk8DJ2Y5dFOnL3WzwocHOp2/FZjg7rvN7H3AL8zsDHfflyW++cB8gAkTJhT6\n9VICV150Dnf+6nla2v7UD11Xk2DWjNOpSUQ/oQNc/+Hz+Oz0M/nDpmYGN9Rz7sTxVFfp8Q3JUKZZ\nLu7+rpk9A8wEVmWr021Cd/dLch0zs+1mNtrdt5rZaNK/EnS2BRifsT0u2AeQ9Xx3byU9GIC7LzOz\nDcCpQFOW+BYCCwEaGxsjNIFIrrpoOpt3vsui51dTW5OgrSPJeaefxF9fcWGlQ+uREQP6M/uM0yod\nhvRBZjYSaA+SeQNwKfCdXPXDTltcBFwD3Br8/GWWOi8Bk81sEulEPhe4sqvzg5vY4+5JMzsZmAxs\nDBmrlFlVlfF/5l3MdR87nze372H08EGMHjao0mGJFI1R8geLRgP3BpNLqoCH3D3ny3vDJvRbgYfM\n7FpgE3AFgJmNAX7k7rPdvcPMbgCWAAngHndf3dX5wIeBW8ysHUgB17n7npCxSoUMG9SPYYP6VToM\nkdIoYUJ39xXAOfnWD5XQ3X03cHGW/W8DszO2FwOLe3D+I8AjYWITESkH8+j09OpJURGRQpXo3aCF\n0nC9iEhMqIUuIhKCVlsUEYkLJXQRkXhQC11EJC4ilNA1KCoiEhNqoYuIFEqvoBMRiREldBGR3q8M\na7n0iPrQRURiQi10EZEwtJaLiEg8RKnLRQldRKRQEVucSwldRCQES1U6gj/RoKiISEyohS4iEoa6\nXERE4kGDoiIiceBo2qKISFxEqYWuQVERkZhQC11EJIwItdCV0EVEChS1xbmU0EVECuUeqUFR9aGL\niMSEWugiIiFEqctFLXQRkTA8ROmGmY03s2fM7FUzW21mX++qvlroIiIhlLiF3gH8lbu/bGYDgWVm\n9pS7v5qtshK6iEihHEiVLqO7+1Zga/B5v5mtAcYCSugiIhEzwsyaMrYXuvvCbBXNbCJwDvBirosp\noYuIhBGugb7L3Ru7q2RmA4BHgG+4+75c9ZTQRURCKPUsFzOrIZ3Mf+buj3ZVVwldRCSMEj5YZGYG\n3A2scffdqmISAAAIFklEQVR/6q6+pi2KiIRgXnjJwweAzwMXmdnyoMzOVVktdBGRiHL335NeMiYv\nSugiIoXK8wGhclFCFxEpUHq1xehkdCV0EZEwUpUO4E80KCoiEhNqoYuIhKAuFxGRONCgqIhIXMTo\njUVmNszMnjKzdcHPoTnqzTSztWa23swWZOz/TLDGb8rMGjud882g/loz+2iYOEVESqXEDxb1SNhB\n0QXAUnefDCwNto9hZgngDmAWMAWYZ2ZTgsOrgE8Cz3U6ZwowFzgDmAn8ILiOiIjkEDahzwHuDT7f\nC1yWpc4MYL27b3T3NuCB4DzcfY27r81x3QfcvdXd3wDWB9cREYmWIy+KLqQUWdiEPipYgB1gGzAq\nS52xwOaM7eZgX1fyPsfM5ptZk5k17dy5M7+oRUSKwcFShZdi63ZQ1MyeBk7McuimzA13d7Pyvy41\nWAx+IUBjY2N0RidEpG+I0KBotwnd3S/JdczMtpvZaHffamajgR1Zqm0Bxmdsjwv2daWQc0RE+rSw\nXS6LgGuCz9cAv8xS5yVgsplNMrNa0oOdi/K47lwzqzOzScBk4A8hYxURKT4PUYosbEK/FbjUzNYB\nlwTbmNkYM1sM4O4dwA3AEmAN8JC7rw7qXW5mzcD5wBNmtiQ4ZzXwEOkXoT4JXO/uyZCxiogUnbkX\nXIot1INF7r4buDjL/reB2Rnbi4HFWeo9BjyW49rfAr4VJj4RkZLrTX3oIiKSg6PVFkVEpPjUQhcR\nKZBRmr7wQimhi4iEoYQuIhITSugiIjGgQVERESkFtdBFRELQoKiISFwooYuIxEGMXkEnIiLRoYQu\nIlIop6RvLDKze8xsh5mtyiccJXQRkTBSIUr3fkz6vcp5UR+6iEgIpZzl4u7PmdnEfOsroYuIhBGh\nQVEldBGRyhlhZk0Z2wuD9yQXRAldRKRQDqRCtdB3uXtjkaJRQhcRKZzmoYuIxEdppy3eD/w3cJqZ\nNZvZtV3VVwtdRCSM0s5ymdeT+mqhi4jEhFroIiKFCj8oWlRK6CIiBXPw6LzhQgldRCQMzXIREZFi\nUwtdRKRQ6kMXEYmRCHW5KKGLiIShhC4iEgd69F9EREpALXQRkUI5kNI8dBGReIhQl4sSuohIGEro\nIiJx4JGah65BURGRmFALXUSkUA6uxblERGIiQl0uSugiImFEaFBUfegiIjGhFrqISKHc9WCRiEhs\nRKjLRQldRCQEj1ALPVQfupkNM7OnzGxd8HNojnozzWytma03swUZ+z9jZqvNLGVmjRn7J5rZYTNb\nHpS7wsQpIlIawWqLhZYiCzsougBY6u6TgaXB9jHMLAHcAcwCpgDzzGxKcHgV8EnguSzX3uDu04Jy\nXcg4RURiL2xCnwPcG3y+F7gsS50ZwHp33+jubcADwXm4+xp3XxsyBhGRyjjyCrpCS5GFTeij3H1r\n8HkbMCpLnbHA5ozt5mBfdyYF3S2/M7MP5apkZvPNrMnMmnbu3Jl34CIiReGpwkuRdTsoamZPAydm\nOXRT5oa7u5kV65+crcAEd99tZu8DfmFmZ7j7vs4V3X0hsBCgsbExOsPNIhJ7DnhvelLU3S/JdczM\ntpvZaHffamajgR1Zqm0Bxmdsjwv2dfWdrUBr8HmZmW0ATgWauotXRKRs3EvS0s5kZjOBfwUSwI/c\n/dZcdcN2uSwCrgk+XwP8Mkudl4DJZjbJzGqBucF5OZnZyGAwFTM7GZgMbAwZq4hIr9LNpJLjhE3o\ntwKXmtk64JJgGzMbY2aLAdy9A7gBWAKsAR5y99VBvcvNrBk4H3jCzJYE1/0wsMLMlgMPA9e5+56Q\nsYqIFJ2nvOCSh5yTSrIJ9WCRu+8GLs6y/21gdsb2YmBxlnqPAY9l2f8I8EiY2EREyqK0XS7ZJpWc\nm6tyrJ4UXbZs2S4z21TpOEIYAeyqdBBloPuMl956nyeFvcB+3lnytD88IsQl6s0sc2xwYTDRoyCx\nSujuPrLSMYRhZk3u3th9zd5N9xkvfeU+s3H3mSX+ih5NKtHyuSIi0dWjSSWxaqGLiMSJu3eY2ZFJ\nJQngniOTSrJRQo+WgvvOehndZ7z0lfusiFyTSrIxj9BaviIiUjj1oYuIxIQSepmVag35KMgVc8Zx\nM7Pbg+MrzGx6vudGScj7vMfMdpjZqvJG3XOF3qeZjTezZ8zs1eDP6tfLH30f5e4qZSzAbcCC4PMC\n4DtZ6iSADcDJQC3wR2BKcOx04DTgWaCx0veTT8wZdWYDvwYMOA94Md9zo1LC3Gdw7MPAdGBVpe+l\nhP8/RwPTg88Dgdej+v8zbkUt9PKL6xry+TyiPAe4z9NeAIYEi7r16PHmCgtzn7j7c0BvWMai4Pt0\n963u/jKAu+8nveRHPktmS0hK6OVXyjXkKymfmHPV6U33G+Y+e5Oi3KeZTQTOAV4seoRyHE1bLIEK\nrSEvEilmNoD0mkzf8CzvMpDiU0IvAa/AGvIRkE/MuerU5HFuVIS5z94k1H2aWQ3pZP4zd3+0hHFK\nBnW5lF9J1pCPgHxiXgRcHcyOOA/YG3Q/9ab7DXOfvUnB92lmBtwNrHH3fypv2H1cpUdl+1oBhgNL\ngXXA08CwYP8YYHFGvdmkZwdsAG7K2H856b7KVmA7sKTS99RVzMB1pNezh/RsiDuC4yvJmKWT636j\nWELe5/2kX7HYHvx/vLbS91Ps+wQ+SPrtbCuA5UGZXen76QtFT4qKiMSEulxERGJCCV1EJCaU0EVE\nYkIJXUQkJpTQRURiQgldRCQmlNBFRGJCCV1EJCb+P1ZkO1CwNfBcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb60c7772e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of these colored clusters is a type of digit. Close clusters are digits that are structurally similar (i.e. digits that share information in the latent space).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the VAE is a generative model, we can also use it to generate new digits! Here we will scan the latent plane, sampling latent points at regular intervals, and generating the corresponding digit for each of these points. This gives us a visualization of the latent manifold that \"generates\" the MNIST digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s8cIvChPTtsHfmPP965/wD/u/B2Ddn3CQtvlwfEF+ZabxomW9EnD+5MT3MLVl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ezn/QWeP7t/G/2V8AD07s+JHw/gWGpRzGS6NI9bfMxsjh6MSA6bNFZts84VzXQc+aL7ROdo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\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lA77O46QcJDcFN1QUbSCu0Krm0cfTK5wcCq9YalS7ZLohBhGoiiQomCxaBHPB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\nwzHyg/vo4F7yidKBVem5X0P3n+V4+cC9dHAv+cTpAEBEzgIvAf/71MuPpR8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb60c6b3438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display a 2D manifold of the digits\n",
    "n = 15  # figure with 15x15 digits\n",
    "digit_size = 28\n",
    "figure = np.zeros((digit_size * n, digit_size * n))\n",
    "# we will sample n points within [-15, 15] standard deviations\n",
    "grid_x = np.linspace(-15, 15, n)\n",
    "grid_y = np.linspace(-15, 15, n)\n",
    "\n",
    "for i, yi in enumerate(grid_x):\n",
    "    for j, xi in enumerate(grid_y):\n",
    "        z_sample = np.array([[xi, yi]]) * epsilon_std\n",
    "        x_decoded = generator.predict(z_sample)\n",
    "        digit = x_decoded[0].reshape(digit_size, digit_size)\n",
    "        figure[i * digit_size: (i + 1) * digit_size,\n",
    "               j * digit_size: (j + 1) * digit_size] = digit\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(figure)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}