Denoise_Autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nikitavemuri/anaconda/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n",
      "/Users/nikitavemuri/anaconda/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Denoising Autoencoder\n",
    "In this lab, you will be applying a noise filter to MNIST images and using a denoising autoencoder to reconstruct the original image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def masking_noise(X, v):\n",
    "    \"\"\" Apply masking noise to data in X, in other words a fraction v of elements of X\n",
    "    (chosen at random) is forced to zero.\n",
    "    :param X: array_like, Input data\n",
    "    :param v: int, fraction of elements to distort\n",
    "    :return: transformed data\n",
    "    \"\"\"\n",
    "    X_noise = X.copy()\n",
    "\n",
    "    n_samples = X.shape[0]\n",
    "    n_features = X.shape[1]\n",
    "\n",
    "    for i in range(n_samples):\n",
    "        mask = np.random.choice(range(n_features), int(v*n_features), replace=False)\n",
    "        for m in mask:\n",
    "            X_noise[i][m] = 0.\n",
    "    return X_noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"files/ae_diagram.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network parameters and Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "learning_rate = 0.01\n",
    "training_epochs = 10\n",
    "batch_size = 256\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a placeholder value for the input images after they are flattened to an array of dimension 784."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, shape = (None, n_input), name = \"X\")\n",
    "X_noise = tf.placeholder(tf.float32, shape = (None, n_input), name = \"X_noise\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill in the weights and biases dictionary for all the layers.\n",
    "\n",
    "Fill in the encoder and decoder functions:\n",
    "* The encoder function takes in a noisy image and returns a 128 dimensional tensor (the middle layer)\n",
    "* The decoder function takes in the middle layer of the network and outputs the restored image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights = {\n",
    "    'encoder_w1': tf.Variable(tf.truncated_normal([n_input, n_hidden_1], stddev=0.1)),\n",
    "    'encoder_w2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2], stddev=0.1)),\n",
    "    'decoder_w1': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_1], stddev=0.1)),\n",
    "    'decoder_w2': tf.Variable(tf.truncated_normal([n_hidden_1, n_input], stddev=0.1))\n",
    "}\n",
    "\n",
    "bias = {\n",
    "    \"encoder_b1\": tf.Variable(tf.truncated_normal([n_hidden_1], stddev=0.1)),\n",
    "    \"encoder_b2\": tf.Variable(tf.truncated_normal([n_hidden_2], stddev=0.1)),\n",
    "    \"decoder_b1\": tf.Variable(tf.truncated_normal([n_hidden_1], stddev=0.1)),\n",
    "    \"decoder_b2\": tf.Variable(tf.truncated_normal([n_input], stddev=0.1))\n",
    "}\n",
    "\n",
    "def encoder(X):\n",
    "    layer1 = tf.nn.sigmoid(tf.matmul(X, weights['encoder_w1'])+bias['encoder_b1'])\n",
    "    layer2 = tf.nn.sigmoid(tf.matmul(layer1, weights['encoder_w2'])+bias['encoder_b2'])\n",
    "    return layer2\n",
    "    \n",
    "def decoder(x):\n",
    "    layer1 = tf.nn.sigmoid(tf.matmul(x, weights['decoder_w1'])+bias['decoder_b1'])\n",
    "    layer2 = tf.nn.sigmoid(tf.matmul(layer1, weights['decoder_w2'])+bias['decoder_b2'])\n",
    "    return layer2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the encoder and decoder layers using the functions above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder_op = encoder(X_noise)\n",
    "decoder_op = decoder(encoder_op)\n",
    "\n",
    "pred = decoder_op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the entropy by taking the Log Loss and calculate the overall loss by taking the mean of the entropy. The predicted value of this network is the output of the decoder and the true value is the original MNIST image *before* the noise filter is added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "entropy = tf.losses.log_loss(X, pred)\n",
    "loss = tf.reduce_mean(entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the optimizer as the RMSPropOptimizer which minimizes the loss calculated above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(loss)"
   ]
  },
  {
   "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 ./data/mnist/train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting ./data/mnist/train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting ./data/mnist/t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting ./data/mnist/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "MNIST = input_data.read_data_sets(\"../data/mnist\", one_hot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start an interactive session and initialize all the variables created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each training epoch, take a batch of the training examples, apply the noise filter to each image, and run the optimizer and loss. At the end of each epoch, print the loss and confirm it decreases with each epoch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100.20810449123383\n",
      "Epoch 1: 45.626348689198494\n",
      "Epoch 2: 35.127207174897194\n",
      "Epoch 3: 31.335228964686394\n",
      "Epoch 4: 28.98599722981453\n",
      "Epoch 5: 27.44351363182068\n",
      "Epoch 6: 26.320942506194115\n",
      "Epoch 7: 25.525962702929974\n",
      "Epoch 8: 24.837533175945282\n",
      "Epoch 9: 24.367572277784348\n"
     ]
    }
   ],
   "source": [
    "num_batches = int(MNIST.train.num_examples/batch_size)\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    total_loss = 0\n",
    "    for batch in range(num_batches):\n",
    "        x, _ = MNIST.train.next_batch(batch_size)\n",
    "        x_noise = masking_noise(x, 0.5)\n",
    "        _, l = sess.run([optimizer, loss], feed_dict = {X: x, X_noise: x_noise})\n",
    "        total_loss += l\n",
    "    print(\"Epoch {0}: {1}\".format(epoch, total_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the code below to plot the original image, the noisy image, and the reconstructed image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NHd4h84Z0Tnv1dADuyp4OwMh4tzcQ8F8+P1/y/Cd5V1anSoVLW3SmyzqFm+dI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\nmPve9QB0b76C3KvEN9pTzT1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wpC72gdgYqdTVxm/11JpckcKeaK+nEP02mrYYHW0xuVdH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2883f7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = MNIST.test.images[:10]\n",
    "x_noise = masking_noise(x, 0.5)\n",
    "pred_img = sess.run(pred, feed_dict = {X: x, X_noise:x_noise})\n",
    "f, a = plt.subplots(3, 10, figsize=(10, 3))\n",
    "plt.axis('off')\n",
    "for i in range(10):\n",
    "    a[0][i].imshow(np.reshape(x[i], (28, 28)))\n",
    "    a[1][i].imshow(np.reshape(x_noise[i], (28, 28)))\n",
    "    a[2][i].imshow(np.reshape(pred_img[i], (28, 28)))\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAA5FJREFUeJzt1MENwCAQwLDS/Xc+tgCJ2BPklTUzHwDv+28HAHCG4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+\nQIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5A\nhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE\n4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QITh\nA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOED\nRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+\nQIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5A\nhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE\n4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QITh\nA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOED\nRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNE\nGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QYPkCE4QNEGD5AhOEDRBg+QIThA0QY\nPkCE4QNEGD5AhOEDRBg+QMQGL4sE9RSocXsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x181c378550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NBwYCq2vXLnF7G0fRsGFDdu3aZT9UJmVUSk1X7vT6AxNAvookfh2eR+LLaNpi\n/Mtn2mJiyJhwbTEYQhlM/Qi0sCyrmWVZ5YEBwEfFnRvCb8WECy+8kC1btrBjxw7y8/PByPhj9EoW\nHuzyZWdnA1gkfh1WIPFlNG0xzjBtsRCJImPCtcVgCHowpZTKBUYCX+AO3HpbKfVzCefGFS6XiylT\npnD11Vfz888/Q5RknDdvHvPmzfP7mWVZWFbJ7ttACULGuMIuX+vWrQEOlgE9/XcZkNH0N3GGaYve\nJJCMCdcWgyIQX2C4HgW+x7gkUL9pOGR8/fXX1euvv+73M0C5qy38BCJjPNehUkoBK1UCyxhNPY0V\nRsb4l08p0xaVkTEuCFRGV2yHcgY7I0aMAODVV18F4Pbbby90jlKe1Zft2rUDYOnSpQCceeaZkS5i\nVMnIyACgTp06ACxYsACAPn36xKxM4UAsi/a6NBgMhmizd+9e/bpBgwYAZGVlATBjxgxGjhzpdZ6c\nYyiM2U7GYDAYDAaDIQSMZcohjBgxQlukfOncuTMrV64sdHzDhg2RLlZMqVWrFgBJSe4xf7xbpJKT\nkwFvi9R7770HwM033xyTMoWCrOi57LLLANi+fXssixNTVq9eDUBKSgrgfKvjiRMnAI/1u3v37gA8\n+OCDMSuTIbKsXbuWt956C4CPPnLHi2/cuLHQeaK7Y8aMYdSoUX4/MxTGDKYcwrRp05g2bRoAXbt2\nBeDo0aMAfPfdd/q8vLw8AC6//HKWLFkCwLPPPgvA2LFjo1beaFKtWrVYFyFoTp48CbgHS1J3gmVZ\ncd05NWrkzowiboGyTKdOnYD4uNlkZWXRvHlzAPbv3x+26xYElbNp06awXTOcnD59GoAKFSpw4403\nAvDBBx/EskhRoUqVKoB7AN2xY0evz0Rf7X2RvzCEcC56SlSMm89gMBgMBoMhBBxtmRowYAAA8+fP\n57HHHgNg4sSJABw+fBiAGjVqxKZwYULcA506ddIWqe+//77I88VVJFYpSHyL1LFjx2JcktIjwfJv\nvPEGAJ9//nksixNWCnLLaPerPYi1tFx44YUALFu2DIDy5cuHWLro88MPP9ClSxcgPixTFStW5ODB\ngwBMmDDB6zkYxDL+66+/Am5dv+aaa0IqYziRvlJcmeCxSJ06dQqASpUqRb9gUUKs4/6sS/6sUHfd\ndRcAw4cPZ/r06V7HnM7zzz/PQw89BKB18OGHHwagQ4cO1KxZM2K/bSxTBoPBYDAYDCHgSMtUy5Yt\nAdiyZQsH7dzYAAAgAElEQVTgtkyJRUpG0pIG4OjRo3oEap/9+86M/FkGfvvtNwAaN24czuKXCom1\ngOItUsLFF18MwPLly7WVqiBxGq1atYpACWOHWKQ2b94MePQiHrjlllsAj3XVjj0QXV4L5557LgAL\nFy7UMUlOQ2Ji2rZtG/K1Vq1aBcSnRSo3150386KLLtLHnJz24qeffgKgffv22roYKvv27WPcuHEA\npKamAoX73lhjt0gBXm1O0tE899xzcWmdeumllwDo2bMn4K5bcC8G6dy5MxCYLp48eRKXyz0cEGuU\nHX/HYs1jjz3GzJkzAUhPTwfcViixTH355Zdezy6XS8eM9erVC4CnnnoqbOVx5GBKbp523nzzTQD+\n8pe/APDyyy8DRQfOffHFFwBaQWTAZN9TyIkdXlHMnTsXgHXr1uljvgHNiYYMnuQmEA/cdtttgMe9\n56+O5EZWt25dmjVrBsC2bdsAj6vEqaSnp+vVe3Xr1gVgzZo1QHADIvkvxBVRuXLlcBQzKuzZ496O\nrGnTpvqYE/sUWbknuyrk5+ezaNEiAK644oqgrrlv3z4A6tWrp2X+5ptvQi1qRBG3tGVZul3KwOqf\n//ynPs/3M6dRsBUPl19+uV6c5Kt3zZs3L7S6NjMzs8hchPHU7oRJkybp19KP3nvvvfqY1N/ll18O\nuAdVTZo0AcI7iBKMm89gMBgMBoMhBBxpmfLHrbfe6vUsbNq0SS95tY/OJX+GzJbr168PQJMmTXRm\n7XhhzZo1DBo0CEA/Q3y6vwIlKSkpbK6IaCIWKbGSymzYjujp6tWrtZt36tSpXuckJyfrHFQ33HBD\nxMpbWp566qm4az+R4t133wW8+x1pn2JJdgKyNH7y5Mn62KWXXhrSNX/80b3Pcu/evbX8YrF0ApmZ\nmdpi+PvvvwN49Sf+rE5nn302AL/88gsAVatWjXApS4dYzCQYXBZtAOTk5ABQrlw5feyss87y+n48\n7pAxfvx4XnvtNcDjypM+NT8/X+v0mDFjAI8nCty6CZ52CrBz506v6ycnJ4fNw2MsUwaDwWAwGAwh\nEDeWqaIoKuhaEsgJK1asAODQoUOOjGsoDt9Ea0IiWqTsXH/99QB8/PHHMS5J8UhyVftMsDgdk1mk\nffGB+PobNmwIeMdaSbygr1U2msjiiLlz5+rksuEkHmM2Fi5cCMDo0aO1BdyJWeDvv/9+AF555RXA\nbT2bPXt2qa7ha/kYMmQI4Nxkjlu2bCEzMxPwWCjsyIKkq666Sh+TGLibbroJ8GQJd0pguljT7HUn\nCXMrVqwYkzJFin//+98APP300zz99NOAZ1/AKVOmAO7YsUceeaTIa9gtUuCOvZUAfSGc3g9jmTIY\nDAaDwWAIgbi3TAWKJNXLz8/XVio55nSqVKmiV+QkOpIAMJ7ipUrrc7fHNfgiq1TtvvyBAwcCcPXV\nV8csSa0klA1HAlWJX0xPT9fxKfGY1uOzzz7TryVu08n4a1NDhw4F0HEpUHj17FdffcUDDzzgdUyS\nJju1nXbu3FmXTeK77NgtUuBub2Jl++qrrwDPqjmncc455wDuFcCJZpFav3494Em0OWLECL0SP5Dk\nwPn5+TpGTjwF9v/Ln76Ga/VmmRlM2YmXQZSQlZWlTZyhZJuOB55//nkAPv30U7182x5U6EQkq669\nMUoDFVdDoMGf0sn36NGj0LXkBhbvyH5wRW3gHW8opXS+H3GpOYnRo0d7vbcv7pg1axbgPZgSV4ic\nc/755xe6pgx+Dx8+7PhdKMTdI9n2A8VJuc8ef/xxnWtRXKyJiOic9Pn2wY/Uo78N7yVf2OrVq3VI\ngiwo2L17N+Ct9xJOsW7dOmrVqhWWshs3n8FgMBgMBkMIOHvKHwZkdiEBlPEQfP7HP/4R8HYjJLpF\n6j//+Q+AXnbfoUMHx1ukfPFnQi7tcmQ5X6xy4AnyHTVqlA4ijkfuvvtuwPM/2RPsxRP33Xcf4OlL\natasyY4dO2JZpGKRTPoSrJyfn8/f//73Is8XV7ucM2jQIG0ZF5zq3rMjlmJ/bvhRo0YBnmSdNWrU\n0PsVOpHp06fHxb0rVGRXBbsVXupRdE76w/z8fB0yYa9je+oEO/b3stggnBjLlMFgMBgMBkMIxNfU\nvxS8+OKLgCeIsF+/foB7BOvUbQJknzK7RQrg5ptvjkVxoorEzsisQ4Ku4wl/CTpLyzvvvANA//79\n9UxKrhsLq5RYdGXbkSVLlgR9Ldnf69VXX/W6ZjyxdetWr62swB3gesYZZ8SyWAFhD1aWJIfybMf3\n2JEjR7RVJF6sI6dOnaJ69ep+P7P3/2KZcvo2Tvv37/ebhkLqY8KECV7PM2fO1HslxhO+i3OysrKK\nXeAjgeqyyGDhwoWFLFJFWarCTUIOpnJycvTqkwoVKgDOX21z6tQpUlJSgMLmaTmeyPzvf/8D3O49\n8N48NpGRPekkSFL2nnTKvovSuUneNnsQp+wL1q1bN6/39mN2fG/E4Rh8RhvJKQYeeWSj30SlevXq\nWlbZVD0cm1xHkkqVKumQAQmQF9eRUkrrsGxWXaNGDT0B91256BR896C1v/a3P63v4MvfQFgCtyOR\nOy4clLRaUXJQSZ0tXLhQ9ysSMiHGifz8/Ij2OfHXmxkMBoPBYDA4iIS0TJUrV67QiN3p/Pvf/+ah\nhx4CCu/BJMHZiUwkAgKjhczSg7EmvfDCC4B76TN40gYkJyfr/CiffPIJAC1atAi5rMEiu6wrpXS2\ncrGq+ZvtFWdSj5bZPRLMnTtXL7GXvuXPf/5zLIsUcSzL0rP8DRs2xLg0pWfp0qVe7+16Z1/kItYN\ncY/997//BZxjJZZ95UTvzj33XLZu3er3XMuyCt377JYq+UwsUtOnT2f48OFhL3OkmTNnDuAJ65Fn\n8IQkdO/ePSplMZYpg8FgMBgMhhBISMuUy+UqtGO203n00UcLZWKVhI+JzuOPP6593/Jc3J5LTkPi\nSPwl7SyJuXPnAh7LlF0HJG4slhYpoU6dOoB7BiuB5JIcT2Klunbtqo/ZkRm/fdYYr7z88ss6AF2S\n/8qOComGWMrBO1Ys3mjTpk2pzp85c6bXs1No0qSJ1/utW7eyadMmwLNQpGfPnoA7tYqvd8ZfHFW8\neXB8ueOOOwBv67hYHv3FbUaSEi1TlmU1sixrsWVZGy3L+tmyrPsKjtewLOsry7K2FDz7XzoRB+za\ntYvLL7+cNm3a0LZtW91ZHj58mKuuuooWLVqwefNm4lXGQOS76qqrdDBmPBKojIAzl3IGQKLrKSS+\njKYtmrYYL5QFGcOKUqrYB1Af6FTwuhqwGWgDPAeMKzg+Dni2pGulpKSoSHDy5El18uRJ/d4tVuDs\n3btXrVq1Siml1O+//65atGihfv75ZzVmzBg1efJkpZRSDRs2VJGUsWLFioWOpaenq/T0dHXixAl9\n7MSJE17v7eTm5qrc3Fx9zokTJ1RGRob66aef1KpVq9Tjjz+uHn744ULyZWdnq8mTJ6u6deuqSMlX\nEpZlKcuygv5+IHU4efJkBaSrCMoIKECtXbtWrV271u9n8rAfC5eMkdbTYJg6daqaOnWqruPi6tqp\nMi5btkwtW7bMq/5efvll9fLLL5fqOoHqaSzbotCgQQPVoEEDpZRSv/32m/rtt98C+p5T2mKojBw5\nUo0cOVIlJSUV+sypehoovn3RnXfeWegcp8qYlpam0tLS1FlnneX3861bt6qtW7eG7fcKyl7yWCmQ\nk7y+AB8CVwG/AvWVZ8D1a0nfjbTSiGK0bt06pOv07t1bffnll6ply5Zq7969Siml2rdvryIpo7/B\nVFJSkm7I8tr+GDFihBoxYoSaOHGimjhxYqHv2R/244CyLMtLvr1796oKFSqoSMlXHC6XS7/etGmT\n2rRpU8jX9FeHe/fuVUCWipCM/jpd+/8fyDF5PPHEEyX+Xiz0NBimTJmipkyZot+XZgDpNBkBVbdu\nXVW3bt1iJzaBUpSexqotRoJYtMVwsHv3brV79+6AznWinvo++vTpU+R5geA0Ge0TsqpVq6qqVauq\nX375Jey/E+hgqlQB6JZlNQU6AiuAukqp9IKP9gF1S3Mtp5KWlsaaNWvo0qUL+/fv1zvcF6z6iHsZ\nlfL4xu3y1atXL65dC3aKqsN69epBgsQJJrqeQuLLWJyemrYYPyS6nkLZkDFkAhlxFdyAqwKrgJsL\n3h/1+fxIEd8bDqwEVjZu3Djso8aDBw8WGl2LabK0HDt2THXq1Em9++67SimlzjzzTP1ZSkqKiqSM\nqamphSwWvpYlX/xZn3ypVKmSqlSpkpd8o0aNUnPmzFGVK1dWc+bM0ecmJyerSMkXKHl5eSovLy/o\n7xdXh0opBeSqKMgoOllc/VDMjDA7O7vIz2Kpp6EQiJtPcJqMQ4YMUUOGDPGqM3GrB0NJeuqEtuiP\nrKwslZWVFdC5TmmLoTJv3rwiP3OanmZnZ6vs7Gw1YsQIv59Lv5ScnKySk5PVnXfe6dfFZ8dpMkab\nsFqmLMsqB7wLzFNKvVdweL9lWfULPq8PHChisDZdKdVZKdW5du3agfxcTMjJyaFPnz7cfvvtevuW\nunXrkp6erj8njmW0yyd5cs444wy9Sic9Pb3IjYXjQT4ouQ4Lnv1O+RNFxnjXU0h8GQPRU9MW41/G\neNdTKBsyhotAVvNZwExgk1LqH7aPPgIGF7wejDuWKmpkZmaSmZlJly5d7CNhADp27FiqaymlSE1N\npXXr1jz44IP6eO/evZk9ezYAhw4dggjKOGPGDGbNmsWsWbP0sby8PPLy8nj11VcZNWoUo0aNIjk5\nWT/k8zFjxnjtpzVjxgwOHDjAgQMHOHnyJCdOnPCSb9CgQQwaNIiBAwdy4sQJAGbPnh31dBIdO3ak\nY8eOJCUl8dprr/Haa6+RlJQUVMr/QOqw4Dkqa7x9dbKoVAnvvfce7733XqHjvntUyTVjrafhID8/\nv8iEnU6VccOGDWzYsAGlFNdeey3XXnutboelIVA9jWVql4svvpiLL77Y69iSJUtYsmQJvXr1olev\nXjzwwANFbrvitLYYKrfddpt+Lf2TU/W0XLlylCtXjueee46BAwcycOBAGjRoQIMGDXQiT6WUTvHh\ne8+x4xQZs7Oz9R67dhy3HVVJpivgEtymwfXA2oLHtUBNYBGwBVgI1CjpWpEOJty5c6fauXNnqb/3\n7bffKkC1a9dOdejQQXXo0EF98sknKiMjQ/Xs2VOde+65qlq1asoJMgZDIPJdccUVqkOHDkrFSD4x\nTwdLoDICa1QUZRT3kD8337p169TRo0fV0aNHA7pWvOppvXr1VL169VTNmjVVzZo11fz589X8+fP9\nnutUGUePHq1Gjx4d8nXioS127dpVde3a1csdK6/Hjh2rxo4dqzIzM1VmZmZIMka7LYaTPn36OFJP\nS+L48ePq+PHjAZ3rtLZIKYLlw0mgbr4SAwCVUkuBwttVu4m/bd/9cMkll3hZEewsWrQIgM6dO7Ny\n5crD0SxXuAhEPnDLGK8EKqNlWc7YGyIIEl1PIfFlNG0xMdpigwYNElpPIfHbYriJu9UUW7ZsATxZ\noe17EDVu3Dhm5TKEhj+3ViLw2muveT3bad++fbSLExMkvuKXX34BoFWrVrEsTlA8//zzsS5C1Fi+\nfDkAffr0AWDq1KnaLeu7S0NZY8CAAYB7v8zx48cD7hiieKFKlSqxLkLQ+GZvdxoOczoaDAaDwWAw\nxBdxZ5n69ttvAWjZsqU+JnsvffPNNwCUhZUDBkO8EY8WqbLMu+++W+hYWbVICfPnzwfc95/du3cD\n8WWZimd8F60cP36cqlWrxqg0hTGWKYPBYDAYDIYQiDvL1NChQwFITU0F3DtDL1y4EIBKlSrFrFwG\ng8FgKBts3rw51kUoM4g1UOLVJJWM5L1yCnE3mBKKWmVgMBgMBoMhMZBBlOC0QZRg3HwGg8FgMBgM\nIWBF08JjWdZB4ASQEbUfDZ5aeJeziVKqxMj2RJcxzuSDxJfR6GkRJLqMcS4fJL6MRk8LKBMyRttd\nZlnWSqWU4zPShVLORJcxXuSDxJfR6GnkvhtNjJ5G5rvRxMgYue9Gk2DLadx8BoPBYDAYDCFgBlMG\ng8FgMBgMIRCLwdT0GPxmMIRSzkSXMV7kg8SX0ehp5L4bTYyeRua70cTIGLnvRpOgyhn1mCmDwWAw\nGAyGRMK4+QwGg8FgMBhCwAymDAaDwWAwGEIgpMGUZVnXWJb1q2VZWy3LGheuc6OJZVmNLMtabFnW\nRsuyfrYs676C4xMsy9pjWdY2y7KyLMvaa2SMWxkzCuQ7bVnWrBKu40j5IPFlNHpaJmSMez2FxJfR\n6Km1x7KstQWPawO6oFIqqAeQDGwDmgPlgXVAm1DPjfYDqA90KnhdDdgMtAEmAGOMjHEv45O4E7DF\ntXxlQcYyrqdlQcaE0NOyIGMZ19MJwEOlvV4olqmLgK1Kqe1KqWxgPnBDGM6NKkqpdKXU6oLXx4BN\nQMOCjxtjZLQTjzI2BA7Fu3yQ+DKWcT2FxJcxIfQUEl/GMq6nQRH0aj7LsvoC1yilhhW8HwR0UUqN\n9DlvODAWOKNKlSq1WrVqFWxZo86RI0fIzMykadOmpKWlcejQoTsogzIWyDccqF6lSpXm8SofwKpV\nq44DsxO8Du8B2iW4jKYtmrboOExb9BDPMtpJS0sjIyPDKvHEEExkfYEZtveDgCnFnZuSkqLiiQUL\nFqjU1FSllFIpKSnKyEjfeJZPKaWAY2WgDr8pAzKathjH8ill2mICyZhwbdFOQdkj6ubbAzSyvT+7\n4Fgg58YFDRs2ZNeuXfZDEZVRKiU/P9/rUVTlhYMgZIwr/MhXkcTX0/NIfBlNfxNnmLZYiESRMeHa\nYjCEMpj6EWhhWVYzy7LKAwOAj4o7N4TfigkXXnghW7ZsYceOHeTn54OR8cfolSw82OXLzs4GsEj8\nOqxA4sto2mKcYdpiIRJFxoRri8HgCvaLSqlcy7JGAl/gjth/TSn1cwnnfhLs78UCl8vFlClTuPrq\nq9m5cyfA2+GQUaxPALm5uQDk5ORw9OhRAHbv3g3A/v379TnnnHMOAHXq1AGgRo0alCtXDoCkJPeY\n2LJKduuGKmPnzo7f9NsLu3x5eXkAB6Opp2JBzM3Nld/XlCtXjuTk5JB/w08d/tu0xcSX0bRF5xFv\nbVH6J/GAgPf9xN89pSy0xWAIKc+UUupTpVRLpdQ5SqmnSzo3lN+KFddeey2bN2+mXbt2GBnjE5Fv\n27ZtUIJ7JEHq8JHizk0QGU1bjENMW/QmQWRMyLZYWoK2TBlKj4z8c3JyyMnJAdwrIwCWLFnCG2+8\nAcD27dsBOHjwIOCeKZx11lkAXHTRRQAMGTJEv65cuTIQnGXKach/lJWVxdy5cwHYsGEDAPfeey8A\nLVu21LMnpyHlFyuU1OGXX37J//73PwCqVKkCwLhx47TFUayM8YDI6Ptsn8kWp4vFxfvFow5nZWWx\nbt06wCPbBRdcAEDFihVjVq7iKKoO4vH/NxSPva6lX8rMzAQgIyMDcN+HpE+tVq0a4PaCVK1aFaCQ\nF8RQGPPPGAwGg8FgMISAsUxFEfsM4eTJkwBi7ubHH39kx44dABw6dAhwz3jB7aM+fvw4AFu2bAFg\n3rx5/P777wD88Y9/BKB8+fJAfM8upey5ubnMnz8fgM2bNwPuwEdwW6acitSx1M3MmTMBePvtt0lL\nSwM8lsSzzz6bBx54AEBbHsMRQxUJ7Ba3EydOAJ56kVlrs2bNtNVN5PCni/Y4DYkdlPOTkpLiRn9F\njn379vHwww8DcOzYMQDmzJkDQNu2bR0jj/zXJ0+e1P1JhQoVAI+11OVyBWRdFJRS2sp++vRpfQ3p\ni0KJ5zQEjq+lMT8/X9eLWMe3bdvG0qVLAVi2bBng6afq1aun60jac5MmTbj11lsBdG4wsVoZC1Vh\nHDeYsgdni0nSrigul7vI8dRI7W4QeZbORm6iDRo0oG3btoBHmWvXrg24ZS5Y/cLy5csB+Oyzz/TA\nSpKhnXfeeRGXJVocPnxYDzSlkz7//PMB59a5UkovKPjll18A+OQTd9zljh07tBzSyc2ZM0cPnIcN\nGwZAhw4dAI+eOwXR4ePHjzNjxgwAVqxYAbgHUQD33HMPlSpVAjyDI3vb9XWBHjp0SJ8nnbS0C3Bu\nPQvST82dO5fVq1cDHrllwBlr7IMd0bXFixezcuVKAM4991wAbrrpJsB7YYtQ3ID49OnTbNy4EYAv\nvvgCgK5du9KlSxfA4+aMZV363k/kvZ2kpCTd5pyud3Z825T0MUeOHGHBggUAfPqpO2Rp/fr1ehIv\nMtaoUQNwu/vkGvb6l4HY2Wef7fV7TsE31MAeSO9737VP1CIxGDTDS4PBYDAYDIYQiPn013dkfeTI\nEdauXQvAxx9/DMC6deu0OVJGyA0aNACgefPmtGjhTmNRt25dAGrVqkW9evUAjxlbRqL2EWlxrohw\nIte3Lz2VGXjz5s0B6N+/P7179wbgzDPPBDzuoNOnT7N161YAvvrqK8CdNkFmDeJKmjx5MuA8q0Zp\nkP9oyZIlHD58GPDUtQRrO3nmeOrUKQDtohQLlRy3c/DgQe0O+vLLLwF47rnnAOjdu7ejgtLF4vbT\nTz/x1ltvARSqn8qVK/ud3fvOFMV9vWzZMt0Gr7rqqkiLEHZEjvfee0/Xr7RZmfHHWlfz8/N1ipVZ\ns2YBbmupWKn69u0LeMqZnJxcrJvPt79ev349TzzxhNc5WVlZ2sIqlspoo5QqtAhEFvisW7dO6514\nBs477zxuv/12AB10Heu6Kwp7e5J0Ol9//TWAduMtX75ch42INSo/P1/Xh1j5JUQkLy+P9PR0wHP/\nadu2rfZ6xHKRk2//IV6aQ4cOaeu43Bc3btzInj3uRaJyj61VqxYAf/jDH7Re9urVC3DXtT3EAIKX\n0VimDAaDwWAwGEIg5iYMmT3ILG/VqlX861//AuDbb78F3PEH4ueWZfL+RpF2f64EVIqFSmYb7dq1\n46677gIgJSUFcPuIozHitvtrZdQsz5UqVSpyRlipUiUaNmzo9Znd7y8zL/kvE8Ey9cYbb2j/v8Tk\nSB06lfz8fG1BtOsuuOtNLE1Shzk5OXqW9dtvvwHwl7/8BXDHy/3hD38AYhuUbk/nAW6rhsgoM9gr\nr7wScM8AA4lFEEvJ4sWL9Uz58ssv15871SIgyH8iy8t37typ26O0vfr168emcAXYZ/NimZAFAwcO\nHNBt6frrrwegZs2agLdlqjhEH9555x3WrFkDQOvWrQHo3Lmz9ghEG7vlTBZ8yCKPb775BnDfa844\n4wwA/ayU0vV53333Ac5La+HPKjh27FjAU7diQT7jjDO0JV/i4tq1a0efPn0Az31R2mt+fr5ePCE6\n7HK59H/g23dFC6WUvg+sWrUKgGnTpgHuRVty7xMdV0rpMsq9de/evQBs3bpV30tEN26++WYaNXLv\ndiM6G6yMjrnryg2jevXqWjhx2x08eFAPtqTypcLz8/MLBRbm5ubqxi7ZxMUMv27dOv3nvv322/pa\n0VQSy7ICMina3YJy05XGkpycrG9m1113HRDfgyhB6nn58uW6Pnv06AE4fwVJbm6udk2LjtlNyNJx\ni8k8NzdXdwZSv/L+/vvv55133gE8g8lYDjKkQ/v666+1Xl5xxRUA9OzZEwh80Cdm+G+++UYPOqS9\nxhMyALYHm8ukxyk3Yru7SwYLeXl5XHbZZYAnH1ZJwde+N/IPPvgAcIcYSN8q/Xa7du1CvjGVFnsA\nMsDRo0d55BF3vkxxoUvfWa1aNdq3bw945Pn111/55z//CXgGGrKSzb4oIlbY61EGr3379uXAgQOA\nx3ggCwluuukmOnXqBHhcrZUqVSpUz/YQFJHTX964aCNlOHbsGK+88gqANrKIazM/P1/3OTI5qFat\nmu5nffU+OTmZffv2Ae76BveAVPqgUCcAzr47GQwGg8FgMDicmJsyfIO/OnbsqFMEyAg0IyNDj5pl\nBC6z+6ysLG3NkGzi+/fv17NFWZr+7rvvAm4LgJgEY7kUNpDftM8aZEQtM946deowcOBAwJ3aH5xv\nuQmETZs2Ae4ZidTPjTfeCDjX/SOzqEOHDrFkyRLAO+cOuK2sXbt2BTzu5by8PL2cXJaqS/6fX3/9\nlUmTJgEwZcoUILbWDpnJHTp0SC/4GDNmDFBycKrvLFgsOgcOHNDBofGI9C05OTlaNllE4pR8YXl5\nedp9U7CPGnl5edraGUg57elqxD0yevRowL0IQYK4hw8fDritc9GU3x7eIdanr7/+WqerEP3s2LEj\nAAMHDtRtUfqbu+++W1uFxUIlLtDq1avHvO9RSumy3nLLLYDb6yLWFHFNSr6zokJXimuj/tKZlPS9\ncKKU0r8t1s5JkybpVCyix9WrVwfcC5LE+iaWxqZNm+p7pfQz69evB9yuUOmXZWxRtWrVQvdN35QK\ngRL/d1+DwWAwGAyGGBJzy5TvrLVcuXI62E2sUBKHYD/Pjr+RtPhLmzRpAnj85r///rve087pGcPt\nifH+7//+D/DMstq2basD6aMdnxAJZOYrs8L8/Hw945U6dCoS7zRr1iyd1kOOieXlqquu0oGwcsyy\nLJ0S4KmnngI8Cfays7P5/PPPAdi1axfgnolF2/oo7UiWXLtcLj1jl7Qegeqd1PGiRYsAd9Co1LET\nkjsGitSt7LWolNKz+gEDBgDOkSM/P18HJ4tFPisrix9//BHwzNAlAN0ed2mPk5JrDB48GEDP/i3L\n4uabbwbQVoJox6DaEcvUypUrtb6JRerJJ5/U70VOqUvw6Ke/eKJY1+eJEyd0HyFxwEopHa8o8WHh\niJv1TeVjfx3J/0Eppb1MkiJm2rRpWm+lr/jzn/8MwJ/+9KdC8U7Z2dk6llhSPcj3s7Oz9XlyTtOm\nTQv9ZyY1gsFgMBgMBkMMiLllypdgRoX+vuMbiyUzkIoVK2prgFPiGnzxTW74xhtv6PgMmUEOHTpU\nr+CjUxQAABe3SURBVJ5JhFgpmT189NFH+lj//v2B0FdZRAqpJ5m1z5gxQ/v1xeopdXTffffp/a3s\nMyFZTn7bbbcBaAtkdna2TlT73XffAe5VfdGua2k3Eq9x5plnBr16VOLBPvvsM8BtcZX/xwkrpgJF\n4sdkGxXwWM8lnswpWJal40dFd06fPq1j++655x7Ak8qhXbt22kooq9q2b9/O66+/DsDPP//sdf1G\njRrpOJ1YWRcty/LadxDclhyxAEsyTomrKV++vLZgSQxY+fLl9ZZGF198MeDdr0bDMuMP+d3du3fr\nOhNrccWKFXU8ZSBt0W5pCjRmN5rbx+Tn5/PTTz8B7i2awF2fcp+WRJuS3qF+/fo6BkqsdRs3bmTd\nunWAxxslFqrc3FydYFgseuGM73PcYCpcSON6+eWXAc/ApHXr1lxyySWAc0zxgm+GV8lq+8orr+gG\n9Kc//Qlw5+UJNfdHrDoIf0gOEXE7lC9fnlGjRgHOHSxKhyxLdvft21dofytp+E2aNPG7LFmOtWnT\nBvAs8T127JhOFyCdQ//+/aOWtR/c+iEDOlle3KxZM33jDbQM8p+8+uqrgCdzerly5QrduJygi8WR\nn5+vM2lL/SQlJWn3ntMGheXKldMpLGSRw+eff64zoMskTXTZ5XLpAa4M/vfs2aNTfch5cs6MGTN0\nPiOntVPfnTFkUHnixAl++OEHwHN/OH36tJ6o+oYVxNLNJ310WlqanqgJDRs21ANG377c3yAomMDy\naPUz4NYtcT/LseTkZB3aIsdkMpaVlaWDy+W5QoUKOvWK7/9Vo0YNvfjn0ksvBdwD0nD1Pc7SfoPB\nYDAYDIY4IyEtU0opvcxVRrFiAUhNTXVMQj07SqlCFqn7778fcCdylCBKmWVWrlw5ZIuU/X2sd3WX\nBJVStrPOOkvvw+hUxKVgd1sJomOStqJ8+fLFztzFcmrPbC91IhnD/e12H0mUUnp2J5a2atWqFUqQ\nWJyZPDc3V+8R9tprr+lj4K5jWQzipGDf4sjOztZWDfkfXC6XrmenlN2+155YmB5//HHAbbGR9ibZ\n7MXKVqtWLe0Ok2SPaWlpWrclCPixxx4D3BZyJyULlpCAJk2a6HvAhx9+COCVUNee6gPc/5e4gMSS\nKlbZypUrxywkRHTs999/10HTUreNGjXSFkOxFtsz3/vu7elyuQrJ4S9BZyw9Fl26dAE8Lr0ffvhB\n90Hff/89gHZ3Zmdn675E7p05OTmFEgCLtbhNmzYMGTIE8Fgrw2lNNZYpg8FgMBgMhhBwzpQijOTk\n5NCvXz/AM7uQ2dntt9/uKN++fe8ziRuSXdhlq4BKlSrRrVs3wCOHPcFZoDMJfxYpIZYz6uzsbB27\nIeXo1q2bYwPPBYnBEAsVeMoviWeLWySglNKzKNn13L41icgvsRyRriN/+mHfuwvc8U5iiRNrjCTR\nS05OLhT3t2XLFqZPnw54gn2F888/X1s6nGLRKYmjR4/qYHyhcuXKtGzZMkYlKh77npCii6NHj9ZJ\nHsUKI9gt5LIP6ooVK7QunnfeeYAndtMpi3h892Pr2bOntkwtW7YM8CwckIUQgLZGNW/eXCcy3bZt\nG+DZEqp27dqFrDzRwp7MWqw29r0xZasVsbCJFdtuaZM9Pnv06OGVZgg87bpixYqFFm3ZiUb7dLlc\nut+UNBbHjh3TKWIk5k/iNw8ePKiTe4rc8mxH+pj7779fx0zZ0wkVtSduqcsf0rcdhnTk//jHP/QK\nKFGQESNGAO4NIJ3QcdtzSIG7gUggszTiOnXqAO7GIPtEBZtTyr63k+/gK9aDy/379+sVF1KW2267\nLeblKg77BrL2oHBx77Vr187rM3/Bnzk5Ofzyyy8AvPnmm4B30KR8V27Ukf4//OmU74bhS5cu5T//\n+Q/gybMkAch2N6R8LzMzk+XLlwOeG7fIdeWVV2pXktOR+vvxxx+9XEPgviHLgNIJfUtRiP4kJSUV\n6ZpTSuk6keBm+ybscjPydTk5BRn0nHPOOTr/leidDKIOHDigB0533nkn4G5jkjF78eLFAHoF4znn\nnFNsvxtJt5hcs3Hjxjz//POAZ+K1cuVKPUGR/HYiY25uru6LZAXmDz/8wPnnnw94/hPpw3r06KEn\n6jJ5s+d89C1POLHfh6TM8ly3bl3uvfdewNO/SNlPnz6tV/HJYqXDhw9rPZf751//+lfAnevPXz2G\nSybn3q0MBoPBYDAY4oCEskzJrP5vf/ubHr3KaFssU06xdvjmRXnzzTe1WV2CCWX/qKFDh+pRtsiV\nlJQUkCz2YGEJchYzvsxAY7WcW8r28ccfa3eX5HqRQHsnI7uTi+n8yJEj2qQsdSgBkvYZntRhenq6\n3n9PXEfymcvl0te/8sor9TWiuVTZsiytI2Id2759uw4olyzYkm+pRo0a2mIhM//zzjuv0E7tYrW6\n7rrr9EzRKe2yKKS9LliwQNep1MUtt9ziuJQIoSDyScC23XViX7LuJHyt7FWqVNEuI7GqSp+yZcsW\n3beK2xI8ljix/EjA888//6xdZcWlJomkhapChQo6ZcOgQYMAd8Z52dtTrKVi4T9x4oTWSen3Dxw4\noOtULFjS765YsYLLLrsMQFv06tevH1X3ZlF9gG/dilW1fPny+jPpW5KTk3XuvrFjxwKe/TIrVqwY\n0f7T2T2YwWAwGAwGg8Mp0TJlWVYjYA5QF1DAdKXUy5Zl1QDeApoCaUA/pdSRyBW1aGRGMHLkSMC9\n47SMqGfPng1QbGzGrl27uOOOO9i/fz+WZTF8+HDuu+8+Dh8+TP/+/UlLS5PPqodDRn+7sK9atUqP\nuCVQtHv37oA7wFdmi/6uUVyitry8PC3fnj17sCyL6667jr59+1KvXj3uuusu9u7dS9OmTQv9RqSR\n8s+YMUOXXWaRYqEKlEDqsMBKGZYptWVZ2nIkM6FffvlFz1xlNn/kiFtd7DEqEhg7YcIEHXcks0fB\n5XLpuCuxru7evZvBgwdHXE/tszeJXZCA41q1ammdtadLAPfsWWb6jRs31tcQS5ZcS6x2TZs2LTQb\njXZbDBSZwX/66adaV0Weu+++O+AZb6B6Gu22KOTn5+ukhytWrADclirRa6nr4uSNdlu0I+VyuVza\nAipJH0XvunfvrtujvV1Kvyt9kOwhOW/ePJ2aRpLq7tq1y6st3nXXXdx///0R01PLsnQdiN61adNG\nx+qJ5VgWLf3000/aWiWceeaZuu3arVXgjm0UuaXPys7O5t577+XAgQNRkbG0HD9+nL59+wKePrVG\njRraC3XDDTcA0du7NhDLVC4wWinVBugK3GtZVhtgHLBIKdUCWFTwPi5xuVy88MILbNy4ke+//56p\nU6eyceNGnnnmGa644gq2bNkiN864lNHlcvHkk08yf/58ZsyYwfvvv09aWhqvvPIK3bt3Z+PGjVxx\nxRXaHROPBFKHBWb+erEua7Akup5C4ssYqJ6atuhsXC4Xzz//PD///DPLly9n2rRpCaWn4Jbxueee\nY/369QkrYzgp0TKllEoH0gteH7MsaxPQELgB6FFw2mzga2BsREpZAuInlkR0SUlJXH311QA6KWBx\n1K9fX89aqlWrRuvWrdmzZw8ffvihTqBZs2ZN9uzZcyNhktG+IgHcK4JkmafMKNasWQO4fcMS51S7\ndm3AvcpBZsi+vuSqVavqWCyxCnz77bdkZGToa2zYsIHPPvuM+fPnA+7d4CdMmBAO0QJG/PY7d+7U\ns4Zgl1wHUoeDBw/mkUceqR6m4uuYBFlp+eWXX+q6kz0GxSJ65ZVX6jQdsq3Kt99+65VWATyzp+rV\nqzN69Giv32nQoIFe7hwNPbUvq5cEqvXr19cWRX/bMPjGlGRnZ+v/QM6vUaOGlst3thiLthgIS5cu\nBdypEaTMnTt3BjyrhgIhUD2NdluUvuTEiRN6hZRYx84880wtcyCrL2PRFv3hu+RddNlfHJBlWdqC\nJVuNLFiwAHCnSti+fTuAVxyWr4y7d++OqJ76xg6VL19ex2hecMEFgGef0B07dmg5JdbKsixtkdq1\naxfgiZ+tVauWTssi95Gzzz6bc88910tGJ7RFKfOll16qY6XE+tS3b1+9z2m0LFJCqQLQLctqCnQE\nVgB1CwZaAPtwuwGjTnZ2tnaHyWCkVq1aegPI0gbQpaWlsWbNGrp06cL+/ft1gylQsLDJKA1Cgh4r\nVqyolVn2LhMT7Keffqo7MRmENW7cWN/U5CYt51SsWFGfJzPc5ORk6tSpw4kTJ9i+fTu9e/cmIyOD\nJk2akJycTL169aLuWpBNLbOysvSAQQZToTSAouqwwIwdtkUXUoeyv1yzZs30EmVJbyHpLubPn6/1\nUwa1vpl6wROcPWLECJ1Xxt9/EW09ledA25N9vy1xLYi8kn24pDqOlozFIXJMnToVcLvBZMAog91g\ng7GL09NYufnsmwDLzSgnJ0e796SPCTRjfbTaYiCUpG9Sj7JPpri9fv/9d91m/WX9Fxm7du0aFT21\nuzLF7Sh7mkpOrbVr1+oyygCqZs2aevIudSz9blJSknbNy6AyOTlZt/udO3fGvC3KPU0C8NevX6//\nCwm1mDRpUiEdjRYBB6BbllUVeBe4Xyn1u/0z5e5x/G4vbVnWcMuyVlqWtVL8mk7l+PHj9OnTh5de\neknHwwgFFRPXMubm5rJ8+XKuu+66QlvqFKd48SIfBFSHfkkwGeNaTyHxZTR6mjgy9u3blxdffDEh\n9RQSvy2Gi4BmBpZllcM9kJqnlHqv4PB+y7LqK6XSLcuqDxzw912l1HRgOkDnzp39/unBIDOE5557\nTptgZRR9xx136GDCQMnJyaFPnz7cfvvtemlo3bp1SU9Pp379+jKrDpuMMmsQF0GXLl30ztdiaRL3\npT1Lq8wk9u7dW6hDkgFSlSpVtBVElL9Vq1YsXryYCy+8kIceeghw75V28uRJLMsiPT29uER+Ya1D\nme1L8sf8/HwdSNmqVaugr1tSHRb8n36n/KHIKLP2e+65R1srJEuvzAQzMzMLZawHj87KNST53Jgx\nY3Rd2+s52noaKvn5+dqaJTNeebYsy+9ycifJKO1I3BqWZWk3pSwlL+0MOBA9jVZbFOwuMXG/Sybw\nU6dOaSuNpL4oiVi1xVCQ/0CSVorr7NSpU9pibE9Mm5OTo91KsdBTe1C6eDVWr14NuFOtSN8ii2Eq\nVKigLVLSL8k9o1OnTtrCLlancuXKaRlj2Ralj5CQlPfff18fFyv3W2+9BbhDXGKVSLZEy5TlLtlM\nYJNS6h+2jz4CBhe8Hgx8GP7iRQelFKmpqbRu3ZoHH3xQH+/du7deDVgQCxOXMiql+Oabb6hVq5bO\nrwLuPD+S5Xf27Nna/x6PBFKHBc9HY1PC0El0PYXElzFQPTVt0dkopRg2bBitWrVKSD2FsiFjOAnE\nMvUHYBCwwbKstQXHHgWeAd62LCsV2An0i0wR/SOB1c8884weuYo16qmnnipVEsBly5Yxd+5c2rVr\np2cjkyZNYty4cfTr14+ZM2eKteiZcJVfRs8yM+jVq5cOLn7ttdcAT/K4rKwsPVOVGVKjRo10YkQJ\nDpYZc0ZGho7FatmyJRs3bmT8+PG0adOG999/nw8//JC//e1vPPLIIwwYMIDZs2fTpEkTPfuMNDI7\nkq1UkpOTtQyBLL32RyB1WBCImV7shYJAdK1Pnz46gaVY3cRCZZ/Ryvn2VAKSvFMSdLpcrkL/QSz0\nNFSSk5O1hU0sUhLUmpubWyjZpdNklC1GJO7Esizat28PeOQpDYHqabTaoiB9aF5eng7qlUD0rKws\nbfmQfsWum77WxVi2xXBgXzQC7tgcabPSd/3www9aRkmbEG09tVumpF7kHrhx40ZtVZX4zeTkZK9t\nWsC9hyHAH//4R72gQvR6+fLljmiLskjnkUceATyxl5UrV+aZZ9w/LeljYmWVgsBW8y0Fiiph1NJU\n++5ld8899wDuP1o65Pfec3sgS7vf1yWXXOI3RxN4co107tyZlStXHi59yQvjz21Xt25d7fKT1SSi\nRMeOHdMySVBocnKydp/45pnyleWaa65hzJgxhVZaWZal5RMZo4GUT/aJSk9P54477gAoFMsVKIHU\nIYBlWXl+TwoB+T+rVKmi94GSDvbZZ58F8HLdSIDr2LFjdWZleyBoUURbT8OF5MCxd+rg7hR9A3qd\nIqOUQQZRMonJzs7W7o5gAs8D1dNotUVf7BsfS924XC69IqxFixaAd1/jewOLZVsMJ1K/Bw4c0Lm3\nZNLXrVs3/V/5rhqMpp76LmQaP3484O5jZPGRfXWmDDqk35FBUu3atb3uLeCMtpiXl8djjz0GeNyV\nUr6LLrqIG2+8EXDGDgqxL4HB8P/t3b9rJGUcx/H3dx0vKWIhaHGJwR/B5ppoCqsrUpoQEKtYpxdT\nWAg29w/ELQVFGxEkoMVBamtR5Dx/HMqdEPQ4JFaKjcF9LGaf2clcLtnd2XnmmWc/L1iSbDI7zyf5\nJnnmmWeeERER6bBO3JvPOVccBRweHgKjNZiyLCvuveOH39sc6ptU9agGeGilW3/6LhV+RM0vX3F6\nenrmctyuMrNiBGN3dxfIT/1BnrG66nJ5hDJVWZYVR49+jTR/KncwGESb37fLj8T4tcFWV1eLUccu\n12pVedqBX2rGX2Z/cnJSnPLyUxFiGAloSnU9tKWlpeKUmV/jqKzNGq5OF/FTP/b394vTYf7UZK/X\nO7Osgn+u/Pa81w6pfC9ZyE+x+vW+/Od8DR4cHBRLQ8TwdyTd3wgRERGRADozMuXvF3R0dASMLlle\nWFhgZ2cHSOtIMWXVo6lp50nFbJxVl1NWnhPoj5ZXVlbOfG5xcTH6EQ4/YurnSUEcR8FNybKM9fV1\nAPr9PpCPTPlFEf3347zV77uuPAkfRndogNGcOT+qWh7liYlvU3nBTT8X6qKvj4X/Gfjvfb/fLxax\n9m31f09iu+I17r9kIiIiIpHrxMjUYDDg+PgYGC0k53upy8vLxbyG6j3DRKRdvV6vuFKxOjoX21Hx\nebrQxlkys4fm3/i388L//1hbWwPyK+X8PB2/aGmsI1NlsbfvPNVFjPf29oq5X36EbWtrC8hv9xPT\n//pOdKayLGNjYwMYTYosT9yuXvIvIvHR76fErHpq3i/poboNz3eSNjc3izsNeLH+POLp1omIiIh0\nkD1qUa5GdmZ2AvwD/Blsp9N7irPtfNY59/RlG6WesWP5IP2MqtNHSD1jx/NB+hlVp0NzkTFkZwrA\nzL5xzrWzvO8E6rQz9YxdyQfpZ1SdNrdtSKrTZrYNSRmb2zakadup03wiIiIiNagzJSIiIlJDG52p\nD1rY5zTqtDP1jF3JB+lnVJ02t21IqtNmtg1JGZvbNqSp2hl8zpSIiIhISnSaT0RERKSGYJ0pM3vV\nzH42s7tm9k6o/V7GzFbN7Esz+8nMfjSzt4bP3zCz+2Z2a/jYHuO1lLEls8oYaz5IP6PqVBkrrxNl\nPkg/o+p0soxAfmPBph/AY8A94AXgCvAdcC3Evsdo21VgY/j+E8AvwDXgBvC2Ms5PxpjzzUNG1aky\ndiHfPGRUnY6f0T9CjUy9Atx1zv3qnPsX+Ax4LdC+L+Sce+Cc+3b4/t/AHWBlipdSxhbNKGO0+SD9\njKrTiaSeMdp8kH5G1enkQnWmVoDfSh//To1GN8XMngNeBr4aPvWmmd02s4/N7MlLNlfGSNTI2Il8\nkH5G1encZ+xEPkg/o+r00oyAJqAXzGwJ+BzYd879BbxPPjT5EvAAOGixeTOhjMrYBannA2VEGTsh\n9Xwwu4yhOlP3gdXSx88Mn4uCmT1O/s381Dn3BYBz7g/n3H/OuQHwIflw5UWUsWUzyBh1Pkg/o+pU\nGYeizgfpZ1Sdjp0RCNeZ+hp40cyeN7MrwBvAzUD7vpCZGfARcMc5917p+aulL3sd+OGSl1LGFs0o\nY7T5IP2MqtOCMkacD9LPqDotjJMxN+mM9WkfwDb5bPl7wLuh9jtGu64DDrgN3Bo+toFPgO+Hz98E\nripj+hljzTcPGVWnytiFfPOQUXU6WUbnnFZAFxEREalDE9BFREREalBnSkRERKQGdaZEREREalBn\nSkRERKQGdaZEREREalBnSkRERKQGdaZEREREalBnSkRERKSG/wEO0FW1g5aoFQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x181c378358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis('off')\n",
    "f, a = plt.subplots(3, 10, figsize=(10, 3))\n",
    "for i in range(10):\n",
    "    a[0][i].imshow(np.reshape(x[i], (28, 28)), cmap='Greys',  interpolation='nearest')\n",
    "    a[1][i].imshow(np.reshape(x_noise[i], (28, 28)), cmap='Greys',  interpolation='nearest')\n",
    "    a[2][i].imshow(np.reshape(pred_img[i], (28, 28)), cmap='Greys',  interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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