STEP1_1_TrainingConvolutionalVariationalAutoEncoder_with_Noise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting DATASETS/MNIST_TF/train-images-idx3-ubyte.gz\n",
      "Extracting DATASETS/MNIST_TF/train-labels-idx1-ubyte.gz\n",
      "Extracting DATASETS/MNIST_TF/t10k-images-idx3-ubyte.gz\n",
      "Extracting DATASETS/MNIST_TF/t10k-labels-idx1-ubyte.gz\n",
      "Encoder\n",
      "Convolution 1 -> Tensor(\"encoder/e_cnn_0/Relu:0\", shape=(?, 14, 14, 8), dtype=float32)\n",
      "Convolution 2 -> Tensor(\"encoder/e_cnn_1/Relu:0\", shape=(?, 7, 7, 16), dtype=float32)\n",
      "Reshape  -> Tensor(\"encoder/Reshape:0\", shape=(?, 784), dtype=float32)\n",
      "FC 1  -> Tensor(\"encoder/e_d_0/Relu:0\", shape=(?, 1024), dtype=float32)\n",
      "Z mean -> Tensor(\"encoder/e_d_1/BiasAdd:0\", shape=(?, 100), dtype=float32)\n",
      "Decoder\n",
      "FC 1  -> Tensor(\"decoder/d_d_0/Relu:0\", shape=(?, 1024), dtype=float32)\n",
      "FC 2  -> Tensor(\"decoder/d_d_1/Relu:0\", shape=(?, 784), dtype=float32)\n",
      "Reshape  -> Tensor(\"decoder/Reshape:0\", shape=(?, 7, 7, 16), dtype=float32)\n",
      "DeConv 1  -> Tensor(\"decoder/Reshape_1:0\", shape=(?, 14, 14, 8), dtype=float32)\n",
      "DeConv 2  -> Tensor(\"decoder/Reshape_2:0\", shape=(?, 28, 28, 1), dtype=float32)\n",
      "Reshape  -> Tensor(\"decoder/Reshape_3:0\", shape=(?, 784), dtype=float32)\n",
      "Decoder\n",
      "FC 1  -> Tensor(\"decoder/d_d_0_1/Relu:0\", shape=(?, 1024), dtype=float32)\n",
      "FC 2  -> Tensor(\"decoder/d_d_1_1/Relu:0\", shape=(?, 784), dtype=float32)\n",
      "Reshape  -> Tensor(\"decoder/Reshape_4:0\", shape=(?, 7, 7, 16), dtype=float32)\n",
      "DeConv 1  -> Tensor(\"decoder/Reshape_5:0\", shape=(?, 14, 14, 8), dtype=float32)\n",
      "DeConv 2  -> Tensor(\"decoder/Reshape_6:0\", shape=(?, 28, 28, 1), dtype=float32)\n",
      "Reshape  -> Tensor(\"decoder/Reshape_7:0\", shape=(?, 784), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# COMMENTS TO DO\n",
    "#\n",
    "#Condensed code based on the code from: https://jmetzen.github.io/2015-11-27/vae.html\n",
    "\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.layers as layers\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import glob\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "def plot(samples, w, h, fw, fh, iw=28, ih=28):\n",
    "    fig = plt.figure(figsize=(fw, fh))\n",
    "    gs = gridspec.GridSpec(w, h)\n",
    "    gs.update(wspace=0.05, hspace=0.05)\n",
    "\n",
    "    for i, sample in enumerate(samples):\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_aspect('equal')\n",
    "        plt.imshow(sample.reshape(iw, ih), cmap='Greys_r')\n",
    "\n",
    "    return fig\n",
    "\n",
    "\n",
    "def encoder(images, num_outputs_h0=8, num_outputs_h1=16, kernel_size=5, stride=2, num_hidden_fc=1024, z_dim=100):\n",
    "        \n",
    "    print(\"Encoder\")\n",
    "\n",
    "    h0 = layers.convolution2d(\n",
    "        inputs=images,\n",
    "        num_outputs=num_outputs_h0,\n",
    "        kernel_size=kernel_size,\n",
    "        stride=stride,\n",
    "        activation_fn=tf.nn.relu,\n",
    "        scope='e_cnn_%d' % (0,)\n",
    "    )\n",
    "    \n",
    "    print(\"Convolution 1 -> {}\".format(h0))\n",
    "\n",
    "    h1 = layers.convolution2d(\n",
    "        inputs=h0,\n",
    "        num_outputs=num_outputs_h1,\n",
    "        kernel_size=kernel_size,\n",
    "        stride=stride,\n",
    "        activation_fn=tf.nn.relu,\n",
    "        scope='e_cnn_%d' % (1,)\n",
    "    )\n",
    "    \n",
    "    print(\"Convolution 2 -> {}\".format(h1))\n",
    "\n",
    "    h1_dim = h1.get_shape().as_list()[1]\n",
    "\n",
    "    h2_flat = tf.reshape(h1, [-1, h1_dim * h1_dim * num_outputs_h1])\n",
    "\n",
    "    print(\"Reshape  -> {}\".format(h2_flat))\n",
    "    \n",
    "    h2_flat =layers.fully_connected(\n",
    "        inputs=h2_flat,\n",
    "        num_outputs=num_hidden_fc,\n",
    "        activation_fn=tf.nn.relu,\n",
    "        scope='e_d_%d' % (0,)\n",
    "    )\n",
    "    \n",
    "    print(\"FC 1  -> {}\".format(h2_flat))\n",
    "\n",
    "    z_mean =layers.fully_connected(\n",
    "        inputs=h2_flat,\n",
    "        num_outputs=z_dim,\n",
    "        activation_fn=None,\n",
    "        scope='e_d_%d' % (1,)\n",
    "    )\n",
    "    \n",
    "    print(\"Z mean -> {}\".format(z_mean))\n",
    "\n",
    "    z_log_sigma_sq =layers.fully_connected(\n",
    "        inputs=h2_flat,\n",
    "        num_outputs=z_dim,\n",
    "        activation_fn=None,\n",
    "        scope='e_d_%d' % (2,)\n",
    "    )\n",
    "\n",
    "    return z_mean, z_log_sigma_sq \n",
    "\n",
    "\n",
    "\n",
    "def decoder(z, num_hidden_fc=1024, h1_reshape_dim=7, kernel_size=5, h1_channels=16, h2_channels = 8, output_channels=1, strides=2, output_dims=784):\n",
    "    \n",
    "    print(\"Decoder\")\n",
    "    \n",
    "    batch_size = tf.shape(z)[0]  \n",
    "    \n",
    "    h0 =layers.fully_connected(\n",
    "        inputs=z,\n",
    "        num_outputs=num_hidden_fc,\n",
    "        activation_fn=tf.nn.relu,\n",
    "        scope='d_d_%d' % (0,)\n",
    "    )\n",
    "    \n",
    "    print(\"FC 1  -> {}\".format(h0))\n",
    "\n",
    "    h1 =layers.fully_connected(\n",
    "        inputs=h0,\n",
    "        num_outputs=h1_reshape_dim*h1_reshape_dim*h1_channels,\n",
    "        activation_fn=tf.nn.relu,\n",
    "        scope='d_d_%d' % (1,)\n",
    "    )\n",
    "    \n",
    "    print(\"FC 2  -> {}\".format(h1))\n",
    "\n",
    "    h1_reshape = tf.reshape(h1, [-1, h1_reshape_dim, h1_reshape_dim, h1_channels])\n",
    "\n",
    "    print(\"Reshape  -> {}\".format(h1_reshape))\n",
    "\n",
    "    wdd2 = tf.get_variable('wd2', shape=(kernel_size, kernel_size, h2_channels, h1_channels), initializer=tf.contrib.layers.xavier_initializer())\n",
    "    bdd2 = tf.get_variable('bd2', shape=(h2_channels,), initializer=tf.constant_initializer(0))\n",
    "    \n",
    "    h2 = tf.nn.conv2d_transpose(h1_reshape, wdd2, output_shape=(batch_size, h1_reshape_dim*2,  h1_reshape_dim*2, h2_channels), strides=(1, strides, strides, 1), padding='SAME')\n",
    "\n",
    "    h2_out = tf.nn.relu(h2 + bdd2)\n",
    "    \n",
    "    h2_out = tf.reshape(h2_out, (batch_size, h1_reshape_dim*2,  h1_reshape_dim*2, h2_channels))\n",
    "    \n",
    "    print(\"DeConv 1  -> {}\".format(h2_out))\n",
    "    \n",
    "    h2_dim = h2_out.get_shape().as_list()[1]\n",
    "\n",
    "    wdd3 = tf.get_variable('wd3', shape=(kernel_size, kernel_size, output_channels, h2_channels), initializer=tf.contrib.layers.xavier_initializer())\n",
    "    bdd3 = tf.get_variable('bd3', shape=(output_channels,), initializer=tf.constant_initializer(0))\n",
    " \n",
    "    h3 = tf.nn.conv2d_transpose(h2_out, wdd3, output_shape=(batch_size, h2_dim*2, h2_dim*2, output_channels), strides=(1, strides, strides, 1), padding='SAME')\n",
    "\n",
    "    h3_out = tf.nn.sigmoid(h3 + bdd3)\n",
    "    \n",
    "    #Workaround to use dinamyc batch size...\n",
    "    h3_out = tf.reshape(h3_out, (batch_size, h2_dim*2, h2_dim*2, output_channels))\n",
    "\n",
    "    print(\"DeConv 2  -> {}\".format(h3_out))\n",
    "\n",
    "    h3_reshape = tf.reshape(h3_out, [-1, output_dims])\n",
    "    \n",
    "    print(\"Reshape  -> {}\".format(h3_reshape))\n",
    "\n",
    "    return h3_reshape\n",
    "    \n",
    "\n",
    "mnist = input_data.read_data_sets('DATASETS/MNIST_TF', one_hot=True)\n",
    "\n",
    "#For reconstructing the same or a different image (denoising)\n",
    "images = tf.placeholder(tf.float32, shape=(None, 784))\n",
    "\n",
    "images_28x28x1 = tf.reshape(images, [-1, 28, 28, 1])\n",
    "\n",
    "images_target = tf.placeholder(tf.float32, shape=(None, 784))\n",
    "\n",
    "is_training_placeholder = tf.placeholder(tf.bool)\n",
    "learning_rate_placeholder = tf.placeholder(tf.float32)\n",
    "\n",
    "z_dim = 100\n",
    "\n",
    "with tf.variable_scope(\"encoder\") as scope:\n",
    "\n",
    "    z_mean, z_log_sigma_sq = encoder(images_28x28x1)\n",
    "    \n",
    "\n",
    "with tf.variable_scope(\"reparameterization\") as scope:\n",
    "    \n",
    "    eps = tf.random_normal(shape=tf.shape(z_mean), mean=0.0, stddev=1.0, dtype=tf.float32)\n",
    "\n",
    "    # z = mu + sigma*epsilon\n",
    "    z = tf.add(z_mean, tf.multiply(tf.sqrt(tf.exp(z_log_sigma_sq)), eps))\n",
    "    \n",
    "\n",
    "with tf.variable_scope(\"decoder\") as scope:\n",
    "        \n",
    "    x_reconstr_mean = decoder(z)\n",
    "    \n",
    "    scope.reuse_variables()\n",
    "\n",
    "    ##### SAMPLING #######\n",
    "    \n",
    "    z_input = tf.placeholder(tf.float32, shape=[None, z_dim])\n",
    "\n",
    "    x_sample = decoder(z_input)\n",
    "\n",
    "\n",
    "#reconstr_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=x_reconstr_mean, labels=images_target), reduction_indices=1)\n",
    "\n",
    "offset=1e-7\n",
    "obs_ = tf.clip_by_value(x_reconstr_mean, offset, 1 - offset)\n",
    "\n",
    "reconstr_loss = -tf.reduce_sum(images_target * tf.log(obs_) + (1-images_target) * tf.log(1 - obs_), 1)\n",
    "\n",
    "latent_loss = -.5 * tf.reduce_sum(1. + z_log_sigma_sq - tf.pow(z_mean, 2) - tf.exp(z_log_sigma_sq), reduction_indices=1)\n",
    "\n",
    "cost = tf.reduce_mean(reconstr_loss + latent_loss)\n",
    "\n",
    "optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate_placeholder).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N. Batches: 550\n",
      "Epoch 0, Mean Loss 203.16, time: 4.317421197891235\n",
      "Epoch 100, Mean Loss 107.23, time: 3.8993914127349854\n",
      "Epoch 200, Mean Loss 106.02, time: 4.037439346313477\n",
      "Epoch 300, Mean Loss 105.38, time: 4.026751518249512\n",
      "Epoch 400, Mean Loss 105.06, time: 4.0390801429748535\n",
      "Epoch 500, Mean Loss 104.72, time: 4.0352044105529785\n",
      "Epoch 600, Mean Loss 104.46, time: 3.9838976860046387\n",
      "Epoch 700, Mean Loss 104.46, time: 3.911322593688965\n",
      "Epoch 800, Mean Loss 104.17, time: 4.028074741363525\n",
      "Epoch 900, Mean Loss 104.10, time: 4.009028196334839\n",
      "Model saved in file: MODELS_CVAE_MNIST/CONV_VAE_MNIST_WITH_NOISE.ckpt\n"
     ]
    }
   ],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "\n",
    "epochs = 1000\n",
    "batch_size = 100\n",
    "\n",
    "n_batches = mnist.train.images.shape[0]//batch_size\n",
    "\n",
    "learning_rate=0.001\n",
    "\n",
    "OUTPUT_PATH = \"OUT_CVAE_MNIST_WITH_NOISE/\"\n",
    "MODELS_PATH = \"MODELS_CVAE_MNIST/\"\n",
    "\n",
    "if not os.path.exists(OUTPUT_PATH):\n",
    "    os.makedirs(OUTPUT_PATH)\n",
    "    \n",
    "if not os.path.exists(MODELS_PATH):\n",
    "    os.makedirs(MODELS_PATH)\n",
    "\n",
    "CVAE_SAVER = tf.train.Saver()\n",
    "\n",
    "print(\"N. Batches: {}\".format(n_batches))\n",
    "\n",
    "# Launch the graph\n",
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        \n",
    "        start_time = time.time()\n",
    "        \n",
    "        mean_loss = 0\n",
    "\n",
    "        for n_batch in range(n_batches):\n",
    "            \n",
    "            batch_xs, _ = mnist.train.next_batch(batch_size)\n",
    "            \n",
    "            #from https://blog.keras.io/building-autoencoders-in-keras.html\n",
    "            noise_factor = 0.5\n",
    "            noise_batch_xs = batch_xs + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=batch_xs.shape) \n",
    "\n",
    "            noise_batch_xs = np.clip(noise_batch_xs, 0., 1.)\n",
    "            \n",
    "            out_recon, out_latent_loss, out_cost, _ = sess.run([\n",
    "                x_reconstr_mean, latent_loss, cost, optimizer], \n",
    "                           feed_dict={\n",
    "                               images: noise_batch_xs, \n",
    "                               images_target: batch_xs, \n",
    "                               is_training_placeholder: True,\n",
    "                               learning_rate_placeholder: learning_rate})\n",
    "\n",
    "\n",
    "            mean_loss += out_cost\n",
    "            #print(\" Loss {}\".format(out_cost))\n",
    "        \n",
    "           \n",
    "        if epoch % 100 == 0:\n",
    "            \n",
    "            print(\"Epoch {}, Mean Loss {:.2f}, time: {}\".format(epoch, mean_loss/n_batches, time.time() - start_time))\n",
    "            \n",
    "            fig=plot(noise_batch_xs, 10, 10, 10, 10)\n",
    "            #plt.show()\n",
    "            plt.savefig(OUTPUT_PATH + 'input_{}.png'.format(str(epoch).zfill(3)), bbox_inches='tight')\n",
    "            plt.close(fig)\n",
    "            \n",
    "            fig=plot(batch_xs, 10, 10, 10, 10)\n",
    "            #plt.show()\n",
    "            plt.savefig(OUTPUT_PATH + 'target_{}.png'.format(str(epoch).zfill(3)), bbox_inches='tight')\n",
    "            plt.close(fig)\n",
    "\n",
    "            fig=plot(out_recon, 10, 10, 10, 10)\n",
    "            #plt.show()\n",
    "            plt.savefig(OUTPUT_PATH + 'output_{}.png'.format(str(epoch).zfill(3)), bbox_inches='tight')\n",
    "            plt.close(fig)\n",
    "\n",
    "            \n",
    "\n",
    "    # Save model weights to disk\n",
    "    save_path = CVAE_SAVER.save(sess, MODELS_PATH + 'CONV_VAE_MNIST_WITH_NOISE.ckpt')\n",
    "    print(\"Model saved in file: {}\".format(save_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restored in file: MODELS_CVAE_MNIST/CONV_VAE_MNIST_WITH_NOISE.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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+JXn9a90eHR0dhZ+n9VJJtv+stznULr74YiBFaE6ZMqWp18ec3KfLaFt9g4wQ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\n5rjMlQTLYmTfYYcdBsAdd9xRV+WxVuunfZSvn5MnT66qkjM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7UWuiNV6izEjcSthn87No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Tvm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\nEDLGGGNMafkHRfrVC6F8ytYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa284c94eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "\n",
    "    CVAE_SAVER.restore(sess, save_path)\n",
    "    print(\"Model restored in file: {}\".format(save_path))\n",
    "    \n",
    "    random_gen = sess.run(x_sample,feed_dict={z_input: np.random.randn(100, z_dim)})\n",
    "    \n",
    "    fig=plot(random_gen, 10, 10, 10, 10)\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model restored in file: MODELS_CVAE_MNIST/CONV_VAE_MNIST_WITH_NOISE.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAElCAYAAADnfOZBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACrtJREFUeJzt3TGLXGUbx+E5cTUoKwQLMWhhNyukV0QE8wmW3WqbGFAI\nO0UEG8FWsLUJM8HCSguRHUIsJCjxO9i42ygIQqpUwqCix1KiIeeezJmdnflfV33vOY/v8B5/PMVt\n07btAAAg1blVHwAAYJXEEAAQTQwBANHEEAAQTQwBANHEEAAQTQwBANHEEAAQTQwBANG25hlumsa6\nauCxtW3bzPs3vjvAIirfHTdDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0\nMQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQA\nRBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBND\nAEA0MQQARBNDAEC0rVUfAAAeZjwed84cHh6ewkn+bzKZdM6MRqNTOAl9cDMEAEQTQwBANDEEAEQT\nQwBANDEEAEQTQwBANDEEAEQTQwBANDEEAERr2ratDzdNfRjgP9q2beb9G9+dzVPZLD0Y9LtdurIx\nuu93Vu3v75fmptPpkk+ymSrfHTdDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARBNDAEA0MQQARLN0\nETg1li5utr6XKZ6cnHTO7OzslJ61CsfHx6W54XBYmqsujhyNRqW5FJYuAgB0EEMAQDQxBABEE0MA\nQDQxBABEE0MAQDQxBABEE0MAQDQxBABEs4Ga3jz55JOluSeeeKI098EHH5TmXnzxxdJcxZUrV0pz\n58+fL81dvHixc+bevXulZ20CG6jXU9+bpav29/c7Z6bTaa/vXIW+N1U3zdz/N9toNlADAHQQQwBA\nNDEEAEQTQwBANDEEAEQTQwBANDEEAEQTQwBANDEEAETbWvUB1tm5c7WWfPbZZ5d8ksdX3Rj7yiuv\ndM7s7u6WnnWW//eoqm5u/+STTzpnDg4OFj0OLNXly5d7fV5ls/RgsBnbpSvu3r1bmqtuoK5sDB+N\nRqVnpXAzBABEE0MAQDQxBABEE0MAQDQxBABEE0MAQDQxBABEE0MAQDQxBABEs4H6Iba3t0tz7733\nXmnuo48+WuQ4rLGvv/561UeAR6psK65uPq5K2Sxd9d1335Xmqv/FAObnZggAiCaGAIBoYggAiCaG\nAIBoYggAiCaGAIBoYggAiCaGAIBoTdu29eGmqQ+vsU8//bQ09+677y75JOtlNpuV5v76669e3/vM\nM890zpw712/3//nnn6W5ixcvds7cv39/0eOsjbZtm3n/JuW7syrz/Dugy2QyKc2NRqPe3pmk+lud\nnJx0zuzs7Cx6nLVR+e64GQIAookhACCaGAIAookhACCaGAIAookhACCaGAIAookhACCaGAIAotlA\n/RBPP/10ae77778vzQ2Hw0WOs1S3bt0qzf3444+dM1988UXpWb/++mtp7s033yzN3blzp3Pm/Pnz\npWf98ccfpbm33367NPfll1+W5lLYQH329LmBumnm/nmZw3g8Ls1dvny5c8YG6ge5GQIAookhACCa\nGAIAookhACCaGAIAookhACCaGAIAookhACCaGAIAom2t+gBn0Ww2K8299tprSz7JZtre3i7Nffzx\nx6W56nbpiupWcZulOeuq24pZH5XN0oPB2f6vHpxVboYAgGhiCACIJoYAgGhiCACIJoYAgGhiCACI\nJoYAgGhiCACIJoYAgGg2UHPqptNpae7111/v7Z2///57ae6zzz7r7Z2wKSaTyaqPwKC+WdrvNT83\nQwBANDEEAEQTQwBANDEEAEQTQwBANDEEAEQTQwBANDEEAESzdJHeVJckvvHGG0s+yf9du3atNPfV\nV18t+SRwOg4PD1d9BIrG43GvzxuNRr0+L4GbIQAgmhgCAKKJIQAgmhgCAKKJIQAgmhgCAKKJIQAg\nmhgCAKKJIQAgWtO2bX24aerDbJQLFy50znzzzTelZ7366quLHucBs9msc+bSpUulZ/3888+LHodH\naNu2mfdvfHcez/HxcWluOBx2zjTN3D8bc+jztxoM/F7/VfnuuBkCAKKJIQAgmhgCAKKJIQAgmhgC\nAKKJIQAgmhgCAKKJIQAgmhgCAKJtrfoArIerV692zqxis/RgMBhcuXKlc8ZmaTbFeDwuzVW3FbM8\ne3t7pbnqb3VycrLIcXgEN0MAQDQxBABEE0MAQDQxBABEE0MAQDQxBABEE0MAQDQxBABEE0MAQDQb\nqMM999xzpbn3339/ySf5v1u3bpXmjo6OlnwSgPn1/W368MMPe30e/3IzBABEE0MAQDQxBABEE0MA\nQDQxBABEE0MAQDQxBABEE0MAQDRLF8N9/vnnpbmXXnqpt3fevXu3NHft2rXe3gk83GQyWfUR1tLx\n8XFvz9rf3y/NTafT3t7Jg9wMAQDRxBAAEE0MAQDRxBAAEE0MAQDRxBAAEE0MAQDRxBAAEE0MAQDR\nbKBeMxcuXCjNXb9+vTT31ltvLXKcB1Q3S+/u7pbmfvvtt0WOAxQcHh52zoxGo1M4ydlQ3Sw9HA47\nZ2yWXh9uhgCAaGIIAIgmhgCAaGIIAIgmhgCAaGIIAIgmhgCAaGIIAIgmhgCAaE3btvXhpqkPsxTV\nTbA3btzo9b2//PJL58ylS5dKz7JZOlfbts28f+O783jm+bZ3mUwmpbmzvKm6z83Sg8FgcHJy0jmz\ns7NTehbLVfnuuBkCAKKJIQAgmhgCAKKJIQAgmhgCAKKJIQAgmhgCAKKJIQAgmhgCAKLZQH2GXL16\ntXPm5s2bpWc99dRTpbnZbFaaOzg46Jy5fft26VnksoH69Ozt7ZXmjo6OentnZSvzYFDfzDwejztn\nDg8PS8+q6vufgdWzgRoAoIMYAgCiiSEAIJoYAgCiiSEAIJoYAgCiiSEAIJoYAgCiba36APzrhRde\n6JypLlOsunfvXmnOQkVYL9PptDRXWTI4HA5Lz6rOzbPsty+TyaQ0NxqNlnwSziI3QwBANDEEAEQT\nQwBANDEEAEQTQwBANDEEAEQTQwBANDEEAEQTQwBAtGaeTaBN05z+2tAN8M4775TmKhtSt7ZqS8Nn\ns1lpbnd3tzT37bfflubgUdq2beb9G9+d1dvb2yvNHR0d9freyjfRxmi6VL47boYAgGhiCACIJoYA\ngGhiCACIJoYAgGhiCACIJoYAgGhiCACIJoYAgGg2UC9ge3u7NPfDDz+U5l5++eUFTvOg69evl+Zu\n3LjR2zuhiw3UwGmzgRoAoIMYAgCiiSEAIJoYAgCiiSEAIJoYAgCiiSEAIJoYAgCiiSEAINrWqg+w\nzg4ODkpzfW6WvnPnTmnu5s2bvb0TADaZmyEAIJoYAgCiiSEAIJoYAgCiiSEAIJoYAgCiiSEAIJoY\nAgCiNW3b1oebpj4coLpM8aeffirN3b9/v3Pm+eefLz3r77//Ls3BaWrbtpn3b3x3gEVUvjtuhgCA\naGIIAIgmhgCAaGIIAIgmhgCAaGIIAIgmhgCAaGIIAIgmhgCAaDZQA6fGBmrgtNlADQDQQQwBANHE\nEAAQTQwBANHEEAAQTQwBANHEEAAQTQwBANHEEAAQba4N1AAAm8bNEAAQTQwBANHEEAAQTQwBANHE\nEAAQTQwBANHEEAAQTQwBANHEEAAQTQwBANH+AQ0y1mgT3m1iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa223821da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIEAAABJCAYAAAAaG0WJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa228e19470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MyJEjSzW+FcqXF7Dmn+u2abA4PuxP6H31ylARylAQBEEQBB1NUwsurFq1qvQyrPvQXxqr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa228dbf7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "\n",
    "    CVAE_SAVER.restore(sess, save_path)\n",
    "    print(\"Model restored in file: {}\".format(save_path))\n",
    "    \n",
    "    INTERPOLATION_STEPS = 10\n",
    "    alphaValues = np.linspace(0, 1, INTERPOLATION_STEPS)\n",
    "    \n",
    "    random_indexes = np.random.permutation(mnist.train.images.shape[0])[:2]\n",
    "    \n",
    "    x_samples = mnist.train.images[random_indexes].copy()\n",
    "    \n",
    "    z_projected_samples = sess.run(z, feed_dict={images: x_samples})\n",
    "    \n",
    "    fig=plot(x_samples, 1, 2, 10, 10)\n",
    "    plt.show()\n",
    "    \n",
    "    fig=plot(z_projected_samples, 10, 10, 10, 10, 10, 10)\n",
    "    plt.show()\n",
    "    \n",
    "    z_samples_interpolated = np.zeros((INTERPOLATION_STEPS, z_dim))\n",
    "    \n",
    "    for i, alpha in enumerate(alphaValues):\n",
    "        z_samples_interpolated[i] = z_projected_samples[0]*(1-alpha) + z_projected_samples[1]*alpha\n",
    "        \n",
    "    \n",
    "    x_interpolated = sess.run(x_sample, feed_dict={z_input: z_samples_interpolated})\n",
    "    \n",
    "    fig=plot(x_interpolated, 1, INTERPOLATION_STEPS, 10, 10)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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