Part 3 - Autoencoders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Utility-Functions\" data-toc-modified-id=\"Utility-Functions-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Utility Functions</a></div><div class=\"lev1 toc-item\"><a href=\"#MNIST\" data-toc-modified-id=\"MNIST-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>MNIST</a></div><div class=\"lev2 toc-item\"><a href=\"#Deep-Autoencoder\" data-toc-modified-id=\"Deep-Autoencoder-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Deep Autoencoder</a></div><div class=\"lev2 toc-item\"><a href=\"#Shallow-Autoencoder\" data-toc-modified-id=\"Shallow-Autoencoder-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Shallow Autoencoder</a></div><div class=\"lev1 toc-item\"><a href=\"#Denoising-Autoencoder\" data-toc-modified-id=\"Denoising-Autoencoder-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Denoising Autoencoder</a></div><div class=\"lev1 toc-item\"><a href=\"#Sparse-Autoencoders\" data-toc-modified-id=\"Sparse-Autoencoders-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Sparse Autoencoders</a></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "!conda install -c conda-forge --yes --prefix {sys.prefix} seaborn\n",
    "!conda install -c conda-forge --yes --prefix {sys.prefix} keras\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Input\n",
    "from keras.datasets import mnist\n",
    "from keras.regularizers import l1\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_autoencoder_outputs(autoencoder, n, dims):\n",
    "    decoded_imgs = autoencoder.predict(x_test)\n",
    "\n",
    "    # number of example digits to show\n",
    "    n = 5\n",
    "    plt.figure(figsize=(10, 4.5))\n",
    "    for i in range(n):\n",
    "        # plot original image\n",
    "        ax = plt.subplot(2, n, i + 1)\n",
    "        plt.imshow(x_test[i].reshape(*dims))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "        if i == n/2:\n",
    "            ax.set_title('Original Images')\n",
    "\n",
    "        # plot reconstruction \n",
    "        ax = plt.subplot(2, n, i + 1 + n)\n",
    "        plt.imshow(decoded_imgs[i].reshape(*dims))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "        if i == n/2:\n",
    "            ax.set_title('Reconstructed Images')\n",
    "    plt.show()\n",
    "\n",
    "def plot_loss(history):\n",
    "    historydf = pd.DataFrame(history.history, index=history.epoch)\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    historydf.plot(ylim=(0, historydf.values.max()))\n",
    "    plt.title('Loss: %.3f' % history.history['loss'][-1])\n",
    "    \n",
    "def plot_compare_histories(history_list, name_list, plot_accuracy=True):\n",
    "    dflist = []\n",
    "    min_epoch = len(history_list[0].epoch)\n",
    "    losses = []\n",
    "    for history in history_list:\n",
    "        h = {key: val for key, val in history.history.items() if not key.startswith('val_')}\n",
    "        dflist.append(pd.DataFrame(h, index=history.epoch))\n",
    "        min_epoch = min(min_epoch, len(history.epoch))\n",
    "        losses.append(h['loss'][-1])\n",
    "\n",
    "    historydf = pd.concat(dflist, axis=1)\n",
    "\n",
    "    metrics = dflist[0].columns\n",
    "    idx = pd.MultiIndex.from_product([name_list, metrics], names=['model', 'metric'])\n",
    "    historydf.columns = idx\n",
    "    \n",
    "    plt.figure(figsize=(6, 8))\n",
    "\n",
    "    ax = plt.subplot(211)\n",
    "    historydf.xs('loss', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
    "    plt.title(\"Training Loss: \" + ' vs '.join([str(round(x, 3)) for x in losses]))\n",
    "    \n",
    "    if plot_accuracy:\n",
    "        ax = plt.subplot(212)\n",
    "        historydf.xs('acc', axis=1, level='metric').plot(ylim=(0,1), ax=ax)\n",
    "        plt.title(\"Accuracy\")\n",
    "        plt.xlabel(\"Epochs\")\n",
    "    \n",
    "    plt.xlim(0, min_epoch-1)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "x_train = x_train.astype('float32') / 255.0\n",
    "x_test = x_test.astype('float32') / 255.0\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "60000/60000 [==============================] - 7s - loss: 0.1416     \n",
      "Epoch 2/3\n",
      "60000/60000 [==============================] - 7s - loss: 0.0997     \n",
      "Epoch 3/3\n",
      "60000/60000 [==============================] - 8s - loss: 0.0935     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x119609b90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_size = 784\n",
    "hidden_size = 128\n",
    "code_size = 32\n",
    "\n",
    "input_img = Input(shape=(input_size,))\n",
    "hidden_1 = Dense(hidden_size, activation='relu')(input_img)\n",
    "code = Dense(code_size, activation='relu')(hidden_1)\n",
    "hidden_2 = Dense(hidden_size, activation='relu')(code)\n",
    "output_img = Dense(input_size, activation='sigmoid')(hidden_2)\n",
    "\n",
    "autoencoder = Model(input_img, output_img)\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "autoencoder.fit(x_train, x_train, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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RRx451jG/8pWvNLNx25NPd1vzpdEm/WMf+9i0HxNWNLNmzRorG0Ur9EVnBg8A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\nAAAAoHAGPAAAAACFM+ABAAAAKNx/ANXuAsEXkmofAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116bd550>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autoencoder_outputs(autoencoder, 5, (28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d59490>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 108,
       "width": 1130
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = autoencoder.get_weights()[0].T\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 5))\n",
    "for i in range(n):\n",
    "    ax = plt.subplot(1, n, i + 1)\n",
    "    plt.imshow(weights[i+0].reshape(28, 28))\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shallow Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 4s - loss: 0.1624     \n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 4s - loss: 0.1041     \n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 4s - loss: 0.0962     \n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 4s - loss: 0.0949     \n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 4s - loss: 0.0944     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x10c34f610>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_size = 784\n",
    "code_size = 32\n",
    "\n",
    "input_img = Input(shape=(input_size,))\n",
    "code = Dense(code_size, activation='relu')(input_img)\n",
    "output_img = Dense(input_size, activation='sigmoid')(code)\n",
    "\n",
    "autoencoder = Model(input_img, output_img)\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "autoencoder.fit(x_train, x_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCSc0s2OPPXasY55++unNbNz25NPd1nxJtEn/2Mc+Nu3HhOXNrFmzxspG0Qp9\n4ZnBAwAAANBzCjwAAAAAPafAAwAAANBzCjwAAAAAPafAAwAAANBzCjwAAAAAPadN+gywxRZbNLMn\nP/nJi3y8o446qpmNaqEOM90ZZ5zRzF7ykpcsxZFMv5e97GVL9XwPPPBAMxu39expp502dPuFF144\n1vG+/e1vj7UfjwwvfelLm9mKK67YzC666KKh288777zFHhPMRKecckozO/roo5vZBhtssCSGs9Tc\ncsstzezyyy9vZocffngzu/HGGxdrTECyYMGCsTKmhxk8AAAAAD2nwAMAAADQcwo8AAAAAD2nwAMA\nAADQcwo8AAAAAD2nwAMAAADQc9qkLyWbbbZZM/v617++yMcb1fby9NNPX+TjQR/sv//+zeyNb3xj\nM1t55ZWnfSxPetKThm4/6KCDpv1cn/rUp5rZ1VdfPdYxTz755GZ2xRVXjHVMWFRrrLFGM9t7773H\nOuaXv/zlodsffPDBsY4HM90111zTzA4++OBmtt9++zWzI488crHGtDS85z3vaWYf+chHluJIgMlW\nW221sfa7++67p3kkyyczeAAAAAB6ToEHAAAAoOcUeAAAAAB6ToEHAAAAoOcUeAAAAAB6ToEHAAAA\noOdmLViwYLwdZ80ab8fl1KhWjm9605sW+Xh77LFHM7vwwgsX+XgsugULFsxa1mMYxnuT5Z33Zn+s\nvPLKzexb3/pWM7v55pub2ctf/vKh2+fNm7fwA2OJ8N7sjxe+8IXN7PDDD29m++67bzM77bTThm7/\nxCc+0dxn1qz2S+ayyy5rZtdee20z4/d5bzKdbrrppma20korNbN3vetdzexf/uVfFmtMfTXOe9MM\nHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICe00VrGu21117N7Iwzzmhmj3rUoxb5\nXLpoLXs6DsDM5L0JM5P3JsxM3ptMp69+9avN7Ljjjmtm55xzzpIYTq/pogUAAACwHFLgAQAAAOg5\nBR4AAACAnlPgAQAAAOg5BR4AAACAnlPgAQAAAOi5lZb1AB5Jnv70pzezcVqhJ8mcOXOGbp87d+5Y\nxwMAAIAlYd99913WQ1iumcEDAAAA0HMKPAAAAAA9p8ADAAAA0HMKPAAAAAA9p8ADAAAA0HMKPAAA\nAAA9p036DHDxxRc3s+c+97lDt992221LajgAAABAz5jBAwAAANBzCjwAAAAAPafAAwAAANBzCjwA\nAAAAPadZIU2+AAAgAElEQVTAAwAAANBzCjwAAAAAPTdrwYIF4+04a9Z4O8IjxIIFC2Yt6zEM473J\n8s57E2Ym702Ymbw3YWYa571pBg8AAABAzynwAAAAAPScAg8AAABAzynwAAAAAPScAg8AAABAzynw\nAAAAAPTc2G3SAQAAAJgZzOABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAA\nAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkF\nHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA\n6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+AB\nAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICe\nU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6LmVlvUA\nAGCmKKXMTvLzh7nb3UluSvKDJO+vtV64pMfVR6WUVZNsUmt9uOdzmVha4yulbJ/kx0lSa521kPsc\nmuTTSa6ptc5eYoMDAB5RzOABgOEuTPI/U/59J8l1STZL8sdJvldKOWiZjXCGKqX8YZJLk7xoWY9l\nmJk+PgCAcZjBAwDDvazWevWwoJSyeZKTkuye5JOllK/XWm9fmoOb4Y5JsuWyHsQIM318AACLzAwe\nAFhEg8t6DkryQJI1kxy8bEcEAMDyToEHAMYwKPLUwZfbLsuxAACAS7QAYHzzB7e/t3juYBHfv0xy\nSJJt0v1PlZrk35N8uNZ6z7ADllJeNNhv1yTrJbkxyX8leWet9cYh998/yRHpLhdbM90C0N9M8o+1\n1iun3PfQdIv3fiTJ2wf/XpzksUluTnL6sPOUUlZJ8rokf5Jku8FjuSHJfyf5QK31isH9npXknEm7\nfqiU8qEk76i1vr2U8vYkb0tydJK7krwlybpJ5iTZL8mfDvKTa60HDnms/5Tk9Uk+W2s9dEq2VpK/\nSjezasskKya5LMmnkny81jr/4cY36VibJ/m7JM9PskmSO5NckOT4WuvZU8c12GejJH+b5CVJNk5y\ndZITkpw77P7jmPT9+5ck70vyziT7Jlkn3eLgJ9RaP1RKmZXk8HTPx9ZJ5iY5K8nRjdfQXklek+Rp\n6V4LSfe6OzvJsbXWnw3ZZ50k/zfdWlSbJflVklPTff9OSfLMJM+utZ47Zb+dkrwxybOSrJ/k9iTf\nGpznh0POs1aSNyR56eCxPJjk2sHjeX+t9fqRTxoALEfM4AGAMZRSSpLtB1/+YEq2bpLzkhyfrlBz\nU5KfJdkxyfuTnF9KWW/IMT+SrsjyonQfZH+SZIN0H74vLKVsOum+K5RS/i3JyekKEXOTXJLuw/6r\nklw8KP4Ms0mS/01XAHggyU+TbDo4z3dLKY+ZdJ5Z6T64fyDJzkmuSXJ5kg2TvDrJD0spTxnc/Y50\ni1H/ZvD1VYOvr51y/gOSfDTJ/ekKIY8a3HcspZTNknwvyT+ke46vTrcY9m6D83x6YcdXSnlBuq5X\nhw8e40/SdU57UZJvllLeNuT8Wyf5fpK/SbJRugWcH5OuEPPhcR/XCJsluSjd9/mWJLelm0X2wVLK\n36Uran0s3WunpiuiHZLk7FLKylPG/pYk3x7kq6Z7vL9MsnmSw9J9f8uUfTZJt+D4W5M8MV0h7f50\nRcDvDc77ewYFqguTvDxdMfLSdMXRiQXL/2LK/VdPV/z5+yRbpXud/jTJ7CRHJrmolPLEhXnCAGB5\noMADAIuolLJzusLKrHQzJ/5jyl0+k2SPdB+Ct661blVr3Sndh+ZvJ3lykk9OOeah6QoudyU5oNb6\n+Frrruk+zJ+Trijz6Um7vCXdh/I7kuxba31CrXX3dDMwjk+yWpIvDNp0T/XSdEWL3WqtT6y1bp/k\nD5LMG5zv8En33Xvw78okm9datxuMa5MkX0myRpL3Jkmt9aJa617pig9JN+Nlr1rrp6acf88k/zw4\n3raDcczP+D6TrsBxQZItaq071lq3STdLZG6SPyulHPJw4yulzE73vVwzybuSrFNr3bXW+vh0M3N+\nk+TtpZT9Jk48KIB9Jsnjk3w9yaaD78Pj0s082WsxHlfLfkl+nWSbwetq0yT/b5C9J91Mq4NrrZvU\nWncejOH+dM/RH00a+05J3pGumPjywf13r7VunmSXJL9I8uh0xZTJPpFuVtr30z3fTx7sc0C6x73d\n1AGXUvZI8q+DL1+XZN1a65PTFcQOSzcb7mOllN0m7faqwTjOT/K4WusOg8ezWbrv9QbpFswGAKLA\nAwAtXyqlnD/l3w9KKdenKxA8Kd2lRS+afLnV4APqvkluTbJfrXXORFZrvS7JgemKDi8ZfMCe8ObB\n7dG11lMm7XNrukLOA0meU0rZpJSyZrriQZIcUWs9fdL97661HpXkP9MVed7aeHx/Vmv930n7fTfJ\niYMv95x0vx0Gt/9Va/3FpPvfme4Sna+nm4mxKO5L8tZa64JJj3EspZQ/yEOFnJfUWq+ZNMZvpStg\nJMkrFuJwb0hX0PhcrfXva633TTrWaeku20q6y5AmPD3JU9MVXA6qtd42uP/8WusHknx+nMe1EA6b\neG0NnsdjB9tXSPLPtdaTJo39u+lmlCXdLKwJz0tX+PlSrfXESdtTa704yccHX/62YFNK2SXdbKa7\n0r2+r5u0zynpLr8a5u/TLQ3w3lrrRyYKerXWBbXW/5euKLlSfrdgM/HaO3nieR3sc3OSo5KckW62\nFgAQBR4AaNkt3Zokk//tlm7dkJPTzS54Uq318in7vWRwe3at9ZapBx18OJ1Yx+WPkqSUslW6S1Ae\nSPK5IfvcmG4mwwa11hvSFRXWSnd5zpca4//gxDlKKStOyX5Va/3ekH0mFo1+9KRtEwWqV5VSXj35\n0rJa69W11hfUWqfO8Hg4l9Za5y7iPi0vGtyePnhup/p4umLcixfiWBP3ObGRfzHJgiQ7D9bcSZIX\nDm6/Vmv99ZB9Pjlk2+K6I93ssMmumfTfXx+yzy8Ht2tNbBgUoFZPcmjjPPMGt2tM2rbv4Pb0Yev5\npHu8907eMLjU6g8HX7ae24ntf1hKmVgjcuK198ZSyh8P1uOZGPt3a60vqrW+q3E8AFjuWGQZAIbb\nvNZ6dfLbBZP/MN1lRVukWzPmtFrrvUP2m5jt8PRSyvmtYw9uJ9Y22WJwe3Wt9a5hO9RaJ8+S2Xpw\ne8mIS5smZuesle4ymMmL0d7Q2Ofuwe3kgtB/pltX5SnpLrH5eCnlB+kWuf1qrfXCxrFGGVYYGNfE\nczd0FtFgptFlD3eQQfHg8YMv3ztYm2aYB9P9/VTSra201WB76xyXPNy5x3DjxOynCbXW+yYtlfN7\nhcV0M3WSKQuC11oXlFIWlFKek64QtkW6Rap3SXcZXvK7/0NwomPc0MdVa727lFLTrYU0Ycskqwz+\n+9OllAeH7Drxmlsz3WVe16S77OyIwZhOSnJ/KeV/0r32Tqu1Puz3FQCWJwo8APAwBoWc00spF6Vb\nUPkFSb5WSnl2rfXuKXefmP2ySR76gNyy9uB2YlbMws5qmZjJcOeI+0w+1lpTsvsy2m+LAIPCwbPT\nda/683Qf1p8y+Pf3pZSfpLtM7H8WZuADQzuIjWlRn7uWybOWdlmI+6895XZoYS7dbJsFGdJpbTG0\nzjVhwcPkv1VKOSLdZXyPm7T5/nSXIV6Sh2YoTZh4vkeNYerrcvJzu2ce3tpJUmu9vZSye7rLF/9k\nMMZnDf69r5TynXSXqin0AEAUeABgodVary+l/Fm6S2CekuS4dC3NJ5v44Ht0rfWfFvLQE5fCrLmQ\n958oZjx6xH3WnvTfi1X8GBSx3p3k3YOOUc9N17nrhelmfZxVSinT2LK6VQxZY8i2RX3uWiYXLDZY\nhHWBbh/cPqqRr5bpLe5Mm1LKYem6bSXdWkFnpJsJ9dNa672llMPz+wWeiedpatFwsqnZxD531Vpb\nz9NQtdbbkxyd5OjBmlXPGYzpOekWBv9GKWWrWuu8EYcBgOWCAg8ALIJa6zdLKf+artPUa0opX661\nnj3pLj8d3G7TOsZgodoFSeYMLiG6chDNLqWsPmRWUEopH0/XLenYPLRWzo6llBUal2k9eXA7L+1L\nsh7WYM2dbZJcWWu9pdZ65WC8J5RSHpeu7fVG6To7fWTc8ww8MLhdtZFvPGTbxHP3e52bkqSUsmG6\n1vNzkryi1vrAsPvVWn9dSrklXWembdJ1bpp6rBWTPDvdwr4/r7U+mIe+FztPvf/Ato3tM8HRg9u3\n11rfMSTfdMi2y9J9r3cYkk1czrjVlM1XpeuStWYpZdPJi3VP2m+tdK/Zq5NcM7h0bKN034sf1Frv\nGiz8fHGS40spT0o3m26TdN+Tr418pACwHLDIMgAsujfmoUVrP1JKWWVSNvFB84BSyvpTdyylrJ1u\nkeWLkvzxYPNlSa5LsnK6S1Gm7rNeuk5ae6e75Of8dC2710/yssYYXzu4PXsxW5B/YXC+v5gaDGbs\nTCwyPXndnonzLerMlYnZMGVqMHjenjlkn7MGt/uUUtYZkr80ye5Jtp1U3GmN74zB7Wsa4zskyTeS\n/CgPzdj5z8Ht3qWUYZfkHdo41kwwe3B70dRgsDDyxGtx8v8QnPx4NxxyzD9Jt3Dzb9Vaf5Pk24Mv\nW8/t/01yTpJz89Dfp+cOtu079c611p+kWwMp+d3XHgAstxR4AGAR1VrvyENtykuSv52UnZuuJfVj\n0q3Ts+VENigAfCXJOukWGv73wT4LkrxvcLfjSinPn7TPBoP7rZnknFrrjwYdqD4wuMvHSykvmnT/\n1Uopx6XrCHVffrel9zj+fXB7zORxDc71snQdvebnoUJL8tAlYZst4rkmOkNtWUr5m0nneWy6RXaH\nFXDOTrcI9NpJTp7U3SqllGcm+YfBl8ctxPiOTbc+0CGllPeUUlabdKwXJPnw4Mt/HbwGUmv9YZKv\nprt87NRSyqaT9nl1kr96uAe9DE3Mfvo/pZTfXu5XStki3WOaeO3+9nmotX4/3ff6UUlOmVzUKqVM\nLEQ+zLvSzVr7u1LKX090diulzCqlvCLJxKLWxw9mRiUPvfaOK6XsNuk8K5RSjky3WPncDJltBQDL\nIwUeABhDrfXfknxr8OWbBx+KJ/xJkp8k2SPJlaWUS0spFyf5eboFYn+TZO/Jl2LVWk9I1zVo7XRr\n2lxVSvlRkmvTrXdzdX53Nsh70rXtXjvdAtDXlFK+n+TmdLMh5iU5tNb6e7MzFtHn07WFf9RgXNeV\nUn5QSrk+yX+km93xllprnbTPRIelvymlXFRKedPCnGgw1lMGXx4/eEwXpXsOnp7k/UP2WZDkoHSX\nYD07ybWllP8tpcxJNwPkMUk+WWud3H5+6PgGi/X+Wbo2329OcvPgsV6d5Mx0a8t8M5MKegOHp1u7\nZo8kV5VSLiylXJuu69gZmbn+Pl3R5XlJri+l/LCUcmW6ywyfm+S/B/fbqJQyebbTq9K9Hp+W5OrB\nfj9NtzbVNXloEe/fXg43uIzxqHR/e/5Lkl8OXq/XJ/lcutlrn0vywUnn+cck3013ad4PBu+JC9PN\n3PnndIXF19Rab5uG5wIAek+BBwDG99p0HYdWy6T1Z2qtN6T7sP/GJD9MN1Nk23Rr4Xw8yU611h9N\nPVit9bAkB6ablbJOunVlrk9X2Ni11nrtpPs+mOTl6Yob30xXfNgxXYHno0l2qbWeuLgPcFBA+ZMk\nRya5IN3Czjun+xvi1CTPrbW+b8pu/5Dks+kKWdsk2X4RTnlwurVhLk3y2HSdk76SZNc8NMNn6hiv\nSbd+yzvTFSe2Hez7P0kOqbW+emHHV2v90uDxfTLJbeme0/XTrffyN+kKc7/ThazWelOSvQbn/3m6\nhacfTPKOdJd1zUi11lPSjfusdJ2vdkxXMPxGkgNqrc9NN9Ns7STPmLTfDeme739O9/rcPt174PjJ\n90vyO2tJ1Vr/OV0XrS+mK6LtnG5m2nnpipeHTm7/Puhe9/x0haiLkmw4GOO96S4d3L3W+oXFfiIA\n4BFi1oIFC91JEwAAmgZr90x0tNp0GjurAQAPwwweAAAWSinlj0opPyulfLRxl4m26rco7gDA0qXA\nAwDAwrooXfetI0opB08OSil75KFLFT+2lMcFAMs9l2gBALDQSilvSdcVK+nW6Lk+3TpFswfb/ivJ\nflPXKgIAliwFHgAAFkkp5RnpFp3eKd1C2HckuSzd4tWfq7XOX4bDA4DlkgIPAAAAQM9ZgwcAAACg\n5xR4AAAAAHpOgQcAAACg5xR4AAAAAHpOgQcAAACg5xR4AAAAAHpupXF3nDVrlv7qLNcWLFgwa1mP\nYRjvTZZ33pswM3lvwszkvQkz0zjvTTN4AAAAAHpOgQcAAACg5xR4AAAAAHpOgQcAAACg5xR4AAAA\nAHpOgQcAAACg5xR4AAAAAHpOgQcAAACg5xR4AAAAAHpOgQcAAACg5xR4AAAAAHpOgQcAAACg5xR4\nAAAAAHpupWU9AICZatasWYuczZ8/f0kNB3iEGPWzZcGCBUtxJADAI4kZPAAAAAA9p8ADAAAA0HMK\nPAAAAAA9p8ADAAAA0HMKPAAAAAA9p8ADAAAA0HPapAMzyoorrtjMVl999Wa22267NbPtt9++mR10\n0EHNbMcdd2xmK6wwvD5+9dVXN/f5zne+08w++clPNrM5c+Y0s7vvvruZ3X///c3sgQceaGaj2jRr\n78xMN+o1Ot1aPwcebhyj3ivz588faz+Y6Vq/w0e9rh988MFmNup3HMDyygweAAAAgJ5T4AEAAADo\nOQUeAAAAgJ5T4AEAAADoOQUeAAAAgJ5T4AEAAADouVnjttycNWuWXp0s1xYsWLD0evEugpny3hzV\nInillVZqZo9//OOb2SGHHNLMXvziFzezbbbZppmtscYazWxUC+SWUT9T77nnnmZ2zTXXNLOPfvSj\nzezTn/50M7vrrrua2WL87B9rv3GMO0bvzeVb6zW68sorN/dZf/31m9kuu+zSzB73uMc1s/PPP7+Z\nXX311c1s1M+JUe+JPrRQ9958ZFhzzTWb2T777NPMDjjggKHbW+3Tk+SCCy5oZieeeGIzG/U7dVTr\n9eWV92Z/jPo7bNTfraP2mz9//tDtS+J3zrh/R/bhd9ySMM570wweAAAAgJ5T4AEAAADoOQUeAAAA\ngJ5T4AEAAADoOQUeAAAAgJ5rt7IBWAyjVrsftYL+Jpts0sye8IQnjLXfKKM61jzwwAOLnI16bKOO\nd+uttzazq666qpndfffdzWxJdByY7mMuza5cLN9GvXZHddPbcccdm9nmm2/ezH75y182sxtvvLGZ\n3Xvvvc1see0iwtI3quvcRhtt1Mxe8IIXNLOnPOUpQ7evs846zX122GGHZnbZZZc1s1/84hfNTBct\nZoJRHa9G/U4alY3qSDfq763bbrtt6PZRf2OOeh+N+l016nGvuOKKY+036nz333//WPv1+fetGTwA\nAAAAPafAAwAAANBzCjwAAAAAPafAAwAAANBzCjwAAAAAPafAAwAAANBzy22b9FGt1ka1hhy13zgt\nlZN+t2FbHKPa9S2vz8kjyajv76g2iGuttVYzW2WVVZrZf//3fzezs846q5mdd955zWzu3LnNbLXV\nVhu6fZdddmnuc+ihhy7y8ZJH9ntl3DbpfX/cLDmt18aolq7z5s1rZqN+f4/6GTGqTfqo82nhzEww\n6mfseuut18xGtUnfcMMNh24f9Xtg1Pthzz33bGbnn39+M7v11lubmd8tLC2jPm+Oeo9ttdVWYx3z\nV7/61SJno95/8+fPb2ajjNpv1VVXbWajnpNRn89H/Z6+8847m9k4v4tHPbal+bPFDB4AAACAnlPg\nAQAAAOg5BR4AAACAnlPgAQAAAOg5BR4AAACAnlPgAQAAAOi53rRJb7VQHNVO7TGPeUwz23777ZvZ\nFlts0cxGtTi7//77m9lVV13VzObMmdPM7r333qHbRz3uRz/60c1sVPu2e+65p5mNahM7qr3zqP1G\nne+OO+5oZvfdd9/Q7VrLziyjWhauueaazWyNNdZoZt/5znea2Te+8Y1mdu211zazUa/RUVo/k0b9\n3Nl6662b2eMe97hmNqrd8tlnn93M7r777mY2U2iT/sgw7vdxlKX5PV5//fWb2ROf+MRmdskllzSz\nWmsza/0eg5lio402amaf/exnm1mrFXqSrLTSon/sWGuttZrZn/3ZnzWz1t/PSfLJT36ymV133XXN\nbNy20Cy/Rv1uXHvttZvZ05/+9Gb23Oc+t5n95Cc/aWZf/OIXm9m8efOGbl/ar/lR59tkk02a2ajf\n06Pe01dccUUza/0NPer396i/W7RJBwAAAGChKfAAAAAA9JwCDwAAAEDPKfAAAAAA9JwCDwAAAEDP\nKfAAAAAA9NyMapM+qpVcqy346quv3txn2223bWb7779/M9tyyy2b2WMf+9hmNqpN99y5c5vZqLZp\nq6yyytDtT3jCE5r7jGozfeeddzazG264oZmNGv+o9vBXXnllMzv33HOb2fe+971mdssttwzdPqq1\nnpbKS07rfbvyyis39xnVQn3OnDnNbFSb9FGtxJdEm8fW4z7mmGOa+2y33XaLfLwk2X333ZtZ62dj\n0o826aN+bvLIMOq1vTR/No96rzzrWc9qZqPar5522mnN7De/+U0z8zuJmWDU39Af/OAHm9nWW2/d\nzEb9fh/HqJ8fj370o5vZq1/96mb20pe+tJm98Y1vbGZnnXVWM9NCnWFWWqn9UftJT3pSMzviiCOa\n2ai/m04//fRmduuttzazmfL6HfV8bbzxxs1sVAv1UZ9hW5+zk+Suu+4aur0PnzfN4AEAAADoOQUe\nAAAAgJ5T4AEAAADoOQUeAAAAgJ5T4AEAAADoOQUeAAAAgJ6bUW3SR2m1hBvVPnFUm+ZRrb3vuOOO\nZnb77bc3s1GtIUe1VBvVurXVlv3ee+9t7vPrX/+6mf34xz9uZqOer1Et59dee+1mtummmzazUW3r\nRrVQb70WZkpruuVN63m/7777mvuM+t5fd911zWzUe3Npt3h89rOfPXT7qParo35GjHrffulLX2pm\no55LmE6jft+OysY95iijft63jtn6fZq038/J6N/RN954YzNb2j+TZko7emaWFVdcsZkddNBBzWyf\nffZpZtPdCn3cv8lH/d4c9X4f9XfrBz7wgWZ22223NbOLL764md1zzz3NjP4b9bN3VGvvI488splt\nscUWzeyyyy5rZqNeh6PeZy3j/o4eZdTPpPXWW6+Z7bXXXs1s1O/pOXPmNLNWK/Sk/TlmVJv6mcIM\nHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6DkFHgAAAICeU+ABAAAA6LkZ1SZ9VBvPVkuyUa0H\nr7jiimZ20003LfK5kuRXv/pVM3vggQea2UortZ/qRz/60WNlLTfccEMzG9Vefauttmpmb37zm5vZ\n7rvv3sxGtbv7+c9/3sxGtX5e2q1nGc+o79Oo9+2o9upLuzXhqFaqX/jCF4ZuX2WVVZr7jHpdv/71\nr29m//Ef/9HMRv3cgek0Tmvyh8uWRPvu1u/bpz71qc19dtlll2Z23XXXNbNbbrmlmS2JxzaTnmdm\njlHf+6c97WnN7Nhjj21mo/5uHWXU7/6bb7556Pbzzjuvuc8FF1zQzEa1Qt9///2b2ZprrtnMRrVb\nPv7445vZUUcd1cy+//3vN7Nx/qYd1abe38hL36i/+57znOc0s5133rmZjfp799RTT21m119/fTMb\n5/fAqNfaqOONyka1Sd92222b2ajPm6N+XrX+Xk+SefPmNbM+tENvMYMHAAAAoOcUeAAAAAB6ToEH\nAAAAoOcUeAAAAAB6ToEHAAAAoOcUeAAAAAB6bka1SR+l1W5tVEvlUa3QR7USH9VicNyWcKPazI1q\nvd4yqnXbuO1Qr7rqqmY2ql37ox71qGZ2++23N7Nvf/vbzazPren+f3v3FmJV/fYBPB2dbBzTMS0z\nKy3TopIyOlNSQRGWEUURgXYiiqiom+giSgQhKiOoiCAMobToQEFJQYoXaWJaF2aa50OOlZOl5ow6\n6v/i5X/xvuznKVcz885v5vO5XF/WnrX3rN9aax43fvl7nbHGqsrqGl944YUwi2pW//rrr3CfWbNm\nhdncuXPDLLvOQXdQdW1WXe9ZLXRUczxp0qRwnyFDhoTZd999F2Z//PFHmFVVtQqd3quhoSHMXnnl\nlTAbNmxYmGXnWvaM9vnnn4fZI488UnP73r17w32y95Y9f65YsSLMbrrppjCbPHlymJ1yyilh9sQT\nT4TZvffeG2ZZTXOkM56FyGXrITsPr7rqqjDr379/mK1bty7MPvzwwzDbv39/mFU5b6o+r2efV1Zp\nPmXKlDA799xzw2zz5s1htnHjxjBrb28Ps5L5Bg8AAABA4Qx4AAAAAApnwAMAAABQOAMeAAAAgMIZ\n8AAAAAAUzoAHAAAAoHDF16RnVY2dUSVeVfbzqlS0VT3+rLZu5MiRYTZ69Ogwy47/k08+CbOs0i6r\n5aNn64y12bdvPMvOahcnTpwYZlEV5cKFC8N93nzzzTCrWoVetTZZzSodqWrdeWc4/vjja27P1nN2\nz5k/f36YZZW0VWVrOruWuW/2Xtkz2vjx48MsO9ey82n27Nlh9vDDD4dZ9FyeHUdWod7c3BxmWTXy\nnj17wiyrac5qrSdMmBBm0TXpmGPUpPcEw4cPD7Psd7979+4wy9bYzp07w6yE+0D2eV1zzTVhVldX\nF2ZfffVVmO3atSvMeupa8g0eAAAAgMIZ8AAAAAAUzoAHAAAAoHAGPAAAAACFM+ABAAAAKJwBDwAA\nAOoUKisAAA6iSURBVEDhiqlJr6InVJ919Hvo1y/+ld92221h1tjYGGbbtm0Ls9deey3MOqNelt4r\nO7cvuOCCMHvxxRfDbNSoUWG2ffv2mtvfeeedcJ+s2rKq7H1nlcpZLXtPuHbSs2W1yqeddlrN7SNG\njAj32bFjR5h9+eWXYRbVPv8b2XvrTnX0dK3svMgqurN7RHbOZPerp556Ksza29vDrMpxVK19zu5x\nGzZsCLOsCr3qGsvuxZQhq+g+8cQTw2zAgAFhlp2HS5YsCbPOuO90tOycv+6668Isu0/v3bs3zLJn\n74MHD4ZZT+WKAwAAAFA4Ax4AAACAwhnwAAAAABTOgAcAAACgcAY8AAAAAIUrvkWrO7VGZA0HVVsx\nOvo4Ro4cGWZZi1Zra2uYzZs3L8w2bdoUZt3pd0cZsnP77LPPDrN33303zLKmrOy8X7p0ac3tixcv\nDvfJ2kWy9ZC1N1Rt/LD+KFl23l911VU1tzc0NIT7zJ8/P8yam5vDrOo6yq5lGWu698rOmQcffLDS\nfm1tbWE2Y8aMMNu1a1eYlSBrFsvut9lnOXTo0DAbNGjQPzswuq3svKivrw+zgQMHhlm2jqq00XW1\nbD00NTWF2aOPPhpm2dpcuXJlmK1duzbMeuO90Td4AAAAAApnwAMAAABQOAMeAAAAgMIZ8AAAAAAU\nzoAHAAAAoHAGPAAAAACFK74mvatllXBZhV5W0Xb48OF/dUz/V1YF+9xzz4XZkCFDwmzVqlVhNmfO\nnDDbv39/mMHRyqqRZ86cGWZjxowJs2xNb9u2Lcxmz55dc3tLS0u4T9Wqxuwaka2xjr620PN1Rn13\nVdmxZNWzF1100VG/3vLly8Ps4MGDYdYZsnXbG+te+R+NjY1hNmzYsDA7dOhQmG3ZsiXMPvroozDr\n6POwb9/435ur3sey15w6dWqYZTXNmaxyfvv27ZVek64X3Seycz471zJDhw4NsyuuuCLM/vzzzzDL\nqteztRS97wEDBoT7ZH83Pv7442F25plnhlm2/hYtWhRm/t7833yDBwAAAKBwBjwAAAAAhTPgAQAA\nACicAQ8AAABA4Qx4AAAAAApnwAMAAABQODXpNWR1d1Wzjq5Zzeqi77rrrjC79tprw2zfvn1h9tZb\nb4VZc3NzmKl0pSNNmDAhzK688sowy+qRs/P+2WefDbNly5bV3J5V0laVraPO+HlVRZ9zVnuZHb+a\n985RtQq96mtWvQ9krzlq1Kgwu+SSS476ONavXx9mXX0fc9+kloEDB4bZ8ccfH2bZOvrll1/C7Pff\nf/9nB3YUomfX7PizSujs/pE9Lzz00ENhVrWyPaucz54z6F6i6292rq1evTrM1qxZE2bZc+sDDzwQ\nZhMnTgyzBQsWhFm23sePH19ze3avbWhoCLMbbrghzBobG8MsqztfsWJFmHWnZ+HuwDd4AAAAAApn\nwAMAAABQOAMeAAAAgMIZ8AAAAAAUzoAHAAAAoHAGPAAAAACF67U16VltZNUK2awKvaNrT4cNGxZm\n999/f5hl9eoff/xxmM2fPz/MVNPRkerr68Ns+vTpYZbVrGbr7+uvvw6zDz74IMyy9d6TZZXngwcP\nrrk9q9JsaWkJM9Wy5eiMau+6urowu/jii8MsOg+3bt0a7rNy5cow64yadzhaTU1NYZY922W136ef\nfnql12xrawuzTLSms+fI7DowfPjwMHvvvffCLHteyGQVzjNnzgwzz8nlO3z4cJjt3LkzzN5///0w\ny+rCJ0yYEGZZvfqFF14YZrt27Qqz6NzOnpHXr18fZpMnTw6zTHbfrHrd6Y18gwcAAACgcAY8AAAA\nAIUz4AEAAAAonAEPAAAAQOEMeAAAAAAKZ8ADAAAAULheW5OeyarwOqMKNhPVQ06dOjXcJ6u9XLNm\nTZi98cYbYbZ79+4w6+rPhJ7tjDPOCLOsGjKrUs2qFWfNmhVm7e3tYRbJKh6zivFsHWUVq9l+2bFk\nn9fo0aPDLKunHjt2bM3tmzdvDvf59NNPwyyrpD355JPDjFzVc6arr/XZern00kvDbMCAATW3L168\nONwnq4+tKvsss6w7PYPQfRw4cCDMsut5VpM+ZMiQMMueJVetWhVm2Tkarc2stjyri3711VfD7Kyz\nzgqzTHb8ixYtCrOFCxdW+nmUITsvWltbw2zp0qVhtmXLljDLnnGydTt06NAwy+4tmzZtqrn9+++/\nD/fp379/mN15551hllW5Z0aOHFlpv97IN3gAAAAACmfAAwAAAFA4Ax4AAACAwhnwAAAAABTOgAcA\nAACgcAY8AAAAAIXrtTXppVSNnnPOOTW3P/744+E+xx57bJi9/fbbYbZ+/fowy6r14GhlFcFXX311\nmDU2NlZ6zcx5550XZt99991Rv96ZZ54ZZueff36YrV69Osx++umnMBs0aFCY3X777ZWycePGhVlW\n1RtdQ37++edwnw0bNoRZc3NzmP36669hRnXd6d54wgknhNnkyZOP+vWymvT29vajfr2/k12Tsurq\n7HfQnX4/dK3smpfVNGcGDhwYZs8880yYzZgxI8yyZ9Bp06bV3H7NNdeE+4wePTrMGhoawixbf9k6\n2r17d5g98cQTYdbW1hZm9GzZ+bRv374w27hxY5hFteXHHJOf29n6y+470TUkuzf26xePEdauXRtm\nhw4dCrOsev2CCy4Is/fffz/MeuPfsL7BAwAAAFA4Ax4AAACAwhnwAAAAABTOgAcAAACgcAY8AAAA\nAIUz4AEAAAAoXK+tSe9OjjvuuDCbO3duze0jRowI98kqlT/77LMwO3jwYJhBV2lqaurw16yvrw+z\n6dOnh9nTTz991K+ZVTxm1ZB//PFHmGVrc+jQoWGWVeBm9ZaZ7FhGjhxZc3tWITplypQwe/7558Ns\nz549YUY56urqwuyee+4Js+weGNXSrly5MtynM2pUs/M++3mq0Kll7969YbZw4cIwu/vuu8Msuw/c\ncsstYXbjjTeGWXYPjLKsvjmrhM5k99s1a9aEWfbetm7dWulYoJbOuO9k532myn0n2yera89kz5g7\nduwIs+waoiYdAAAAgOIY8AAAAAAUzoAHAAAAoHAGPAAAAACFM+ABAAAAKJwBDwAAAEDh1KR3kawK\n9tlnnw2zc889t+b29vb2cJ+XX345zFpaWsIMukpWrfjBBx+E2UMPPRRmp556aphl9YmDBg2qlEWy\n95ZlAwYMCLOsJjbLsvedyWoq//rrrzCLqsu//fbbcJ8ff/wxzLLqeHqGxsbGMLvjjjvCLDu3d+7c\nWXN7V1ccV607V5NOLVnV7/Tp08Ps8ssvD7OxY8eGWX19fZhVrUDuaG1tbWE2b968MHvsscfCLKuj\ntzbp7rryHO3XLx4jZM/P+/fvD7PsuW/dunVhVvV5t6fyaQAAAAAUzoAHAAAAoHAGPAAAAACFM+AB\nAAAAKJwBDwAAAEDhDHgAAAAACqcmvQNldcXjx48Ps6lTpx71ay5dujTcZ/bs2WGm4pHubtOmTWF2\n3XXXhdmtt94aZtdff32YjRs3LsyamprCLFqbra2t4T5Z/WNWP57V1Q4ePDjMsur1DRs2hNkXX3wR\nZt98802Ybd68ueb27HeaVf8eOnQozChHdm8cMWJEmLW3t4fZ7t27w2z+/Pk1t2f1x53B/ZausmXL\nljC77777wuyNN94Is+y5tX///mGWrfdIttaz9/bwww+H2cKFC8Ps4MGD/+zA4B+ocs7/nRLuH9kz\n5m+//RZma9asCbPm5uYwy+rVS/i8upJv8AAAAAAUzoAHAAAAoHAGPAAAAACFM+ABAAAAKJwBDwAA\nAEDhDHgAAAAACqcmvQMde+yxYTZt2rQwGzRoUJjt2bOn5vbXX3893Ef9IyXLqrGzau+XXnqpUtbR\nsrrMvn3jmXqWZa+Z1VRm+7W1tVU6lo6uqVRt2fNl51Nra2uYzZ49O8yGDBkSZnPnzq25Pati7mrZ\neV+1ctda6r0OHz4cZkuWLAmzyy67LMwuvPDCMLv55pvDbMyYMWG2bNmymtsXLFgQ7rN69eowy64f\n1gMdKbsuV8064xzt6Nesq6sLs4aGhjD74Ycfwiz7O3Xv3r1hllWoZ8eZfSbZ3xwlX0N8gwcAAACg\ncAY8AAAAAIUz4AEAAAAonAEPAAAAQOEMeAAAAAAK16fq/xDdp0+fcv9r6X+hX7+4eGzSpElhNmfO\nnDA78cQTw2zr1q01t99xxx3hPsuXLw+zkv9H8O7myJEj1apOOllvXZvwX9ZmObLmi/r6+jDL7mUH\nDhyouT1rGupOurp1pStZm9A9WZvVVW3RymTX+q68D2T36MGDB4fZuHHjwiy7t1dt0m1paQmzrEGz\nhOeCKmvTN3gAAAAACmfAAwAAAFA4Ax4AAACAwhnwAAAAABTOgAcAAACgcAY8AAAAAIWLO797uKqV\ndiNGjAizJ598MsyamprCLKto27x5c83tUX36MceUX6MKQO+QVaK2trZ24ZF0H+7hAOXoLpXmnSG7\nR+/atSvMli1bFmbZ39lZLXv293JWhV7676AK3+ABAAAAKJwBDwAAAEDhDHgAAAAACmfAAwAAAFA4\nAx4AAACAwhnwAAAAABSu19akZ/r2jedeo0ePDrOTTjopzLJKuJaWljD78ssva27//fffw30AAACg\nM2T141m9eiarO+ef8w0eAAAAgMIZ8AAAAAAUzoAHAAAAoHAGPAAAAACFM+ABAAAAKJwBDwAAAEDh\n+mQVZ+mOffpU27FwWYV6v35x63xdXV2YHT58OMwOHDhQc3vV3xsd58iRI33+v4+hlt66NuG/rE3o\nnqxN6J6sTeieqqxN3+ABAAAAKJwBDwAAAEDhDHgAAAAACmfAAwAAAFA4Ax4AAACAwhnwAAAAABSu\nck06AAAAAN2Db/AAAAAAFM6ABwAAAKBwBjwAAAAAhTPgAQAAACicAQ8AAABA4Qx4AAAAAApnwAMA\nAABQOAMeAAAAgMIZ8AAAAAAUzoAHAAAAoHAGPAAAAACFM+ABAAAAKJwBDwAAAEDh/gNkmqxT+RN2\n8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1292dd790>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autoencoder_outputs(autoencoder, 5, (28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f26850>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 108,
       "width": 1130
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = autoencoder.get_weights()[0].T\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 5))\n",
    "for i in range(n):\n",
    "    ax = plt.subplot(1, n, i + 1)\n",
    "    plt.imshow(weights[i+20].reshape(28, 28))\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Denoising Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11971c650>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_factor = 0.4\n",
    "x_train_noisy = x_train + noise_factor * np.random.normal(size=x_train.shape) \n",
    "x_test_noisy = x_test + noise_factor * np.random.normal(size=x_test.shape)\n",
    "\n",
    "x_train_noisy = np.clip(x_train_noisy, 0.0, 1.0)\n",
    "x_test_noisy = np.clip(x_test_noisy, 0.0, 1.0)\n",
    "\n",
    "n = 5\n",
    "plt.figure(figsize=(10, 4.5))\n",
    "for i in range(n):\n",
    "    # plot original image\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    if i == n/2:\n",
    "        ax.set_title('Original Images')\n",
    "\n",
    "    # plot noisy image \n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    if i == n/2:\n",
    "        ax.set_title('Noisy Input')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1690     \n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1322     \n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1255     \n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1222     \n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1201     \n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 7s - loss: 0.1187     \n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1176     \n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1166     \n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 10s - loss: 0.1159    \n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 8s - loss: 0.1151     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11949c9d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_size = 784\n",
    "hidden_size = 128\n",
    "code_size = 32\n",
    "\n",
    "input_img = Input(shape=(input_size,))\n",
    "hidden_1 = Dense(hidden_size, activation='relu')(input_img)\n",
    "code = Dense(code_size, activation='relu')(hidden_1)\n",
    "hidden_2 = Dense(hidden_size, activation='relu')(code)\n",
    "output_img = Dense(input_size, activation='sigmoid')(hidden_2)\n",
    "\n",
    "autoencoder = Model(input_img, output_img)\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "autoencoder.fit(x_train_noisy, x_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qx4AAAAACpnwAMAAABQOQMeAAAAgMrN2zXpc8mSJUti27BhQ9PjpXVwzz33XGwTExOn/sLg32Ry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\nAAAAUDkDHgAAAIDKGfAAAAAAVO6/ALP3zcmHHN08AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116fd8d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 394,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "images = autoencoder.predict(x_test_noisy)\n",
    "\n",
    "for i in range(n):\n",
    "    # plot original image\n",
    "    ax = plt.subplot(3, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    if i == n/2:\n",
    "        ax.set_title('Original Images')\n",
    "\n",
    "    # plot noisy image \n",
    "    ax = plt.subplot(3, n, i + 1 + n)\n",
    "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    if i == n/2:\n",
    "        ax.set_title('Noisy Input')\n",
    "        \n",
    "    # plot noisy image \n",
    "    ax = plt.subplot(3, n, i + 1 + 2*n)\n",
    "    plt.imshow(images[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    if i == n/2:\n",
    "        ax.set_title('Autoencoder Output')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Sparse Autoencoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1606     \n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1042     \n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0964     \n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0950     \n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0945     \n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0943     \n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0941     \n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0940     \n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0938     \n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0938     \n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0937     \n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0936     \n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0936     \n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0935     \n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0935     \n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0934     \n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0934     \n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0933     \n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.0933     \n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.0933     \n"
     ]
    }
   ],
   "source": [
    "input_size = 784\n",
    "code_size = 32\n",
    "\n",
    "input_img = Input(shape=(input_size,))\n",
    "code = Dense(code_size, activation='relu')(input_img)\n",
    "output_img = Dense(input_size, activation='sigmoid')(code)\n",
    "\n",
    "autoencoder_standard = Model(input_img, output_img)\n",
    "autoencoder_standard.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "history_standard = autoencoder_standard.fit(x_train, x_train, epochs=20)\n",
    "\n",
    "encoded_standard = Model(input_img, code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1836     \n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1283     \n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1183     \n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1144     \n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1120     \n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1104     \n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1091     \n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1081     \n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1073     \n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1066     \n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1060     \n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1056     \n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1051     \n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1047     \n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1044     \n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1041     \n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1038     \n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1036     \n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 5s - loss: 0.1034     \n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 4s - loss: 0.1032     \n"
     ]
    }
   ],
   "source": [
    "input_size = 784\n",
    "code_size = 32\n",
    "\n",
    "input_img = Input(shape=(input_size,))\n",
    "code = Dense(code_size, activation='relu', activity_regularizer=l1(10e-6))(input_img)\n",
    "output_img = Dense(input_size, activation='sigmoid')(code)\n",
    "\n",
    "autoencoder_regularized = Model(input_img, output_img)\n",
    "autoencoder_regularized.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "history_regularized = autoencoder_regularized.fit(x_train, x_train, epochs=20)\n",
    "\n",
    "encoded_regularized = Model(input_img, code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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20kknNbMTTjhhrGN+8YtfbGbjtief7rbmy6NN+oc+9KFpPyasbmbNmjVWNopW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b276a10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 572
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autoencoder_outputs(autoencoder_regularized, 5, (28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UaqV4EUlPlBBCCCFECgQEbAegVas2VgEUQIYMGfjwwz4AbN26+Ym37Xmxffs2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\noiRJkiQpwf8HAtt7C1/fq+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e3b0450>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 424
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_compare_histories([history_standard, history_regularized], \n",
    "                       ['Standard Autoencoder', 'Regularized Autoencoder'], plot_accuracy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9920/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.092041103911399844"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder_standard.evaluate(x_test, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9792/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.10194810169935227"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder_regularized.evaluate(x_test, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.60018\n",
      "0.776899\n"
     ]
    }
   ],
   "source": [
    "print(encoded_standard.predict(x_test).mean())\n",
    "print(encoded_regularized.predict(x_test).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fa56b50>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4cQ/zwQfTATDG25KxZMl3YcdGCsjp6emUl5eFVbLXrFld77g++uh9AO655176\n9Tu2NowDrFjxc73n12Xp0u954IGxtGy5B3/964NkZmaGHdOY+/Ltt99E/Kx42Hff/cjMzOL775dQ\nUVERtv+bb74CoFOn/cnKyqZjx04sW2bDfjkqKipi9ergByMecEBnqqurWbZsadh1Fy/+lgkT/lV7\nfbdRIHeZyJM6vduKAirkGakZdMhrF3RctBVYREREkl3//seRl5fHSy9NYtWqlUH7Hn/8n7z++iu1\nAa1fvwE0a9acKVNeZe3aNbXHrV//Gy+/HLwEIcB++3WkurqaBQs+rd22bdtWpk59vd5x+UPy5s3B\nc7nee+8dvv56EeBdFWRnbdq0kb/85VZqamq4994HgyZhBtqZ+3L00cfQokVLpkx5NejY5cuXM23a\nWzs9xsbIzMzkxBNPYsOGwrCe9wULPmXmzA/Ze+99anv9Bw4cRGlpCRMmPFb7C5PH4+HJJx+jOmTx\n9FNOGQzAv/71SFBVvaRkOw8/fD8vvfRC2DlukVz1/N1BhB7y2gp55Y4KeX5GHi2yg/u9tpRvI3gp\nfRERkV1DQUEBI0bczejRd3L55RfSv/9xtGrViq++WsT333/HQQcdzAUXXAx4Wxduvvk2Ro++i6FD\nL2bAgONITU3lv/+dXXu9tLS02q8HDz6DefP+y8iRd3DSSX8gIyOD2bNnsvfe+4SF3FAnn3wKM2d+\nyF/+cisnnngyeXl5LFnyHV9/vYiWLfdg8+ZNtT3tO2PMmLtZv/43Dj/8SL777lsWLfo8aAIjeANo\n+/YdGnxfcnNzGTHiTu66awTDhl3CsceeQHZ2Bu+//z7Nm7eo7cFuatdeeyPffvsNL730Al9/vYju\n3Xuwdu0aPvnkv+Tm5nL33WNrW3DPPfdPzJv3X6ZMeZWlS5fQrVt3Fi/+lp9+Wk7LlnsEXbd378M5\n++zzmTLlVS6++DyOPvoYMjIy+e9/Z7N+/W+cfvpZrn04kAK5yzS0Qp6fmUfzzGZBx20t39a0gxMR\nEUmg448/kTZt2vDvfz/PggWfUlZWRvv27bn00iu44IKLyM3d8cTqE088mezsHCZNeo6PPnqf7Oxs\nTjjhJHr27M0999xBVtaOSaPHHNOPkSPv5eWXJ/Hee+/QokVLBg4cxDnnnM9pp/2hzjH16dOX0aPv\n46WXXuDDD98jKyubDh324uabR9C9+yFcfvlFLFjwCb//fd3XCeWvan/xxcKoDzM69NDDaN++w07d\nl379jmXv+TALAAAgAElEQVTcuAk899xTzJz5Ebm5OZx77rnst19n7rnnjp0aY2O1aNGCJ5+cyKRJ\nz/Hxx7OYOvU1WrRoyR/+8EcuuWQoe+21Y4WdjIwMHn10PBMnPsNHH73Pm2++QefOXXj00cd46KEH\nwn6JuOmmWznooIN5880pfPDBdNLS0th33/0YOvQqBg4cFJfvrzFSIj0ZcldSWFiUVN/ga8ve4uPV\nnwZtG3HEDexbsDcPfv4vVhZ5/wM9aI8uXNPjMm6c85faEP+H/Y5n8AE79x/87q51a++EEf+DJ6Rp\n6D7Hh+5zfOg+x0cs93n79mJKSkpo1ap12GIH7777NvffP4bRo+/nhBN+78hYk5n+PjurdeuCRq2u\noR5yl4n0+5H/l6bAVVbyM/JIS02jIHPHskFbKlQhFxER+eWXVZxxxincf/+YoO3l5WVMnep9AmTP\nnr2inC0Sf2pZcZ3oLSuBq6j4H0vcPKsZ2yq8v9WqZUVERAS6dOnKQQd1Y/r0aaxbt5aDDupGeXkZ\nn346j3Xr1jJs2LVNsh65SGM5EsiNMenA9cCVQCdgHfA88IC1trKO844FZkfb72et3W0W147UX+PB\nQ2V1JeXVO5YHyvMF8hZZzfilyDuDXIFcRETE+9j4Rx8dz+TJLzF79gymTn2N9PQMDjywM9deewPH\nHXdioocoEsSpCvl4YBgwD3gbOAYYA/QEzq7jvBXA6Cj7jgQGAv91aIxJIfKkzhq2VwWvQZ6f6Z2g\nETixc0u5O58+JSIiEm/5+fkMHXoVQ4deleihiNQr5kBujOmDN4xPAc611nqMMSnARGCIMWaQtfad\nSOdaa1cAoyJcsznwLbABOC/WMSaVCE3kHo+H0qrgBfFz0r1P5mqWtSOQl1SVUl1TTVpqGiIiIiKS\nHJyY1Hmd73W0tdYD4Hu9A28HxhWNuOZDwD7AjdbaXx0YY9KI1rISHsi9yzXlZeQGbQ+tpIuIiIiI\nuzkRyPsDG6y1Qc+itdauBZYBA3bmYsaY7sDlwDxr7csOjC/JRG5ZiRbI/ZM7/QIfHiQiIiIi7hdT\nIDfGZAF7A8ujHLICaGGM2ZmpzPf5xjUilrElq4jLHuKhLCSQZ6dFrpAXV2xHRERERJJHrD3k/meW\nRnsmrH+WYXOgsL6LGWM6A4PwVsc/re/4hvAveJ8ssn8O/5E0a5ZDRUlp0La92u5Jq9wCitPbBG1P\nz/Uk3ffsBrpn8aH7HB+6z/Gh+xwfus/xofucWLG2rGT4Xsuj7Pdvz46yP9RwIAV4MJZBJbPIPeQ1\nlFQGB/Jc36TOgszglpVt5cWIiIiISPKItULuT4mZUfZn+V7r7aMwxqQBfwLWAhFXZWmMZHsUbGlZ\nRdi2zVu2s3HbjiUNU0ihaEsF21OqqKgOjvC/bdpEYbPk+p4TSY8Mjg/d5/jQfY4P3ef40H2OD91n\nZzX2XxpirZBvBWrwtqRE0jzguPr0AVoBb/hXa9ktReohD1n2MCsti9QU748uMy2TjNQdv1cFPs1T\nRERERNwvpkBura0AVuJ9OmcknYBCa+2mBlzuFN/rlFjGlOwiPRgodNnD7PSsoP15ASutaJUVERER\nkeTixLKH84B2xpgugRuNMR2ALsCCBl7nKKAS+MyBMe1Sajweyqp3tOn7lzz0C1xpRRVyERERkeTi\nRCCf5Hu9zxiTCuB7Uuf9vu1PNfA6vYAl1tpoE0R3CzWRntQZUiEPD+SqkIuIiIgkq5gDubV2BjAZ\nOAuYb4x5APgYGIK3/eRd/7HGmFHGmFGh1zDG7Am0wDuhczcXIZB7aoLWIfevQe7nX3EFoLQqeDUW\nEREREXE3JyrkABcDI/FOyrwJaOd7f1HIBM17fH9C7el7bcjkz11apB7yGk/wg4FCK+SB70Of6Cki\nIiIi7hbrsocAWGsrgbG+P3UdlxJl+zK8649LBB48lFZHn9QZHMhVIRcRERFJJk5VyMUhnkg95B4P\nFdU71ifPSoseyCtqKqmuqW66AYqIiIiIoxTIk0CNp4aK6sra95mpGUH7cwJ6yEFtKyIiIiLJRIHc\nZSL1kFfUVAZtz0wLfjBqbkggL1HbioiIiEjSUCB3mUiPKC2vCl4JMjSQZ4dM8ixThVxEREQkaSiQ\nu02EHvLAhwIBZKYFt6zkhgRyVchFREREkocCuctEqpCHBfLU0Aq5eshFREREkpUCueuER/LysAp5\naA95cIVcgVxEREQkeSiQu0xDesizQgJ5+CoralkRERERSRYK5G7TiB7y0AcFqUIuIiIikjwUyF0m\nYoW8nh7y1JRUstP0tE4RERGRZKRA7jKR1iEvC1v2MCPsmMCndapCLiIiIpI8FMhdxhOhZaW+SZ2g\nQC4iIiKSrBTIXaf+HvLQSZ0QGsjVsiIiIiKSLBTIXSZyD3lF0PvQHnIIXmlFFXIRERGR5KFA7jKR\nesiraqpqv05LSSMtNS3smOBArgq5iIiISLJQIHebSCXyAJEmdEJwy0qJKuQiIiIiSUOB3GUiVcgD\nRWpXgeCndZZVlVHjqXF0XCIiIiLSNBTIk0y0Cnl2QCD34AnrOxcRERERd1Igd5lIyx4GirTkIUBu\nQA85eKvkIiIiIuJ+CuRJJtKShwA5GcGBvEQTO0VERESSggK5yzS2hzwnLTvovZY+FBEREUkOCuQu\nU88iK1FbVnIyQgO5KuQiIiIiyUCB3G3q7SGPsuyhKuQiIiIiSUmB3GUa27ISuMoKaFKniIiISLJQ\nIE8y0SZ1ZqVlBb0vqy6Px3BEREREJEYK5C5TU0/LSkaUlpWstExSSKl9X16lQC4iIiKSDBTIXafu\nQB6tQp6SkhK0TxVyERERkeSgQO4y9a6ykhq5Qg7BbSsK5CIiIiLJQYHcdRr3pE4IntiplhURERGR\n5KBA7jL1tJDXHchVIRcRERFJOgrkrtP4CnlW+o5AXq5ALiIiIpIUFMhdpr4e8qwo65BDSIVcLSsi\nIiIiSUGB3GXqezBQtGUPIXhSpyrkIiIiIskh3YmLGGPSgeuBK4FOwDrgeeABa21lA87PBm4DLgL2\nBdYAbwOjrbVbnBhj0qinRB5t2UOA7HT1kIuIiIgkG6cq5OOBR4CNwDi8gXoM8Ep9JxpjMoD3gNHA\nWuCfwC/ATcD7xpjoCXQXVF+FvK5lD9WyIiIiIpJ8Yg7kxpg+wDBgCtDfWns70B+YBJxljBlUzyVu\nBI4F/m6tPdZae5u19li8If93wPmxjjG5xDCpMyCQV3uqqaypcmxUIiIiItI0nKiQX+d7HW2t9QD4\nXu/Amy6vqOf84cAK4M6Q7Q8BLwClDowxadT7YKAGtqyA1iIXERERSQZO9JD3BzZYaxcHbrTWrjXG\nLAMGRDvRGHMwsB/wz9Bec2vtCuBSB8aXXOpZiLyhFXLw9pHnk+fIsERERESkacQUyI0xWcDewGdR\nDlnhPcy0ttYWRtjf3ff6nTHmFLxV8kOBLXj7z0daa7fHMsZkU1ccT01JJT0lLer+sAq5JnaKiIiI\nuF6sFfI9fK/RVkLZ6nttDkQK5B18r4OBQcB04Am8PeU3A0caY45vyEot0bRuXdDYUxMiLT16F1FW\nWiZt2jSLur9tVcug99n5aUn3/SeK7lN86D7Hh+5zfOg+x4fuc3zoPidWrD3k/iU/opVi/duzo+z3\n91MMAoZZa/9orb0ZOAJ4HegLXBvjGJOKp46Wlaz0uhecyckIaVmpKnNkTCIiIiLSdGKtkPsnXEZL\niv6EGK3tpMb3+pW19mn/RmtttTHm/4BzgHPxLqXYKIWFRY09NSGqqqqj7ksnvc7vp7Q4+NzfNm2h\nMD25vv9481cEku3vSbLRfY4P3ef40H2OD93n+NB9dlZj/6Uh1gr5VryhunmU/c0Djot2PsCi0B3W\n2pV4W2EOiGWAyaaudcjrmtAJweuQg9YiFxEREUkGMQVya20FsBLv0zkj6QQUWms3Rdn/g+81WtJM\nB0oaP8LkU9ekzrqe0gmQpUmdIiIiIknHiXXI5wHtjDFdAjcaYzoAXYAFdZy7EKgABhhjgpYPMcZ0\nBfKB/zkwxuRRRw95hirkIiIiIrscJwL5JN/rfcaYVABjTApwv2/7U9FOtNZuBSYD+wK3+7cbYzKA\nB31vn3NgjEmj7gp5Rh17IT01PWhZxLJqTeoUERERcbuYHwxkrZ1hjJkMnAfMN8bMBvoA/YApwLv+\nY40xo3znjAq4xK3A0cC9xphjgW+AE4BewGRr7duxjjG51NFDnlp3hRy8bStVld4unzK1rIiIiIi4\nnhMVcoCLgZFAK+AmoJ3v/UXW2sCEeY/vTy1r7XrgKOCfQFdgOJAD3AZc6ND4kkYskzoBstN2rDBZ\nrpYVEREREdeLuUIO4Htwz1jfn7qOS4myfSNwo+/P7q2OnpXMelpWIPhpnaqQi4iIiLifUxVycUid\nFfKGtKwETOxUhVxERETE/RTIXSaWZQ8heKUVVchFRERE3E+B3G3qXPaw/paVwLXItQ65iIiIiPsp\nkLuMoxVytayIiIiIuJ4CuevE1kMeGMhVIRcRERFxPwVyl6mpo2WlIcseBresVOCp43oiIiIikngK\n5EmkQcseBlTIPXgor65oyiGJiIiISIwUyF3GyWUPQW0rIiIiIm6nQJ5EGjSpMz04kGvpQxERERF3\nUyB3mbp6vvMz8+o9P6xCrpVWRERERFxNgdx16gjkGfn1np2dpgq5iIiISDJRIHeZutZEadCkznT1\nkIuIiIgkEwVyl6lrUmdDhLas6OFAIiIiIu6mQO42MS4brkmdIiIiIslFgdxlnK6Qq2VFRERExN0U\nyJNEekpag44LXRpRLSsiIiIi7qZA7jLRKuQ5GTkNOj81JZXMgFCuCrmIiIiIuymQu02UjpXmmc0a\nfInApQ9VIRcRERFxNwVyl4lWIT+ny2kNvkZgIFeFXERERMTd0hM9AAkWGMf3b96Rdrmt6dS8Iwc0\n79jga2QFrLSiVVZERERE3E2B3GU8nh2RfL+CvTm7y6k7fQ21rIiIiIgkD7WsuE5AjTylcVfIUsuK\niIiISNJQIHeZwB7ylEYm8my1rIiIiIgkDQVyl4nxQZ1ASIVcLSsiIiIirqZA7jYeByrkalkRERER\nSRoK5C4TVCFvbA95QMtKRU0lNZ6amMYkIiIiIk1Hgdx1nK2Qg6rkIiIiIm6mQO4ygRVypwK5lj4U\nERERcS8FcrfxxD6tM7BlBVQhFxEREXEzBXKXCaqQpzhUIVcgFxEREXEtBXKX8Tiw8GGWWlZERERE\nkoYCuYs58WAgUMuKiIiIiJspkLuMJ2gd8sZRhVxEREQkeaQ7cRFjTDpwPXAl0AlYBzwPPGCtrWzA\n+XOBvlF2X2OtfcKJcSadxvaQq0IuIiIikjQcCeTAeGAYMA94GzgGGAP0BM5uwPk9AAu8GmHfFw6N\nMSl4mmAdck3qFBEREXGvmAO5MaYP3jA+BTjXWusxxqQAE4EhxphB1tp36ji/I9AMeNZaOyrW8SQz\nT8iSh41tWclIzSCFlNpwX66WFRERERHXcqKH/Drf62hrrQfA93oH3lX8rqjn/B6+1/85MJakFrbC\nSiNbVlJSUoLaVkpVIRcRERFxLScCeX9gg7V2ceBGa+1aYBkwoJ7zFcijaGyFHCA7Lbv267KqstgH\nIyIiIiJNIqZAbozJAvYGlkc5ZAXQwhjTuo7L9MBbSe9rjFlkjNlujFltjPmHMaZ5LONLNuEtK42P\n5DnpOwJ5qQK5iIiIiGvF2kO+h+91S5T9W32vzYHCKMf0wFsMHoO3D/2/eKvqNwInGGOOsdZua+wA\nW7cuaOypcVdVUx30Pi8vq9Hjb5aTx9rt3q+rUyuT6j4kgu5PfOg+x4fuc3zoPseH7nN86D4nVqyB\nPMP3Gq1J2b89O9JOY0wq3jD/NTDIWrsmYPvjwFXAKODmGMeZHEIr5I3sIQfIzcyt/bqkorTR1xER\nERGRphVrIPcnvcwo+/0zC7dH2mmtrQGOirTdGHMrcDFwATEE8sLCosaeGneVNVVB77dvr2j0+NOq\nd/xot5VtT6r7EE/+ioDuT9PSfY4P3ef40H2OD93n+NB9dlZj/6Uh1kmdW4EavC0pkTQPOG6nWGuL\n8U4KbWeMiVhh3+U4tOwhBPeQa1KniIiIiHvFFMittRXASrxP54ykE1Bord0UaacxpoUxpo8xpkuU\n83PwBv56n/a5KwhZ9DDGSZ05tV+XVpeFTRgVEREREXdwYtnDeXir2EGh2hjTAegCLKjj3N7AJ8BD\noTuMMe2B/YGvrLXVoft3TaHrkDf+SoEV8hpPDeXVFY2/mIiIiIg0GScC+STf632+yZj4ntR5v2/7\nU3WcOw/4FTjFGNPfv9EYkwk8hnfS6HgHxpgUnKyQZ6cHd/mUVattRURERMSNYg7k1toZwGTgLGC+\nMeYB4GNgCN5lDN/1H2uMGWWMGRVwbgVwJd4sOsMY86IxZhzeVVfOBF4FJsY6xmThZFtJTkgg11rk\nIiIiIu7kRIUcvKuhjARaATcB7XzvL7LWBqbMe3x/allr3wH6AR8Bg/AudVgJXA9cGHL+Ls65ZQ8D\ne8hBgVxERETErWJd9hAAa20lMNb3p67jIiZMa+0C4I9OjCWZOfmbR3iFXGuRi4iIiLiRUxVycUBo\ny0pqTKusqGVFREREJBkokLtK6CorCuQiIiIiuzoFchdpqnXIQQ8HEhEREXErBXIX8TjYRZ6ZmkFq\nyo4fb4l6yEVERERcSYHcTcKeC9T4CnlKSgo5aTvaVlQhFxEREXEnBXIXCauQx/CkTgh+OJB6yEVE\nRETcSYHcxWKpkAPkBgVytayIiIiIuJECuYuEVshjLJCrQi4iIiKSBBTIXcQTNqcztkgeuNKKArmI\niIiIOymQu0pIhTzGEnmOKuQiIiIirqdA7iLhyx7GWiFXIBcRERFxOwVyF4u1hzwwkJdXl1PjqYnx\niiIiIiLiNAVyF/F4Qid1xhbJAyd1evBQXl0e0/VERERExHkK5G4WY4k8N2BSJ6htRURERMSNFMhd\nJHzZw9h+PIEVclAgFxEREXEjBXIXCW9ZiU2OArmIiIiI6ymQu0j4MuTOrbICelqniIiIiBspkLuK\n0xVy9ZCLiIiIuJ0CuYuEPqkz1lVW1LIiIiIi4n4K5C4S/mCg2OSkKZCLiIiIuJ0CuauEtKzE2EOe\nkZZBekpa7fsyBXIRERER11EgdxFn6+NegX3kJZrUKSIiIuI6CuRu4vCTOiG4j1wVchERERH3USB3\nkdAKeexxPPjhQOohFxEREXEfBXJXCV1mxdkKudYhFxEREXEfBXIXaYoKeWAPuSrkIiIiIu6jQO4i\nntCFyB3uIVcgFxEREXEfBXIXi3XZQwgJ5NUK5CIiIiJuo0DuIk4/GAiCA3lFdQXVNdWOf4aIiIiI\nNJ4CuYulOtKykhP0XlVyEREREXdRIHeRmtAecgdaVgKXPQStRS4iIiLiNgrkrhL6YKDY5YQEck3s\nFBEREXEXBXIXCe8hjz2S54YFcq1FLiIiIuIm6U5cxBiTDlwPXAl0AtYBzwMPWGsrd/JaacAnwO+s\ntU4UiZNHaMeKA5cMbVlRhVxERETEXZyqkI8HHgE2AuOANcAY4JVGXOsm4HcOjSuphFbIHVn2MC1k\nUqcCuYiIiIirxBzIjTF9gGHAFKC/tfZ2oD8wCTjLGDNoJ651IDA21jHJDuohFxEREXE3Jyrk1/le\nR1trPQC+1zvwNmFc0ZCLGGNSgGeAtcAyB8aVdMIq5A4/qRPUQy4iIiLiNk4E8v7ABmvt4sCN1lp/\nsB7QwOtc5Tv2SmC3TI2hqx46IS01jczUjNr3qpCLiIiIuEtMgdwYkwXsDSyPcsgKoIUxpnU919kH\neBB41lo7O5YxJTfne8ghuEqudchFRERE3CXWVVb28L1uibJ/q++1OVBYx3WeBIqBW2McT5jWrQuc\nvmST2ZySG/S+RfNcR8afn5XH1ooiAKrTqpLqnsSL7kl86D7Hh+5zfOg+x4fuc3zoPidWrIHc3wtR\nHmW/f3t2lP0YY4YAA4GzrbXRgv1uoSnWIQfIzdyx0kpJ5W7ZDSQiIiLiWrEGcn+6y4yyP8v3uj3S\nTmNMW+BR4E1r7RsxjiWiwsKiprhsk9i8pSTo/batpRSmxz7+dM+OHvJtJduT6p40NX9FQPekaek+\nx4fuc3zoPseH7nN86D47q7H/0hDrpM6tQA3elpRImgccF8l4II0dK7Xs1sIq5A49Fimwh7y0WhVy\nERERETeJqUJura0wxqzE+3TOSDoBhdbaTVH2n+V7XWuMCdtpjPEAK621HWMZZ7JyYtlDgJz0HS0r\npZWa1CkiIiLiJrG2rADMAy42xnSx1tauH26M6QB0AabVce7oKNuvBtr69u82feUeT03Qe+cCeWCF\nXIFcRERExE2cCOSTgIuB+4wx51pra3wP+bnft/+paCdaa0dF2m6MOR1oG23/ripsSmcTtKxU1VRR\nWV1JRlpGHWeIiIiISLzE/GAga+0MYDLe9pP5xpgHgI+BIcAU4F3/scaYUcaYUbF+5q6raVZZyQ59\nWqeq5CIiIiKu4cSTOsFbIR8JtAJuAtr53l9krQ1Mmff4/kgEoU/qdKhATm5ADznoaZ0iIiIibuJE\nywrW2kpgrO9PXcc1KGNaa3s5Ma5k01TrkOeEVMj1tE4RERER93CqQi5NIMWhJvLstJCWFQVyERER\nEddQIHeR8Aq5M0Ir5CVVWotcRERExC0UyN0krIfc+XXIQS0rIiIiIm6iQO4ioRXyplj2ENSyIiIi\nIuImCuQuEt6w4tSyh1lB7xXIRURERNxDgdxVQirkDl01NSU1aGJnSVWJQ1cWERERkVgpkLuIJ3Qh\ncsciOeRl5NZ+vb1SgVxERETELRTIXSS0hzzVqSZyID8jr/ZrBXIRERER91AgdzGnVlmB0Ar5dseu\nKyIiIiKxUSB3kZrQlhXn8rhaVkRERERcSoHcVUIndTZNhbxYFXIRERER11Agd5HwKZ1N00NeXl1B\nVU2VY9cWERERkcZTIHeR0FVWUhyc1BlYIQe1rYiIiIi4hQK5i4Q9qbOJWlZAgVxERETELRTIXSR8\nHXLn5AW0rID6yEVERETcQoHcVZpuHfLQQK4KuYiIiIg7KJC7SFM+qTM/rGVFFXIRERERN1Agd5Gw\nOK5JnSIiIiK7PAVyFwmf1OmczLRMMlIzat+rh1xERETEHRTIXaQpW1ZAT+sUERERcSMFchdzclIn\nKJCLiIiIuJECuYvURHhWp5MCV1pRIBcRERFxBwVyNwl9UqfDgTw/qEKuHnIRERERN1Agd5GwSZ3O\n5nFVyEVERERcSIHcRcJXWWm6HvKSqlJqPDWOXl9EREREdp4CuYuErrLi5DrkAPkBFXIPHkqqSh29\nvoiIiIjsPAVyFwmf0tl0FXKA7RXqIxcRERFJNAVyF4nnOuQA26vURy4iIiKSaArkrtLUkzpDArkm\ndoqIiIgknAK5izR1y0pgDzlAsQK5iIiISMIpkLtI2KTOpm5Z0VrkIiIiIgmnQO4i4euQOxvIs9Oy\nSU3Z8SNXy4qIiIhI4imQu0j4OuTOSklJIS9dT+sUERERcZN0Jy5ijEkHrgeuBDoB64DngQestZUN\nOL8bMBY4GigAvgYesdZOdWJ8SaOJ1yEHyMvMo6iyGFAPuYiIiIgbOFUhHw88AmwExgFrgDHAK/Wd\naIzpCSwE/gC8BzwN7AW8YYz5P4fGlxRqIkzrdJoq5CIiIiLuEnMgN8b0AYYBU4D+1trbgf7AJOAs\nY8ygei4xAcgA+llrL7fW/hk4BPgRGGOM2TPWMSYrpyd1AuRnBAZyVchFREREEs2JCvl1vtfR1loP\ngO/1Drwr+V0R7URjTDMgD3jHWvulf7u1thiYBmQDhzowxqQQtspKU7SsBCx9WKwKuYiIiEjCOdFD\n3h/YYK1dHLjRWrvWGLMMGBDtRGvtNqBnlN1dfa+/OTDGpNDUkzoheOnDkspSPB5PkwR/EREREWmY\nmAK5MSYL2Bv4LMohK7yHmdbW2sIGXC8N76TQG4CBeCvn38YyxqQS0kLeFC0rgYG82lNNeXU52enZ\njn+OiIiIiDRMrBXyPXyvW6Ls3+p7bQ7UG8iBOUBf39efAOc3emQ+rVsXxHqJuMkpzAh637p1ARlp\nGVGObpx2RXvA8h3vs5ql0jovee5RU0mmvyfJTPc5PnSf40P3OT50n+ND9zmxYu0h96fF8ij7/dsb\nWoKdAzwEzAeOAWYZY/ao84xdWEqK88vE52fmBb0vLi92/DNEREREpOFirZCX+l4zo+zP8r02aPag\ntfZu/9fGmAeB/8O7Pvl1UU+qR2FhUWNPjbvi4rKg9xsKi0hLTXP0M6pLg0P+6vUbKKjebX/nqa0I\nJNPfk2Sk+xwfus/xofscH7rP8aH77KzG/ktDrCXYrUAN3paUSJoHHLez7gJKgNMacW5SCpvU2SSr\nrOQGvdda5CIiIiKJFVMgt9ZWACvxTsSMpBNQaK3dFGmnMWYPY8xgY0yPKNdeB7SKZYzJJGzZwyae\n1AlQXKW1yEVEREQSyYkm5XlAO2NMl8CNxpgOQBdgQR3nHgS8DdwTusMY0xzYj6ApiLu2sOd0NkWF\nPD20Qq5ALiIiIpJITgTySb7X+4wxqQDGmBTgft/2p+o4dwGwCjjNGONfXQVjTDowHm+P+3MOjDEp\nBLasNEV1HCAtNY3stB1zbBXIRURERBIr5gcDWWtnGGMmA+cB840xs4E+QD9gCvCu/1hjzCjfOf7X\namPMUN8xM40xrwEbgN8D3Xzbx8U6xqQR2LLShM/qycvIpazaO4FUPeQiIiIiieXUunoXAyPx9nvf\nBLTzvb/IWhvYiXEPIe0p1toZeAP8h8Bg4Bq83Rs3A6dZa6scGqPr1QRUyFObMJEH9pGrQi4iIiKS\nWDFXyAGstZV4lyccW89xEVOmtfZLvGFc/JrwcfaBa5EXV2gdchEREZFEcv7JM9JogausNGWFvFnG\njtf7gTMAACAASURBVDUytymQi4iIiCSUArmLBK1D3oQV8oLM/NqviyqLw5ZbFBEREZH4USB3EU+c\nesgDA3mNp4aSqtI6jhYRERGRpqRA7iJBleo4VcgBiir0uFwRERGRRFEgd6l4VcgBitRHLiIiIpIw\nCuQu4onTOuTNMguC3mtip4iIiEjiKJC7SCJ6yEEVchEREZFEUiB3kXitspKXnktKQOAvqlQgFxER\nEUkUBXIXCe5YabpAnpaaFvS0Tk3qFBEREUkcBXJX2ZHIU5qwQg7BfeRFFdub9LNEREREJDoFchep\nCQzkTfxZ+YEPB1KFXERERCRhFMjdxBNYIW/aH02zgECuVVZEREREEkeB3EU8cayQF2QEVMg1qVNE\nREQkYRTIXST4QZ1NG8kDlz6sqK6gvLqiST9PRERERCJTIHeR4Ap5Uwfy4IcDqY9cREREJDEUyF3E\nE8dVVgoy84Le6+FAIiIiIomhQO4iHk/8KuTNQirkmtgpIiIikhgK5C7V5JM6A3rIAYoVyEVEREQS\nQoHcReLaspIRHMhVIRcRERFJDAVyF4lny0pGWgbZadm174sqNalTREREJBEUyF0knhVyCH44kCZ1\nioiIiCSGArmLxLNCDpCvQC4iIiKScArkLhXvCrl6yEVEREQSQ4HcRWri+GAgCH44kFZZEREREUkM\nBXI38cS3h7wgY8fDgbZXlVBdU93knykiIiIiwRTIXSRoUmccPq8g5OFARZWqkouIiIjEmwK5i3gC\n38SjQh7ycCBN7BQRERGJPwVyNwloWUmNSw+5Hg4kIiIikmgK5C4SOKkzHhXyZiGBXBM7RUREROJP\ngdxVEl0h19M6RUREROJNgdxFAh8MFI9Zndlp2aSnpte+Vw+5iIiISPwpkLtI4KTO1Dj8aFJSUijI\nCHhap1ZZEREREYk7BXIXiXeFHILbVlQhFxEREYm/9PoPqZ8xJh24HrgS6ASsA54HHrDWVjbg/MOA\nu4F+QAHwC/A6MNZau92JMSYDT5x7yCF4YqcCuYiIiEj8OVUhHw88AmwExgFrgDHAK/WdaIw5DvgU\nGAh8APzTd50RwGxjTLZDY3S94Ap5fAJ54MOBNKlTREREJP5iDuTGmD7AMGAK0N9aezvQH5gEnGWM\nGVTPJR73jaOftfZP1tpbgd8BTwNHANfGOsZklBKnCnnzgEBeVFFMdU11XD5XRERERLycqJBf53sd\nba31APhe78A7T/GKaCcaYw4GugL/sdYu9G/3nT/G93agA2NMCoHrkKfEqULeIrt57dcePKqSi4iI\niMSZE4G8P7DBWrs4cKO1di2wDBhQx7nb8LamPBdhX7nvNT/Cvl1TQMtKnOZ00iKredD7zeVb4/TJ\nIiIi/9/enUfHdRx2vv/ebuw7iB1cQHArUqRELaQoUiYlWRK1WN7iRHISWyM/J8ri8cR5mXcynryx\nZXtGcXxO5iWe+L1Eji3Hjh1rRp5IojbbihabpERtpESKZJEEF5AASOz72t33/XEbIBobQaLRtwH8\nPudAt/ve6u7iRQn96+q6VSICM7yo0xiTDiwB9k1S5LRXzJRYa5vGHrTWngO+NcljPxndfjCTOs4l\nbkwPeWImwClIL4i5365ALiIiIpJQM51lZVF02z7J8eF0lw+MC+STMcaUcXHIymNXVjVPSUnupQsl\niZTU4Mhth8TUPT1vMbx18f5QSv+cOmfxshD/zX7QeU4MnefE0HlODJ3nxNB59tdMu2FTo9uBSY4P\n75/2TCnGmHzgOaAM+PboseXzX+LHkOemZZM6arXO1t62hLyuiIiIiHhm2kPeF92mTXI8Pbqd1lzi\nxpgS4EXgeuBZ4M9mVDugqWnuXKQ4MBgaue3gJKzu+en5NPe1AFDf3jSnztlMDfcILKR/sx90nhND\n5zkxdJ4TQ+c5MXSe4+tKv2mYaQ95BxDBG5IykfxR5aZkjFkJvI4Xxp8BftNaG5r6UfNN4nvIAQpH\nXdipMeQiIiIiiTWjQG6tHQTO4K3OOZFqoMla2zrV8xhjrsVbHGgl8E/Ap6y1kw2DmbfcmFlWEhfI\nR8+00tavQC4iIiKSSPGYymM3UG6MWTN6pzGmElgDvDHVg40xq4BfAKV4q31+buH1jHtGrdOZqIU6\ngdhA3jHYScSNJO7FRURERBa4eATyH0a3jxpjAgDGGAf4y+j+SWdJiZb/F6AE+Ftr7Z8NLy60EPnW\nQz5qcaCIG6FrsDthry0iIiKy0M30ok6stS8ZY54AHgBeN8a8AmwDtgNP4s2YAoAx5pHoYx6J7voE\nsAlvNpbu4eNjnLfW/v1M6zkXuEkwhhy8ceT56XkJe30RERGRhWzGgTzqs3gL+DwEfAmoBb4CfGtM\nj/dXo9tHotsd0W068BeTPPd7wIII5DEXdfo0hhy81TqrWJqw1xcRERFZyOISyK21Q8A3oj9TlXPG\n3P8SXoAXYoesJHIQ+bjVOnVhp4iIiEjCJGZ9dpmW0V8lBBLYQ56blk3QubhKaEv/lJPiiIiIiEgc\nKZAnkdFjyBPZQx5wAhRnLhq539TXnLDXFhEREVnoFMiTyOghK4nsIQcoySweud3Y25LQ1xYRERFZ\nyBTIk0hsD3liX7s062Igb+5rIRwJJ7YCIiIiIguUAnkSiR1DnthfTWlWycjtsBumtb89oa8vIiIi\nslApkCeR2FlWEvvapaOGrAA09jUltgIiIiIiC5QCeVLxZx5yiB2yAtDYqws7RURERBJBgTyJ+LVS\nJ0B+eh6pgdSR+wrkIiIiIomhQJ5ERg9ZSfCIFQJOIKaXvLFXQ1ZEREREEkGBPImMvqjTcRL/qxk9\njlxzkYuIiIgkhgJ5EvGzhxygZFQPeWt/O0PhIR9qISIiIrKwKJAnFf/GkEPs1IcuLo3qJRcRERGZ\ndQrkSSTi+jfLCkBFdmnM/fM9FxJeBxEREZGFRoE8SfnRQ16eFRvIG3oaE14HERERkYVGgTyJuD7O\nQw6QkZJBYXrByH31kIuIiIjMPgXyJOL3RZ0A5aOGrTT0qodcREREZLYpkCeV0Rd1+vOrqcguG7nd\n2NtEOBL2pR4iIiIiC4UCeRKJ4H8P+ehAHnEjmo9cREREZJYpkCeTUSsD+XFRJ0D5qEAOUK9x5CIi\nIiKzSoE8ifh9USeMn2lFF3aKiIiIzC4F8iTi+rwwEEBWaib5aXkj989r6kMRERGRWaVAnkRcnxcG\nGjZ6HHmDeshFREREZpUCeRIZNYTctx5y0EwrIiIiIomkQJ5EkmEecoidizzkhmnub/WxNiIiIiLz\nmwJ5UomZZsW3WoydaUUXdoqIiIjMHgXyJDJ6HvKAj33ko3vIQePIRURERGaTAnkycZOjhzwnNZvc\ntJyR+wrkIiIiIrNHgTyJjL6oM+BjIAeoyLo4bEVTH4qIiIjMHgXyJOLGzrPiWz0AKnIuBvILvY1E\n3IiPtRERERGZvxTIk8moISt+95CXj+ohH4qEaOlr87E2IiIiIvNXit8VkIsiMT3k/qoYc2Hn+d4L\nlGQV+VSb2RWOuPT2h+gfDNE/GCYUjhCOuERcl0jE+wkEHFKCAVKDAYJBh9RggJSUACnBAClB71gw\n4Pg6f7yIiIjMTQrkScr3HvIxUx829Fzg6uKrfKrNzHT3DXGqoZO6ph5au/pp7xqgrXuAtq4BuvtC\nDA7FZ+GjYMAhNyuV/Jx08rPTKMhJY1FeBsvL86iuyCU3Ky0uryMiIiLziwJ5Ehm9MJDfY8hz03LI\nSc2me6gHmFszrbR09HPwZAs1dR3U1HdyvrU3Ia8bjri0dw/S3j044fHSgkyqK/NYtTifq1csorQw\nKyH1EhERkeQWl0BujEkBvgj8PlANNACPA9+01g5d5nPdB+wCrrPWHohH/eaK0Rd1Oj4HcvDmIz/R\nfgpI/sWBWjv7eds28daRC9TUd/pdnQk1tvfR2N7HvsPeuSxflMU1K4u4emURZmkBKUFd0iEiIrIQ\nxauH/DvAw8Bu4BngZuDrwEbgN6f7JMaYdXhBfsGJ7R0nKcYiV2SXjwrk3kwrASd5QmPfQIi3jjay\n52ADx891TOsxqSkBCnPTKcxJpzA3nfKSHLLSUwiHwmSkBUlPC5IaDBIMOAQCDoGAN3woEnEZCkcI\nh71tKBwhFHYJhSKEIhFCoQj9Q2E6uwfp6PF6yTt6Bujqnfzz6PnWXs639vKLt86SnhbkqqpCNq4q\n5uoVRRTmpsfrNImIiEiSm3EgN8ZswwvjTwL3W2tdY4wD/AB40Bhzn7X22Wk8z23AE0DxTOs0F7lj\nLuj0P47Hrtg5GBmirb+dosxFPtbI++BSU9/Jr96r560jjQxMMf47PTVIdUUuKxfns6IyjxUVeeRl\np8V82CkpyQWgqalrVurb2TPIqYZOTjV0crLe++kdCI0rNzAYZv/xZvYfbwZgaWkO164q5gZTwtLS\nnKT4gCYiIiKzIx495F+Ibr9mrXUBoqH8y8Bngd8DJg3kxphM4H8AnwPagHeB6+NQrznNSYKe6NGL\nA4E3jtyvQN43EGLvofO8sr+O+uaeScvlZaVyw9pSblxbyqol+QQD/p7HvOw0Nq4qZuMq73NmJOJy\nsqGT92taOFjTwpkLE38QONvYzdnGbnbtPU1JQQY3rCnlelPCyso8hXMREZF5Jh6BfAfQbK09NHqn\ntbbeGHMMuOUSjy8DPo83bvyPgP/GAgzk44as+FSP0cbOtHK+t5ENrEtoHRpaenj5nTr2HGqgf3Di\n3vDM9CCb15Zy47oyzLIC30P4VAIBh1WL81m1OJ/f2LGCtq4BDp1s4f2TLXxwqnXCf2NTez8vvlnL\ni2/WUpyfwbYN5WzbUK6LQkVEROaJGQVyY0w6sATYN0mR014xU2KtbZqkTBvwIWvtnuhzzqRKc9a4\nIStJ0Aual5ZDVkomvaE+ILEzrdTUdfDU7lN8cKp10jJrluSzfWMlm0wp6WnBhNUtngpz09m+sZLt\nGysJhSMcP9fB+zXNHDjezIW2vnHlmzv6eWbPaZ7Zc5pVS/LZtqGcG9eWkpWR6kPtRUREJB5m2kM+\nPH6hfZLjw1fa5QMTBnJrbQewZ4b1mNTwGOFkNxiOvfjPwUmKui8tqMQ21wDQPNA863WqOdfOP794\nlLePTBz+czJTuXNLFTu3LGNJafzqkgznGqCiPJ8dm5bhui6157vYe7CBve/Xc7ph/MwxJ851cOJc\nB//y0nG2rC/nw5uWcp0pTerZWpLlPM93Os+JofOcGDrPiaHz7K+ZBvLhbrmBSY4P78+Y4evMf0k4\nywrA0ryKkUB+trNh1mZaOXuhix+/eJQ979dPeHxFZT73faia7dctJiNt/k+f7zgOVRV5VFXk8ds7\nDfXN3bz2bh2vvH2WhpbYMfRDoQi736tn93v1FOSkc8v1S7hzyzKqyvN8qr2IiIhcjpkmm+Hv1Cdb\ngnB47rbJr8KbZbM1e0a8DYRjF5NxcJKi7oUpRSO3B0ID2LO1FGcWTfGIyzMwGOaZPaf4xVtnCUfc\nccevXVXMvTdVsXKxdzFjV0cf8Twrsz3LSrykAndcV8nt11Zwoq6DvYfO8+aRRvrGzNjS3j3A07+q\n4elf1bCyMo/tGyu5cV2p7x9i5sp5nut0nhND5zkxdJ4TQ+c5vq70m4aZvkt3ABG8ISkTyR9VTqYw\nfh5ynyoyxuLsipj7dd0NcQvk+4818ZOXjtHSOf4LlvXVi/jk9hWsqFQv72iO47B6SQGrlxTwO3es\n5sCJFvYebODgyVYiY9pQTX0nNfWd/Mu/Heemq8q4Y9NSFhdn+1RzERERmcyMArm1dtAYcwZvdc6J\nVANN1trJr8wTYKJ5yJNjHPDinPKY+3XdDWws2TCj52zu6OMnvzzOgRPN446tXuLNPmKWFc7oNRaC\n1BRvdpnNa0vp6Blk3+EL7DnYwNnG7phyA4NhXjtQz2sH6tlQvYidm5eyvnpR0gyLEhERWeji8T32\nbuCzxpg11tpjwzuNMZXAGrzpDOWSkrOHPCs1i8L0AtoGvOt2z3ZNPMZ7OiKuy8vvnONnr50ct6BP\nfnYaD9y+ii3ryhQUr0B+dho7Ny/lzk1LOHOhi1+918C+w+fpG4g9z4dOtXLoVCsVRVncvWUZW9eX\nJ/VFoCIiIgtBPAL5D/EWAHrUGHO/tTYSXanzL6PHH4vDa8x7Y0Yb4CTFTOSeZXlLaGvyAnlN+6kr\nurCzoaWHH7xwdNwS944DH75uCZ/csYKsjPl/seZscxyH5eV5LC/P44HbVvG2beTld89xqiF2bGBD\nSy+PP3+U5/ae4b5ty9m6oSyp528XERGZz2acgKy1LxljngAeAF43xrwCbAO2A08Czw2XNcY8En3M\nIzN93fkmGechH7amYCXvNXnrPvWEeqnrPs/S3MppPTYcifDzN8/y1K9PEQpHYo4tL8/lwbsNyzUb\nyKxITwty89UVbNtQTk1dJ794q5Z3jjXFfPhrbO/j+88f4dnXT/PRbcu5ab2CuYiISKLFq0vys8AH\nwEPAl4Ba4CvAt6y1o5PmV6PbR+L0uvPG+DHkSRTIC1fG3D/edmJagfxCay+P7TrMqTFzaKemBPjk\n9hXcuXmJwl8COI7DqiX5rFpyNc3tfbz0zjlePVDH4NDFD0iNbX1877kjPLv3NB+9eTlbrlIwFxER\nSZS4BHJr7RDwjejPVOUumTKttQ/hBfsFZfwsK8kTyCuyy8hJzaZ7yJu98lh7DR9etmPS8q7r8tqB\nen768vGY0Afe6pqfu3cdZYu07Lsfigsy+fTtq7l3axUv7qvl5XfPxfyOLrT18Y/PHmHX3jN87Obl\nbFlXRiCQPG1RRERkPtKg3SQxdsq6ZOohdxyH1YUr2d/4PgDH204RjoQJBsYvV9/RPcDjLxzl/ZqW\nmP3paUF+69aV3HrdYgJJ9GFjocrLSuP+21Zx943LeGHfGV55t47B0Khg3trLd3cdZtee09x141K2\nri8nLXX871tERERmToE8SUTc2NkwUoPJ9atZU3AxkPeH+znXXU9V3tKYModOtvDYrsN09w3F7F+1\nJJ/fu+8qSgsyE1ZfmZ687DQe+PBq7t5SxQtvnOGV/XUMjQrm51t7+acXLT977SQfvn4xt12/hPzs\nydYBExERkSuRXKlvAQtFYgN50Emu3kgzZhz5sbaakUAecV2e3Xuap399KmYkfDDg8Int1dyzpUrD\nHpJcfnYan759NfdsWcYL+2rHBfPuviGe2XOa5984w01XlbNz81KWlOb4WGMREZH5Q4E8SYTd2CXQ\nU4LJFchLs0rIT8ulY9CbPu9YWw13Vt1KT/8Q3911eNwQlYq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ExAczDuTGmG14YfxJ4H5r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ea9ced0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "standard_scores = encoded_standard.predict(x_test).ravel()\n",
    "regularized_scores = encoded_regularized.predict(x_test).ravel()\n",
    "sns.distplot(standard_scores, hist=False, label='standard model')\n",
    "sns.distplot(regularized_scores, hist=False, label='regularized model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13021c750>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y/JzhyNYfKatm3bZOK6JEREREPCmmgNxaWwfsJrg7Z3vGA8XW2tIuXO6C5sfl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\nf7vHkD/8vKV2XytXnzeHkOMEWJ30Rb9f+UevWX7R65U5R/o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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e470190>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 370
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(regularized_scores, hist=False, label='regularized model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
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    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "66px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": true,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}