{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Nets\n",
    "\n",
    "This type of neural nets are predominantly (and heavily) used in image processing. This lesson is on the CIFAR-10 dataset.\n",
    "\n",
    "## Useful terms:\n",
    "\n",
    "1. Conv2D\n",
    "2. MaxPool2D\n",
    "3. BatchNormalization\n",
    "\n",
    "## Further Readings:\n",
    "https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /root/miniconda3/lib/python3.6/site-packages\r\n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir\n",
    "from tqdm import tqdm\n",
    "\n",
    "import tarfile\n",
    "import pickle\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Conv2D, MaxPool2D, Flatten, BatchNormalization, Dropout\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile('cifar-10-python.tar.gz'):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
    "            'cifar-10-python.tar.gz',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(cifar10_dataset_folder_path):\n",
    "    with tarfile.open('cifar-10-python.tar.gz') as tar:\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "        \n",
    "label_dict = dict(zip(range(10), \n",
    "                      ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']))  \n",
    "width = height = 32\n",
    "channels = 3\n",
    "train_examples = 50000\n",
    "\n",
    "# Get the test set\n",
    "with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:\n",
    "    batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "    test_x = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)/255\n",
    "    test_y = batch['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size):\n",
    "    n_batches = 5\n",
    "    while(1):\n",
    "        for batch_id in range(1, n_batches + 1):\n",
    "            with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "                batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "                features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "                labels = batch['labels']\n",
    "                for start in range(0, len(features), batch_size):\n",
    "                    end = min(start + batch_size, len(features))\n",
    "                    yield features[start:end]/255, np.array(labels[start:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ca8fe550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/YzPrBfBtAEOotev6iLuHsw3qVKt5TE+QZJYqfx/KWkdwfGSEr6Aeeuk4tbVk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\nCn4hEsr/AwmU/m7ZA1RAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ca8558d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ESqJZfRWtoJAPW3q5vF3A8+ChsJV233/fY/Z59Cc/MWOitn01NG7bPMOHjwbbs7bDg6KT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\nPAXg6STHAeAbqL5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6b8a48dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GzM60Is9mQohsuanVfndfAPB1AA8CuGxmMwAw/B2s0uDup939pLufLJNFICFE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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ca901390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J6CI1/GLJQD932xZuUwLwq+5+ORrtuG8ws6sA3ArgAXffCeCB5v9CiNcJcwa/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\nvxCJouAXIlEU/EIkioJfiERR8AuRKP8fHfrYoWBq62kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6b89d6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = next(get_batch(5))\n",
    "for im, label in zip(x,y):\n",
    "    plt.imshow(im)\n",
    "    plt.title(label_dict[label])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic logistic multiclass classification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 1000\n",
    "gen = get_batch(batch_size)\n",
    "x_train, y_train = next(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic = LogisticRegression()\n",
    "logistic.fit(x_train.reshape(batch_size,-1), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 1, 8, 0, 4, 3, 3, 6, 1, 1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = logistic.predict(test_x.reshape(len(test_x), -1))\n",
    "y_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting the probabilities for the first 3 images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.11780589e-02,   5.22601998e-02,   1.55926385e-01,\n",
       "          1.91240503e-01,   3.91236477e-04,   1.54297967e-01,\n",
       "          2.80945273e-01,   1.19926488e-06,   1.13659809e-01,\n",
       "          9.93687013e-05],\n",
       "       [  2.16303715e-02,   7.31044039e-01,   2.69961983e-03,\n",
       "          1.05058501e-01,   4.19521439e-04,   4.01069801e-03,\n",
       "          7.30693763e-06,   2.05908231e-04,   2.05482815e-02,\n",
       "          1.14375752e-01],\n",
       "       [  6.08332707e-02,   8.16219690e-02,   7.55828475e-03,\n",
       "          1.26261683e-01,   2.92891682e-02,   9.79890820e-03,\n",
       "          7.44308146e-03,   4.27092877e-03,   6.19882712e-01,\n",
       "          5.30399941e-02]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.predict_proba(test_x[:3].reshape(3,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy of the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.275"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of parameters for a fully connected network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30720"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32*32*3*10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Keras Multilayered Perceptron (Neural Net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size):\n",
    "    n_batches = 5\n",
    "    while(1):\n",
    "        for batch_id in range(1, n_batches + 1):\n",
    "            with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "                batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "                features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "                labels = batch['labels']\n",
    "                for start in range(0, len(features), batch_size):\n",
    "                    end = min(start + batch_size, len(features))\n",
    "                    x = features[start:end]/255\n",
    "                    y = labels[start:end]\n",
    "                    yield x.reshape(len(x),-1), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that when we do classification problems we use the **Categorical Crossentropy Loss**. When its only two classes we can use Logistic Loss (Binary Crossentropy Loss). Finally for regression problems we use **Mean Squared Error**.\n",
    "\n",
    "The Cross Entropy loss is defined as:\n",
    "$$\\mathcal{L} = -\\frac{1}{N}\\sum_i \\mathcal{I}(y_i=1)\\log(p_{i1})+\\mathcal{I}(y_i=2)\\log(1-p_{i2})+\\cdots++\\mathcal{I}(y_i=K)\\log(1-p_{iK})$$\n",
    "where $N$ is the number of training instances, $K$ is the number of classes and $p_{ik}$ is the probability that instance $i$ belongs to $k$.\n",
    "\n",
    "Softmax takes a $D$ dimensional vector and squeezes them through a function such that we have $D$ outputs whos values are positive and sums to one.\n",
    "$$\n",
    "\\text{softmax}(\\mathbf{y})_d = \\frac{\\exp(-y_d)}{\\exp(-y_1)+...+\\exp(-y_D)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 100)               307300    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 308,310.0\n",
      "Trainable params: 308,310\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "# TODO: Do a 'Normal' 1 Hidden layer NN (Refresher https://keras.io/#getting-started-30-seconds-to-keras)\n",
    "# Note that the number of inputs is width*height*channels\n",
    "# The last layer is a softmax layer (it outputs the probability of the ten classes)\n",
    "# The loss function is 'sparse_categorical_crossentropy' and use either 'adagrad' or 'adadelta' as the optimizer\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "195/195 [==============================] - 3s - loss: 2.0330     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7faada64e710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "model.fit_generator(get_batch(batch_size=batch_size), train_examples//batch_size, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9152/10000 [==========================>...] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3337"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(test_x.reshape(len(test_x),-1))\n",
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolution Neural Networks (CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Points to note **\n",
    "1. One CNN, connected to **one** node above is simply a Dense layer with most weights set to zero.\n",
    "2. The same CNN, connected to multiple nodes is weight tying/ sharing.\n",
    "\n",
    "Consider the following convolution mask:\n",
    "<img src='https://ujwlkarn.files.wordpress.com/2016/07/screen-shot-2016-07-24-at-11-25-24-pm.png?w=74&h=64'>\n",
    "<img src='https://ujwlkarn.files.wordpress.com/2016/07/convolution_schematic.gif?w=536&h=392'>\n",
    "\n",
    "![](cnn.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size):\n",
    "    n_batches = 5\n",
    "    while(1):\n",
    "        for batch_id in range(1, n_batches + 1):\n",
    "            with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "                batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "                features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "                labels = batch['labels']\n",
    "                for start in range(0, len(features), batch_size):\n",
    "                    end = min(start + batch_size, len(features))\n",
    "                    x = features[start:end]/255\n",
    "                    y = labels[start:end]\n",
    "                    yield x, np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the max pooling layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Conv2D?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MaxPool2D?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "# TODO: Get 3 layers of Conv2D followed by MaxPool2D\n",
    "# The first layer requires input_shape = (width,height,channels)\n",
    "# Set activation='relu' in all layers and padding='same', Maxpool does not have an activation\n",
    "# All you need to specify is the kernel_size and filters parameters\n",
    "# As a thumb rule the number of filters double. eg. choose 4, 8, 16 for the 3 layers\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "# Note: You so not apply dropout on final layer.\n",
    "model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 32, 32, 8)         224       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 16)        1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 11,162.0\n",
      "Trainable params: 11,162.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "32*32*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "224"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*3*3*8+8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1168"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*3*8*16+16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "195/195 [==============================] - 28s - loss: 2.0986    \n",
      "Epoch 2/5\n",
      "195/195 [==============================] - 29s - loss: 1.7812    \n",
      "Epoch 3/5\n",
      "195/195 [==============================] - 29s - loss: 1.6117    \n",
      "Epoch 4/5\n",
      "195/195 [==============================] - 29s - loss: 1.5217    \n",
      "Epoch 5/5\n",
      "195/195 [==============================] - 33s - loss: 1.4660    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa693789668>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "model.fit_generator(get_batch(batch_size=batch_size), train_examples//batch_size, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9792/10000 [============================>.] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.489"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(test_x)\n",
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7mW2u5rXnAt0XEf+lnwuV/q7PDvocZo/5/llobUoRmQmHAayNttckr53tPiIi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\n9P8O5tzNUeiijgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6932cf048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "idx = np.random.choice(len(test_x),5,replace=False)\n",
    "\n",
    "\n",
    "p = model.predict(test_x[idx])\n",
    "for i in range(len(idx)):\n",
    "    plt.subplot(5,2,2*i+1)\n",
    "    plt.imshow(test_x[idx[i]])\n",
    "    plt.title(label_dict[test_y[idx[i]]])\n",
    "#     plt.show()\n",
    "    pred_label = np.argsort(-p[i])[:3]\n",
    "    pred_prob = [p[i][l] for l in pred_label]\n",
    "    pred_label = [label_dict[l] for l in pred_label]\n",
    "    \n",
    "    plt.subplot(5,2,2*i+2)\n",
    "    plt.bar(range(3),pred_prob)\n",
    "    plt.xticks(range(3), pred_label)\n",
    "#     plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Normalization\n",
    "\n",
    "Batch Normalization makes the output of multiplying by weights 0 mean and variance of one **before** passing through an activation layer. This makes sure that the gradients are neither large or too small. Making the learning process faster. \n",
    "\n",
    "https://www.quora.com/Why-does-batch-normalization-help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BatchNormalization?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(8, kernel_size=(3,3), padding='same', input_shape = (width,height,channels)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(16, kernel_size=(3,3), padding='same'))\n",
    "# TODO: add a BatchNormalization() layer\n",
    "model.add(Activation('relu'))\n",
    "# TODO: add a MaxPool2D layer\n",
    "# TODO: Add another set of Conv2D followed by BatchNormalization, followed by relu activation, followed by maxpool (4 lines of code)\n",
    "# TODO: flatten the layer\n",
    "# TODO: Add the last softmax layer\n",
    "model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_22 (Conv2D)           (None, 32, 32, 8)         224       \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 32, 32, 8)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_22 (MaxPooling (None, 16, 16, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 16, 16, 16)        1168      \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 16, 16, 16)        0         \n",
      "=================================================================\n",
      "Total params: 1,392.0\n",
      "Trainable params: 1,392.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "195/195 [==============================] - 38s - loss: 2.0036    - \n",
      "Epoch 2/5\n",
      "195/195 [==============================] - 43s - loss: 1.6267    \n",
      "Epoch 3/5\n",
      "195/195 [==============================] - 46s - loss: 1.4977    \n",
      "Epoch 4/5\n",
      "195/195 [==============================] - 49s - loss: 1.4123    \n",
      "Epoch 5/5\n",
      "195/195 [==============================] - 49s - loss: 1.3460    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa690de7a20>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 256\n",
    "model.fit_generator(get_batch(batch_size=batch_size), train_examples//batch_size, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 2s     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5076"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(test_x)\n",
    "np.count_nonzero(y_pred == test_y)/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vfvs7bnvN6jWN9pt/+i0u9oILLgjnLPr7ZdSfowcPu9iRoYNho2/CxVatCu/Oir5lLrZ7\nPMRGJ/xx57SH9+bZwyEPzbQckkzvMgDbzewpACB5B4BrADSSIc3s3mj/7wNY3dQeiogsUHoyJiLz\nYRWA56Ltnelr03kfgH+aKkDyepJbSG7Zv3//PHZRRGRh0mBMRJqK5E+gPhj7yFRxM7vVzDaa2cbB\nwcHmdk5EpAWaOk1ZyOdx7vKlje3ujlCNf3xyzO0bTzHmMuUr4inGWnburBa2mQnFpS2yo9BKNE3Z\n0eYr2cNCrDTpS1swCVOIHZlyFeevXee2e7tDuY7qpC9DsW7teY12T/8SF9u1L5SoeHrHbhcr1cL1\nlw36EiCXLg+/yGx8yMV2j4fq/LWS70tXdE4bCfcbT+WKZOwCsCbaXp2+5pC8BMCnAbzZzA5m4yIi\ni5GejInIfNgMYD3J80kWAbwLwKZ4B5JrAXwZwHvM7Ect6KOIyILU1CdjInJ2MrMKyRsA3AUgB+A2\nM9tK8gNp/BYA/w3AUgCfTL+UUjGzja3qs4jIQqHBmIjMCzO7E8CdmdduidrvB/D+ZvdLRGSha+pg\nLGGCtkJnY7taCUv7VGu+1EQS5WLVKn4JIES5S5mFi1CphPynasXnQrEQctSYKXuRRDO2XZ1dmViU\nT9bu88JK5ZDrNlHzpS0ue82r3XZHW7j3I4cOudjRoWi5ory/q6GRkO91dNgfVyqEEiCHqj4vrDYR\n+j10wOfk7Rje22g/P3LExZbun2y0l090N9qF2jhERERkfilnTERERKSFNBgTERERaSENxkRERERa\nqKk5YwagZNFyRXH9sILvSjXK0yqVM7lf4yGnKbNyEXL5sG85789ZjuqMZZdYYrSwUltbwcWSqGBZ\nkvjiZW3FUJNseMznZe074quH93b1NdrbHtvuYt/417sb7Re/6sUuhiTkzHV3ZZaGWhXqgHHlqIvV\nusP1enMrXWxyaEejvffoXhcr5kNe3NrVYcmqQlFloUREROabnoyJiIiItJAGYyIiIiIt1OQ6YwSS\ncMnJWph+yxd8yYi2Yi6KZaYUo+nGXGaesloJ5SVGhjNLLEVTpOzw10ty0Xky6yi1t0fTljVfZqM6\nGaZTa2Vf2mL/vj1ue7xtpNF+9hk/Tfn9b3270Z4w3+8XvuRFjfbqtetdbGhVWB7J+od9rBquN1Lz\nyzhNVsJUb1dPp4uhK0y9jkUrQ9X87K2IiIjMAz0ZExEREWmhEw7GSK4h+Q2Sj5LcSvLX0tcHSN5N\n8on0z/7T310RERGRs8tsnoxVAPyGmW0AcAWAD5LcAOBGAPeY2XoA96TbIiIiInISTpgzZma7AexO\n28MkHwOwCsA1AK5Kd7sdwDcBfGTGcwGoVOMlkEIZirZ2n7eUROUV8lH5CMCXpWgr+ESmapTTVS75\n5XsmSiGny6LSGQDQ0RGuQRRdLIlLYhQzJTiiVZw6y76f5SM+T+vQ4V2NdvHgPhd73ZoLG+181V8/\nx9DXwb7lLraksKzR3jXiQtiZ3xauXfbLKA2NT4R+53pcrKM3LIFUao/es1x28SkRERGZq5PKGSO5\nDsDLAdwPYEU6UAOAPQBWzGvPRERERBaBWQ/GSHYD+BKAD5uZW5HazAyATXPc9SS3kNwyOjoy1S4i\nIiIii9asSluQLKA+EPu8mX05fXkvyZVmtpvkSgD7pjrWzG4FcCsArF17nhWiaUXG1fhzOX8cw3YN\nM8Tob6HY3tFo9yzpczHUwpTb2OhRFypXwvQmM+Uy4k0mfvya5MJ2R7svl2G9fnw6XgzXOLi0w8U6\nXxhNDZ4z6WL780812j0j/pzn9m5otCt2kT+uHPqzb3iLizEqyZHk/PUmR6LPKHp7bcrhtoiIiMzF\nbL5NSQB/BeAxM/tEFNoE4Lq0fR2Ar85/90RERETObrN5MvYaAO8B8EOSD6Wv/S6AmwB8geT7AOwA\n8M7T00URERGRs9dsvk35XQDTfY3uDfPbHREREZHFpcnLIQFJlIDVXgw5TYWCL+fAfJhBNZ8yhijV\nDMzE8m3hluL8MQDoaFsS2p3dLjY6FJYumpyccLHODt+3WK0a7mdkxJfSODp+2G0P1Q6G2AW+tEbl\nolDa40jBfT8CpcOjjXYhU65jaM/+Rntp/8Uudk4Slk7aPbTLxTo7w3JM+WV+rF2qhPudnAx5djXf\nZREREZkHWg5JREREpIU0GBOReUHyapLbSG4nedyKHCQvJnkfyUmSv9mKPoqILERNn6a0aPxXLISK\n9YWinwq0JEydJZmMtVw0hZnk/TxlLio1kWTKUJBxJX0/hVkohmnLidKoi02WQiX9Qt5Pb1Zq4ZzD\nY75cxjOHnnHbQ/m9jXbPQMXFVnSHjyJBl4sdTML1nz/ij3t+4vFG+5ySL1Hx8t6fbLQvWv5iF9ub\nf7rRPtrlpzAr0coB1YkwZWumeUqZGut/uW4G8CYAOwFsJrnJzB6NdjsE4EMA3taCLoqILFh6MiYi\n8+EyANvN7CkzKwG4A/Ul0xrMbJ+ZbQZQnuoEIiKLlQZjIjIfVgF4Ltremb4mIiInoMGYiCwo8RJq\n+/fvP/EBIiJnuKbmjJkB1UrIO6qUoxykxOdCxcsM5YrZvLDQ7Vw+P23MMuv3lKphdqS9zS9d1L80\nrHM+fHS3i01MhhyqtqLPm6pFS3KO5nzZicklh9x2e3vo2/IOf/0VXSGHbcewP27EwvbhUb8ee6E9\n5HTtzz3rYkfzIResr33QxfoOhPd3fL8v5TESfRZjFu69VlXOmExrF4A10fbq9LWTFi+htnHjRi3C\nJSJnPT0ZE5H5sBnAepLnkywCeBfqS6aJiMgJNP3blCJy9jGzCskbANwFIAfgNjPbSvIDafwWkucA\n2AKgF0CN5IcBbDCzoWlPLCKyCDR1MEbSlbDIt0XtYsHtm4+mH4uZshdxLJfzU5i1qPxCPIUIAMWo\nDEa+4G89n4Trt1d6XKw0Hn5XlCt+qq6KMI13uOugi6Gv5DZr4+H61S7f7yXnhAr8a3r8NbY9sa/R\nHuzxU4q9SwYa7R2je13s0erDjXZxl58WLUSrAQwW/P1Wu8I18tbbaOdy+p0p0zOzOwHcmXntlqi9\nB/XpSxERiejJmIiIyCKw7savt7oLZ6xnbnrLaT2/csZEREREWkiDMREREZEWauo0ZZIkKLaHJZDa\nO0I5h1whU74iifK7MuUrZsoZY7QEErPLIUXb1czSPkQ4T1uHz6EqR8sMlasuhKEo9/hoty8tMTzh\ny3UUi+H6B5eOuNjDlZBfVjbf77F8KMmRq/myG7koLW14ZJ+LHToU8sKKY77jveeF9mTOvxe5asif\naxsLnxeZWZdKRERE5kxPxkRERERaSIMxERERkRZqbgX+9L9jKtUwjce8n25Mour5tVqm6n1thkrw\n8VRaZlqtGlfkr2bmGxnGpfmcL6WR5ENZiLJNutiBcliu5Sh9KYvSpJ+mbO8LZSIqHX6t5L3RdOCh\n3T42djDcx8Qzfpqy0hmmFJMuf0+1qFrIZG+fi+0ZC/fYvcyXxMBkeH/L1bCqgEHF0EVEROabnoyJ\niIiItJAGYyIiIiItpMGYiIiISAs1vwJ/MnV5BJrPR/LpXj5HzKJ9LXNcEuV+MTvWjE+T6Ua8Wcgs\nv9Tdu7TRPjz8nIs9uT8cub/W72IrB4f95auhAyOH/PX7+sNySMwfdbHDz4flibrh89DWdgyGjTb/\nPu3sHGu0jx5tc7HhPWHf/sz/BbnozW+P2lquQUREZP7pyZiIiIhIC2kwJiIiItJCTa7AT7S1hemy\nYjQdmGSr5UfzhjPFMoX03TZzfi4yruqfcPqK/6S/XndPmH48PL7TxcYs3h5zsULRv70Jw/ZkzZev\naCuEshSDg/64h8dDtf6Jor+nfX1hu5Kp3J+P7qNv6QEXO7crTIu+EEtdrO1odI2u8HndQ3/vIiIi\nMnd6MiYiIiLSQiccjJFsJ/lvJH9AcivJP0hfHyB5N8kn0j/7T3QuEREREfFm82RsEsDrzexlAC4F\ncDXJKwDcCOAeM1sP4J50W0REREROwglzxqxeO+JY0lIh/c8AXAPgqvT12wF8E8BHZj4bj8vHakQy\nSxfFeWL5fMHHotwrZnK/4iIVSeJjhUKcr+bPmc/HOWPH9/uYXM73f3nnwXDOnF8Oqa3W5ba7oxy5\n8oC/SFdHuP5E4vPJerpD+Y723h4X25kPeWr5zP0m0Vg7+/4u7+totFeiw8Wq4+Ge9oyGe6pll5AS\nERGROZtVzhjJHMmHAOwDcLeZ3Q9ghZkdWyhxD4AVp6mPIiIiImetWQ3GzKxqZpcCWA3gMpIvycSz\na4A3kLye5BaSW4aHj061i4iIiMiidVKlLczsCMlvALgawF6SK81sN8mVqD81m+qYWwHcCgAXXLje\nstOD0X6ZV+JpNT9mLBTCdF+x2O5iyQwlKuKpz/gcAJDPx1Of2d6F49oL/nrnRmUh2swPNqtjfvpv\naWc48UH6++3uCu9LfnWfi61/6apGu3Ofv8YhjDbaQz1+GrG3J5omtU4XO3QklMvYPOTP2d4d3sOh\ncqj4X8pNOd4WERGROZjNtykHSfal7Q4AbwLwOIBNAK5Ld7sOwFdPVydFZOEjeTXJbSS3kzzuCz2s\n+/M0/jDJV7SinyIiC81snoytBHA765nyCYAvmNnXSN4H4Ask3wdgB4B3nsZ+isgClv58uBn1f6zt\nBLCZ5CYzezTa7c0A1qf/XQ7gU+mfIiKL2my+TfkwgJdP8fpBAG84HZ0SkTPOZQC2m9lTAEDyDtS/\ncR0Pxq4B8Jk0x/T7JPuOpTo0v7siIgtHU5dDevqp7Qeufcd/2AFgGYADJ9p/EVro78t5re6ALFir\nADwXbe/E8U+9ptpnFQA3GCN5PYDr080Rktvmt6sLwoL9u86Pt7oHTbVgPwdAn8VCMofPYla/N5s6\nGDOzQQAgucXMNjbz2mcCvS8i/ks/Zyv9XV8Y9DksHIv9s9DalCIyH3YBWBNtr05fO9l9REQWHQ3G\nRGQ+bAawnuT5JIsA3oX6N65jmwC8N/1W5RUAjipfTESkydOUkbN6CmIO9L7IGcnMKiRvAHAXgByA\n28xsK8kPpPFbANwJ4KcAbAcwBuCXW9XfBUB/1xcGfQ4Lx6L+LHh8sVURERERaRZNU4qIiIi0kAZj\nLUbymyTfP01sLcmRtKDmjPuKiIjImampg7ETLZfSKs0e5JBcQ/IbJB8F8CqkxXNJDpC8m+QTJO8G\nMGxm3WZWnfGEIrJgkFxH8pEpXv80yQ2zOP4qkl87Pb1bnEh+lORvtrofMnvp34NXt7ofzdK0wVi0\nXMqbAWwAcO1sfjCdpSoAfsPMNgB4EMBPpO/FjQDuMbP1AO5Jt0XkLGBm788sDwWg8bNRFjiSrfrC\n22J1FQANxk6DxnIpZlYCcGy5lHlD8kaST5IcJvkoybenr3+U5Oei/daRNJJ5kv8dwI8B+It0SvAv\n0n1eTXIzyaPpn6+Ojv8myT8ieW96zD+SXEry8ySH0v3XRfu7cwE438weTMNVAEMAvgzgNwBcRXIA\nwO0A3nGsn9Pc76+QfIzkYZJ3kVSFfJGFIZ/+PHiM5N+T7Ex/bmwEgPTnxp+S/AGAK9NZg8dJPgjg\nZ1rb9bMDyd8j+SOS3wVwUfrahST/f5IPkPwOyYvT1wdJfin9Ob2Z5GvS1z9K8rMkvwfgs627m7MH\nyfeSfJjkD9L39q0k7yf57yT/heSK9PfnBwD8nyQfIvljre316dfMwdh0S6HMpydRH1gtAfAHAD5H\ncuVMB5jZ7wH4DoAb0inBG9LB0NcB/DmApQA+AeDrJJdGh74LwHvSe7gQwH0A/hrAAIDHAPw+UJ96\nPMG52gFcAOA6AMMARtN99wAYnK7fJK8B8Luo/+AeTO/hb2d+e0SkSS4C8EkzexHq/9j6L5l4F4D7\nzexlALYA+EsAbwXwSgDnNLOjZyOSr0T9Z/SlqJdTeVUauhXAr5rZKwH8JoBPpq//GYD/aWavAvCz\nAD4dnW4DgDea2bXN6PvZjOSLAfxfAF6f/r//awC+C+AKM3s56g9pftvMngFwC+qfyaVm9p1W9blZ\nzqoEfjP7opk9b2Y1M/s7AE+g/kTuZL0FwBNm9lkzq5jZ3wJ4HPUflsf8tZk9aWZHAfwTgCfN7F/M\nrALgiwix4AyyAAAgAElEQVSLq097LpLdAF4MYJOZ3Z/u/18BvBP1z2amuiMfAPAxM3ssveYfA7hU\nT8dEFoTnzOx7aftzAF6biVcBfCltXwzgaTN7Il1E/XOQufoxAF8xszEzG0K94HA76tNeXyT5EID/\nBeDYP9bfiPrsyEPpvr3pz2eg/vN5vLndP2u9HsAXzewAAJjZIdRX4riL5A8B/BbqvxMXnWbOgZ/2\npVBIvhfArwNYl77UjfrioyfrXAA7Mq/tgH+Stzdqj0+xfewv8nTnWoP6D+O9qOeHHTvnBIAC6v8a\nOwCgd5o+ngfgz0j+afQa0z5mrycizZX9h1R2e0JfzGm6BMARM7t0mtgVZjYRv0gSqM9WyOnz/wH4\nhJltInkVgI+2tjut0cwnY7NZLuWUpU+E/hLADQCWmlkfgEdQH6CMAuiMds9OA2R/UD6P41daX4tT\nGzxOd66rUZ/O3IkwSN0E4IMAygB+GsDdM5z3OQD/ycz6ov86zOzeU+ijiMyvtSSvTNvvRn0qZjqP\nA1hH8sJ0W9Nhc/dtAG8j2UGyB/VZjTEAT5P8OQBg3cvS/f8ZwK8eO5jkVAM2mbt/BfBzx9J00jSe\nJQi/W6+L9h0G0NPc7rVO0wZj6VTaseVSHgPwBTPbOo+X6EJ9ULUfAEj+MoCXpLGHALyO9bpdSwD8\nTubYvajnbR1zJ4AXknx3muT/86g/qTqVr5tPda6Xov64/PUANgL4dZL/CfW8hV9E/enYGwB8aobz\n3gLgd9I5eJBccuyHjIi03DYAHyT5GIB+zPB3OX0acz3quaQPAtjXnC6evdIvSP0dgB+gnkayOQ39\nAoD3pV+c2IrwJbIPAdiYJpY/inoaiMyz9Hf+fwfwrfQz+ATqT8K+SPIB1GeDjvlHAG9fLAn8Z9Vy\nSOk3I/8zgBqAz6CeDPtZM/s0yZtR/4t4AMDHUU/kLKRr6l2J+rcXB9P9P0TytagPjl6A+lp6v2Zm\n302v800AnzOzT6fbfwRgtZn9Urr9RgC3mNkL0u0Tnes+1AdfFwP4FoBfNrMD6TdKno76mb3uewD8\nNupP3o4CuNvMfmU+31MRERE5vc6qwZiIiIjImeas+jaliIiIyJlGgzERERGRFtJgTERERKSF5jQY\n4wJd+FtERETkTHHKCfysL277IwBvQr1W1mYA1061EO4x+WK7FTu6Gts1Y6NdzfTDLIwTcyy6WJKE\nWomJr9EHq5bCRq2WOWe4BjOlxfwW3VYuGrLmEx/r7Qk1Wbt6fEmUJFfw12A41p8lI/Ne1CrhnqxW\nzexam7J93Hmy7+/0l3PvUy16Dw8cGcbw6PiMXReZT8uWLbN169a1uhsiIqfkgQceOGBm0y5teMxc\nKvA3Fv4GAJLHFv6edjBW7OjC+ive0tgereYa7aOlstu3Wu1utHuK57pYT9tIo91ZftzFSkPPNto2\n4QsnV0phUJOv+oFLiWGQk8/5wd+SqFxsf7uPvfH1b2i0L3vtG1ysc8D3u5aEUV2SGf4lufBR1Cb8\nyhtjB54P9zB22MWqlbBvuTzmY/HAtFRxMYsGwuXMaGxycrLRHh8L5/z9T30JIs20bt06bNmypdXd\nEBE5JSRntSLOXKYpZ7XwN8nrSW4huaVSmsyGRURERBa1057Ab2a3mtlGM9uYL7ad7suJiIiInFHm\nMk150gt/V1DEYYaHZ9XOMBVZoJ82rI2H6bHh8SMuNnZ0T6M9kPNTbEXrb7ST4lIf6wzXSMpDLjY+\nElZhKFT9OTvbOhrtvj6fF9bRGWLlSmaqteanBsvReatl/5SwLR/ll5V8HlyxGKZza9H0LQBYlNCW\nSzK5ZtElLJPpVYv6Vs3kmsW5bsX2cD0m+vKtiIjIfJvLb9fTuvC3iIiIyGJwyk/G0rUSjy38nQNw\n2zwv/C0ickrW3fj1VnfhjPXMTW858U4iMq/mMk0JM7sTwJ2z3Z/5InKD54UX+sKUZTLsp+16832N\ndlex3cX2PfmVRnts97MuNj66r9GuVPyDv47eMMW4cnm/ixV6w9Tc+O69LtbTHt6mvjZ/zmQyfLux\ndOgZF5tM/PRfriNcP1fx91srhanIQt6XxChVw5RiLVtYIppSTNjlQsUkfPOzYv6blnG5jAS+XAai\nb5oWC9E3QDVNKSIiMu/021VERESkhTQYExEREWkhDcZEREREWmhOOWMnK8kBHT0h6Wly5bJG2wrP\nu327cqG8w9LuC12sve3tjfYe+lvgngcb7YFaycWePxJKZBSjKv4AsHp1KIPBcX/O8ahC/YFhXwG/\nHK0cwPKwi5WO+tyzPENeWK6908WqUS5YNfE5Y7nukN9WzCzxBIbxNKu+tMboSLjHQkemfEVUrb+3\n09d/G5sI+WyMlqJKCqoTJyIiMt/0ZExE5gXJq0luI7md5I0z7PcqkhWS72hm/0REFioNxkRkzkjm\nANwM4M0ANgC4luSGafb7OIB/bm4PRUQWrqZOU7bliQsHw1TdE9FYkJ1+am58f1hv/PDhnS6WL/Y2\n2l0DK12sJ3dxo7007xfc3vXwtkZ7ZMxP2x09Ei0UPnC+i40tf1WjXTzyHd+XJEyF1qqZivtjftpy\nshCmBpOCr6RfKISPYmzUV+Dv6AilPUj/PrX1DDTauZqfppyYCHOfnd1+WnTsSCgBUjI/Jq8Uon2j\n8hgWTbOKZFwGYLuZPQUAJO8AcA2ARzP7/SqALwF4FUREBICejInI/FgF4Lloe2f6WgPJVQDeDuBT\nM52I5PUkt5Dcsn///nnvqIjIQqPBmIg0y/8L4CNmmcVQM8zsVjPbaGYbBwcHm9Q1EZHWaeo0pYic\ntXYBWBNtr05fi20EcAdJAFgG4KdIVszsH5rTRRGRhampg7G8VTBYDaUhau0hhypX9F0pTYS8qaPP\nPuhi1WjpoOpoZn2gXCi/sOJ8N0uCJc+H8hlHD/vliA5GFSvOKe5zsXaEXKwaVrhYW1vIC2vP+3sw\n8zlkBYb8Mqv4fLb68p513Tm/PFFHEu5xdOSoPywJfatmcr+6opy5fM33pastXC/7nKI6FkqAxKUz\nkDmHSGQzgPUkz0d9EPYuAO+OdzCzRjImyb8B8DUNxERE9GRMROaBmVVI3gDgLtT/ZXGbmW0l+YE0\nfktLOygisoBpMCYi88LM7gRwZ+a1KQdhZvZLzeiTiMiZoKmDscrEKA5s/X5je+UrlzfavX0Dbt+L\nXndVo739EXOx+757b6M9Pu4r6e8dDVN8XTk/pdfRFW63WFziYuNHoxUAxn2JiJFt4ffLUGnIxb5W\nCaUmzvmRn97sGejz2/2hryuWLnWxF79gdaNdHvb9LiXh/lny05ul4YONdrXq36dKNUyLjlX91Ge1\nGs9N+ulNlsM1apXovSj7khsiIiIyd/o2pYiIiEgLaTAmIiIi0kIajImIiIi0UFNzxlirojAZSkFs\n/97XG+1KVJICAA6ue2GjbTmfFzawPCyBNDbsV1spWcihGj7gc7+WrX5Ro/3SS17pYs9u/V6jPXn4\nWRcbKYRzFs3nZd3/SCiltHqNzwNr273HbY+P/aDR7u3scrH3v/faRvuiVatdbOxQuH4u78fPFvUn\nu1hRR0d/o13K5pqNh/IVNfh8sny0NNNkLeSdIVNFREREROZOT8ZEREREWkiDMREREZEWauo0ZS6X\noLu3s7H9xPanG+3xcV8GfnzPzka7u7fgY0nYt3+JnxrsjkpdLOtvd7H9Q6H0RH+7L9Ow+hUXNNpb\nvvuUP2ex2Giv3/gSF0NUdb+3x0+13vvt77jtga5w7yNjwy72vftDuY4L3/GLLtbRu6zRHh3OLJyc\njyrpl3zIGN63XJufxLTJsPqBVf1qBJaE45J8R6NNjd1FRETmnX67ioiIiLSQBmMiIiIiLaTBmIiI\niEgLNXc5pEoNhw6NNrbHS6H0RFumJ0t7QrmF4dExF6tFZSGSbl8iovfCixvtc5dnSkRsf7LRZs0v\nazQ0eqDR7urvcbGnn94RznmuL7Oxak3INXtm9/MuNrD6XLd98YVh3we+f6+L7d8bcuS2/mi7i120\n/qKwkfd5cPkkjKeT3qKLVaqVsF/Nl+Ro6+hutCfGfAmQai1sM4lyzajaFiIiIvNNT8ZEREREWuiE\ngzGSt5HcR/KR6LUBkneTfCL9s3+mc4iIiIjI1GYzTfk3AP4CwGei124EcI+Z3UTyxnT7Iyc6Ubla\nxd4jRxvbl/zUzzbaSwd9iYo1S8I02tDe3S42MR7KQjz/zNMutudImH7sONdPUy7tCCUbDh895GJE\nKJdRTvxUIPNhKvTe+3/gYiP/uqXRXrlquYtdfNGA265NhH53Fv306tBoKC/xhX/4qou9+CUvbbTf\n+hM/7vtWiarnF31pjWIulKWojfu6F/m2cFyu7Mt8TI6H9zCemLTM6gMiIiIydyd8MmZm3wZwKPPy\nNQBuT9u3A3jbPPdLRM4wJK8muY3k9vQfadn4NSQfJvkQyS0kX9uKfoqILDSnmsC/wsyOPa7aA2DF\ndDuSvB7A9QBQLBan201EzmAkcwBuBvAmADsBbCa5yczixWPvAbDJzIzkJQC+AODi488mIrK4zDmB\n3+pzV9POX5nZrWa20cw25vNN/fKmiDTPZQC2m9lTZlYCcAfqT9AbzGzEwlx3F2b4uSEispic6uho\nL8mVZrab5EoA+054BIBKrYaDw6G0xcbz1zXaA+eucvt2doacsfNf7JcgGjkcykA8f8QvD3Tw0cca\n7TXjR1xsaChs7937rIslFuWMjVdcbLwaSl109HW4WM/yKL+srdvF9o51uu0DO8I1xstLXKxcDnla\nSzp9zlpXLuR3FfOZ8XMulJ6olv2SUklUloJ5n0+WK4Tfg4WiL+VRmgw5ZHGZC5EZrALwXLS9E8Dl\n2Z1Ivh3AxwAsB/CW5nRNRGRhO9UnY5sAXJe2rwPw1Rn2FREBAJjZV8zsYtTzTP9wqn1IXp/mlG3Z\nv3//VLuIiJxVZlPa4m8B3AfgIpI7Sb4PwE0A3kTyCQBvTLdFZPHaBWBNtL06fW1K6ReDLiC5bIpY\nI7VhcHBw/nsqIrLAnHCa0syunSb0hpO9WK1Ww/BYmAJ77Ft3N9pLlvW5fasMZSiW9PhptL7eUBai\nt8cft2JtKC+RP+RLYowfOthoVzK3bqUwFTg05MtAVGqhL0OTPgaGaTzDuAvt2ZVz22Zh+tMqvpzE\nQDQ1ed5KX7ZtsK+30Z4Y8jPChd6wb5Lz5TKsEqYtq9VMeg6jcXiu4EJJVOW/MqlpSpmVzQDWkzwf\n9UHYuwC8O96B5AsAPJkm8L8CQBuAg8edSURkkVFGvYjMmZlVSN4A4C4AOQC3mdlWkh9I47cA+FkA\n7yVZBjAO4OdNxetERDQYE5H5YWZ3Argz89otUfvjAD7e7H6JiCx0WptSREREpIWa+mSMIIr5sMDO\njgcfaLSLBZ+3lGsLXWPe517lCyHW2eFLTaw879xGu1LxJSry0TUq5aqLWVQGIun0b0u+FnKvCvB9\nqVbiBYN8rFKtZPYN+Wb+6sCewyONdttzB1xsohzO016gi52/Nip7MeBLaVjU73Jl0sWq1ZALVjVf\nEgNRvl6CuFCvv7aIiIjMnZ6MiYiIiLSQBmMiIiIiLdTUaUqrVVEdDdNx1ham31j161bmq2HKL5fz\n02g5hi9gLe0/z8U6+0JZhkMHD/tzRlORnR2+DMQEwjU6Mss2WS1MBZJ+OrVcCsfVLDOdWvNj3fh7\nY9WqL5FhlbFGu5QpNXHfD0Nh80pmRvHaqOxHV22Pi7EYqu6XMtOUFt1vLTOdCwv3W4veM81SioiI\nzD89GRMRERFpIQ3GRERERFpIgzERERGRFmpuaQsCST4kThWLoZ3L+2IPOYacpu7MUj6dUamLy152\niYstu3h9o/3I1sddrC3KeVq9ZpWL3f/go432eMXnbI1PhJyqJPOWLT1vRaP9/AFfkqKS6bfVQgfK\nlUxpjWooS0HzOVwDK1Y32oPnvcjFDh0JSzAdfe4BF2N7OGc1U0yj0B5y65JCJkcuysmLS35Ua5mE\nNREREZkzPRkTERERaSENxkRERERaqKnTlLmE6G0P5Ra6oumx9pwfF8YV+XPMjBnbQhmMCy/a4EKX\nv/LyRvuilee72EMPhWm8pe09Llaohem4g8NjLlaNZuc6o/4DwLkrQsX/IxO+m739/X7fc89ptHfs\n3OlitFARf92A79uVl4SpyUte4u/3iW9/vdEe2fek7/fQwUa7XPalNMajcXgVfjq1ElXkryXRqgUT\n4xARORnrbvz6iXeSKT1z01ta3QVpEj0ZExEREWkhDcZEREREWkiDMREREZEWamrOWGJAVzmUbeis\nhiV62jLlFXK1kBdWaGt3sXw+jCELOb+M0pGjId9r9Zp1LvbUk8802nf987dcbMfzoSzFZKnsYnFJ\nh5G8X0YpiXYdrfr1gvp7fO5XZz7Kg6v4PK0V569ptN/x41e62IXnDTbaY6N+WaMlPb2N9vLlAy5W\nmxgNfZvwCW1jE6HjE9lYOWyPRcs9serLY4iIiMjc6cmYiMwLkleT3EZyO8kbp4j/AsmHSf6Q5L0k\nX9aKfoqILDQajInInJHMAbgZwJsBbABwLckNmd2eBvDjZvZSAH8I4Nbm9lJEZGHSYExE5sNlALab\n2VNmVgJwB4Br4h3M7F4zO5xufh/AaoiISHNzxjoT4JW9Yfy3pj/kew0u8flVlULIv5rIDBkPVULe\n2eTQkItVLdQLK2Xyq5gLsWf37nGxvfujpYwqPmeMSejLZHmfix04+Hyjnc/kr+176odue9vmJY32\n6ITPvzpn1dsb7VXLlvnrR+llk5N+qaTh/fsb7b6qX64o3xly7Qo5n8/WxuieEsvEwv0XEc6Z96cQ\nia0C8Fy0vRPA5dPsCwDvA/BPUwVIXg/gegBYu3btfPVPRGTB0pMxEWkqkj+B+mDsI1PFzexWM9to\nZhsHBwen2kVE5KzS1CdjInLW2gVgTbS9On3NIXkJgE8DeLOZHczGRUQWo6YOxrraElx2XigN0b0k\nTKP1dPhlhjp7w3Znpy8nsetImH781pb7XYzLVjXaS4oFFytFy/zkzE/ptVmYNhwZHXWxfCE8QExq\nfkoPFpYZKmeWbSqXM+U6cmH6r62918WWLwn32NbT6S8R3cbOod0u9sNHH2m0l3QccbGe3nBOy5Sl\nKERTr5aZpixHU5Pl6H1hZtkkkchmAOtJno/6IOxdAN4d70ByLYAvA3iPmf2o+V0UEVmY9GRMRObM\nzCokbwBwF4AcgNvMbCvJD6TxWwD8NwBLAXyS9ZzFipltbFWfRUQWihMOxkiuAfAZACsAGIBbzezP\nSA4A+DsA6wA8A+Cd0TelRGSRMbM7AdyZee2WqP1+AO9vdr9ERBa62STwVwD8hpltAHAFgA+m9YNu\nBHCPma0HcE+6LSIiIiIn4YRPxsxsN4DdaXuY5GOof439GgBXpbvdDuCbmObbUcd05fPYuDx8O6oS\nKk0g1+bLQuRyYZw4WfblHMqjw432tm2bXezpKDVq1YpzXCy+3rJVy12ssz1cf++eAy5WLoe8sJEx\nXy4jicpZJJmxbVvRL+O0ctnKRnvgPP+V/YHV4X350WF//cNRDtt9//6gi43sCTnS5TX++ocPhxy1\nTGULJFHOXDWzHFK1FN5vqypPTERE5HQ6qdIWJNcBeDmA+wGsSAdqALAH9WnMqY65nuQWklsOT1Sm\n2kVERERk0Zr1YIxkN4AvAfiwmblKq2ZmwNRftYtrBvW36/sCIiIiIrFZjY5IFlAfiH3ezL6cvryX\n5Eoz201yJYB9058hvVguwbIlHY3tXGmk0S4X/PRfzcI4sSPvy14MDvY32v/86F4Xe3DrDxrtkag6\nPQDkBgca7aUDS1ysZ2mI9WemEIeHwzThyFjJxdo7QomKWt6/nV09vnzFRRe/uNHuXb7UxY6Uwnn/\n8TvfdrHDB0PJiqf+/SEXe9FwVM6i7K83WYn76sfKcWmP8qR/70vRtHCpHOZ9a6YpSxERkfk2m29T\nEsBfAXjMzD4RhTYBuA7ATemfXz0tPRQREZE5W3fj11vdhTPWMze95bSefzZPxl4D4D0Afkjy2GOZ\n30V9EPYFku8DsAPAO09PF0VERETOXrP5NuV3AUy3RPQb5rc7IiIiIotLUzPq84Uclp/b3diuTIay\nEGMTZbdvUgk5TYXMUj5LiqFGxev6fEmMRx5/vtEePzLsYpM/Cnlo+zp92Yn23pBvVS75b332D4T8\nrmKbP66QhH1Hx8dd7OjoUbe9PbqP/BMdLrZ7985G+8hBn343vD8s4Vc77PPgXnNJyJ8Dfc4YaqFv\nlYq/p8moL7WaXxqqFB0X76eUMRERkfl3UqUtRERERGR+aTAmIiIi0kJNnaZkPkG+vzvaDtON3Zmu\n5FlotK3sSy+QYfucA34qsvBomBo8OD7qYp3RPNtwZirwaC70hfR9sSPhPPnMNCULoZ8j4678GoaG\n/DaSMKVabO/0/Y7KSSSTfrqzUgoV8nvzfkrxnCVrGu2OvB9blyfC/Vaq/rjJyVD2omI+Nh6V2RiN\npmyrmelMERERmTs9GRMRERFpIQ3GRERERFpIgzERERGRFmruYpGkWzKISSjvwKJf8qgW5WKZ+fyq\nYhJymjqXDrpYUnum0R4e9+Ucjlg4LpfLXK8axqX5KH8MAHbu2dVoT2bKXjDat5ZZcqjQ5vu9avWq\nRnvpiuUuNnL4UKN9+PldLlZsD7lmq9r8NZb2RqUtcr5cRpILOV6kz/eqJaHf8ZJHAFCKdp2ohuup\nsoXMhOTVAP4MQA7Ap83spkz8YgB/DeAVAH7PzP6f5vdSRGTh0crdIjJnJHMAbgbwJgA7AWwmucnM\nHo12OwTgQwDe1oIuiogsWJqmFJH5cBmA7Wb2lJmVANwB4Jp4BzPbZ2abAZSnOoGIyGLV5GnKBLli\nmLqrdK1otJO2Pr9vLpSQqNX8NJpVQzmLvg5fkb6tMNZoj4/6sebkZPgdkBT9Ck9MounTki+lAYbr\nJzl/XKGQj2L+ekm7nzYslcM06ZEDvt+7oqnQ0aHDLra6I0xTXrl+pYutaA/9STKlJxitYsXET73m\nCtHKBdXsFGbYnqyE96KmEvwyvVUAnou2dwK4vEV9ERE5o+jJmIgsKCSvJ7mF5Jb9+/ef+AARkTOc\nBmMiMh92AVgTba9OXztpZnarmW00s42Dg4MnPkBE5AynwZiIzIfNANaTPJ9kEcC7AGxqcZ9ERM4I\nzc0ZM8Kq4ZK17lDqIVn7crcr+0LphzgvCwBqI2HJo75nj7rY5f0h1+yRoz73a6Qacr/KY34ZJZ8J\nlmRiyTT7Acl4dA3zuW1lO+S2dz/3DKaTi0p+LCv42MZlSxrti5f43K/eWrRUk/m86EItKuVR9bFK\ntORRuerLdYxF5TuOjIXjqjXljMnUzKxC8gYAd6Fe2uI2M9tK8gNp/BaS5wDYAqAXQI3khwFsMLOh\naU8sIrIIqLSFiMwLM7sTwJ2Z126J2ntQn74UEZGIpilFREREWqjpFfgZVdZHPipn0ev/wcz+ZY22\n5TMlI3pCSYyBl1zqYm+9bH04fc8+F3vycJjSe2645GJ7R8PU3EjFT8dVa2GqjpmZulwS+tae96Us\nuvN+SnFFVyjrMdju3/pl3WHfNUv86gAv6Anv2UCHP2d71LdSZsUBVqJp04qPlSfC9Gp2VYHR0Ylw\nzui9UGULERGR+acnYyIiIiItpMGYiIiISAtpMCYiIiLSQk3NGatZguFyyJvq7gg5Y7Wcz4WqMc5V\n8sv1WLTsUHHpOS72ivWhXMZFKzpdbGQs5IkNlXyRiv2j0VJF475ExehYyKmqlHysmA/5XD3tPqmq\nI+e3+9vDvr3tPi+sWIyWVTKfz2alkMM1mclnm5wYb7QzXUM5WuZoctIHS9HSUOXJ6fPJyqVwDlPS\nmIiIyLzTkzERERGRFtJgTERERKSFmjpNOTQ6jm/c92hj+5WdL2m0z73QjwuNYRoxOztm0QxjLely\nsepkmFZrh5/u6+gsNtrL/QwmLhqI3oqc70s5mhqcGMtMIVZCZ5Kqn/qsZiryx1OvtUwp/0rU18nS\nmL/+RFS+ouQr6Y8x9HsyUyG/NNM05UTYt5yZ32Q1Ok90DmiWUkREZN7pyZiIiIhIC51wMEayneS/\nkfwBya0k/yB9fYDk3SSfSP/sP/3dFRERETm7zObJ2CSA15vZywBcCuBqklcAuBHAPWa2HsA96baI\niIiInIQT5oxZvZ7BSLpZSP8zANcAuCp9/XYA3wTwkZnOlSPQUwg5SN/+3rca7ZesWOv2felrrwh9\nyORCxYUuLDOcHBoO+VbF4WEXS4ohZyxfzJTSSKLtxJ+0GpV3qJQzfYmqQkxUM3lZmVysPMP1JzKJ\ncJVqKFFRmRx1sXI5nKdU8eecQFR2I5OjVqpFOWM+1Q3lUrh+aSJT2iK6Xny/Km0hIiIy/2aVM0Yy\nR/IhAPsA3G1m9wNYYWa70132AFgx7QlEREREZEqzGoyZWdXMLgWwGsBlJF+SiRum+a4dyetJbiG5\n5ej45FS7iIiIiCxaJ1XawsyOkPwGgKsB7CW50sx2k1yJ+lOzqY65FcCtAPDicwZsWVuYqls2GOpL\nbLv/PnfckWh67LJXv8rFCm1hDJkU2v0Fe8IDuiMHdrpQElWdz8HXlsgVC9GOfoxaiirwlyt+NYAk\nfgdzvqp+pVZw2+V8R9j13Bf4ffc+2WiPH/XTq5Pl0O9siYpyVK2/DN+3iajUxkjZx8bK4Z6qmar+\nY5Ph/kuqwC8iInJazebblIMk+9J2B4A3AXgcwCYA16W7XQfgq6erkyKy8JG8muQ2kttJHveFHtb9\neRp/mOQrWtFPEZGFZjZPxlYCuJ1kDvXB2xfM7Gsk7wPwBZLvA7ADwDtPYz9FZAFLfz7cjPo/1nYC\n2Exyk5k9Gu32ZgDr0/8uB/Cp9E8RkUVtNt+mfBjAy6d4/SCAN5yOTonIGecyANvN7CkAIHkH6t+4\njpD2qMwAACAASURBVAdj1wD4TJpj+n2SfcdSHZrfXRGRhaOpyyE9uvfwgUv+5O92AFgG4EAzr73w\n/MtULy709+W8VndAFqxVAJ6Ltnfi+KdeU+2zCoAbjJG8HsD16eYIyW3z29UFYcH+XefHW92Dplqw\nnwOgz2IhmcNnMavfm00djJnZIACQ3GJmG5t57TOB3hcR/6Wfs5X+ri8M+hwWjsX+WWhtShGZD7sA\nrIm2V6evnew+IiKLjgZjIjIfNgNYT/J8kkUA70L9G9exTQDem36r8goAR5UvJiLS5GnKyFk9BTEH\nel/kjGRmFZI3ALgLQA7AbWa2leQH0vgtAO4E8FMAtgMYA/DLrervAqC/6wuDPoeFY1F/FlQhTxER\nEZHW0TSliIiISAtpMCYiIiLSQk0djJ1ouZSzEclnSL4x89oakt8g+SjJrSR/LX19gOTdJJ9I/+xv\nTa9FZLbS4rX/ZZ7OdRXJr83HuRY7kutIPjLF658muWEWx+uzOEkk3zab93aO15jyc01jjc82/d27\n7HT2ZT41bTAWLZfyZgAbAFx7uj+0BawC4DfMbAOAKwB8MH0vbgRwj5mtB3BPui0iC1sfgOMGYyRb\n9QUpmYGZvT+zTBeAxu8omZu3of77vSWm+2zPBM18MtZYLsXMSgCOLZdyxkifaH2Z5H6SB0n+BckL\nSf5run2A5OejhdU/C2AtgH8kOULytwHAzHab2YNpexjAY6hXIr8GwO3p5W5H/X9sEVnYbgJwIcmH\nSG4m+R2SmwA8mv1XPMnfJPnRtP0Ckv9C8gckHyR5YXxSkq8i+e/Z1+Wk5NOfyY+R/HuSnSS/SXIj\nAKQ/l/+U5A8AXJnO3jxO8kEAP9Pari8MJP+B5APpLM716WsjUfwdJP+G5KsB/AcAf5L+XbiQ5KUk\nv0/yYZJfOTbbk34G/5PklvSzeVX6u/UJkn8UnfvXST6S/vfhqFvHfa7ReY8rHEvyF0n+W9qv/7UQ\nB97NHIxNtxTKGSH98L6G+qLo61Dv+x0ACOBjAM4F8CLUi1p+FADM7D0AngXwVjPrNrP/McV516G+\n9uf9AFZEdZf2AFhxuu5HRObNjQCeNLNLAfwWgFcA+DUze+EJjvs8gJvN7GUAXo1oWaj0F9stAK4x\nsydPT7cXhYsAfNLMXgRgCMc/wewCcH/6GWwB8JcA3grglQDOaWZHF7BfMbNXAtgI4EMkl061k5nd\ni3otwd8ys0vT/28/A+AjZnYJgB8C+P3okFJacf8WAF8F8EEALwHwSySXknwl6uVvLkd9Buk/kjy2\nTvaJPtcGki8C8PMAXpP+Ha0C+IVTeSNOJyXwz95lqA+4fsvMRs1swsy+a2bbzexuM5s0s/0APgHg\nx2dzQpLdAL4E4MNmNhTH0sWUVXdE5Mzzb2b29Ew7kOwBsMrMvgIA6c+TsTT8ItRrLr3VzJ49vV09\n6z1nZt9L258D8NpMvIr6z2AAuBjA02b2RPrz93NN6uNC96H0yeH3UX/YsH42B5FcAqDPzL6VvnQ7\ngNdFuxwrCv1DAFvTGaNJAE+l13ktgK+kv29HAHwZwI+lx5zoc429AfXB9WaSD6XbF8zmHpqpmTkN\nZ/pSKGsA7DCzSvwiyRUA/gz1/0l6UB/gHj7RyUgWUP8h8Hkz+3L68l6SK81sN8mVAPbN5w2ISFOM\nRu0K/D9622dx/O50v5cDeH4e+7UYZf9Bm92eMLNqszpzpiF5FYA3ArjSzMZIfhP1/zfj93E2/09P\nZTL9sxa1j22faGxyos81RgC3m9nvnFz3mquZT8Zms1zKQvYcgLVTJOX+Mer/I7zUzHoB/CLqH/4x\nx/1PQpIA/grAY2b2iSi0CcB1afs61B/disjCNoz6P8SmshfA8nTapQ3ATwONXNGdJN8GACTbjuW9\nADgC4C0APpb+MpRTt5bklWn73QC+O8O+jwNYF+XoXXtae3ZmWALgcDoQuxj16UKg/uDgRSQTAG+P\n9m/8XTCzowAOkzz2NOs9AL6F2fsOgLeleX5d6XW+k8ZO5nO9B8A7SC4HGlULzjuJfjRF0wZj6ROl\nY8ulPAbgC2a2tVnXnwf/hvq/WG8i2UWyneRrUP8fbwTAUZKrUM8Zie3F8Y9EX4P6/5ivTxMKHyL5\nU6gnAr+J5BOo/2vkptN4PyIyD8zsIIDvpYn6f5KJlQH836j//Lgb9V/4x7wH9SmghwHciyhHycz2\noj5wu5nk5af3Ds5q21D/tvpjAPoBfOp/s3fnUXKd5Z34v9/aunpTL1Kr1drcsi1vYDAgjBMgMRAP\nBoafSTJhMPmBk2OiOAMEZpIZezKZ/DInyTnOMNkIi8chBJOQECeQ4GCDcRz2xUgGL8iyJVlry5J6\nUe/d1bU9vz+qdN/7FN1SS2pVtaTv5xwdvbeeW/e+dUtqvbrvc593oR3NLAdgK4AHqwn8mpkAvoxK\nsvxOVP49+l719btQyaH+DmK5jqjkUf/X2IMnt6GS0P8UgOtQ+buwKNWH3D6Fyt+dxwB8wsx+WA2f\nzvf6DIDfBvCVaj8eAdC32H7Ui5ZDOg0kNwL4MCpTkgbgbwH8X1SSFK9EZc29vwbwn81sffU9twD4\ncwArAPy+mf2fBnRdRERElikNxkREREQaSE9TioiIiDSQBmMiIiIiDaTBmIiIiEgDndVgjBfhwt8i\nIiIiS+mME/irywPtAnATKksbbQNw68kW6UyQlmSsBNdJqnG1NDdF7Z5V3TXHCctKzc7MuNjYRChk\nny+6+qyIjz1ZE7F4B2qDzsLBk77tFPyl+PHendCS9fX1uju7onYm7UugFQqFqD2Ty7nY2MRk1C6X\nywuczSuUSiiVy2fzMUVOy6pVq6y/v7/R3RAROSOPP/74sJn1nGq/s6nAHy38DQAkTyz8veBgLEli\nVSYMskrJ8O96ouSHAK94USjNtfWX/DJSzZnWqP30kz9wsS8+8kjUPnhsrKYHYSDDhB9TlJGPxWre\nFn/BatcXDbEUfSFn1gxr4lska/YNxy0nfAfiBaJfftXVLnbr28JatutW++97cDAscPDDHbtc7IFH\nQu29idy0ixXj4+Vy6PXB48chUk/9/f3Yvn17o7shInJGSB5YzH5nM025qIW/SW6trsy+vawyGiIi\nIiLOOU/gN7N7zWyLmW1JUDNcIheqU+WQkuwg+S8knyS5g+QvN6KfIiLLzdlMU572wt8lM4znQ+4S\n4zlORT/Ft3Zjb9R+/b9/vYu1NXdG7Zddd4WLrVndEbX/7nMPutjuw8NRe6Im9SkfzycrFVysWAhT\nmOVSzfRibAozSf8+wudiZWJTtJlM2sWsHLbLOX8tbrj2qqj93nf/vy728quujdqpor/zONISpnMP\nHx1xsWIqfI4cfT+LDMeJz+bqvqYspJpD+lHEckhJPlCTQ/peAM+Y2VtJ9gB4juRnzCw/zyFFRC4a\nZ3Nn7Hxf+FtElk6UQ1odXJ3IIY0zAO2sJEy2ATgOoPYpGxGRi84Z3xkzsyLJEwt/JwF88jxb+FtE\nls58OaS1C1x/BJX/sL0AoB3AfzSzcs0+ILkVlQWbsXHjxjPqTP9dD556J5nX/rvf0uguiFx0zmaa\nEmb2EICHTus9sbZ7orHm6caZ2dAuo8vFyomw3ZT2pS1+8iWvjdqpki8D8fBjj0Xtrz3tH/qcy4We\nre3zTyyuWbM+aicTGRdLJJKxmJ9tSdU8eDk0NBS1J2IlOACAsRsEL79qs4vd+sabova67pUutm/X\n7nD+gp9ILBXCRbSaaeBy/AFR1Ijn9iUXKEUicvreCOAJAK8HcBmAR0h+08zcXwYzuxfAvQCwZcsW\nzY6LyAVPFfhFZCksJof0lwF83ir2ANgH4CqIiFzkNBgTkaWwmBzSgwDeAAAkewFcCWBvXXspIrIM\nndU0pYgIsHAOKck7qvF7APwegE+RfBqVSe87zWx4wYOKiFwk6joYI4BkLAMk8WPRYGhwNGrnyz75\nik2hZAPR5GKtyVDa4kX9PverGDtMV99qFys3h+r1L3qJL6XR1RVi05OzLnb8eOjn9PSQi61a1eG2\nW1raonYy6T9Ta0ssZ63Nf6aOuZDjPDXuVxUYHhuP2sWapaHyY6Fvc7k5FytZvHzFwslgiVgFftW2\nkJOZL4e0Ogg70X4BwL+rd79ERJY7TVOKiIiINJAGYyIiIiINVPecsfjoLxGb9mLNNOV4rPTDxISf\nmutsbY/a2Sb/vlRrc9SeHPOlLVa1roja6zpXuFjPxv6o3dLiy0AcGwhlMAaP+cWyd+3aF7WTKV+B\nv2+NL8mxNjY1evXV/iGyjb1h34EdO11sdCKUzJgY94t6j8+GqclCzYLfmdjKAQcHj7jYbOx9ydqa\nFeUwLZqwEFNlCxERkaWnO2MiIiIiDaTBmIiIiEgDaTAmIiIi0kANrTMWX5Quk/TjwsHjoYTE3l0+\n32l9JuRXTR8+6mKlWH5ZcW7KxYrTuag9cWTQxQ7uD8vqvej6n3CxlV3hfM+PHHCxFw6GmpWdXb6U\nRWdrTT7b6lCyYmZ4n4t97TuPhL485+tgrll3adRmqtnFCsVw3VJlv1TTdCHkvu0+9IKLlUshvy1J\n/8fAFtEWERGRpaE7YyIiIiINpMGYiIiISAM1djmkeBX4pO/K+HgobfHII//qYj/14lBZ3/K+nMTQ\nkbA28WxuwsVmxkeidlPCT7qNjxyL2nuf96UlbnjNT0bt48f9dF9uJkyLHpwad7FUapXbvvaajeEc\nu593scEDod/NK3xJjEODYcWY2Tnf71XdYXWAdNmFMDoRynAcGfarA5DxkhU1RStUw0JERKRudGdM\nREREpIE0GBMRERFpIA3GRERERBqooTljrmyC+VyoWEoTvv29b7rYwRfeFrW7zS9dVIot8zM8XFP2\nojgXtbNN/qNns+movWfPHn++IwNR+9CAz70qWjjO0SGfM3aspnzF7HTIWetZ0elibdmwxFNPz1r/\nvqGQ+zV4xJfWGD0eYitXtLnY4ZFQvmMyN+NiiUQyalvZX3v3vZRrEtFERERkSenOmIiIiEgDaTAm\nIiIi0kDLprRF2fx0WCYTqtUPDvtq+bv2747aK8f81ODIvjA1OJfx02+zxWI4Zmx6DwDGJqej9lTB\nj1Fnjo9G7Usv2+xi+w+GqceZg77sBZB3W3tjVf5La3zfMqlw/nzRX4umVKisn0n7uhNjg6EkRylf\nU8qjHD5vAf6YtPAZyzVTkeX4vq6bqsEvIiKy1HRnTESWBMmbST5Hcg/JuxbY50aST5DcQfLr9e6j\niMhy1Ng7YyJyQSCZBPBRADcBGACwjeQDZvZMbJ9OAB8DcLOZHSS5ujG9FRFZXnRnTESWwvUA9pjZ\nXjPLA/gsgFtq9nkngM+b2UEAMLNBiIjI8r0zZrH8pLGpMRfbczDkhRWmcy528Pm9UbtlY5+LHY+V\ndxjL+WWURqdD2YtcscnFmls7Qrt5hYuNjIRljYw+nyvb5I/TlG2O2u0rul1sbCSUzNi54ykX624P\n52xpyrgYi7NRe3rWf6bpZOhPqSZnLFkOMavJ1zO3r9ZGkkVZB+BQbHsAwKtq9rkCQJrk1wC0A/gz\nM/t0fbonIrJ8LdvBmIhccFIAXgHgDQCaAXyX5PfMbFd8J5JbAWwFgI0bN/7YQURELjSnnKYk+UmS\ngyR/FHutm+QjJHdXf+862TFE5IJ3GMCG2Pb66mtxAwAeNrNpMxsG8A0AL609kJnda2ZbzGxLT0/P\nOeuwiMhysZg7Y58C8BEA8emEuwA8amZ3V5+augvAnad78vhUZG0F/mIpTBvOFX2V/e9vezxqt/Rf\n4mIHJ0IaSsuIC2EyH6bxJnNFH0yFKcRU1k8vTs2GkhG79jznYkOjYXqxVPLThETWbXfGpiZbW3y1\n/IF9obJ+Yc4f51ghfJBMpqbsxkyYep2Z8NONM7HpRyb9V11CvKxITcmK2GYxMe/LIrW2AdhMchMq\ng7B3oJIjFvcFAB8hmQKQQWUa80/q2ksRkWXolHfGzOwbAI7XvHwLgPuq7fsAvA0ictEysyKA9wF4\nGMBOAPeb2Q6Sd5C8o7rPTgBfBvAUgO8D+ISZ/WihY4qIXCzONGes18yOVNtHAfQutGM8/0Op4CIX\nLjN7CMBDNa/dU7P9IQAfqme/RESWu7MubWGV+cUFZ7Di+R8ajImIiIh4Z3pn7BjJPjM7QrIPwJnV\nC7J4c+G8JZgfM+7cHUpb9K9e5WJtm9ZF7dlZvzxQrhRyz9o6On2MoWTEdE1fjh4LechTLwy52Ew+\nHNNqcsZiaW+Vc8zGylBM+WWc+tbG+z3rYgcOh887PTnlYlOz4SQzcz63rr2tNWq3tqR93xjL16sZ\nJcdCKMT3g4iIiCy1M70z9gCA26rt21BJzBURERGR07SY0hZ/B+C7AK4kOUDydgB3A7iJ5G4AP1Pd\nFhEREZHTdMppSjO7dYHQG5a4Lw7Lyajd0uzLQGTaV0btn3jzv3exq9eFkmd7n/6hix08GmZTZ/J+\n0u3wUCgfse/gQRdrbwolKqYnfUmMYiEcJ53w830tTUm3PZcLU4xM+inF5kw4R8n8OZLp8DVNj+Vd\nDLFzZtI15SvKobRFuezLXpRj76vN5VNun4iISP1obUoRERGRBtJgTERERKSBNBgTERERaaCGLhRO\nct42AKQSodTE7KzPd9p0xTVRe8tP3ehi+cH9UfvaV7zSxa4oh/yuf3nwSy7W3BrGpZdf2u77WQjL\nI6XSPp+rgFDOIptsdrHuDp/rtiK2vWr1Cheby4W+jU74Ehm5QixPrGZZo/hla81mXCyXC2UvSiWf\no1ZZkabCyrVlReJLJUFERETOId0ZExEREWkgDcZEREREGqih05Q4yTRloRim5jpXdrvYW9/yprBR\n9FN6E2Ohsv3UkRdcrCNWrb9YU0/+0JFDUfuyy/35XvTi/qi9os1X4GesZERTk58m7O3tcdtdq8L5\nk35X7Nm7P2pPTPjS/bP5cC3yBf95E7Fpy6baqd5UKK0xl/clMZqSYRyeqCnJYcXwmUzTlCIiIueU\n7oyJiIiINJAGYyIiIiINpMGYiIiISAM1NmfM8XlL7Stao/b73n+Hi73lTTdF7XR5xsX2j45G7WND\nNfldzemonUj5ZKijw+F9RfrY6mwobdHX1ediY23h/Hnz+VxNGX954/0p0ZeayBVCnlhbhy+tkRoJ\nSzWlUj7ZrBxL6srPzS0YK5V8eZBErERHS6bJxcpuFSffTxEREVlaujMmIiIi0kAajImIiIg0UF2n\nKQ1AvH49Y2UZLJV1+97yH34+at/xn253sXSs14MDh11s9949Ubslk3axHTuejNoPPfhFF5vIh3Hp\nc7uPuVhXUzjOlquvcDGLzf7VThM+v98f57mDg1F7be9KF2vvCuU0ho4Pu1hHc/jAyZKbQ8RcMUxF\nFop+dYBSOT416aeBaeE4tVOY5VjZj2S8BL/qXMhJkLwZwJ8BSAL4hJndvcB+rwTwXQDvMLN/rGMX\nRUSWJd0ZE5GzRjIJ4KMA3gTgGgC3krxmgf3+EMBX6ttDEZHlS4MxEVkK1wPYY2Z7zSwP4LMAbpln\nv/cD+ByAwXliIiIXJQ3GRGQprANwKLY9UH0tQnIdgJ8F8PGTHYjkVpLbSW4fqnkiWkTkQlTXnDGS\nSKdC/pXFyjSs7lvr9n3rz74tajdnfTfHho9G7emJMRfLxJYAyqZ9ztiajZeGY8LnqO3afzBqp2pK\nPYznclF7zwGfozY9PhG1p2YnXWxkpiaHi+G4x8d8SY7R2HF6ulpcrKMjXKdsTUmMciJ8jslZn7M2\nNhXOUfBpYW6ppFTKj8nzFs6RKMeWrKrJOxM5TX8K4E4zK9cufxZnZvcCuBcAtmzZokRFEbngLaM6\nYyJyHjsMYENse331tbgtAD5bHYitAvBmkkUz++f6dFFEZHnSYExElsI2AJtJbkJlEPYOAO+M72Bm\nm060SX4KwBc1EBMR0WBMRJaAmRVJvg/Aw6iUtvikme0geUc1fk9DOygisozVdTCWYALN2eZou5gJ\nuVGXXbrZ7XvFVaGeV2FuysWyDMsOrWzz+VWXbVgTtY/u3+VivWsvi9o3vORqFzsUywVLpX2aStfK\ncMzZvI8NT4W8sKmczxHL1+RpzRVnQzvnc8bWrgz5ZOu7fM5aqhxyuNJlH5u1kBeXK+RdLJsJuWA1\nqz8hlQgv/Fj5sPi2Cyp9RxZmZg8BeKjmtXkHYWb2S/Xok4jI+UBPU4qIiIg0kAZjIiIiIg1U39IW\nIJIM05RltIeOJNrdvtmWtqidTMy6GAph2nJg97Mu9MyTj0ftoSM7XSw3HspX9K3xZS9uuD5MYU6M\nFVzsxje8ORx/j39A7Itf+beoPT3ny060NvtzdLWFMhQtrW0utqYzlJpYkfGP/eemw/RjMuFj0xOh\nJMbstJ/OjZeioPn3JROhb0XfbeSL4fMzthySJilFRESWnu6MiYiIiDTQKQdjJDeQ/CrJZ0juIPmB\n6uvdJB8hubv6e9e5766IiIjIhWUxd8aKAH7DzK4BcAOA91YXAL4LwKNmthnAo9VtERERETkNp8wZ\nM7MjAI5U25Mkd6Ky5twtAG6s7nYfgK8BuPNkx2IihWxmVbTd2dUfzlNa4fYdHw+lH1rM50Id3PGD\nqH1oz14XO7D3SNSePO6XB8pPDkTtDZsvcbH+TaEv27f5khj9l0S1KrEmVh4DAL7xze9E7aMln3zV\nlvE5Y92xFZgSSb9vZ3vIISsW/LJK8eoS0zmfzzY9G5ZqaqrJNTOGPLTa5ZDMQhmOvK+IgVKslAZj\n57Yfq4EhIiIiZ+u0EvhJ9gN4GYDHAPRWB2oAcBRA7wLv2QpgKwCkEun5dhERERG5aC06gZ9kG4DP\nAfigmU3EY1a5ZTLvbRMzu9fMtpjZlkQiOd8uIiIiIhetRd0ZI5lGZSD2GTP7fPXlYyT7zOwIyT4A\ng6c8DohkbECWiZ09N3nc7Xtg3/NRu++lfS42PRWbfjQ/nly75vKoPZpa72KF/FjU7ui6xh+zdCxq\np1MHfMeT4Ry5CT9leumaUJ0/m/WXszXr5/+mRsIlSpT8vkmG7XxNrBQrS9HUlHGxnu6wbfDnyxXD\n3ORUTdmNXGzecm7Ov2+2EKYw08nwfWmWUkREZOkt5mlKAvhLADvN7I9joQcA3FZt3wbgC0vfPRER\nEZEL22LujL0awLsAPE3yieprvwXgbgD3k7wdwAEAbz83XRQRERG5cC3macpvAeAC4TcsbXdERERE\nLi51XQ6ptSWD67f0R9uXX31F1N64ZqXbtz0V8pjSTd0u1rX+2qg9NZt1MaRDPtm6jf6YxvA056Yr\nNrlYfyHkk02OjrvYitXh/C0dq1zs2itC7tmNa9e4WN6G3fY3v/nlqN3Z7ve9ZNOVUfvw4YMuNn48\n5JqlUz6/qzsZcr8s2eJiIzNh3xz8klJEeF9bm3/KNRmvnlEK+80Wfb6ciIiInD0thyQiIiLSQBqM\niYiIiDRQXacpV63qwq+8++ej7fau1qjdVLNvy6qeqJ1o6nCx5pXronbXdNHFWleEacp0utPFUplQ\n5T6ZbXaxvv5QPuNlr3q1i+XKYcxqSV9aorUnvG/T1S9xsSJ9uY7JuVBZvy3rp17bWsMKBJPTrowb\nmtLh/KV8zsVmZ8MxRyfGa2JhmrJcU4E/kQjHzDb7acpEJpSzYGyaciI3AxEREVlaujMmIiIi0kAa\njInIkiB5M8nnSO4hedc88V8k+RTJp0l+h+RLG9FPEZHlRoMxETlrJJMAPgrgTQCuAXAryWtqdtsH\n4KfN7FoAvwfg3vr2UkRkeaprzlgShtZUyPHqzITyZaWCL2WWSYd8snLKZ5S1rQ5lIcp5nwx1/PDR\nqD025nOvSuWQ8zR7dM7FcsWQo3bJFTVLJZXDOkBTMz5HbeOLQ55YubnNxZJJ/5muvvb60JecP/+B\n5/dE7dYWn5fWlOkK55+adLGZYljmqAB/zOncdNiv5nzGkBeWTvnzJWNLICG2FNOC1eZEgOsB7DGz\nvQBA8rMAbgHwzIkdzOw7sf2/B8CvVyYicpHSnTERWQrrAByKbQ9UX1vI7QC+NF+A5FaS20luHxoa\nWsIuiogsTxqMiUhdkXwdKoOxO+eLm9m9ZrbFzLb09PTMt4uIyAWlrtOUiUQCbbEpuBTClN9Mzs+B\ndaRilfXpY4lsOMbRY8dc7Mjzu8N+SV+y4fhomKYcGvXV8Y8dC9XrN23ud7HejaFaf2+fr8DfvTr8\n5z8346cCp8aOuu3izGjU3r/3eRcbHjoctVnzeediU5HD437qdWw8TEWOTRdcLBfbLJdcCGWEF5pi\npSwAgLFSHmCYoqXmKWVhhwFsiG2vr77mkHwJgE8AeJOZjdSpbyIiy5rujInIUtgGYDPJTSQzAN4B\n4IH4DiQ3Avg8gHeZ2a4G9FFEZFmq650xEbkwmVmR5PsAPAwgCeCTZraD5B3V+D0AfgfASgAfq979\nLZrZlkb1WURkudBgTESWhJk9BOChmtfuibXfA+A99e6XiMhyV9fBGBMJZJrDMkTpTMjpsrTPW0ol\nQixR9uUk0rHlgVa0tbjYjqMDUfvQoYMuNjoWcsbmSj6/q/mFkIe2b/+zLrZh0+aovf7yK12sfVUo\nszEXW34IAAYPHnDbh3Y/FbVHDvlz5HJTUfvwMZ/PNjIR8sLy5Zp8slheWKnkr2HJwnUqFn0JECbC\ncVwpCwDpZPhjYVBpCxERkXNJOWMiIiIiDaTBmIiIiEgD1XWasljI49gLL0TbPV2hsvyK1kvcvk1N\nsarwVlOXgWF745W+ruT+nd1Re9u2h13s4KHBqF22mnFobKauSH++Jx//ftRe2eeLhq9YGeoglWum\nAseHB932yLFQE7MwM+Zis8UwbTo6OeNixXLoXBl+SjEZKwGSSPqvM5UK29mMX8Uglw/nK8fKVwBA\nKr5v7HSkxu4iIiJLTf+6ioiIiDSQBmMiIiIiDaTSFiIics703/Vgo7tw3tp/91sa3QWpk/rmvdwF\nJgAAIABJREFUjBWLGBsKZRusEHKsejf2u30T6ZC3VLaFayqkW33sqpeF4+x7zh/TmIvaAwd9+Yjp\nyZBDNZ2frOlL2Hd0xC9xZLE8qlLZ54yVir4kx1wunGMu78tgWCokZ8WXPwKAdCqcg/DnKMdKdKT8\n6k+YmwvnqEkLQ+yQKJb8+VKxGhaJhEsag4iIiCwtTVOKiIiINJAGYyIiIiINVOecsQQsEaYfk62h\nLESptdvtmU+G/bL03czPjEftsSFf5T6ZD1ORa3tXuljusg1RuzznpwlHRidCX8ZzLjZbCNONs9N+\nCrMcG8/WVsePV7kHACTCvuWU/0zlUphHzNSUqOhYEcpXGPzUZyFWTiORrJkmTcS3fV+y2Vj5iprV\nD7LZcD4UQr8SmqYUERFZcrozJiIiItJApxyMkcyS/D7JJ0nuIPm/qq93k3yE5O7q712nOpaIiIiI\neIu5MzYH4PVm9lIA1wG4meQNAO4C8KiZbQbwaHVbRERERE7DKXPGzMwATFU309VfBuAWADdWX78P\nwNcA3HnyYwH5YshPyrSFnK72ntVu32RTqNNQO2JkLBVqeOCIiw3sfDJq799/yMVGR8MSRJPT4y5m\nFnKxEj6FCtNjIb9s1qeaIRVbtWnMr2KEck05iVKs3+maMhTxVLANa33+3CUb+qJ2vjBdc45wdYZG\nplwseZLli9as6Y3arZ29LlZOtoX2XOj0rgO7FzyeiIiInJlF5YyRTJJ8AsAggEfM7DEAvWZ2YiR0\nFEDvggcQERERkXktajBmZiUzuw7AegDXk3xxTdxQuVv2Y0huJbmd5PaJ6en5dhERERG5aJ1WaQsz\nGyP5VQA3AzhGss/MjpDsQ+Wu2XzvuRfAvQBw+caNlkiEsgmZWMmKNGbd+8ozoSr8bM30X7wK/Zo1\nl7jYxJEwbdncPuZiqekwN7hyTc0xY1N6PQVfPmJDvAxGws8vJtJhnvL4uB9s5vP+OPEK/ZmMP04y\n9pm6OtpcrKW1OWqXy75aPhjmVFua/fmT8VIaJd+Xzu7OqN3R3eNiBYTvqJSL9Tml1bNERM5XWprq\nzJ3rpakW8zRlD8nOarsZwE0AngXwAIDbqrvdBuAL56qTIrL8kbyZ5HMk95D8sQd6WPHhavwpki9v\nRD9FRJabxdzq6ANwH8kkKoO3+83siyS/C+B+krcDOADg7eewnyKyjFV/PnwUlf+sDQDYRvIBM3sm\nttubAGyu/noVgI9XfxcRuagt5mnKpwC8bJ7XRwC84Vx0SkTOO9cD2GNmewGA5GdReeI6Phi7BcCn\nqzmm3yPZeSLVof7dFRFZPuqaBPT8oUPDt3zwAwcArAIwXM9znyeW+3W55NS7yEVqHYB4LZkB/Phd\nr/n2WQfADcZIbgWwtbo5RfK5pe3qsrBs/67zDxvdg7patt8DoO9iOTmL72JR/27WdTBmZj0AQHK7\nmW2p57nPB7ouIv6hnwuV/q4vD/oelo+L/bvQ2pQishQOA9gQ215ffe109xERuehoMCYiS2EbgM0k\nN5HMAHgHKk9cxz0A4N3VpypvADCufDERkTpPU8Zc0FMQZ0HXRc5LZlYk+T4ADwNIAvikme0geUc1\nfg+AhwC8GcAeADMAfrlR/V0G9Hd9edD3sHxc1N8FKw82iYiIiEgjaJpSREREpIE0GDsFkleSfILk\nJMlfb3R/RERE5MJS18HYqZZLWab+G4Cvmlm7mX14KQ5IcgPJr5J8huQOkh+ovt5N8hGSu6u/dy3F\n+URk+SJ5I8mfbHQ/LgYkf5fkbza6Hxc6kv0kf9TofpxP6jYYiy2X8iYA1wC4leQ19Tr/WbgEwI75\nAtXPdCaKAH7DzK4BcAOA91avxV0AHjWzzQAerW6LyIXtRgAajIkAINmoBwsbqp53xqLlUswsD+DE\ncinLFsl/A/A6AB8hOUXyb0l+nORDJKcBvI5kB8lPkxwieYDkb5NMVN+fJPlHJIdJ7iP5PpIGYMjM\nfgAAZjYJYCcqlchvAXBf9fT3AXhbvT+ziCwNku+uLoj+JMm/JvlWko+R/CHJfyXZS7IfwB0A/nM1\nHeK1je31hYfk/yC5i+S3AFxZfe06kt+rfj//dGIWguQrq689QfJDurtzVpIk/6I6+/MVks0nue5f\nI/mnJLcD+ADJXyD5o+rfnW9U90lWv5Nt1ff/akM/3RKr52BsoaVQli0zez2AbwJ4n5m1AcgDeCeA\nPwDQDuBbAP4cQAeASwH8NIB3Izyy/yuo3Am8DsDLMc/gqvrD+GUAHgPQG6u7dBRA7zn4WCJyjpF8\nEYDfBvB6M3spgA+g8vPiBjN7GSr/Gf1vZrYfwD0A/sTMrjOzbzaqzxcikq9ApebddaiUVXllNfRp\nAHea2UsAPA3g/6u+/lcAftXMrgNQqnN3LzSbAXzUzF4EYAzAz2Ph6w4AGTPbYmZ/BOB3ALyx+nfn\n/6nGb0elNuErUfkef4Xkpjp9lnNOCfyn7wtm9m0zKwMooPIX/b+b2WT1B+sfAXhXdd+3A/gzMxsw\ns1EAd8cPRLINwOcAfNDMJuKx6mLKqjsicn56PYB/MLNhADCz46isOPAwyacB/FcAL2pg/y4WrwXw\nT2Y2U/0Z+wCAVgCdZvb16j73Afgpkp0A2s3su9XX/7b+3b2g7DOzJ6rtxwFchnmue2z/v4+1vw3g\nUyR/BZW6hQDw71ApGv0EKjcvVqIy4Lsg1HMwdqEshRK/u7cKQBrAgdhrBxDu+K2t2T9qk0yjMhD7\njJl9vvryMZJ91XgfgMGl7bqINNCfA/iImV0L4FcBZBvcH5FzaS7WLgHoPMX+0ycaZnYHKneWNwB4\nnORKAATw/uod5OvMbJOZfWWpO90o9RyMLWa5lPNB/G7VMCp3x+Krsm9EGGQeQWXQeUJ8MPqXAHaa\n2R/HXnsAwG3V9m0AvrAUHRaRuvs3AL9Q/UcEJLtRSWc48bPhtti+k6ikPcjS+waAt1XzldoBvBWV\nf/RHY/l57wLwdTMbAzBJ8lXV199R/+5e0MYxz3Wfb0eSl5nZY2b2OwCGUPm382EAv1a9kQGSV5Bs\nrUO/66JuTy0stFxKvc5/LphZieT9AP6A5LsBdAP4LwD+T3WX+1FJRnwQlR8Ad1ZffzUqfxCfrt5y\nBYDfQmUa836St6Nyh+3t9fkkIrKUqktB/QGAr5MsAfghgN8F8A8kR1EZrJ3Id/kXAP9I8hZU/uev\nvLElYmY/IPn3AJ5EZaZhWzV0G4B7SLYA2IuQ53s7gL8gWUZloDBe5y5f6Ba67rU+RHIzKnfDHkXl\n+3sKQD+AH5AkKoO0C+YhNy2HdAokvwbgb8zsEyQ/BWDAzH47Fu9CZfrhjQByAP4CwO+bWbn6iO6H\nUEnqnwDwYQD/G5VERV14EZFlhGSbmU1V23cB6DOzDzS4W3IR0GCsjki+CcA9ZnbJKXcWEZG6Ivkf\nAfx3VGaNDgD4JTMbamyv5GKgwdg5RLIZlTplX0GlTMXnAHzPzD7Y0I6JiIjIsqHB2DlUnRf/OoCr\nAMwCeBDAB2rLWIiIiMjFS4MxERERkQY6q9IWPD8X/hYRERFZNs74zlh1kexdAG5CZWmjbQBuNbNn\nFnpPpjVhLV3xtbUZtRL0+1aeXD3RrjlQrMu1IXeMmrGmOw4X/ty1kXI51q65XmWLHdRqzldz/rKV\n40F/Ttc3HyMXDvpIbd8WOoYfhdde32Qi/r2E9vhQDrOT+ZNdcpEltWrVKuvv7290N0REzsjjjz8+\nbGY9p9rvbOqMRQt/AwDJEwt/LzgYa+lK4qfe3xVeKIfTN2V8V5qyYdCWqrl/lyyGUUaSPmixwUO6\npsB1Kj4OTOVdjMlwzGLJH3MmF2LT+aKL5fPpcO58sz9fqsUfJz8b9s3UDJxSse10zaAuGc6RQsbF\n0m7gVHCxuXwY/CUSaRfLxi53tuZ8bdmwb1vsov31b38fIvXU39+P7du3N7obIiJnhOSBU+91dtOU\ni1r4m+RWkttJbs9Pl2vDIiIiIhe1c74ckpndW12JfUumVeuSi4iIiMSdzTTlaS/8bSDmLEyBZWNT\nZ0z6fdOxqbMm81NsuZmwnW72HyGRnYraybKfUkww7FuuGYcWy2G6r1jynSnFpi0LRT8VWCiE2Nx0\nycVsOue2OztXRe3p/Iw/RyocN1kzaE23xqYmaxK8SuVwTivVZLvFNhM1SXlMhHPU5raVYn8sxmOf\nvWRKFxMREVlqZ3Or6kJZ+FtERESkYc74ztiFuPC3iFwY+u96sNFdOG/tv/stje6CyEXnbKYpYWYP\nAXho0W8gkEqHKcBMLJ8/m/RTYC3J8CTk6EE/3bj7yeGo3d3T4WLrN4f3da3004blWI2KUsE/6Tg8\nGKYJJyf8k5Yt7U1RO+dnKTEzE/pWmKmZ7hvzDyy0J8JnT6dW+OPkR8NG3k83NqXDNGUhWfPEZCls\np+DPl0iGrzeVrpnOjRXFSKX8E5qxQyKRiL1P9YFFRESWnDLqRURERBpIgzERERGRBtJgTERERKSB\nzipn7HQlmUBLKlSpb8/HcpoKPt8pPxy2pw/73K+1bZdE7UPPH3Wx6YlQ2uKaV3S5WKEQcsGGj465\n2AsHQqyjvdvFLu0NFTwGR4+7WDa2PMCew77QbluTzwvLJkPu2fSMz4NLzYVyHYXJCRcbHgnbmT5f\n5b9pZciRSyZ9CZBEbHWC2uWQkrFcMLPapZLC+xJuNQKVthAREVlqujMmIiIi0kAajInIkiB5M8nn\nSO4hedcC+9xI8gmSO0h+vd59FBFZjuo6TYlSAonpMFU3fiRMKdL8NGUxtsh1s/npvkIxViF/1k+d\nHds3GbVnZn0F/GxLOHci78eh44dDRfzey9a6WHc6TFuOF6ZcrDlWgiNT9NOER4dfcNu9q8Nx1/Vs\ndLFnnw3lOmbGx12MqbDAeDHZ6s/fuSZqW02V/WLsmiasZkH1WHtuzpfLSMWmMDPJ+PtU20LmRzIJ\n4KMAbkJlndptJB8ws2di+3QC+BiAm83sIMnVjemtiMjyojtjIrIUrgewx8z2mlkewGcB3FKzzzsB\nfN7MDgKAmQ3WuY8iIsuSBmMishTWATgU2x6ovhZ3BYAukl8j+TjJd893IJJbSW4nuX1oaOgcdVdE\nZPnQYExE6iUF4BUA3gLgjQD+J8krancys3vNbIuZbenp6al3H0VE6q6uOWPFuSJGd4WSEuWRUE6i\na5UvQ9GeCSUcetasdLEnth+J2seHfRmIcmxZpWTNUDPRGWI16VWwUiif8dRTT/t+M5xjdZ9Pc1nd\nG/6x6Oj0OWJHjvpZmOf3PBe1V6xocrHNV4UcsqeeqCmfkQ65WnNDfqmmwdKxqN15qe+bNYcLMFf2\n75tLhnyyTM2YvCmeGhbLOyubcsZkQYcBbIhtr6++FjcAYMTMpgFMk/wGgJcC2FWfLoqILE+6MyYi\nS2EbgM0kN5HMAHgHgAdq9vkCgNeQTJFsAfAqADvr3E8RkWWnvk9TisgFycyKJN8H4GEASQCfNLMd\nJO+oxu8xs50kvwzgKQBlAJ8wsx81rtciIstDXQdjVjTMjYQyDU2FcPqx476cQ3dnuGmXyfgbeGWE\nkhXZFh8rxarEtzT5UhOre0KJikMvHHOx9Zt6o/bQUT/1OTQUzremr93HBsO+R46M+H6W/FzoXG46\nag+8sNvFsi3huJnWjItZrFj/2JFhF+NUKEtRQNLFVmwKnzfR7GOlZJhypA8hxRBLxeZzVX9fTsbM\nHgLwUM1r99RsfwjAh+rZLxGR5U7TlCIiIiINpMGYiIiISANpMCYiIiLSQHXNGSuXgFwsHat7ZVja\nZ7rkc8ZyuZBbNjzsc7GmpsO+nV1ZF8sVQw5VTcoWCuUw9uxa2etjubmo3dra5mNzIalqZNAvh3R8\nPORwTU3MuNjKlX4Zp5YV4fyplE/UKhRCXlqxXLOMU6zMR1d3t4vlSyGhbG5o2sVsdWfs3D7XLZUK\nJSuyKX+hkrFjkrHxOpU1JiIistR0Z0xERESkgTQYExEREWmg+lbgLxqGBkOl++62MMWYTJfdvvnp\nMI339BOHXKxYCNNl6bQvA5FJhcr2xZpZtcGjoQr9qtV9PpgM06J5G/WhZHjfyPEj8MHQ7w0b/RRi\nc0uL27ZYyYiZGT8V2dHZEbVXrmp2seJcOP/URNHFOjvClOpcyR9z4Jn9Ubs/s9nFuleHaUsrFVws\nlw/nMITp23LZf0ciIiJy9nRnTERERKSBNBgTERERaSANxkREREQaqL6lLcwwYyE/ade+sCRROmFu\n397Onqi9bq3Pdzp46HDUHh3x5RzWrAu5YO2dnS7W1bM2ak9M5l3s+FSsL0lfdmJDf3hfuslfsnws\nTyuXn3WxWLUMAMBMLnbOhC/JcXjgeNRuaWtysUs3bYjanSt8SY6x0VDmwwp+bJ2eCmU4ju0+7GKt\n2cuidrFmqaSyxfPwQqysBZFERESWnO6MiYiIiDTQKQdjJD9JcpDkj2KvdZN8hOTu6u9d57abIiIi\nIhemxUxTfgrARwB8OvbaXQAeNbO7Sd5V3b7zlCfLJNC1MUzPNRdCBf7xF3w5iWOx7b6Na12sqSlM\no41N+Mr92Wxsys18qYdSMUxpXrqp38XSibDv4BFf8X8ktgJAKuOn9JpbQ/mKiXE/L9mU9RX4u1aE\nMet0zk9plmOfaWZywsV27RyI2q957Rtc7OqrQzmNp5560sUKhTD1myv58x3edzRqr7l8g4uV4+VB\nYuUsyn4mWURERJbAKe+Mmdk3AByvefkWAPdV2/cBeNsS90tERETkonCmCfy9Znai+ulRAL0L7Uhy\nK4CtAJDKKkVNREREJO6sR0dmZgAWnMAys3vNbIuZbUmlNRgTERERiTvTO2PHSPaZ2RGSfQAGF/Mm\nSxgKmVDaYm405Ea11iwdNLg/zIw+8dhTLtbVG5by2XhJj4t1dIfjDA3VLGuUCXlZre2XuFh7ezpq\nN6U2+r4MhZytjg5fkqI1ljOGkh9sTs36nLVkU3jvyna/dNLYVMh9mxj1s8L52bA80VNP7HCx18Zy\nyK65+hUuZsWQ7/XcAX8NGau7MdPa6mLJ3tC3nIVyFmYqbSEiIrLUzvRW1QMAbqu2bwPwhaXpjoiI\niMjFZTGlLf4OwHcBXElygOTtAO4GcBPJ3QB+protIiIiIqfplNOUZnbrAqE3LPD6ghKpBFp7whTj\n4X1hGjFdU5G+FGu/cNRPNw5NhOnNy6/xU4oduTCVVi76abWurlBqoqvTT81Nj3VE7YHjMy527Ego\niZFK+WOu6V0XtdtbfAmO42PDbntyOlTELxcLLjY7Fc7Z3truYk1NodTEbOwYAPCNrz8ata+8+kUu\ntvnSq6J2qezPVyiMRe38qF+NYCYRO8fKcM1U2UJERGTpKaNeREREpIE0GBMRERFpIA3GRGRJkLyZ\n5HMk91RX5lhov1eSLJL8D/Xsn4jIcnWmpS3OSDKTROf6zmj7wA9DCYeR49Nu36YVIVcpky66GBmy\nlwYH/dJBHStWh42SC2Hw6JGoPXbcLwE0MhiWPBo4eNTFLtlwRdTu7fMlOAqxFZAKBZ971dGx0m3P\n5kK8ZGUXa2oKpTVSaZ8/NzcXPn9LNu1io+NDUfvZZ3/o+zYRLkCCfhmn9WvDdRqfHnOxgUPhe2mN\nlb0wrYckCyCZBPBRADcBGACwjeQDZvbMPPv9IYCv1L+XIiLLk+6MichSuB7AHjPba2Z5AJ9FZdm0\nWu8H8DkssjahiMjFQIMxEVkK6wAcim0PVF+LkFwH4GcBfPxkByK5leR2ktuHhoZOtquIyAWhrtOU\nRqDYHKa6Vl8Vpsr2fXXA7dvTEco5rL/UL33ZlA7lJQ7uPexiO3aECvUru9tcbDIXxp6PPfaYix0d\nCNOUibK/LJls6HMqXTNVxzDduGpVpwvl5vxUZEtLmHqdnJ50sWIpTEWWSv59szOhkn8uN+tiTU1h\n+jGf92UvhmNTuLufPehiG9aFa9rW7ct8FHrCtW9uDteQCT/VKXKa/hTAnWZWJhdezcHM7gVwLwBs\n2bJFc+MicsGr62BMRC5YhwHEEzHXV1+L2wLgs9WB2CoAbyZZNLN/rk8XRUSWJw3GRGQpbAOwmeQm\nVAZh7wDwzvgOZrbpRJvkpwB8UQMxERENxkRkCZhZkeT7ADwMIAngk2a2g+Qd1fg9De2giMgyVtfB\nWNkMk4WQ/9Te3xy1V169wu177JmQuFve60tG9K0JSxe1t/lSE8dnQomMAwP+ga11m0KeVKLJf/R1\nl6yJ2vnZORc7OLAzak9M+/yqNb3hfeWSfx6iZ/UlbruruydqD44ccbHpXFgO6cB+P7vTnAmlLgol\nv6xRrhByyNpX+L61dYTt7r5uFzsyGnLk0uZzzdZecXXUZjLkj50sz0fEzB4C8FDNa/MOwszsl+rR\nJxGR84GephQRERFpIA3GRERERBqovjljBMAw/ku2hafWL3mZL1+Rmgv7je72ZSAmR8K0WmdHu4sV\nC2EqrXVFh4ulW8K06LHjwy7W3RGmSZO+AD4S06Gf+bwv63/gQKjW39HuY21ta9329EyYipyJtQGg\nkA/TjzPTPlYuhPN3dvnPNBObUj121FfSb23LhPetrbkWXWEKk7EyIgDQtnZV1J6dDVPEZVXgFxER\nWXK6MyYiIiLSQBqMiYiIiDSQBmMiIiIiDVTXnLFUIoHVLaEURTYZxoLtK5rdvn3ZsAzP/ja/Pt2x\nZ0ej9ujAhIuVYssTdazPuNi69bGl8pK+nENuLuSlrejwZTbK5dCX8bFpF2vOhtyr8dkRF/vGt7/s\ntovF8Hlnpn25jvhySO1tfhmn7lUhh2u6Jp+srT3kzLUnfL9npsK1mRvzJTE2b35J1M4Vfa5bcV/o\nS+elIYEuaRq7i4iILDX96yoiIiLSQBqMiYiIiDSQBmMiIiIiDVTXnLF0IoHVsVpfmdhYsDWTdvuW\nNoSuWd4v5dPZHPKkhgfGXWwyF3K/CvB5UocP7o/aV1y1oaZvIU8K9Plcq9eG809M+ZytXCHs29zs\n63UhVXabc7Nhuynr66P1dIQ8sUxNnbPp2ZCnNpefdbF0Kly32lyz3ESsrtmI/0yF1aGdn/P9PDb2\nQtRu7Qz9tKLfT0RERM6e7oyJiIiINJAGYyIiIiINVNdpymSC6GgJc3ApC0sXZWqHhanQtQ0bW1yo\nDWEJoBWrWl2siFzULs35qbkdP9wXtX8046f7LtsclmNKNvtSD6VY2Yl0yk9FHn0hLEGUyfjztXf4\nJYjWrOuK2s1ZX4aiJXZdZmf9skYz02EqNmG+b1YKU4czU34acXYqfMZMzdznkaFDUbuc9Rc/vSpc\n+0Jsytag5ZBERESWmu6MiYiIiDTQKQdjJDeQ/CrJZ0juIPmB6uvdJB8hubv6e9epjiUiIiIi3mLu\njBUB/IaZXQPgBgDvJXkNgLsAPGpmmwE8Wt0WERERkdNwypwxMzsC4Ei1PUlyJ4B1AG4BcGN1t/sA\nfA3AnSc/GJCKpR0lLOQ4JZh0uw4fDUsgTR7zuVDpYijhUJjzuV8tbSGnK9NEF+vfEPLCDteUxGAp\ndCyb9pdlLh/ypkol35eR4bDk0Pr1l7hY7TYYxr5TNSUyZnOhDMfQ0FEXGx0ejNpl8+df2R3KbnR1\n+xIgLr+MPmds7aZQ2mOcvi+ZnrCM1Or1oQbGrmxdUwxFREQuCqeVM0ayH8DLADwGoLc6UAOAowB6\nF3jPVpLbSW6fnSrMt4uIiIjIRWvRgzGSbQA+B+CDZuZW5zYzA+Z/1M7M7jWzLWa2pbktPd8uIiIi\nIhetRc07kUyjMhD7jJl9vvryMZJ9ZnaEZB+AwYWPUGVAKR+mzoqxKbey+bFcIlZZPjfnpxSHhsJY\nsC3jnxvobAnbo7FK8gDQ0hY+7pZXXeliPatDGYqJ6WEXa28JpTU6O/106rp14fOsXbvGxfKzObfd\n1BQ+U8LmXGz8ePiMbc1+0Lr26s1RO5v1pTVSsSnVo0f99ObK1aHsRybtq/O3ZMP1npjz05Qre8J0\nZyZWVYR69lZERGTJLeZpSgL4SwA7zeyPY6EHANxWbd8G4AtL3z0RERGRC9ti7oy9GsC7ADxN8onq\na78F4G4A95O8HcABAG8/N10UERERuXAt5mnKbwHgAuE3LG13ROR8RfJmAH8GIAngE2Z2d038F1F5\n4poAJgH8mpk9WfeOiogsM/WtVcAEkpnmaDOXC2UpikWfM9bc1R61N7y43cVGj4T3TQ36vKxcKeRi\nzcz6MhAz+bBvXwdrYpNRO56vBgBzhfAUaGtrs4tdenkoX9HS7POyjh4ecNtta3qidmlu0sV6V4ac\ntTW9PS6WYPgc6Yzv2+jx0aidhP+8KzpXhnb7Khfbt/dI1J6hLw/SnI5f70ysvdCYXC52JJMAPgrg\nJgADALaRfMDMnonttg/AT5vZKMk3AbgXwKvq31sRkeVFKdkishSuB7DHzPaaWR7AZ1GpRRgxs++Y\n2Yn/PXwPwPo691FEZFlSFU8RWQrrAByKbQ/g5He9bgfwpXPaI1kW+u96sNFdOG/tv/stje6C1Eld\nB2OpVAqdnWEKbnJqKmqXa6bYSuVieJ+f/cPGq0L5isOZYy42sDv8e1DOuxBmZ8N042zOl5YYHgkV\n/8s103FzxXADcXKs6GL96y+PHd+XiFi7YaXbfulLXhzOUfTnWNMTauZOTh53sQMDu6L2nj27XCw/\nG0prXLrpahebnAkX4KknnnOxodFwjrb1vnJ/GWEqNMn4xfdlPUTOBMnXoTIYe80C8a0AtgLAxo0b\n69gzEZHG0DSliCyFwwA2xLbXV19zSL4EwCcA3GJmI/MdKF4ouqenZ75dREQuKBqMichS2AZgM8lN\nJDMA3oFKLcIIyY0APg/gXWa2a55jiIhclJQzJiJnzcyKJN8H4GFU5rM/aWY7SN5Rjd8D4HcArATw\nsUotaRTNbEuj+iwislzUdTCWTKTQ0bY62m7NhtyvsvmcsemZkE+WL/jyFWYhT6q7zy/6igRUAAAg\nAElEQVSHNBVbgujAj4642LHhUAYi3dziYmmGZYZmZvz5xqdDLli56POmrBRKcuTLPg/t8s2b3HZv\nb8gLGzjgc9327T0QtY8c2e9iBYQyGJmmjIulEuErPHZs1MX27AnHzBV8+YpENnyOQtKXFSnG1j1K\nMFZKgyptIQszs4cAPFTz2j2x9nsAvKfe/RIRWe40TSkiIiLSQBqMiYiIiDRQXacpyRSyyTCtaAxl\nItIZPwXW2RLKQkzNTrnYxPRw2Ej4j7Bhc3gU3uir5U/nw1To/oFhF0vMhKm60pyftiuVwvuufck1\nLtaaDdObxZqp1tFhf45vf/2rUXtk0D9I1tUVKvAXir4mhzH0J5Xw05THp8MU5uS4L4mRzIT+NGV9\n5f5YtQ6s7PO1N5vbOsN+8UvhL4uIiIgsAd0ZExEREWkgDcZEREREGkiDMREREZEGqn9pi/ZV0XYu\nlguWpE9ISmbCODGd9Llfzc0hT2u6MOlik9PhmJe/xL+vuyfkZT2/fZ+LzQ2GshSzI74MxPhIOEdr\nNutixVh+11zRL4dkJZ8HF8+La2v3n7dvXXvUHhv3xzl8eCxqz8zVlKjIhK8w2+HH1oXpUAIkVyq5\n2Kq166L2pVde5WLFQuhbmWEJKVPSmIiIyJLTnTERERGRBtJgTERERKSB6jpNWUYZM6XpaLutNVTB\nzyR9V4r5MD1G+jFjNhumGzvoK+lPNbVF7em8n9Jb3R7ehwk/FZjYGCrSZ+GnIo8dHIzahwb82set\n+XC+9Rv7XKwl66v1k2FKM93U5GJDx8NU5Fzef95EpjVqd7S3udjA0dCfsSlfAqS1I3zeS/s3u1jv\npsuidqq51cXKsQod2Wz4XpIJVeAXERFZarozJiIiItJAGoyJiIiINJAGYyIiIiINVNecsUJxDi8M\nhZISHdmwNNLqrl63b3M2lKXI0i/lU4iVXkgn/fJAbbGcqlyh4GLlWDKUXeHLNAwcPBS1x2qWMUp3\nhON05X2O2sjgRNQ+fNDnVO2d9Tlr6UzIIevpW+1iG1ZuCudoanexqf27o/Zz+3e5WB4hD+3SF13h\nYms2XBLO3e6PiUzIWYtfawBoSYfrnW0K1yyd1NhdRERkqelfVxEREZEG0mBMREREpIHqOk1p5QLm\nckej7eHZ41E7nx93+65oC5X6V3Z0u1gyNl2WTPjyEc1NYcqtNV1TLiNWhL7zaj8tevVlL47a48cH\nXezQwf1R+7mnn3cxK4apyY6OlS6Wm5xz22vXhar3q9b6acqjI6NR+4ntj7vYyMxQ1N6weaOLXfHi\nq6N2d7c/P1JhCncmX3ShZCLEWjL+GrZlw/VtbUrG3qPSFiIiIktNd8ZEREREGuiUgzGSWZLfJ/kk\nyR0k/1f19W6Sj5DcXf2961THEhERERFvMdOUcwBeb2ZTJNMAvkXySwB+DsCjZnY3ybsA3AXgznPY\nVxERETlD/Xc92OgunLf23/2Wc3r8Uw7GzMwAnFhnJ139ZQBuAXBj9fX7AHwNpxyMlUELS/YYQgmF\nqZwvNVG0UE5iNu+X+eloC8v3sFh2sWw6lGzoaPE5VM0t4eZdwadQoSUdjrlyhX9fz8qwzNHoUd+X\npIXP0Nnd42Jjw6Nue3VfKC9xcNDnnu174WDUTq/wn+nlL39p1O7t3+BiFltGanzO9w1zIcerKe1L\ncrSkw03RTKLkYknGylkkwvVUxpiIiMjSW1TOGMkkyScADAJ4xMweA9BrZkequxwF0LvgAURERERk\nXosajJlZycyuA7AewPUkX1wTN1Tulv0YkltJbie5fWYiP98uIiIiIhet0yptYWZjJL8K4GYAx0j2\nmdkRkn2o3DWb7z33ArgXANZe3mHpdJjsKhSLsXbOv4+hsv1cycdKFirbp2omzyamw5hw6PiQi63o\nXBO121r9lGJbJkxTls0fM5nJRu0XX3udiw0Nho9dgp/ua27zl5fZMP136Uo/3XjZS/ujdrol62Ll\nZCgvMVZT1X9qLpTPSNQMhxn7HMmyL7PRlAjTqy1pv4pBUyqM0Yul8B1VxtwiIiKylBbzNGUPyc5q\nuxnATQCeBfAAgNuqu90G4AvnqpMisvyRvJnkcyT3VB/qqY2T5Ier8adIvrwR/RQRWW4Wc2esD8B9\nJJOoDN7uN7MvkvwugPtJ3g7gAIC3n8N+isgyVv358FFU/rM2AGAbyQfM7JnYbm8CsLn661UAPl79\nXUTkoraYpymfAvCyeV4fAfCGc9EpETnvXA9gj5ntBQCSn0Xliev4YOwWAJ+u5ph+j2TniVSH+ndX\nRGT5qOtySEeenxj+vZ97+ACAVQCG63nu88Ryvy6XNLoDsmytA3Aotj2AH7/rNd8+6wC4wRjJrQC2\nVjenSD63tF1dFpbt33X+YaN7UFfL9nsA9F0sJ2fxXSzq3836rk1p1gMAJLeb2ZZ6nvt8oOsi4h/6\nuVDp7/ryoO9h+bjYvwutTSkiS+EwgPgjwuurr53uPiIiFx0NxkRkKWwDsJnkJpIZAO9A5YnruAcA\nvLv6VOUNAMaVLyYiUudpypgLegriLOi6yHnJzIok3wfgYQBJAJ80sx0k76jG7wHwEIA3A9gDYAbA\nLzeqv8uA/q4vD/oelo+L+rugCnmKiIiINI6mKUVEREQaSIOxUyC5n+TPzPP6a0/3kXuSnyL5+0vX\nOxERETnf1XUwdqrlUs4nZvZNM7vyTN5LcgPJr5J8huQOkh+ovt5N8hGSu6u/dy1tr0XkXCP56yR3\nkvxMo/siFSR/l+RvNrofFyuS/SR/NM/rnyB5zSLefyPJL56b3i0PdRuMxZZLeROAawDcupgv4XxE\n8lQPRhQB/IaZXQPgBgDvrV6LuwA8amabATxa3RaR88t/AnCTmf3iiRcW8TNBljl9h0vPzN5Ts2Qa\ngGi8cFGp552xaLkUM8sDOLFcyvngldW7WKMk/4pktjpSHzixQ3U6806STwGYJpki+TKSPyA5SfLv\nAWQBwMyOmNkPqu1JADtRqUR+C4D7qoe8D8Db6vkhReTskLwHwKUAvkRynORfk/w2gL+u/tz4K5JP\nk/whyddV39NC8v7qz5h/IvkYyYu2+OVSIfk/SO4i+S0AV1Zfu4zkl0k+TvKbJK+qvt5D8nMkt1V/\nvbr6+u/Gv8PGfZoLQorkZ6p3jf+x+uf+ayf+rJOcIvlHJJ8E8BPVmbRnSf4AwM81tuvnXj0HYwst\nhXI++EUAbwRwGYArAPz2AvvdCuAtADpRubb/jMpf4G4A/wDg52vfQLIflbU/HwPQG6u7dBRA71J9\nABE598zsDgAvAHgdgD9BZRbgZ8zsVgDvrexi16Lys+I+kllU7qSNVu+U/08Ar2hI5y8gJF+BSq27\n61App/LKauheAO83s1cA+E0AH6u+/mcA/sTMXonKz+lPxA4X/w7lzF0J4GNmdjWACVT+3Me1AnjM\nzF4KYDuAvwDwVlT+PqypZ0cbQbddF+cjZnYIAEj+AYA/B/Cv8+z34dh+PwUgDeBPqwsj/yPJ/xLf\nmWQbgM8B+KCZTZCMYmZmJFV3ROT89oCZzVbbr0HlZwfM7FmSB1D5z91rUBkMwMx+VL27LmfntQD+\nycxmAIDkA6jMTPwkgH+I/axtqv7+MwCuib2+ovrzGfDfoZy5Q2b27Wr7bwD8ek28hMq/hwBwFYB9\nZrYbAEj+DcJ6tRekeg7GzuelUOJ39A4AWLuI/dYCOGy+kNuBEw2SaVT+4H3GzD5fffkYyT4zO0Ky\nD8Dg2XddRBpoutEdkEgCwJiZXbdA7AYzy8VfrA7O9B0ujdqbC7XbOTMr1aszy009pykXs1zKchUf\nRG5EZRpiPvE/XEcArGP8dlflvai+9pcAdprZH8fiDwC4rdq+DcAXzqbTIrKsfBOVlAeQvAKVnwfP\nAfg2gLdXX78GwLWN6uAF5BsA3kaymWQ7KtNdMwD2kfwFoPJzmORLq/t/BcD7T7yZ5HwDNjk7G0n+\nRLX9TgDfOsm+zwLoJ3lZdfuCnyKu22DMzIoATiyXshPA/Wa2o17nP0vvJbmeZDeA/wHg7xfxnu+i\n8tTkr5NMk/w5VB5iAIBXA3gXgNeTfKL6680A7gZwE8ndqNw2v3vJP4mINMrHACRIPo3Kz5BfMrO5\n6us9JJ8B8PsAdgAYb1w3z3/VB6T+HsCTAL6Eys0AoDIYvr2aJL4D4SGyXwewheRT1e/hjjp3+WLw\nHCr/lu4E0AXg4wvtWL1DuRXAg9UE/gt+lkjLIZ0Cyf0A/i8qg6e1qNyt+jVUBlZ/Y2brY/u9x8z+\nNfbeLagkIV6Oyrp8ALDbzBZ6AEBELjLVx/jTZpar3gn4VwBXVp86F5GLgAZjIiINVJ1G+yoqD/wQ\nwJ1m9qXG9kpE6kmDMREREZEG0tqUIiIiIg2kwZiIiIhIA53VYIwX0MLfIiIiIo1wxjlj1SeAdgG4\nCZWljbYBuHW+RT9PyLanrH1lU3ghfmrW7HyyWCm8MDdRcKFivhzelqh5Y2zomahZhjSZSsba/n2J\nZNhmsuaYXKA9D3Mf6mTXveZAJ/2KTnHShQ5ysrctcO0nR+aQmywu9oQiZ23VqlXW39/f6G6IiJyR\nxx9/fPj/b+/eYyQ9q/SAP0/dq7qrb3Npz9Xj22Im3DMybICEy1oySyKTrEJgEetERiNrl7BIrISV\nlaKNkijmj2xEElivBU68Cwk4AcRkMQHHAS2wQDzLgu2xGWawx56xZ7rn0vfuqq7LyR/1ud73fMy4\n257uqp7p5ydZfr86X9X3VpXtef2dU+c1s20rnXc5Hfi7G38DAMkXN/6+5GKsuqWI3/rD/d3jeB3I\n9PojjmX9Dbz2TJj28W+fdrHpk2HXisxAalFVCuPSkF+NjY5Xu+OB0aKLVcYK3XFhMPWaxXDcTn2a\nlloAta0ZxZqpk6NFJPKpWDaKpddC0fFLLNqMbf/AS+20dInv5Sv/9pJfrci62LdvHw4fPtzvaYiI\nvCLJtmcrupw05ao2/iZ5kORhkodrc810WERERGRTW/cCfjO7z8wOmNmBUlX7kouIiIjELmd19PI3\n/jaffnypcrW4li3T8mvGhfP1aOybVBeaA+Fg0b+91my4Mzf3Qt3FbCLEJjKzfjLlUJdWHPbpzYEt\n4XrlsaqLDaXTndXouJBKvWbD9VupfKMhlWL0wYhPYb5kOdtLfvaruZaIiIishcu5M3Ylb/wtIiIi\nsiG84jtjZtYk+eLG31kA919BG3+LyFVs393f6PcUrlgn7nlvv6cgsulcVhGXmT2EsAH2y5b+BeWl\nr+NPPHd2ujuemptxsYEoIVdKpQLzUauLUqq3RbEZUoFjI+MuVh0b7Y5PnX7BxX7+RPg1Z6t41sVu\nvNG/zq7dQ91xtuxzftmB8FPPwpj/WqwYUpjt9K8is+F1WvQ/kGjHqd62/ywyL/Hhr/Z7ERERkcun\nDvwiIiIifaTFmIiIiEgfaTEmIiIi0kcbt/FXVLfUXvJFTItnolYT7YqLlYqhfUSt7ltUDJXDuUPD\nvg1FNuryf8MN17lYdTDUelWyBRcrZsL1Ji5ccLFruNUdb62Vu+P5C3MudnYuzPX6/de7WDsf2nDM\nNf17apdb3XFuyHfuz1aibZwKvtasHXX8t3aqlYbrwK8CMhERkfWkO2MiIiIifaTFmIiIiEgf9TRN\nSQAZ14E/rAXbmZY7N+480ZjzKbbmufC8gfygi1UrUUd88+0r0ArXyBdKLnTNjtCGYrDiY83mYnc8\nPOS76u9h2Iw9n/fXG66U3TEa4frtJf+eWrPh+NwvfAqzXAnXLJZ9enW6fr47Xij43QgW2mHe7VQr\njS07Q7uOgWH/fvO5ME8rhOcpZSkiIrL2dGdMREREpI+0GBMRERHpIy3GRERERPqo560tMtHWRhbV\ndDVTW/nkGWIL074WKtMO084P+3YOjA4LLV/f1aiH10lXP1XLob4rn6qNGh3d0h3PLtRcbHohtNmo\nlH3t1amJ0+64ENVfDVR8rVszuuTJMxP+ebnwWdx0/W4XG6+EWrfnJvxWTVMTYa5zC/4znNsZ1uHj\nuwZcLFcIrTQsdPVAs+br+kRiJG8D8Gl09qr9nJndk4rfDuBfA2gDaAL4uJl9v+cTFRHZYDZunzER\nuWKQzAL4DIBbAZwC8CjJQ2b2ZHTaIwAOmZmRfB2ABwHc3PvZiohsLEpTishauAXAcTN72syWAXwJ\nwO3xCWY2b9ZtKTwAwCAiIr2/M2bRf3+NoZ1Dum0Cm2Fqi9OLLtZuhnRZM/286D/v1QGffitE3flL\nlcolY3F3egDIRHOuFH1atFIIx7Wcb23RyPpjZsPrtFN/DuVy4X2U6Z/XbocU7uSFMy62Z+/O7nhg\nIPWestPdcTXvY/VzIRU5teDf79hISNlO23x33Ey14xCJ7AJwMjo+BeDN6ZNI/kMA/w7AdgDvvdgL\nkTwI4CAA7N27d80nKiKy0ejOmIj0jJl9zcxuBvA+dOrHLnbOfWZ2wMwObNu27WKniIhcVbQYE5G1\n8DyAPdHx7uSxizKzvwRwPZnawFVEZBPSYkxE1sKjAG4ieR3JAoAPADgUn0DyRib1CCTfBKAI4Pyv\nvJKIyCbT119TtqN2FumaMauFWqz6rG+pEJWaYblZd7Fs9DrNtq9xWlwMtWfbU+mPkZGR8JqLs34u\nrdAWIrXBEoYHQq3Zgt/FCK1SwR0vNML156YuuFgpF9WstXwbiqFqqOHK+JI1nDpzqjvO5Xwrj+pQ\nqJmbWJ52saWozcf2sWtc7OZX7e+Oj104EV4/PwmRizGzJsmPAvgWOv+a3G9mR0jelcTvBfBbAH6H\nZAPAEoB/EhX0i4hsWmptISJrwsweAvBQ6rF7o/GnAHyq1/MSEdnolKYUERER6aO+3hkzxulHP5XG\nYshe1GZ8d/5yPqTjWjmfiqwth/RbY3bGxer1kNLMF31Kb+fO0CJi4lSq47+F68dd/AEgH+VM23Xf\ngqOZSjciE5KcS8u+k//IULU7Lgz4z2JqOqQ0zX8UqI6EFvn5fKqVRpSyZd6/Ziv6nHZe67v6j+4c\n7o7by1EaOKOMkoiIyFrTnTERERGRPtJiTERERKSPtBgTERER6aPe14xFHSzirZGY2mln/kKoqYrr\nxwAg2w7H2axfT+aj2iimYvNLoUZt8uw5FxsZHeuOl+d9G4i5C2fDa5pvs9GohTqxeJsmABioVt1x\nvhC9+bxv5RHv47Rrl281sfWaMLfHjz7hYpVW2OYoX/F9L+pLoS5sedF/wFwO5z599ISLTZx9ujvO\n7AvztPSXJCIiIpdNd8ZERERE+mjFO2Mk7wfw9wFMmtlrksfGAHwZwD4AJwC838ym1m+aIiJyJdp3\n9zf6PYUr1ol73tvvKUiPrCZN+V8B/GcAfxY9djeAR8zsHpJ3J8efXOmFDESTof1CO8rUFRr+Jt3c\n6dCWor6UmnSULmvUfIuIqHsE8vmSi8VdIU6d9t3kTz4XttHbXq242OxkaO/QWPaTabZDavI1B25x\nsdf/3be743O1+e74S//tCy72/MkT3fH5Kd+SY8eu8e542/btLrZcb3TH83P+s7CotcXIVv9ZZNsh\nTbm04FtyFKLWIdVqOI+ZVGpVRERELtuKacpkQ98LqYdvB/BAMn4AwPvWeF4iIiIim8IrrRkbN7PT\nyfgMgPFLnUjyIMnDJA/X5huXOk1ERERkU7rsAv5ko99LtmY3s/vM7ICZHSgN5i91moiIiMim9Epb\nW0yQ3GFmp0nuADC54jMAdJpZhHVbxkKBV3PRt4VYmgn1T/ls2b9M1AaikPELvHY7qgxLlTjlstH1\nlv1WRc/88lh3XLzO3+jLFMPzhqtbXex1r35Nd/zGt7/LxQa27HDH88tRfVfNt4m4/08/3R1PTvm2\nG7XoPe3e6ec2H7XhGEotdreNh5YYdfj3W84NdsdnJidcrJkL1ytXQ61ZJqsf34qIiKy1V/qn6yEA\ndyTjOwB8fW2mIyIiIrK5rLgYI/nfAfwQwKtIniJ5J4B7ANxK8hiA30iORURERORlWjFNaWYfvETo\n3a/kgkRIR+as0B0vzvm0XasWcoyZVElaJupfkc9lXWx5OaQ3Wy3/mpmo1UOlXHCxpXpo7/DMyedc\nbGTrSHe8+7obXKxZDinUWsZfb37yvDt+/Imj3fGN193kYrv37e6Oj876NOXCQmitcfqMj2UQrjm3\n4FtiFFrhszg/5XcVGKwMd8ctNl1suRk+i1wppGVJtbYQERFZayoCEhEREekjLcZERERE+kiLMRER\nEZE+eqWtLV4hA6Map1y0H1J7ztd+lRBaLyy1fVuGRiscW8PXk7UtapGR8a/ZboZYeWzQxRYaoe3E\n7ISv9apsDVsQHfnl8y529MSPwusXx1zsb732be54Zj7MJ5v3DXBffWOoIcsuz7vYM08/2x0326mW\nbu3weS7VZl0otxjW2oOVIReLtzZaNr+N0vD28Nlki1FAS3cREZE1pz9eRURERPpIizERWRMkbyN5\nlORxkndfJP4hko+RfJzkX5F8fT/mKSKy0fQ2TUmAUfd8RGnDhSmfimwuhfNKBd9Zfinqnp/N+HYL\n+agjfzPVgn+oGlJ1Y9f47vgz9ZCq27fjWhdrRC04pmbqLpZhSE3+zWO+tcRUxqcNZwauD/Ms+NdZ\nqId84Pbx7S7WjNKys1OLLrYwtxDm4nKKwOBApTvOpXYqqFtIk1rW734wvKXaHTP+J0SdLeQSSGYB\nfAbArQBOAXiU5CEzezI67RkAf8/Mpki+B8B9AN7c+9mKiGwsujMmImvhFgDHzexpM1sG8CUAt8cn\nmNlfmdlUcvgjALshIiJajInImtgF4GR0fCp57FLuBPDNdZ2RiMgVose/phSRzY7kO9FZjL3tEvGD\nAA4CwN69e3s4MxGR/ujpYowgMgj1V62l0OphYdq3c7Co7UW55NtQWFTvlM35FhEDg2F7onrDv72R\n0T3d8dCob0Nx4XxoZ7FYX3Kx5vwz3fHUYmobJQvbIz131tdsTf7klDse2nldd9zOVFysunVnd3xm\n+oiLZbLhBmY2498TLRyXyv76rWgbqaUF377CCuGzzwym2ooMRceMt3hKtdUQCZ4HsCc63p085pB8\nHYDPAXiPmZ1PxwHAzO5Dp54MBw4c0D90InLVU5pSRNbCowBuInkdyQKADwA4FJ9Aci+ArwL4sJn9\nog9zFBHZkJSmFJHLZmZNkh8F8C0AWQD3m9kRkncl8XsB/EsAWwB8Ntl0vmlmB/o1ZxGRjaLHi7EM\nslGashllzgqpe3Rj48PhvKVUKjJqZ7Flm28DkcmFt5TN+a7zZiGFOT016WK1WmhL8fSFKRcrbQkT\ntYKvSbbsm8L1iltcrEifNsw1oxSn+VYTpYEQs4xPKc7OhvmUUu+pFqUtW03fogKZ8KG26b/qpUZo\nl7FldMDPsxwdmLJEsjpm9hCAh1KP3RuNPwLgI72el4jIRqc0pYiIiEgfaTEmIiIi0kdajImIiIj0\nUW9rxsxgzWb3MNMOl9++das7NdcI9VbLRb8FUKMZ2i3UGm0XQzOsL4eG/dubnQt1YnU2XSw3GK5R\npN/3h9HHlKFvAwGEeWZSdWCZQqoWqxieW8r7OrinT5/ojmfmfc1aoRDqthoLC6lYmNuyf0k06+F5\n7ZZ/v8sI7TvKQ74OLRsV8LWQqkMTERGRNaU7YyIiIiJ9pMWYiIiISB9pMSYiIiLSR73dDsmATDus\n/6wRrQWbvqZpcSlsj1QZ9FsQ1WZDj6wzL6R6gpVGQ+z8WRerjoTXKQ35HmCGenecz/qaserIju44\nV361i4G7u8NMydeItbKpXmKl8Lqteb9V0sQLx7rj4YGyi1WiHmHz8IVhS1HJXKvp69kacd8x+tqv\nfDl89sWq/8egnQnfhVn0WajlmIiIyJrTnTERERGRPtJiTERERKSPerwdEoEolWb1qIVC3acGa/WQ\nilxspFpbNEKqrpRKYU7NTHTH84u+DUQd1e54a77kYoNRijFb8tc7Px22ShrO7nCxLeMhLWolP5d6\nwacps1E7iQsnfupirakXuuO5wpyLlbNhbtfu8dsxTZ6e7Y5PLZx3MWuHHCZzvgXIcLQFUmnIz7sZ\nbceUaUfvwX9FIiIisgZ0Z0xERESkj1ZcjJHcQ/I7JJ8keYTk7yePj5F8mOSx5O+jK72WiIiIiHir\nuTPWBPAJM9sP4C0Afo/kfgB3A3jEzG4C8EhyLCIiIiIvw4o1Y2Z2GsDpZDxH8ikAuwDcDuAdyWkP\nAPgugE++1Gs1Gy1MT4aWFVPHQk3XYNZvydNshRqn+Xlf+1WuhNYPW7b5dhIjW0NdWK227GLMhN4M\nQwO+AGpscFt33C5MuFiusj28Rs7XbLEYaqqa8NfLF1NbJ9VCq42F555woV0jYd7L5p/XXA7zHk5t\nXVSbD/Vz5bJ/T825MJ980deFVcfC55Yr+euZa2GhQjEREZH19LJqxkjuA/BGAD8GMJ4s1ADgDIDx\nSzznIMnDJA/XF5sXO0VERERk01r1YozkIICvAPi4mc3GMTMzXKIlqJndZ2YHzOxAsdLjH2+KiIiI\nbHCrWh2RzKOzEPuimX01eXiC5A4zO01yB4DJlV6n3TQsnAvtHZamQoqNA0vu3FwlpMdKpVSLiLhD\nvvk02o4t4QZdq1V3MbNwZ2552V+vGHXHR9GnPi1KTVqx6mLLURqvBT/P0YJvJ7H13JPd8Q2tCy72\nvQuhncZsy69rcwxr5nbTpxuHR7Z0x6WS/wpardB1v1n0X3VhLByz4LvzsxViGaoDv4iIyHpaza8p\nCeDzAJ4ysz+OQocA3JGM7wDw9bWfnoiIiMjVbTV3xt4K4MMAHif5YqfSfwHgHgAPkrwTwLMA3r8+\nUxQRERG5eq3m15Tfx6V/UvfutZ2OiIiIrId9d3+j31O4Yp24573r+vq9rag3oB1tgZTPhvqnUsVv\nT1RvhXYW1aERF2u0Q43T6clzLnb27FR3PDbq67tgoUat0fT1ZKWBSjjNfPb2/Iz3kjMAAA0hSURB\nVFxYiw5sr7gYlsJHWBn1bScKsyfd8fXP/aw7PmC+DcaZRrjmd5581sUy2XDN50/7X6SWy8XueG7O\nF3UVoto35hsulh+I2lnQPy8Tvf94Fa4mFyIiImtP2yGJyJogeRvJoySPk/yVJtAkbyb5Q5J1kn/Q\njzmKiGxE6jUhIpeNZBbAZwDcCuAUgEdJHjKzJ6PTLgD4GID39WGKIiIbVk8XY9Y2NBdDijFXCDfm\nMjnfBqJSDGnLctGnMEcHQ9puccGnG6NuDihWfNrw2ZO/DNfO+NRc+1RoC9FIpRDLw68N47b/yKwV\ndhTIzfnWErunHnPH186F62cHfdJvtBJaVCzMP+NizWxIMbYz/v02zoeWb+22b1GRyYXj8S2+7cbA\n0Fh0lNqpIEpImhuLXNItAI6b2dMAQPJL6OzS0V2MmdkkgEmS61t8ISJyhVGaUkTWwi4AcZHkqeSx\nly3etePs2bMrP0FE5AqnxZiIbCjxrh3btm1b+QkiIlc4LcZEZC08D2BPdLw7eUxERFbQ05qxdquN\nxdnQsqJo4fLlqm8ZMb49bGs0e2HOxSxqSzG+dczF4tYWZ8/5theDg6HVRS7ra7ZmpqfD68PXr+26\nJqpZq/nXvHDhRHc8ZM+52EjDb3l0fH6iO/5f53zt1/99aiZcYsnXftWz4TMrpLY1qg5F7StSvScs\nG65RGfPPy0ZvqWX+/TL6Xkz9LGR1HgVwE8nr0FmEfQDAb/d3SiIiVwb9mlJELpuZNUl+FMC3AGQB\n3G9mR0jelcTvJXkNgMMAhgC0SX4cwH4zm73kC4uIbAJajInImjCzhwA8lHrs3mh8Bp30pYiIRHq6\nGMtls9gyOtw93rdrZ3c8Wz/vzp2eC7+iytO3ZWgsLYYD35AehWxowNBItXoYqISUXr6QdbG9O8Nc\nKgVfSsfodX7y+MMudmHmeHc8vnOriz0869/T40+f6I6fm/ftJBbaIR+Yy/ivJZ8N81mY9+nNei20\nvSiXfAuQwS3hdQbHfBrYMtEH1/ZNK+LWFm01tBAREVlXKuAXERER6SMtxkRERET6SIsxERERkT7q\nac0YC0A+6sk9nQ/tJOqNVA3VdKgLy9X8NAcs1EY1m76mqVqMtkqq+fqq5kKor7JWw8Wqe0a74+uH\ntrvYU4+HdhlnJn/pYtNToZ3FyYKf5/nFmjueyITtmbIVvw4uW5hP3neaAKMatkbbz7u9HOrZFubn\nXYwj4Xn5IV8zhqh9R1wjBgBtRhNQawsREZF1pTtjIiIiIn2kxZiIiIhIH/U0TZkpZlC5sdg9btdD\ne4XccsGdO7AlrBNrMz41NzUdUprtmm9fMZgPLStGBwddLD53obbkYucnQxuK6nnf9qJxLrTEKJlP\n97EZ2m4cm3jBxarDw+5429aR7rhYKvu5tUK6tTbv05sLy1H6seVzmBmGr7CeSmEOjYfPulj1X7W5\ndhZ+TW4ZtbMQERHpFd0ZExEREekjLcZERERE+kiLMREREZE+6u3elDRYLmqpkAt9EzKDvodCNppa\ncZevr2oshzVka97vh5RdDPVOrUVfQ9WaDbVmmay/Xnk4tLZoLPmPZa4e9jEu5/zzhouhLm1mcc7P\nM7X/cTYTatFGhv06eGwkXL8abdsEAIXFMJ/ZOd++ooWoXUfWv+boNWFuuVTbDYuep+4VIiIi/aM7\nYyIiIiJ9pMWYiIiISB/1tgO/ZVBoRq0tLGrTkFoWui7w8OnGQj4k1jjq21BgKBy3fQYT2UaI5Wu+\nfcMcF7rjxVR6cXFH1IJjzD8vfzK0yMhM+gvOzpz352bCe8+k5paJNiAYGvXtMwaqIU2by/kWIPWF\nsFPBUmHRxYa2h7YbVLsKERGRDUl3xkRERET6aMXFGMkSyf9H8mckj5D8V8njYyQfJnks+fvoSq8l\nIiIiIt5q7ozVAbzLzF4P4A0AbiP5FgB3A3jEzG4C8EhyLCIiIiIvw4o1Y2ZmAF7sp5BP/jIAtwN4\nR/L4AwC+C+CTK7wajBevXUq3V8jaS6wTm+E1jH47pHZ0bL68CiiG52V99wjUo5qq3KC/9tB4qDWz\nhi/2Gtsbtjwam/AtOM48f8Edz1+od8cLS+f89RdDy4qFtp/c0HDYRmmwtMXFKsOl7rg85Ld4qsT1\ndPTbKMHU0EJERGQjWFXNGMksyZ8CmATwsJn9GMC4mZ1OTjkDYHyd5igiIiJy1VrVYszMWmb2BgC7\nAdxC8jWpuKFzt+xXkDxI8jDJw0upBq0iIiIim93Lam1hZtMkvwPgNgATJHeY2WmSO9C5a3ax59wH\n4D4A2LavYo1c1MMhypwxtS5knLhMpyyZjc7z6bdMdMy2j6XP9cFwjXberyszhZD6HMr4VhqFkdCu\nYvR6n17cVR9xx0uzYTE6c67mYjPnQ2uNxrRv5bG4EFKareVlFxsshWsOjvk0aaEcWltYqj3IS4rf\nvrKZIiIi62o1v6bcRnIkGZcB3Arg5wAOAbgjOe0OAF9fr0mKyMZH8jaSR0keJ/krP+hhx39M4o+R\nfFM/5ikistGs5s7YDgAPkMyis3h70Mz+guQPATxI8k4AzwJ4/zrOU0Q2sOS/D59B53/WTgF4lOQh\nM3syOu09AG5K/nozgD9J/i4isqmt5teUjwF440UePw/g3esxKRG54twC4LiZPQ0AJL+Ezi+u48XY\n7QD+LKkx/RHJkRdLHXo/XRGRjaOn2yGde3bp3Oc/8tNnAWwFcG6l8zehjf65XNvvCciGtQvAyej4\nFH71rtfFztkFwC3GSB4EcDA5nCd5dG2nuiFs2H/X+al+z6CnNuz3AOi72Egu47tY1Z+bPV2Mmdk2\nACB52MwO9PLaVwJ9LiL+Rz9XK/27vjHoe9g4Nvt3ob0pRWQtPA9gT3S8O3ns5Z4jIrLpaDEmImvh\nUQA3kbyOZAHAB9D5xXXsEIDfSX5V+RYAM6oXExHpcZoyclWnIC6DPhe5IplZk+RHAXwLQBbA/WZ2\nhORdSfxeAA8B+E0AxwEsAvhn/ZrvBqB/1zcGfQ8bx6b+Ltj5YZOIiIiI9IPSlCIiIiJ9pMWYiIiI\nSB/1dDG20nYpmwXJPSS/Q/JJkkdI/n7y+BjJh0keS/4+2u+5isjLQ3IfyScu8vjnSO5fxfPfQfIv\n1md2mwvJj5F8iuQX+z0XAUj+Eck/6Pc8NqKeLcai7VLeA2A/gA+u5j9MV6kmgE+Y2X4AbwHwe8ln\ncTeAR8zsJgCPJMcichUws4+ktocC0P1vo6yP3wVwq5l96MUHSPbrh2uyBq7W76+Xd8a626WY2TKA\nF7dL2XTM7LSZ/SQZzwF4Cp1O5LcDeCA57QEA7+vPDEXkMuVIfjG5K/M/SVZIfpfkAQAgOU/y35P8\nGYBfT7IGPyf5EwD/qL9TvzqQvBfA9QC+SXKG5J+T/AGAPydZIvlfSD5O8m9IvjN5ToXkg0nW4msk\nf/zidyavDMk/JPkLkt8H8KrksRtI/m+Sf03yeyRvTh7fRvIrJB9N/npr8vgfxd9f/97N+unlCnM1\n26VsOiT3obP3548BjEd9l84AGO/TtETk8rwKwJ1m9gOS96NzhyY2AODHZvYJkiUAxwC8C522H1/u\n7VSvTmZ2F8nbALwTwEcB/AMAbzOzJZKf6Jxir00WAt8m+WvofE9TZraf5GsA/LRvb+AqQPJvo9Nz\n8A3orDd+AuCv0WljcZeZHSP5ZgCfReef/08D+A9m9n2Se9FplfPq5OX2I/n+evw2euKqvN13pSA5\nCOArAD5uZrMkuzEzM5LqOyJyZTppZj9Ixl8A8LFUvIXOv/sAcDOAZ8zsGACQ/ALC3pyydg5Ff5C/\nDcB/AgAz+znJZwH8WvL4p5PHnyD5WF9mevV4O4CvmdkiAJA8BKAE4O8A+B/Rn3nF5O+/AWB/9PhQ\n8uck4L+/q04vF2PaCiVCMo/Of4y/aGZfTR6eILnDzE6T3AFgsn8zFJHLkP4fqfRxzcxavZqMAAAW\n+j0BAdApj5o2szdcIvYWM6vFDyaLs6v6++tlzdhqtkvZFNj5J+vzAJ4ysz+OQocA3JGM7wDw9V7P\nTUTWxF6Sv56MfxvA91/i3J8D2EfyhuT4g+s6MwGA7wH4EAAk6cm9AI4C+AGA9yeP7wfw2n5N8Crx\nlwDeR7JMsopOqngRwDMk/zHQ+fOQ5OuT878N4J+/+GSSF1uwXZV6thgzsyY6eftvoVOw/qCZHenV\n9TeYtwL4MIB3kfxp8tdvArgHwK0kj6Fzu/aefk5SRF6xo+j8SvopAKMA/uRSJyZ3AQ4C+EZSwK87\n4uvvswAyJB9Hp0bvn5pZPXl8G8knAfwbAEcAzPRvmle25IdqXwbwMwDfROemDNBZCN+Z/IDlCMKP\n+T4G4ADJx5Lv4K4eT7lvtB2SiIgIum1G8mZWS+5U/h8Ar0o6AIisGxXwi4iIdFQAfCep6SWA39VC\nTHpBd8ZERERE+kh7U4qIiIj0kRZjIiIiIn2kxZiIiIhIH2kxJiIiItJHWoyJiIiI9NH/Bxikr3wn\n//HYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa687d9c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "idx = np.random.choice(len(test_x),5,replace=False)\n",
    "\n",
    "\n",
    "p = model.predict(test_x[idx])\n",
    "for i in range(len(idx)):\n",
    "    plt.subplot(5,2,2*i+1)\n",
    "    plt.imshow(test_x[idx[i]])\n",
    "    plt.title(label_dict[test_y[idx[i]]])\n",
    "#     plt.show()\n",
    "    pred_label = np.argsort(-p[i])[:3]\n",
    "    pred_prob = [p[i][l] for l in pred_label]\n",
    "    pred_label = [label_dict[l] for l in pred_label]\n",
    "    \n",
    "    plt.subplot(5,2,2*i+2)\n",
    "    plt.bar(range(3),pred_prob)\n",
    "    plt.xticks(range(3), pred_label)\n",
    "#     plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}