tensorflow1_ipynb/autoencoder/ae-conv-nneighbor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.1\n",
      "IPython 6.0.0\n",
      "\n",
      "tensorflow 1.2.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- Convolutional Autoencoder with Nearest-neighbor Interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A convolutional autoencoder using nearest neighbor upscaling layers that compresses 768-pixel MNIST images down to a 7x7x4 (196 pixel) representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "\n",
    "##########################\n",
    "### DATASET\n",
    "##########################\n",
    "\n",
    "mnist = input_data.read_data_sets(\"./\", validation_size=0)\n",
    "\n",
    "\n",
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Hyperparameters\n",
    "learning_rate = 0.001\n",
    "training_epochs = 5\n",
    "batch_size = 128\n",
    "\n",
    "# Architecture\n",
    "input_size = 784\n",
    "image_width = 28\n",
    "\n",
    "# Other\n",
    "print_interval = 200\n",
    "random_seed = 123\n",
    "\n",
    "\n",
    "##########################\n",
    "### GRAPH DEFINITION\n",
    "##########################\n",
    "\n",
    "g = tf.Graph()\n",
    "with g.as_default():\n",
    "    \n",
    "    tf.set_random_seed(random_seed)\n",
    "\n",
    "    # Input data\n",
    "    tf_x = tf.placeholder(tf.float32, [None, input_size], name='inputs')\n",
    "    input_layer = tf.reshape(tf_x, shape=[-1, image_width, image_width, 1])\n",
    "\n",
    "    ###########\n",
    "    # Encoder\n",
    "    ###########\n",
    "    \n",
    "    # 28x28x1 => 28x28x8\n",
    "    conv1 = tf.layers.conv2d(input_layer, filters=8, kernel_size=(3, 3),\n",
    "                             strides=(1, 1), padding='same', \n",
    "                             activation=tf.nn.relu)\n",
    "    \n",
    "    # 28x28x8 => 14x14x8\n",
    "    maxpool1 = tf.layers.max_pooling2d(conv1, pool_size=(2, 2), \n",
    "                                       strides=(2, 2), padding='same')\n",
    "    \n",
    "    # 14x14x8 => 14x14x4\n",
    "    conv2 = tf.layers.conv2d(maxpool1, filters=4, kernel_size=(3, 3), \n",
    "                             strides=(1, 1), padding='same', \n",
    "                             activation=tf.nn.relu)\n",
    "    \n",
    "    # 14x14x4 => 7x7x4\n",
    "    encode = tf.layers.max_pooling2d(conv2, pool_size=(2, 2), \n",
    "                                     strides=(2, 2), padding='same', \n",
    "                                     name='encoding')\n",
    "\n",
    "    ###########\n",
    "    # Decoder\n",
    "    ###########\n",
    "    \n",
    "    # 7x7x4 => 14x14x4\n",
    "    deconv1 = tf.image.resize_nearest_neighbor(encode, \n",
    "                                               size=(14, 14))\n",
    "    # 14x14x4 => 14x14x8\n",
    "    conv3 = tf.layers.conv2d(deconv1, filters=8, kernel_size=(3, 3), \n",
    "                             strides=(1, 1), padding='same', \n",
    "                             activation=tf.nn.relu)\n",
    "    \n",
    "    # 14x14x8 => 28x28x8\n",
    "    deconv2 = tf.image.resize_nearest_neighbor(conv3, \n",
    "                                               size=(28, 28))\n",
    "    # 28x28x8 => 28x28x8\n",
    "    conv4 = tf.layers.conv2d(deconv2, filters=8, kernel_size=(3, 3), \n",
    "                             strides=(1, 1), padding='same', \n",
    "                             activation=tf.nn.relu)\n",
    "    # 28x28x8 => 28x28x1\n",
    "    logits = tf.layers.conv2d(conv4, filters=1, kernel_size=(3,3), \n",
    "                              strides=(1, 1), padding='same', \n",
    "                              activation=None)\n",
    "    \n",
    "    decode = tf.nn.sigmoid(logits, name='decoding')\n",
    "\n",
    "    ##################\n",
    "    # Loss & Optimizer\n",
    "    ##################\n",
    "    \n",
    "    loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=input_layer,\n",
    "                                                   logits=logits)\n",
    "    cost = tf.reduce_mean(loss, name='cost')\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "    train = optimizer.minimize(cost, name='train')    \n",
    "\n",
    "    # Saver to save session for reuse\n",
    "    saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minibatch: 001 | Cost:    0.724\n",
      "Minibatch: 201 | Cost:    0.124\n",
      "Minibatch: 401 | Cost:    0.103\n",
      "Epoch:     001 | AvgCost: 0.181\n",
      "Minibatch: 001 | Cost:    0.101\n",
      "Minibatch: 201 | Cost:    0.097\n",
      "Minibatch: 401 | Cost:    0.092\n",
      "Epoch:     002 | AvgCost: 0.093\n",
      "Minibatch: 001 | Cost:    0.089\n",
      "Minibatch: 201 | Cost:    0.085\n",
      "Minibatch: 401 | Cost:    0.089\n",
      "Epoch:     003 | AvgCost: 0.087\n",
      "Minibatch: 001 | Cost:    0.085\n",
      "Minibatch: 201 | Cost:    0.088\n",
      "Minibatch: 401 | Cost:    0.085\n",
      "Epoch:     004 | AvgCost: 0.084\n",
      "Minibatch: 001 | Cost:    0.088\n",
      "Minibatch: 201 | Cost:    0.082\n",
      "Minibatch: 401 | Cost:    0.085\n",
      "Epoch:     005 | AvgCost: 0.083\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "##########################\n",
    "### TRAINING & EVALUATION\n",
    "##########################\n",
    "    \n",
    "with tf.Session(graph=g) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    np.random.seed(random_seed) # random seed for mnist iterator\n",
    "    for epoch in range(training_epochs):\n",
    "        avg_cost = 0.\n",
    "        total_batch = mnist.train.num_examples // batch_size\n",
    "\n",
    "        for i in range(total_batch):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "            _, c = sess.run(['train', 'cost:0'], feed_dict={'inputs:0': batch_x})\n",
    "            avg_cost += c\n",
    "\n",
    "            if not i % print_interval:\n",
    "                print(\"Minibatch: %03d | Cost:    %.3f\" % (i + 1, c))\n",
    "\n",
    "        print(\"Epoch:     %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)))\n",
    "    \n",
    "    saver.save(sess, save_path='./autoencoder.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./autoencoder.ckpt\n"
     ]
    },
    {
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99tuBMP4OOOCA7BytycWTTz4JhCph+j+EtYMqX+mcjTbaqHwfoIEorowcK/ub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\nwIDsHGUwlT3W77z11ltAYYawODO43Xb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127163748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "##########################\n",
    "### VISUALIZATION\n",
    "##########################\n",
    "\n",
    "n_images = 15\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=n_images, sharex=True, \n",
    "                         sharey=True, figsize=(20, 2.5))\n",
    "test_images = mnist.test.images[:n_images]\n",
    "\n",
    "with tf.Session(graph=g) as sess:\n",
    "    saver.restore(sess, save_path='./autoencoder.ckpt')\n",
    "    decoded = sess.run('decoding:0', feed_dict={'inputs:0': test_images})\n",
    "\n",
    "for i in range(n_images):\n",
    "    for ax, img in zip(axes, [test_images, decoded]):\n",
    "        ax[i].imshow(img[i].reshape((image_width, image_width)), cmap='binary')"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}