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    "\"\"\" Auto Encoder Example.\n",
    "Using an auto encoder on MNIST handwritten digits.\n",
    "References:\n",
    "    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \"Gradient-based\n",
    "    learning applied to document recognition.\" Proceedings of the IEEE,\n",
    "    86(11):2278-2324, November 1998.\n",
    "Links:\n",
    "    [MNIST Dataset] http://yann.lecun.com/exdb/mnist/\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.01\n",
    "training_epochs = 20\n",
    "batch_size = 256\n",
    "display_step = 1\n",
    "examples_to_show = 10\n",
    "\n",
    "# Network Parameters\n",
    "n_hidden_1 = 256 # 1st layer num features\n",
    "n_hidden_2 = 128 # 2nd layer num features\n",
    "n_input = 784 # MNIST data input (img shape: 28*28)\n",
    "\n",
    "# tf Graph input (only pictures)\n",
    "X = tf.placeholder(\"float\", [None, n_input])\n",
    "\n",
    "weights = {\n",
    "    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),\n",
    "    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\n",
    "}\n",
    "biases = {\n",
    "    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'decoder_b2': tf.Variable(tf.random_normal([n_input])),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Building the encoder\n",
    "def encoder(x):\n",
    "    # Encoder Hidden layer with sigmoid activation #1\n",
    "    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n",
    "                                   biases['encoder_b1']))\n",
    "    # Decoder Hidden layer with sigmoid activation #2\n",
    "    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n",
    "                                   biases['encoder_b2']))\n",
    "    return layer_2\n",
    "\n",
    "\n",
    "# Building the decoder\n",
    "def decoder(x):\n",
    "    # Encoder Hidden layer with sigmoid activation #1\n",
    "    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n",
    "                                   biases['decoder_b1']))\n",
    "    # Decoder Hidden layer with sigmoid activation #2\n",
    "    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n",
    "                                   biases['decoder_b2']))\n",
    "    return layer_2\n",
    "\n",
    "# Construct model\n",
    "encoder_op = encoder(X)\n",
    "decoder_op = decoder(encoder_op)\n",
    "\n",
    "# Prediction\n",
    "y_pred = decoder_op\n",
    "# Targets (Labels) are the input data.\n",
    "y_true = X\n",
    "\n",
    "# Define loss and optimizer, minimize the squared error\n",
    "cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "# Initializing the variables\n",
    "init = tf.initialize_all_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost= 0.218654603\n",
      "Epoch: 0002 cost= 0.173306286\n",
      "Epoch: 0003 cost= 0.154793650\n",
      "Epoch: 0004 cost= 0.146902516\n",
      "Epoch: 0005 cost= 0.141993478\n",
      "Epoch: 0006 cost= 0.132718414\n",
      "Epoch: 0007 cost= 0.125991374\n",
      "Epoch: 0008 cost= 0.122500181\n",
      "Epoch: 0009 cost= 0.115299642\n",
      "Epoch: 0010 cost= 0.115390278\n",
      "Epoch: 0011 cost= 0.114480168\n",
      "Epoch: 0012 cost= 0.113888472\n",
      "Epoch: 0013 cost= 0.111597553\n",
      "Epoch: 0014 cost= 0.110663064\n",
      "Epoch: 0015 cost= 0.108673096\n",
      "Epoch: 0016 cost= 0.104775786\n",
      "Epoch: 0017 cost= 0.106273368\n",
      "Epoch: 0018 cost= 0.104061618\n",
      "Epoch: 0019 cost= 0.103227913\n",
      "Epoch: 0020 cost= 0.099696413\n",
      "Optimization Finished!\n"
     ]
    }
   ],
   "source": [
    "# Launch the graph\n",
    "# Using InteractiveSession (more convenient while using Notebooks)\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(init)\n",
    "\n",
    "total_batch = int(mnist.train.num_examples/batch_size)\n",
    "# Training cycle\n",
    "for epoch in range(training_epochs):\n",
    "    # Loop over all batches\n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        # Run optimization op (backprop) and cost op (to get loss value)\n",
    "        _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})\n",
    "    # Display logs per epoch step\n",
    "    if epoch % display_step == 0:\n",
    "        print(\"Epoch:\", '%04d' % (epoch+1),\n",
    "              \"cost=\", \"{:.9f}\".format(c))\n",
    "\n",
    "print(\"Optimization Finished!\")\n",
    "\n",
    "# Applying encode and decode over test set\n",
    "encode_decode = sess.run(\n",
    "    y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})\n",
    "# Compare original images with their reconstructions\n",
    "f, a = plt.subplots(2, 10, figsize=(10, 2))\n",
    "for i in range(examples_to_show):\n",
    "    a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\n",
    "    a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\n",
    "f.show()\n",
    "plt.draw()\n",
    "plt.waitforbuttonpress()"
   ]
  }
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