Chapter15.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"autoencoders\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import fully_connected\n",
    "\n",
    "n_inputs = 3\n",
    "n_hidden = 2\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "hidden = fully_connected(X, n_hidden, activation_fn=None)\n",
    "outputs = fully_connected(hidden, n_outputs, activation_fn=None)\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(reconstruction_loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy.random as rnd\n",
    "\n",
    "rnd.seed(4)\n",
    "m = 200\n",
    "w1, w2 = 0.1, 0.3\n",
    "noise = 0.1\n",
    "\n",
    "angles = rnd.rand(m) * 3 * np.pi / 2 - 0.5\n",
    "data = np.empty((m, 3))\n",
    "data[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * rnd.randn(m) / 2\n",
    "data[:, 1] = np.sin(angles) * 0.7 + noise * rnd.randn(m) / 2\n",
    "data[:, 2] = data[:, 0] * w1 + data[:, 1] * w2 + noise * rnd.randn(m)\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(data[:100])\n",
    "X_test = scaler.transform(data[100:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_iterations = 1000\n",
    "codings = hidden\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        training_op.run(feed_dict={X: X_train})\n",
    "    codings_val = codings.eval(feed_dict={X: X_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure linear_autoencoder_pca_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAADQCAYAAADcQn7hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d0c29b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,3))\n",
    "plt.plot(codings_val[:,0], codings_val[:, 1], \"b.\")\n",
    "plt.xlabel(\"$z_1$\", fontsize=18)\n",
    "plt.ylabel(\"$z_2$\", fontsize=18, rotation=0)\n",
    "save_fig(\"linear_autoencoder_pca_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 28 * 28\n",
    "n_hidden1 = 300\n",
    "n_hidden2 = 150\n",
    "n_hidden3 = n_hidden1\n",
    "n_outputs = n_inputs\n",
    "\n",
    "learning_rate = 0.01\n",
    "l2_reg = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
    "with tf.contrib.framework.arg_scope(\n",
    "        [fully_connected],\n",
    "        activation_fn=tf.nn.elu,\n",
    "        weights_initializer=tf.contrib.layers.variance_scaling_initializer(),\n",
    "        weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg)):\n",
    "    hidden1 = fully_connected(X, n_hidden1)\n",
    "    hidden2 = fully_connected(hidden1, n_hidden2)\n",
    "    hidden3 = fully_connected(hidden2, n_hidden3)\n",
    "    outputs = fully_connected(hidden3, n_outputs, activation_fn=None)\n",
    "\n",
    "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n",
    "\n",
    "reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
    "loss = tf.add_n([reconstruction_loss] + reg_losses)\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_epochs = 5\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        n_batches = mnist.train.num_examples // batch_size\n",
    "        for iteration in range(n_batches):\n",
    "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
    "            sess.run(training_op, feed_dict={X: X_batch})"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}