IMDB_In_Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing IMDB Data in Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the data\n",
    "This dataset comes preloaded with Keras, so one simple command will get us training and testing data. There is a parameter for how many words we want to look at. We've set it at 1000, but feel free to experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Loading the data (it's preloaded in Keras)\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000,)\n",
      "(25000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000,)\n",
      "(25000,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Examining the data\n",
    "Notice that the data has been already pre-processed, where all the words have numbers, and the reviews come in as a vector with the words that the review contains. For example, if the word 'the' is the first one in our dictionary, and a review contains the word 'the', then there is a 1 in the corresponding vector.\n",
    "\n",
    "The output comes as a vector of 1's and 0's, where 1 is a positive sentiment for the review, and 0 is negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 14, 22, 16, 43, 530, 973, 2, 2, 65, 458, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 2, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 2, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 66, 2, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 2, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 2, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0])\n",
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. One-hot encoding the output\n",
    "Here, we'll turn the input vectors into (0,1)-vectors. For example, if the pre-processed vector contains the number 14, then in the processed vector, the 14th entry will be 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[ 0.  1.  1.  0.  1.  1.  1.  1.  1.  1.  0.  0.  1.  1.  1.  1.  1.  1.\n",
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      "  1.  0.  1.  1.  0.  0.  0.  1.  0.  0.  1.  0.  1.  0.  1.  1.  1.  0.\n",
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      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoding the output into vector mode, each of length 1000\n",
    "tokenizer = Tokenizer(num_words=1000)\n",
    "x_train = tokenizer.sequences_to_matrix(x_train, mode='binary')\n",
    "x_test = tokenizer.sequences_to_matrix(x_test, mode='binary')\n",
    "#print(x_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we'll also one-hot encode the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 2)\n",
      "(25000, 2)\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoding the output\n",
    "num_classes = 2\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building the  model architecture\n",
    "Build a model here using sequential. Feel free to experiment with different layers and sizes! Also, experiment adding dropout to reduce overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_records, num_features = x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: Build the model architecture\n",
    "#my_model = Sequential()\n",
    "#my_model.add(Dense(8, activation='relu', input_dim=num_features))\n",
    "#my_model.add(Dropout(0.4))\n",
    "#my_model.add(Dense(20, activation='relu'))\n",
    "#my_model.add(Dropout(0.4))\n",
    "#my_model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "my_model = Sequential()\n",
    "my_model.add(Dense(16, activation='relu', input_shape=(num_features,), kernel_regularizer=keras.regularizers.l2(0.1)))\n",
    "my_model.add(Dropout(.4))\n",
    "my_model.add(Dense(16, activation='relu', input_shape=(num_features,), kernel_regularizer=keras.regularizers.l2(0.1)))\n",
    "my_model.add(Dropout(.4))\n",
    "my_model.add(Dense(2, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_36 (Dense)             (None, 16)                16016     \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dropout_24 (Dropout)         (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 2)                 34        \n",
      "=================================================================\n",
      "Total params: 16,322\n",
      "Trainable params: 16,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "my_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Compile the model using a loss function and an optimizer.\n",
    "adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "my_model.compile(loss = 'categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Training the model\n",
    "Run the model here. Experiment with different batch_size, and number of epochs!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 100\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25000, 1000), (25000, 2), (25000, 1000), (25000, 2))"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# TODO: Run the model. Feel free to experiment with different batch sizes and number of epochs.\n",
    "my_model_history = my_model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          validation_data=(x_test, y_test), \n",
    "          verbose=0).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['acc', 'val_acc', 'val_loss', 'loss'])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model_history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KAL7oe+Z80l12A4D3AbgKwJUA3kdEM+78f8nMzwXwIgAvI6LXDPM4VMrUSvUi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\nooACAAAAgEoUUAAAAABQ6RdJGOOiY47M+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d4015f3a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 4))\n",
    "ax[0].plot(range(1, epochs + 1), my_model_history['loss'])\n",
    "ax[0].plot(range(1, epochs + 1), my_model_history['val_loss'])\n",
    "_ = ax[0].set(xlabel='Epochs', ylabel='Losses')\n",
    "ax[1].plot(range(1, epochs + 1), my_model_history['acc'])\n",
    "ax[1].plot(range(1, epochs + 1), my_model_history['val_acc'])\n",
    "_ = ax[1].set(xlabel='Epochs', ylabel='Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluating the model\n",
    "This will give you the accuracy of the model, as evaluated on the testing set. Can you get something over 85%?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.84484\n"
     ]
    }
   ],
   "source": [
    "score = my_model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Accuracy: \", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Evaluating the Udacity model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Building the model architecture with one layer of length 100\n",
    "udacity_model = Sequential()\n",
    "udacity_model.add(Dense(512, activation='relu', input_dim=num_features))\n",
    "udacity_model.add(Dropout(0.5))\n",
    "udacity_model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_28 (Dense)             (None, 512)               512512    \n",
      "_________________________________________________________________\n",
      "dropout_18 (Dropout)         (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_29 (Dense)             (None, 2)                 1026      \n",
      "=================================================================\n",
      "Total params: 513,538\n",
      "Trainable params: 513,538\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "udacity_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compiling the model using categorical_crossentropy loss, and rmsprop optimizer.\n",
    "udacity_model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      " - 9s - loss: 0.3961 - acc: 0.8304 - val_loss: 0.3397 - val_acc: 0.8572\n",
      "Epoch 2/10\n",
      " - 7s - loss: 0.3354 - acc: 0.8650 - val_loss: 0.3510 - val_acc: 0.8614\n",
      "Epoch 3/10\n",
      " - 6s - loss: 0.3242 - acc: 0.8776 - val_loss: 0.3578 - val_acc: 0.8592\n",
      "Epoch 4/10\n",
      " - 7s - loss: 0.3130 - acc: 0.8861 - val_loss: 0.3746 - val_acc: 0.8604\n",
      "Epoch 5/10\n",
      " - 7s - loss: 0.3043 - acc: 0.8904 - val_loss: 0.3846 - val_acc: 0.8603\n",
      "Epoch 6/10\n",
      " - 7s - loss: 0.2977 - acc: 0.8967 - val_loss: 0.4055 - val_acc: 0.8559\n",
      "Epoch 7/10\n",
      " - 8s - loss: 0.2851 - acc: 0.9036 - val_loss: 0.4341 - val_acc: 0.8596\n",
      "Epoch 8/10\n",
      " - 8s - loss: 0.2781 - acc: 0.9088 - val_loss: 0.4420 - val_acc: 0.8565\n",
      "Epoch 9/10\n",
      " - 7s - loss: 0.2734 - acc: 0.9139 - val_loss: 0.5290 - val_acc: 0.8442\n",
      "Epoch 10/10\n",
      " - 7s - loss: 0.2537 - acc: 0.9203 - val_loss: 0.4825 - val_acc: 0.8560\n"
     ]
    }
   ],
   "source": [
    "# Running and evaluating the model\n",
    "udacity_model_history = udacity_model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          validation_data=(x_test, y_test), \n",
    "          verbose=2).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ya22x2zXJN9Vt/f3k9CFcMlCtv0WkeWto17u9wOS6+7xT7/7ii6Ja\ngqnDEtmSU8xzH++kd3wY147s6nZJrcuRXG84egt2Lwes03Bh3H2QdpkzpU5EBDDGPAI8Zq0t9G5H\nAj+z1j7obmUCav0tIi3XmfTf/CltOCgB/HJSH7ZkF/PQfzbSPTaUUVqIemaO5tWOHO1eDtbjTKc7\n9xeQdrlzDyMRkW/6jrX2/poNa22BMeYiQEHJZWr9LSIt2Zn8tmrz/Tv9/Qx/u2YIlz+9gu+/spYF\nd48lKUotpU/J0UOw+W0nHO1aBrYaonvA2T/zhqN+oFaxIvLt/I0xQdbacnDuowQEuVxTm6fW3yLS\n0p1JULKNVkULFh7SjudvSOeyp1Zw+0sZvPH9s/Rp2cmU5MPmd5xwtPMjJxxFdYOxP3bCUVx/hSMR\nORWvAO8bY2q6s94MzHKxnjavpvV3aLBaf4tIy/Wtf9EbY4o5fiAyQIhPKmqBusWG8uT0odz0wmp+\n+vpnPHvdMPz89If+MUoLYPN/veFoKXiqIDIFxvzIWXMUP1DhSEROi7X2MWPM58CFOO9PiwAtHHVB\n/dbfT00fSie1/haRFupbg5K1VqstG+icXrE8eHE/Hn5nE395bys/naBmA5QWwpaFTjja8SF4KiGi\nK4y+2xk56jxI4UhEGks24AGuBnYB890tp+3JPlzGna+uZd3eQrX+FpFWQXPEGtHNY1LYnF3E3z7Y\nTq/4sLbZ+rTsMGz5nxOOtr/vhKPwZBj1fSccdRmicCQijcIY0wuYRu29/V7HaQ9+nquFtUErd+Tx\nw9nr1fpbRFoVBaVGZIzht5f1Z8fBo9wzbwMp0R3onxDudlm+V1YEWxd5w9F7UF0BHRNh5Pcg7QpI\nGKpwJCK+sBlYBlxqrd0OYIz5ibsltS3WWv7x8U4eW7SZ1JgOzJkxih6dNBlFRFoHBaVGFhTgz7PX\nDWPKk8u5/aUM/nP3GDqFtcL52eXFsHWxE462vQvV5RDWBYbf7owcJQwDP025EBGfmoozovShMWYR\nMAd1ZG0yRWWV/HzeBhZvzOHiAZ3545UD1cxIRFoV/UbzgdiwIJ67IZ2rnl3FHS+vZfaMUQQF+Ltd\n1pkrPwLb6oSjqjII6wzptzjhKHG4wpGINBlr7ZvAm8aYDsBlwE+AOGPMM8Cb1tolrhbYihWWVHDF\n0yvV+ltEWjUFJR/pnxDO41cN4q7X1vHAm1/ypysHtqw3kbIiKNgF+bucr/vWecNRKYTGwdAbnXCU\nNFLhSERcZa09CrwKvGqMiQKuAu4FFJR8ZOaK3ezMO8qrt41kTI8Yt8sREfEJBSUfunhgZ7bk9ORv\n72+jT3wYt53dze2SalkLR3Jqg1D9ryWHjj0/rAsMuc4JR8mjwK8VjJCJSKtjrc0H/uF9iA8cKa9i\n1srdjO8Xp5AkIq2agpKP/fiCnmzJLuKRhV/RMy6Mc3vFNt2LV1dC4d46AWh3bRAq2A2VJbXnGj8I\nT4TIVOh7qfM1KtX5GpkCwR2brm4REWm2Zn+6l8Olldw5rrvbpYiI+JRPg5IxZhLwV8AfeN5a+4d6\nx+8A7gKqgSPADGvtJmNMCvAVsMV76ifW2jt8Wauv+PkZ/nz1YKY+s5K7X1vHW3eNoXtsaOO9QPmR\n448I5e+Cw1lgq2vPDQh2Qk9kKnQbVxuGorpBeBIEBDZeXSIi0uqUV1Xz/PKdjO4WzZDkSLfLERHx\nKZ8FJWOMP/AUMB7IAtYYYxZYazfVOe01a+2z3vMnA38GJnmP7bDWDvZVfU2pQ1AA/7whnSlPreD2\nWRm8edcYwkPaNexia+Fo3onD0NHcY88PiXQCUGI6DLiqdlQoKhVC47WeSERETtub6/aRU1TO41cN\ncrsUERGf8+WI0ghgu7V2J4AxZg4wBfg6KFlri+qc3wGwPqzHVUlR7Xnm2qFc+/yn/GD2el64aTj+\nfvWaO1SUwP71kLXaaZ5QE4gqjtQ5yUDHBCf49Jp4bBCKTIWQiCb9uUREpG2o9jj3TBqQEM5YrU0S\nkTbAl0EpAciss50FjKx/kjHmLuCnQCBwfp1DqcaY9UAR8KC1dpkPa20SI7tF89vL+nPfv7/g0f9u\n4sGzOzqhKNP7yP4cPFXOyVHdILonpIw5NghFJEO7VnhfJhERadb+9+UBduUd5Zlrh7asLq4iIqfJ\nl0HpeL9FvzFiZK19CnjKGDMdeBC4ETgAJFtrDxljhgFvGWPS6o1AYYyZAcwASE5Obuz6G1dVBWR/\nzjXVnzIw/l2iMjbA2nznWLv2zg1az/qh0247cTh0iHa3XhGRNqABa2mTgVlAhPece621C73HBuJ0\n1+sIeIDh1tqyJiy/yVhrefrDHQyNrmQiq+CdZc7Mh/bR3rWvKRDZtfZ5cLi7BYuINAJfBqUsIKnO\ndiKw/1vOnwM8A2CtLQfKvc/XGmN2AL2AjLoXWGufA54DSE9Pb17T9opzjh0t2r8eqssB6BeRzKr2\nA/nnkRSuumwqfQePBv8GrlkSEZFG0cC1tA8Cc621zxhj+gELgRRjTADwCnC9tXaDMSYaqGziH8H3\nSvJhzwr2rV/Mnw8tpY9fJswHAsMgYSiU5sP+dVBacOx1IZF1AlSdR0RXp8Oq3vNEpAXwZVBaA/Q0\nxqQC+4BpwPS6Jxhjelprt3k3Lwa2effHAvnW2mpjTDegJ7DTh7WemeoqyN1YG4oyP4XCPc4x/0Do\nPBhG3A5JIyBxBKZjZ/oereC+p1ew4H8VLOheRZcIvWmIiDSxk66lxZkJUXN/hHBqP/CbAHxurd0A\nYK2td/O5FqqsCPashN3LYNfHkP0FYIkliCz/vlSNu5mA7uc672v+df6EKDsMBXucW0/UfRz4HL56\nBzx1MqTx996OIuX4j5BIaO5T+6orobTQCYilBU5g/Pq591FeDBjvz+L9Wvc5xrk1xzHH/U5wnJMc\nP973qvda7aMh9RxnKr+INIjPgpK1tsoYczewGGe6wkxr7UZjzMNAhrV2AXC3MeZCnE/hCnCm3QGc\nAzxsjKnCaR1+h/cmgs1DST5kZTiBKPNTZ/pB5VHnWGicM31uxO3O186DICDoG98iskMgz9+QzuVP\nr2TGyxnM+95ZhATqJq4iIk2oIWtpfwMsMcb8AKfp0IXe/b0Aa4xZDMQCc6y1j/m2XB+oOAp7P3FC\n0e5lsP8z57YS/kHOh3vn3c/mkEFc+u8y7rtkIKPGnuCP7OBw6DzQedTnqYai/bXhqbBOoNqyEI4e\nPPb8oI7HTuP7+pHa+LeyqKqAMm/gKTlO2DleACopgIriE39P4wfBERAU5mxbC9g6Xz3H7rOe4xzn\n+MePe673eUNFpkD386HbeU5wUhMokRPy6X2UvPO4F9bb9+s6z390guvm4wzuu8/jgbyt3lC02plO\nl7fVOWb8IX4ADLm2dm1RRHKDPwnrGRfGX6cN5raXMvj5Gxv4+zVDtEBWRKTpNGQt7TXAi9ba/2eM\nGQ28bIzpj/P+ORYYDpQA7xtj1lpr3//GizSn9bSVZc772C7viNG+tc5oj18AJKTD2T91/nhOHPF1\n46A/vbiG0PYFTBuRdJJvfgJ+/hCR5DxSz/7m8fIjx4angt3O6NTBrbB1ydfT1h2mdjQqol6YCot3\nusR+I9gcLwB5H8d0la3H+DmjWyGREBLl3GIjtq+z3T6qzrGI2nNCIp2g58atOKz99lBWmAk7P4Qd\nH8LncyFjpvMzdhkK3c9zglPi8LZxT8XqSme0s+YD76w1zv9P4wd6HwOc0N8xofmPbopP+TQotUhl\nRc4bR00oylrjTCkA5xdg0kgYNM15E0kYCoEdzujlLugbxy8m9uGPizbTJz6Mu8/v2Qg/hIiINEBD\n1tLeivf+ftbaVcaYYCDGe+1H1to8AGPMQmAo8I2g5Op62qoK5z2tZipd5moneBg/6DIERt/lhJek\nURD0zZuhb84u4v3Nufx0fC/aB/roT4agUIhLcx71eTxwJOebU/oKdsP29+BI9sm/v/GvDTXto6Bj\nF+e1vg469R41ISgwrGXde/Dr6XYnENvLeYz8nhMUsjJqg9OyP8PHf4J2HSBlbG1wiu3dOoJCSX7t\n0oiamUBVpc6x8GRIHu2MpGZ/AZv/y9efl4REOqGpboCK6XXstFNp1fS/NDifsiz/s/OPKHeTd2jb\nQKe+kHa5E4qSRkJ0d5/8wrjj3G5syS7i8SVb6RUXxoS0+EZ/DRER+YaTrqUF9gIXAC8aY/oCwcBB\nnGnlvzDGtAcqgHOBJ5qq8BOqroIDG2DXR0442vsJVJYAxvkjb8TtkHI2dB3doM50zyzdQYdAf24c\nneLz0o/Lzw86dnYeXUd/83hlKRTudYJT8QFnutsxwSfK2dca/thvTP7tnP+eXUfDefc7HwjvWlYb\nnLYtds4L6wLdxnmD0zgI7eRezQ1lLeRt84aiT5y/7WpmAvkFOEsi0m92/q5LGuEE57rKj0DORueW\nLdlfOF9X/7N2ZNM/COL6HRug4tKO+0GDtHzG2ubVLO50paen24yMjJOfeDxF++GpkU6L7pp/OInp\nTdretKyymu/+YxXbco/w7zvPok98x5NfJCLSDHmnoKW7XUdDGGMuAv5C7Vra39ddS+vtdPdPIBTn\nY+ZfWGuXeK+9DrjPu3+htfYXJ3u9M3qvOh6PB3K+cEaLdi1zGjHUrJ+J7etMo0s9G7qOcUZKTsHe\nQyWMe/xDbju7G/df1Lfxapbmr2BPbWja9VFtV8O4AdB9nDPa1PUsaBfiapkAVJQ4nRdrlkhkflpb\nb81MoJpHlyEQ2P7UX6O6Cg5tc6br1Q1QX3d7NM6H6fVHn8LiGu3HlMbV0PcpBaUanmpnfqqLsg+X\nMfnJ5QQG+LHg7rFEdWgD84RFpNVpSUGpqZ3xe5W1kPtV7VS63cudZgQA0T2c0aLUc5yvobFnVOsD\nb37BvIwslv3yPOI66kbnbZan2hmlrAlOmZ9CdYUzspI8qnaaXvzAppmqWHSgdqRo7ydOYPFUOcdi\neh0bjGJ6+m400Voo2ueEproBqqbrMTgNvuIHHBugorq1rCmdrZSCUgv1WWYhV/9jFYMTI/j5pN4M\nSAgnuJ264YlIy6GgdGKn/V519BAs/JkzalSS5+yL6OqMFqV4R43qTyE6A7nFZYz944dMHZrAo1cc\np5OdtF0VR2HPqtrglLvR2d8+GlLPrQ1OEafZ/KOuurdf2esNR4f3OscCguvMBPLOBjrFUVOfKC2E\nnC+94ekL53Hwq9ow164DxPc/NkB16vd18xRpGg19n9IapWZmcFIEf7pyID+du4Grnl1FO39Dv84d\nGZIcydCukQzrGkmX8GB1xxMRaUuCw511Ez0u9Iajs50W2j7yr+W7qKr28L1zuvvsNaSFCuwAPS90\nHgDFObBzKez4wPm68d/O/ugeTmDqfr7TICK4AUsKyg47TbT21jRdWFvbmTCssxOIRn0fkkc60wCb\nY4e+kAjn500ZW7uvqhwObq4z+vQFbHgd1jzvHDf+zmhY54G1ASr5rOb587UxGlFqpg4dKWfd3kLW\n7S1g3Z4CPs86TGllNQBxHYMYmhzpPLpGkNZFo04i0nxoROnEWsJ71eHSSsb84QPG9Y7lyelD3S5H\nWpKaqaE1o017VjjNRIy/03q8ZrQpYZiz3KFgV20oqmmohXW6Msb1d4JR8ihntCg8qXU15fB4oHB3\n7ahTTYAq9jbejB8IV73orH2SY+1f76w3OwOaetfKVFZ72Hyg2AlO3kdmvtPaMtDfj35dOjKsa214\n6hzeDBZYikibpKB0Yi3hverJD7bx+JKt/PeHY0nr0nRNjaQVqip3AlBNcNq/HrDOvaYCguFornNe\nUEcnSNWEooRhtTfsbWuO5jn/rRbe43RhnvIk9JvidlXNQ2UpLHnQGYmbNhv6XHTa30pBqQ3ILS5j\n/d5C1u1xgtPnWYcpr/IA0Dk8mKHJkQxJjmBo10jSunQkKECjTiLiewpKJ9bc36tKK6oZ88cPGJgY\nzos3j3C7HGltSvKdJiQ7P3RCVJL39iuxfVxvqNXsFO6FeTfDvgwYeQeM/23bnoqXswnm3+qMOo6+\nGy546Iz+e2iNUhvQKSyYiWnxTPTed6miysNXB4q8I05OgPrvFwcACAzwY0BCOEOTI7yjTpHqYiQi\nIsd4fc1e8o9WcOe4Hm6XIq1R+yhIu8x5yLeLSIab/wfvPQSfPO2MzF31ok/XJjZL1kLGTFh8vzPK\neO382vVxTUBBqRUJDPBjUFIEg5IiuHmMsy+nqOzrEad1ewuZtWoP/1y2C4CEiBBnxCnZaRLRt3NH\nAgPUslJEpC2qrPbwz2W7SO8ayYjUZtA9TKStCwiESY9C8mj4z93wj7PhsmfPaMpZi1KSDwt+AJvf\nge4XwOXPNvlNjxWUWrm4jsF8Z0BnvjOgMwDlVdVs2l/09YjT2j0FvPO5M+oUFODHwMRw75Q9Z61T\npzCNOomItAX/+Ww/+wpL+e1laW6XIiJ19ZvsdMKbdyPMucaZenbhb8C/nduV+c7uFfDv2+FILkz4\nHYy6y5X7TykotTFBAf4M8QahW8emAnDgcCnr9hR+3SRi5opdVH68E4CkqJDaDnvJkfTpHEY7f406\niYi0Jh6P5dmPdtAnPozzejftJ7Yi0gBRqXDLEljyAKx60jsV7wUIT3S7ssZVXQUf/wk+fgwiU+DW\nJZDgXvdNBSWhc3gIFw8M4eKBzqhTWWU1/7+9O4+Psrr3OP75ZbJOJntCgGwQNtkEJCLgLtVqbcW6\nVGhttbXSWq2tba/WtrfX621vvd311g3321qpSqu01eIGriCLgiiKQgJJ2ElYskAgybl/zBCSIQmg\nmTyT5Pt+vfKamWeemfnleSVz5jvnPOe8t2l3S3haXFrF0yuC01Umxfk4Pj+t1Qx7GWQm9+GTC0VE\neoHnVm9l7bZabp8xXuv0iUSruEQ4/zdQNBXmfQfuORUumg3Dzva6sq6xqyLYi1S+CMbNhM/8yvPZ\nDxWU5DCJcT4mFmUysSg4Rt05x8Zde1uG671VvpPZr5TS2BycMXFwdnLLtOQTizIY1i8FX4waWhGR\nnsA5x90L11KY6ef80DBtEYliYy6G/uOCQ/EevQROuQHO/An4evDH+tVPB89Ham6Cz8+GcZd5XRGg\noCRHwczIz/CTn+HngnEDgeAUsqs27mZ5KDgtXLONuW9VAhBIiGVCYToTQpNEjC9IJy2pF4+jFRHp\nwd5YV8XKyt38/PNjiNXQapGeIXsofP0FePYmeO13waF4Fz8AqT3sy4799TD/Zlj+MAw8AS55ADKL\nva6qhYKSfCxJ8T4mDc5smRnJOUd5dX1LcFq+YRd/eOkjml1wIe2hOYE2w/WKs5OJUa+TiIjn7l64\njpyUBC4+oZed6yDS28UlwQV3QNHJ8I/vwj2nwMX3wZCzvK7s6Gx5F578GuxYAyd/F878cdStFaWg\nJF3CzCjKSqYoK5mLQn9B8KMAABsrSURBVI1tbUMj71TsaglPz767hTlLKwBIS4prs6bTuIJ0Agn6\ncxQR6U7vVO7itbU7uPm840iM04KfIj3SuMtg4Hh4/Ar440Vw+k1w+o3Ru4ivc7D0fpj/Y0hKhy//\nLWrDnT6ZSsQEEmKZOjSbqUOzgeCsSqU76oKz64XC04I12wGIMRjRP5WJRYfWdSrM9OukYhGRCLpr\nwTpSE2P50uQ+toilSG+TMwKufhH++QN4+bbghAgX39/t6w4dUX01PH0trHkGhp0D0++CQI7XVXVI\nQUm6TUyMMbRfgKH9AnyhpACA3XsPsCLU6/R2+U6efnsTf1pcDkB2ID64nlMoOB2fn6ZvPEVEusja\nbbXMX72F684cqh59kd4gPhk+fzcMOhn++f3QULwHYPCpXlcWVPYq/HUW1G2HT/8CJl8TPD8jiumd\nUTyVlhTH6cNzOH148NuEpmbHR9tqeGvDofD0/OqtAMTGGKMHpobWgUpnRP8UirMDxMfq5GMRkWN1\nz8vrSIiN4cqpg7wuRUS60oTLYeCE4FC8/7sAzvwRnPJ9TxZsBYJrI718G7zya8gaAl98EQaM86aW\nY6SgJFHFF2Mc1z+V4/qn8sWTCgGortvP2+U7W851+svSCh5+Yz0QDE/FOckMz03huP4pDM9NYUT/\nFAoy/JosQkSkAxt37eWptzdy+eQisgIJXpcjIl0tdzTMWgD/uAFe+hlsWBRccyk5u3vr2LkB5n4d\nKpcEA9y5/wMJge6t4RNQUJKol5kcz7SRuUwbmQtAY1Mz67bXsWZrDWu27GHNllpWVu7iH+9sbnlM\nUpyP4bkBRrQKTyP6p5ATSNB5TyLS5933SikAV58WPdPwikgXS0iBi+4LLlD77A+DC9Re8iAUTeme\n1393Lvz9BsAFhwCOvaR7XrcLKShJjxPri2kJPoTWdQKoa2jko221LeFpzdY9vPTBdh5fVtmyT4Y/\nLvjY3BSG9w/2Qg3LTSE1Ues8iUjfUFXbwJyl5Uwfn0deepLX5YhIJJlBydcgbyI8cSU8fD5M+ylM\nvT5yQ/H21wXXd3r7j5B/YnBSiYxBkXmtCFNQkl4jOSGW8QXpjC9Ib7O9qraBNVtr+HBLTagXqoa5\nb22ktqGxZZ+89CSG5wZawtPw3BSG5AQ0eYSI9DoPv7GehsZmrjlDvUkifcaAcTBrIcz7NrzwH8FZ\n8S68G/yZXfs6m98Jro1UtRZO/T6ccTP4eu6X0QpK0utlBRKYGkhg6pBD43Kdc2zctZcPt9bwwZaD\nIaqW19dWsb+pGQieLzUoyx/qgUplRP8Aw3NTKMpKxqfzn0SkB6rZd4BH3ljPOaNyGdovxetyRKQ7\nJabBpY/Akvtg/o/g3tPg0ochv+STP7dz8Oa98Py/gz8LvvI0FJ/+yZ/XYwpK0ieZGfkZfvIz/Jx1\nXG7L9gNNzWyoqgsO3duyhzVba1i9aQ/PvrsF54L7JMTGMCw30CY8De0XYEBakgKUiES1P79Zzp59\njXzrjKFelyIiXjCDk2ZBfmgo3oPnwtm3frKpuut2wFPfgo/mw/DzYPqdkJzVpWV7JaJByczOBW4H\nfMD9zrnbwu7/JnAt0ATUArOcc6tD990MXBW673rn3PxI1ioCEOeLYWi/FIb2S+H84we0bN+7v4m1\n22r5YMuell6o19ZuZ+5bla0ea+SlJ1GQ6acw9FPQ6jItqed2PYtIz7fvQBP3v1bGyUOzGBc2RFlE\n+pi8ifCNV+Cpa2H+zbDh9WDASTrG94bShfDXb8DenXDer2DS1VG/NtKxiFhQMjMfcCdwNlAJLDWz\neQeDUMifnXP3hPa/APgtcK6ZjQJmAKOBgcALZjbcOdcUqXpFOpMU72Nsfhpj89PabN9Vv581W2oo\n21FHeXU95dX1VFTX88yqzeysP9Bm37SkuJYAlZ+Z1HK9MNPPwPQk4nxaD0pEImfuW5Vsr2ng95eN\n97oUEYkGSRkw41FYdGfwvKV7T4MvPBJcg+lImg7Agp/Da7+H7GFw+ZPQf2zka+5mkexRmgSsdc6V\nApjZHGA60BKUnHN7Wu2fDIQGNzEdmOOcawDKzGxt6PkWRbBekWOW7o/npOIsTio+vIu5Zt8BKqr3\ntoSng0Hq/c17eH711pZzoQBiDAamJ1GQEQpPWf42PVMZ/jhNay4iH1tjUzP3vlzKuPw0pg7pHUNi\nRKQLmMHU66BgEjzxVXjgHPj0f8OJX++4Z6i6DOZeBRuXw8Qr4dO/gHh/t5bdXSIZlPKAila3K4GT\nwncys2uB7wHxwFmtHrs47LF5kSlTJDJSEuMYNTCOUQNTD7uvqdmxdc++lhDVOki9+ME2dtQ2tNk/\nOd7XJjgdDFIFGX7yM5I0O5+IdOqfqzZTXl3Pjz4zUV+6iMjhCibBN1+Fv30DnvlBcCje5+6AxLDP\nMKuehL9/Nzi1+KWPwOgLvam3m0QyKLX3TuwO2+DcncCdZvZF4CfAFUf7WDObBcwCKCws/ETFinQn\nX4wxMD2JgelJTG6nN6p+fyMV1XvbBKiK6nrKdtTx8ofbaWhsbrN//9TENudEHTcghcmDs0jz67wo\nkb7OOcfdC9cxtF+Ac0blHvkBItI3+TNh5l/gjdvhxf8KTvX9hUeCQ+oaauHZG2HFo1AwGS6+D9J7\n/2fvSAalSqCg1e18YFMn+88B7j6WxzrnZgOzAUpKSg4LUiI9lT8+9tCiumGcc2yvaQiGp531lFcd\nGt73+todzN2zDwj2mI8akMrk4iymFGcxqThTC+uK9EEL1mzjgy01/PrSccRoZk4R6UxMDJxyAxSc\nFFwP6b5pcNq/wcrHYGcZnH4TnHYj+PrGxNmR/C2XAsPMbDCwkeDkDF9svYOZDXPOfRS6eT5w8Po8\n4M9m9luCkzkMA5ZEsFaRHsPM6JeaSL/UREoGHb5Q3L4DTays2MWi0ioWl1bxx8UbeOC1MmIMxuSl\ntQSnEwdnEkjoG290In3ZXQvWkZeexPTxA70uRUR6iqKp8I1X4a9Xw4KfQWoeXPF3GHSK15V1q4h9\nSnLONZrZdcB8gtODP+ice8/MbgWWOefmAdeZ2aeAA8BOgsPuCO33OMGJHxqBazXjncjRSYzztZlg\nYt+BJt4q38ni0moWr6viodfLmP1KKb4YY+zB4DQki5KiDJIVnER6lSVl1SzbsJNbPjdKM2uKyLEJ\n5MDlc+HDf0HhlODQvD7GnOsdI9ZKSkrcsmXLvC5DJOrt3d/E8g07WVxaxaLSKlZW7KKx2REbY4wr\nSGdycSZTirOZWJRBUrwmiZBjZ2bLnXNdsNR779PdbdWVDy1hVeVuXrvpLP0/i4iEHG07pa+PRfqY\npHgfpwzL5pRh2QDUNTSyfMNOFpVWsWhdFfe8XMqdC9YR5zPGF6QzpTiLyUOyOKEwQ7PrifQg723a\nzcI12/nBOcMVkkREPgYFJZE+LjkhltOG53Da8BwAahsaWbo+OExvUWkVf1iwljteWkt8bAwTCtKZ\nMiR4jtP4wnQSYvXhSyRa3b1wHYGEWL48ZZDXpYiI9EgKSiLSRiAhljNH9OPMEf0A2LPvAEvLqlkU\nCk63v/gRv3/hIxJiY5hYlMGU0DlOx+enEx+rcyBEosH6HXU8s2ozV59aTFqSZrsUEfk4FJREpFOp\niXFMG5nLtJHB9Vd21x/gzbKq0Kx61fzm+Q/heUiK81EyKKNlcoixeWk6eVyinpmdC9xOcNKh+51z\nt4XdXwg8AqSH9vmhc+6ZsPtXA7c4537dbYUfwb2vlBLri+GqUwZ7XYqISI+loCQixyTNH8c5o/tz\nzuj+AOys28+bZcHQtGhdFb+avwaA5HgfJYMymTIki9EDUxmUlcyAtERiFZ4kSpiZD7gTOJvg+n1L\nzWyec251q91+AjzunLvbzEYBzwCDWt3/O+DZbir5qGzds4+5yyu5pCSffqmJXpcjItJjKSiJyCeS\nkRzPuWMGcO6YAQDsqG3gzdLqlln1bnv2g5Z943xGfoafoiw/g7KS21zmZ/g1dE+62yRgrXOuFMDM\n5gDTCfYQHeSA1ND1NFotfm5mFwKlQF23VHuUHnitjMbmZr5xWrHXpYiI9GgKSiLSpbIDCZx//ADO\nP/5QcFq7rZYNVXVsqKpnQ1U966vqWFpWTd3+Q8ujxRjkZSSFBahkBmX5Kcj0a8Y9iYQ8oKLV7Urg\npLB9bgGeM7NvA8nApwDMLBm4iWBv1A8iXulR2l1/gEcXb+Czxw+kKCvZ63JERHo0BSURiajsQALZ\ngQQmhxbAPcg5R1XdfjZU1bF+R33wsip4OW/FJvbsa2zZ1wwGpCYGg1O2n8LMYIAqCoUqLZQrH5O1\nsy18ccGZwMPOud+Y2RTgj2Y2BvhP4HfOuVqz9p6m1YuYzQJmARQWFn7yqjvxyKL11O1v4pozhkT0\ndURE+gJ9uhART5hZS4iaWHT4at+76ve3BKeDvVAbqup5fvVWdtTub7NvTkpCS3A6dJlMUbaf1ETN\n+CUdqgQKWt3Op9XQupCrgHMBnHOLzCwRyCbY83SJmf2S4EQPzWa2zzn3h/AXcc7NBmZDcMHZLv8t\nQur3N/LQ62WcdVw/Rg5IPfIDRESkUwpKIhKV0v3xjPfHM74g/bD7avYdaDOM72Bv1KsfbefJ5Q1t\n9s1Mjm9zLlRRlp/c1ET6pSSQE0gkNSmWI/UISK+1FBhmZoOBjcAM4Ith+5QD04CHzWwkkAhsd86d\nenAHM7sFqG0vJHWnOUsq2Fl/gG+pN0lEpEsoKIlIj5OSGMeYvDTG5KUddt/e/U2UV7cNUBuq6lhS\nVs1TKzbiwr7Pj/fFkJOSQHZKAjmBeHJSEsgJJAQvD/4EEslOiccfr7fM3sQ512hm1wHzCU79/aBz\n7j0zuxVY5pybB3wfuM/MbiA4LO9K58L/iry3v7GZ+14tZdKgTEoGHd5DKyIix06tvoj0KknxPkb0\nT2FE/5TD7mtobKKiei/bavaxo3Y/22saDv3UNrBx1z5WVOymuq6B5nY+CifH+8ICVHDoYJttKQlk\nJSdoBr8eIrQm0jNh237a6vpq4OQjPMctESnuGDy1YiObd+/jvy8a63UpIiK9hoKSiPQZCbE+hvYL\nMLRfoNP9mpod1XX7WwJUeKDaXrOPD7fW8vraKnbvPdDuc2T449qGqFbXW2/P9McTE6Ohf/LxNTU7\n7nl5HaMGpHLG8ByvyxER6TUUlEREwvhirCXIHElDY9PhvVM1DeyoPRSs3i7fxbaafew70HzY4+N9\nMZw9KpcZkwo4eUi2QpMcs+fe20Lp9jr+d+YEnW8nItKFFJRERD6BhFgfeelJ5KUndbqfc466/U1t\nQ1RNcI2peSs38c9VmynITOKykgIuLSkgNzWxm34D6cmcc9y1cB2Dsvx8ZuwAr8sREelVFJRERLqB\nmRFIiCWQEMvg7LYLgf74/JHMf28Ljy0p59fPfcjvXviIM0f0Y+akAk4fnkOsT+c7SfteW7uDVRt3\n84uLxuJTb6SISJdSUBIR8VhinI/p4/OYPj6Psh11zFlaztzllbzw/lb6pybyhZJ8vnBiAfkZfq9L\nlShz14J15KYmcNEJeV6XIiLS6+hrShGRKDI4O5mbzxvJGz+cxt1fOoHh/VP43wVrOfWXC/jKg0t4\ndtVmDjQdfq6T9D1vl+9kUWkVXz+lmIRYn9fliIj0OupREhGJQvGxMZw3dgDnjR1ARXU9Tyyr4PFl\nlVzz6FtkB+K5eGI+M04sPGwYn/Qddy1cR1pSHDNPKvS6FBGRXklBSUQkyhVk+vneOSO4ftowXv5w\nO48tqeD+V8u49+VSJhdnMnNSIZ8e3Z/EOPUq9BUfbq3h+dVbuf6soQQS1JSLiESC3l1FRHqIWF8M\n00bmMm1kLlv37OOJZRX8ZVkF35mzgnR/HJ+fkMfMSYUMzz18sV3pXe5ZuI6kOB9XnjzY61JERHot\nBSURkR4oNzWR684axrfOGMrr63YwZ0kFf1q8gYdeX88JhenMmFTIZ48fgD9eb/O9TUV1PU+v3MRX\nphSRmRzvdTkiIr2WWlARkR4sJsY4dVgOpw7Loaq2gblvVTJnaQU3PvkO//X31VwwfiAzJxUyJi/N\n61Kli9z3aikxBlefWux1KSIivZqCkohIL5EVSGDWaUO4+tRilpRVM2dpBU8ur+TRN8sZk5fKjBML\nmT5+ICmJcV6XKh/T9poG/rK0ggvH5zHwCIsci4jIJ6OgJCLSy5gZJxVncVJxFrd8bjRPrdjIY0vK\n+clT7/Lzf77PZ48fwIxJhZxQmI6ZFintSR56vYz9Tc1884whXpciItLrKSiJiPRiaf44rpg6iK9M\nKWJl5W7mLCln3spNPLG8kuG5AWacWMhFJ+SR7te5LtFuz74D/HHRBs4d3Z8hOQGvyxER6fUUlERE\n+gAzY3xBOuML0vnJZ0fx95WbmLOknFv/sZrb/vUB543pz4wTC5lcnKlepij16OJyahoa+dYZQ70u\nRUSkT4hoUDKzc4HbAR9wv3PutrD7vwd8HWgEtgNfc85tCN3XBKwK7VrunLsgkrWKiPQVgYRYZk4q\nZOakQlZv2sOcpeX87e2NPL1iE4Ozk7n3yxM1xXiU2XegiQdeK+PUYdmMzdfEHCIi3SFiQcnMfMCd\nwNlAJbDUzOY551a32u1toMQ5V29m1wC/BC4L3bfXOTc+UvWJiAiMGpjKrdPHcPN5I3lm1WaeXrmJ\nggy/12VJmNqGRqYOyWLmpEKvSxER6TMi2aM0CVjrnCsFMLM5wHSgJSg55xa02n8xcHkE6xERkQ4k\nxfu4eGI+F0/M97oUaUd2IIE7Zk7wugwRkT4lJoLPnQdUtLpdGdrWkauAZ1vdTjSzZWa22MwujESB\nIiIiIiIi7Ylkj1J7ZwO7dnc0uxwoAU5vtbnQObfJzIqBl8xslXNuXdjjZgGzAAoLNRxBRERERES6\nRiR7lCqBgla384FN4TuZ2aeAHwMXOOcaDm53zm0KXZYCC4HDxhw452Y750qccyU5OTldW72IiIiI\niPRZkQxKS4FhZjbYzOKBGcC81juY2QTgXoIhaVur7RlmlhC6ng2cTKtzm0RERERERCIpYkPvnHON\nZnYdMJ/g9OAPOufeM7NbgWXOuXnAr4AA8ERo3Y6D04CPBO41s2aCYe62sNnyREREREREIiai6yg5\n554Bngnb9tNW1z/VwePeAMZGsjYREREREZGORHLonYiIiIiISI+koCQiIiIiIhLGnGt3xu4ex8y2\nAxu8rqMLZQM7vC4iiun4dE7Hp3M6Ph3rimNT5JzTVKTtUFvVp+jYdE7Hp3M6Pp37pMfnqNqpXhOU\nehszW+acK/G6jmil49M5HZ/O6fh0TMdGjoX+XjqmY9M5HZ/O6fh0rruOj4beiYiIiIiIhFFQEhER\nERERCaOgFL1me11AlNPx6ZyOT+d0fDqmYyPHQn8vHdOx6ZyOT+d0fDrXLcdH5yiJiIiIiIiEUY+S\niIiIiIhIGAWlKGNmBWa2wMzeN7P3zOw7XtcUjczMZ2Zvm9k/vK4lmphZupk9aWYfhP6GpnhdUzQx\nsxtC/1fvmtljZpbodU1eMrMHzWybmb3balummT1vZh+FLjO8rFGij9qpo6N2qmNqqzqmdqotr9sp\nBaXo0wh83zk3EpgMXGtmozyuKRp9B3jf6yKi0O3Av5xzxwHj0DFqYWZ5wPVAiXNuDOADZnhblece\nBs4N2/ZD4EXn3DDgxdBtkdbUTh0dtVMdU1vVDrVT7XoYD9spBaUo45zb7Jx7K3S9huCbR563VUUX\nM8sHzgfu97qWaGJmqcBpwAMAzrn9zrld3lYVdWKBJDOLBfzAJo/r8ZRz7hWgOmzzdOCR0PVHgAu7\ntSiJemqnjkztVMfUVh2R2qlWvG6nFJSimJkNAiYAb3pbSdT5PXAj0Ox1IVGmGNgOPBQa7nG/mSV7\nXVS0cM5tBH4NlAObgd3Ouee8rSoq5TrnNkPwAzHQz+N6JIqpneqQ2qmOqa3qgNqpo9Zt7ZSCUpQy\nswAwF/iuc26P1/VECzP7LLDNObfc61qiUCxwAnC3c24CUIeGTbUIjWGeDgwGBgLJZna5t1WJ9Fxq\np9qnduqI1FZ1QO1U9FFQikJmFkew8XnUOfdXr+uJMicDF5jZemAOcJaZ/cnbkqJGJVDpnDv4ze6T\nBBsjCfoUUOac2+6cOwD8FZjqcU3RaKuZDQAIXW7zuB6JQmqnOqV2qnNqqzqmdurodFs7paAUZczM\nCI7bfd8591uv64k2zrmbnXP5zrlBBE9wfMk5p29bAOfcFqDCzEaENk0DVntYUrQpByabmT/0fzYN\nnUDcnnnAFaHrVwBPe1iLRCG1U51TO9U5tVWdUjt1dLqtnYqN1BPLx3Yy8GVglZmtCG37kXPuGQ9r\nkp7j28CjZhYPlAJf9bieqOGce9PMngTeIjhr19v08ZXPzewx4Awg28wqgf8AbgMeN7OrCDbal3pX\noUQptVPySamtaofaqcN53U6Zcy5Szy0iIiIiItIjaeidiIiIiIhIGAUlERERERGRMApKIiIiIiIi\nYRSUREREREREwigoiYiIiIiIhFFQEukiZtZkZita/XTZSuNmNsjM3u2q5xMRkb5H7ZTIsdE6SiJd\nZ69zbrzXRYiIiHRA7ZTIMVCPkkiEmdl6M/sfM1sS+hka2l5kZi+a2Tuhy8LQ9lwz+5uZrQz9TA09\nlc/M7jOz98zsOTNLCu1/vZmtDj3PHI9+TRER6aHUTom0T0FJpOskhQ1puKzVfXucc5OAPwC/D237\nA/B/zrnjgUeBO0Lb7wBeds6NA04A3gttHwbc6ZwbDewCLg5t/yEwIfQ834zULyciIj2e2imRY2DO\nOa9rEOkVzKzWORdoZ/t64CznXKmZxQFbnHNZZrYDGOCcOxDavtk5l21m24F851xDq+cYBDzvnBsW\nun0TEOec+5mZ/QuoBZ4CnnLO1Ub4VxURkR5I7ZTIsVGPkkj3cB1c72if9jS0ut7EoXMMzwfuBCYC\ny81M5x6KiMixUjslEkZBSaR7XNbqclHo+hvAjND1LwGvha6/CFwDYGY+M0vt6EnNLAYocM4tAG4E\n0oHDvi0UERE5ArVTImGU6EW6TpKZrWh1+1/OuYNTryaY2ZsEv5yYGdp2PfCgmf0bsB34amj7d4DZ\nZnYVwW/krgE2d/CaPuBPZpYGGPA759yuLvuNRESkN1E7JXIMdI6SSISFxn6XOOd2eF2LiIhIOLVT\nIu3T0DsREREREZEw6lESEREREREJox4lERERERGRMApKIiIiIiIiYRSUREREREREwigoiYiIiIiI\nhFFQEhERERERCaOgJCIiIiIiEub/AY4YQixqgSTSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d4000fa3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(14, 4))\n",
    "ax[0].plot(range(1, epochs + 1), udacity_model_history['loss'])\n",
    "ax[0].plot(range(1, epochs + 1), udacity_model_history['val_loss'])\n",
    "_ = ax[0].set(xlabel='Epochs', ylabel='Losses')\n",
    "ax[1].plot(range(1, epochs + 1), udacity_model_history['acc'])\n",
    "ax[1].plot(range(1, epochs + 1), udacity_model_history['val_acc'])\n",
    "_ = ax[1].set(xlabel='Epochs', ylabel='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}