autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "print(sys.executable)\n",
    "\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoding_dim = 32\n",
    "input_img = Input(shape=(784,))\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = Model(input_img, encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "\n",
    "decoder = Model(encoded_input, decoder_layer(encoded_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "\n",
    "(x_train, _),(x_test, _) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float') / 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",
    "print x_train.shape\n",
    "print x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize = (20,4))\n",
    "for i in range(n):\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",
    "    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",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_img = Input(shape=(784,))\n",
    "\n",
    "encoded = Dense(128, activation='relu')(input_img)\n",
    "encoded = Dense(64, activation='relu')(encoded)\n",
    "encoded = Dense(32, activation='relu')(encoded)\n",
    "\n",
    "decoded = Dense(64, activation='relu')(encoded)\n",
    "decoded = Dense(128, activation='relu')(decoded)\n",
    "decoded = Dense(784, activation='sigmoid')(decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n",
    "autoencoder.fit(x_train, x_train, epochs=100, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
   ]
  }
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