NoisyRecons.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from models.variational_autoencoder import VariationalAutoencoder\n",
    "from models.bayesian_autoencoder import BayesianAutoencoder\n",
    "from models.variational_conv_autoencoder import VariationalConvAutoencoder\n",
    "from models.bayesian_conv_autoencoder import BayesianConvAutoencoder\n",
    "from models.bayesian_vae import BayesianVAE\n",
    "from models.GAN import AAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE VAE\n",
      " --> Encoder\n",
      "    w:  (784, 128)  |  b: (128,)\n",
      "    w:  (128, 64)  |  b: (64,)\n",
      " --> Latent Space\n",
      "    Mu.     w: (64, 2)  |  b: (2,)\n",
      "    Sigma2. w: (64, 2)  |  b: (2,)\n",
      " --> Decoder\n",
      "    w:  (2, 64)  |  b: (64,)\n",
      "    w:  (64, 128)  |  b: (128,)\n",
      "    w:  (128, 784)  |  b: (784,)\n",
      "INFO:tensorflow:Restoring parameters from DUMPS/vae/VAE_128_64_2\n",
      "Model restored from file: DUMPS/vae/VAE_128_64_2\n"
     ]
    }
   ],
   "source": [
    "VAE = VariationalAutoencoder('VAE', n_neurons_encoder=[128, 64], n_latent=2, n_neurons_decoder=[64, 128])\n",
    "VAE.restore('DUMPS/vae/VAE_128_64_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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KsQqVwbHc5Nlnn50dY/gg+1P7mv07derUZGso8Hve855kU3IYEfHBD34w2eedd16y2e+K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\ny1IffPDByWbOC91HMVfhggULsmM9LQ9aV1DlryLyualtyXnFPJMsgxx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tz0W9B+U4yDmz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\nCAAAAGAGvAQCAAAAmIH/AJQfCX/Kr95WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x276b35459b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VAE.plot_noisy_recon()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x276a1fdfe80>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VAE.plot_recon()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VCAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VCAE VCAE\n",
      " --> Encoder\n",
      "(?, 28, 28, 1)\n",
      "(?, 14, 14, 10)\n",
      "(?, 7, 7, 10)\n",
      "(?, 3, 3, 10)\n",
      "(?, 90)\n",
      "(?, 64)\n",
      " --> Latent space\n",
      "(?, 2)\n",
      " --> Decoder\n",
      "(?, 64)\n",
      "(?, 90)\n",
      "(?, 3, 3, 10)\n",
      "(?, 7, 7, 10)\n",
      "(?, 14, 14, 10)\n",
      "(?, 28, 28, 1)\n",
      "(?, 784)\n",
      "INFO:tensorflow:Restoring parameters from DUMPS/vcae/VCAE_3x3_10_3\n",
      "Model restored from file: DUMPS/vcae/VCAE_3x3_10_3\n"
     ]
    }
   ],
   "source": [
    "VCAE = VariationalConvAutoencoder('VCAE', n_inputs=784,\n",
    "                 n_conv_maps = [10, 10, 10],\n",
    "                 n_conv_filters = [3, 3, 3], \n",
    "                 n_conv_strides = [2, 2, 2], \n",
    "                 n_conv_padding = ['SAME', 'SAME', 'VALID'], \n",
    "                 n_dense = [64],\n",
    "                 n_latent=2)\n",
    "VCAE.restore('DUMPS/vcae/VCAE_3x3_10_3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2573c7055f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VCAE.plot_recon(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zUn5doaSKoYsMp4yI2HvvvUv7pz/9aWlr6Bzn2fve977qGEM2tVRjCy35t/HG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\nn8z3EL3G3XffXdqcd3peViEyk6CORjgefEfXtARcxyZOnFgda9mY7hu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2NkuSV/W5a1Yt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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25743e67710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VCAE.plot_noisy_recon(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "AdvAE = AAE('AAE_128_64_2', n_inputs = 784, n_neurons_encoder=[128, 64], n_latent=2, n_neurons_decoder=[64, 128])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------Epoch 0/50------------------\n",
      "Autoencoder Loss: 173.1112060546875\n",
      "Discriminator Loss: 1.4092826843261719\n",
      "Generator Loss: 0.5582042932510376\n",
      "------------------Epoch 1/50------------------\n",
      "Autoencoder Loss: 164.56973266601562\n",
      "Discriminator Loss: 1.3816967010498047\n",
      "Generator Loss: 0.6596517562866211\n",
      "------------------Epoch 2/50------------------\n",
      "Autoencoder Loss: 160.25360107421875\n",
      "Discriminator Loss: 1.382715106010437\n",
      "Generator Loss: 0.6902976036071777\n",
      "------------------Epoch 3/50------------------\n",
      "Autoencoder Loss: 159.45547485351562\n",
      "Discriminator Loss: 1.3855531215667725\n",
      "Generator Loss: 0.6894305944442749\n",
      "------------------Epoch 4/50------------------\n",
      "Autoencoder Loss: 149.436767578125\n",
      "Discriminator Loss: 1.3852852582931519\n",
      "Generator Loss: 0.691820502281189\n",
      "------------------Epoch 5/50------------------\n",
      "Autoencoder Loss: 152.19961547851562\n",
      "Discriminator Loss: 1.3852039575576782\n",
      "Generator Loss: 0.6928458213806152\n",
      "------------------Epoch 6/50------------------\n",
      "Autoencoder Loss: 148.01953125\n",
      "Discriminator Loss: 1.3860034942626953\n",
      "Generator Loss: 0.6928032040596008\n",
      "------------------Epoch 7/50------------------\n",
      "Autoencoder Loss: 148.6840362548828\n",
      "Discriminator Loss: 1.3862714767456055\n",
      "Generator Loss: 0.6935080289840698\n",
      "------------------Epoch 8/50------------------\n",
      "Autoencoder Loss: 145.54342651367188\n",
      "Discriminator Loss: 1.386217474937439\n",
      "Generator Loss: 0.6932545304298401\n",
      "------------------Epoch 9/50------------------\n",
      "Autoencoder Loss: 139.97525024414062\n",
      "Discriminator Loss: 1.3862431049346924\n",
      "Generator Loss: 0.692994236946106\n",
      "------------------Epoch 10/50------------------\n",
      "Autoencoder Loss: 144.50918579101562\n",
      "Discriminator Loss: 1.3861942291259766\n",
      "Generator Loss: 0.6931145787239075\n",
      "------------------Epoch 11/50------------------\n",
      "Autoencoder Loss: 137.0306396484375\n",
      "Discriminator Loss: 1.386236310005188\n",
      "Generator Loss: 0.6934635639190674\n",
      "------------------Epoch 12/50------------------\n",
      "Autoencoder Loss: 146.12095642089844\n",
      "Discriminator Loss: 1.3862338066101074\n",
      "Generator Loss: 0.6930806040763855\n",
      "------------------Epoch 13/50------------------\n",
      "Autoencoder Loss: 143.43453979492188\n",
      "Discriminator Loss: 1.386217474937439\n",
      "Generator Loss: 0.6930742263793945\n",
      "------------------Epoch 14/50------------------\n",
      "Autoencoder Loss: 135.82546997070312\n",
      "Discriminator Loss: 1.3862673044204712\n",
      "Generator Loss: 0.6931312680244446\n",
      "------------------Epoch 15/50------------------\n",
      "Autoencoder Loss: 136.83128356933594\n",
      "Discriminator Loss: 1.3862905502319336\n",
      "Generator Loss: 0.6928359270095825\n",
      "------------------Epoch 16/50------------------\n",
      "Autoencoder Loss: 140.06924438476562\n",
      "Discriminator Loss: 1.3862926959991455\n",
      "Generator Loss: 0.6925182342529297\n",
      "------------------Epoch 17/50------------------\n",
      "Autoencoder Loss: 137.34552001953125\n",
      "Discriminator Loss: 1.3862841129302979\n",
      "Generator Loss: 0.6935663223266602\n",
      "------------------Epoch 18/50------------------\n",
      "Autoencoder Loss: 137.69093322753906\n",
      "Discriminator Loss: 1.3862855434417725\n",
      "Generator Loss: 0.6929722428321838\n",
      "------------------Epoch 19/50------------------\n",
      "Autoencoder Loss: 131.07839965820312\n",
      "Discriminator Loss: 1.3862919807434082\n",
      "Generator Loss: 0.6944228410720825\n",
      "------------------Epoch 20/50------------------\n",
      "Autoencoder Loss: 135.0315704345703\n",
      "Discriminator Loss: 1.386295199394226\n",
      "Generator Loss: 0.6911253333091736\n",
      "------------------Epoch 21/50------------------\n",
      "Autoencoder Loss: 137.4248046875\n",
      "Discriminator Loss: 1.38629949092865\n",
      "Generator Loss: 0.6952998638153076\n",
      "------------------Epoch 22/50------------------\n",
      "Autoencoder Loss: 133.47909545898438\n",
      "Discriminator Loss: 1.3862724304199219\n",
      "Generator Loss: 0.6943923234939575\n",
      "------------------Epoch 23/50------------------\n",
      "Autoencoder Loss: 132.2054443359375\n",
      "Discriminator Loss: 1.3862926959991455\n",
      "Generator Loss: 0.6932045221328735\n",
      "------------------Epoch 24/50------------------\n",
      "Autoencoder Loss: 136.5889892578125\n",
      "Discriminator Loss: 1.386293888092041\n",
      "Generator Loss: 0.6934949159622192\n",
      "------------------Epoch 25/50------------------\n",
      "Autoencoder Loss: 131.45648193359375\n",
      "Discriminator Loss: 1.3862988948822021\n",
      "Generator Loss: 0.6908645629882812\n",
      "------------------Epoch 26/50------------------\n",
      "Autoencoder Loss: 136.35784912109375\n",
      "Discriminator Loss: 1.386294960975647\n",
      "Generator Loss: 0.6944102048873901\n",
      "------------------Epoch 27/50------------------\n",
      "Autoencoder Loss: 130.7783966064453\n",
      "Discriminator Loss: 1.3862934112548828\n",
      "Generator Loss: 0.6932418346405029\n",
      "------------------Epoch 28/50------------------\n",
      "Autoencoder Loss: 141.00502014160156\n",
      "Discriminator Loss: 1.3862941265106201\n",
      "Generator Loss: 0.6934825778007507\n",
      "------------------Epoch 29/50------------------\n",
      "Autoencoder Loss: 135.5159912109375\n",
      "Discriminator Loss: 1.3863043785095215\n",
      "Generator Loss: 0.6963866949081421\n",
      "------------------Epoch 30/50------------------\n",
      "Autoencoder Loss: 137.28956604003906\n",
      "Discriminator Loss: 1.3862941265106201\n",
      "Generator Loss: 0.6931056976318359\n",
      "------------------Epoch 31/50------------------\n",
      "Autoencoder Loss: 133.5808868408203\n",
      "Discriminator Loss: 1.3862935304641724\n",
      "Generator Loss: 0.6931352615356445\n",
      "------------------Epoch 32/50------------------\n",
      "Autoencoder Loss: 132.14932250976562\n",
      "Discriminator Loss: 1.3862941265106201\n",
      "Generator Loss: 0.693123459815979\n",
      "------------------Epoch 33/50------------------\n",
      "Autoencoder Loss: 134.42176818847656\n",
      "Discriminator Loss: 1.386293888092041\n",
      "Generator Loss: 0.6930631995201111\n",
      "------------------Epoch 34/50------------------\n",
      "Autoencoder Loss: 135.35272216796875\n",
      "Discriminator Loss: 1.3862941265106201\n",
      "Generator Loss: 0.6931476593017578\n",
      "------------------Epoch 35/50------------------\n",
      "Autoencoder Loss: 125.16806030273438\n",
      "Discriminator Loss: 1.3863134384155273\n",
      "Generator Loss: 0.6975449323654175\n",
      "------------------Epoch 36/50------------------\n",
      "Autoencoder Loss: 121.98428344726562\n",
      "Discriminator Loss: 1.3862942457199097\n",
      "Generator Loss: 0.69343101978302\n",
      "------------------Epoch 37/50------------------\n",
      "Autoencoder Loss: 131.18246459960938\n",
      "Discriminator Loss: 1.386295199394226\n",
      "Generator Loss: 0.6921980381011963\n",
      "------------------Epoch 38/50------------------\n",
      "Autoencoder Loss: 128.47328186035156\n",
      "Discriminator Loss: 1.3862944841384888\n",
      "Generator Loss: 0.693454384803772\n",
      "------------------Epoch 39/50------------------\n",
      "Autoencoder Loss: 134.42633056640625\n",
      "Discriminator Loss: 1.3862941265106201\n",
      "Generator Loss: 0.6931133270263672\n",
      "------------------Epoch 40/50------------------\n",
      "Autoencoder Loss: 132.42645263671875\n",
      "Discriminator Loss: 1.3862944841384888\n",
      "Generator Loss: 0.6927366256713867\n",
      "------------------Epoch 41/50------------------\n",
      "Autoencoder Loss: 128.22027587890625\n",
      "Discriminator Loss: 1.3862942457199097\n",
      "Generator Loss: 0.6931058168411255\n",
      "------------------Epoch 42/50------------------\n",
      "Autoencoder Loss: 129.22315979003906\n",
      "Discriminator Loss: 1.3862942457199097\n",
      "Generator Loss: 0.6931470036506653\n",
      "------------------Epoch 43/50------------------\n",
      "Autoencoder Loss: 134.1287078857422\n",
      "Discriminator Loss: 1.3862943649291992\n",
      "Generator Loss: 0.6931465864181519\n",
      "------------------Epoch 44/50------------------\n",
      "Autoencoder Loss: 129.18853759765625\n",
      "Discriminator Loss: 1.3862942457199097\n",
      "Generator Loss: 0.6931469440460205\n",
      "------------------Epoch 45/50------------------\n",
      "Autoencoder Loss: 131.56480407714844\n",
      "Discriminator Loss: 1.3862943649291992\n",
      "Generator Loss: 0.6931486129760742\n",
      "------------------Epoch 46/50------------------\n",
      "Autoencoder Loss: 131.60183715820312\n",
      "Discriminator Loss: 1.3862943649291992\n",
      "Generator Loss: 0.6927796602249146\n",
      "------------------Epoch 47/50------------------\n",
      "Autoencoder Loss: 123.93226623535156\n",
      "Discriminator Loss: 1.386318564414978\n",
      "Generator Loss: 0.6980804204940796\n",
      "------------------Epoch 48/50------------------\n",
      "Autoencoder Loss: 128.57826232910156\n",
      "Discriminator Loss: 1.3862942457199097\n",
      "Generator Loss: 0.693146824836731\n",
      "------------------Epoch 49/50------------------\n",
      "Autoencoder Loss: 132.21286010742188\n",
      "Discriminator Loss: 1.3862943649291992\n",
      "Generator Loss: 0.6931467652320862\n"
     ]
    }
   ],
   "source": [
    "AdvAE.learn(n_epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved in file: DUMPS/aae/AAE_128_64_2\n"
     ]
    }
   ],
   "source": [
    "AdvAE.serialize('DUMPS/aae/AAE_128_64_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from DUMPS/aae/AAE_128_64_2\n"
     ]
    }
   ],
   "source": [
    "AdvAE.restore('DUMPS/aae/AAE_128_64_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed27f7ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AdvAE.plot_enc_dec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NGJG3QS10mt+l4cu10N7XAsNVdWzzd+g8YnlULb1MdA4T/kbKF7RtGQLM+aAy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\n778/OzZ8+PBkz5w5s21zceWVV079qGObaPgww76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yMvqDiNyvqFSsU0vEl0JyI3I/yRK3\n73znO7Pz2Iec6yrbu/LKK5Otcj+Wut5jjz2SrXN29913TzYlghF5iDx/V2+ZlxyzKmekDHLs2LHZ\nMcrBGGbM8t8REQ8//HCyKWNW/821tRZ2zfvQ8xhCrSWrOQ49L/8D+577FEp+IvK+obRk6tSp2XmU\nWqu0nWOJ+yP+e8QrpRe1++8NlHyltgNlXiNHjsyOUS5JyRf3GBG5XJJ7JJVf0i9TfhmRzzmOmdp5\nmmKBv5P7Ut2jrig+tiSpVbkqny3GjBmTHaOchPtQyjQjcvkWn3dUtsd9icoHWca89LwQ0TvXu6bQ\nN1ISFJGvM9xjROT7Q45zroMRuWyH6yKlQ/p3ukel76StfcVnHpXMlyTaKr19I8sxSel5USVyXJ+4\nHkXkfch25byJiNhmm22SzTGhbcU5q+OAc537L+5/I/I1WfdpvP51112XbD7fdAeOBDLGGGOMMcYY\nY4zpAPwSyBhjjDHGGGOMMaYD8EsgY4wxxhhjjDHGmA7gDZMTiGj+CepkmcdFy5JSb8eyixER/fr1\nSzb1uarZpoZUc4MQaj615B//jnpf1RhSi00dfkTEbbfdluynnnqqeI03qsa3pvtn26nenXpm/a38\nO9qqe6UmnZpR1QTzGqo1Vb1m6Z74O3W8rCj6256G7UQNvebxYpnvKVOmJFvzMtVyWLAfOdd13jCv\nlB7j507S19fGNn3X+PHjk615C+jLme/rkksuyc675ZZbkq05JTiPqOfWcuf0/6rFpoZb89isqJT6\nR7XxzJ3E/GYRuVaeOeqYdyQiz0fw6KOPJltzH3D+6drKuUjtvc5ZjhkddzxWmpd6rLdR80/se81l\nwrwF8+fPT7bmz2JbamlrHmOeLfrqiFeOixK90Z+WSo1H5HNA97nsH+ZFmzVrVnYe+457Q50r9HnM\n/RXxyvyIXf1NRJ5rRHMe9pb+WkZTf8pcS5qXlOOe+WLoMxWuW5onimuc7l/pN3X/2smU8snU1hLd\nE3DcP/jgg8lesGBBdt706dOTzZxAtCPyZxTtKz7H0tY1jXn6NO8T881yDPaGZxD2k/pT7kN1reJe\nh/NI5xjnMPtp8eLF2Xl8RtC8lWxz5iLSPRDfQ+gx7ns5HnU/XHsv0QqOBDLGGGOMMcYYY4zpAPwS\nyBhjjDHGGGOMMaYDeEPKwTSUmHKrWlgdwyM11JahrLyefhevX5N8MVxeQ+cZonbAAQckW+UulLap\n/IjhYL0h7Jbtz3bVsMlaqdEStZK5NclATfrH0D+W7mMIvF6/FqbfydTahWVnNSSXIa+18pu8hpb6\n3GGHHZI9cODAZDPENyIPtWWIfUQ5pLa3S1D4+9Q/sZzx3nvvnWwthUt5weWXX55slfTxPG1vXpNh\nzxoezzB9lYMxbJs+vrf0GddClUmzjDFDlSPydW3evHnJpnQvIp8vDJVXKS5DnOlDI/J+rcmFeU31\nt1wTeL3e0o8lausM24S+UPdHXINZRljnG8PjdR4xHJ8+WUPnlyxZUrzf3khJgqJwjVOJEP0Z/aHu\nkbg+cW+ie15K7VUqQfke+1/3srwPytUi8nnKsbWi7oNK+xJd0zgH9LmAPnTu3LnJ1r7m9bnP0fPo\n/1TCxzbn2NFx0HQf3RupyU05frmmReQl2Nknd999d3YeZdKcp7qX5fOFynS1v5ah6yevr+OE/pbf\nvaKkD1HYb2wflWbymVql7pR9cW+4zjrrZOdxLaSEc8aMGdl5d9xxR7JVjse1ln2t8jXuj7RveE3K\nDnl/Ee2fz44EMsYYY4wxxhhjjOkA/BLIGGOMMcYYY4wxpgN4w8jBGKamcg+GrjLsWMO6KFnQkCmG\nSzIMi2F0EXk4GEPgtWoC70O/i2Fo++23X7I1NIxhY5ptXEPAlrGihPPVQoIZVlyrBKSwvRhOq+3K\nzwzlVSkEK1PpmGO4JUOkVSrEkFL9LStKX/U0bJeSTDMiHyfs05pEYa+99sqOjR49OtkcP5QHReQV\nWCg5iijLTnp7/zIMV0PiKbPbZpttkq3+dPLkycmeNGlSslU+UqtowTBZzj9tf4YK69rQ22H/DB8+\nPDtGiaT6KMogufZp1Ua2u0q0SC1UudTH+jc8T7+rJjvpFGprKyupatU1hqxTis6KexG51JPnReT7\nlFo1Oe5fOmFdLK0Luq+gfEt9IH0WZQOaRoD+kO2s/c2+47iIyNda9uldd92VnUcJisrBeps0s2kf\n8rPu2+lPKTHSa3BtZX+qBIjXqMk2WXWI8zKid8qfm1JKDRGRt7X2D9edkn+NyOeYzj9C38u+isif\nS/hd6lPpO3SPyvvlvFxR+7tUYVGr8THFA+2IcpUuTQVBid+dd96Z7JkzZ2bn8R2Apg/ZdNNNu/xe\nlebzuVVlvqwIzucRrbypVRpfK44EMsYYY4wxxhhjjOkA/BLIGGOMMcYYY4wxpgPwSyBjjDHGGGOM\nMcaYDuANkxOIqOaNek3qIufPn5+dR82s5hch1FHPmTMnO8a8CNRd6j1RE6j5JzbccMNkU8OopQCp\nBaYeMKKeI2NFhNrUVsrAR5RLulNnGRGx9tprJ3uzzTZLNnOXROT9puOF2m5qrDVHRW/Qwnc32i6c\nS9TFavlN9iNL3PLfI/LcTprfgt81ZcqUZF911VXZedT2a9nr3jD/mqCljanF7tu3b3ZswoQJyWY+\nGs0pcdlllyW7No/4We+Dc5M+U8cV71fLY/dG2E7MeTd48ODsPPaP5pxg3gHmn9Dz6KdLfjgiz8un\nfpn3yPwJeo1SDqjafXRSGeRaTiDm5NK8Tuutt16yWTJXc6wx74WOg4ULFyabJXQ1b0HTXHm9Zc0s\n7W80tyP3r9xrRuT7EebxUt/LPmZOEs6viHzvo3tPlsCePXt28Z64B9ay170h90gJ/ladA4sWLUq2\n5qNkm9Ty0tH/rbXWWsnWHFy1vHycw8wzo/dUy+HWG+FY5N5N93FcP/QY253PcLU+4HrHHDQRuT/U\nY9yjco+kzySci3qM1+ht+1XOFc2PxvbX/Dtcx/icMW3atOy822+/Pdlc37SN2Yd8xo+IGDNmTLJ3\n3333ZDNXUESeV01zDv31r39NNvPFtTsHkOJIIGOMMcYYY4wxxpgOwC+BjDHGGGOMMcYYYzqAN6Qc\nrCYfYUgcQ88j8rAxDYFkqDtDw7QMH2VYvI+aVEJDOPfZZ59kr7vuusnWcFpKwDQMt7eVwi2FaNbQ\nNi/1h4ZBM3yTYdUMj47I+1DLnzIcj+NFQ/NqY6Q39Ft3wP7nfNbSjTyPEhed9wy71j646aabkn35\n5ZcnW6VnvI+atKQ392lN2jNkyJDs2NChQ5PNOTF16tTsPMptGV6rvpDXUOkC56aGuhMt80p0XJT+\nfUXq31IZVV2PGBbNdTAibzOGs2spXH4X56XK7rjesdS4fqY0Se+J0kztU95HLZx/RerH1wr91aOP\nPprsf/zjH9l5lH3RVn9KCROlLxER8+bN6/K7tJ962/5leWB/6L6C8kbtH/YD5QZaUpoSA7az+jJ+\n1jnG/Q33nuzTiHwf3dtkJgrHKfuNEr6IvJ9YDjoi32PSx6mkrNSWKnXnZ/XrHGecz7of5nc39ZMr\n8rpI+Ht1716TrPIY25bSvYh8nazJpHk93d9wLlJWq9eozcUVtX9KcPxxvukcoFRW5w73m3zOnzt3\nbnYe25x7IO1rfh43blx27D3veU+ymXZE5xHTjDA9RUS+tqqMuDtxJJAxxhhjjDHGGGNMB+CXQMYY\nY4wxxhhjjDEdH/9MTAAAFrdJREFUwBtSDqYw9I0yAsp0IvKQeK2MwYoKDI/UMOZSmKaG8K2xxhrJ\npvwrImLHHXfs8hoa/vvHP/4x2fpbenM4de33lGQbeqwmf2AGeWaM17D3WgUISpM4Rmoh17Vjva0P\nXwslaaCGznPOMcxaKzTw71SONGnSpGRTfqnzrWk1m96M+jhKe0aMGJEdYxgupQYLFizIzmM7s5+0\njTkOaj6Aflf7heG/6v97o5SBocuUQus8YjUSnTuUlrA9tbIN5wclYBqyzjEzcODA7NigQYO6vF8N\nfWZ4vP4W3qN+d6dSqoqqc6BU3UQlltx76LpI2Sz7rVZRsVbNrDdSqhQWkY9nXYNYsYvyBa0wQwkS\nJUcK+67mb+m/m+6HezulFBQReXupbHb8+PHJHj16dLLVV1HySpmPymsp7dK+4Hdzz6uyGMrZalL3\n3rhfLT07RuR9zDUnIm93zlndm7A9uR7XKpdq1Uymr+DY4loake/Panuk3gDnS6mSXkT+7Kf9y/WJ\nfajPi3wu5DV0XeR+5pBDDsmOMT0C+54+PSKvAHbHHXcU77cn8S7KGGOMMcYYY4wxpgPwSyBjjDHG\nGGOMMcaYDsAvgYwxxhhjjDHGGGM6gBUiJxCpaTypgddygNRu8lhN80xdomp/WTL5oIMOyo4xB8MT\nTzyR7D//+c/ZeXfeeWey9bf0Fk3ua0F1r9TSUrPL/AYRuYaex6gtjcjzRLFMakSu2S7dQ8Qr86gQ\n9qH78/9gmzFHieYhoVaa/ai6ec57Lbt46623Jvvhhx9Odm2+dVIOC84xHcvMLaD5XTgPOFc07xnz\nHTBviLZp7T7Y9xwT6ruZu0FLHfeG3BbqD7k+sT+YFyainAsmImLUqFHJZp4nzcXD7+b6prkuODc1\np0EpD436Ws5nvQ9SyzvTSbBvuM/h3Isot5G2MddF9ZOE39XbSxa3irYD957aP9wrcg5w3YrI5ynz\nWajf5J5Vv4tzkfO0lptS99S13EcrOvxtOgeYa0mfC5566qlkc4+qeUj4d2xzzcHFHDGLFi3KjrF0\nOddq5s2LyNcGzd1Vyqum43ZF9bW8V813xXmlc4fjmf5V+5F/pzlHCftA+4f7Xs5t5pmJiPj73/+e\nbI6LiNyHr0j90wS2nfoZzrdank+Oc+bP0mPsQ+2nkSNHJlv7hm3O/cy1116bnXfTTTclW+dzba3t\nThwJZIwxxhhjjDHGGNMB+CWQMcYYY4wxxhhjTAewwsnBGHal4akssVYLiWtaopwh6/369cvOmzhx\nYrLHjBmTHWMY2g033JDsCy64IDuP4Yg1uUJvLN24jFpZdZX9sDwg+2PLLbfMzttqq62SvcEGGyRb\nw94ff/zxZGuJSIYWcpzpPTFUUfuQv4Uhh71BmrI8aMgxwywZ0rzNNttk51H6Q4mCth+lP+zTiHKJ\n8lof9LY51hTtJ4asa5li+jhKwLQkZqnNa9ImlQUy9JbzXkOD58+fn2ydz72xT0trIctLR+RS1803\n3zw7xrZmH+jaSr9Xkh/p32mpYsojeN7dd9+dncd+rEnb+N29sX9L6NyhJIHrkcqfeR7LRmsbMyxd\nS5DTd9fKFHdSf9RYHlkN253SMJVLbr311smmzEvlBNy/qlRl8ODBXV6f4yKiXh67U9C9Attcj9H3\nzpw5M9kqw6V0mdKe2r6E0peI/HmHc11LW9N3q79mn/K79bxWn6deb2qyPu4bNc0Dfz/7qtYuXNN0\nrnAvpc+S3N8MHz482cOGDcvO4zrJNVLvsTfAscj1adasWdl59JP6bMa+Yv/qPpcSP85T3Suxb9Sf\ncq5fdtllyb788suz87g/VokufW1PymsdCWSMMcYYY4wxxhjTAfglkDHGGGOMMcYYY0wH4JdAxhhj\njDHGGGOMMR3ACpcTiHRHKWfqBVm+es8998zOe9e73pVslsyNiJg7d26yzzrrrGRTNxiRa1Q7KU8M\n9bK13CDMIRGR5yWhXna77bbLztt4442TzRwSWkJw4cKFyVYtPP+OuRS0r2vaTf4d9cL6Nytq+c0a\n7Fcto8o8QDvuuGOyt9122+w8lkudPHlysllGU9F8MhxPtTLw5pVzkflAmFsrIp8f1DmrNr3k11SX\nTV+r83mvvfZKNn2Aztnbb7892VoivjRPV6RxUMsvwvxys2fPzs5jvoghQ4Zkx6htp/Ze+43+i6V2\nmasrIvePe++9d/G7OE70fpnXS/M48L46ac0kOk+ZA4R5mJgbLyJvyzvuuCPZWm6YudnU12622WbJ\n5vq2Is2j15PaGsTxzHmlZb2ZS4JlozUXGP9uwIAB2TH2I3NdzJs3LzuvlBuxk1A/w7VE24T51+hP\ndQ/Evq+VD6/l5WOOIOYH0u9iLk0thc77qOVMLOUOWpFhP2r5ePpE9qOex3lay5/Fdtf8UFwXuT5r\nKXPmAtO8T70N9g19kLY/n+E0Tw/7gO2vz5Vc47jnZd60iHyPev/992fHmAfot7/9bbI1RyZ/l+6B\n6Utod/d8cySQMcYYY4wxxhhjTAfgl0DGGGOMMcYYY4wxHcAKLQdrB7XQapbuO/roo7PzGEKmMqOL\nLroo2ZSxMGQzonnZxd4caq3tzzJ/KkFh2PLb3va2Lv89Ig97Z4i0lsKtSc/YvwwD1D4sheRG5OHY\nDBXV+2B4d0+WBuxOGH6pZYZHjx6d7LFjxyZbwyOnTJmSbJbH3G+//bLzKF+g1Czilf1qcjj/tP1r\nbccwaPo/DV3lNfldnFMRESNHjkz2/vvvnx2bMGFCsjmuKP+KiLjxxhuTXfO1vcWfMmSY4et33XVX\ndh59lEqv6G8Zaq0SFPqlmmyMa+a+++6bHeNYYP/o+lkrldpb+m55KYW2R+QSPJa1pSw6Im/zv/71\nr8nW8sgDBw5MNuVlEREbbbRRl/dklp/aWOa8opwzIt8vMPXA1KlTi9fnOhuRS265z1IJCuUWKn1S\nH9Fb0XHOdlX5Mz9zz6JzjNKVWnl3zmddM9kflEZrygL+nf4W+nyV3pIVye+WfKX6Tba1lhfnWsV2\nUfkz18Ja+9VSYHA+s+9030zfq9Kn3kZTaSz7SecY4TG2Y0QuwRs/fnyytUQ872PSpEnZsbPPPjvZ\nnPe6f6mNA86xnpRcOhLIGGOMMcYYY4wxpgPwSyBjjDHGGGOMMcaYDqDj5WAqgVh//fWTffjhhyd7\n1KhR2XkM3bruuuuyY+edd16ya1KJ0vV6I6UwOG1/hsb2798/O0ZZyIgRI5KtsjHKH1gZQUMJWUmK\n1cYi8jA+2qzCE5H37+LFi7NjDz74YLKfeOKJZC9atCg7j/elY2RFGRc1WZ9WtWAoOkOV58+fn513\n/fXXd/ldDJ+NyCtqqASC/W/5Qh1tH457zqmIiHXXXTfZDKum/9RrMBybsqGIiAMPPDDZBxxwQHaM\n0kzKma644orsPI4frSLRGyvw8TdxTmhYOn0P5R0Red/xeuorS9UNVb7AuaiVbgj7QOWxvH5v6at2\nomHvXMe4Zqr8YdasWcmeM2dOsvv165edx7GkaytlCPQX7qf2wn2QSlVYPY99On369Oy8mtxlyy23\nTDYlf9rfPKbztFP6XPdk9I2sYBpRrjilshDuezlPdT/MKpdaDZN+netxTSqkawMlor2lWnFJIqSy\nSu4Ntaos/45tVKvoV6tcWZMtcX/MdAb/X3t38mpHtcUBeAceSEAR1ExEjAYNKgRscGAzEIcqIujA\n/8Cp/45kohOxRY0tOLBDTDDGJnYRDYrG2CABERz6BvL2++2Veyp598WbnKrvG61LVc7NrapdtU+x\n11o1HX+qK+ecx+LUfCCfQfXazmOe57re47L7bHYorr/rwIEDPc4OYK2NXRWnuiiei+fJSiAAAACA\nBfASCAAAAGABvAQCAAAAWIBF1gTKXMGaJ5p1Z+6///4e19oHR48e7fHevXuHbdmWXH2Dk03VBMrj\nnLVAWhtbwe/evbvH27dvH/bLWhSZK11ryWSefM2/zTztzL+traePHz/e408++WTYlnUX8prIds71\nM9f1Gqn1ZPKcZP2B1saWjNkSM2sdtDbWIMhrodadyd9V26PmtszFXtfaS2fCqtpItW5BXqdZ36q1\nMYc+86jrGMt/l/UlavvwvO/WFp5Zd2vfvn09fu+994b9plqXr3ONg9OR12/NSc+fs/16a+P9d6rG\ny6rxMVX7oI7F/IyspVFrjcz9XG1Gnps6F8mW7jfccEOPa52LrCmSn1Hvzzn+spZCa+O1NPUcdw43\nNjXGcj5y2WWX9bi2Ks5zkHOHel+fqjWzql5QbRE/VddrzvLc1Odi1maptefy362Kp9R6MVn3p27L\n53PWQqw1GPO7Ss5XWxufB/l3rvP4zWs9r+16LWdtmFoXLffNeoj12ZrzndxWa3Dl94usZ9paa3fc\ncUePc25b6zBm3Zn6PWQppq7Lev/L+cdVV13V4wceeGDY78477+xx3hdr/cT8nn/48OFhW61HtE6s\nBAIAAABYAC+BAAAAABZgMelguTQ2l1XWpdAPPfRQjzMFpS5Zf/TRR3t88ODBYVsuDVtSmsnpymV7\ndYlr/lyX961K56lLnXfs2NHjXK5Z2x7nv6tL7HNpZ57DqRaedaliXgdT10T9zHU0tRS9tizNpbJT\nrbtzbGYKWV26+/vvv/f4+++/H7bl+V+V+rLR716iutT5559/7nGmM7bW2pVXXtnj6667rsc1lSuv\n+7yf5vLc1lq74IILelzTJZ9++ukeP/XUUxv+/+r/f52Xs/+T6nWeKQCr0gRPtS1l+lC9r+W1kOO0\nnm9jcdpUi/hMI6opzplOvWvXrh7ffvvtw345tjMVs7XWjhw50uNVqWH812bH1NQcKc93pgLWttE5\np9mzZ8+wLa+TTGnK+3Br4320pjzMeZzm3zb1LDkTx2AqrXJqW85n85qoabgff/zxyt+9av61Tud2\nau6ZY6emtuZcpc4pM70u55f52a2N5UTyflhLFuTn3XPPPcO2a6+9tsc5Ft9///1hv/379/d4SS3i\nT1e9T+Yc8+677+7xXXfdNeyXJUNynptzzdZae+utt3o8p+O//t8+AQAAADglL4EAAAAAFsBLIAAA\nAIAFmG1NoKnc2syfffDBB4f9brrpph5nnt+HH3447Pfcc8/1ONvd1n/Hyabab2be+Q8//DBse/PN\nN3uc7cQvv/zyYb+shZB5orV9df5ct+U5zVaNNRf0xx9/7PFnn302bMu6F/m3ZB2h1ta7veB/1Lzs\n/LnWmsm852zTefPNNw/7XX311T3euXNnj2vubx73AwcODNuy3si65rxvlToWs+ZOvf9lLYobb7yx\nx7t37x72W9Xauh7/bMf55JNPDtueeOKJDfdbUo2KrZDHb7M1XrZv397jqRppeW+s9171ZU42VRsk\nZY2K2oo4W0fnM7LWyjh27FiPc57TWmsfffRRj+t9nZPVe9JUi/hs1/3LL7+s3C9rWGR76azN1tpY\ng+36668ftuV18tVXX/X422+/HfbL/8dSz/fZfK7kMzmvj9bGMZw1E+v94euvv+5xthlvbZzn1uf/\nusoxltd5xq2NNYFqzayc32QNy9qaPWumZT2t/I5Zf651E/O85jzr8ccfH/Y7evRoj2t906XKc12P\na9a6y5pAWZ+ptXFO+frrr/f4mWeeGfY7ceJEj+dUc9JKIAAAAIAF8BIIAAAAYAFmlQ42tYw8lwLe\neuutPb733nuH/c4///weZ6rP3r17h/1yaV5dRikt4WSrWm7WZY1//PFHjz/99NNhWy5Tz6V/F110\n0bBftsjMc5NpSPXnmuaVKQr5/63tN7N95FS6WaYl1f/Hui4tzPFWx17+TTmOWmvt8OHDPc5Uotqe\nNpe95/GrKV+HDh3qcW2rmct181pb8hhdlfZTj0kuFc/2mK2N4yrHQE0Hy/tuphbU85TLcOu4z3TM\nXDo9l+Xr56Kp9M6p+1Wm/NV7arbXzXNa2+5y8vHPFI86TvP+mqkf2SK3tTFtOlPzMsWrtdZefvnl\nHr/wwgvDtnwG52es6zPsbKrnMecF3333XY/zednaeF4zzau2pc65bPXTTz9t+Pn12ZrPz3qOl/wM\nPZPyONb5cD7j8p7Z2piunec6U+dbG+dRNZV+jinyecymUhjz7z3vvPOGbTkXzeM59V0j79H12Zfl\nII4cOTJsy3IGOQ+q4z7nwEu939bnYp6nmg5722239fjSSy/tcaait9ba/v37e/zKK6/0ONPEWpvv\n885KIAAAAIAF8BIIAAAAYAFmlQ6WaoX87EJ033339XjXrl3DfrnkK5eJ1WWyqrNv3lR3sFwSXdOm\ncmnyN998c1qfn+pSwqmlsKtSnaY+Y2pbLh+cy7LbqfOYXRS++OKLYVumGX3wwQc9rql2ef4zTTA7\nw7U2djSp2+a6hPNMWXWNtjamXmUaSGutvfjiiz1+9913e1xT+lIuka4d8vJ6qUu45zh2zkVTHahW\n3efqfjn+alpfdg7Lpe6ZTtiacbqRPP61S1Cmpj/22GM9fvvtt4f9MpUhPyO7Q7U2nrd85rY2jk3p\nmP+7qTlHHs+8V9Z0vexWdMstt/S4zmVzbNbz+M477/T4+eef73F9Vucz2L33n1ePcd4La4pRjttM\nMarP4EzRPdXvW0f1b8h7VM4bMwWytXEc1ZTkTL07fvx4j3fs2DHsd+GFF274eV9++eWw36uvvtrj\n2kU40+Qz5at2Isu/aw7nbTNq2l52XatdojNFMrvi1XvcSy+91OPPP/+8x7W8x1znJVYCAQAAACyA\nl0AAAAAAC+AlEAAAAMACzKom0FR9lswXvOaaa3pc8/yyxeprr73W48zb3OjfcWZM5bpuZR7sUnNu\nN6uOh6wlUGtY/Prrrz3Oegene8y1qj1zpmpU5HGudXrynJ44ceIf+t+xlaZqfKWpdvHZVnXfvn3D\ntqz9kzX2ak0g43m6XkzWuWhtrB2RrYiz7ktrq2vb1XM9x7bR54qpY5s/5znNOhWtjfVfnn322ZWf\nlzWBsjZea2PtrvxdzvfWy7FYa9Pktnpusm7NoUOHelxrK+Ycawn11/JvynlorQmUdQlrnZ6so/Sv\nf/33a3KdB+W9c6pu21Q9n1Vjzlj8W97H6vf6/Lke8xwT+czMGpatjffXrFc69VycEyuBAAAAABbA\nSyAAAACABdi2lUuctm3btmW/rLZF3LNnT48ffvjhHl9xxRXDfm+88UaPH3nkkR7XpYTr1rb4r7/+\n2nbqvU5tK88hozN1DltzHs8mY3H9GYt/q8uzsw18bVWcLY4zBeVspiQYi+vPWJwHY3Favdfmd5yd\nO3f2OL/rtNbawYMHe3zs2LFhW01v+n8Zi/NwLo3FvO4zNay1cQxccsklK7fldf7bb78N++W8ZE7p\nkad7Dq0EAgAAAFgAL4EAAAAAFsBLIAAAAIAFmG1NoJo/m20TL7744h5nPmBrrf35558rt6V1qAOU\nzqUcTzZHvvU8GIvrz1icB2Nx/RmL82Asrj9jcR7WZSzW7/mnY92+u2+WmkAAAAAAdF4CAQAAACzA\nlqaDAQAAAHB2WAkEAAAAsABeAgEAAAAsgJdAAAAAAAvgJRAAAADAAngJBAAAALAAXgIBAAAALICX\nQAAAAAAL4CUQAAAAwAJ4CQQAAACwAF4CAQAAACyAl0AAAAAAC+AlEAAAAMACeAkEAAAAsABeAgEA\nAAAsgJdAAAAAAAvgJRAAAADAAngJBAAAALAAXgIBAAAALICXQAAAAAAL4CUQAAAAwAJ4CQQAAACw\nAF4CAQAAACyAl0AAAAAAC/BvoLJeQ5GfOFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed296179e8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AdvAE.plot_noisy_recon(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BCAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "BCAE = BayesianConvAutoencoder('BCAE', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from DUMPS/bcae\n"
     ]
    },
    {
     "ename": "DataLossError",
     "evalue": "Unable to open table file DUMPS\\bcae: Unknown: NewRandomAccessFile failed to Create/Open: DUMPS\\bcae : Acceso denegado.\r\n; Input/output error\n\t [[Node: save/RestoreV2_15 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/cpu:0\"](_recv_save/Const_0, save/RestoreV2_15/tensor_names, save/RestoreV2_15/shape_and_slices)]]\n\t [[Node: save/RestoreV2_32/_19 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_79_save/RestoreV2_32\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\nCaused by op 'save/RestoreV2_15', defined at:\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2808, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-18-d867b43befdb>\", line 1, in <module>\n    BCAE.restore('DUMPS/bcae')\n  File \"C:\\Users\\AlbertoIbarrondo\\AnacondaProjects\\Variational Autoencoders\\models\\bayesian_conv_autoencoder.py\", line 472, in restore\n    saver = tf.train.Saver()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1056, in __init__\n    self.build()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1086, in build\n    restore_sequentially=self._restore_sequentially)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 691, in build\n    restore_sequentially, reshape)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 407, in _AddRestoreOps\n    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 247, in restore_op\n    [spec.tensor.dtype])[0])\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_io_ops.py\", line 669, in restore_v2\n    dtypes=dtypes, name=name)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 768, in apply_op\n    op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2336, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1228, in __init__\n    self._traceback = _extract_stack()\n\nDataLossError (see above for traceback): Unable to open table file DUMPS\\bcae: Unknown: NewRandomAccessFile failed to Create/Open: DUMPS\\bcae : Acceso denegado.\r\n; Input/output error\n\t [[Node: save/RestoreV2_15 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/cpu:0\"](_recv_save/Const_0, save/RestoreV2_15/tensor_names, save/RestoreV2_15/shape_and_slices)]]\n\t [[Node: save/RestoreV2_32/_19 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_79_save/RestoreV2_32\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mDataLossError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1038\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1040\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1021\u001b[1;33m                                  status, run_metadata)\n\u001b[0m\u001b[0;32m   1022\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m     87\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[1;34m()\u001b[0m\n\u001b[0;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[0;32m    467\u001b[0m   \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDataLossError\u001b[0m: Unable to open table file DUMPS\\bcae: Unknown: NewRandomAccessFile failed to Create/Open: DUMPS\\bcae : Acceso denegado.\r\n; Input/output error\n\t [[Node: save/RestoreV2_15 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/cpu:0\"](_recv_save/Const_0, save/RestoreV2_15/tensor_names, save/RestoreV2_15/shape_and_slices)]]\n\t [[Node: save/RestoreV2_32/_19 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_79_save/RestoreV2_32\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mDataLossError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-d867b43befdb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mBCAE\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DUMPS/bcae'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AnacondaProjects\\Variational Autoencoders\\models\\bayesian_conv_autoencoder.py\u001b[0m in \u001b[0;36mrestore\u001b[1;34m(self, path)\u001b[0m\n\u001b[0;32m    472\u001b[0m         \u001b[0msaver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    473\u001b[0m         \u001b[0msess\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m         \u001b[0msaver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    475\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\u001b[0m in \u001b[0;36mrestore\u001b[1;34m(self, sess, save_path)\u001b[0m\n\u001b[0;32m   1455\u001b[0m     \u001b[0mlogging\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Restoring parameters from %s\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1456\u001b[0m     sess.run(self.saver_def.restore_op_name,\n\u001b[1;32m-> 1457\u001b[1;33m              {self.saver_def.filename_tensor_name: save_path})\n\u001b[0m\u001b[0;32m   1458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1459\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    776\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 778\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    779\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    980\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m--> 982\u001b[1;33m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[0;32m    983\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1030\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[1;32m-> 1032\u001b[1;33m                            target_list, options, run_metadata)\n\u001b[0m\u001b[0;32m   1033\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1050\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1051\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1052\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1054\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDataLossError\u001b[0m: Unable to open table file DUMPS\\bcae: Unknown: NewRandomAccessFile failed to Create/Open: DUMPS\\bcae : Acceso denegado.\r\n; Input/output error\n\t [[Node: save/RestoreV2_15 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/cpu:0\"](_recv_save/Const_0, save/RestoreV2_15/tensor_names, save/RestoreV2_15/shape_and_slices)]]\n\t [[Node: save/RestoreV2_32/_19 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_79_save/RestoreV2_32\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\nCaused by op 'save/RestoreV2_15', defined at:\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2808, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-18-d867b43befdb>\", line 1, in <module>\n    BCAE.restore('DUMPS/bcae')\n  File \"C:\\Users\\AlbertoIbarrondo\\AnacondaProjects\\Variational Autoencoders\\models\\bayesian_conv_autoencoder.py\", line 472, in restore\n    saver = tf.train.Saver()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1056, in __init__\n    self.build()\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1086, in build\n    restore_sequentially=self._restore_sequentially)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 691, in build\n    restore_sequentially, reshape)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 407, in _AddRestoreOps\n    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 247, in restore_op\n    [spec.tensor.dtype])[0])\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_io_ops.py\", line 669, in restore_v2\n    dtypes=dtypes, name=name)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 768, in apply_op\n    op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2336, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1228, in __init__\n    self._traceback = _extract_stack()\n\nDataLossError (see above for traceback): Unable to open table file DUMPS\\bcae: Unknown: NewRandomAccessFile failed to Create/Open: DUMPS\\bcae : Acceso denegado.\r\n; Input/output error\n\t [[Node: save/RestoreV2_15 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/cpu:0\"](_recv_save/Const_0, save/RestoreV2_15/tensor_names, save/RestoreV2_15/shape_and_slices)]]\n\t [[Node: save/RestoreV2_32/_19 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_79_save/RestoreV2_32\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "BCAE.restore('DUMPS/bcae')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BCAE.plot_enc_dec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BCAE.plot_noisy_recon(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "BAE = BayesianAutoencoder('BAE', n_inputs = 784, n_neurons_encoder=[128, 64], n_latent=2, n_neurons_decoder=[64, 128])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from DUMPS/bae/BAE_128_64_2/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "BAE.restore('DUMPS/bae/BAE_128_64_2/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed29606978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BAE.plot_enc_dec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHsiO6RjdAMNNFY4zvV/+Hcsxb+wabQnT3Bgask322GOPZGv7s08ZYqySC8IQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed25f66860>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BAE.plot_noisy_recon(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BVAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BVAE = BayesianVAE('BVAE', n_inputs = 784, n_neurons_encoder=[128, 64], n_latent=2, n_neurons_decoder=[64, 128])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from DUMPS/bvae/bvae_128_64_2/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "BVAE.restore('DUMPS/bvae/bvae_128_64_2/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda4edc2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BVAE.plot_enc_dec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H3QmfbRpe3h3YJuyXiFxmp1LHEjoXOQdUSskQc85L9UHsK87Tz3zmM9l5LOWr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\nO+lj9NrMNaNzvTegj+tOflP6OLaX7l1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bmpZH5TqrYddN9zW9EfZeY4899sg+X3vttcnm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\nAAAT8BIIAAAAYAJeAgEAAABM4D9/zC2JrtURAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda5edcb0f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BVAE.plot_noisy_recon(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}