image_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e95c533",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score, confusion_matrix\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff5a25e",
   "metadata": {},
   "source": [
    "## Load and Prepare Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b947c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable to hold MNIST dataset \n",
    "mnist = tf.keras.datasets.mnist "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "045ab4a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n",
      "11501568/11490434 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset into training and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d234b4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize image pixel value from [0, 255] to [0, 1]; split into train, validation, and test sets\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0 \n",
    "\n",
    "x_train = x_train.reshape(-1,28,28,1)\n",
    "x_test = x_test.reshape(-1,28,28,1)\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(\n",
    "\tx_train, y_train, test_size = 1/6, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97ba5b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape = (50000, 28, 28, 1)\n",
      "y_train.shape = (50000,)\n",
      "x_val.shape = (10000, 28, 28, 1)\n",
      "y_val.shape = (10000,)\n",
      "x_test.shape = (10000, 28, 28, 1)\n",
      "y_test.shape = (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Check each array shape\n",
    "print('x_train.shape =', x_train.shape)\n",
    "print('y_train.shape =', y_train.shape)\n",
    "print('x_val.shape =', x_val.shape)\n",
    "print('y_val.shape =', y_val.shape)\n",
    "print('x_test.shape =', x_test.shape)\n",
    "print('y_test.shape =', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c8211c",
   "metadata": {},
   "source": [
    "## Build the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fef1ba17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-19 18:57:10.894369: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "# Building the DNN Model with a Dropout Layer (2 Dense Layers)\n",
    "dnnd = tf.keras.models.Sequential() \n",
    "# Add the Flatten Layer\n",
    "dnnd.add(tf.keras.layers.Flatten()) \n",
    "\n",
    "# Add 2 hidden neuron Dense layers\n",
    "dnnd.add(tf.keras.layers.Dense(units=400, activation=\"relu\")) \n",
    "dnnd.add(tf.keras.layers.Dense(units=100, activation=\"relu\"))\n",
    "\n",
    "# Add a dropout layer\n",
    "dnnd.add(tf.keras.layers.Dropout(0.25))\n",
    "# Add the output layer\n",
    "dnnd.add(tf.keras.layers.Dense(units=10, activation=\"softmax\"))\n",
    "\n",
    "# Compile the model\n",
    "dnnd.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c667006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a CNN model (1 Dense Layer)\n",
    "cnn = tf.keras.models.Sequential() \n",
    "\n",
    "# Add the 1st Convolutional Layer\n",
    "cnn.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), padding = 'Same', \n",
    "                  activation ='relu', input_shape = (28,28,1)))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "# Add the 2nd Convolutional Layer\n",
    "cnn.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (3,3), padding = 'Same', \n",
    "                  activation ='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "# Add the Flatten Layer\n",
    "cnn.add(tf.keras.layers.Flatten())\n",
    "\n",
    "# Add 1 hidden neuron Dense Layer\n",
    "cnn.add(tf.keras.layers.Dense(units=400, activation=\"relu\"))\n",
    "\n",
    "# Add a Dropout Layer\n",
    "cnn.add(tf.keras.layers.Dropout(0.25))\n",
    "# Add the Output Layer\n",
    "cnn.add(tf.keras.layers.Dense(units=10, activation=\"softmax\"))\n",
    "\n",
    "cnn.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee0d7a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a Deep CNN model (1 Dense Layer)\n",
    "dcnn = tf.keras.models.Sequential() \n",
    "\n",
    "# Add the 1st Deep Convolutional Layer\n",
    "dcnn.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), padding = 'Same', \n",
    "                  activation ='relu', input_shape = (28,28,1)))\n",
    "dcnn.add(tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), padding = 'Same', \n",
    "                  activation ='relu'))\n",
    "dcnn.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "# Add the Dropout Layer\n",
    "dcnn.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "# Add the 2nd Deep Convolutional Layer\n",
    "dcnn.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (3,3), padding = 'Same', \n",
    "                  activation ='relu'))\n",
    "dcnn.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = (3,3), padding = 'Same', \n",
    "                  activation ='relu'))\n",
    "dcnn.add(tf.keras.layers.MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "# Add the Flatten Layer\n",
    "dcnn.add(tf.keras.layers.Flatten())\n",
    "\n",
    "# Add 1 Dense Layer\n",
    "dcnn.add(tf.keras.layers.Dense(units=400, activation=\"relu\"))\n",
    "# Add a Dropout Layer\n",
    "dcnn.add(tf.keras.layers.Dropout(0.25))\n",
    "\n",
    "# Add the Output Layer\n",
    "dcnn.add(tf.keras.layers.Dense(units=10, activation=\"softmax\"))\n",
    "\n",
    "# Compile the model\n",
    "dcnn.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a472085",
   "metadata": {},
   "source": [
    "## Train the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aedcad79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Deep Neural Network Model with a Dropout Layer (DNND) ----------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-19 18:57:11.708882: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1563/1563 - 13s - loss: 0.2526 - accuracy: 0.9232 - val_loss: 0.1128 - val_accuracy: 0.9663\n",
      "Epoch 2/20\n",
      "1563/1563 - 14s - loss: 0.1034 - accuracy: 0.9693 - val_loss: 0.0867 - val_accuracy: 0.9745\n",
      "Epoch 3/20\n",
      "1563/1563 - 14s - loss: 0.0718 - accuracy: 0.9778 - val_loss: 0.0790 - val_accuracy: 0.9752\n",
      "Epoch 4/20\n",
      "1563/1563 - 15s - loss: 0.0537 - accuracy: 0.9833 - val_loss: 0.0733 - val_accuracy: 0.9792\n",
      "Epoch 5/20\n",
      "1563/1563 - 12s - loss: 0.0418 - accuracy: 0.9865 - val_loss: 0.0932 - val_accuracy: 0.9766\n",
      "Epoch 6/20\n",
      "1563/1563 - 10s - loss: 0.0360 - accuracy: 0.9884 - val_loss: 0.0856 - val_accuracy: 0.9765\n",
      "Epoch 7/20\n",
      "1563/1563 - 13s - loss: 0.0298 - accuracy: 0.9906 - val_loss: 0.0874 - val_accuracy: 0.9755\n",
      "Epoch 8/20\n",
      "1563/1563 - 18s - loss: 0.0265 - accuracy: 0.9913 - val_loss: 0.0797 - val_accuracy: 0.9807\n",
      "Epoch 9/20\n",
      "1563/1563 - 17s - loss: 0.0220 - accuracy: 0.9926 - val_loss: 0.0950 - val_accuracy: 0.9788\n",
      "Epoch 10/20\n",
      "1563/1563 - 20s - loss: 0.0225 - accuracy: 0.9926 - val_loss: 0.0931 - val_accuracy: 0.9772\n",
      "Epoch 11/20\n",
      "1563/1563 - 23s - loss: 0.0183 - accuracy: 0.9940 - val_loss: 0.0872 - val_accuracy: 0.9828\n",
      "Epoch 12/20\n",
      "1563/1563 - 20s - loss: 0.0172 - accuracy: 0.9947 - val_loss: 0.1051 - val_accuracy: 0.9791\n",
      "Epoch 13/20\n",
      "1563/1563 - 18s - loss: 0.0170 - accuracy: 0.9944 - val_loss: 0.0958 - val_accuracy: 0.9819\n",
      "Epoch 14/20\n",
      "1563/1563 - 12s - loss: 0.0162 - accuracy: 0.9950 - val_loss: 0.1110 - val_accuracy: 0.9799\n",
      "Epoch 15/20\n",
      "1563/1563 - 13s - loss: 0.0153 - accuracy: 0.9953 - val_loss: 0.0876 - val_accuracy: 0.9838\n",
      "Epoch 16/20\n",
      "1563/1563 - 14s - loss: 0.0136 - accuracy: 0.9958 - val_loss: 0.1042 - val_accuracy: 0.9812\n",
      "Epoch 17/20\n",
      "1563/1563 - 17s - loss: 0.0131 - accuracy: 0.9955 - val_loss: 0.0999 - val_accuracy: 0.9802\n",
      "Epoch 18/20\n",
      "1563/1563 - 13s - loss: 0.0117 - accuracy: 0.9965 - val_loss: 0.0987 - val_accuracy: 0.9834\n",
      "Epoch 19/20\n",
      "1563/1563 - 12s - loss: 0.0153 - accuracy: 0.9959 - val_loss: 0.1177 - val_accuracy: 0.9818\n",
      "Epoch 20/20\n",
      "1563/1563 - 12s - loss: 0.0103 - accuracy: 0.9969 - val_loss: 0.1260 - val_accuracy: 0.9816\n",
      "Model: Convolutional Neural Network Model (CNN) -----------------------------------------------------------\n",
      "Epoch 1/20\n",
      "1563/1563 - 58s - loss: 0.1440 - accuracy: 0.9556 - val_loss: 0.0525 - val_accuracy: 0.9840\n",
      "Epoch 2/20\n",
      "1563/1563 - 53s - loss: 0.0481 - accuracy: 0.9854 - val_loss: 0.0441 - val_accuracy: 0.9854\n",
      "Epoch 3/20\n",
      "1563/1563 - 52s - loss: 0.0332 - accuracy: 0.9889 - val_loss: 0.0417 - val_accuracy: 0.9886\n",
      "Epoch 4/20\n",
      "1563/1563 - 53s - loss: 0.0260 - accuracy: 0.9915 - val_loss: 0.0290 - val_accuracy: 0.9916\n",
      "Epoch 5/20\n",
      "1563/1563 - 55s - loss: 0.0203 - accuracy: 0.9931 - val_loss: 0.0358 - val_accuracy: 0.9907\n",
      "Epoch 6/20\n",
      "1563/1563 - 57s - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0391 - val_accuracy: 0.9899\n",
      "Epoch 7/20\n",
      "1563/1563 - 54s - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.0378 - val_accuracy: 0.9914\n",
      "Epoch 8/20\n",
      "1563/1563 - 51s - loss: 0.0127 - accuracy: 0.9960 - val_loss: 0.0353 - val_accuracy: 0.9909\n",
      "Epoch 9/20\n",
      "1563/1563 - 51s - loss: 0.0112 - accuracy: 0.9963 - val_loss: 0.0584 - val_accuracy: 0.9880\n",
      "Epoch 10/20\n",
      "1563/1563 - 54s - loss: 0.0093 - accuracy: 0.9966 - val_loss: 0.0380 - val_accuracy: 0.9913\n",
      "Epoch 11/20\n",
      "1563/1563 - 51s - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0390 - val_accuracy: 0.9912\n",
      "Epoch 12/20\n",
      "1563/1563 - 55s - loss: 0.0088 - accuracy: 0.9970 - val_loss: 0.0437 - val_accuracy: 0.9908\n",
      "Epoch 13/20\n",
      "1563/1563 - 52s - loss: 0.0057 - accuracy: 0.9981 - val_loss: 0.0481 - val_accuracy: 0.9906\n",
      "Epoch 14/20\n",
      "1563/1563 - 53s - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.0426 - val_accuracy: 0.9927\n",
      "Epoch 15/20\n",
      "1563/1563 - 52s - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.0590 - val_accuracy: 0.9911\n",
      "Epoch 16/20\n",
      "1563/1563 - 52s - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.0533 - val_accuracy: 0.9901\n",
      "Epoch 17/20\n",
      "1563/1563 - 54s - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.0439 - val_accuracy: 0.9925\n",
      "Epoch 18/20\n",
      "1563/1563 - 54s - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.0469 - val_accuracy: 0.9916\n",
      "Epoch 19/20\n",
      "1563/1563 - 52s - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.0468 - val_accuracy: 0.9910\n",
      "Epoch 20/20\n",
      "1563/1563 - 52s - loss: 0.0045 - accuracy: 0.9985 - val_loss: 0.0621 - val_accuracy: 0.9908\n",
      "Model: Deep Convolutional Neural Network Model (DCNN) -----------------------------------------------------\n",
      "Epoch 1/20\n",
      "1563/1563 - 191s - loss: 0.1408 - accuracy: 0.9561 - val_loss: 0.0536 - val_accuracy: 0.9845\n",
      "Epoch 2/20\n",
      "1563/1563 - 194s - loss: 0.0496 - accuracy: 0.9847 - val_loss: 0.0467 - val_accuracy: 0.9859\n",
      "Epoch 3/20\n",
      "1563/1563 - 192s - loss: 0.0363 - accuracy: 0.9890 - val_loss: 0.0294 - val_accuracy: 0.9925\n",
      "Epoch 4/20\n",
      "1563/1563 - 195s - loss: 0.0281 - accuracy: 0.9910 - val_loss: 0.0328 - val_accuracy: 0.9905\n",
      "Epoch 5/20\n",
      "1563/1563 - 195s - loss: 0.0240 - accuracy: 0.9924 - val_loss: 0.0348 - val_accuracy: 0.9894\n",
      "Epoch 6/20\n",
      "1563/1563 - 193s - loss: 0.0213 - accuracy: 0.9933 - val_loss: 0.0318 - val_accuracy: 0.9922\n",
      "Epoch 7/20\n",
      "1563/1563 - 194s - loss: 0.0171 - accuracy: 0.9946 - val_loss: 0.0286 - val_accuracy: 0.9916\n",
      "Epoch 8/20\n",
      "1563/1563 - 193s - loss: 0.0145 - accuracy: 0.9956 - val_loss: 0.0337 - val_accuracy: 0.9909\n",
      "Epoch 9/20\n",
      "1563/1563 - 194s - loss: 0.0134 - accuracy: 0.9960 - val_loss: 0.0328 - val_accuracy: 0.9933\n",
      "Epoch 10/20\n",
      "1563/1563 - 194s - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0455 - val_accuracy: 0.9918\n",
      "Epoch 11/20\n",
      "1563/1563 - 190s - loss: 0.0108 - accuracy: 0.9963 - val_loss: 0.0446 - val_accuracy: 0.9907\n",
      "Epoch 12/20\n",
      "1563/1563 - 195s - loss: 0.0122 - accuracy: 0.9963 - val_loss: 0.0516 - val_accuracy: 0.9904\n",
      "Epoch 13/20\n",
      "1563/1563 - 190s - loss: 0.0098 - accuracy: 0.9968 - val_loss: 0.0354 - val_accuracy: 0.9925\n",
      "Epoch 14/20\n",
      "1563/1563 - 197s - loss: 0.0094 - accuracy: 0.9972 - val_loss: 0.0387 - val_accuracy: 0.9924\n",
      "Epoch 15/20\n",
      "1563/1563 - 194s - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.0435 - val_accuracy: 0.9927\n",
      "Epoch 16/20\n",
      "1563/1563 - 194s - loss: 0.0084 - accuracy: 0.9974 - val_loss: 0.0623 - val_accuracy: 0.9896\n",
      "Epoch 17/20\n",
      "1563/1563 - 192s - loss: 0.0088 - accuracy: 0.9976 - val_loss: 0.0465 - val_accuracy: 0.9917\n",
      "Epoch 18/20\n",
      "1563/1563 - 193s - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.0443 - val_accuracy: 0.9908\n",
      "Epoch 19/20\n",
      "1563/1563 - 194s - loss: 0.0094 - accuracy: 0.9971 - val_loss: 0.0369 - val_accuracy: 0.9927\n",
      "Epoch 20/20\n",
      "1563/1563 - 192s - loss: 0.0074 - accuracy: 0.9976 - val_loss: 0.0455 - val_accuracy: 0.9935\n"
     ]
    }
   ],
   "source": [
    "# Train the models\n",
    "epochs = 20\n",
    "batch_size = 32\n",
    "\n",
    "\n",
    "print(\"Model: Deep Neural Network Model with a Dropout Layer (DNND) ----------------------------------------------\")\n",
    "dnnd_history = dnnd.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val), verbose=2)\n",
    "\n",
    "print(\"Model: Convolutional Neural Network Model (CNN) -----------------------------------------------------------\")\n",
    "cnn_history = cnn.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val), verbose=2)\n",
    "\n",
    "print(\"Model: Deep Convolutional Neural Network Model (DCNN) -----------------------------------------------------\")\n",
    "dcnn_history = dcnn.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_val, y_val), verbose=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "683e621f",
   "metadata": {},
   "source": [
    "## Plot Accuracies for each Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d12ce185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 400)               314000    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               40100     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 355,110\n",
      "Trainable params: 355,110\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summarize the model\n",
    "dnnd.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ed4d5f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.1120 - accuracy: 0.9803\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8d86e23ad0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0gzepNkBHjx5FQECA1rrAwED1KLgpKSk4ffq0VhozMzMEBASo0+iSnJyM2NhYrYWIiIiKL5MKgB4+fAhXV1etda6uroiNjUViYiKePHmC9PR0nWkePnyY7XFnzpwJJycn9eLl5VUg+SfjOXDgABQKBZ4/fw4AWLFiBUqXLp3jPlOnTkX9+vVf+tyGOg4RERmOSQVABWX8+PGIiYlRL3fv3jV2lgpEv379oFAooFAoYGlpCVdXV3To0AHLly/PMou5j48PFAoFjh07prX+s88+Q5s2bdTPp06dCoVCgSFDhmilCw8Ph0KhwK1btwDIEYBV51YoFHBwcEDt2rUxbNgwXLt2rUCuNyc9e/bUmszTUHQ15h89enSOk4QSEVHhM6kAyM3NTWsCRgB49OgRHB0dYWtrCxcXF5ibm+tM4+bmlu1xra2t4ejoqLUUV506dUJUVBRu3bqFnTt3om3bthgxYgS6dOmCtLQ0rbSqCS5zY2Njg2XLluUpkNm7dy+ioqJw9uxZzJgxAxEREfDz8yv0AMHW1hbly5cvlHPZ29vD2dm5UM5VlKSnp2cJrImIigqTCoCaNWuW5YcyJCQEzZo1AyAnimzUqJFWGqVSidDQUHWaks7a2hpubm7w9PREw4YN8eWXX+L333/Hzp07swwpMHjwYBw7dgx//fVXjsesUaMG2rZtq57sMyfOzs5wc3ND5cqV0bVrV+zduxf+/v748MMPkZ6ernOf5s2bZwnEHj9+DEtLS/z9998AgNWrV6Nx48ZwcHCAm5sb3nvvvRx7/umqAps1axZcXV3h4OCADz/8MMvErydPnkSHDh3g4uICJycntG7dGmfOnFFv9/HxAQB0794dCoVC/TxzFZhSqcTXX3+NChUqwNraGvXr11c34gc0pWVbtmxB27ZtYWdnBz8/vxzbsQHA3LlzUbduXZQqVQpeXl4YOnQo4uLitNIcPnwYbdq0gZ2dHcqUKYPAwEA8e/ZMna/Zs2ejatWqsLa2RsWKFTF9+nQAWasQgaylfKrXdPv27ahVqxasra1x586dXF83AHj+/Dk++ugjuLq6wsbGBnXq1MGff/6J+Ph4ODo6Zhk9etu2bShVqhRevHiR42tCRJQdowZAcXFxCA8PR3h4OADZzT08PBx37twBIKumMo6kO2TIENy8eRNjxozB5cuXsWjRImzcuBEjR45Upxk1ahSWLl2KlStXIiIiAh9//DHi4+PRv3//grsQIYD4+MJfDNSBr127dvDz88OWLVu01leqVAlDhgzB+PHjc/1PftasWdi8eTNOnTql17nNzMwwYsQI3L59G6dPn9aZpk+fPli/fr3WSMIbNmyAh4cHWrVqBUDONTVt2jScPXsW27Ztw61bt9CvX78852Pjxo2YOnUqZsyYgVOnTsHd3R2LFi3SSvPixQsEBQXh0KFDOHbsGKpVq4bOnTurf4RPnjwJAAgODkZUVJT6eWbz5s3DnDlz8L///Q/nzp1DYGAg3nzzzSwlaBMmTMDo0aMRHh6O6tWro3fv3llK6TIyMzPD/PnzcfHiRaxcuRL79u3DmDFj1NvDw8PRvn171KpVC0ePHsWhQ4fwxhtvqAPP8ePHY9asWZg0aRIuXbqEtWvXZmlPl5uEhAR8++23+OWXX3Dx4kWUL18+19dNqVTitddew+HDh7FmzRpcunRJPcN9qVKl0KtXLwQHB2udJzg4GG+//TYcHBz0yh8RkZowov379wsAWZagoCAhhBBBQUGidevWWfapX7++sLKyEpUrVxbBwcFZjrtgwQJRsWJFYWVlJZo2bSqOHTumV75iYmIEABETE5NlW2Jiorh06ZJITEzUrIyLE0KGI4W7xMXpdV1BQUGia9euOrf17NlT+Pr6qp97e3uL77//XkRHRwsHBwexatUqIYQQI0aM0LonU6ZMEX5+fkIIIXr16iXatWsnhBAiLCxMABCRkZFCCCEiIyMFABEWFpbl3BEREQKA2LBhg868RUdHCwsLC/H333+r1zVr1kyMHTs222s9efKkACBevHghhNC81549eyaEECI4OFg4OTlpHW/o0KFax/D391dfmy7p6enCwcFB/PHHH+p1AMTWrVu10mV8jYQQwsPDQ0yfPl0rTZMmTdTnV71Wv/zyi3r7xYsXBQARERGRbX4y27Rpk3B2dlY/7927t2jRooXOtLGxscLa2losXbpU5/bMr58QWe9xcHCwACDCw8NzzFfm12337t3CzMxMXLlyRWf648ePC3Nzc/HgwQMhhBCPHj0SFhYW4sCBAzrT6/yMElGJkNPvd2ZGLQFSzQ+UeVFVxaxYsQIHDhzIsk9YWBiSk5Nx48YNnf/lDx8+HLdv30ZycjKOHz8Of3//gr8YEyeEgEKhyLK+XLlyGD16NCZPnoyUlJQcj/HNN9/gn3/+wZ49e/Q+NwCd51floWPHjvj1118ByJLCo0ePok+fPuo0p0+fxhtvvIGKFSvCwcEBrVu3BgB1aWJuIiIisrxPMlebPnr0CIMGDUK1atXg5OQER0dHxMXF5fkcgByj4sGDB2jRooXW+hYtWiAiIkJrXb169dSP3d3dASDHar29e/eiffv28PT0hIODAz744AM8ffoUCQkJADQlQLpEREQgOTk52+15ZWVlpZVvIPfXLTw8HBUqVED16tV1HrNp06aoXbs2Vq5cCQBYs2YNvL298eqrr75UXomoZDOpNkBFlp0dEBdX+IudncEuISIiApUqVdK5bdSoUUhMTMxSJZRZlSpVMGjQIIwbN06viS9VP/zZnR+Q1WC//fYbUlNTsXbtWtStWxd169YFIEcUDwwMhKOjI3799VecPHkSW7duBYBcgzZ9BAUFITw8HPPmzcORI0cQHh4OZ2dng54jI0tLS/VjVXCYXVXkrVu30KVLF9SrVw+bN2/G6dOn1SOsq/Jna2ub7bly2gbI6jUAWvc1NTVV53EyB7K5vW65nRsABg4cqP7HKDg4GP379882YCYiygsGQIagUAClShX+YqAfgH379uH8+fPo0aOHzu329vaYNGkSpk+fnmuj08mTJ+Pq1atYv359ns6tVCoxf/58VKpUCQ0aNMg2XdeuXZGUlIRdu3Zh7dq1WqU/ly9fxtOnTzFr1iy0atUKNWvW1HvqE19fXxw/flxrXeYhAA4fPoxPP/0UnTt3Ru3atWFtbY0nT55opbG0tMy2MTcAODo6wsPDA4cPH85y7Fq1aumV54xOnz4NpVKJOXPm4JVXXkH16tXx4MEDrTT16tXLtrddtWrVYGtrm+32cuXKAQCioqLU61Rt93KT2+tWr1493Lt3L8dhCd5//33cvn0b8+fPx6VLlxAUFJSncxMRZYcBUAmTnJyMhw8f4v79+zhz5gxmzJiBrl27okuXLloNzjMbPHgwnJycsHbt2hyP7+rqilGjRmH+/Pk6tz99+hQPHz7EzZs3sX37dgQEBODEiRNYtmwZzM3Nsz1uqVKl0K1bN0yaNAkRERHo3bu3elvFihVhZWWFBQsWqI87bdq0XF4JbSNGjMDy5csRHByMq1evYsqUKbh48aJWmmrVqmH16tWIiIjA8ePH0adPnyylFz4+PggNDcXDhw/Vvasy++KLL/Dtt99iw4YNuHLlCsaNG4fw8HCMGDFCrzxnVLVqVaSmpqpfg9WrV+Pnn3/WSjN+/HicPHkSQ4cOxblz53D58mX89NNPePLkiXrIgzFjxmDVqlW4ceMGjh07hmXLlqmP7+XlhalTp+LatWvYsWMH5syZk6e85fa6tW7dGq+++ip69OiBkJAQREZGYufOnVo948qUKYO33noLX3zxBTp27IgKFSrk+7UiIgJg3EbQRZXejaBNRFBQkLqhuYWFhShXrpwICAgQy5cvF+np6VppVY2gM1q7dq0AkG0jaJWYmBjh4uKisxG0arGzsxO+vr5i6NCh4tq1a3nK/19//SUAiFdffTXLtrVr1wofHx9hbW0tmjVrJrZv367V6Dq3RtBCCDF9+nTh4uIi7O3tRVBQkBgzZozWtZ05c0Y0btxY2NjYiGrVqolNmzZleZ22b98uqlatKiwsLIS3t7fO1yg9PV1MnTpVeHp6CktLS+Hn5yd27typ3q6rwfizZ88EALF///5sX5+5c+cKd3d3YWtrKwIDA8WqVauyNFw+cOCAaN68ubC2thalS5cWgYGB6u3p6enim2++Ed7e3sLS0lJUrFhRzJgxQ73voUOHRN26dYWNjY1o1aqV2LRpU5ZG0Jlf07y+bk+fPhX9+/cXzs7OwsbGRtSpU0f8+eefWscJDQ0VAMTGjRuzfQ2EMO3PKBG9HH0aQReZyVCLEk6GSlT0rF69GiNHjsSDBw9gZWWVbTp+RolKLn0mQ7UopDwREeVLQkICoqKiMGvWLHz00Uc5Bj9ERHnFNkBEVKTNnj0bNWvWhJubG8aPH2/s7BBRMcEAiIiKtKlTpyI1NRWhoaGwt7c3dnaIqJhgAEREREQlDgMgIiIiKnEYABEREVGJwwCIiIiIShwGQERERFR4cpguqDAxACIiIqKCo1QCJ04AU6cCTZsC/foZO0cAOBAiERERGdq//wJ79gB//QXs2gU8fqzZFhkpgyIz45bBsASoBOnXrx8UCgUUCgUsLS3h6uqKDh06YPny5VAqlVppfXx8oFAossyI/tlnn6FNmzbq51OnToVCocCQIUO00oWHh0OhUODWrVsAgFu3bqnPrVAo4ODggNq1a2PYsGG4du1ajvlW7ZvX2cfzSqFQYNu2bQY9JhFRgUlPB44fB6ZMAZo0AXx8gDfeACZOBH77DUhLM17eMs+q1aED0Ls3sHq1DH4cHYG33waWLwfOnTN68AOwBKjE6dSpE4KDg5Geno5Hjx5h165dGDFiBH777Tds374dFhaat4RqhvCDBw/meEwbGxssW7YMn3/+OapVq5Zj2r1796J27dpISEjA+fPnMW/ePPj5+eGPP/5A+/btDXKNxV1KSgqngyAqiTp0APbv1153+zbw55+AkxPQo4dm/S+/yL/16wO1awO2tobPT0wMsHevLOXZvx84fx4oVUpu69QJSEkBOneWS/PmgKWl4fPwEowfglGhsra2hpubGzw9PdGwYUN8+eWX+P3337Fz506sWLFCK+3gwYNx7Ngx/PXXXzkes0aNGmjbti0mTJiQ6/mdnZ3h5uaGypUro2vXrti7dy/8/f3x4YcfIj2bhnGVKlUCADRo0AAKhUKrBOqXX36Br68vbGxsULNmTSxatEi9LSUlBcOHD4e7uztsbGzg7e2NmTNnApAlXADQvXt3KBQK9XNdxo4di+rVq8POzg6VK1fGpEmTkJqaqpXmjz/+QJMmTWBjYwMXFxd0795dvS05ORljx46Fl5cXrK2tUbVqVSxbtgwAsGLFCpQuXVrrWNu2bYNCoVA/nzp1KurXr49ffvlFa4LPXbt2oWXLlihdujScnZ3RpUsX3LhxQ+tY9+7dQ+/evVG2bFmUKlUKjRs3xvHjx3Hr1i2YmZnh1KlTWul/+OEHeHt7ZykRJKJColQCZ84A06cDbdoA8fGaba+8IgOdd94BgoOBgweBhQuBgQOBXr2ADN8bmDULGDRIlhQ5OMggqE8f4LvvsgZReSUEcPGiPEbbtoCLi6ZUJzISCA3VpP36axkQffst0Lp1kQt+AJYAGZbqjWpnp3kjpqQAqamAhQVgbZ01ra2tpigwNVWmNzcHMs5irSutAbVr1w5+fn7YsmULBg4cqF5fqVIlDBkyBOPHj0enTp1glsO5Z82ahSZNmuDUqVNo3Lhxns9tZmaGESNGoHv37jh9+jSaNm2aJc2JEyfQtGlTdemRqvTj119/xeTJk7Fw4UI0aNAAYWFhGDRoEEqVKoWgoCDMnz8f27dvx8aNG1GxYkXcvXsXd+/eBQCcPHkS5cuXR3BwMDp16gRzc/Ns8+jg4IAVK1bAw8MD58+fx6BBg+Dg4IAxY8YAAHbs2IHu3btjwoQJWLVqFVJSUrSCxr59++Lo0aOYP38+/Pz8EBkZiSdPnuT5NQKA69evY/PmzdiyZYs6r/Hx8Rg1ahTq1auHuLg4TJ48Gd27d0d4eDjMzMwQFxeH1q1bw9PTE9u3b4ebmxvOnDkDpVIJHx8fBAQEIDg4WOt+BQcHo1+/fjneayIysJgYICRElqTs3Ak8fKjZtn8/0KWLfDxuHPDVV9rBxKuvZj2eELI0KCxMLk+eAJcuyWXtWqBRIyDjPz/z5gHu7oCfH1C1qvwN0mXpUuCjj7TX1aihKeVp1UqzPofv1CJDUBYxMTECgIiJicmyLTExUVy6dEkkJiZm3VG+7YSIjtas++YbuW7gQO20dnZyfWSkZt3338t1772nndbFRa6/cCHf1ySEEEFBQaJr1646t/Xs2VP4+vqqn3t7e4vvv/9eREdHCwcHB7Fq1SohhBAjRowQrVu3VqebMmWK8PPzE0II0atXL9GuXTshhBBhYWECgIj87/oiIyMFABEWFpbl3BEREQKA2LBhg868ZbdvlSpVxNq1a7XWTZs2TTRr1kwIIcQnn3wi2rVrJ5RKpc7jAhBbt27VuS0n3333nWjUqJH6ebNmzUSfPn10pr1y5YoAIEJCQnRuDw4OFk5OTlrrtm7dKjJ+NKdMmSIsLS1FdMb3lQ6PHz8WAMT58+eFEEIsXrxYODg4iKdPn+pMv2HDBlGmTBmRlJQkhBDi9OnTQqFQqO+ZqcrxM0pU1KxfL4SFheb3AxCiVCkhunYVYvFiIR4+fLnjK5VC3L8vxI4dQkyfLsQ77wgxaZJme3KyEJaWmnPb2QnxyitCDB4sRMeOQqxcqUl79aoQ1tZCdOokxPz5Qly79nJ5KwA5/X5nxhIgAgAIIbSqXVTKlSuH0aNHY/LkyejZs2eOx/jmm2/g6+uLPXv2oHz58nqdG4DO82cnPj4eN27cwIcffohBgwap16elpcHJyQmAbPTdoUMH1KhRA506dUKXLl3QsWPHPJ9DZcOGDZg/fz5u3LiBuLg4pKWlwdHRUb09PDxcKw8ZhYeHw9zcHK1bt9b7vBl5e3ujXLlyWuuuXbuGyZMn4/jx43jy5Im62urOnTuoU6cOwsPD0aBBA5QtW1bnMbt164Zhw4Zh69at6NWrF1asWIG2bdvmWB1IZJJSU4E7d4AbN2QpekCAZlvv3kBcHODsrFnKlpV/PT1l2xVDiIsD9u2TpTwBAbLqCJBtdNLSspakZKwxeBkKBeDhIZfOnXXnq39/IDxcNk5OSACOHZMLANjbA337ysfVqsneXXZ2hsmbkTEAMqS4OPk345vjiy+Azz6TVWAZRUfLvxkbpg0bJutsMxcd/teTqkAasf0nIiJC3dYms1GjRmHRokVa7Wt0qVKlCgYNGoRx48ap27jk9dwAsj2/LnH/vdZLly6Fv7+/1jZVFVHDhg0RGRmJnTt3Yu/evXj33XcREBCA3377Lc/nOXr0KPr06YOvvvoKgYGBcHJywvr16zFnzhx1Gtsc7ktO2wBZBSgy9Z7I3L4IAEqpGhZm8MYbb8Db2xtLly6Fh4cHlEol6tSpg5SUlDyd28rKCn379kVwcDDeeustrF27FvPmzctxHyKT8MMPwOXLwM2bMui5fVsz+J6/v3YAtHevrCLSxc9PBgYqDRrItKoAKWPAVLmybIujcucO8OKFpmrr4EHZxAGQx1AFQNWry/YzxvrHo2xZYPFi+Tg9Hbh2TRMMubgAr7+unb6YBD8AAyDD0vEjBSsrueQlraWl7oZiutIa0L59+3D+/HmMHDlS53Z7e3tMmjQJU6dOxZtvvpnjsSZPnowqVapg/fr1eTq3UqnE/PnzUalSJTRo0EBnGlWbn4yNpF1dXeHh4YGbN2+iT58+2R7f0dERPXv2RM+ePfH222+jU6dO+Pfff1G2bFlYWlpm2/Ba5ciRI/D29tZq4H379m2tNPXq1UNoaCj69++fZf+6detCqVTi4MGDCMj4pfufcuXK4cWLF4iPj1cHOXnp7v/06VNcuXIFS5cuRav/6t0PHTqUJV+//PKL+np1GThwIOrUqYNFixYhLS0Nb731Vq7nJjIKIWTbmBs35KIKbm7cAMqVA7Zv16RdsEBuz8jGRgYpNWpor1+6VHbTfvpUlm48fapZMvdqvXtXrr93L2v+/Py0A6AOHYCrV7XTVK4sS2G6ddOsUyiMF/xkZm4O1Kwpl169jJ2bAscAqIRJTk7Gw4cPtbrBz5w5E126dEFfVTGnDoMHD8b333+PtWvXZilxycjV1RWjRo3Cd999p3P706dP8fDhQyQkJODChQv44YcfcOLECezYsSPbhsjly5eHra0tdu3ahQoVKsDGxgZOTk746quv8Omnn8LJyQmdOnVCcnIyTp06hWfPnmHUqFGYO3cu3N3d0aBBA5iZmWHTpk1wc3NT97ry8fFBaGgoWrRoAWtra5QpUybLuatVq4Y7d+5g/fr1aNKkCXbs2IGtW7dqpZkyZQrat2+PKlWqoFevXkhLS8Nff/2FsWPHwsfHB0FBQRgwYIC6EfTt27cRHR2Nd999F/7+/rCzs8OXX36JTz/9FMePH8/SG0+XMmXKwNnZGUuWLIG7uzvu3LmDcePGaaXp3bs3ZsyYgW7dumHmzJlwd3dHWFgYPDw80KxZMwCAr68vXnnlFYwdOxYDBgzItdSIyKCSkoBnz2TgkfHvs2eyqurTTzVpa9cG/istziJzlfvAgbIqp0oVuVSuLBv56mrcnzEYyc2pU9oBUsbFzU07bVqaDLpatZJBz2uvydIePar6qYAVdIMkU5TvRtBFXFBQkAAgAAgLCwtRrlw5ERAQIJYvXy7S09O10qoaQWe0du1aASDbRtAqMTExwsXFRWcjaNViZ2cnfH19xdChQ8W1PDSkW7p0qfDy8hJmZmZa5//1119F/fr1hZWVlShTpox49dVXxZYtW4QQQixZskTUr19flCpVSjg6Oor27duLM2fOqPfdvn27qFq1qrCwsBDe3t7ZnvuLL74Qzs7Owt7eXvTs2VN8//33WRoub968WZ0PFxcX8dZbb6m3JSYmipEjRwp3d3dhZWUlqlatKpYvX67evnXrVlG1alVha2srunTpIpYsWZKlEXTm11gIIUJCQoSvr6+wtrYW9erVEwcOHMjSsPvWrVuiR48ewtHRUdjZ2YnGjRuL48ePax1n2bJlAoA4ceJEtq+BKTHlz6hJyvTdIfbvF2LNGiEWLBDi66+F+OwzIYKChHjjDSEydxaoW1e78W/GpXx57bTt2wthZiaEj498PGiQELNmCbFpkxAZPtdUsunTCFohRObhGyk2NhZOTk6IiYnRauwKAElJSYiMjNQaj4XIlE2bNg2bNm3CuXPnjJ0Vg+BntIAIIdsjnj0r24ioFjs72b1apV49Of6LLq6u2l28W7cGDh0CypSRS9mymseurrItj8rjx3IMHA4CSjnI6fc7M1aBEZVQcXFxuHXrFhYuXIhvvvnG2NmhoiQ9XbszxnvvyYa8MTFZ07q6aj/395dtcjIGM6rHLi7aaXftkr2d8jLuVKZekEQviwEQUQk1fPhwrFu3Dt26dcOAAQOMnZ2SJSVF9lI6d06OrGthIduxlC8vf+gbNZKlHYXh6dOspTqPHwMPHmjaqyQmyuDH0hKoU0c2+K1fXy6+vtrHW7o07+dmmzMyIlaB6cAqMCLTVaQ+o0IAjx7JAKNFCzmmCgCMHg1kGEohi6NH5bQHgJzT6fvvNcGRKlBSPW/TRnbFzkteMjbAnTxZTqegq0cTILtxe3nJx2fPyn1r1mQVFBVprAIjIipsycmyl9LZs7Jk59w5+fjxY7n94EHNtAX16skSnnr1ZImKQiHTRUfLJWOPoshIzTQGuhw7pgmAFiwAZs7UDpbs7GS+zp2TbXhUQyIkJmqCnypVtEt16tcHKlTQnMPPzzCvEVERwgAon1hwRlQ0FfhnUwggKkoGFHXrytGCATkh5NChWdObmcnxZDJOavnee8AHH+StS/SQIUC7djIwyhgkqZ67u2vSPngg8xYVpftYZ8/KSSwB2VW8a1cZhOXynzJRccQASE+W/w1UmJCQwDFTiIqghIQEAJrP6ku7cQP45x9Nic65c5qRg3/5BfjwQ/m4Xj2gdGlZWlKvnuZv7dpZR8/NPDJ8Try8NFVRuRk9Ws4UnjFYio2V48/Ur689CGCNGlkHBSQqQRgA6cnc3BylS5dG9H9TWdjZ2ek1hxURFQwhBBISEhAdHY3SpUtnO7CmXrZtA7p3z7rezEwGFRnP0ayZHMjPmN8HqqkZiChXDIDywe2/+nlVEERERUfp0qXVn1G9Xb8uS07+Gykb7drJ6iE/P6BhQ03JTq1aWXsw5aUrNxEVGQyA8kGhUMDd3R3ly5fXOXElERmHpaWl/iU/KSnA778DS5bIiTH9/ICwMFmS4+goe0MVVpd0Iio0DIBegrm5uWGK2Ymo8N28KcesCQ6WXdUBGfR4eABxcYCDg1zH4IeoWGIAREQlz9SpwFdfaZ67ucnGzAMHFp2ZuYmoQDEAIqLi7/Zt2WZHNWt448byb8eOwEcfAW+8IUc5JqISg632iKh4SkuTbXs6dwYqVQLmz9dse+012b19927grbcY/BCVQCwBIqLi5e5dOT7PsmXA/fua9bdvax6bmwOVKxd+3oioyGAARETFR58+wPr1gFIpn5crB/TvDwwaBFStaty8EVGRwgCIiApOTIwMSHbsABYt0swvtWIFsHZt9vstWqQJWNatkz21sjN3rpxPC5Dd1pVKOd3DRx8B3boB1taGuBIiKmYYABGRYSmVwP79MmjZvBlISpLr//c/TZrr14GQkOyP8eKF5nFkZM5pnz/XPB43DvjsM07xQES5YgBERIbx4AGweDGwcqV2e5vatYH339ee4fztt4GaNbM/lre35vGbbwIVK2aftnp13fsREeVAITiteRaxsbFwcnJCTEwMHDlLMlHeHDkCtGghHzs5Ab17AwMGyC7nnC+PiAqBPr/fLAEiIv0IIYOd4GCgbFlg9my5vlkzGfAEBMi2N5nnyiIiKkJYAqQDS4CIdLh/H1i1SjZgvnpVrnNyAqKiGOwQUZHAEiAiMpydO4EFC+Sggaru5XZ2wDvvyC7m7GVFRCaIARARZSWEpt3OoUMyCAKAli1l0PPOO5rJQomITBADICKSnjwBfv1Vtu355hugSxe5vn9/+bdfP6BaNaNlj4jIkBgAEZVU0dHA6dPAmTPAsWOyiis1VW5btUoTAFWtCkyfbrx8EhEVAAZARCVBVBTw7BlQq5Z8npAAuLtr2vSoNGokS3x69y78PBIRFSIGQETFiRByQEJVyc7p03KJigKaNwcOH5bp7OwAX18gPR1o2FAGPh06AHXrGjf/RESFhAEQkakSAvj3X8DZWbOucWMZ+GRmZiartzI2bg4LAywtCyevRERFDAMgIlMgBHDnjqZER1W6o1AADx9qghpPTyA8XFZ1NWokl4YNgfr1gVKltI/J4IeISjAGQERF3dGjwNChMrDJzNxcNmZ2dZXPf/4ZKF1aVnEREVG2zIydgR9//BE+Pj6wsbGBv78/Tpw4kW3a1NRUfP3116hSpQpsbGzg5+eHXbt2aaV58eIFPvvsM3h7e8PW1hbNmzfHyZMnC/oyiArOkiUy+LG0BBo0AAYOBBYtAo4fB+LiNMEPAHh4MPghIsoDo5YAbdiwAaNGjcLPP/8Mf39//PDDDwgMDMSVK1dQvnz5LOknTpyINWvWYOnSpahZsyZ2796N7t2748iRI2jQoAEAYODAgbhw4QJWr14NDw8PrFmzBgEBAbh06RI8PT0L+xKJ9CeE7KWlqrL67jv5eOpUwMXFqFkjIioujDoXmL+/P5o0aYKFCxcCAJRKJby8vPDJJ59g3LhxWdJ7eHhgwoQJGDZsmHpdjx49YGtrizVr1iAxMREODg74/fff8frrr6vTNGrUCK+99hq++eabPOWLc4GR0dy4Iau7rKyA7ds5izoRkR70+f02WhVYSkoKTp8+jYCAAE1mzMwQEBCAo0eP6twnOTkZNjY2WutsbW1x6NAhAEBaWhrS09NzTJPdcWNjY7UWokKVkgLMmAHUqQPs2QOEhGgmHCUiIoMzWgD05MkTpKenwzVj+wUArq6uePjwoc59AgMDMXfuXFy7dg1KpRIhISHYsmULoqKiAAAODg5o1qwZpk2bhgcPHiA9PR1r1qzB0aNH1Wl0mTlzJpycnNSLl5eX4S6UKDf//CPb9kyYACQlAQEBwPnzQI0axs4ZEVGxZfRG0PqYN28eqlWrhpo1a8LKygrDhw9H//79YWamuYzVq1dDCAFPT09YW1tj/vz56N27t1aazMaPH4+YmBj1cvfu3cK4HCrpYmNlg+ZXXwUuXQLKlQPWrJElQJxzi4ioQBktAHJxcYG5uTkePXqktf7Ro0dwc3PTuU+5cuWwbds2xMfH4/bt27h8+TLs7e1RuXJldZoqVarg4MGDiIuLw927d3HixAmkpqZqpcnM2toajo6OWgtRgbO0BA4ckI8HDQIuXwb69GG7HyKiQmC0AMjKygqNGjVCaGioep1SqURoaCiaNWuW4742Njbw9PREWloaNm/ejK5du2ZJU6pUKbi7u+PZs2fYvXu3zjREhe7GDTn9BADY2sqZ1//5R3Z1L1vWuHkjIipBjFoFNmrUKCxduhQrV65EREQEPv74Y8THx6N///4AgL59+2L8+PHq9MePH8eWLVtw8+ZN/PPPP+jUqROUSiXGjBmjTrN7927s2rULkZGRCAkJQdu2bVGzZk31MYmMIjkZ+OorOULzokWa9a1aAS1bGi9fREQllFHHAerZsyceP36MyZMn4+HDh6hfvz527dqlbhh9584drbY7SUlJmDhxIm7evAl7e3t07twZq1evRunSpdVpYmJiMH78eNy7dw9ly5ZFjx49MH36dFhy2H8ylgMHgCFDgCtX5PNDh4BPPjFqloiISjqjjgNUVHEcIDKIJ0+A0aOBlSvlczc3YN484J132M6HiKgA6PP7zbnAiArCjh1AUBDw9KkMdj7+GJg+Xc7TRURERscAiKggVKgAPH8O1KsnGzj7+xs7R0RElIFJjQNEVGQlJsrxe1T8/IDQUODUKQY/RERFEAMgopeRng6sWAFUrw68/rocwVmldWs51g8RERU5rAIjyg8hgF27gLFjNUGPlxfw+LFx80VERHnCEiAifZ0+Lefr6txZBj+lSwOzZ8tu7u3aGTt3RESUBywBItJHQgLQsSPw77+AlZUcz+fLLzmKMxGRiWEARJSbZ89kKY9CAdjZyYDn7Flg2jTA29vYuSMionxgFRhRdhIS5Ng93t5yXB+VUaOAVasY/BARmTAGQESZpaUBv/wCVKsGTJwIvHgBrF2r2c5RnImITB4DICIVIYDt2+UYPoMGAQ8eAD4+wJo1ciEiomKDbYCIVAYNApYtk4/LlpWlP0OHAtbWxs0XEREZHEuAiFTefBOwsQHGjQNu3ABGjmTwQ0RUTLEEiEqmR4+Ar74CatQARoyQ6954A4iMlLO2ExFRscYAiEqWuDhgzhzgu++A+HigTBngww8Be3vZuJnBDxFRicAqMCoZ0tOBn38GqlYFpk6VwU+TJsDWrTL4ISKiEoUlQFT8JSUBffoAW7bI51WqADNnAm+/zS7tREQlFAMgKv5OngR+/11OXTF7NvDxx/IxERGVWAyAqPhr1QpYvhyoWBFo08bYuSEioiKAARAVTxcuALa2sroLAPr2NW5+iIioSGEjaCp+/v5blvoEBgLR0cbODRERFUEMgKh42bwZ6NgReP4ccHUFLFjISUREWTEAouJj4ULgnXeA5GSgWzdg7145pQUREVEmDIDI9AkBjB8PfPKJfDxkCPDbb7INEBERkQ4MgMj0zZwJzJolH0+bBixaBJibGzdPRERUpDEAItM3aBDg6wv88oucwZ2DGxIRUS7YQpRMU2KipoqrXDkgPJyDGxIRUZ6xBIhMz/XrQN26ssRHhcEPERHpgQEQmZZTp4DmzYEbN+S0FsnJxs4RERGZIAZAZDp27ZJTWTx+DDRoIAc8tLY2dq6IiMgEMQAi07ByJfDGG0B8PNChA3DwIODmZuxcERGRiWIAREXfrFlAv35AWhrQpw/w55+Ag4Oxc0VERCaMARAVfWlp8u8XXwCrVrHBMxERvTR2g6eib8IEoFkzoH17Y+eEiApTaKhs59eypbFzQsUQS4Co6Hn2DBg+HIiLk88VCgY/RCXNL78AAQFAq1bAp5+yxycZHAMgKlru3ZNfeD/+CAwcaOzcEJExbN8OfPSR5vmCBbIU+No14+WJih0GQFR0XLwov+QuXgQ8PIAvvzR2joiosB0+DPTsCSiVwIABwB9/AM7OQFgY0LAhsGaNsXNofEol8M038nXat09OAk16YwBERcPdu7Lk5949Oa/X0aNAvXrGzhURFaaLF4EuXYCkJPl38WL5NzwcePVVWS3+wQeyV6iqirykSUkB+vYFJk0CNm6UzQMaNQLWrgVSU42dO5PCAIiMTwhgyBDZ9qdhQ+DQIaBiRWPniogK0507QGAg8Py5HO19wwbA4r9+OhUqyJKOKVMAMzM5LljjxsDZs0bNcqGLiwPefBP49Vf52vTsKedEDAuTQ4RUqQLMnQvExho7pyaBARAZ37p1wF9/ye7ta9YAZcsaO0dEVJiePpXBz/37QK1astrLzk47jbk5MHWq7Bnm4QFcuQL4+wOLFpWMKqDHj4F27YDdu+Vr88cfwPr1svR82jSgfHn5+PPPAS8vYMwYWaJO2WIARMbn7Q1Urw5MnCirv4iKCiGAb7+VP7yq8ajIsOLjZTXX5cuypGfXrpz/CWrTRpb8vP667Bk2bBjw9tuyBLm4unVLDgVw8qRsD7VvH9Cpk9zm7Cy/O2/fBpYuBWrWlCVA330HVKokq8tKWklZHimEKAmhs35iY2Ph5OSEmJgYODo6Gjs7JUNSkiza5iCHVJRs2AD06iUf9+kjB+I04/+NBpOaCnTvDuzYAZQpI6u/a9XK275CAN9/D4wbJ4/j7S1Lk5s1K9g8F7Zz52SwExUlmwbs2QPUqJF9eqVSlqj/739yyiCVDh2A0aPlX4Wi4PNtJPr8fvOTTMaT8T9qGxsGP1S0PHsGjBihef7rr8DHH5eM6pbCIAQwaJAMfmxt5d+8Bj+A/BEfNUr2GqtcWZaAtGolp85RKgsu34Xp779l4++oKKBOHeDIkZyDH0AG6F26AAcOACdOyHZCZmZASIisZvTzk4F8SkqhXEJRxgCIjOPFC6BuXWDOHFYtUNE0dizw6JGsUli5Uv7gLlki21gwCHp548fL19XcXPZmym/JTZMmshFwr15Aero87muvyXtnyrZuBTp2BGJiZGD3zz+Ap6d+x2jSRLYTun5dBvOlSgHnzwNBQbJ6bPZs2ei8pBKURUxMjAAgYmJijJ2V4mv4cCEAIXx8hIiLM3ZuiLT9/bd8fwJCHDwo1y1bplk3aZJx82fq5s7VvJbLlxvmmEqlEL/8IoStrTyuq6sQISGGOXZhW7xYCDMzeR3dugmRkGCY4/77rxAzZwrh7q55/e3thRg5UohbtwxzDiPT5/ebAZAODIAK2OHDQigU8sO3e7exc0OkLSlJiJo15ftz4EDtbfPna344Zs0yTv5M3a+/al7DmTMNf/yLF4WoU0ceX6EQYvx4IVJTDX+egqBUCvHVV5rXZ9Cggsl7UpIQwcFC1K6tOZe5uRC9ewtx6pThz1eIGAC9JAZABSgpSQhfX/mBCwoydm6IslL9ALm6yv+YM5s5U/OjsXBh4edPH0ePCjFggBB79hg7J9Lu3UJYWsrXbsQI+YNfEBIShBg8WHOfmjcX4vbtgjmXoaSlCTF0qHYpY0G9PipKpRA7dwrRvr3mvIAQbdsKsWOHEOnpBXv+AqDP7zd7genAXmAFaPJkzZgVEREc84eKlitX5AjkKSmyR5GqB1hmkybJqQgAYPlyoH//wstjXgghR1H+9FPN6MCvvy57BtWsaZw8nTwJtG0ru7337i3H/CroHnUbN8qG1rGxQOnS8l51716w58yPpCQ5wvVvv8m2ZgsWyO79hSksTLbJXL9etqUCZPssR0fAyUn/vxkfW1oW2mXo9fudnwhr3759+dnNZLAEqICcOyeEhYX8D2PjRmPnhkibUilE69by/dmpU87/fSuVQnz2mUxrZibE+vWFls1cJSbKUh/Vf/NNmmg+dxYWQnzyiRBPnhRunq5cEcLFReYhIECI5OTCO/fNm0I0bap5PYYNk69RUfH8uRBt2si8WVkZ/7vx9m0hPv9cCAcH7VKhl1lsbGSJarVqQjRuLES7dkJ07y7E998bPPsFXgJkbW2NChUqoH///ggKCoKXl1f+QrUiiiVABSQ4WM7w3Lmz7OFQjMeiIBO0fDnw4YdylN0LF2QvmZwIId/PS5fKaQm2bAHeeKNw8pqdu3eBHj1kaYuZGTBzJvDFF3IW9S++kLOsA3LMncmTgaFDC374iagoObXFrVtyzqr9+wEHh4I9Z2YpKXKwwO++k8/r15clHbl1KS9oUVGyx9rZs/I12bZNjvZcFKSkyNGnY2NlT7T8/I2Pz/kc770nh5cwoAIvAXr8+LGYO3eu8PPzExYWFqJjx45iw4YNIrkwo/oCxBKgAnT+vBD37xs7F0TaHj0SokwZ+d/qd9/lfb+0NCH69NH8927MXkf79wtRrpzMS9myutv97N0rRL16mv/Mq1UT4vffC66tyfPnmvNVrSpfZ2P66y9NSVSpUkKsXGm8vFy9KkSlSpr2ZmfOGC8vBSU1Vbaji4wU4uxZIf75R4g//xRi7VohfvqpQDrBFGoj6NOnT4vhw4cLZ2dn4ezsLD755BMRHh7+soc1KgZARCXMe+/JH6L69fXvdZOaKovzASHs7OSXfGFSKmW3cnNzzTVERmafPi1NiKVL5Y+uKhBq104IQ39vJyZqqhRdXYW4ccOwx8+v+/dlI1/VtTdqJBscHz5ceL3FTp3SBKtVqghx/XrhnLcEKPReYPfv3xdTpkwR1tbWolSpUsLc3Fy0bNlSXLhwwRCHL3QMgAxIqZS9PU6fNnZOiHTbuVPTlufkyfwdIylJthsChHB0zP9x9BUXJ7suq37M339fiPj4vO0bGyu7iFtba7qMDxwoRFTUy+crLU2IHj3kcR0chAgLe/ljGlJamhDTpmmCRtVSpowQ774rxyZ68KBgzr1njxx7BxCiYUMhHj4smPOUUIUSAKWkpIhNmzaJ1157TVhYWIhXXnlFLF26VMTFxYnIyEjRp08f4evrm9/DGxUDIANasUIz2NazZ8bODZG2uDg5GCcgGzW/jPh4TYlH2bKy0X9BunFDU71kYSHHKMpPVVZkpBA9e2oPjDdjRv4bCiuVQgwZoqkWLMqdZqKi5Hg4PXtqqkAzLn5+QowbJ8SBA0KkpLz8+dat0wwD0L69EPyNMbgCD4BUVV5ly5YVI0aMEOfPn8+SJioqSigUivwc3ugYABnIw4eaLxUOGld0paTIsW2WLTPMl7wp+eIL+f708hLixYuXP15srBD+/ppqnytXXv6YuuzcqflslS+vGa36ZRw+rN1byttb9m7TN6hSjaOkUAixadPL56uwpKYKceSIrA5r0kQzWKtqcXCQVZ1Llghx547+x583T3Osd9+VpYZkcAUeALVr106sXbtWJOVwA1NTU8WBAwfyc3ijYwBkIO++Kz/sDRqYzkisJdHUqZov5urVhfjtt4IfgK0oCAvTVIFs32644/77ryw5AISoUCHn9jj6UiqFmD5d8+Ps7y/EvXuGO356uhBr1sh8q94TzZoJcexY3vb/+WfNfj/+aLh8GUN0tHwt+vTRNJzOuNSqJbuLh4TkHMwolbIUSbXfJ5+Y5ACDpoIjQb8k9Qv44IH2D0Fysiwyz/xmj4uTS8Y3dUqKXJe5GFmftPHxcn1ammZdaqpcl3luGH3SJiTI9RmDkrQ0/dNmbmuQmCjXp6TIniWqdhWHD+ecViU9XfP6ZJSUJNdl7GWoT1qlUpNW1/3UJ21e7r0h3ie67qch3ieq+6lKe/q0JgjIOO5H06ZC7NqV93uf3/eJSnb382XfJxnvZ+bXrGFDea1vv501rT73XlfaO3c002lUqSLEtWsv/x3x4IEQb76puUeDB8tgqyC+I548EWLiRM28WoAQvXoJcfly9u+pTZs081dNmJD3e2+I90lBf0ckJgpx4oQQX38txCuvZC0dsrMTonNn2Rj90iXt1/f99zXppk+Xxzel74i8pC1C3xEFHgDNmDFDLFu2LMv6ZcuWiVl6VnUsXLhQeHt7C2tra9G0aVNx/PjxbNOmpKSIr776SlSuXFlYW1uLevXqiZ07d2qlSUtLExMnThQ+Pj7CxsZGVK5cWXz99ddCqcd/tOoXEJD/Bah88418E2eeH8jOTq7P+J/e99/Lde+9p51W9Z9ExgbiS5bIdV27aqf19pbrT5zQrFuzRjOYWEa1asn1+/dr1m3dKtc1b66dtnFjuf7PPzXr9uzR1HlnpGrTkHFwrkOHNN1aM+rcWfOfn4eHfNyvn/zr4aGd9u235fqMUwlcvSrXOTlppw0Kkutnz9asu3dP0/YhI9VQ8lOmaNY9e6b5Asr4gRo9Wq4bPVqzLiVFkzZjm6UpU+S6oUO1z6caYC7jf+GzZ8t1maf6cHKS669e1axbuFD7R1hF9fplbDwaHCzXde6snbZqVbn+0CHNuo0b5brWrbXTqkom9uyRX74Z5wJq0ECIyZNl9+CMX+5z52r2379f899vRgEBcv2aNZp1J05oqlIy6tpVrl+yRLPuwgW5zsVFO62qd1bGAdMiIzU/OhkNHCjXf/ONZl10tOY6MlK9r62tNY1d4+I0aTN+yX75pVw3YoT2MVRps/uOuHdP081Z9YOZ3++IjPNDWVnJXlxCFPx3xMqV8jOc8Qff1VW7ulD1Wqo+C4MGyZ5wOX1HBAdr1oWFme53RO/eQri5ZS0dql5diE8/1VwvoD28gql8R6j8+adc17ixdtrmzeX6rVs164z8HaFPAJSvccgXL16MmjqGU69duzZ+/vnnPB9nw4YNGDVqFKZMmYIzZ87Az88PgYGBiI6O1pl+4sSJWLx4MRYsWIBLly5hyJAh6N69O8LCwtRpvv32W/z0009YuHAhIiIi8O2332L27NlYsGCB/hdK+XPwIPDgAVCtmhwojoqmKVOAixflUPWAHPb+q6+A69flAHkqo0bJYfpv3TJKNg3u7l3gyBH5uG1bwN29YM7j6QmEhgIVKsifQEAODqevbduAGTPkYxsb4O+/gYEDDZbNHDk7ywFMT50C6taV6x49AqpXl+uVSs1gd2lpQLduwKJFJWeQ00WL5HddWBjQvr1cp1AAV68C8+cDf/2lSdu7t3HySNnLNUTSwdraWty8eTPL+hs3bghra+s8H6dp06Zi2LBh6ufp6enCw8NDzMxmhmB3d3exMNPkg2+99Zbo06eP+vnrr78uBgwYkGOazJKSkkRMTIx6uXv3rowgWQWWv2LLpCRZCnTwoGkUb5fEKrC//9ZUV/z2m+60589rujKrSh4+/VSIW7dMtwpMqdRUI/n7a7+WhqwCy5j28mXNmC/+/prSk9y+I1JSZFWS6vVv0UK+9hkV5ndEaqqcyV3Va05VaqgqAWnZUnMcfe59cfuOePRIiM2bZUnYq6/KNkKm+B3BKjDdqlatKlavXp1l/apVq0SlSpXydIzk5GRhbm4utmYsOhNC9O3bV7z55ps69ylbtqz45ZdftNb16dNHeGcoPps+fbrw9vYWV/7rfREeHi7Kly8v1mQsdstkypQpAkCWhY2gqViKi9MUifftm3v6U6e0Z4t2cJBjqGT+IjIFmzfLa7C01K5iKmhnz2p6bbVtm/WHJLN//9WMK6SqfisqPfSSkmR1jqOjJn916nCYCyoSCjwA+vbbb4Wzs7NYvny5uHXrlrh165ZYtmyZcHZ2FjNmzMjTMe7fvy8AiCNHjmit/+KLL0TTpk117tO7d29Rq1YtcfXqVZGeni727NkjbG1thZWVlTpNenq6GDt2rFAoFMLCwkIoFIpc85RtCRADIP3s2GGaP4olzSefyB8tT0/9frT27JH/8at+9Nzc5HD2ReWHOTfPnwvh7i7zPmFC4Z//xAlNQ/PXXst+QtCzZ4WoXFmms7UVQsc/m0VCdLR8L3XubNieaEQvocADIKVSKcaMGSNsbGyEmZmZMDMzE3Z2duKrr77K8zHyEwBFR0eLrl27CjMzM2Fubi6qV68uhg4dKmxsbNRp1q1bJypUqCDWrVsnzp07J1atWiXKli0rVqxYkee8sRt8Ppw5I3sTVapU+DNNU96FhmoCmPzMw5OeLgdzU/1AA3I+qY0bi37XeVXj12rVjDcb+N9/a3pW9eiRdXiIdes0nSp8fIreCMpERVyhdYN/8eKFOHHihDh//nyOYwLpkp8qMJXExERx7949dSBWK0Nr8woVKmRpJzRt2jRRo0aNPOeNAZCeUlM1XYrfecfYuaHsxMRoeg199NHLHSs5WYgFCzRtWwA5eFxRHfX3yBFNT6bQUOPmZfdu2Z4KEOKDD2RQmZoqx5RRvZYdO/IfCaJ8KPBeYCr29vZo0qQJ6tSpA2tra732tbKyQqNGjRAaGqpep1QqERoaimbNmuW4r42NDTw9PZGWlobNmzeja9eu6m0JCQkwM9O+LHNzcyiVSr3yR3qYOxc4cwYoUwbIb287IYCVK7V7TZBhff45cPs2UKkS8N13L3csKytg+HDgxg1g6lSgVCng5EmgXTvgtdeAs2cNkmWDSE0FBg+W77F+/WQejaljR2DjRtnrbvVqYNAgIDAQmDNHbh83Tn4OnJ2Nm0+i4i6/UdbJkyfFF198IXr27Cm6d++uteTV+vXrhbW1tVixYoW4dOmSGDx4sChdurR4+N/kcB988IEYN26cOv2xY8fE5s2bxY0bN8Tff/8t2rVrJypVqiSeZWjHEBQUJDw9PcWff/4pIiMjxZYtW4SLi4sYM2ZMnvPFEiA9XL0qhI1N1rE99KUaZwcQYsAAtiUytB07NOPRFMQI7Q8fCjF8uGYsGIVCjqCro7dooZsxQzN+SFEqVVm3Tnt8nVKlZI88Isq3Aq8CW7dunbC0tBRdunQRVlZWokuXLqJ69erCyclJ9OvXT69jLViwQFSsWFFYWVmJpk2bimMZhlxv3bq1CMowUNSBAweEr6+vsLa2Fs7OzuKDDz4Q9+/f1zpebGysGDFihKhYsaJ6IMQJEyaI5OwaHOrAACiPlEoh2rSRX94dOuS/Dcgvv2h+BFQ/CL6+BT+ZZEnx9Kmm8e/IkQV7ruvXtWcnt7SUPZgyDhZYmK5d08x2XhQbEy9bJocjqFZNiIsXjZ0bIpNX4AFQ3bp11e1s7O3txY0bN4RSqRSDBg0SkydPzs8hixQGQHmkClzs7PL/n/5ff2mmYpg4UbYhUf1Y29jIXkZFvXFtUacaKbVGjdy7XxvK6dMyKM7YdX7cOCEy/cNSoJRKTff9lwnQC9r9+9n3CCMivRR4AGRnZyci/xvSvWzZsuLcf/+pX7p0Sbi5ueXnkEUKA6A8un9fiG7dtKdK0MepU5ppF/r21fxARUfLbsKqH8+33+YYI/n122/yNTQzEyKHaWYKTEiIEI0aaZcI9esnB1ksaCtXagLp69cL/nxEZHQF3gi6TJkyePHiBQDA09MTFy5cAAA8f/4cCQkJL98wiUyDhwewZQvw2Wf67xsZCbz+uhxGv0MHYOlSzfD55coBf/4pG4VaWgK//QY0aAAcP27Q7Bd70dHAkCHy8fjxQNOmhZ+HgADgxAng99+BVq1kg+QVK+S0Cp06AXv3aqaJMKQnT+QUHoCc8qNKFcOfg4hMWr4CoFdffRUhISEAgHfeeQcjRozAoEGD0Lt3b7RXzYdCxdeTJ5rHCoX+8/48fSp7Cj16BPj5yQDHyko7jZmZ/AE7fFj2Wrp1C2jZEpg9W84/RDkTQs7D9uQJUK8eMHmy8fJiZga8+aacw+rYMeCdd+S63btl8NugAbBmjQyODGX0aPk+q1tX9n4jIspEIYT+/379+++/SEpKgoeHB5RKJWbPno0jR46gWrVqmDhxIsqUKVMQeS00sbGxcHJyQkxMDBwdHY2dnaLl2TOgVi058d+PP2om0syrxERZKnDkCODlJX8QPTxy3icmRnZj3rhRPg8MBFatAsqXz981lARr1sgJTC0tZfd0Pz9j50jbzZvADz8Ay5YBqlLjChWAESNkt3B931cZhYbK95hCId9nr7xikCwTUdGn1++3vvVrqampYuXKlequ6sUR2wDloH9/2a6iZs2sE/nlJi1NiLfekvuXLq3fXExKpRBLl2pG0XVzE2LvXv3OX1LcvSuEk5N8nb75xti5ydnTp0JMny6Eq6t2g+nPPxfizh39j5eQoJnnLMNEy0RUMhR4I2hbW1txK/OsxMUIA6BshIRouqofOqTfvkqlnElcNat4fseiuXBBiNq1NfmYMCHrdAIlmVIpRGCgZmRmU3ltkpJkl3BfX00gZGEhxxI6cybvx/nyS808Z/z8EpU4Bd4IumnTpggPD8/PrmSqlErgk0/k46FDgRYt9Nv/+++B+fPl41WrgNat85eP2rVlo9pBg+TP5PTpQJs2wJ07+TtecbN0qWxbY2MjX2cLC2PnKG+srYEBA4ALF4AdO4C2bYG0NODXX4GGDWWV1q5dOTeYvnBBthED5IjkrL4mohzkqw3Qxo0bMX78eIwcORKNGjVCqVKltLbXq1fPYBk0BrYB0iEkRA7h7+AA3Lun34/Lhg1Ar17y8f/+Z7hGqRs2yLZBsbFyGo7ly4Fu3QxzbFMUGSkb/cbHy+lJRo40do5ezunTsifgxo1AerpcV6eOfP/07i2DJhWlUjaSP3pUvge2bjVKlonIuAq0DZAQQigUiiyLmZmZ+q+pYxWYDt26yaqF4cP12+/gQc3Ej59+avjB6G7ckFU9qmqTTz4x3kzfxpSeLkTr1vI1aNVKPi8ubt2SI1jb22vus5ubnOLi339lmkWLNO2H7t41bn6JyGj0+f3OVwnQ7du3c9zu7e2t7yGLFJYAZRIfD1SuLMeVuXQJ8PXN236XLsmqsufPgbfe0kwAaWgpKcCECbJ0CQDq15elQ9WrG/5cRdW8eXI8plKl5ESkxXHcm+fPgSVL5LU+eCDXlSolJzhdvVqWBC5YICdpJaISSZ/f73wFQMUdAyAdkpKA/fvl+D158eAB0KyZbJvTvLkc8M7WtmDz+NdfQFCQHPumVCngp59kV/Di7soVGfQlJclrVg1+WFylpADr18uA9/x5zXp/fzluVEEE2URkEgo8AFq1alWO2/v27avvIYsUBkAvKTYWePVVWRJRvboci8XZuXDOff8+8P77wIED8nlQELBwIWBvXzjnL2xpabLty/HjclDB3bv1H5jSVAkh26bNmQPcuAFs2ybbCBFRiVXgAVDmgQ5TU1ORkJAAKysr2NnZ4d9//9X3kEUKA6AMHj+WwYtZHjsMpqbKKS5CQuRAhceOyZGcC1N6OjBjBjB1qmwcW6OGrBIraoMBGsLMmcCXX8qBA8+fl4NLEhGVUPr8fuerG/yzZ8+0lri4OFy5cgUtW7bEunXr8pVpKqJ69ABq1pS9a3IjhOyeHhIiq6B27Cj84AeQVSCTJskqO09PWUXk7y9Hrn7xQlahFIea33Pn5DxXgBxigMEPEVGeGbQN0KlTp/D+++/j8uXLhjqkUbAE6D/nz8t5pMzNgdu3ZTCRk8mTgWnTZPrt24HOnQsnnzl5+lQ2kv3zT+31CoXsRq1abGxe/rmTkxyTqGLFgr+ulBQZ1IWHA127ym7fJaXqi4goG/r8fht0lDQLCws8UPXOINP300/yb7duuQc/S5fK4AcAfv65aAQ/gKy+275dlpBMnAjExcn1QshGw0lJhj+nr69sLN6pk5wB3cbG8Of45hsZ/Dg7A4sXM/ghItJTvkqAtm/frvVcCIGoqCgsXLgQXl5e2Llzp8EyaAwsAYJsyOzpKQOGffvkyLzZ2bFDlkKkp8tSoK++Krx86kOpBJKTZdCTnKxZDPX87l3ZGDnjbPV2dvK169RJBkWG6J5+8qTsYZeeLocWeOedlz8mEVExUOCNoM0yNYhVKBQoV64c2rVrhzlz5sDd3V3fQxYpDIAALFoEDBsm2/9cupR9CcOpU3Jai4QEWdW0fHnJLo149kx2+d+1Sy6ZS0SrVtWUDrVpIwMkfSQmAo0aARERcnRttrkjIlLjOEAvqcQHQELI7sSXLsmqI9UcYJndvClLIqKj5TQZf/4JWFoWbl6LMiFkO6qdO2UwdOiQ7LauYm0tg0dVQFSjRu7B4+jRstu3m5uc+6qwhhcgIjIBDIBeUokPgI4elYMX2tnJEgwnp6xpnjyRaa5dk4Pw/f23nCeMshcbK6sTd+2SQVHmCVx9fDRVZe3aZR276J9/ZMAkBPDHH0CXLoWWdSIiU1DgAVCPHj3QtGlTjB07Vmv97NmzcfLkSWzatEnfQxYpJT4AEkIGNNeuAQMHZt2emAi0by8DpYoV5Vg/Jl7tWeiEAC5f1pQOHTwoe3apWFrKBtSqgMjHR45jdPOmnDV92TKjZZ2IqKgq8ACoXLly2LdvH+rWrau1/vz58wgICMCjR4/0PWSRUuIDoJykp8tGt1u3AqVLy1Ge8zo3GGUvPl6OXr1zp1xu3tTebm8vG6R7eclqNV2lckREJVyBd4OPi4uDlZVVlvWWlpaIjY3NzyGpqBAi+3YoQsgJN7duBaysZPdyBj+GUaqUHEH79dfl82vXNFVl+/druu8vX87gh4jIAPI1EnTdunWxYcOGLOvXr1+PWrVqvXSmyEjS0uTAh599JmfeBmSJT0QE8OuvQP/+cl4tAFizRlbRUMGoVk02Pv/rL+Dff4E9e2Qj6oAAY+eMiKhYyFcJ0KRJk/DWW2/hxo0baNeuHQAgNDQU69atM/n2PyXatm2yZ9Ht2zIYOnsWCAuT1TMZzZ3LsWcKk62tnOiUiIgMJt+9wHbs2IEZM2YgPDwctra2qFevHqZMmYLWrVsbOo+FrkS0AUpLk93cz5wBTp+WS+ZB/FTs7IAGDYCGDWV3d/Y+IiKiIojd4F9SsQuAUlOBixdlkKMKeM6ezX4aiKZNZRf3hg3loHs1asj5vYiIiIqwAm8EffLkSSiVSvj7+2utP378OMzNzdG4ceP8HJYMITlZVmNlLNk5d067i7WKg4MmyLl0STa67dJFjjFDRERUjOUrABo2bBjGjBmTJQC6f/8+vv32Wxw/ftwgmSM9rVkjx+1JTs66zclJE+yolipVADMz2cZHNdnpsGGFm2ciIiIjyFcAdOnSJTRs2DDL+gYNGuDSpUsvnSnKh7Q0YMIEGfyUKSMDnIwBT+XK2XdvX7cOiImRaTp2LNx8ExERGUG+AiBra2s8evQIlStX1lofFRUFC4t8HZJe1h9/yKkVnJ3lrOS2tnnft3174IsvNCVCRERExVy+fu06duyI8ePHIyYmRr3u+fPn+PLLL9GB3XWNY/58+XfwYP2CHwCoVAmYPRv46CPD54uIiKgIylcvsPv37+PVV1/F06dP0aBBAwBAeHg4XF1dERISAi8vL4NntDCZXC+wc+fkPFHm5kBkpJwugYiIqITR5/c7XyVAnp6eOHfuHGbPno1atWqhUaNGmDdvHs6fP2/ywY9JUpX+9OihX/Dz5Ikc0HDvXjnNBRERUQmR7wY7pUqVQsuWLVGxYkWk/NfFeufOnQCAN9980zC5o9w9eSKnqQCATz/Vb9/ly4HffpOlRidPGj5vRERERVS+AqCbN2+ie/fuOH/+PBQKBYQQUGToYZSenm6wDFIufvlFDmjYsKEcvDCv0tOBn3+Wj4cNy76HGBERUTGUryqwESNGoFKlSoiOjoadnR0uXLiAgwcPonHjxjhw4ICBs0jZSk0FfvxRPh4xQr8gZvduWfJTpgzQs2fB5I+IiKiIylcJ0NGjR7Fv3z64uLjAzMwM5ubmaNmyJWbOnIlPP/0UYWFhhs4n6bJtG3DvHlC+vP5BzKJF8m///nKuLyIiohIkXyVA6enpcHBwAAC4uLjgwYMHAABvb29cuXLFcLmjnKkaP3/0EWBtnff9IiOBv/6Sj4cMMXy+iIiIirh8lQDVqVMHZ8+eRaVKleDv74/Zs2fDysoKS5YsyTI4IhWQM2eAQ4cACwv9g5jFi2Wvr44dgWrVCiZ/RERERVi+AqCJEyciPj4eAPD111+jS5cuaNWqFZydnbFhwwaDZpCyoSr9efddwMNDv319feUydKjh80VERGQC8jUQoi7//vsvypQpo9UbzFQV+YEQo6PleD8pKcCxY0CmSWnzRAi5cOoLIiIqJvT5/TbYxF1ly5Y11KEoN0uWyOCnadP8BT+A7DFWDIJVIiKi/OC//6YmJUXTg0vfgQ8vXgSWLQMSEgyfLyIiIhPCAMjUbN4MREUBbm5yGgt9/PADMHCgHPiQiIioBGMAZGpUjZ8//hiwssr7fs+fa6bMGDDA4NkiIiIyJQyATMmJE7LRs6WlHPtHHytXAomJQN26QMuWBZM/IiIiE8EAyJSoSn969QJcXfO+n1KpaTc0dCgbPxMRUYnHAMhUREUBGzfKxyNG6Lfvvn3A1auAgwPQp4/h80ZERGRiGACZisWL5eSnzZsDjRrpt6+q9KdvXxkEERERlXAMgExBcjLw88/ysb5d39PTZdd5hUI2nCYiIiLDDYRIBWjjRuDRI8DTE3jrLf32NTcH/vxTzhpfoULB5I+IiMjEsASoqBMCmDdPPh46VPYAyw8GP0RERGoMgIq6Y8eA06cBa2tg0CD99j1/Xpb8EBERkRYGQEWdquv7e+8B5crpt+8nnwA+PsC6dQbPFhERkSljAFSU3b8P/PabfJyfeb8OHpSPW7UybL6IiIhMXJEIgH788Uf4+PjAxsYG/v7+OHHiRLZpU1NT8fXXX6NKlSqwsbGBn58fdu3apZXGx8cHCoUiyzLM1ObA+uknIC0NePVVoH59/fcFgK5d2f6HiIgoE6MHQBs2bMCoUaMwZcoUnDlzBn5+fggMDER0dLTO9BMnTsTixYuxYMECXLp0CUOGDEH37t0RFhamTnPy5ElERUWpl5CQEADAO/pOHmpMSUly7B9A/9KfFy+AVavk46FDDZsvIiKiYkAhhBDGzIC/vz+aNGmChQsXAgCUSiW8vLzwySefYNy4cVnSe3h4YMKECVqlOT169ICtrS3WrFmj8xyfffYZ/vzzT1y7dg0KHdNAJCcnIzk5Wf08NjYWXl5eiImJgaOj48teYv6sWAH07w9UrAjcuAFY6DFiwU8/ycCnRg0gIoJTXxARUYkQGxsLJyenPP1+G7UEKCUlBadPn0ZAQIB6nZmZGQICAnD06FGd+yQnJ8PGxkZrna2tLQ4dOpTtOdasWYMBAwboDH4AYObMmXByclIvXl5e+bwiA8nY9X3YMP2CHyE0Iz9//DGDHyIiIh2MGgA9efIE6enpcM00saerqysePnyoc5/AwEDMnTsX165dg1KpREhICLZs2YKoqCid6bdt24bnz5+jX79+2eZj/PjxiImJUS93797N9zUZxKFDQHg4YGsLDByo3743bwKRkXLfoKACyR4REZGpM3obIH3NmzcP1apVQ82aNWFlZYXhw4ejf//+MDPTfSnLli3Da6+9Bg8Pj2yPaW1tDUdHR63FqFRd399/HyhbVr99q1SRvcf++AMoXdrgWSMiIioOjBoAubi4wNzcHI8ePdJa/+jRI7i5uencp1y5cti2bRvi4+Nx+/ZtXL58Gfb29qhcuXKWtLdv38bevXsxUN9SFGO6cwfYulU+/uST/B3DyQlo395weSIiIipmjBoAWVlZoVGjRggNDVWvUyqVCA0NRbNmzXLc18bGBp6enkhLS8PmzZvRtWvXLGmCg4NRvnx5vP766wbPe4FZtEhOYNquHVC3rrFzQ0REVCwZvQps1KhRWLp0KVauXImIiAh8/PHHiI+PR//+/QEAffv2xfjx49Xpjx8/ji1btuDmzZv4559/0KlTJyiVSowZM0bruEqlEsHBwQgKCoKFPo2IjSkhAVi6VD7Wt+s7IBtM164NbNpk2HwREREVM0aPDHr27InHjx9j8uTJePjwIerXr49du3apG0bfuXNHq31PUlISJk6ciJs3b8Le3h6dO3fG6tWrUTpTe5e9e/fizp07GDBgQGFezstZuxb49185fUWXLvrvf+0acOkSkKFLPxEREWVl9HGAiiJ9xhEwGCGAevWACxeA//0P+Pxz/Y/RsCEQFgbs3Al06mT4PBIRERVhJjMOEGVw4IAMfuzsgA8/zN8xnjyRf52dDZYtIiKi4ogBUFGh6voeFJT/7utPn8q/Li4GyRIREVFxxQCoKIiMBLZvl4+HD8/fMRIS5AIwACIiIsoFA6Ci4McfAaUS6NABqFUrf8dQlf5YWgL29obLGxERUTFk9F5gJV5cHPDLL/LxiBH5P05yMlCnjpw3jPN/ERER5YgBkLGtWQPExMgpLF57Lf/HqVoVOH/ecPkiIiIqxlgFZkxCaBo/f/IJkM18ZkRERGRY/MU1pr17gYgI2WYnh9nqiYiIyLAYABmTqvSnf385genLmDtXNqD+3/9ePl9ERETFHAMgY7l+HdixQz7Ob9f3jCIjZWnS8+cvfywiIqJijgGQsfz4o2wD9NprQPXqL388jgJNRESUZwyAjOHFC2D5cvn4Zbq+Z8RRoImIiPKMAZAxrFwJxMYCNWrIwQ8NQVUCxACIiIgoVwyACptSCSxYIB8bsuu7qgSIVWBERES5YgBU2PbsAa5eBRwdgb59DXdclgARERHlGUeCLmzz5sm/H34IODgY5pipqXIk6SdPWAJERESUBwyACtOVK8CuXXKurmHDDHdcS0vg3DnDHY+IiKiYYxVYYdq8Wf7t0kWW2BAREZFRsASoMI0fD7z6KlCqlLFzQkREVKKxBKgwKRRAy5ZAgwaGPe727XIajE8/NexxiYiIiikGQMXB3btyGowHD4ydEyIiIpPAAKg44BhAREREemEAVBxwDCAiIiK9MAAqDhgAERER6YUBUHHAKjAiIiK9MAAqDlgCREREpBcGQMVB+fKAh4f8S0RERLniQIjFwc6dxs4BERGRSWEJEBEREZU4DICIiIioxGEAZOouXgR8fYFu3YydEyIiIpPBNkCm7uFD4PJlwIyxLBERUV7xV9PUsQs8ERGR3hgAmTrVIIgMgIiIiPKMAZCpU5UAcRRoIiKiPGMAZOpYBUZERKQ3BkCmjlVgREREemMAZOocHQFPT8DNzdg5ISIiMhkKIYQwdiaKmtjYWDg5OSEmJgaOjo7Gzg4RERHlgT6/3ywBIiIiohKHARARERGVOAyATFlSElCzJtCyJZCQYOzcEBERmQxOhWHKnj4FrlwBrl8HbG2NnRsiIiKTwRIgU6bqAu/sDCgUxs0LERGRCWEAZMo4CCIREVG+MAAyZZwGg4iIKF8YAJkyjgJNRESULwyATBlLgIiIiPKFAZAps7KS02B4eBg7J0RERCaFU2HowKkwiIiITA+nwiAiIiLKAQMgIiIiKnEYAJmygACgRQs5GjQRERHlGafCMGXHjwNxcYAZ41giIiJ98JfTVCUny+AH4DhAREREemIAZKpUgyCamQFOTsbNCxERkYkxegD0448/wsfHBzY2NvD398eJEyeyTZuamoqvv/4aVapUgY2NDfz8/LBr164s6e7fv4/3338fzs7OsLW1Rd26dXHq1KmCvIzCl3EiVFaBERER6cWov5wbNmzAqFGjMGXKFJw5cwZ+fn4IDAxEdHS0zvQTJ07E4sWLsWDBAly6dAlDhgxB9+7dERYWpk7z7NkztGjRApaWlti5cycuXbqEOXPmoEyZMoV1WYWDE6ESERHlm1EHQvT390eTJk2wcOFCAIBSqYSXlxc++eQTjBs3Lkt6Dw8PTJgwAcOGDVOv69GjB2xtbbFmzRoAwLhx43D48GH8888/+c6XSQyEuGkT8O67QMuWwEtcKxERUXFhEgMhpqSk4PTp0wgICNBkxswMAQEBOHr0qM59kpOTYWNjo7XO1tYWhw4dUj/fvn07GjdujHfeeQfly5dHgwYNsHTp0hzzkpycjNjYWK3FJFSoIKfCICIiIr0YLQB68uQJ0tPT4erqqrXe1dUVDx8+1LlPYGAg5s6di2vXrkGpVCIkJARbtmxBVFSUOs3Nmzfx008/oVq1ati9ezc+/vhjfPrpp1i5cmW2eZk5cyacnJzUi5eXl2EusiC98w5w9y6wfr2xc0JERGRyTKr17Lx581CtWjXUrFkTVlZWGD58OPr37w+zDI2AlUolGjZsiBkzZqBBgwYYPHgwBg0ahJ9//jnb444fPx4xMTHq5e7du4VxOURERGQkRguAXFxcYG5ujkePHmmtf/ToEdzc3HTuU65cOWzbtg3x8fG4ffs2Ll++DHt7e1SuXFmdxt3dHbVq1dLaz9fXF3fu3Mk2L9bW1nB0dNRaiIiIqPgyWgBkZWWFRo0aITQ0VL1OqVQiNDQUzZo1y3FfGxsbeHp6Ii0tDZs3b0bXrl3V21q0aIErmaaGuHr1Kry9vQ17AcY2eLCcBmPvXmPnhIiIyOQYdSqMUaNGISgoCI0bN0bTpk3xww8/ID4+Hv379wcA9O3bF56enpg5cyYA4Pjx47h//z7q16+P+/fvY+rUqVAqlRgzZoz6mCNHjkTz5s0xY8YMvPvuuzhx4gSWLFmCJUuWGOUaC0x4OHDyJJCYaOycEBERmRyjBkA9e/bE48ePMXnyZDx8+BD169fHrl271A2j79y5o9W+JykpCRMnTsTNmzdhb2+Pzp07Y/Xq1ShdurQ6TZMmTbB161aMHz8eX3/9NSpVqoQffvgBffr0KezLK1gcB4iIiCjfjDoOUFFlEuMAOToCL17ImeCrVzd2boiIiIzOJMYBopeQkiKDH4AlQERERPnAAMgUZZwINUP1HxEREeUNAyBTpGr/U7YsJ0IlIiLKB6M2gqZ8SkqS02CUK2fsnBAREZkkBkCmqEkTOQ0GERER5QvrT4iIiKjEYQBEREREJQ4DIFM0axbQvDmwYoWxc0JERGSSGACZokuXgKNHgcePjZ0TIiIik8QAyBSpusE7Oxs3H0RERCaKAZApUg2EyFGgiYiI8oUBkCniRKhEREQvhQGQKWIVGBER0UthAGRqUlOB2Fj5mCVARERE+cIAyNTExgJeXkCpUpwIlYiIKJ84FYapcXYG7twxdi6IiIhMGkuAiIiIqMRhAEREREQlDgMgU7Nxo5wG45tvjJ0TIiIik8UAyNRcvy6nwYiMNHZOiIiITBYDIFPDMYCIiIheGgMgU8NpMIiIiF4aAyBTwxIgIiKil8YAyNRwHjAiIqKXxgDI1LAKjIiI6KUxADI1dnZyGgxWgREREeUbp8IwNefOyb9CGDcfREREJowlQKZKoTB2DoiIiEwWAyAiIiIqcRgAmZKTJ4FmzYCPPzZ2ToiIiEwa2wCZkrt3gWPHADPGrURERC+Dv6SmhF3giYiIDIIBkCnhKNBEREQGwQDIlHAUaCIiIoNgAGRKVFVgLAEiIiJ6KQyATAlLgIiIiAyCAZApsbDgNBhEREQGwG7wpmTbNvmX02AQERG9FJYAmSJOg0FERPRSGAARERFRicMAyFQ8eQK88grwxhusAiMiInpJbANkKqKjgePHgbJlWQVGRET0klgCZCo4BhAREZHBMAAyFRwDiIiIyGAYAJkKBkBEREQGwwDIVLAKjIiIyGAYAJkKlgAREREZDAMgUyEEYG/PEiAiIiIDUAjBQWUyi42NhZOTE2JiYuDo6Gjs7GgTgt3giYiIdNDn95slQKaGwQ8REdFLYwBEREREJQ4DIFPRoQPw+utAVJSxc0JERGTyOBWGKUhLA0JDZfsfC94yIiKil8USIFPw7Jmm8XOZMsbODRERkcljAGQKVGMAlS7NEiAiIiIDYABkClSjQHMQRCIiIoMoEgHQjz/+CB8fH9jY2MDf3x8nTpzINm1qaiq+/vprVKlSBTY2NvDz88OuXbu00kydOhUKhUJrqVmzZkFfRsHhKNBEREQGZfQAaMOGDRg1ahSmTJmCM2fOwM/PD4GBgYiOjtaZfuLEiVi8eDEWLFiAS5cuYciQIejevTvCwsK00tWuXRtRUVHq5dChQ4VxOQVDFQBxFGgiIiKDMHoANHfuXAwaNAj9+/dHrVq18PPPP8POzg7Lly/XmX716tX48ssv0blzZ1SuXBkff/wxOnfujDlz5mils7CwgJubm3pxMeXSk5QUOQ2GKV8DERFREWLUFrUpKSk4ffo0xo8fr15nZmaGgIAAHD16VOc+ycnJsLGx0Vpna2ubpYTn2rVr8PDwgI2NDZo1a4aZM2eiYsWK2R4zOTlZ/TwmJgaAHFK7SHj/fbkolUBRyRMREVERo/rdztMsX8KI7t+/LwCII0eOaK3/4osvRNOmTXXu07t3b1GrVi1x9epVkZ6eLvbs2SNsbW2FlZWVOs1ff/0lNm7cKM6ePSt27dolmjVrJipWrChiY2N1HnPKlCkCABcuXLhw4cKlGCx3797NNQYx6mSoDx48gKenJ44cOYJmzZqp148ZMwYHDx7E8ePHs+zz+PFjDBo0CH/88QcUCgWqVKmCgIAALF++HImJiTrP8/z5c3h7e2Pu3Ln48MMPs2zPXAKkVCrx77//wtnZGQoDz70VGxsLLy8v3L17t+hNtGpgvNbiqyRdL6+1+CpJ11tSrlUIgRcvXsDDwwNmZjm38jFqFZiLiwvMzc3x6NEjrfWPHj2Cm5ubzn3KlSuHbdu2ISkpCU+fPoWHhwfGjRuHypUrZ3ue0qVLo3r16rh+/brO7dbW1rC2ts6yT0FydHQs1m/CjHitxVdJul5ea/FVkq63JFyrk5NTntIZtRG0lZUVGjVqhNDQUPU6pVKJ0NBQrRIhXWxsbODp6Ym0tDRs3rwZXbt2zTZtXFwcbty4AXd3d4PlnYiIiEyX0XuBjRo1CkuXLsXKlSsRERGBjz/+GPHx8ejfvz8AoG/fvlqNpI8fP44tW7bg5s2b+Oeff9CpUycolUqMGTNGnWb06NE4ePAgbt26hSNHjqB79+4wNzdH7969C/36iIiIqOgx+rwKPXv2xOPHjzF58mQ8fPgQ9evXx65du+Dq6goAuHPnjlY9XlJSEiZOnIibN2/C3t4enTt3xurVq7WqrO7du4fevXvj6dOnKFeuHFq2bIljx46hXLlyhX15WVhbW2PKlClZqtyKI15r8VWSrpfXWnyVpOstSdeaV0ZtBE1ERERkDEavAiMiIiIqbAyAiIiIqMRhAEREREQlDgMgIiIiKnEYABWAH3/8ET4+PrCxsYG/vz9OnDiRY/pNmzahZs2asLGxQd26dfHXX38VUk7zb+bMmWjSpAkcHBxQvnx5dOvWDVeuXMlxnxUrVkChUGgtmed1K4qmTp2aJd81a9bMcR9TvKcqPj4+Wa5XoVBg2LBhOtOb0n39+++/8cYbb8DDwwMKhQLbtm3T2i6EwOTJk+Hu7g5bW1sEBATg2rVruR5X3898YcnpelNTUzF27FjUrVsXpUqVgoeHB/r27YsHDx7keMz8fB4KQ273tl+/flny3alTp1yPWxTvbW7Xquvzq1Ao8N1332V7zKJ6XwsSAyAD27BhA0aNGoUpU6bgzJkz8PPzQ2BgIKKjo3WmP3LkCHr37o0PP/wQYWFh6NatG7p164YLFy4Ucs71c/DgQQwbNgzHjh1DSEgIUlNT0bFjR8THx+e4n6OjI6KiotTL7du3CynHL6d27dpa+c48+W5GpnpPVU6ePKl1rSEhIQCAd955J9t9TOW+xsfHw8/PDz/++KPO7bNnz8b8+fPx888/4/jx4yhVqhQCAwORlJSU7TH1/cwXppyuNyEhAWfOnMGkSZNw5swZbNmyBVeuXMGbb76Z63H1+TwUltzuLQB06tRJK9/r1q3L8ZhF9d7mdq0ZrzEqKgrLly+HQqFAjx49cjxuUbyvBSpPs5ZSnjVt2lQMGzZM/Tw9PV14eHiImTNn6kz/7rvvitdff11rnb+/v/joo48KNJ+GFh0dLQCIgwcPZpsmODhYODk5FV6mDGTKlCnCz88vz+mLyz1VGTFihKhSpYpQKpU6t5vqfQUgtm7dqn6uVCqFm5ub+O6779Trnj9/LqytrcW6deuyPY6+n3ljyXy9upw4cUIAELdv3842jb6fB2PQda1BQUGia9eueh3HFO5tXu5r165dRbt27XJMYwr31dBYAmRAKSkpOH36NAICAtTrzMzMEBAQgKNHj+rc5+jRo1rpASAwMDDb9EVVTEwMAKBs2bI5pouLi4O3tze8vLzQtWtXXLx4sTCy99KuXbsGDw8PVK5cGX369MGdO3eyTVtc7ikg39Nr1qzBgAEDcpwY2FTva0aRkZF4+PCh1r1zcnKCv79/tvcuP5/5oiwmJgYKhSLXuRD1+TwUJQcOHED58uVRo0YNfPzxx3j69Gm2aYvLvX306BF27NihcyLwzEz1vuYXAyADevLkCdLT09WjWKu4urri4cOHOvd5+PChXumLIqVSic8++wwtWrRAnTp1sk1Xo0YNLF++HL///jvWrFkDpVKJ5s2b4969e4WYW/35+/tjxYoV2LVrF3766SdERkaiVatWePHihc70xeGeqmzbtg3Pnz9Hv379sk1jqvc1M9X90efe5eczX1QlJSVh7Nix6N27d46TZer7eSgqOnXqhFWrViE0NBTffvstDh48iNdeew3p6ek60xeXe7ty5Uo4ODjgrbfeyjGdqd7Xl2H0qTDI9A0bNgwXLlzItb64WbNmWpPcNm/eHL6+vli8eDGmTZtW0NnMt9dee039uF69evD394e3tzc2btyYp/+qTNmyZcvw2muvwcPDI9s0pnpfSSM1NRXvvvsuhBD46aefckxrqp+HXr16qR/XrVsX9erVQ5UqVXDgwAG0b9/eiDkrWMuXL0efPn1y7Zhgqvf1ZbAEyIBcXFxgbm6OR48eaa1/9OgR3NzcdO7j5uamV/qiZvjw4fjzzz+xf/9+VKhQQa99LS0t0aBBA1y/fr2AclcwSpcujerVq2ebb1O/pyq3b9/G3r17MXDgQL32M9X7qro/+ty7/HzmixpV8HP79m2EhITkWPqjS26fh6KqcuXKcHFxyTbfxeHe/vPPP7hy5Yren2HAdO+rPhgAGZCVlRUaNWqE0NBQ9TqlUonQ0FCt/5AzatasmVZ6AAgJCck2fVEhhMDw4cOxdetW7Nu3D5UqVdL7GOnp6Th//jzc3d0LIIcFJy4uDjdu3Mg236Z6TzMLDg5G+fLl8frrr+u1n6ne10qVKsHNzU3r3sXGxuL48ePZ3rv8fOaLElXwc+3aNezduxfOzs56HyO3z0NRde/ePTx9+jTbfJv6vQVkCW6jRo3g5+en976mel/1YuxW2MXN+vXrhbW1tVixYoW4dOmSGDx4sChdurR4+PChEEKIDz74QIwbN06d/vDhw8LCwkL873//ExEREWLKlCnC0tJSnD9/3liXkCcff/yxcHJyEgcOHBBRUVHqJSEhQZ0m87V+9dVXYvfu3eLGjRvi9OnTolevXsLGxkZcvHjRGJeQZ59//rk4cOCAiIyMFIcPHxYBAQHCxcVFREdHCyGKzz3NKD09XVSsWFGMHTs2yzZTvq8vXrwQYWFhIiwsTAAQc+fOFWFhYepeT7NmzRKlS5cWv//+uzh37pzo2rWrqFSpkkhMTFQfo127dmLBggXq57l95o0pp+tNSUkRb775pqhQoYIIDw/X+hwnJyerj5H5enP7PBhLTtf64sULMXr0aHH06FERGRkp9u7dKxo2bCiqVasmkpKS1McwlXub2/tYCCFiYmKEnZ2d+Omnn3Qew1Tua0FiAFQAFixYICpWrCisrKxE06ZNxbFjx9TbWrduLYKCgrTSb9y4UVSvXl1YWVmJ2rVrix07dhRyjvUHQOcSHBysTpP5Wj/77DP16+Lq6io6d+4szpw5U/iZ11PPnj2Fu7u7sLKyEp6enqJnz57i+vXr6u3F5Z5mtHv3bgFAXLlyJcs2U76v+/fv1/m+VV2PUqkUkyZNEq6ursLa2lq0b98+y2vg7e0tpkyZorUup8+8MeV0vZGRkdl+jvfv368+Rubrze3zYCw5XWtCQoLo2LGjKFeunLC0tBTe3t5i0KBBWQIZU7m3ub2PhRBi8eLFwtbWVjx//lznMUzlvhYkhRBCFGgRExEREVERwzZAREREVOIwACIiIqIShwEQERERlTgMgIiIiKjEYQBEREREJQ4DICIiIipxGAARERFRicMAiIiIiEocBkBERDocOHAACoUCz58/N3ZWiKgAMAAiIiKiEocBEBEREZU4DICIqEhSKpWYOXMmKlWqBFtbW/j5+eG3334DoKme2rFjB+rVqwcbGxu88soruHDhgtYxNm/ejNq1a8Pa2ho+Pj6YM2eO1vbk5GSMHTsWXl5esLa2RtWqVbFs2TKtNKdPn0bjxo1hZ2eH5s2b48qVK+ptZ8+eRdu2beHg4ABHR0c0atQIp06dKqBXhIgMiQEQERVJM2fOxKpVq/Dzzz/j4sWLGDlyJN5//30cPHhQneaLL77AnDlzcPLkSZQrVw5vvPEGUlNTAcjA5d1330WvXr1w/vx5TJ06FZMmTcKKFSvU+/ft2xfr1q3D/PnzERERgcWLF8Pe3l4rHxMmTMCcOXNw6tQpWFhYYMCAAeptffr0QYUKFXDy5EmcPn0a48aNg6WlZcG+MERkGMaejp6IKLOkpCRhZ2cnjhw5orX+ww8/FL179xb79+8XAMT69evV254+fSpsbW3Fhg0bhBBCvPfee6JDhw5a+3/xxReiVq1aQgghrly5IgCIkJAQnXlQnWPv3r3qdTt27BAARGJiohBCCAcHB7FixYqXv2AiKnQsASKiIuf69etISEhAhw4dYG9vr15WrVqFGzduqNM1a9ZM/bhs2bKoUaMGIiIiAAARERFo0aKF1nFbtGiBa9euIT09HeHh4TA3N0fr1q1zzEu9evXUj93d3QEA0dHRAIBRo0Zh4MCBCAgIwKxZs7TyRkRFGwMgIipy4uLiAAA7duxAeHi4erl06ZK6HdDLsrW1zVO6jFVaCoUCgGyfBABTp07FxYsX8frrr2Pfvn2oVasWtm7dapD8EVHBYgBEREVOrVq1YG1tjTt37qBq1apai5eXlzrdsWPH1I+fPXuGq1evwtfXFwDg6+uLw4cPax338OHDqF69OszNzVG3bl0olUqtNkX5Ub16dYwcORJ79uzBW2+9heDg4Jc6HhEVDgtjZ4CIKDMHBweMHj0aI0eOhFKpRMuWLRETE4PDhw/D0dER3t7eAICvv/4azs7OcHV1xYQJE+Di4oJu3boBAD7//HM0adIE06ZNQ8+ePXH06FEsXLgQixYtAgD4+PggKCgIAwYMwPz58+Hn54fbt28jOjoa7777bq55TExMxBdffIG3334blSpVwr1793Dy5En06NGjwF4XIjIgYzdCIiLSRalUih9++EHUqFFDWFpainLlyonAwEBx8OBBdQPlP/74Q9SuXVtYWVmJpk2birNnz2od47fffhO1atUSlpaWomLFiuK7777T2p6YmChGjhwp3N3dhZWVlahatapYvny5EELTCPrZs2fq9GFhYQKAiIyMFMnJyaJXr17Cy8tLWFlZCQ8PDzF8+HB1A2kiKtoUQghh5BiMiEgvBw4cQNu2bfHs2TOULl3a2NkhIhPENkBERERU4jAAIiIiohKHVWBERERU4rAEiIiIiEocBkBERERU4jAAIiIiohKHARARERGVOAyAiIiIqMRhAEREREQlDgMgIiIiKnEYABEREVGJ83/vrsrwjZEzBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation accuracies for the models\n",
    "dnnd_test_loss, dnnd_test_acc = dnnd.evaluate(x_test, y_test)\n",
    "\n",
    "plt.plot(dnnd_history.history[\"accuracy\"], \"r--\",label=\"DNND training accuracy\")\n",
    "plt.plot(dnnd_history.history[\"val_accuracy\"], \"r-\",label=\"DNND validation accuracy\")\n",
    "plt.axhline(dnnd_test_acc,color=\"r\", linestyle=\":\", label=\"DNND test accuracy\")\n",
    "\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0.95, 1.005)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f54ed71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 28, 28, 64)        640       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 32)        18464     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1568)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 400)               627600    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                4010      \n",
      "=================================================================\n",
      "Total params: 650,714\n",
      "Trainable params: 650,714\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summarize the model\n",
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc25989a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_2 (Conv2D)            (None, 28, 28, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 28, 28, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 14, 14, 32)        18464     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 14, 14, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1568)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 400)               627600    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                4010      \n",
      "=================================================================\n",
      "Total params: 696,890\n",
      "Trainable params: 696,890\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Summarize the model\n",
    "dcnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e2233fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 3s 10ms/step - loss: 0.0582 - accuracy: 0.9909\n",
      "313/313 [==============================] - 13s 40ms/step - loss: 0.0324 - accuracy: 0.9934\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8d8565a6d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation accuracies for the models\n",
    "cnn_test_loss, cnn_test_acc = cnn.evaluate(x_test, y_test)\n",
    "dcnn_test_loss, dcnn_test_acc = dcnn.evaluate(x_test, y_test)\n",
    "\n",
    "plt.plot(cnn_history.history[\"accuracy\"], \"b--\",label=\"CNN training accuracy\")\n",
    "plt.plot(cnn_history.history[\"val_accuracy\"], \"b-\",label=\"CNN validation accuracy\")\n",
    "plt.axhline(cnn_test_acc,color=\"k\", linestyle=\"--\", label=\"CNN test accuracy\")\n",
    "\n",
    "plt.plot(dcnn_history.history[\"accuracy\"], \"g--\",label=\"DCNN training accuracy\")\n",
    "plt.plot(dcnn_history.history[\"val_accuracy\"], \"g-\",label=\"DCNN validation accuracy\")\n",
    "plt.axhline(dcnn_test_acc,color=\"k\", linestyle=\":\", label=\"DCNN test accuracy\")\n",
    "\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0.95, 1.005)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43f5a30",
   "metadata": {},
   "source": [
    "## Plot Accuracy and Loss for each Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "26088d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics_plot(metrics, history):\n",
    "  f, ax = plt.subplots(1, len(metrics), figsize=(5*len(metrics), 5))\n",
    "  for idx, metric in enumerate(metrics):\n",
    "    ax[idx].plot(history.history[metric])\n",
    "    ax[idx].set_xlabel(\"Epochs\")\n",
    "    ax[idx].set_ylabel(metric)\n",
    "    ax[idx].plot(history.history['val_' + metric]);\n",
    "    ax[idx].legend([metric, 'val_' + metric])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8bcf7d79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Deep Neural Network Model with a Dropout Layer (DNND)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model: Deep Neural Network Model with a Dropout Layer (DNND)\")\n",
    "metrics_plot(['loss', 'accuracy'], dnnd_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3fb04c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Convolutional Neural Network Model (CNN)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model: Convolutional Neural Network Model (CNN)\")\n",
    "metrics_plot(['loss', 'accuracy'], cnn_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "89694569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Deep Convolutional Neural Network Model (DCNN)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model: Deep Convolutional Neural Network Model (DCNN)\")\n",
    "metrics_plot(['loss', 'accuracy'], dcnn_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06beb47e",
   "metadata": {},
   "source": [
    "## Get Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8eb9870e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Deep Neural Network with Dropout Layer (DNND)\n",
      "Precision score is: 0.9803890083838953\n",
      "Recall score is: 0.9803\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions using the Deep Neural Network with Dropout Layer (DNND)\n",
    "print (\"Model: Deep Neural Network with Dropout Layer (DNND)\")\n",
    "\n",
    "# Generate predictions on unseen test set.\n",
    "predictions_dnnd = dnnd.predict(x_test)\n",
    "y_pred_dnnd = np.argmax(predictions_dnnd, axis=1)\n",
    "\n",
    "# Calculate precision and recall scores \n",
    "p = precision_score(y_test, y_pred_dnnd , average=\"weighted\")\n",
    "print(f\"Precision score is: {p}\")\n",
    "r = recall_score(y_test, y_pred_dnnd , average=\"weighted\")\n",
    "print(f\"Recall score is: {r}\")\n",
    "\n",
    "# Confusion Matrix\n",
    "label_names=['0','1','2','3','4','5','6','7','8','9']\n",
    "cm =confusion_matrix(y_test, y_pred_dnnd)\n",
    "CM = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "CM = CM * 100\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(CM, annot=True, fmt='.2f', xticklabels=label_names, yticklabels=label_names, cmap=\"BuPu\", vmin=0.0, vmax=1.0)\n",
    "plt.ylabel('Actual labels')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6bde1224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Convolutional Neural Network (CNN)\n",
      "Precision score is: 0.9909335467901598\n",
      "Recall score is: 0.9909\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions using the Convolutional Neural Network (CNN)\n",
    "print (\"Model: Convolutional Neural Network (CNN)\")\n",
    "\n",
    "# Generate predictions on unseen test set.\n",
    "predictions_cnn = cnn.predict(x_test)\n",
    "y_pred_cnn = np.argmax(predictions_cnn, axis=1)\n",
    "\n",
    "# Calculate precision and recall scores \n",
    "p = precision_score(y_test, y_pred_cnn , average=\"weighted\")\n",
    "print(f\"Precision score is: {p}\")\n",
    "r = recall_score(y_test, y_pred_cnn , average=\"weighted\")\n",
    "print(f\"Recall score is: {r}\")\n",
    "\n",
    "# Confusion Matrix\n",
    "label_names=['0','1','2','3','4','5','6','7','8','9']\n",
    "cm =confusion_matrix(y_test, y_pred_cnn)\n",
    "CM = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "CM = CM * 100\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(CM, annot=True, fmt='.2f', xticklabels=label_names, yticklabels=label_names, cmap=\"Reds\", vmin=0.0, vmax=1.0)\n",
    "plt.ylabel('Actual labels')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe01c7f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Deep Convolutional Neural Network (DCNN)\n",
      "Precision score is: 0.993412860643631\n",
      "Recall score is: 0.9934\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions using the Deep Convolutional Neural Network (DCNN)\n",
    "print (\"Model: Deep Convolutional Neural Network (DCNN)\")\n",
    "\n",
    "# Generate predictions on unseen test set.\n",
    "predictions_dcnn = dcnn.predict(x_test)\n",
    "y_pred_dcnn = np.argmax(predictions_dcnn, axis=1)\n",
    "\n",
    "# Calculate precision and recall scores \n",
    "p = precision_score(y_test, y_pred_dcnn , average=\"weighted\")\n",
    "print(f\"Precision score is: {p}\")\n",
    "r = recall_score(y_test, y_pred_dcnn , average=\"weighted\")\n",
    "print(f\"Recall score is: {r}\")\n",
    "\n",
    "# Confusion Matrix\n",
    "label_names=['0','1','2','3','4','5','6','7','8','9']\n",
    "cm =confusion_matrix(y_test, y_pred_dcnn)\n",
    "CM = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "CM = CM * 100\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(CM, annot=True, fmt='.2f', xticklabels=label_names, yticklabels=label_names, cmap=\"Blues\", vmin=0.0, vmax=1.0)\n",
    "plt.ylabel('Actual labels')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.show(block=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0c120a",
   "metadata": {},
   "source": [
    "## Make an Example Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "903f4724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Digit :\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Digit :  7\n"
     ]
    }
   ],
   "source": [
    "# Example prediction\n",
    "plt.imshow(x_test[0], cmap=plt.cm.binary)\n",
    "plt.xlabel('Actual Digit')\n",
    "print(\"Actual Digit :\")\n",
    "plt.show()\n",
    "print(\"Predicted Digit : \", y_pred_dcnn[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9785deed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
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