Image_classification_on_fashion_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "665f4ba5",
   "metadata": {},
   "source": [
    "### Importing the Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7edcbc4",
   "metadata": {},
   "source": [
    "#### Import the libraries for data analysis and data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41b066a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "016efea6",
   "metadata": {},
   "source": [
    "#### Reading the csv file to generate dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bf4b769",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(r'fashion-mnist_train.csv')\n",
    "test_df = pd.read_csv(r'fashion-mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82fb23b1",
   "metadata": {},
   "outputs": [
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d976878",
   "metadata": {},
   "outputs": [
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07310079",
   "metadata": {},
   "source": [
    "#### Converting the images to numpy arrays to feed into the tensorflow model, and splitting into features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8eed2b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.array(train_df)\n",
    "test_data = np.array(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87d2460e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data[:, 1:]\n",
    "y_train = train_data[:, 0]\n",
    "\n",
    "X_test = test_data[:, 1:]\n",
    "y_test = test_data[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a81266",
   "metadata": {},
   "source": [
    "#### Checking the size of train and test arrays, which contain the images and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f41543e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))\n",
    "print(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43e63531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "print(len(y_train))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0f49f47f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b1ddc96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a057f1ad",
   "metadata": {},
   "source": [
    "#### Reshaping the images array to convert into 28x28 input shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2998bb04",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1,28,28)\n",
    "X_test = X_test.reshape(-1,28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed71264a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57374e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4dd5a3",
   "metadata": {},
   "source": [
    "#### Reshaping the labels array to plot the images and creating the plot_image function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80f2e852",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.reshape(-1,)\n",
    "y_test = y_test.reshape(-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3358a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "def plot_image(X, y, index):\n",
    "    \n",
    "    plt.figure(figsize=(15,2))\n",
    "    \n",
    "    plt.imshow(X[index], cmap=plt.get_cmap('gray'))\n",
    "    plt.xlabel(labels[y[index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42ac6e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image(X_train, y_train, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b6f23c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image(X_train, y_train, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26094797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image(X_train, y_train, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8fb898c",
   "metadata": {},
   "source": [
    "#### Normalizing to improve accuracy, can be easily achieved through tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd0df4f5",
   "metadata": {},
   "source": [
    "Normalization is a process that changes the range of pixel intensity values. The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "458f35fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "X_train = X_train/255.0\n",
    "X_test = X_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9755b370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2283b96a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train) #last layer will have 10 neurons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0572285",
   "metadata": {},
   "source": [
    "#### Spliting the training data further into training, and validation data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4957a145",
   "metadata": {},
   "source": [
    "The splitting is basically done is to prevent our model from overfitting i.e., the model becomes really good at classifying the samples in the training set but cannot generalize and make accurate classifications on the data it has not seen before, and also to get the stats from the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ea8b8c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c41b71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_validate, y_train, y_validate = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81554376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dc79de6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5dde1e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286e9ddc",
   "metadata": {},
   "source": [
    "### Importing the necessray libraries to construct the desired CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cba350e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d9762f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(X_train.shape[0],28,28,1)\n",
    "X_validate = X_validate.reshape(X_validate.shape[0],28,28,1)\n",
    "X_test = X_test.reshape(X_test.shape[0],28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d1cb696e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48000, 28, 28, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9c6745e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "578f433b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 28, 28, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3ec3ebb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train shape: (48000, 28, 28, 1)\n",
      "X validate shape: (12000, 28, 28, 1)\n",
      "X test shape: (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print('X train shape: {}'.format(X_train.shape))\n",
    "print('X validate shape: {}'.format(X_validate.shape))\n",
    "print('X test shape: {}'.format(X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ba98673",
   "metadata": {},
   "source": [
    "#### Building 3 deep CNN models by changing the number of Convolutional layer, and hence to increase the number of tunable parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2a4cfef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "name1 = 'Model_1'\n",
    "model_1 = Sequential()\n",
    "\n",
    "model_1.add(Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=(28,28,1), name='Conv2D_1'))\n",
    "model_1.add(MaxPool2D(pool_size=(2,2), name='MaxPool2D'))\n",
    "model_1.add(Dropout(0.2, name='Dropout'))\n",
    "\n",
    "model_1.add(Flatten(name='Flatten'))\n",
    "\n",
    "model_1.add(Dense(32, activation='relu', name='Dense'))\n",
    "model_1.add(Dense(10, activation='softmax', name='Output'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "name2 = 'Model_2'\n",
    "model_2 = Sequential()\n",
    "\n",
    "model_2.add(Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=(28,28,1), name='Conv2D_1'))\n",
    "model_2.add(MaxPool2D(pool_size=(2,2), name='MaxPool2D'))\n",
    "model_2.add(Dropout(0.2, name='Dropout_1'))\n",
    "\n",
    "model_2.add(Conv2D(filters=64, kernel_size=3, activation='relu', name='Conv2D_2'))\n",
    "model_2.add(Dropout(0.25, name='Dropout_2'))\n",
    "\n",
    "model_2.add(Flatten(name='Flatten'))\n",
    "\n",
    "model_2.add(Dense(64, activation='relu', name='Dense'))\n",
    "model_2.add(Dense(10, activation='softmax', name='Output'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "name3 = 'Model_3'\n",
    "model_3 = Sequential()\n",
    "\n",
    "model_3.add(Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=(28,28,1), kernel_initializer='he_normal', name='Conv2D_1'))\n",
    "model_3.add(MaxPool2D(pool_size=(2,2), name='MaxPool2D'))\n",
    "model_3.add(Dropout(0.25, name='Dropout_1'))\n",
    "\n",
    "model_3.add(Conv2D(filters=64, kernel_size=3, activation='relu', name='Conv2D_2'))\n",
    "model_3.add(Dropout(0.25, name='Dropout_2'))\n",
    "\n",
    "model_3.add(Conv2D(filters=128, kernel_size=3, activation='relu', name='Conv2D_3'))\n",
    "model_3.add(Dropout(0.4, name='Dropout_3'))\n",
    "\n",
    "model_3.add(Flatten(name=\"Flatten\"))\n",
    "\n",
    "model_3.add(Dense(128, activation='relu', name='Dense'))\n",
    "model_3.add(Dropout(0.4, name='Dropout'))\n",
    "model_3.add(Dense(10, activation='softmax', name='Output'))\n",
    "\n",
    "models = [model_1, model_2, model_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "23bee5c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "___________________________________________________________________________\n",
      " Layer (type)                    Output Shape                  Param #     \n",
      "===========================================================================\n",
      " Conv2D_1 (Conv2D)               (None, 26, 26, 32)            320         \n",
      "                                                                           \n",
      " MaxPool2D (MaxPooling2D)        (None, 13, 13, 32)            0           \n",
      "                                                                           \n",
      " Dropout (Dropout)               (None, 13, 13, 32)            0           \n",
      "                                                                           \n",
      " Flatten (Flatten)               (None, 5408)                  0           \n",
      "                                                                           \n",
      " Dense (Dense)                   (None, 32)                    173088      \n",
      "                                                                           \n",
      " Output (Dense)                  (None, 10)                    330         \n",
      "                                                                           \n",
      "===========================================================================\n",
      "Total params: 173,738\n",
      "Trainable params: 173,738\n",
      "Non-trainable params: 0\n",
      "___________________________________________________________________________\n",
      "\n",
      "\n",
      "Model: \"sequential_1\"\n",
      "___________________________________________________________________________\n",
      " Layer (type)                    Output Shape                  Param #     \n",
      "===========================================================================\n",
      " Conv2D_1 (Conv2D)               (None, 26, 26, 32)            320         \n",
      "                                                                           \n",
      " MaxPool2D (MaxPooling2D)        (None, 13, 13, 32)            0           \n",
      "                                                                           \n",
      " Dropout_1 (Dropout)             (None, 13, 13, 32)            0           \n",
      "                                                                           \n",
      " Conv2D_2 (Conv2D)               (None, 11, 11, 64)            18496       \n",
      "                                                                           \n",
      " Dropout_2 (Dropout)             (None, 11, 11, 64)            0           \n",
      "                                                                           \n",
      " Flatten (Flatten)               (None, 7744)                  0           \n",
      "                                                                           \n",
      " Dense (Dense)                   (None, 64)                    495680      \n",
      "                                                                           \n",
      " Output (Dense)                  (None, 10)                    650         \n",
      "                                                                           \n",
      "===========================================================================\n",
      "Total params: 515,146\n",
      "Trainable params: 515,146\n",
      "Non-trainable params: 0\n",
      "___________________________________________________________________________\n",
      "\n",
      "\n",
      "Model: \"sequential_2\"\n",
      "___________________________________________________________________________\n",
      " Layer (type)                    Output Shape                  Param #     \n",
      "===========================================================================\n",
      " Conv2D_1 (Conv2D)               (None, 26, 26, 32)            320         \n",
      "                                                                           \n",
      " MaxPool2D (MaxPooling2D)        (None, 13, 13, 32)            0           \n",
      "                                                                           \n",
      " Dropout_1 (Dropout)             (None, 13, 13, 32)            0           \n",
      "                                                                           \n",
      " Conv2D_2 (Conv2D)               (None, 11, 11, 64)            18496       \n",
      "                                                                           \n",
      " Dropout_2 (Dropout)             (None, 11, 11, 64)            0           \n",
      "                                                                           \n",
      " Conv2D_3 (Conv2D)               (None, 9, 9, 128)             73856       \n",
      "                                                                           \n",
      " Dropout_3 (Dropout)             (None, 9, 9, 128)             0           \n",
      "                                                                           \n",
      " Flatten (Flatten)               (None, 10368)                 0           \n",
      "                                                                           \n",
      " Dense (Dense)                   (None, 128)                   1327232     \n",
      "                                                                           \n",
      " Dropout (Dropout)               (None, 128)                   0           \n",
      "                                                                           \n",
      " Output (Dense)                  (None, 10)                    1290        \n",
      "                                                                           \n",
      "===========================================================================\n",
      "Total params: 1,421,194\n",
      "Trainable params: 1,421,194\n",
      "Non-trainable params: 0\n",
      "___________________________________________________________________________\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    model.summary(line_length=75)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaae6dff",
   "metadata": {},
   "source": [
    "#### Training the CNN models and further evaluating on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "08c8eb47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training sequential:\n",
      "Epoch 1/50\n",
      "94/94 [==============================] - 15s 150ms/step - loss: 0.7453 - accuracy: 0.7558 - val_loss: 0.4700 - val_accuracy: 0.8304\n",
      "Epoch 2/50\n",
      "94/94 [==============================] - 14s 147ms/step - loss: 0.4386 - accuracy: 0.8466 - val_loss: 0.4076 - val_accuracy: 0.8559\n",
      "Epoch 3/50\n",
      "94/94 [==============================] - 14s 144ms/step - loss: 0.3925 - accuracy: 0.8643 - val_loss: 0.3761 - val_accuracy: 0.8692\n",
      "Epoch 4/50\n",
      "94/94 [==============================] - 14s 144ms/step - loss: 0.3653 - accuracy: 0.8735 - val_loss: 0.3538 - val_accuracy: 0.8743\n",
      "Epoch 5/50\n",
      "94/94 [==============================] - 14s 145ms/step - loss: 0.3445 - accuracy: 0.8793 - val_loss: 0.3342 - val_accuracy: 0.8823\n",
      "Epoch 6/50\n",
      "94/94 [==============================] - 14s 154ms/step - loss: 0.3237 - accuracy: 0.8873 - val_loss: 0.3206 - val_accuracy: 0.8874\n",
      "Epoch 7/50\n",
      "94/94 [==============================] - 14s 151ms/step - loss: 0.3128 - accuracy: 0.8924 - val_loss: 0.3171 - val_accuracy: 0.8874\n",
      "Epoch 8/50\n",
      "94/94 [==============================] - 16s 166ms/step - loss: 0.2994 - accuracy: 0.8944 - val_loss: 0.3061 - val_accuracy: 0.8925\n",
      "Epoch 9/50\n",
      "94/94 [==============================] - 15s 156ms/step - loss: 0.2936 - accuracy: 0.8983 - val_loss: 0.2991 - val_accuracy: 0.8964\n",
      "Epoch 10/50\n",
      "94/94 [==============================] - 15s 158ms/step - loss: 0.2862 - accuracy: 0.8996 - val_loss: 0.2941 - val_accuracy: 0.8943\n",
      "Epoch 11/50\n",
      "94/94 [==============================] - 16s 164ms/step - loss: 0.2749 - accuracy: 0.9038 - val_loss: 0.2888 - val_accuracy: 0.8987\n",
      "Epoch 12/50\n",
      "94/94 [==============================] - 14s 146ms/step - loss: 0.2654 - accuracy: 0.9079 - val_loss: 0.2807 - val_accuracy: 0.8995\n",
      "Epoch 13/50\n",
      "94/94 [==============================] - 14s 150ms/step - loss: 0.2623 - accuracy: 0.9076 - val_loss: 0.2839 - val_accuracy: 0.8993\n",
      "Epoch 14/50\n",
      "94/94 [==============================] - 15s 159ms/step - loss: 0.2552 - accuracy: 0.9102 - val_loss: 0.2805 - val_accuracy: 0.9016\n",
      "Epoch 15/50\n",
      "94/94 [==============================] - 15s 164ms/step - loss: 0.2471 - accuracy: 0.9139 - val_loss: 0.2733 - val_accuracy: 0.9042\n",
      "Epoch 16/50\n",
      "94/94 [==============================] - 15s 159ms/step - loss: 0.2414 - accuracy: 0.9145 - val_loss: 0.2763 - val_accuracy: 0.9018\n",
      "Epoch 17/50\n",
      "94/94 [==============================] - 15s 163ms/step - loss: 0.2368 - accuracy: 0.9166 - val_loss: 0.2658 - val_accuracy: 0.9078\n",
      "Epoch 18/50\n",
      "94/94 [==============================] - 16s 170ms/step - loss: 0.2369 - accuracy: 0.9171 - val_loss: 0.2671 - val_accuracy: 0.9063\n",
      "Epoch 19/50\n",
      "94/94 [==============================] - 17s 178ms/step - loss: 0.2300 - accuracy: 0.9169 - val_loss: 0.2696 - val_accuracy: 0.9026\n",
      "Epoch 20/50\n",
      "94/94 [==============================] - 17s 186ms/step - loss: 0.2281 - accuracy: 0.9194 - val_loss: 0.2596 - val_accuracy: 0.9086\n",
      "Epoch 21/50\n",
      "94/94 [==============================] - 19s 198ms/step - loss: 0.2214 - accuracy: 0.9215 - val_loss: 0.2595 - val_accuracy: 0.9081\n",
      "Epoch 22/50\n",
      "94/94 [==============================] - 14s 149ms/step - loss: 0.2153 - accuracy: 0.9231 - val_loss: 0.2564 - val_accuracy: 0.9112\n",
      "Epoch 23/50\n",
      "94/94 [==============================] - 17s 176ms/step - loss: 0.2140 - accuracy: 0.9231 - val_loss: 0.2738 - val_accuracy: 0.9053\n",
      "Epoch 24/50\n",
      "94/94 [==============================] - 14s 154ms/step - loss: 0.2110 - accuracy: 0.9249 - val_loss: 0.2569 - val_accuracy: 0.9118\n",
      "Epoch 25/50\n",
      "94/94 [==============================] - 17s 183ms/step - loss: 0.2095 - accuracy: 0.9249 - val_loss: 0.2578 - val_accuracy: 0.9075\n",
      "Epoch 26/50\n",
      "94/94 [==============================] - 16s 168ms/step - loss: 0.2051 - accuracy: 0.9266 - val_loss: 0.2524 - val_accuracy: 0.9138\n",
      "Epoch 27/50\n",
      "94/94 [==============================] - 18s 187ms/step - loss: 0.2003 - accuracy: 0.9290 - val_loss: 0.2530 - val_accuracy: 0.9116\n",
      "Epoch 28/50\n",
      "94/94 [==============================] - 14s 146ms/step - loss: 0.1993 - accuracy: 0.9282 - val_loss: 0.2527 - val_accuracy: 0.9118\n",
      "Epoch 29/50\n",
      "94/94 [==============================] - 14s 146ms/step - loss: 0.1974 - accuracy: 0.9290 - val_loss: 0.2549 - val_accuracy: 0.9139\n",
      "Epoch 30/50\n",
      "94/94 [==============================] - 14s 147ms/step - loss: 0.1941 - accuracy: 0.9308 - val_loss: 0.2540 - val_accuracy: 0.9130\n",
      "Epoch 31/50\n",
      "94/94 [==============================] - 14s 147ms/step - loss: 0.1897 - accuracy: 0.9331 - val_loss: 0.2487 - val_accuracy: 0.9137\n",
      "Epoch 32/50\n",
      "94/94 [==============================] - 14s 145ms/step - loss: 0.1852 - accuracy: 0.9337 - val_loss: 0.2491 - val_accuracy: 0.9143\n",
      "Epoch 33/50\n",
      "94/94 [==============================] - 15s 156ms/step - loss: 0.1804 - accuracy: 0.9354 - val_loss: 0.2464 - val_accuracy: 0.9153\n",
      "Epoch 34/50\n",
      "94/94 [==============================] - 15s 155ms/step - loss: 0.1799 - accuracy: 0.9352 - val_loss: 0.2472 - val_accuracy: 0.9143\n",
      "Epoch 35/50\n",
      "94/94 [==============================] - 17s 180ms/step - loss: 0.1792 - accuracy: 0.9352 - val_loss: 0.2492 - val_accuracy: 0.9096\n",
      "Epoch 36/50\n",
      "94/94 [==============================] - 15s 154ms/step - loss: 0.1759 - accuracy: 0.9360 - val_loss: 0.2453 - val_accuracy: 0.9147\n",
      "Epoch 37/50\n",
      "94/94 [==============================] - 14s 146ms/step - loss: 0.1746 - accuracy: 0.9378 - val_loss: 0.2499 - val_accuracy: 0.9128\n",
      "Epoch 38/50\n",
      "94/94 [==============================] - 14s 149ms/step - loss: 0.1706 - accuracy: 0.9382 - val_loss: 0.2485 - val_accuracy: 0.9143\n",
      "Epoch 39/50\n",
      "94/94 [==============================] - 14s 147ms/step - loss: 0.1708 - accuracy: 0.9382 - val_loss: 0.2454 - val_accuracy: 0.9158\n",
      "Epoch 40/50\n",
      "94/94 [==============================] - 14s 146ms/step - loss: 0.1652 - accuracy: 0.9402 - val_loss: 0.2468 - val_accuracy: 0.9143\n",
      "Epoch 41/50\n",
      "94/94 [==============================] - 14s 146ms/step - loss: 0.1615 - accuracy: 0.9434 - val_loss: 0.2488 - val_accuracy: 0.9148\n",
      "Epoch 42/50\n",
      "94/94 [==============================] - 14s 145ms/step - loss: 0.1618 - accuracy: 0.9415 - val_loss: 0.2502 - val_accuracy: 0.9136\n",
      "Epoch 43/50\n",
      "94/94 [==============================] - 14s 144ms/step - loss: 0.1587 - accuracy: 0.9421 - val_loss: 0.2493 - val_accuracy: 0.9152\n",
      "Epoch 44/50\n",
      "94/94 [==============================] - 14s 145ms/step - loss: 0.1552 - accuracy: 0.9438 - val_loss: 0.2482 - val_accuracy: 0.9148\n",
      "Epoch 45/50\n",
      "94/94 [==============================] - 14s 148ms/step - loss: 0.1538 - accuracy: 0.9440 - val_loss: 0.2497 - val_accuracy: 0.9155\n",
      "Epoch 46/50\n",
      "94/94 [==============================] - 14s 151ms/step - loss: 0.1548 - accuracy: 0.9436 - val_loss: 0.2497 - val_accuracy: 0.9165\n",
      "Epoch 47/50\n",
      "94/94 [==============================] - 14s 148ms/step - loss: 0.1504 - accuracy: 0.9448 - val_loss: 0.2559 - val_accuracy: 0.9138\n",
      "Epoch 48/50\n",
      "94/94 [==============================] - 15s 157ms/step - loss: 0.1484 - accuracy: 0.9464 - val_loss: 0.2526 - val_accuracy: 0.9159\n",
      "Epoch 49/50\n",
      "94/94 [==============================] - 15s 161ms/step - loss: 0.1440 - accuracy: 0.9470 - val_loss: 0.2511 - val_accuracy: 0.9147\n",
      "Epoch 50/50\n",
      "94/94 [==============================] - 14s 152ms/step - loss: 0.1443 - accuracy: 0.9478 - val_loss: 0.2621 - val_accuracy: 0.9119\n",
      "\n",
      "\n",
      "Training sequential_1:\n",
      "Epoch 1/50\n",
      "94/94 [==============================] - 28s 295ms/step - loss: 0.7175 - accuracy: 0.7431 - val_loss: 0.4612 - val_accuracy: 0.8342\n",
      "Epoch 2/50\n",
      "94/94 [==============================] - 27s 289ms/step - loss: 0.4410 - accuracy: 0.8409 - val_loss: 0.3802 - val_accuracy: 0.8639\n",
      "Epoch 3/50\n",
      "94/94 [==============================] - 27s 288ms/step - loss: 0.3690 - accuracy: 0.8673 - val_loss: 0.3440 - val_accuracy: 0.8729\n",
      "Epoch 4/50\n",
      "94/94 [==============================] - 26s 276ms/step - loss: 0.3381 - accuracy: 0.8801 - val_loss: 0.3141 - val_accuracy: 0.8867\n",
      "Epoch 5/50\n",
      "94/94 [==============================] - 27s 287ms/step - loss: 0.3178 - accuracy: 0.8863 - val_loss: 0.3028 - val_accuracy: 0.8903\n",
      "Epoch 6/50\n",
      "94/94 [==============================] - 28s 300ms/step - loss: 0.2959 - accuracy: 0.8942 - val_loss: 0.2870 - val_accuracy: 0.8945\n",
      "Epoch 7/50\n",
      "94/94 [==============================] - 28s 295ms/step - loss: 0.2827 - accuracy: 0.8992 - val_loss: 0.2753 - val_accuracy: 0.9028\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/50\n",
      "94/94 [==============================] - 28s 293ms/step - loss: 0.2692 - accuracy: 0.9037 - val_loss: 0.2904 - val_accuracy: 0.8947\n",
      "Epoch 9/50\n",
      "94/94 [==============================] - 28s 294ms/step - loss: 0.2547 - accuracy: 0.9092 - val_loss: 0.2728 - val_accuracy: 0.9012\n",
      "Epoch 10/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.2463 - accuracy: 0.9116 - val_loss: 0.2561 - val_accuracy: 0.9093\n",
      "Epoch 11/50\n",
      "94/94 [==============================] - 26s 275ms/step - loss: 0.2378 - accuracy: 0.9139 - val_loss: 0.2468 - val_accuracy: 0.9100\n",
      "Epoch 12/50\n",
      "94/94 [==============================] - 26s 282ms/step - loss: 0.2273 - accuracy: 0.9183 - val_loss: 0.2489 - val_accuracy: 0.9103\n",
      "Epoch 13/50\n",
      "94/94 [==============================] - 26s 275ms/step - loss: 0.2174 - accuracy: 0.9206 - val_loss: 0.2356 - val_accuracy: 0.9131\n",
      "Epoch 14/50\n",
      "94/94 [==============================] - 26s 281ms/step - loss: 0.2112 - accuracy: 0.9238 - val_loss: 0.2423 - val_accuracy: 0.9158\n",
      "Epoch 15/50\n",
      "94/94 [==============================] - 26s 278ms/step - loss: 0.2068 - accuracy: 0.9244 - val_loss: 0.2266 - val_accuracy: 0.9193\n",
      "Epoch 16/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.1973 - accuracy: 0.9283 - val_loss: 0.2267 - val_accuracy: 0.9197\n",
      "Epoch 17/50\n",
      "94/94 [==============================] - 26s 279ms/step - loss: 0.1912 - accuracy: 0.9302 - val_loss: 0.2333 - val_accuracy: 0.9161\n",
      "Epoch 18/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.1865 - accuracy: 0.9316 - val_loss: 0.2228 - val_accuracy: 0.9192\n",
      "Epoch 19/50\n",
      "94/94 [==============================] - 26s 275ms/step - loss: 0.1804 - accuracy: 0.9339 - val_loss: 0.2185 - val_accuracy: 0.9212\n",
      "Epoch 20/50\n",
      "94/94 [==============================] - 26s 279ms/step - loss: 0.1789 - accuracy: 0.9337 - val_loss: 0.2236 - val_accuracy: 0.9191\n",
      "Epoch 21/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.1687 - accuracy: 0.9375 - val_loss: 0.2172 - val_accuracy: 0.9210\n",
      "Epoch 22/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.1647 - accuracy: 0.9397 - val_loss: 0.2237 - val_accuracy: 0.9212\n",
      "Epoch 23/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.1609 - accuracy: 0.9413 - val_loss: 0.2200 - val_accuracy: 0.9196\n",
      "Epoch 24/50\n",
      "94/94 [==============================] - 26s 275ms/step - loss: 0.1553 - accuracy: 0.9434 - val_loss: 0.2176 - val_accuracy: 0.9233\n",
      "Epoch 25/50\n",
      "94/94 [==============================] - 26s 278ms/step - loss: 0.1495 - accuracy: 0.9461 - val_loss: 0.2186 - val_accuracy: 0.9233\n",
      "Epoch 26/50\n",
      "94/94 [==============================] - 27s 291ms/step - loss: 0.1419 - accuracy: 0.9491 - val_loss: 0.2143 - val_accuracy: 0.9263\n",
      "Epoch 27/50\n",
      "94/94 [==============================] - 26s 274ms/step - loss: 0.1401 - accuracy: 0.9493 - val_loss: 0.2181 - val_accuracy: 0.9227\n",
      "Epoch 28/50\n",
      "94/94 [==============================] - 26s 278ms/step - loss: 0.1337 - accuracy: 0.9507 - val_loss: 0.2161 - val_accuracy: 0.9264\n",
      "Epoch 29/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.1295 - accuracy: 0.9531 - val_loss: 0.2142 - val_accuracy: 0.9277\n",
      "Epoch 30/50\n",
      "94/94 [==============================] - 26s 276ms/step - loss: 0.1268 - accuracy: 0.9526 - val_loss: 0.2223 - val_accuracy: 0.9237\n",
      "Epoch 31/50\n",
      "94/94 [==============================] - 26s 278ms/step - loss: 0.1220 - accuracy: 0.9547 - val_loss: 0.2170 - val_accuracy: 0.9287\n",
      "Epoch 32/50\n",
      "94/94 [==============================] - 26s 276ms/step - loss: 0.1200 - accuracy: 0.9554 - val_loss: 0.2274 - val_accuracy: 0.9231\n",
      "Epoch 33/50\n",
      "94/94 [==============================] - 27s 282ms/step - loss: 0.1187 - accuracy: 0.9559 - val_loss: 0.2231 - val_accuracy: 0.9233\n",
      "Epoch 34/50\n",
      "94/94 [==============================] - 31s 334ms/step - loss: 0.1124 - accuracy: 0.9581 - val_loss: 0.2271 - val_accuracy: 0.9266\n",
      "Epoch 35/50\n",
      "94/94 [==============================] - 29s 303ms/step - loss: 0.1092 - accuracy: 0.9595 - val_loss: 0.2284 - val_accuracy: 0.9258\n",
      "Epoch 36/50\n",
      "94/94 [==============================] - 25s 271ms/step - loss: 0.1085 - accuracy: 0.9598 - val_loss: 0.2194 - val_accuracy: 0.9274\n",
      "Epoch 37/50\n",
      "94/94 [==============================] - 26s 281ms/step - loss: 0.1038 - accuracy: 0.9617 - val_loss: 0.2393 - val_accuracy: 0.9200\n",
      "Epoch 38/50\n",
      "94/94 [==============================] - 29s 310ms/step - loss: 0.1050 - accuracy: 0.9613 - val_loss: 0.2340 - val_accuracy: 0.9245\n",
      "Epoch 39/50\n",
      "94/94 [==============================] - 27s 290ms/step - loss: 0.0943 - accuracy: 0.9651 - val_loss: 0.2282 - val_accuracy: 0.9247\n",
      "Epoch 40/50\n",
      "94/94 [==============================] - 26s 275ms/step - loss: 0.0920 - accuracy: 0.9660 - val_loss: 0.2261 - val_accuracy: 0.9253\n",
      "Epoch 41/50\n",
      "94/94 [==============================] - 28s 298ms/step - loss: 0.0908 - accuracy: 0.9666 - val_loss: 0.2301 - val_accuracy: 0.9253\n",
      "Epoch 42/50\n",
      "94/94 [==============================] - 28s 296ms/step - loss: 0.0937 - accuracy: 0.9654 - val_loss: 0.2251 - val_accuracy: 0.9269\n",
      "Epoch 43/50\n",
      "94/94 [==============================] - 26s 275ms/step - loss: 0.0859 - accuracy: 0.9679 - val_loss: 0.2444 - val_accuracy: 0.9245\n",
      "Epoch 44/50\n",
      "94/94 [==============================] - 26s 281ms/step - loss: 0.0837 - accuracy: 0.9693 - val_loss: 0.2388 - val_accuracy: 0.9255\n",
      "Epoch 45/50\n",
      "94/94 [==============================] - 26s 276ms/step - loss: 0.0818 - accuracy: 0.9703 - val_loss: 0.2400 - val_accuracy: 0.9245\n",
      "Epoch 46/50\n",
      "94/94 [==============================] - 26s 274ms/step - loss: 0.0800 - accuracy: 0.9701 - val_loss: 0.2564 - val_accuracy: 0.9220\n",
      "Epoch 47/50\n",
      "94/94 [==============================] - 26s 280ms/step - loss: 0.0773 - accuracy: 0.9717 - val_loss: 0.2450 - val_accuracy: 0.9254\n",
      "Epoch 48/50\n",
      "94/94 [==============================] - 27s 282ms/step - loss: 0.0742 - accuracy: 0.9731 - val_loss: 0.2513 - val_accuracy: 0.9243\n",
      "Epoch 49/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.0725 - accuracy: 0.9735 - val_loss: 0.2511 - val_accuracy: 0.9252\n",
      "Epoch 50/50\n",
      "94/94 [==============================] - 26s 277ms/step - loss: 0.0687 - accuracy: 0.9746 - val_loss: 0.2503 - val_accuracy: 0.9262\n",
      "\n",
      "\n",
      "Training sequential_2:\n",
      "Epoch 1/50\n",
      "94/94 [==============================] - 50s 523ms/step - loss: 0.8185 - accuracy: 0.6968 - val_loss: 0.4685 - val_accuracy: 0.8299\n",
      "Epoch 2/50\n",
      "94/94 [==============================] - 51s 539ms/step - loss: 0.5024 - accuracy: 0.8168 - val_loss: 0.3863 - val_accuracy: 0.8580\n",
      "Epoch 3/50\n",
      "94/94 [==============================] - 47s 504ms/step - loss: 0.4306 - accuracy: 0.8435 - val_loss: 0.3434 - val_accuracy: 0.8692\n",
      "Epoch 4/50\n",
      "94/94 [==============================] - 47s 505ms/step - loss: 0.3832 - accuracy: 0.8610 - val_loss: 0.3088 - val_accuracy: 0.8850\n",
      "Epoch 5/50\n",
      "94/94 [==============================] - 51s 540ms/step - loss: 0.3547 - accuracy: 0.8711 - val_loss: 0.2986 - val_accuracy: 0.8859\n",
      "Epoch 6/50\n",
      "94/94 [==============================] - 50s 538ms/step - loss: 0.3319 - accuracy: 0.8808 - val_loss: 0.2824 - val_accuracy: 0.8956\n",
      "Epoch 7/50\n",
      "94/94 [==============================] - 47s 498ms/step - loss: 0.3109 - accuracy: 0.8855 - val_loss: 0.2706 - val_accuracy: 0.8999\n",
      "Epoch 8/50\n",
      "94/94 [==============================] - 51s 538ms/step - loss: 0.2995 - accuracy: 0.8914 - val_loss: 0.2597 - val_accuracy: 0.9024\n",
      "Epoch 9/50\n",
      "94/94 [==============================] - 49s 525ms/step - loss: 0.2808 - accuracy: 0.8988 - val_loss: 0.2568 - val_accuracy: 0.9039\n",
      "Epoch 10/50\n",
      "94/94 [==============================] - 48s 513ms/step - loss: 0.2676 - accuracy: 0.9016 - val_loss: 0.2549 - val_accuracy: 0.9046\n",
      "Epoch 11/50\n",
      "94/94 [==============================] - 48s 511ms/step - loss: 0.2592 - accuracy: 0.9036 - val_loss: 0.2467 - val_accuracy: 0.9053\n",
      "Epoch 12/50\n",
      "94/94 [==============================] - 47s 502ms/step - loss: 0.2512 - accuracy: 0.9070 - val_loss: 0.2419 - val_accuracy: 0.9096\n",
      "Epoch 13/50\n",
      "94/94 [==============================] - 48s 515ms/step - loss: 0.2448 - accuracy: 0.9105 - val_loss: 0.2364 - val_accuracy: 0.9127\n",
      "Epoch 14/50\n",
      "94/94 [==============================] - 49s 518ms/step - loss: 0.2331 - accuracy: 0.9128 - val_loss: 0.2368 - val_accuracy: 0.9105\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/50\n",
      "94/94 [==============================] - 48s 511ms/step - loss: 0.2250 - accuracy: 0.9173 - val_loss: 0.2285 - val_accuracy: 0.9134\n",
      "Epoch 16/50\n",
      "94/94 [==============================] - 47s 497ms/step - loss: 0.2189 - accuracy: 0.9186 - val_loss: 0.2263 - val_accuracy: 0.9179\n",
      "Epoch 17/50\n",
      "94/94 [==============================] - 47s 496ms/step - loss: 0.2134 - accuracy: 0.9205 - val_loss: 0.2268 - val_accuracy: 0.9187\n",
      "Epoch 18/50\n",
      "94/94 [==============================] - 48s 508ms/step - loss: 0.2084 - accuracy: 0.9224 - val_loss: 0.2250 - val_accuracy: 0.9197\n",
      "Epoch 19/50\n",
      "94/94 [==============================] - 51s 545ms/step - loss: 0.2008 - accuracy: 0.9252 - val_loss: 0.2285 - val_accuracy: 0.9166\n",
      "Epoch 20/50\n",
      "94/94 [==============================] - 52s 552ms/step - loss: 0.1980 - accuracy: 0.9274 - val_loss: 0.2251 - val_accuracy: 0.9196\n",
      "Epoch 21/50\n",
      "94/94 [==============================] - 53s 564ms/step - loss: 0.1914 - accuracy: 0.9275 - val_loss: 0.2313 - val_accuracy: 0.9171\n",
      "Epoch 22/50\n",
      "94/94 [==============================] - 48s 515ms/step - loss: 0.1886 - accuracy: 0.9286 - val_loss: 0.2190 - val_accuracy: 0.9215\n",
      "Epoch 23/50\n",
      "94/94 [==============================] - 49s 520ms/step - loss: 0.1824 - accuracy: 0.9309 - val_loss: 0.2295 - val_accuracy: 0.9213\n",
      "Epoch 24/50\n",
      "94/94 [==============================] - 48s 514ms/step - loss: 0.1829 - accuracy: 0.9311 - val_loss: 0.2257 - val_accuracy: 0.9220\n",
      "Epoch 25/50\n",
      "94/94 [==============================] - 50s 531ms/step - loss: 0.1757 - accuracy: 0.9338 - val_loss: 0.2247 - val_accuracy: 0.9212\n",
      "Epoch 26/50\n",
      "94/94 [==============================] - 54s 576ms/step - loss: 0.1699 - accuracy: 0.9342 - val_loss: 0.2284 - val_accuracy: 0.9243\n",
      "Epoch 27/50\n",
      "94/94 [==============================] - 50s 531ms/step - loss: 0.1707 - accuracy: 0.9348 - val_loss: 0.2244 - val_accuracy: 0.9225\n",
      "Epoch 28/50\n",
      "94/94 [==============================] - 49s 523ms/step - loss: 0.1660 - accuracy: 0.9369 - val_loss: 0.2225 - val_accuracy: 0.9241\n",
      "Epoch 29/50\n",
      "94/94 [==============================] - 49s 520ms/step - loss: 0.1611 - accuracy: 0.9386 - val_loss: 0.2203 - val_accuracy: 0.9222\n",
      "Epoch 30/50\n",
      "94/94 [==============================] - 49s 519ms/step - loss: 0.1590 - accuracy: 0.9401 - val_loss: 0.2349 - val_accuracy: 0.9238\n",
      "Epoch 31/50\n",
      "94/94 [==============================] - 49s 524ms/step - loss: 0.1558 - accuracy: 0.9417 - val_loss: 0.2296 - val_accuracy: 0.9222\n",
      "Epoch 32/50\n",
      "94/94 [==============================] - 47s 502ms/step - loss: 0.1535 - accuracy: 0.9416 - val_loss: 0.2288 - val_accuracy: 0.9229\n",
      "Epoch 33/50\n",
      "94/94 [==============================] - 47s 502ms/step - loss: 0.1523 - accuracy: 0.9431 - val_loss: 0.2207 - val_accuracy: 0.9264\n",
      "Epoch 34/50\n",
      "94/94 [==============================] - 48s 507ms/step - loss: 0.1458 - accuracy: 0.9435 - val_loss: 0.2279 - val_accuracy: 0.9231\n",
      "Epoch 35/50\n",
      "94/94 [==============================] - 47s 502ms/step - loss: 0.1446 - accuracy: 0.9441 - val_loss: 0.2303 - val_accuracy: 0.9262\n",
      "Epoch 36/50\n",
      "94/94 [==============================] - 47s 503ms/step - loss: 0.1458 - accuracy: 0.9436 - val_loss: 0.2278 - val_accuracy: 0.9232\n",
      "Epoch 37/50\n",
      "94/94 [==============================] - 48s 506ms/step - loss: 0.1406 - accuracy: 0.9459 - val_loss: 0.2335 - val_accuracy: 0.9243\n",
      "Epoch 38/50\n",
      "94/94 [==============================] - 47s 503ms/step - loss: 0.1420 - accuracy: 0.9469 - val_loss: 0.2325 - val_accuracy: 0.9227\n",
      "Epoch 39/50\n",
      "94/94 [==============================] - 48s 507ms/step - loss: 0.1326 - accuracy: 0.9500 - val_loss: 0.2272 - val_accuracy: 0.9252\n",
      "Epoch 40/50\n",
      "94/94 [==============================] - 51s 540ms/step - loss: 0.1315 - accuracy: 0.9497 - val_loss: 0.2372 - val_accuracy: 0.9249\n",
      "Epoch 41/50\n",
      "94/94 [==============================] - 47s 503ms/step - loss: 0.1303 - accuracy: 0.9500 - val_loss: 0.2344 - val_accuracy: 0.9241\n",
      "Epoch 42/50\n",
      "94/94 [==============================] - 48s 507ms/step - loss: 0.1275 - accuracy: 0.9519 - val_loss: 0.2412 - val_accuracy: 0.9248\n",
      "Epoch 43/50\n",
      "94/94 [==============================] - 50s 530ms/step - loss: 0.1255 - accuracy: 0.9514 - val_loss: 0.2364 - val_accuracy: 0.9246\n",
      "Epoch 44/50\n",
      "94/94 [==============================] - 48s 508ms/step - loss: 0.1237 - accuracy: 0.9517 - val_loss: 0.2397 - val_accuracy: 0.9254\n",
      "Epoch 45/50\n",
      "94/94 [==============================] - 47s 502ms/step - loss: 0.1243 - accuracy: 0.9519 - val_loss: 0.2375 - val_accuracy: 0.9253\n",
      "Epoch 46/50\n",
      "94/94 [==============================] - 49s 522ms/step - loss: 0.1233 - accuracy: 0.9526 - val_loss: 0.2472 - val_accuracy: 0.9239\n",
      "Epoch 47/50\n",
      "94/94 [==============================] - 48s 508ms/step - loss: 0.1192 - accuracy: 0.9550 - val_loss: 0.2494 - val_accuracy: 0.9211\n",
      "Epoch 48/50\n",
      "94/94 [==============================] - 49s 517ms/step - loss: 0.1161 - accuracy: 0.9547 - val_loss: 0.2406 - val_accuracy: 0.9243\n",
      "Epoch 49/50\n",
      "94/94 [==============================] - 48s 507ms/step - loss: 0.1176 - accuracy: 0.9546 - val_loss: 0.2460 - val_accuracy: 0.9236\n",
      "Epoch 50/50\n",
      "94/94 [==============================] - 54s 573ms/step - loss: 0.1132 - accuracy: 0.9565 - val_loss: 0.2431 - val_accuracy: 0.9249\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "history_dict = {}\n",
    "\n",
    "for model in models:\n",
    "    model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
    "    \n",
    "    print('Training {}:'.format(model.name))\n",
    "    history = model.fit(x=X_train, y=y_train, \n",
    "              validation_data=(X_validate, y_validate), \n",
    "              batch_size=512, epochs=50, verbose=1)\n",
    "    \n",
    "    history_dict[model.name] = history\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee668e34",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation of Model 1:\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 0.2505 - accuracy: 0.9188\n",
      "Test loss: 25.054%. Test accuracy: 91.880%\n",
      "\n",
      "\n",
      "Evaluation of Model 2:\n",
      "313/313 [==============================] - 1s 5ms/step - loss: 0.2348 - accuracy: 0.9304\n",
      "Test loss: 23.479%. Test accuracy: 93.040%\n",
      "\n",
      "\n",
      "Evaluation of Model 3:\n",
      "313/313 [==============================] - 3s 8ms/step - loss: 0.2122 - accuracy: 0.9303\n",
      "Test loss: 21.216%. Test accuracy: 93.030%\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "for model in models:\n",
    "    \n",
    "    print('Evaluation of Model {}:'.format(i))\n",
    "    test_loss, test_accuracy = model.evaluate(x=X_test, y=y_test)\n",
    "    \n",
    "    print('Test loss: {:.3f}%. Test accuracy: {:.3f}%'.format(test_loss*100, test_accuracy*100))\n",
    "    print('\\n')\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "284008c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1_metrics = pd.DataFrame(history_dict['sequential'].history)\n",
    "model2_metrics = pd.DataFrame(history_dict['sequential_1'].history)\n",
    "model3_metrics = pd.DataFrame(history_dict['sequential_2'].history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5320bceb",
   "metadata": {},
   "outputs": [
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    "model1_metrics.head()"
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   "id": "156268de",
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    "model2_metrics.head()"
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  {
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   "execution_count": 43,
   "id": "043726a9",
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   "id": "5fc9398f",
   "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "\n",
    "for history in history_dict:\n",
    "    \n",
    "    accuracy = history_dict[history].history['accuracy']\n",
    "    val_accuracy = history_dict[history].history['val_accuracy']\n",
    "    \n",
    "    plt.plot(accuracy)\n",
    "    plt.plot(val_accuracy)\n",
    "    plt.title('Model-{} Accuracy'.format(i))\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "efface20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "\n",
    "for history in history_dict:\n",
    "    \n",
    "    loss = history_dict[history].history['loss']\n",
    "    val_loss = history_dict[history].history['val_loss']\n",
    "    \n",
    "    plt.plot(loss)\n",
    "    plt.plot(val_loss)\n",
    "    plt.title('Model-{} Loss'.format(i))\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3404fc49",
   "metadata": {},
   "source": [
    "#### Comparison of the validation accuracy and validation loss of all the 3 models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "817efcd2",
   "metadata": {},
   "source": [
    "The point of this is to highlight the fact that more tunable/trainable parameters often result in a more accurate model. They might require a little more testing and training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5f1e8a12",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy and loss\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, figsize=(14, 10))\n",
    "\n",
    "for history in history_dict:\n",
    "    val_acc = history_dict[history].history['val_accuracy']\n",
    "    val_loss = history_dict[history].history['val_loss']\n",
    "    ax1.plot(val_acc, label=history)\n",
    "    ax2.plot(val_loss, label=history)\n",
    "    \n",
    "ax1.set_ylabel('validation accuracy')\n",
    "ax2.set_ylabel('validation loss')\n",
    "ax2.set_xlabel('epochs')\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4ac120",
   "metadata": {},
   "source": [
    "#### Generating the classification report and confusion matrix for each model, to define the performance of the CNN models. A confusion matrix visualizes and summarizes the performance of a classification algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f35a0d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "def plot_confusion_matrix(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import itertools\n",
    "\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d7dd73a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report of Model 1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.81      0.86      1000\n",
      "           1       0.99      0.99      0.99      1000\n",
      "           2       0.88      0.87      0.87      1000\n",
      "           3       0.91      0.94      0.92      1000\n",
      "           4       0.91      0.86      0.88      1000\n",
      "           5       0.99      0.97      0.98      1000\n",
      "           6       0.73      0.83      0.78      1000\n",
      "           7       0.95      0.98      0.96      1000\n",
      "           8       0.98      0.98      0.98      1000\n",
      "           9       0.97      0.96      0.97      1000\n",
      "\n",
      "    accuracy                           0.92     10000\n",
      "   macro avg       0.92      0.92      0.92     10000\n",
      "weighted avg       0.92      0.92      0.92     10000\n",
      "\n",
      "\n",
      "\n",
      "Confusion matrix of Model 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Classification Report of Model 2\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.91      0.88      1000\n",
      "           1       0.99      0.99      0.99      1000\n",
      "           2       0.91      0.88      0.90      1000\n",
      "           3       0.93      0.94      0.93      1000\n",
      "           4       0.93      0.87      0.90      1000\n",
      "           5       0.99      0.98      0.98      1000\n",
      "           6       0.78      0.81      0.79      1000\n",
      "           7       0.96      0.97      0.97      1000\n",
      "           8       0.99      0.98      0.99      1000\n",
      "           9       0.96      0.98      0.97      1000\n",
      "\n",
      "    accuracy                           0.93     10000\n",
      "   macro avg       0.93      0.93      0.93     10000\n",
      "weighted avg       0.93      0.93      0.93     10000\n",
      "\n",
      "\n",
      "\n",
      "Confusion matrix of Model 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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W8JbPuvX8u/TfmvTNV9W7MlhnjDEmUqSM5X6OC5de7gGhqgNDHYMxxhgTDBGdoZ8t3xoAY4wxUSBMevZnhWXoxhhjopxYlbsxxhgTESKghH7uX5IYY4wxxkroxhhjjFW5G2OMMee6MJn+NKssQzfGGGMioIR+7n8CY4wxxlgJ3RhjjLEqd2OMMeacZ/ehG0+dSiWYHwZzkRducFuoQ0i1Z8noUIcAhNcc0+Zk4TIPebgIlznIo1YEHP9z/5LEGGOMMVZCN8YYE+VstjVjjDEmElgbujHGGBMZrA3dGGOMMeHASujGGGOMVbkbY4wxESACqtwtQzfGGBPdJDI6xZ37n8AYY4wxVkI3xhhjIqHK3UroYeCmgQMoW7o49WrXCEp6g/q0YunEh1n28SPcdlUrAGpWjmPW2HtYMuFhPn7pJvLnzQVAkYJ5mfbmHeyaP5IXH+gRlPiCfTwyU7XSBTSoU4tG9evQrHGDkMUxY/o0aiVUIaFqRUYMHxb1cWzdupV2bS+mds1q1L0wgdEvjwpZLOFyTCyOrBGRLD9CzTL0MNC3X38mTZ0WlLSqVyjFdVc2pUXfETTs9SztW9agQtlivDbkKv7v5Uk06PkMk7/7gbv6tQHgyNEknhgzlYde/Cwo8UFwj8fp+Orrb1m0dAXzFy4JSfrJyckMvmMQk6Z8xYpVa5k47iPWrV0btXEAxMbGMmz4SFauXsfseQt54/VXo/qYWBxZI1iGbvykeYuWFClSJChpVb2gJItXb+bwkSSSk08wd9lGulx8IZXOL868ZRsB+HbhT3RtUxuAQ0eOsWDlrxw5mhSU+CC4x+NcsGTxYipUqMgF5cuTI0cOevTqzdQpk6I2DoBSpUpRp25dAPLnz0/VqtXYvj0x6HGEyzGxOAxYhh511vyyneZ1K1KkYF5y58rOZc0TiC9ZmLW/7KBjq5oAXHlJXeJLFA5xpOFBROh0eTuaNqrPf996MyQxbN+eSHx8mdTXcXHxJCYGP/MKlzjS2rJ5MytXrqBBw0ZBTztcjonFkUXip0eIhW2nOBE5D5jpvSwJJAO7vNcNVfVYSAI7x/286Q9GvvM1U1+7jYOHj7JqfSLHjydz09APGHl/dx66oT1fzF7NsaTkUIcaFmbOmkfp0qXZuXMnndpfSpUqVWneomVQY0hvCthQVO+FSxy+Dhw4QJ+e3Rgx8iUKFCgQ9PTD5ZhYHFkVHlXmWRW2Gbqq/gnUBhCRocABVX0+Zb2IxKrq8WDFIyIxqhoRudzYz79n7OffA/D4bZ1I/GMv6zf/QadbXwWgYtnitG+REMoQw0bp0qUBKF68OJ26dGXpksVBz9Dj4uLZtm1r6uvExG2pcUVjHCmSkpLo07MbvfpcTdcrrgxJDOFyTCyOrIuEDP2cqnIXkXdE5AUR+Q54TkRqi8hCEVklIp+JSGFvu1kiUt97XlRENnvPE0RksYis9N5TyVt+jc/yN0Qkxlt+QESeEJFFQJOQfOgAKFY4HwBlShamS+sLmTBtaeoyEeHBG9rxn4/nhTLEsHDw4EH279+f+nzmN19TPSH4Pe/rN2jAxo0b2LxpE8eOHWPi+HF06Ng5auMAVxK8+YbrqVK1GnfedXdIYoDwOSYWh4EwLqFnojLQVlWTRWQVcLuqzhaRJ4DHgMGZvPdmYJSqfiAiOYAYEakG9AKaqWqSiIwBrgbeBfICP6rqkLQ7EpEbgRsBypQtm6UPdO01fZg7exa7d++mQrl4Hh3yOP0HXJ+lfWbmo+cHUqRQXpKOJzN42AT27j/MoD6tuKmXK3lO+nYl705amLr9T188Tv68uciRPZZOF9ei462v8tOvvwcsvmAfj4zs/OMPevdwJb/jx4/Ts3cfLm13WdDjiI2N5cVRo+nUoR3Jycn06z+A6gnBr0EJlzgAFsyfz4cfvEeNGjVpVK82AI8/9QyXtb88qHGEyzGxOLIuEkrokl6bR7hJqXIHagDfqepYESkIrFbVst42FYCJqlpXRGYB96rqUhEpCixV1XIichXwCC6z/lRVN4jIbcDDwE4vudzAR6o6VESOAzlPVdVer159nb9oqd8/95kq3OC2UIeQas+S0aEOAUi/TS8UIuHHwphgy51dlqlq/UCnE1PkAs3X7oks72ffuGuDEm9GzsUS+sHT2OY4/zQn5EpZqKofetXnHYDpIjIQ1zdxrKo+lM5+jkRKu7kxxpgMhEkv9aw6p9rQfanq38AeEWnhLeoLzPaebwbqec+7p7xHRMoDv6rqy8BkoBauJ313ESnubVNERM4P/Ccwxhhj/OdcLKH76ge8LiJ5gF+B67zlzwMTRKQv8K3P9r2Aa0QkCfgdeEJV/xKR/wNmiEg2IAkYBGwJ1ocwxhgTOmK3rQWPqg7NYPlKoHE6y3/Clb5T/J+3/Fng2XS2Hw+MT2d5vrMK2BhjzDnFMnRjjDEmAkRChn7OtqEbY4wx5h9WQjfGGBP1IqGEbhm6McaY6BYht61Zhm6MMSbqRUIJ3drQjTHGmCAQkbtEZI2I/CgiH4lILm/sk69FZIP3f2Gf7R8SkY0i8rOItDvV/i1DN8YYE9VS7kPP6iPTNETigDuA+qpaA4gBegMPAjNVtRJuoLMHve2re+sTgMuAMSkTh2XEMnRjjDFRL9AZuicWyC0isUAeYDvQBRjrrR8LdPWedwHGqepRVd0EbAQaZrZzy9CNMcYY8cMjE6qaiBvF9DdgB/C3qs4ASqjqDm+bHUBx7y1xwFafXWzzlmXIMnRjjDHGP4qKyFKfx40pK7y28S7ABUBpIK+IXJPJvtK7RMh0+kjr5W6MMSa6id96ue/OZPrUtsAmVd0FICKfAk2BP0SklKruEJFS/DOV9zagjM/743FV9BmyDN0PFEg6fiLUYYTNHOQAha94LdQhALD7k5tDHQIAJ5JDf34AZI8Nn0q5g0eOhzoEAPLmsp9BE5Tb1n4DGnuTiR0G2gBLcVOC9wOGef9P8rafDHwoIi/gSvSVgMWZJWBnsjHGmKgX6AxdVReJyMfAcuA4sAJ4E8iHmx30elym38Pbfo2ITADWetsPUtXkzNKwDN0YY4wJAlV9DHgszeKjuNJ6ets/DTx9uvu3DN0YY0xUs/nQjTHGmEhx7ufnlqEbY4yJcv7r5R5S4dPl1RhjjDFnzUroxhhjol4klNAtQzfGGBP1LEM3xhhjIsG5n59bG3oobNu6lQ7t2lC/dgIN69ZkzOiXU9e9PmY0dWtVo2Hdmjz68ANBjeumgQMoW7o49WrXCEp6t3epxbJXe7F0dC/G3tuWnNljqHXBecwecSULR/Vg3gvdqF+peOr293avw49vXMUPr/WhbZ0ymez57N1y4wDKxZegQZ2aqcseefA+6tSsRqN6F9K7x5Xs3bs3IGn7yuwcAXj5xZEUyB3Dn7t3BzwWXzOmT6NWQhUSqlZkxPBhQU0boE5CRVo0qk2rpvVo07JR6vL/vD6aRnUSaNbgQob+34NBjSnUxyRFsL+/GQmX4xGNLEMPgdjYWJ4eNoKlK9cwc/YC/vPGGH5at5Y5s7/jy6mT+X7JShYvX80dg+8Jalx9+/Vn0tRpQUmrdJG83NqpJs3u+pj6t40nJkbo0bIiT1/XhKfHLaXxnRN58oMlPH1dYwCqlilMj5YVqTtoHJ2HTmXULS3Ils3/l9RX9+3P51O+OmlZ6zaXsGTFahYt+4FKlSoxcvizfk83rYzOEXCZ/bfffk2ZMmUDHoev5ORkBt8xiElTvmLFqrVMHPcR69auDWoMAJ9/8Q2zFixj5pxFAMydM4uvvpjCnIXLmb/kBwbdeXfQYgmXYwLB/f5mJJyOx5kK0vSpAWUZegiULFWK2nXqApA/f36qVK3K9u2J/PfN17nr3vvJmTMnAMWKF89sN37XvEVLihQpErT0YrNlI3eOWGKyCblzxrLjr4OoKgVyZwegYN4c7PjrEAAdG5Vj4pyNHDt+gi1/7OeXHX/ToJL/j0/zFi0pXPjkY9DmkkuJjXWtUw0aNSYxMdHv6aaV0TkC8ND9d/Pk088F/QdkyeLFVKhQkQvKlydHjhz06NWbqVMmnfqNAfbOW29w590+35tiwfvehNMxCfb3Nz3hdDzOhD8yc8vQDVu2bGbVypXUb9CIjRs3sGD+PC5u0YT2l1zMsqVLQh1ewGz/6yAvfbaS9W/3ZdO7/dh38BgzV2zjvv/M55kBTdjwdl+eHdCEIWMXAhB3Xl627T6Q+v7E3QcpfV7eoMf93jv/49J2lwU1Td9z5MupkylVOo6atS4MagwA27cnEh//T1NHXFx8UC5ufIkI3bu2p3WLhox9+z8A/LJxPd8vmMelFzel02WtWb4seN+bcDgm4eRcPh6RkKGHVac4EUkGVuPiWgf0U9VDmWw/C7hXVZeKyGagvqoGt1ExCw4cOEDfPj0YNuIFChQowPHjx9m7Zw/fzlnAsqVL6H9Nb1at2xgWJ4q/Fcqbg46NLqDawPfZe/AYHz54Kb1bVaJB5eLc/9YCPl/wK92aV+C1Oy6mw6NTIJ1jkOnEwAEwfNjTxMTG0qvP1UFL0/cciY2NZcRzz/J5iKpVVf99xIN9bn7x9WxKlSrNrl076d75MipVrsrx48n8vXcP07+dz4plSxjY7yqWrV4flNjC4ZiEEzseoRVuJfTDqlpbVWsAx4CwmPtSHL8eq6SkJK7p052eva6ic9crASgdF0fnrlcgItRv0BDJli3onZ6CpXXteDb/sY/d+45wPPkEny/4lcbVSnJ16yp8vuBXAD6Z9wv1K7vq08TdB4gvmi/1/XFF87Ljz4NBi/eD98Yy7csveHvs+0H7gUp7jmz69Re2bNlEs4Z1qFGlPImJ22jRpD5//P57UOKJi4tn27atqa8TE7dRunTpoKSdolQpl16xYsW5vFNXli9bQum4ODp0dt+buvUbki2I35twOCbh5Fw+HpFQQg+3DN3XXKCiiLQSkakpC0VktIj0z+yNInK3iPzoPQZ7y54TkVt9thkqIvd4z+8TkSUiskpEHveWlRORdSIyBjfdnd+6Vasqg24eSJUq1bjtzrtSl3fs1IXZs74DYMOG9SQdO8Z5RYv6K9mwsnXXARpWLUHunK6S6OIL4/l56x52/HWIFjXcD0CrWnFs3P43AF8s3kyPlhXJEZuN80vkp2LpQizZsDMosX49fRovPD+c8Z9MIk+ePEFJM71zJKFGTX797Xd+/PlXfvz5V+Li4pn7/VJKlCwZlJjqN2jAxo0b2LxpE8eOHWPi+HF06Ng5KGkDHDx4kP3796c+nzXza6pVT6B9x87Mne2+Nxs3rOdYEL83oT4m4eacPh7ih0eIhVWVewoRiQXaA2dctygi9YDrgEa4Q7xIRGYD44CXgDHepj2By0TkUtzE8Q297SeLSEvcvLRVgOtU9VbSEJEbgRuBM+5tvHDBfMZ9+D4JNWrSrJHr+DTk8afo228At950PY3q1SJHjhy8/tb/gnrVd+01fZg7exa7d++mQrl4Hh3yOP0HXB+QtJas38ln83/l+5e6czxZ+eHXXfx32lp++HU3I25oTmyMcPRYMreNngXAut/28Mm8X1gxpjfHk5XBr8/lxAn/V7r373sVc+fM4s/du6lcvgyPPDqUkcOHcfTYUTpffikADRo24uVXX/d72r4yOkfaXXZ5QNPNTGxsLC+OGk2nDu1ITk6mX/8BVE9ICFr6u3b+Qb+rugNw/Hgy3Xr2ps0l7Th27Bh33DqQ5g1rkz1Hdka/8XbQvjehPia+gvn9zUg4HY8zFQ4l7KyS9No8QsWnDR1cCf0eoCmunbyjt81oYKmqvpNeGzpwNXCeqg7xtn8S2KWqL4vIOty8s8WAMaraTESeB7oDe7108wHPAjOB71T1glPFXbdefZ09f3GWP39WZY8NnwqXwle8FuoQANj9SVi02gTk4uNshNM5cvDI8VCHAEDeXGFZrjFA7uyyTFXrBzqdnCUqadzVo7K8n00vdghKvBkJtzP5sKrW9l0gIsc5uWkg1yn2kdll1se4zLskrsSesv2zqvpGmnTLAcFrpDXGGBMaNtta0GwBqotIThEpiCthZ2YO0FVE8ohIXuAKXGkfXCbeG5epf+wtmw4MEJF8ACISJyLBvQHcGGNMyAjuRpqsPkIt3Ero/6KqW0VkArAK2ACsOMX2y0XkHSClDvwtVV3hrVsjIvmBRFXd4S2bISLVgO+9K7QDwDVAciA+jzHGmHATHr3UsyqsMnRVzZfB8vuB+9NZ3srneTmf5y8AL2Swr5rpLBsFpNeAEtpBkY0xxpjTFFYZujHGGBMKEVBAtwzdGGOMiYQq93OhU5wxxhhjTsFK6MYYY6JbmPRSzyrL0I0xxkQ1AbJlO/dzdMvQjTHGRL1IKKFbG7oxxhgTAayEbowxJupFQi93y9CNMcZEN+sUZ4wxxpz73Fju536Obm3oxhhjTASwErqfhMMtD+E0t/2ez24JdQgAFL54SKhDAGD3zMdDHULYyZndyhMmXNjkLMYYY0xEiID83DJ0Y4wxJhJK6FbnZYwxxkQAK6EbY4yJbnbbmjHGGHPui5Tb1ixDN8YYE/UiID+3NnRjjDEmElgJ3RhjTNSzKndjjDEmAkRAfm5V7qFwy40DKBdfggZ1aqYue/rJoVS6IJ4mDerQpEEdpn/1ZVBjWv/zzzSqXyf1UeK8gox++aWgxpDipoEDKFu6OPVq1whKeoO6N2bp2EEse/c2buvR5KR1g3s34/DcJzivYB4AihTIzbRR17Fr+iO8OLhDwGJK7xx55MH7qFOzGo3qXUjvHleyd+/egKWfkRnTp1EroQoJVSsyYviwoKa9betWLr+0DfUuTKBBnZqMGf0yAE8OHULj+rVp2rAuXTq0Y8f27UGNK5THxFewvzcZCZfjcUbEldCz+gg1y9BD4Oq+/fl8ylf/Wn7b7YP5fskKvl+ygnbtLw9qTJWrVGHR0hUsWrqCBYuWkjtPHjp3uSKoMaTo268/k6ZOC0pa1S8oznWd6tHixjdpeN0Y2jetTIX4IgDEFy9A6wYV+O33vanbHzl2nCfemslDY6YHNK70zpHWbS5hyYrVLFr2A5UqVWLk8GcDGkNaycnJDL5jEJOmfMWKVWuZOO4j1q1dG7T0Y2Njeea5ESz7YQ3fzlnAm6+P4ad1a7nz7ntZuHQlCxYv57LLOzLsmSeDFlOoj4mvYH5vMhJOxyMaWYYeAs1btKRw4SKhDiND3307k/LlK1D2/PNDkn7zFi0pUiQ4x6fq+cVYvHYbh48mkZx8grkrN9OlZXUAht/enkfGTD9pjPxDR5JYsPo3jhw7HtC40jtH2lxyKbGxrpWsQaPGJCYmBjSGtJYsXkyFChW5oHx5cuTIQY9evZk6ZVLQ0i9ZqhS169QFIH/+/FSpWpXtiYkUKFAgdZuDBw8GtaQU6mPiK5jfm4yE0/E4E+62taw/Qs0y9DDyxuuv0qjehdxy4wD27NkTsjgmThhHj169Q5Z+MK3Z9AfNLzyfIgVykztndi5rXJn44gXo0KwK23ftY/Uvf4Q6xHS9987/uLTdZUFNc/v2ROLjy6S+jouLD/pFRYotmzezauVK6jdsBMDjQ/6PqhXOZ8K4D3lkSPAmwgmnYxIOzt3jkfXqdqtyzyIRSRaRlSKyRkR+EJG7ReSc/EwDb7yF1es28v2SFZQoWYqHH7gnJHEcO3aML6dO4cpuPUKSfrD9vGU3Iz+Yx9QX+zH5+b6s2vg7x5NP8MC1F/HEf78NdXjpGj7saWJiY+nV5+qgppvebH6h+BE7cOAA1/TpwbDnX0gtnT/2xFP89MsWeva+ijdfezVosYTLMQkX5/LxsBJ66B1W1dqqmgBcAlwOPJZ2IxEJ+978JUqUICYmhmzZsnHdgBtYumRJSOKYPu0ratepS4kSJUKSfiiM/WI5Ta9/nUtuf5s9+w+zZcdezi9ViMX/u5WfJtxFXLECfP/fmylRJF+oQ+WD98Yy7csveHvs+0H/oYyLi2fbtq2prxMTt1G6dOmgxpCUlMQ1vbvTs/dVdOl65b/W9+zVh0mffxq0eMLhmIQTOx6hda5n6KlUdSdwI3CbOP1FZKKITAFmiEheEXlbRJaIyAoR6QIgIgkistgr6a8SkUretl94pf4fRaRXoOP/fceO1OdTJn1G9YTQ9FSdOD56qttTFCuUF4AyxQvSpWU1Ppi2kvM7D6dqzxep2vNFEnfto8n1r/PHXwdCGufX06fxwvPDGf/JJPLkyRP09Os3aMDGjRvYvGkTx44dY+L4cXTo2Dlo6asqg24aSJWq1bj9zrtSl2/cuCH1+ZdfTKFylSpBiynUxyTcnMvHIxKq3MO+5HomVPVXr8q9uLeoCVBLVf8SkWeAb1V1gIgUAhaLyDfAzcAoVf1ARHIAMbiS/nZV7QAgIgX9GWf/vlcxd84s/ty9m8rly/DIo0OZO2c2q35YiYhw/vnlePnV1/2Z5Gk5dOgQ3878mlfGBD9tX9de04e5s2exe/duKpSL59Ehj9N/wPUBS++jp3pTpGBuko6fYPCLX7D3wJFMt/9pwl3kz5uTHLExdGpRlY73vMtPm3f5Nab0zpGRw4dx9NhROl9+KQANGjYK6nkSGxvLi6NG06lDO5KTk+nXfwDVExKClv73C+bz0Yfvk1CjJk0bus5xjz3xFO++8zYb1q8nW7ZslClbllGvvBa0mEJ9THwF+3uTnnA6HmckTKrMs0rSa/M4V4jIAVXNl2bZXqAK0B64SFWv85YvBXIBKd2TiwDtgDrAI8C7wKequkFEKgPTgQnAVFWdm07aN+JqBChTtmy9dRs2+/3znalsYXRChsPVKkDhi4eEOgQAds8MXketzMSE0UlyPPlEqEMAIDYmYioqI07u7LJMVesHOp38Zapq7cFvZXk/8+5tEZR4MxJRZ7KIlAeSgZ3eooO+q4FuXpt7bVUtq6rrVPVDoDNwGJguIq1VdT1QD1gNPCsi/8oVVPVNVa2vqvWLFi0W0M9ljDHGnErEZOgiUgx4HRit6Vc7TAduF6/oKCJ1vP/LA7+q6svAZKCWiJQGDqnq+8DzQN1gfAZjjDGhYW3ooZdbRFYC2XFV6e8BL2Sw7ZPAS8AqL1PfDHQEegHXiEgS8DvwBNAAGCEiJ4Ak4JbAfQRjjDGhFgb5cZad0xm6qsZksu4d4B2f14eBm9LZ7lkg7Ria072HMcaYKBAOJeysipgqd2OMMSaandMldGOMMSbLIuS2NcvQjTHGRDUhPDq1ZZVl6MYYY6JeBOTn1oZujDHGRAIroRtjjIl62SKgiG4ldGOMMVEvGNOnikghEflYRH4SkXUi0kREiojI1yKywfu/sM/2D4nIRhH5WUTanWr/lqEbY4yJai5DDspIcaOAaapaFbgQWAc8CMxU1UrATO81IlId6A0kAJcBY0Qkw7FXwDJ0Y4wxJuBEpADQEvgvgKoeU9W9QBdgrLfZWKCr97wLME5Vj6rqJmAj0DCzNCxDN8YYE/WySdYfp1Ae2AX8T0RWiMhbIpIXKKGqOwC8/1Om/44Dtvq8f5u3LOPPcBaf2xhjjIkofqpyLyoiS30eN/okEYub6Os1Va2Dmw30wcxCSmdZpvOdWy93PxDCa57pcHA0KTnUIQCw8+uhoQ4BgKK9sj7Xsj/smXhDqENIdfBoeJwjBfNYucb47T703ZnMh74N2Kaqi7zXH+My9D9EpJSq7hCRUvwz/fc2oIzP++OB7ZklbmeyMcYYE2Cq+juwVUSqeIvaAGtx03b385b1AyZ5zycDvUUkp4hcAFQCFmeWhpXQjTHGRDXBDf8aBLcDH4hIDuBX4DpcwXqCiFwP/Ab0AFDVNSIyAZfpHwcGqWqm1VqWoRtjjIl6wWg1VdWVQHpV8m0y2P5p4OnT3b9l6MYYY6Lb6d9HHtasDd0YY4yJAFZCN8YYE/UioIBuGboxxpjoJkTG5CyWoRtjjIl6EZCfWxu6McYYEwksQw+xrVu30q7txdSuWY26FyYw+uVRURfHkSNHaN2iMc0a1aVxvVo88+RQAD7/9GMa16tF4bzZWbFsaVBi2bZ1Kx3ataF+7QQa1q3JmNEvn7T+5RdHUiB3DH/u3h2Q9G/vVINlo7qzdFQ3xt59MTmzx/DePa1Z+MKVLHzhSn56ozcLX7gydft7r7yQH8f05IfRPWhbOz4gMfmaMX0atRKqkFC1IiOGDwt4er42bviZNs3rpz4qxp/Hm2NeZs9ff9GzS3ua1KlOzy7t2btnT1DjCuUxCcc4bho4gLKli1Ovdo2QxXA2gjTbWkBZhh5isbGxDBs+kpWr1zF73kLeeP1V1q1dG1Vx5MyZk8lffcP8RcuZu3AZM7+ezpLFC6lWPYH3PppI0+YtghIHuOPw9LARLF25hpmzF/CfN8bw0zp3HLZt3cq3335NmTJlA5J26SJ5uLVDDZrd9xn17/yEmGzZ6NG8PH1Hfkvjuz+l8d2f8vn3m5i0cBMAVeML0aN5Bere8TGdn5jGqJuakS2AN9MmJycz+I5BTJryFStWrWXiuI+Ceq5WrFSFmfOWMnPeUmbMXkTu3Hlo37ELr7w4nBYXXcz3K9bS4qKLeeXF4UGLKdTHJNziAOjbrz+Tpk4LSdpnyx9zoYdBfm4ZeqiVKlWKOnXrApA/f36qVq3G9u2JURWHiJAvXz4AkpKSSEo6jiBUqVqNSpWrnOLd/lWyVClq1/nnOFSpWjX1ODx0/908+fRzAb0Sj40RcueIJSabkDtnLDv+OnTS+m7NyjNh7i8AdGx4PhPn/cKx4yfYsnM/v+zYR4NKxQIW25LFi6lQoSIXlC9Pjhw56NGrN1OnTDr1GwNg7qxvKXdBecqUPZ/pX06h51V9Aeh5VV+mfTE5aHGEyzEJlzgAmrdoSZEiRUKSdlZkE8nyI9Qy7BQnIq+QycwuqnpHQCKKYls2b2blyhU0aNgo6uJITk7moqYN2fTrRgbedAv1Q3wMALZs2cyqlSup36ARX06dTKnScdSsdWHA0tv+1yFemrSK9W/24fCx48xcmcjMH/65qGpWvSR/7D3MLzv2ARB3Xl4Wrd+Zuj7xz4OULpI3cPFtTyQ+/p+5IuLi4lm8eFEm7wiczz+dQNfuvQDYtWsnJUqWAqBEyVLs3rUraHGEyzEJlzhMaGVWQl8KLMvkEbZEpKSIjBORX0RkrYh8KSKVz3AfhUTk1kDFmNaBAwfo07MbI0a+RIECBYKVbNjEERMTw7xFy1izYQvLli5h7Zofg5Z2eg4cOEDfPj0YNuIFYmNjGfHcszwy5PGAplkobw46NixHtZvHUf76D8ibK5beF1VMXd+zRQUmeqVzIN3JFVUznV0xS9LbdyjaDY8dO8aML6fSuWu3oKedVrgck3CJ41wmfniEWoYldFUd6/taRPKq6sHAh5Q14s7iz4CxqtrbW1YbKAGsP4NdFQJuBcb4OcR/SUpKok/PbvTqczVdr7jy1G+I4DgKFSpE8xYXMfPr6VRPCE2nmqSkJK7p052eva6ic9crWfPjarZs2USzhnUASEzcRosm9flu7kJKlCzpt3RbXxjH5j/2s3vfEQA+X7iZxlVKMG72RmKyCV0al6PZvZ+nbp+4+yDx5/1TIo87Ly879hxKu1u/iYuLZ9u2rf+kn7iN0qVLByy9jHz79TRqXliHYsVLAFCsWHH++H0HJUqW4o/fd1C0WOCaHdIKl2MSLnGcyyLhAuiUbegi0kRE1gLrvNcXikjAM7ksuBhIUtXXUxZ4A+LPE5ERIvKjiKwWkV4AIpJPRGaKyHJveRfvbcOACiKyUkRGBCpYVeXmG66nStVq3HnX3YFKJqzj2L1rF3v37gXg8OHDzP5uZtDbzlOoKoNuHkiVKtW47c67AEioUZNff/udH3/+lR9//pW4uHjmfr/Ur5k5wNZdB2hYuTi5c8QAcHGt0vy8bS/gMvv1iX+T+Oc/19RfLPmNHs0rkCM2G+cXz0/FUgVYsiFw1c31GzRg48YNbN60iWPHjjFx/Dg6dOwcsPQy8tnH41Or2wEubd+JCR++B8CED9+j3eWdghZLuByTcInjXOUGlsn6I9ROp1PcS0A74E8AVf0BaBnAmLKqBuk3CVwJ1AYuBNoCI7zJ5I8AV6hqXdzFwEivlP8g8Iuq1lbV+wIV7IL58/nwg/eY/d23NKpXm0b1ajPtqy8DlVxYxvH77zvodFlbmjasQ+sWjWnVui2XXd6RKZM+p3rF81myaCE9u3Xmys7tAx7LwgXzGffh+8yZ/R3NGtWlWaO6TJ8WnOOwZMMuPvv+V74feSVLR3Ujmwj/nbEOgB7NK6R2hkuxbusePlnwKyte6cHkIZcx+D/zOXEicFXusbGxvDhqNJ06tKN2zWp069GT6gkJAUsvPYcOHWLOdzPp0Klr6rLb776P2d/NpEmd6sz+bia333V/0OIJh2MSTnEAXHtNH1q1aML6n3+mQrl43nn7vyGJIxrJqdrcRGSRqjYSkRWqWsdb9oOqBq53UBaIyB3ABap6V5rlLwKrVfVt7/V7wETgK+BF3EXKCaAKcAGQC5iqqunW+4rIjcCNAGXKlq23/pctgflA56ijSZlO2xs04dDzFKB4n/D4Udsz8YZQh5Dq70NJoQ4BgIJ5soc6BJOB3NllmaqmN92oX51XPkHbP/FhlvfzQd/aQYk3I6dTQt8qIk0BFZEcInIvXvV7mFoD1EtneUa/7FcDxYB6qlob+AOXmWdKVd9U1fqqWr9Y0eC12RljjPG/aLkP/WZgEBAHJOKqrQcFMKas+hbIKSKpRRERaQDsAXqJSIyIFMOVyBcDBYGdqpokIhcD53tv2w/kD27oxhhjQiESRoo75eQsqrobV4o9J6iqisgVwEsi8iCujXwzMBjIB/yAu7/+flX9XUQ+AKaIyFJgJfCTt58/RWS+iPwIfBXIdnRjjDEmq06ZoYtIeWAU0BiXEX4P3KWqvwY4trOmqtuBnumsus97+G67G2iSwX6u8n90xhhjwklKL/dz3elUuX8ITABKAaVxHck+CmRQxhhjTDBFQpX76WTooqrvqepx7/E+mQwJa4wxxpxrInqkOBFJGV3/O68tehwuI+8FfBGE2IwxxhhzmjJrQ1+Gy8BTLjxu8lmnwJOBCsoYY4wJFpHwGbMiKzIby/2CYAZijDHGhEoE5Oen7uUOICI1gOr4DLiiqu8GKihjjDEmmMKhU1tWnc5ta48BrXAZ+pdAe2AeYBm6McYYEyZOp5d7d6AN8LuqXoeb3CRnQKMyxhhjgigShn49nSr3w6p6QkSOi0gBYCdQPsBxGWOMMUEhSGR3ivOxVEQKAf/B9Xw/gBsD3RhjjDn3hUkJO6tOZyz3W72nr4vINKCAqq4KbFjGGGOMOROZDSxTN7N1qro8MCGZSJAze0yoQwgr4TIPeeHGg0MdQqo9C18KdQgAJJ8Ij4EvY8JoMPETYXJMginSe7mPzGSdAq39HIsxxhgTEqfTQzzcZTawzMXBDMQYY4wJBSEySuiRcFFijDHGRL3TGinOGGOMiWRh1IXhrFmGbowxJupFQoZ+yip3ca4RkSHe67Ii0jDwoRljjDGB50Z6kyw/Qu102tDHAE2APt7r/cCrAYvIGGOMMWfsdKrcG6lqXRFZAaCqe0QkR4DjMsYYY4ImEqrcTydDTxKRGNy954hIMeBEQKMyxhhjgigMasyz7HSq3F8GPgOKi8jTuKlTnwloVMYYY4w5I6fM0FX1A+B+4FlgB9BVVScGOrBoMmP6NGolVCGhakVGDB8W9XGEUyzRGseg3i1ZOv4Blo1/gNv6XATAe8/0Y+EH97Hwg/v4afIQFn5wHwC9L6uXunzhB/dxcPEL1KocF/AYQ/W3ueXGAZSLL0GDOjVTlz3y4H3UqVmNRvUupHePK9m7d2/Q4gHYunUr7dpeTO2a1ah7YQKjXx4V1PR9vfrKKOrXqUn92jUY/fJLIYvjTAiQTSTLj1A7nV7uZYFDwBRgMnDQW2b8IDk5mcF3DGLSlK9YsWotE8d9xLq1a6M2jnCKJVrjqF6hJNdd0YQW175Aw6tG0L55dSqUKUrfh8fS+OoRNL56BJ9/+wOTvnNzNI2btix1+fVD3mfL9r9YtT4xYPFBaP82V/ftz+dTvjppWes2l7BkxWoWLfuBSpUqMXL4s0GJJUVsbCzDho9k5ep1zJ63kDdefzUk5+qaNT/yv7ffYs78RSxcupKvvvyCjRs2BD2Os5HND49QO50YvgCmev/PBH4Fvsr0Hea0LVm8mAoVKnJB+fLkyJGDHr16M3XKpKiNI5xiidY4qpYrweLVmzl8NInk5BPMXf4LXS6uddI23drWZsL0Zf96b892dZkwI/DzNoXyb9O8RUsKFy5y0rI2l1xKbKzrktSgUWMSEwN7QZNWqVKlqFPXzaeVP39+qlatxvbtwY0B4Oef1tGwUSPy5MlDbGwsLVq2ZPKkz4Iex9kQyfoj1E6nyr2mqtby/q8ENMS1oxs/2L49kfj4Mqmv4+Lig/5jEE5xhFMs0RrHml9+p3mdChQpmIfcObNzWbPqxJcolLq+WZ3y/PHXfn7Zuvtf7+1+aR0mTA98hh4uf5v0vPfO/7i03WUhS3/L5s2sXLmCBg0bBT3t6tVrMH/uXP78808OHTrE9Glfkbhta9DjiFZnPFKcqi4XkQaBCCYzIvIIcBWQjOtlf5OqLsriPmcB96rq0qxskxWq/56mMBQDFIRLHBA+sURrHD9v/oOR785k6qu3cPDQMVZtSOR48j83tvRsV4+J6WTaDRLO59CRY6z95feAxZYiXP42aQ0f9jQxsbH06nN1SNI/cOAAfXp2Y8TIlyhQoEDQ069arRp333s/nS6/lHz58lGzZi1iYsN/QFIJkzbwrDrlkRaRu31eZgPqArsCFlH6MTQBOgJ1VfWoiBQFIuJe+Li4eLb5XMEmJm6jdOnSURtHOMUSzXGMnbSIsZPc9fLjt3YgcedeAGJistHl4lo06/v8v97To11wSucQPn8bXx+8N5ZpX37B1GnfhOTiIikpiT49u9Grz9V0veLKoKefot9119PvuusBeOzRh4mLiw9ZLGciAvLz02pDz+/zyIlrS+8SyKDSUQrYrapHAVR1t6puF5EhIrJERH4UkTfF+xaJyCwReU5EFovIehFp4S3PLSLjRGSViIwHcqckICKvichSEVkjIo8H64PVb9CAjRs3sHnTJo4dO8bE8ePo0LFzsJIPuzjCKZZojqNY4XwAlClRiC6ta6Vm1K0bVmb95j9I3Pn3SduLCFe2qc3EGSsCGleKcPnbpPh6+jReeH444z+ZRJ48eYKevqpy8w3XU6VqNe686+5TvyGAdu7cCcDW335j8uef0aNXn1O8Izxkk6w/Qi3TEro3oEw+Vb0vSPFkZAYwRETWA98A41V1NjBaVZ8AEJH3cKX4Kd57YlW1oYhcDjwGtAVuAQ6pai0RqQX4FiceUdW/vM88U0RqqeqqjAISkRuBGwHKlD37Tv+xsbG8OGo0nTq0Izk5mX79B1A9IeGs93euxxFOsURzHB8Nv44iBfOSdDyZwc99zN79hwHocWn6nd6a161A4s69bE78M6BxpQjl36Z/36uYO2cWf+7eTeXyZXjk0aGMHD6Mo8eO0vnySwFo0LARL7/6elDiAVgwfz4ffvAeNWrUpFG92gA8/tQzXNb+8qDFkOLq3t35688/ic2enRdGjaZw4cJBjyFaSXptUQAiEquqx0Vkpqq2CXJc6cUTA7QALgZuAh7EjSt/P5AHKAK8oqrDvHbvR1R1voiUAOarakUR+Rx4WVW/9fa5HLhRVZeKyM24DDoWVyNwu6qOO5029Hr16uv8RQFpYjfGrwo3HhzqEFLtWfhSqEMAIPlE+r+BwRYTDkU8z4kwOSZ5c2Zbpqr1A51OXOWaetOrWe+N/9illYISb0YyK6EvxrWXrxSRycBE4GDKSlX9NMCxnURVk4FZwCwRWY3L1GsB9VV1q4gMBXL5vOWo938yJ3/Of52pInIBcC/QwBur/p00+zLGGBPBoqUNvQjwJ9AaV6Xdyfs/aESkiohU8llUG/jZe75bRPIB3U9jV3OAq7191sBdEAAUwF2s/O2V6Nv7I25jjDHnAD+0n4dDBUtmJfTiXg/3H3GlWt9wg10fkw94RUQKAceBjbjq8b3AamAzsOQ09vMa8D8RWQWsxNVCoKo/eLPJrcENnDPfr9EbY4wxAZZZhh6Dy0jTu+4IaoauqsuApums+j/vkXb7Vj7PdwPlvOeHgd4ZpNE/g+Wt0ltujDEmcki6Wd25JbMMfUdKD3JjjDEmUrnJWUIdRdZllqFHwMczxhhjTi0SMvTMOsWF/FY1Y4wxxpyeDEvoqvpXMAMxxhhjQiUc5gLIqvAfNd8YY4wJoGhoQzfGGGMiX5jMZ55VpzOwjDHGGGPCnGXoxhhjol42b070rDxOh4jEiMgKEZnqvS4iIl+LyAbv/8I+2z4kIhtF5GcRaXfKz3DWn94YY4yJAClt6EEa+vVOYJ3P6weBmapaCZjpvUZEquMGQksALgPGeJOUZcgydGOMMVFPJOuPU6ch8UAH4C2fxV2Asd7zsUBXn+XjVPWoqm7CDXneMLP9W4ZujDHG+EdREVnq87gxzfqXcFN+n/BZVkJVdwB4/xf3lscBW3222+Yty5D1cveDpBPK7v1HT71hgBXNnzPUIaQ6ciw51CEA4TPHdHjMLh0+c5ADxN8wLtQhALDtP+lO7xDVsoXJ9yZ4hGz+GRx1d0bzoYtIR2Cnqi4TkVanFdS/ZfpTYhm6McaYqCYE5ba1ZkBnEbkcyAUUEJH3gT9EpJSq7hCRUsBOb/ttQBmf98cD2zNLwKrcjTHGRLcgzIeuqg+paryqlsN1dvtWVa8BJgP9vM36AZO855OB3iKSU0QuACrhTfmdESuhG2OMMaEzDJggItcDvwE9AFR1jYhMANYCx4FBqpppW6Zl6MYYY6Le6d5H7g+qOguY5T3/kwwmQ1PVp4GnT3e/lqEbY4yJakFqQw84y9CNMcZEvWCW0APFOsUZY4wxEcBK6MYYY6JeBBTQLUM3xhgT3YTIqK6OhM9wTvrv66Np07QubZrU4a3XXgFgzeof6HxJS9q1bMjlrZuyYtmSoMZ008ABlC1dnHq1awQ13SNHjtCmZWOaN6pLk/q1ePapoQCsXvUDl17cjKYNatO7exf27dsX8Fi2bd1Kh3ZtqF87gYZ1azJm9MsAPPPU41QpX4ZmjerSrFFdpk/7MuBxdGzXhga1E2hUtyaveXGs+mElbVo2pXmjulzUrCHLlmR6W6rfzZg+jVoJVUioWpERw4cFPL2bL63MvKfaM/fJy3jzpibkjM1Gobw5+PjeViwe1oGP721FwTzZU7evHl+Qrx5py7yn2jPnycvIGRv4n7hgH5P0HDlyhOZNGtKw7oXUvTCBJx9/LCRxQHgcjzMmICJZfoSaZegh8NPaNXz47ttM/WYe0+cuYeaML9n0y0aefuxh7rr/EabPWcy9Dw3hmaEPBzWuvv36M2nqtKCmCZAzZ04mffkN8xYtZ873y5j59XSWLF7InYNu4rEnnmHBkpV07NSVV156PuCxxMbG8vSwESxduYaZsxfwnzfG8NO6tQAMun0w8xctZ/6i5bS77PKAx/HUsBEsWbmGb3ziGPLIAzz4yKPMW7ScRx4dypBHHgxoHL6Sk5MZfMcgJk35ihWr1jJx3EesW7s2YOmVLJSbG9pWpu3jM2jx6DSyZROuaHQ+d15ejTlr/6Dhg18wZ+0f3NmhOuCG+X3txibc++5Smv/fV3QZ9i1JyYEddDfYxyQjOXPmZNrX37J4+Q8sWrqSGdOnsWjhwqDHES7HI1pZhh4CG9f/RN36DcmdJw+xsbE0atqCaV9MQkTYv9+VQvft+5sSJUsFNa7mLVpSpEiRoKYJ7so4X758ACQlJZGUdBwRYeOGn2navCUArdq0ZcqkzwIeS8lSpahdpy4A+fPnp0rVqmzfnhjwdE83DhFJranY9/fflCwVvHNkyeLFVKhQkQvKlydHjhz06NWbqVMmnfqNWRAbk41cOWKIySbkyRHD73sP075OHOPnbwJg/PxNXF7HzVdxcY2SrN22lzVb9wKw5+AxTmhgM/RQHJP0pP0OHU9KCkmJMVyOx9kQPzxCzTL0EKhSLYFF389jz19/cvjQIb77ejrbE7cx9Jnnefqxh2hYowJPDXmIB4c8GepQgyY5OZkWjetRuVwpWrVuQ/0GjahaPYGvvpgCwKRPPyZx29ZT7MW/tmzZzKqVK6nfoBEAb77+Kk0a1ObWm65nz549IYlj2IgXGfLwA1SveD7/99D9PPbEM0GLY/v2ROLj/xlaOi4unsTEwF3s/L73MK9O+4mVz3dizUtd2Hc4iVlrfqdYwVz88fcRAP74+whFC+QCoEKJ/KjChHsu4tuhl3J7+6oBiy1FsI9JZpKTk2lUrzZlSxenddtLaNioUdBjCKfjcSbcfOiS5UeoRWSGLiKPiMgaEVklIitFpJGIbBaRouls21lE0q23FJFWItLU3/FVqlKVW++4h6uu7MA1PTpRvUZNYmJiee9/b/LY0yNY/OMvPPbUcO6742Z/Jx22YmJimLtwGWvWb2H5siWsXfMjo197i7feGEOrZg05cGA/2XPkCFo8Bw4coG+fHgwb8QIFChRg4A0388PaDcxftJySJUvxyIP3BjWOZ704/vvm6zwzfCRrN27hmeEjue2WG4ISB4CmU9oNZCmwYJ7stK8TR737p1LjrknkyRlLjybnZ7h9bIzQqFJRbn7jezo8M5PL68bTolqJgMUHwT8mmYmJiWHRspVs3LyNpUsWs+bHH4MeQzgdjzNlJfQwJCJNgI5AXVWtBbTl5DllT6Kqk1X1Xz03RCQWaAX4PUMH6N33Or6atZBPvphJwcKFuaBCRT7+6H3ad+oKQMeu3Vi5bGkgkg5rBQsVonmLi5j59XQqV6nKp1OmMWv+Yrr16M0FF5QPSgxJSUlc06c7PXtdReeuVwJQvEQJYmJiyJYtG/0GDGTZ0sB3WExKSqJvmjg++uDd1OdXdOvB8qXB6xQXFxfPNp9aksTEbZQuXTpg6V1UvSRbdh3kz/1HOZ6sTF22jQYVi7Lr7yOUKOhK5SUK5mL3Plda3/7XYRb8vIu/Dhzj8LFkvlm1gwvPLxyw+CD4x+R0FCpUiJYXtWLGjOD3hwnH4xFNIi5DB0rh5qQ9CqCqu1U1Zcq520VkuYisFpGqACLSX0RGe8/fEZEXROQ7YDxwM3CXV8pv4c8gd+9yM+QlbvuNaVMn0aVbT0qULMXC+XMAmD/nOy6oUNGfSYat3bt28ffevQAcPnyYWd/NpFKVKuza6Y7RiRMneP65Z7ju+psCHouqMujmgVSpUo3b7rwrdfnvO3akPp8y6XOqVU8IeBy3pRNHyVKlmTd3NgCzZ31L+YqVAhqHr/oNGrBx4wY2b9rEsWPHmDh+HB06dg5Yetv+Okj9CueRO0cMAC2rl2D99n1MW5lIr2YXANCr2QV8tcJV6X774w4SyhQkt9fm3rRKMX7e/nfA4oPgH5OM7Nq1i70+36FvZ35DlSqBb3JIK1yOx9kQyfoj1CLxPvQZwBARWQ98A4xX1dneut2qWldEbgXuBQam8/7KQFtVTRaRocABVfV79+ob+/Vm719/EZs9O08Nf4lChQrz3KgxDH3oXo4fP07OnLkY9uKr/k42U9de04e5s2exe/duKpSL59Ehj9N/wPUBT/f333dw640DSE5O5sSJE1zRrTuXte/I66++zFtvvgZAx85dufra/gGPZeGC+Yz78H0SatSkWSPXKW3I40/x8YRxrF71AyJC2fPPZ9QrrwctjuY+cbz86hs8cN9dJHvnyKjRgY3DV2xsLC+OGk2nDu1ITk6mX/8BVE8I3IXN8l//YsrSrXw7tB3Hk0+w+re9vDv7F/LmjOW/tzbjmpbl2fbnIQaMmQ/A34eSeG36z3w95FJUlW9W7eDrVTtOkUrWBPuYZOT3HTu4YUA/9x3SE3Tr3pPLO3QMehzhcjzOXHjcdpZVkl6bx7lORGKAFsDFwE3Ag8BQoJmqJopII+BpVW0rIv2B+qp6m4i8A3ynqmO9/QwlgwxdRG4EbgSIiy9Tb+GqDQH/XKdSNH/OUIeQ6sixTGf5C5qYU01SHCTh8i3LEYT7sk9X/A3jQh0CANv+0zvUIZgM5M4uy1S1fqDTqVD9Qn3mg6yPLdG7bnxQ4s1IJJbQ8eaMnQXMEpHV/DN5/FHv/2Qy/uwHTzONN4E3AWrVqRcuv9fGGGOiVPhcrvuJiFQREd+GxdrAlrPc3X4gf5aDMsYYE9ZspLjwlA8YKyJrRWQVUB1X3X42pgBXBKJTnDHGmPARCbetRVyVu6ouI/1bzcr5bLMUd0saqvoO8I73vH+afa0HagUiTmOMMWFCzp375TMTiSV0Y4wxJupEXAndGGOMORORMn2qZejGGGOiXiRUuVuGbowxJuqd+9l5ZNQyGGOMMVHPSujGGGOiXgTUuFuGbowxJrq5TnHnfo5uGboxxpioFwkldGtDN8YYYyKAldCNMcZEOUGsyt0YY4w590VClbtl6H6QPZuE1Vzk4SBXjphQh2DCXLjMQ1646T2hDgGAPQtGhjqEVMeOnwh1CEEVKZ3irA3dGGOMiQBWQjfGGBPdxKrcjTHGmIhgGboxxhgTASKhl7u1oRtjjDERwEroxhhjopoA2c79Arpl6MYYY0wkVLlbhm6MMSbqRUKnOGtDN8YYYyKAZehhYMb0adRKqEJC1YqMGD4s6uMIp1gsjn9s3bqVdm0vpnbNatS9MIHRL48KSRwpgn1MBvVqwdKP7mXZuPu4rXcLAN57ui8L37+bhe/fzU+fP8LC9+9O3f7efq358ZOH+GHiA7RtXCXg8YXqHNm2dSsd27WhQe0EGtWtyWujXwZg1Q8radOyKc0b1eWiZg1ZtmRx0GI6G+KHf6FmVe4hlpyczOA7BvHFV18TFx9P88YN6NixM9WqV4/KOMIpFovjZLGxsQwbPpI6deuyf/9+mjaqR5u2l0TFOVK9fEmu69qIFv1Hcex4MpNH3cBX89fR95H3UrcZdmcn/j5wBICqF5Sgx6V1qNt7OKWKFeTL0TdRs/swTpzQgMQXynMkNjaWp4aNoHYdd15c1LQBF7dpy5BHHuDBRx7lknbtmTHtS4Y88iBfzPg24PGcjUjpFGcl9BBbsngxFSpU5ILy5cmRIwc9evVm6pRJURtHOMVicZysVKlS1KlbF4D8+fNTtWo1tm9PDHocEPxjUvWC4iz+8TcOH00iOfkEc5f/QpdWNU/aplvb2kyYsQKAji0TmDhjBceSktmy/S9+2fYnDRLKBiy+UJ4jJUuVonadf86LKlWrsn17IiLCvn37ANj399+ULFUqKPGcHX+Uz0N/RWAZeoht355IfHyZ1NdxcfEkJgb/RzJc4ginWCyOjG3ZvJmVK1fQoGGjkKQf7GOy5pffaV6nPEUK5iF3zuxc1qwa8SUKpa5vVqc8f/y1n1+27nbxFCvItj/2pq5P3LmX0sUKBiy+cDlHtmzZzKqVK6nfoBHDRrzIkIcfoHrF8/m/h+7nsSeeCXo80SbsMnQReURE1ojIKhFZKSJ++8UQkVYiMtVf+/MH1X9XwUkIuluGSxwQPrFYHOk7cOAAfXp2Y8TIlyhQoEBIYgj2Mfl5805GvvstU1+5ickv38CqDds5npycur7npXWYOH2FbzD/2kd6MftLOJwjBw4coG+fHjw74gUKFCjAf998nWeGj2Ttxi08M3wkt91yQ1DjOSPeWO5ZfYRaWGXoItIE6AjUVdVaQFtga2ijckQkIP0N4uLi2bbtn4+YmLiN0qVLByKpcyKOcIrF4vi3pKQk+vTsRq8+V9P1iitDEgOE5piMnbyYpte+yCU3jWHP34fY+JsrjcfEZKNLq5p8/M3Kf+LZufekEnxc8ULs2L0vYLGF+hxJSkqib5/u9Ox1FZ27uvPiow/eTX1+RbceLF8a7p3isv4ItbDK0IFSwG5VPQqgqrtVdbuIbBaRx0VkuYisFpGqACKSV0TeFpElIrJCRLp4y8uJyFxv++Ui0jRtQiLSwHtPeRGpJyKzRWSZiEwXkVLeNrNE5BkRmQ3cGYgPXL9BAzZu3MDmTZs4duwYE8ePo0PHzoFI6pyII5xisThOpqrcfMP1VKlajTvvuvvUbwigUByTYoXzAVCmRCG6XFwrtb28dYNKrN+yk8Sdf6du+8XcNfS4tA45ssdwfukiVCxTlCVrfgtYbKE8R1SV224eSJUq1bjtzrtSl5csVZp5c2cDMHvWt5SvWCko8ZwN1ylOsvwItXDr5T4DGCIi64FvgPGqOttbt1tV64rIrcC9wEDgEeBbVR0gIoWAxSLyDbATuERVj4hIJeAjoH5KIl4G/wrQBdgBvA90UdVdItILeBoY4G1eSFUvCtQHjo2N5cVRo+nUoR3Jycn06z+A6gkJgUou7OMIp1gsjpMtmD+fDz94jxo1atKoXm0AHn/qGS5rf3nQYwnFMfnouX4UKZCHpOQTDB7xKXv3Hwagx6V1UjP3FOt+/YNPvlnJivH3czz5BIOHfxqwHu4Q2nNk4YL5jPvwfRJq1KR5I9c5bsjjT/Hyq2/wwH13kXz8ODlz5mLU6NeDEk80k0C265wNEYkBWgAXAzcBDwJDgWaqmui1qT+tqm1FZCmQCzjuvb0I0A7YDowGagPJQGVVzSMirYD/AoeBS73Sfw1gAfCrt48YYIeqXiois4DHfC4qfOO8EbgRoEzZsvXW/7LFn4fBGBMkhZveE+oQANizYGSoQ0h17PiJUIcAQMHcMctUtf6pt8yaajXr6P8++y7L+2lSqXBQ4s1IuJXQUdVkYBYwS0RWA/28VUe9/5P5J24Buqnqz777EJGhwB/AhbhmhSM+q3fgLgLq4DJ+AdaoapMMQjqYQZxvAm8C1KtXP7yuiowxxpyZ0NeYZ1lYtaGLSBWvijxFbSCzou904HbxunOKSB1veUFcKfsE0BdX6k6xF+gAPOOV2H8Ginkd8hCR7CISmrpmY4wxIWH3oftfPmCsiKwVkVVAdVx1e0aeBLIDq0TkR+81wBign4gsBCqTppStqn8AnYBXcSX17sBzIvIDsBL4Vyc6Y4wxJpyFVZW7qi4j/cy0nM82S4FW3vPDuHb2tPvZANTyWfSQt3wWrjofVf0N8C2Jt0xnP63OJH5jjDHnpjDopJ5lYZWhG2OMMaEQAfm5ZejGGGNMJOTo4daGbowxxpizYCV0Y4wxUc0N3XruF9EtQzfGGBPdwmRylayyKndjjDEmAlgJ3RhjTNSLgAK6ZejGGGNMJOToVuVujDEmyvlj4NfMrwhEpIyIfCci60RkjYjc6S0vIiJfi8gG7//CPu95SEQ2isjPItLuVJ/CMnRjjDEm8I4D96hqNaAxMEhEquNmFJ2pqpWAmd5rvHW9cSOaXgaM8WYjzZBl6MYYY6KeSNYfmVHVHaq63Hu+H1gHxAFdgLHeZmOBrt7zLsA4VT2qqpuAjUDDzNKwNnRjooiqzfSbVrjMQ1644e2hDiHVnsWvhDqEoBL81oReVESW+rx+05tq++T0RMrhJgZbBJRQ1R3gMn0RKe5tFgcs9HnbNm9ZhixDN8YYY/yTo+9W1fqZJiOSD/gEGKyq+yTjon16KzK9Ircqd2OMMSYIRCQ7LjP/QFU/9Rb/ISKlvPWlgJ3e8m1AGZ+3xwPbM9u/ZejGGGOiXhB6uQvwX2Cdqr7gs2oy0M973g+Y5LO8t4jkFJELgErA4szSsCp3Y4wxUS8IQ782A/oCq0VkpbfsYWAYMEFErgd+A3oAqOoaEZkArMX1kB+kqsmZJWAZujHGmKgX6PxcVedlkkybDN7zNPD06aZhVe7GGGNMBLASujHGmOjmx/vWQskydGOMMVHP5kM3xhhjznGCzYdu/ODIkSM0b9KQhnUvpO6FCTz5+GMhiWPr1q20a3sxtWtWo+6FCYx+eVRI4gC4aeAAypYuTr3aNUIWQ4oZ06dRK6EKCVUrMmL4sKiPAyA5OZnGDepyZddOUR9HKL43g/pcxNIJD7Fs4sPcdlUrAGpVjmP22LtZ+NEDzHv/PuonnH/Se8qULMyuec8zuG/rgMcXTudqtLEMPcRy5szJtK+/ZfHyH1i0dCUzpk9j0cKFp36jn8XGxjJs+EhWrl7H7HkLeeP1V1m3dm3Q4wDo268/k6ZOC0navpKTkxl8xyAmTfmKFavWMnHcRyE5JuESR4pXXxlF1arVQpZ+OMUR7O9N9QqluO6KprS49nka9h5G+xY1qFCmGE/f2YWn35hG4z7P8eRrX/D0nV1Oet/we65kxvzAnzPhdq6eCfHDI9QsQw8xESFfvnwAJCUlcTwpiUyGAgyYUqVKUaduXQDy589P1arV2L49MehxADRv0ZIiRYqEJG1fSxYvpkKFilxQvjw5cuSgR6/eTJ0y6dRvjNA4ALZt28a0r76k/4DrQ5J+uMUR7O9N1QtKsHj1Zg4fSSI5+QRzl22gS+taKFAgXy4ACubLzY5df6e+p1OrWmxK3M3aX3cELK4U4XSunrEIyNEtQw8DycnJNKpXm7Kli9O67SU0bNQopPFs2byZlStX0KBhaOMIte3bE4mP/2fkxbi4eBITg3+REy5xANx/z1089exzZMsW2p+OcInDVzC+N2t+2UHzuhUpUjAPuXNl57LmCcSXKMx9z3/CM3d2YcOXT/DsXV0ZMnoyAHly5eCe/m15+o2vAhaTr3A6V89UoEeKC4bw+TYEiIgki8hKEflBRJaLSNNQx5RWTEwMi5atZOPmbSxdspg1P/4YslgOHDhAn57dGDHyJQoUKBCyOMJBejOThaL2JFzi+PKLqRQrXoy6desFPe1wjMNXsL43P2/6g5HvfM3UMbcxefStrFqfyPHkE9zYvTn3j/yUSpcP4f6Rn/LakKsBePTmy3nlg+84ePhYwGLyFS7narSKhl7uh1W1NoCItAOeBS4KaUQZKFSoEC0vasWMGdNIqBH8DmFJSUn06dmNXn2upusVVwY9/XATFxfPtm1bU18nJm6jdOnSURvHwgXz+WLqFKZP+4ojR46wf98+BvTry9tj34vKOFIE+3szdtJCxk5y/Wwev60TiX/s5YnbOnHPiE8A+OTrFYx5tA8ADWqW44q2tXn6zi4UzJ+bEyeUI8eO8/r4OQGJLVzO1bMRCdcdEV9CT6MAsAfcFHYiMtMrta8WkdReJCLyqIj8JCJfi8hHInJvoALatWsXe/fuBeDw4cN8O/MbqlSpGqjkMqSq3HzD9VSpWo0777o76OmHo/oNGrBx4wY2b9rEsWPHmDh+HB06do7aOJ54+lk2btrKTxs28e77H3HRxa1DkomGSxwQmu9NscKuz02ZkoXpcvGFTJi2lB27/6ZFvYoAtGpYmY1bdwHQ9vqXqNpxKFU7DmX0h7MY8faMgGXmED7n6tmIgCb0qCih5/YGws8FlAJS7ts4AlzhzUdbFFgoIpOBekA33OTzscByYFmggvt9xw5uGNCP5ORkTugJunXvyeUdOgYquQwtmD+fDz94jxo1atKoXm0AHn/qGS5rf3nQY7n2mj7MnT2L3bt3U6FcPI8OeTwknZ9iY2N5cdRoOnVoR3JyMv36D6B6QkLUxmH+LRTfm4+eH0iRgnlIOn6Cwc9NYO/+wwx68iNG3NeN2JgYjh5N4ranxgUs/cyc0+dqOOTIWSTptXlEEhE5oKr5vOdNgLeAGrjM+kWgJXACqAJcAPQGCqvqY957XgC2q+rzafZ7I3AjQJmyZeut/2VLcD6QMVkQ6d/3sxEubbyFG94e6hBS7Vn8SqhDACB3dlmmqvUDnU6NC+vqpzPmZXk/VUrmDUq8GYmqKndV/R4oChQDrvb+r+e1sf+BK8Wf1rdbVd9U1fqqWr9Y0WIBitgYY0yguSpz6+V+ThGRqkAM8CdQENipqkkicjGQMrTSPKCTiOQSkXxAh9BEa4wxJijEdYrL6iPUoqkNHdyFWD9VTRaRD4ApIrIUWAn8BKCqS7y29B+ALcBS4O9/7dUYY0zECIP8OMsiPkNX1ZgMlu8GmmTwtudVdaiI5AHmACMDFZ8xxhjjDxGfoZ+lN0WkOq5NfayqLg91QMYYYwIoAorolqGnQ1WvCnUMxhhjgiU8OrVllWXoxhhjol44dGrLqqjq5W6MMcZEKiuhG2OMiWrhMnRrVlmGbowxxkRAjm4ZujHGmKgXCZ3irA3dGGOMiQBWQjfGGBP1IqGXu2Xoxhhjol4E5OeWoRtjjIlyYTK5SlZZG7oxxhgTAayEbkwUkUgohvjZ8eQToQ4BgD2LXwl1CKkKNx4c6hBC4Nz/bliGbowxJqoJkVHlbhm6McaYqBcB+bm1oRtjjDGRwEroxhhjop5VuRtjjDERIBKGfrUM3RhjjDn383NrQzfGGGMigZXQjTHGRL0IKKBbhm6MMSa6iQ39avxlxvRp1EqoQkLViowYPizq4winWCyOk900cABlSxenXu0aIYshRaiOybatW7n80jbUuzCBBnVqMmb0ywB89slEGtSpSYHcsSxftjRo8aQI9vEY1LslS8c/wLLxD3Bbn4sAeO+Zfiz84D4WfnAfP00ewsIP7gMgNiYb/xl6FUvG3c+KiQ9xb/+2AY/vTIkf/oWaZeghlpyczOA7BjFpylesWLWWieM+Yt3atVEbRzjFYnH8W99+/Zk0dVpI0vYVymMSGxvLM8+NYNkPa/h2zgLefH0MP61bS7WEGnww/mOaNW8ZlDh8Bft4VK9QkuuuaEKLa1+g4VUjaN+8OhXKFKXvw2NpfPUIGl89gs+//YFJ360CoFvb2uTMEUuD3sNpes3zDLyyKWVLFQlYfNHKMvQQW7J4MRUqVOSC8uXJkSMHPXr1ZuqUSVEbRzjFYnH8W/MWLSlSJPQ/xKE8JiVLlaJ2nboA5M+fnypVq7I9MZGqVatRuXKVoMSQVrCPR9VyJVi8ejOHjyaRnHyCuct/ocvFtU7aplvb2kyYvgwABfLkykFMTDZy58rOsaTj7D94JGDxnRXxwyPELEMPse3bE4mPL5P6Oi4unsTExKiNI5xisTjCV7gcky2bN7Nq5UrqN2wU9LR9Bft4rPnld5rXqUCRgnnInTM7lzWrTnyJQqnrm9Upzx9/7eeXrbsB+PSblRw6coxN055g/dTHeOn979iz71DA4jsbEZCfB7ZTnIhcAXwKVFPVn05j+81AfVXdnWb5AVXNdwbpntH2meynPzBDVbdndV8ZUdX00g1UcmEfB4RPLBZH+AqHY3LgwAGu6dODYc+/QIECBYKadlrBPh4/b/6Dke/OZOqrt3Dw0DFWbUg8ada6nu3qMXH68tTXDWqcT3LyCcpfNoTCBfLwzVt38O3i9WxO/DNgMZ6pSPhKBbqE3geYB/QOcDqB0h8oHcgE4uLi2bZta+rrxMRtlC4d0CTDOo5wisXiCF+hPiZJSUlc07s7PXtfRZeuVwYt3YyE4niMnbSIpteM5JIbX2HP34fY+NsuAGJistHl4lp8/PWK1G17tqvLjO9/4njyCXbtOcD3P2yiXrUyGe3anKWAZegikg9oBlyPT4YuIq1EZJaIfCwiP4nIB5LmUlJEcovINBG5IZ393iciS0RklYg8nkn6I0VkuYjMFJFi3rLaIrLQe+9nIlI4o+Ui0h2oD3wgIitFJLdfDkwa9Rs0YOPGDWzetIljx44xcfw4OnTsHIikzok4wikWiyN8hfKYqCqDbhpIlarVuP3Ou4KS5qmE4ngUK+wqQcuUKESX1rWY4JXIWzeszPrNf5C48+/Ubbf9sZdW9SsBri29YY3z+XnzHwGN78z4o4976Iv4gaxy7wpMU9X1IvKXiNRV1ZQ6mDpAArAdmI/L+Od56/IB44B3VfVd3x2KyKVAJaAhrslisoi0VNU5adLOCyxX1XtEZAjwGHAb8C5wu6rOFpEnvOWD01uuqoNF5DbgXlX91z0oInIjcCNAmbJlz/IQuR6zL44aTacO7UhOTqZf/wFUT0g46/2d63GEUywWx79de00f5s6exe7du6lQLp5HhzxO/wHXBz2OUB6T7xfM56MP3yehRk2aNnSd4x574imOHj3KfXffye5du+h+RSdq1bqQz4N0R0AojsdHw6+jSMG8JB1PZvBzH7N3/2EAelxalwkzlp+07esT5vLmY1exbPwDiAjvTVnEjxt3BDS+MxEp86FLem0vftmxyBfAS6r6tYjcAZRR1ftEpBXwiKpe4m33GjBfVd/32tD/Boar6gc++zqgqvlE5HmgO7DXW5UPeFZV/5sm7WQgp6oeF5HyuHb8i4DVqlrW26YCMBG4OL3lqlpXRGaRQYbuq169+jp/UfDvOzXGZJ1v228oxcaETx/lwo0HhzoEAI4sG7VMVesHOp06devrt/MWZXk/RfLGBiXejASkhC4i5wGtgRoiokAMoCJyv7fJUZ/Nk9PEMR9oLyIf6r+vNgSXgb9xhiEF5qrFGGOMCROBuiTsjqsyP19Vy6lqGWAT0Pw03jsE+BMYk8666cAAr30eEYkTkeLpbJfNiwHgKmCeqv4N7BGRFt7yvsDsjJZ7z/cD+U8jZmOMMeewlOFfs/IItUC1ofcB0o49+Akucx1/Gu8fDLwtIsNVNaVUj6rOEJFqwPdeP7oDwDXAzjTvPwgkiMgyXBV+L295P+B1EckD/Apcd4rl73jLDwNNVPXwacRujDHmHBMOndqyKiAZuqq2SmfZyz4vZ/ksv83neTmfba7zWZ7P5/koYNQp0k/Z/tE0y1cCjdPZPqPln+AuRIwxxkSqMClhZ1X49MIwxhhjzFmz6VONMcZEtXAZujWrLEM3xhhjIiBHtwzdGGNM1IuETnHWhm6MMcZEACuhG2OMiXqR0MvdMnRjjDFRLwLyc8vQjTHGmEjI0a0N3RhjjAkCEblMRH4WkY0i8qC/928ldGOMMVEv0L3cRSQGeBW4BNgGLBGRyaq61l9pWAndGGNMVEuZDz3Ak7M0BDaq6q+qegwYB3Tx5+ewErofLF++bHfu7LLFD7sqCuz2w36yyuI4WbjEAeETi8VxsnCJA8InFn/Ecb4/AjmV5cuXTc+dXYr6YVe5RGSpz+s3VfVN73kcsNVn3TagkR/STGUZuh+oajF/7EdElqpqfX/sy+KIvDggfGKxOMIzDgifWMIljtOhqpcFIZn0yvDqzwSsyt0YY4wJvG1AGZ/X8cB2fyZgGboxxhgTeEuASiJygYjkAHoDk/2ZgFW5h5c3T71JUFgcJwuXOCB8YrE4ThYucUD4xBIucYQFVT0uIrcB04EY4G1VXePPNETVr1X4xhhjjAkBq3I3xhhjIoBl6MYYY0wEsAzdnBGRSJiTyBgTjkQkm/e//c6cBcvQzWkTEVGv04WIXCIiceEQU6hjMCcTkXyh+LuISFh38vU9Jl4vZ+NDRAoDBbyXtUMYyjnLMvRzVMqPg4jkFpE8wUjTJzNvBjwA7A9GuhlJc4HRSUQKBTPtYKWVTtpFfJ5XCVUc6RGRSsB7BPkHWUQKAg2855eISPVgpn8qac7Va4GrUkqjgU430Gn4UQvgARF5HHgvVBeG5zLL0M9Rqqoi0gV3H+M3ItJXRPIGOl0R6Q58CIxS1X2hLGn41hYA9+BuBQm4ND/OrYOZeXiZQGsReVlEbsb9ABY41fuCRVU3AJuAB0WkVhCTLgG0FJFJwCuAP4Zi9huf86U+0Bn4WFVPBDJNEakMXHuu1Aao6mTceOd3Areq6gG127DOiGXo5ygRqQoMAh4Bngb6Ald76/x2VZvOviYB+4DBAKp6zJtFKCS82oL/AS+o6p8ikjPQafr8OA8GngMOBzpNn7RPqOrHuNLMM8Bj3oVV9mDFkB5xsnkx3o3L1B8LdKaecn6q6nrcWNkNgQnA0UCmezZEpB7wFq5mKyDnjE/NXcr5cRvQNRjfi7ORzu/Ly7i/3zXeb5w5A5ahn4NEpCLwJLBPVRer6hfAY8D/iUgzf13VpimJthORtsB5uOrU4iIyFkBVk4OVqafzA7DSewz1YjkajFhEpDXuAqq5qm4SkToi0iaA6fm2v+YEZgLzgGdFJFZVkwKV9unEps4JETkPQFUfBNYCjwcqU09zfvYDCgMP4wbMGiwipb11RUPUpn9Smqq6DDd9ZiWgbiBi8mruWgCvAf/FfTdaAr3CraSe5u/XTUR6AcmqeiPwN+73rJiI3OT9fc0p2MAy54g0J38scB/QFhgGzFfVQyIy3Hs+yc9p3wt0AJYBFwK3A78AC4GtqtrVn+llEofvMWgL5ML9YB0AXsBdbFyZcoGhqsmBSNt7XQl4ENgLnMDNmnQIeEdVx/kr3bRpi0g13IVcovf6IyBWVXuISCsgp6pO92f6ZxDnbbjjsBX4r6r+IiJDgQTgWVVd7se0CqjqPu95Y1w17Q2qesBriroY+BXIB5QE7lfVI/5K/wxjHQiUwpXKRwM34b67TwDL/F31LiL3A/lUdYh38Xc9cAWuduAzb+rOsCEid+AujmcCVXHnd38ReQEoDjTGfa9XhTDMc4KV0M8BKT/oItJcRPoAl6vqs8BXQA9caaQ10Av4089pVwSaqerFuGrMQ8Amr0TYGDhPREoHowTkk6ndAzwKXAa8D1QD7gB2AjNFJFugMnMRuUVErsQdh5lAaWAirl10DgEYTtkn7buAN4B3ReQNr4r7RiBGRJYDLwIb/Z3+6RCRG4CewEPe/8+KSAtVHQpsBu7yV7WviFQABolILnE9ox/AlXprAXgXtF8DBYFOwFshzMzvxI3ZPR+4Ftc2PApXu/ICgek8+BPQSESqq+pRVR3jLW8MhFsnyly4i6+rVfVh3LHKLiIPeE03TwFNLTM/PZahnwO8zLwdrhqtBnCniHyqqs8DPwJdgS7AQFWdJ1noPZtOxpwE7BeRUbjSeU+vWvtKXPVYC1XdHqzOK+I6+jRT1YtwJcHDwCJcBnsPsAbXluo3PhnqbcAAYJWqJqrqh6raR1UX42owegBLM9nVWRORa3CllJa4z3sNLqPar6pX4tpLr1DVXwKRfjrxNBCRLiKSU0TyAZVxGfmVuAz8V1yVaQtVvQ+4S1X91a59AvddqIqrlRmMq95v7GX2eM1QTwNtg5kZ+H73vOflgEuAurjz9VWv9ug5YDywK4vppbSZ1xORNiJSFpiGq027QkQaet+Z40BZ3G9FyKTz+5INKIJ3oeHVHryP6+SIqv6kqjuDGuQ5LKzv2zSO98NwNfCUqo73ln0lIq+q6iCvlHIecEhEsp9te2qakmgvYBWuan0frhNWZy8zHwjcgitlBO3LJiJFgR3ANhF5G1eV2tlru70S+Aa4LRAXF17aXYH+wO9eTUkp3A/nEVxGf62q/uSn9CTN51gFXC0ig3BV2KWB5SLyKe4i62N/pHsGKuEy0uOq+oWIPIKbGrKDqrYFEJFfgMtFZJmq7s5qginHxOuzkB93AVUS15/kaeD/ABWRqaq6wavKPpjVdM9ESvW5dwG+EHdMvsY1zVyhrhPpzSLyi6qOzkpaPjV37YFRwLu4O1A6AJ/gqvVfxs3D3Q+oCTTwd3PUmcbrPW+Cq038HXgWd6GzR1UX4KYVreCV3o8Gq7AQCayEHoZEJId49xeLSDlcZr0X8D2xb8a1D4IrnSXhSuln3dvZ58s2CPfjeNS7Yp4EzMDdG/oM7of82mBeOYtIbeBe/vl8lYDbVTVJRK7DdYrL468vfzolib24qswhwFjcj2UTXK3FcqCbqq72V9o+f4uCXnvxKtx8yo2A11X1b+ADXMZe2B/pnmZsKT3ZP8TNGvWQiPTwzpMk4HwRaSwil+H6N4xW1UN+SPekCxxV3Q+8jqsJeBDXHPQkcBFwqQR5kBkRqSuuMxrexcbj3t/oNeACYLyXmffDtfdnuSbFy8wTcN//9rjpOcGVcAt5tQCdcE1T5wOPA++GIjNPiRdARG7HXWg8grvt9gSuX85EERkD3AU8pKpHLDM/M9YpLsyI6yWcgKuCqgi0A5rhqjKH46oQ14jIxbgfsCtUdZf3A1ZYVc+4Ci9NBlITeAdX8k0UkYtw93fvx2Ue2YEVwara9YmxAa79+CogJ3ArrsPMdqAVrpTql6kI0xyPLkAeXKadG/c3WeaVEm/GZey9VfW4P9JOE8e9QHNcte0IYDbQx3u9E3cxcYeqbvN32qcR2624WpsTuF7Ud6jqZ94x6YvrsHitP/4mIlJeVX/1ng/Gfe5NuPMhGXculABG4goph1R1e1bTPYP4YoEbcDUGj6rqfBFZjDs3jgEdcd/dObhS8lkfF69J4UJcc9ckb1lloBhubIj6IvIA7rfhMlX9Vtw4BSOAl/31HTlb4jqTjsPVIuwDmuKOTS/c3y4G2K+qW0MW5LlMVe0RJg8gB+6+0W643rBHcfcZp6y/E1iHu7pdi6veBMjmxxjy4gbmeN17zAU+x2WYoTgmJVM+H65mYByuqag87gezB3BBgNK+BViA65WchGu7B1eFeR2wGqjux/Tq4e6jLgS0BhbjhsLsicu8+uNK6HcCU4CEEPw9BKiA6ytQxlvWB1c67O69Lg4U91N65wE/42qMGnrn4zW4+/8n45o9zsPd7fECrsd/KM7T83AdFKcAbXC9+gFivP8r4S6Iz/q44Pop/Ii7cFkA3OyzbiAwxnt+EfAt0NhnffYQHZeU725K4bE88EWabYbi+v8EPb5Ie1gbehhRVyWnQHVcproPyCEivYFPVHWUiCz1lv9PVVd4pcks3/bitfk9rKoXicgMXCeez1R1lbihGOsBE9Jp2w0YEWmE+6FSr7T6Me4HvKy6Etuvfk7Pt2ReHFc70h53gTUH1yYKrjRUA+ilqmv9lHYHXNXpC7hah2LAz+puzZogIntx7aQdvPNgjAbp3nPf46KqKiJbgQ1AORHZoaofibud7n8ickBVp/kp3Y642qpbcMelBfCcqk4VkWK4gZVe8/4fjss8/F5Tkkl8vsflTxFJudh8Fqgvbq6DOBH5HVfDNUjPsrpb3GiEH+CqoqeI6ySZX0Rqq+pK3EXPpeI6rzb10lrs0+8gVOMUpPxWlBGR31X1VxFJEpGxqppyb3l2XK2TyapQX1HY4+QHrgT0FXCL9/pO4CXgclz16834oUSOd8Xs8zobrq18YprlVwErgKrB+Ow+z7Pjqm1r4EokM3BVqz/g2mUDmXYfXCn0AeA/uF7D2b119+BKoH4rCeJKVBuBRj7LauPa6n2XvY0byOZff78g/U3KAXHe8+G4WwfLea8vx43wFeendDt6f+uUUn8crlbgPz7bnIerSh6HVxIO1iPNcWmDaxYr4b2+BZiF6ygZ7x23CllMrzlwwuf1Ku93YqV3XsTgaqyG4qrag3YsMoi3Da55EFz7+Epc2/5IXA3UJ7g+GI96f+cqoY45Eh4hDyDaH16mld97Xtb7vyKuSrE4rsr1LlxJZCdeNbsf068M5PWex+BupfnSe13N+5GuGeRjcov35f8PcJG3rDXuPt4NuOaGwgFKuwuut3wFXJPDj0ABb10P78ennJ/TvBu403se6/1fEFdifw7XGbA/riNVfBD/DpImxjW4KuV7cf0J3sFNxDLey2zL+yndksB3QAPvdcr52QLXdn6bz7ZFgKLBPD/TxHobrmnkUe/cTPkO34xrHmjmx7Ta42qlvgGGeMtyAOuBezL624XouPTB9a+4AxiDu8WwBm70undxBYibcE1XAS8sRMsj5AFE+8P7kt7pZVbrcFWtRb0f807eNnlwnX6qeK+z/GX1vlDn4267ug7XQxzcBcb3wFTvdb4gH48rcG3TCbiqy5HANT7rqxC4NvP6uMFiUmpHins/nu/gSoFLgRp+TC+lXfEV3C2J4GpoUtodC+Pajt/yfgiD3mbuxdEId4F1gffDvAx4wFtXB9cJLksl0DTpFcbVyNT0zsehuBLveFzTx1ZcL/KgH4s0cbbFDRiTF7gf+A13N0R5b/0NeP0M/JhmG9w95dl8ll2fNkMP8XFJOa9TBmB6z3sdiyugfIx3sWYPPx/7UAcQ7Q8vY/0O18bW3Wd5K1xHo3p+TOtfFwK4qryZuAuKlJLQw7ge1X6pPj1FTC2B9j6v7wce8Z7H4Eqmn+JdcPg57Uq40bNa40rEpYA3vczjQm+bgt4xugSv9BWAOFp7Fw71fM6JlJL6XbhalJwhODcF16N6FV61rrf8fFypdFQA070HVyW7DXdBNRBXrT0MV0MwDygW7OOR5nUR75zpB3zjLXsP17Pdrxl5mnQvBzZ6zyvibqe8NNjnxyn+fimZejdcSf0in/Uf4gZJCnmskfawTnGhVw33g3UIqCMiS4DtqjpLRJ4DOorIT6qa5QEyNOXb5u7bbgr8gatSH4xrpy/m3UN7Ie7iIkujWJ2mEsAYEemrrjPVGuAmEaml7t7rd0TkKlwVuF/u84bUTmhP4qbZzI/7YeyIG1/7BmCgiLyhqj/iMo9AWuSl0UtEUDeJxwmvM2Rf3FSbQZk9LG0HOOAHEXnFi6ORiCxR1S3iBh56W0RKADtT3uMPqqoi8gauJ3cZYFLK5xeRm3AXoCP9meappOkwWQVXQl7nvS4DfOZtOgtXwxCw2c1U9UsROSEih3BNEINVdUag0jtT3t9PvGP2iYj0xU3x/Dju4jABN5mU8TO7Dz2ERKQ8bvztm3Dt4x/hMtmHcNW/TXG3ujyrZ3lfbcoPkc//N3jpjfD2PQB3u83fuPHILwSeUT8NkpJJXPVw1fmzxY3y9jyu09t8XGe0GNwPejbcl7+dvy4wvEFPhuKqjWd7yx7DNT20x41gdQuuivlZVf3ZH+meIqY4XNVpG1zNzBGgO+7C6sdAp59OPN1xJdBFqvqDl5F2w13wLFI3oE+sBrdXeQ/cIDK9VDVUY9bfjbuHOgl3t8nNuNvoauD+ZrVxt3j+HoRY2uD6d3x2yo0DG0e6d76IG4RIvd+dHrgmk9dxvy9BHzshKoS6iiCaHrjOPg1wmVRFXAeX533W58OVmN/C3brUDp+qqrNMs1ya/d+Jm9wlZVlXXO/2lOp2v93Tfoq47saVSlt4r3vgOn21xGWkN+N68U7Eq/72U7pFcFWAHb3XuXzWPY7r2JQbN9HHfUDJIJ4fuXHV+0NxF12Vg5h2Hp/ng3Eduh7DNQfd5C0fiKtNaBKsuLx0S3kxrcGPfRjOIo5LgK+8508B033OqT64DoxBj48QdoDzTRt3MdwT188ld8p6/ik4Xo71Zg/s3yPUAUTTw/tB9D3Zn8J1hCvrs01uXMm8tvdafP8/w/Q64nrA5sKVwp/BDVDyuc82JXBtWoWCdAx8fwAG4ca5bum97ull6m18jkUg2s474Krvz/Ne5/RZNwuok3Z5JD+84/ES7tawRrhhSsG1VS/H9Su42Vt2LQHqS5BJfLm9GCuG+DjVxdVoPYVr30+5lTGoFzjh8Ej7e4S7QJ+D6+OwCO+WtZRtQ3nREU2PkAcQbQ9cz+m3gYu91895X4DSGWx/Vl8EXOl+rXcBUQL41GfdLOB/uGrtlKkd/TKy1xnElzLK2M1pMvVuuJ7CAe3kgytN/IJ3+5vPj/MkoFaoz5Mg/h1S7vfu6r3O7WXsHbzzJBZXY7AGr6QeDY90MizB1dosBr7knwvtgbjajMLRlGnh3SrIP7WN47zXg3HjNmTDjSURNcckHB42OUuApXQOSXmtbkKTbUAfEWmuqg/gOvl87bWjnkS9b8kZpnkp7l7Pdbgeya/hOrwleJt0xA148S6uF/VNGtyJVmrh5svuqqqv43qxPypuqs1PcKVAv44Cl5aqfoW7h3ipiBRW1yZ8La5ZJODtn+FAREriepMPVNXPRSQ3rjlCcJ01Z6hrI/8VV/oKaVttMKV870TkThH5L+72wS24YZcLALeKyBO4+6xvVtU9Z/NdPdd4P2fFgS0i0lndKJV/AbtE5ENcQaKjt7wbrgBjgsR6uQeQiOTUf3rnNsXdY/69qg7xhjId4PUneVhEUoY/TMximm1w48Dfhcuc6uKql8vhpk78W12HlEtEJC/uVqR9WUnzFPEInHxhom442RVAOxE5oaqviRvydqSIDFbVyYGKx5eqfiVujvM54mZ56gtcH8yLmxA7iuvcdUTcVJUP4Nrwj+PaheuLSEVcE1DnaDguIpJHvdnhRKQ5rmPiG7jS+Tu4i82duE5wMbhOi+tDE21oqOpOERmAG+q3v7qhaA/ixiS4UVWPi5tV7l5c7YUJEuvlHiAiUgg3qcnNuOqnybjhPX/DDdoy2esx2wB4TVXn+CndBrjq4wXixtfuDRzGDbd4Hq4j2ixV3eKP9E4jntSe0OLGiy+uqu95r+/A9QqeoKrTvB+Jb1T1t2DE5hNjR1wtQR0N8WxUweRdbN0NXIq7legb3PmxDteBKRF3O+U8DVGv8mDybmW8BDesbUvcXRfD9Z+x42/HZeSDg32OhiPvbpEJuNEV1+GaZuKAXbi+GH6bAdGcHsvQA0jcNIbdcVWWj6nqT97tPzVx1ZmTReR+YJq6e679mXY2VT3h3TN7FXAQN0hKBdzFxXgN8LzIInIJrhPRD7jOeeDu8X5bVSd627yNK/086lWDh4RvySyaiEg+3PmY9n7vscBkrwkk4nkXdU/jhlSd5N1b/gWwQFVv9rY5D1eLcQHuO5WsfpgY6VyQ8nviPb8KKKWqI73bG9/G3Tkzz6uJPA/4wS56gs/a0APAp5r5Odx9l51w0waCuw1rFdBFRK5Q1eH+zsy9tE94//+M68WeC1e9ug74LgiZ+WW4H8gFuKExLwMO4L78fUWkp7fpDNxIV8sCGc+pRGNmDqCqB1T1e1Wd4JOZ98CVRFeGNLggSdOXYJKI5FU3H/etuGah28DNqIbrxX2LqiZFUWZ+IfCF10QHrhT+J4CqfowbzXGS93u2QFWnWGYeGtaG7mc+A7g0B/5W1f+KSFHgORH5Q1WXicjHuPa3oLS9qerPXpqdcbNV/RnI9ESkCK4ncBevfa0srhozJ24cZ4AnRKQLrqq3ezS0z4Y7ESkF9MLVovRS1V9CHFKwpO1LcJ+ItMIN8rQVeEBEiqnqY6r6V+jCDA11AwsdB8aLyBW45rtdPus/9QoxL4vI18ChaLnYCTdW5R4AItIeeBXop6pzvWW34qqfB6nqomCPsuXFkF2DN4d2B1wm3kRV94nIB8AcVX3DW5+Amz3r6yjKOMKa18u9NW4e9ohvM09xir4EnYDduAGYrtDgDIccFrzjki2lNk9EPsGNU/+L9/863MUQuFtfD6nq4VDEahzL0P3Mu/XsS+AGVV3sVVcVwI1AdiWuGq8pcCDSr2K9C5uXcYNwlAauVtXDKbUYoY3OmH9k0pfgXdyEK99E0znr+x0VkThVTfSev4GrwXmDf/rl5AYetmr20LMMPYtEpDpuvvDx3uuCuLmRD+FuU6uEu6/3I1V9S0QuUNVNIQs4yESkLa6dvKR3u0suVT0S6riMORUJg7HjQyFNZn4bcDWuj8sYVV0rIq/iBobq7G2TQ1WPhS5ik8I6xWWBiFQGPsB1+gJAVf/GjdCWAzfEaltcu3E9b33UZOYAqvoNbtSx70SkuGXmJtyJSCkRGYy7DatfNGXmcNKgOl1xTTC34QolN4lIE1UdBGQTkZS286A2HZqMWae4s+TdDjYVN7Xl296y3F4b0nvACVVNFpFGuIk2HghdtKGlbgCXHMA0EamPNwNTqOMyJgN7cU1kXaItM08hIlVxM+t96HXk/RU3sVNvEYlR1Y4iUtr7Htt3OUxYCf0seNXs7wObgb9FpBmA1z5cHvgPUFLcEKd34e5tnZ5yO1s0UtVJuPHaT1hmbsKZqh5W1S+iKTMXN6+9r324wZau9krle4CRuM5wnb2ms7Oa0tkEjrWhnyGvJ/CXuClOp+LuX82BGxVuKW5yjwWq+rS3fSlV3WEdwYwx4cgrja/Fzbi3TlX/4y3PhRvpsg1uDvPvvc6DuVR1d6jiNRmzDP0siEhJVf3de14F12kkFpehr1fVH31HVjLGmHDljYo3DjeCZGvc/fcTcANQHRSRQbjxCe5X1YWhi9ScilW5nwWfzDybNxLbe7iOIRfiBl3AMnNjzLnAGxVvMW4ipw7AV7hb074UkXq4oZtHk8WJo0zgWYaeBT7Dq27AZeq5cO1LhUMamDHGnAaffj0P4Dq3FQV24O7J/wl4BDfB03Qv4zdhzKrc/UhEKkFqBm+MMWHPy9Rz4MbPKI8rqT+oqp97t+bu8jrFmTBnGboxxpiU/kBzgVdU9clQx2POnFW5G2OMSZmZ8QEgRkTyhDoec+YsQzfGGJPie7xRLc25x6rcjTHGpBKRPKp6KNRxmDNnGboxxhgTAazK3RhjjIkAlqEbY4wxEcAydGOMMSYCWIZujDHGRADL0I0JMBFJFpGVIvKjiEzMyj2+IvKOiHT3nr/lTeWb0batRKTpWaSxWUSKnu7yNNscOMO0horIvWcaozHm3yxDNybwDqtqbVWtgZtP+mbflSISczY7VdWBqro2k01aAWecoRtjzk2WoRsTXHOBil7p+TsR+RBYLSIxIjJCRJaIyCoRuQncONsiMlpE1orIF0DxlB2JyCwRqe89v0xElovIDyIyU0TK4S4c7vJqB1qISDER+cRLY4mINPPee56IzBCRFSLyBiCcgoh8LiLLRGSNiNyYZt1IL5aZIlLMW1ZBRKZ575nrzcFtjPGj2FAHYEy0EJFYoD0wzVvUEKihqpu8TPFvVW0gIjmB+SIyA6gDVMHNflUCWAu8nWa/xYD/AC29fRVR1b9E5HXggKo+7233IfCiqs4TkbLAdKAa8BgwT1WfEJEOwEkZdAYGeGnkBpaIyCeq+ieQF1iuqveIyBBv37cBbwI3q+oGEWkEjMHNvW2M8RPL0I0JvNwistJ7Phf4L64qfLGqbvKWXwrUSmkfBwoClYCWwEeqmgxsF5Fv09l/Y2BOyr5U9a8M4mgLVP9nxkwKiEh+L40rvfd+ISKnM7PWHSJyhfe8jBfrn8AJYLy3/H3gUxHJ533eiT5p5zyNNIwxZ8AydGMC77Cq1vZd4GVsB30XAber6vQ0212Om6c6M3Ia24BrYmuiqofTieW0h4wUkVa4i4MmqnpIRGYBuTLYXL1096Y9BsYY/7I2dGPCw3TgFhHJDiAilUUkLzAH6O21sZcCLk7nvd8DF4nIBd57i3jL9wP5fbabgav+xtuutvd0DnC1t6w9UPgUsRYE9niZeVVcDUGKbEBKLcNVuKr8fcAmEenhpSEicuEp0jDGnCHL0I0JD2/h2seXi8iPwBu4GrTPgA3AauA1YHbaN6rqLly796ci8gP/VHlPAa5I6RQH3AHU9zrdreWf3vaPAy1FZDmu6v+3U8Q6DYgVkVXAk8BCn3UHgQQRWYZrI3/CW341cL0X3xqgy2kcE2PMGbDJWYwxxpgIYCV0Y4wxJgJYhm6MMcZEAMvQjQkgEckpIuNFZKOILPIGfElvu15e2/YaERnus/xmEVnttYPP8x3qVUT6icgG79EvnX2+cqZDsZ7is3QWkQfP4n2pA+AEg4jU847ZRhF5WXzulUuz3UPeNj+LSDuf5TlE5E0RWS8iP4lIN2/5+d5gOau8zxTv856y3uA868QNAlQu4B/UmDQsQzdRxxvgJViux/UIrwi8CDyXTjznASOANqqaAJQQkTbe6g9VtaZ3y9dw4AXvPUVwg7Y0wg1Q85iIFPbZZ32gkD8/iKpOVtVh/txngLyG6yRYyXtclnYD78KoN5DgrR8j/wzB+wiwU1UrA9X5pyPi88C7qloL19nvWZ9dvguMUNVquL/HTn9/KGNOxTJ0EzYyGk5U0gxr6i3LJyL/+//2zj/Ur7KO4693rl9mW5azcitmpVlQKNgoSBht0z8ETfwFUboiQtuCGqNMa2CLSieSMNE/FrtuCP4Iy/mjH2stgi1CY+76AyY3g7ybWOYc2XI49+6Pz+dsZ9+dc+8N1vV2+bzgwrnPec7znOc5X87n+Xye57yf9MSGW17Uy63rLpE0lMdDkm6WtAW4QdJ8SdsUcqfbJH048x0n6aZWuV+TtFDSz1rlLpZ03wSbdSFwRx7/FFjY4TF+AHg6V6sD/Aa4GCA/+Wp4G4e/Fz8P2GT7Rdt7gE2k4UrDtBr45kD/ni1p7eANSpqXnuhaxQYyd0paJGlrev/zM98SSWvy+NLMu0PS7/v6rqOu2yQ9ms/4+lb6j9KzHZZ0U18d46H4tG+m7T84VvyuBz7bkfVC4C7b+1OQZ4QwxABfIo217YO2X8j0jwKb83hLltEMDmbY3pTXvGx730TutyiOJSUsU0wljpITJQadR8iaZt7vElKpHwNoe6djcDqwyPZrkmZmmQckLQJ+QBjRrwCnAmfluXcCe4BbJc1Oo/tFYF3WezchzTrIzbbXA3OAZwGyvL3Au4AXWnlHgDMyTDtKGKA3NSclLQWWZ1ojl3qo3GQ00yC+Nd9o+7n22MH2o8CXe/rmQ8Cl2f5HiG/IPw1cAFzL0UZxJXCe7V2S3pFpXX03yHX5jI8DNkv6eN77RcAZtt0q76g6cuB1d0e5EJvRzMnyGtr90mYOR35uNwrMadW9SiGg82dgme3ngR3Eb+SWvN+3Z3TldOClHOSdSgzIrkl1v6KYNMqgF1OJLjnR2XTLmi4iQqZk+kTkSu9tvWRnAXdIOo3wet/YKvd22wfa9UnaAHxe0jrgU8AVef7ycersmr894ltR23skXU0YqoPANsJrb87fSgwoPgd8B7iyr1xJpxCGecE49zXIX2w/DiDpSWBzGtfHgXkd+bcCQ5LuAZpoRWffDXBZRl9mAO8lvN6ngFeAtYoNaB7sq8P2TuDMvkZ0RD+gWwWvL98MYC6w1fZyScuJUPsXgBXAGklLCDGeXcCBvOYcQnf/r8RzXEJI/BbFpFEGvZgSqF9OtE/WtC+9nTYoR9qWWl0FbLF9UXrGvxun3HWEUMsrxMDgQN73eB76KDE4GVXM3c8CjjJ0th/I8kmD1+Xd3UXMD5PlLmidm5ttOIvwtkfSth0vaSTn8Mdif+v4YOv/g3S8J2xfpdhk5XzgMYXq3JgStAoluxXAJ3IQMwS8Jb35+cBCYpC2DPhMTx0nMbaHPkr0RcNcYHdH3ua5DOb7B7CPEPQBuJdYB4Ht3aTmvUKf/mLbeyWNAtttP5Pnfk6o55VBLyaVmkMvpgp9cqJ9sqaDMqZNyP15SR+R9AYiLDpWfbvyeEkr/dfAVWl8D9WXL/PdhIc81GS2fXnudT74tz6zbCQ8aghJ1N+6Q81J0smtdnyVUI4jIwgN5xOqcRBSsedKOjGvORf4le2HbL/H9jzb84B9jTFXrBtYzzFA0gdt/9H2SmL64H309F2LmcSgaq+kdxM7zzXGcZbth4Gvkx54Vx22d/b095m2X7L9HPBPSZ9Mb/0K4P6OJmwkJHXfnL+t04jNckwMrBZkvoVEBAFJJ+XvCuDbHN717hHgROVWscS0yFj71BfF/4Ty0Iupwi8JYzAM7CTnN23/PT3W+/Jl+jdgMfB9Igz9BOHNXk+EZa8hQrbPAk8AJ/TUdyMRcl8OtHcwW0vMiQ5LepWYv1+T5+4EZtv+b17WPwE2SBohPPND0wSSHmttWHKLDuubf8/203m8LOf4XyXm8q/MfnlR0irCmDTX9O2y1vB+4N/j5Jkoq3OwIWKh2A6iv/v6Dts7JG0npF+fIULqEHrz90tqIjLfGKOOiXA1Meh6K/CL/EPSBcDZtlfafjJD+U8RYfOlremYbxHP7MdAs2YCwsj/UJKJkPvSbNdrklYQawIE/CnbXhSTSkm/FsUEUazw3m77/zKUKmk1sMH28Ot9L0VRHHvKoBfFBFBsNvIvYLHt/ePlL4qimGzKoBdFURTFNKAWxRVFURTFNKAMelEURVFMA8qgF0VRFMU0oAx6URRFUUwDyqAXRVEUxTSgDHpRFEVRTAP+A79DcFSXmxvsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "Classification Report of Model 3\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.89      0.89      1000\n",
      "           1       0.99      0.99      0.99      1000\n",
      "           2       0.90      0.88      0.89      1000\n",
      "           3       0.94      0.93      0.94      1000\n",
      "           4       0.88      0.91      0.89      1000\n",
      "           5       0.98      0.98      0.98      1000\n",
      "           6       0.81      0.79      0.80      1000\n",
      "           7       0.96      0.97      0.96      1000\n",
      "           8       0.99      0.99      0.99      1000\n",
      "           9       0.97      0.96      0.97      1000\n",
      "\n",
      "    accuracy                           0.93     10000\n",
      "   macro avg       0.93      0.93      0.93     10000\n",
      "weighted avg       0.93      0.93      0.93     10000\n",
      "\n",
      "\n",
      "\n",
      "Confusion matrix of Model 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "for model in models:\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "    y_predClasses = [np.argmax(element) for element in y_pred]\n",
    "    \n",
    "    \n",
    "    print('Classification Report of Model {}'.format(i))\n",
    "    print(classification_report(y_test, y_predClasses))\n",
    "    print('\\n')\n",
    "    \n",
    "    print('Confusion matrix of Model {}'.format(i))\n",
    "    plot_confusion_matrix(confusion_matrix(y_test, y_predClasses), normalize=False, \n",
    "                      target_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'])\n",
    "    print('\\n')\n",
    "    print('\\n')\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "befa38aa",
   "metadata": {},
   "source": [
    "### Testing the Model (Only tested model_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a848b556",
   "metadata": {},
   "source": [
    "###### Importing Libraries for uploading the image and preprocessing it to feed into the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ebf28837",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from skimage import data\n",
    "from skimage.color import rgb2gray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c77517",
   "metadata": {},
   "source": [
    "#### Creating functions to preprocess and reshape the image, then converting into grayscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d01b60e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image):\n",
    "    \n",
    "    image = image.resize((28, 28))\n",
    "    image = img_to_array(image)\n",
    "    #img = preprocess_input(img)\n",
    "    image = np.expand_dims(image, axis=0)\n",
    "    \n",
    "    return image\n",
    "\n",
    "\n",
    "def reshape_normalize(image):\n",
    "    \n",
    "    image = image.reshape(1, 28, 28, 1)\n",
    "    image = image.astype('float32')\n",
    "    image = image/255.0\n",
    "    \n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b54ba796",
   "metadata": {},
   "source": [
    "##### Creating a function to output the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "24caa643",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(preprocessed_image):\n",
    "    \n",
    "    y = model_2.predict(preprocessed_image)\n",
    "    val = np.argmax(y)\n",
    "    \n",
    "    labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "    \n",
    "    result = ''\n",
    "    \n",
    "    for i, dress in enumerate(labels):\n",
    "        \n",
    "        if val == i:\n",
    "            result = dress\n",
    "            break\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71523bd",
   "metadata": {},
   "source": [
    "#### Uploading the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "90d39867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c8e2f271c0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_bag = \"purse_ladies.jpg\"\n",
    "\n",
    "image1 = Image.open(image_bag)\n",
    "\n",
    "plt.imshow(image1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be777bd5",
   "metadata": {},
   "source": [
    "Preprocessing the image, and converting into grayscale since the model takes a grayscale image as input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1dd18722",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_array = preprocess_image(image1)\n",
    "\n",
    "img_gray = rgb2gray(image_array)\n",
    "\n",
    "img_normalize = reshape_normalize(img_gray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5e5980c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bag'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction(img_normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1dfb897d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c8e1204640>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_trouser = \"trousers.jpg\"\n",
    "\n",
    "image2 = Image.open(image_trouser)\n",
    "\n",
    "plt.imshow(image2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d73d6e3",
   "metadata": {},
   "source": [
    "Preprocessing the image, and converting into grayscale since the model takes a grayscale image as input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1b949ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_array2 = preprocess_image(image2)\n",
    "\n",
    "img_gray2 = rgb2gray(image_array2)\n",
    "\n",
    "img_normalize2 = reshape_normalize(img_gray2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "811337b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Trouser'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction(img_normalize2)"
   ]
  }
 ],
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