14_cifar10_classification_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7280e46",
   "metadata": {
    "id": "a7280e46"
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, Dropout\n",
    "from keras.datasets import cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ece7314e",
   "metadata": {
    "id": "ece7314e"
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26b3f8b2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "26b3f8b2",
    "outputId": "464f38d8-e8e6-4904-f6dd-e05d12323966"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e8f6b57",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0e8f6b57",
    "outputId": "5f7e716a-9512-4f97-f795-09a56f86428e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "418f3d1c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "418f3d1c",
    "outputId": "31c57969-b331-4074-9fd6-80982c432ebc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c065e96",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7c065e96",
    "outputId": "b99fd8a5-2940-4a2a-f4a8-9b79a425ef44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4981ef7f",
   "metadata": {
    "id": "4981ef7f"
   },
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40667455",
   "metadata": {
    "id": "40667455"
   },
   "outputs": [],
   "source": [
    "# Normalize images \n",
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "KcKtEe9LEkLz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KcKtEe9LEkLz",
    "outputId": "186480af-76ce-4fc2-bb5a-2975049d1288"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3662fc",
   "metadata": {
    "id": "9f3662fc"
   },
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3a008c3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f3a008c3",
    "outputId": "c533ad91-8e9d-4ce0-d745-c50855df29ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 32, 32, 6)         456       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 6)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1536)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               786944    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               65664     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 854,354\n",
      "Trainable params: 854,354\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Input(shape=(32, 32, 3)))\n",
    "\n",
    "# conv layers \n",
    "model.add(Conv2D(filters=6, kernel_size=5, strides=(1, 1), padding='same', activation='relu'))\n",
    "\n",
    "# pooling \n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "\n",
    "# flatten \n",
    "model.add(Flatten())\n",
    "\n",
    "# Fully Conncted NN \n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "# output layer \n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec39d352",
   "metadata": {
    "id": "ec39d352"
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"SparseCategoricalCrossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7077bdf5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7077bdf5",
    "outputId": "49af98a6-c8b6-41bc-a3e3-ff8dcff1be45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1563/1563 [==============================] - 121s 75ms/step - loss: 1.5347 - accuracy: 0.4491 - val_loss: 1.3198 - val_accuracy: 0.5251\n",
      "Epoch 2/20\n",
      "1563/1563 [==============================] - 111s 71ms/step - loss: 1.2301 - accuracy: 0.5611 - val_loss: 1.2734 - val_accuracy: 0.5490\n",
      "Epoch 3/20\n",
      "1563/1563 [==============================] - 112s 72ms/step - loss: 1.0687 - accuracy: 0.6193 - val_loss: 1.1581 - val_accuracy: 0.5913\n",
      "Epoch 4/20\n",
      "1563/1563 [==============================] - 113s 73ms/step - loss: 0.9221 - accuracy: 0.6739 - val_loss: 1.1517 - val_accuracy: 0.6031\n",
      "Epoch 5/20\n",
      "1563/1563 [==============================] - 111s 71ms/step - loss: 0.7856 - accuracy: 0.7243 - val_loss: 1.1576 - val_accuracy: 0.6164\n",
      "Epoch 6/20\n",
      "1563/1563 [==============================] - 110s 71ms/step - loss: 0.6565 - accuracy: 0.7691 - val_loss: 1.2094 - val_accuracy: 0.6127\n",
      "Epoch 7/20\n",
      "1563/1563 [==============================] - 102s 65ms/step - loss: 0.5276 - accuracy: 0.8148 - val_loss: 1.3577 - val_accuracy: 0.5907\n",
      "Epoch 8/20\n",
      "1563/1563 [==============================] - 99s 63ms/step - loss: 0.4247 - accuracy: 0.8516 - val_loss: 1.5331 - val_accuracy: 0.5964\n",
      "Epoch 9/20\n",
      "1563/1563 [==============================] - 104s 67ms/step - loss: 0.3340 - accuracy: 0.8820 - val_loss: 1.6786 - val_accuracy: 0.5952\n",
      "Epoch 10/20\n",
      "1563/1563 [==============================] - 99s 63ms/step - loss: 0.2661 - accuracy: 0.9062 - val_loss: 1.7762 - val_accuracy: 0.5954\n",
      "Epoch 11/20\n",
      "1563/1563 [==============================] - 114s 73ms/step - loss: 0.2180 - accuracy: 0.9228 - val_loss: 1.9205 - val_accuracy: 0.5913\n",
      "Epoch 12/20\n",
      "1563/1563 [==============================] - 121s 78ms/step - loss: 0.1762 - accuracy: 0.9396 - val_loss: 2.2109 - val_accuracy: 0.5961\n",
      "Epoch 13/20\n",
      "1563/1563 [==============================] - 121s 77ms/step - loss: 0.1675 - accuracy: 0.9423 - val_loss: 2.4504 - val_accuracy: 0.5737\n",
      "Epoch 14/20\n",
      "1563/1563 [==============================] - 121s 78ms/step - loss: 0.1433 - accuracy: 0.9492 - val_loss: 2.5163 - val_accuracy: 0.5880\n",
      "Epoch 15/20\n",
      "1563/1563 [==============================] - 120s 77ms/step - loss: 0.1284 - accuracy: 0.9563 - val_loss: 2.6380 - val_accuracy: 0.5951\n",
      "Epoch 16/20\n",
      "1563/1563 [==============================] - 124s 80ms/step - loss: 0.1237 - accuracy: 0.9581 - val_loss: 2.7980 - val_accuracy: 0.5678\n",
      "Epoch 17/20\n",
      "1563/1563 [==============================] - 126s 81ms/step - loss: 0.1130 - accuracy: 0.9616 - val_loss: 2.7861 - val_accuracy: 0.5908\n",
      "Epoch 18/20\n",
      "1563/1563 [==============================] - 131s 84ms/step - loss: 0.1026 - accuracy: 0.9653 - val_loss: 2.8509 - val_accuracy: 0.5875\n",
      "Epoch 19/20\n",
      "1563/1563 [==============================] - 120s 76ms/step - loss: 0.1010 - accuracy: 0.9650 - val_loss: 3.1390 - val_accuracy: 0.5813\n",
      "Epoch 20/20\n",
      "1563/1563 [==============================] - 121s 77ms/step - loss: 0.0925 - accuracy: 0.9688 - val_loss: 3.2933 - val_accuracy: 0.5793\n"
     ]
    }
   ],
   "source": [
    "result = model.fit(X_train, y_train, verbose=1, epochs=20, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "716ad17a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "716ad17a",
    "outputId": "9eeccf2e-fd85-4583-fcae-0b96db9c1f19"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"loss\"])\n",
    "plt.plot(result.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\", \"Test\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7b94b59",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "a7b94b59",
    "outputId": "ad3429b8-a7e0-44c5-d30d-7a7640e4cc84"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZpYZGnYOn69sh71YGhDIHQgj8+lLrR+7X2soKSqUCNlZWsLFSwNo6/6tSAWsrKSAUBA8hFfLQ68LP7W2spIQhChYJiAfXk3YDKwVgnX8FJsDdHlYKBRr6S1dbrLOlkZZr+tSAh9P9CSznzPkc8+bNwxdffIHGjRvDyckJEyZMQG5ubonH+XBHZIVCAZ1O98ifT3kw3BARUbWUo9FKgSVTrQ8uD75OeWC5Oq/4D+NAFyUG1PGBTggoIIUWewdr2CgVsFFawTr/qxRcrO5fCakiIxorrUp3b9G+ffswYMAADB8+HIA00eWlS5fQsGHDiiyvTBhuiIjI7Gl1Alm5echSS49MtRZ3snINw0tBaMkPLBnqPKPew8XOGt4udvBytoOXiy18Xe3h62qPQBclvK0zEerpBGcnRyitqkZoMbU6depg3bp1OHjwIDw8PDB37lwkJSUx3BAREQH37yjKzMlDpjoPWWpt/tc8ZOXm6Z9nqrUPBJaC8KLVB5mC75f17iRbayv45AcWb5f8R/7zgmUF33ewLXrclpycHMTHx8PORmmxwQYA3n//fcTHx6Nnz55wdHTEiy++iIEDByI9PV3u0gpRiIJ7wKoJlUoFNzc3pKenw9XV9dEbEBFRueh0AlfTsnH6RjrO3FThzM10nL2pQmpWyX01ysLaSgEnO2s421nDzcEGPq73w4r3wyHGxQ4udtblnqyxINyEhYXB3t7eREdSPZX0szTm85tXboiIyGQ0Wh0u3crEmZv3g8y5xAxkFtMEpFAATrbWcLJT6kOJ9Noazg8uszNc9uC60vel5XbWVpydmxhuiIiobLJz83AuMQNn84PM6ZvpuJiUiVxt4c63ttZWaOjngvAANzQKcEVEoBtqezvB2QRXTogexnBDRESPlJ6twZmb6TitvyKjQtztTBQ1pIqLnTXCA1zRKD/INAp0RW1vZ9goOeMPVQ6GGyIi0hNCIEmVgzM3VDibKDUrnb6hwo2794pc38vZDhGBrlKIyQ8zQR6OsLLgjrVU9THcEBFVU3laHeJSsnC2oJNvogpnb6pwJ1tT5PpBNRzQyN8tP8xIQcbHlR1oqephuCEiqgay1Hk4nySFF+mKjArnkzKQW8TgdEorBep4O+c3LUlBJjzAFW4ONkXsmajqYbghIrIwyaocnEm8H2TO3VQhPjULRQ384WSrREN/KcSEB7gi3N8NdX2dYW9T9JguROaA4YaIyExpdQJXUrNw5ub9IHP2pgopmeoi1/d1tUO4v6u+s2+4vyuCa7B/DFkehhsioipAqxO4m52LO9ka/dc72bkGy9KyDL9/NzsXGm3hyzFWCqCWt7M+yBR89XK2k+HIiCofww0RUQVJzsjB5VuZBkElLasgnBgGFVWOpshmo0ext7FCA78Hm5Vc0cDPtdipAoiqA4YbIiITO3tThe/3xWHziZvIK2ogmBK42FvDw9EWHo42cM//6uFk+9AyW7jnL/dztbfo+YyoYjxq4MTo6GgsX768TPsODQ3FhAkTMGHChDJtbwoMN0REJiCEwIHLqfhu73/YdylFv7yWlxO8nO2kMOJoC3cnm0JBpYaT9NzNwYYD3VGlSExM1D9fvXo1PvjgA1y4cEG/zMHBQY6yTIbhhoioHDRaHbaeSsR3e+JwNlEFQOrz0ruxP17sWAtNarrLWyBREfz8/PTP3dzcoFAoDJZt3rwZ06ZNw5kzZxAQEIDo6Gi89957sLaWYsO0adOwdOlS3Lp1C56ennj66afx1VdfoXPnzrh69SomTpyIiRMnApCCf2VjuCEiKoMsdR5WHbmGpfvj9aP3OtgoMaRlEJ5vH4agGo4yV0iyEQLQZMvz3jaO0myk5fDnn39i+PDh+Oqrr9ChQwf8999/ePHFFwEAU6dOxdq1azFv3jysWrUKjRo1QlJSEk6cOAEAWL9+PZo2bYoXX3wRL7zwQrkPp6wYboiIjJCckYPlB67gp0NXocqRZrr2crZFdJtQDG8dAg8nW5krJNlpsoGZAfK897s3AVuncu3i448/xuTJkxEdHQ0AqFWrFj788EO88847mDp1KhISEuDn54fu3bvDxsYGwcHBePzxxwEANWrUgFKphIuLi8GVoMrGcENEVAqXkzPx/d44bIi9oZ/1upaXE8Z2qIVBzQM56B1ZjGPHjuHIkSP4+OOP9cu0Wi1ycnKQnZ2NwYMH44svvkCtWrXQq1cv9O7dG/369dM3WVUFVacSIqIqRgiBI1fuYNHe/7DjXLJ+eWSIB17sWAtPNPTlAHhUmI2jdAVFrvcuJ51Oh+nTp2PQoEGFvmdvb4+goCBcuHABMTEx2LFjB1555RV8+umn2LNnD2xsqsYUHQw3REQP0eoEtp9Jwnd743D82l0AUjeG7g198VLHWmgRWkPeAqlqUyjK3TQkp+bNm+PChQuoU6dOses4ODigf//+6N+/P1599VU0aNAAp06dQvPmzWFrawutVluJFRfGcENElC9Ho8WaY9exZF8crqRKHUJtra3wVPNAjO1QC7W9nWWukKjiffDBB+jbty+CgoIwePBgWFlZ4eTJkzh16hQ++ugjLF++HFqtFq1atYKjoyN+/PFHODg4ICQkBIA0zs3evXsxdOhQ2NnZwcvLq9KPgeGGiKq9tKxcrPj7Clb8fRVpWbkAADcHG4xoHYLotqHwduG0BVR99OzZE7///jtmzJiBOXPmwMbGBg0aNMDYsWMBAO7u7pg9ezYmTZoErVaLxo0bY/PmzfD09AQAzJgxAy+99BJq164NtVoty63gCiHHu8pIpVLBzc0N6enpcHV1lbscIpLR1dQsLNkfj1+PXkOORuokXNPDAc+3D8MzLYLgZMe//+jRcnJyEB8fj7CwMNjb28tdjlkr6WdpzOc3/+cSUbVz7OodLN4Xhz/OJOnnc4oIdMWLHWujd4QfrDlKMJFZY7ghompBqxOIOZuE7/fF49jVO/rlnep546WOtdCmtucj59shIvPAcENEFi07Nw9rj13Hkv3xuFrQSVhphQGPBWBsh1qo7+cic4VEZGoMN0RkkZIzcrDi4FX8dPgq7mZrANzvJDyybQh8XNg3gshSMdwQkUW5eCsDi/fFYWPsTf1IwsE1HPF8+zAMblETjrb8tUcVo5rdn1MhTPUz5P9yIjJ7Qgj8/V8qFu2Lw+4Lt/XLmwe7SyMJh/tByZGEqYIUjMqbnZ0NBwcHmasxb7m50lAMSmX5pjNhuCEis6XR6rDlZCIW7Y3D2UQVAGlw2J7hfnihYxgiQziSMFU8pVIJd3d3JCdLU3Q4Ojqyc3oZ6HQ63L59G46OjuWep4rhhojMjipHg1X/JGDZgStITM8BADjYKDG4RU2MaReGUC/zHfqezFPBDNgFAYfKxsrKCsHBweUOhww3RGQ2bty9h2X747HqyDVkqvMAAF7OdhjVNgTPtQqBh5OtzBVSdaVQKODv7w8fHx9oNBq5yzFbtra2sLIq/zhTDDdEVOWdup6O7/fFYcupRGh1UofDuj7OeKFDLQxoFgA76/K1zxOZilKpLHd/ESo/hhsiqrJOXr+LmVvP4VBcmn5ZuzqeGNuhFjrX82a/BiIqkuxjjM+fP18/h0RkZCT27dtX4vrffvstGjZsCAcHB9SvXx8rVqyopEqJqLLkaLT45I/zeHL+QRyKS4O1lQJPNgvEljfaY+XY1uhS34fBhoiKJeuVm9WrV2PChAmYP38+2rVrh++++w5RUVE4e/YsgoODC62/YMECTJkyBd9//z1atmyJf/75By+88AI8PDzQr18/GY6AiEzt34Q7eGftSVxOzgQA9G3ij3d7N0SAO2+xJaLSkXVW8FatWqF58+ZYsGCBflnDhg0xcOBAzJo1q9D6bdu2Rbt27fDpp5/ql02YMAFHjx7F/v37S/WenBWcqGrK0WgxN+YiFu+Lg05IHYU/GhiBXhF+cpdGRFWAWcwKnpubi2PHjmHy5MkGy3v06IGDBw8WuY1arS40BbqDgwP++ecfaDQa/UBKD2+jVqv1r1UqlQmqJyJTOnolDe+sPYm4lCwAwJPNAvFB33De/UREZSJbn5uUlBRotVr4+voaLPf19UVSUlKR2/Ts2ROLFy/GsWPHIITA0aNHsXTpUmg0GqSkpBS5zaxZs+Dm5qZ/BAUFmfxYiKhssnPzMH3zGQz+7m/EpWTB19UOS6JbYN6QxxhsiKjMZO9Q/HCnQCFEsR0F33//fURFRaF169awsbHBgAEDMGrUKADFD9U8ZcoUpKen6x/Xrl0zaf1EVDaH4lIR9eU+LDtwBUIAT0fWxPYJndCtoe+jNyYiKoFs4cbLywtKpbLQVZrk5ORCV3MKODg4YOnSpcjOzsaVK1eQkJCA0NBQuLi4wMvLq8ht7Ozs4OrqavAgIvlkqfPwwW+nMXTRIVxNzYa/mz2WjW6JzwY3hZtj4aZlIiJjyRZubG1tERkZiZiYGIPlMTExaNu2bYnb2tjYoGbNmlAqlVi1ahX69u1rkhENiahiHbicgp5f7MWKv68CAIY9HoQ/J3ZEl/o+MldGRJZE1lvBJ02ahBEjRqBFixZo06YNFi1ahISEBIwbNw6A1KR048YN/Vg2Fy9exD///INWrVrhzp07mDt3Lk6fPo0ffvhBzsMgokfIyNFg1rbz+PlwAgAg0N0Bs59qjA51vWWujIgskazhZsiQIUhNTcWMGTOQmJiIiIgIbN26FSEhIQCAxMREJCQk6NfXarX4/PPPceHCBdjY2KBLly44ePAgQkNDZToCInqUvRdvY/K6k7iZP8Hl8NbBmBzVEM52HCCdiCqGrOPcyIHj3BBVDlWOBh//fg6rj0qd+INqOOCTp5qgbe2i+8cREZXELMa5ISLLtet8MqasP4UklXS1ZlTbULzTqz4cbfkrh4gqHn/TEJHJpGdrMP33M1j/7w0AQKinI+Y83RSPh9WQuTIiqk4YbojIJGLO3sK7G07hdoYaCgXwfLswvNmjPhxsix6DioioojDcEFG53MnKxbTNZ/Db8ZsAgFreTvj06aaIDPGQuTIiqq4YboiozI5cScPLP/2LlEw1rBTACx1rYWL3erC34dUaIpIPww0RlcmvR67hvY2noNEK1PVxxqeDm+KxIHe5yyIiYrghIuPkaXWYte08luyPBwD0buyHzwY35Z1QRFRl8LcREZWaKkeD13+OxZ6LtwEAE7rXxRtd68LKqujJbomI5MBwQ0SlEp+ShbE/HMF/t7Ngb2OFuc88ht6N/eUui4ioEIYbInqk/ZdS8OrP/yL9ngb+bvb4fmQLRAS6yV0WEVGRGG6IqFhCCPx46Cqmbz4LrU6gWbA7vhsRCR8Xe7lLIyIqFsMNERVJo9Vh6qYz+pm8BzULxMxBjXmbNxFVeQw3RFTInaxcvLzyGA7FpUGhACb3aoAXO9aCQsGOw0RU9THcEJGBi7cyMPaHo0hIy4aznTW+HPoYujX0lbssIqJSY7ghIr2/zt3C+FXHkanOQ3ANRyyOboF6vi5yl0VEZBSGGyKCEAKL9sZh9h/nIQTQKqwGFgyPRA0nW7lLIyIyGsMNUTWXo9Hi3Q2nsP7fGwCAZ1sFY1q/RrC1tpK5MiKismG4IarGkjNy8NKPxxCbcBdKKwWm9gvHiNYh7DhMRGaN4Yaomjp9Ix0vrDiKxPQcuNpbY/5zkWhf10vusoiIyo3hhqga2noqEZN+PY4cjQ61vJ2wJLolwryc5C6LiMgkGG6IqhGdTuCrnZfwxY5LAICO9bzx9bBmcHOwkbkyIiLTYbghqibu5Wrx1poT2HIqEQDwfPswTIlqAGslOw4TkWVhuCGqBm7evYcXVhzFmZsq2CgV+HhgYzzTMkjusoiIKgTDDZGF+zfhDl5ccQwpmWp4Otli4YhItAytIXdZREQVhuGGyIL9/V8qxiw/gnsaLRr4uWBxdAvU9HCUuywiogrFcENkoR4MNp3qeWP+c83hZMf/8kRk+fibjsgCHYq7H2w61/fGwuGRsLdRyl0WEVGl4G0SRBbmUFwqRi9jsCGi6ovhhsiCPBhsOtVjsCGi6onhhshCPBxsvhvBYENE1RPDDZEFOMxgQ0Skx3BDZOYOx6ViVH6w6chgQ0TEcENkzh4ONosYbIiIGG6IzNXhuFSMXs5gQ0T0MIYbIjNUEGyycxlsiIgexnBDZGb+iU/TB5sOdb0YbIiIHsJwQ2RG/olPw6hl/+iDzfcjWzDYEBE9hOGGyEww2BARlQ7DDZEZYLAhIio9hhuiKu7IFQYbIiJjMNwQVWFHrqQheimDDRGRMRhuiKooBhsiorJhuCGqgo5cScOo/GDTvg6DDRGRMRhuiKqYgmCTlR9sFkcz2BARGYPhhqgKOfpQsOEVGyIi4zHcEFURR/P72DwYbBxsGWyIiIzFcENUBTwYbNrV8WSwISIqB4YbIpkdu3rHINgsHtmSwYaIqBwYbohkdOPuPbyw4iiDDRGRCTHcEMkkR6PFSz8eRVpWLhoFuDLYEBGZCMMNkQyEEJiy/hRO31ChhpMtvhsRyWBDRGQiDDdEMlh64Ao2xN6A0kqBb55thpoejnKXRERkMRhuiCrZwf9SMHPrOQDAu70bom1tL5krIiKyLAw3RJXo+p1svPZzLLQ6gUHNAjGmXajcJRERWRzZw838+fMRFhYGe3t7REZGYt++fSWuv3LlSjRt2hSOjo7w9/fH6NGjkZqaWknVEpXdvVwtXvrxGNKyctE40A0zBzWGQqGQuywiIosja7hZvXo1JkyYgPfeew+xsbHo0KEDoqKikJCQUOT6+/fvx8iRI/H888/jzJkzWLNmDY4cOYKxY8dWcuVExhFCYPL6kzhzUwVPJ1ssHBHJaRWIiCqIrOFm7ty5eP755zF27Fg0bNgQX3zxBYKCgrBgwYIi1z906BBCQ0PxxhtvICwsDO3bt8dLL72Eo0ePVnLlRMZZsj8evx2/md+BuDkC3R3kLomIyGLJFm5yc3Nx7Ngx9OjRw2B5jx49cPDgwSK3adu2La5fv46tW7dCCIFbt25h7dq16NOnT7Hvo1aroVKpDB5ElenA5fsdiN/v0xBtanvKXBERkWWTLdykpKRAq9XC19fXYLmvry+SkpKK3KZt27ZYuXIlhgwZAltbW/j5+cHd3R1ff/11se8za9YsuLm56R9BQUEmPQ6iklxLy8ZrP/8LnQCeal4T0W1D5S6JiMjiyd6h+OEOlUKIYjtZnj17Fm+88QY++OADHDt2DH/88Qfi4+Mxbty4Yvc/ZcoUpKen6x/Xrl0zaf1ExbmXq8WLPx7DnWwNmtR0w8dPRrADMRFRJbCW6429vLygVCoLXaVJTk4udDWnwKxZs9CuXTu8/fbbAIAmTZrAyckJHTp0wEcffQR/f/9C29jZ2cHOzs70B0BUAiEE3ll3EucSVfBytsXC4exATERUWWS7cmNra4vIyEjExMQYLI+JiUHbtm2L3CY7OxtWVoYlK5XSB4YQomIKJSqD7/fFYfOJm7C2UuDbZ5sjgB2IiYgqjazNUpMmTcLixYuxdOlSnDt3DhMnTkRCQoK+mWnKlCkYOXKkfv1+/fph/fr1WLBgAeLi4nDgwAG88cYbePzxxxEQECDXYRAZ2HfpNmZvOw8A+KBfOFrVYgdiIqLKJFuzFAAMGTIEqampmDFjBhITExEREYGtW7ciJCQEAJCYmGgw5s2oUaOQkZGBb775Bm+++Sbc3d3RtWtXfPLJJ3IdApGBhNRsvP5LLHQCGBxZEyNah8hdEhFRtaMQ1aw9R6VSwc3NDenp6XB1dZW7HLIg2bl5GDT/IM4nZaBpkDtWv9ia/WyIiEzEmM9v2e+WIrIEQgi8vfYkzidlwMvZDguHN2ewISKSCcMNkQl8tzcOW04mwtpKgQXDm8PfjR2IiYjkwnBDVE57Lt7GnD+kDsRT+zdCy9AaMldERFS9MdwQlcPV1Cy8nj8C8ZAWQRjeKljukoiIqj2GG6IyylLn4cUVx6DKyUOzYHfMGNiIIxATEVUBDDdEZSB1ID6BC7cy4O1ih4XDI2FnzQ7ERERVAcMNURks2PMftp5Kgo1SgYXDm8PX1V7ukoiIKB/DDZGRdl1Ixqd/XgAATO8fgcgQdiAmIqpKGG6IjHAlJQvjf4mFEMCwx4PxLDsQExFVOQw3RKWUqc7Diz8ehSonD82D3TGtf7jcJRERUREYbohKQQiBt349gYu3MuHDDsRERFUaww1RKXy76zL+OJMEW6UVFo6IhA87EBMRVVkMN0SPsPP8LXwecxEAMGNAIzQP9pC5IiIiKgnDDVEJLidn4I1fjkMI4LlWwRj6ODsQExFVdQw3RMW4m52LsT8cRaY6D4+H1cDUfo3kLomIiEqB4YaoCHlaHV77ORZXUrMR6O6ABc81h601/7sQEZkD/rYmKsLMreex/3IKHG2VWBzdAp7OdnKXREREpcRwQ/SQX49cw9ID8QCAuc80RUN/V5krIiIiYxgdbkaNGoW9e/dWRC1Esjt6JQ3vbTwFAJjYvR56RfjLXBERERnL6HCTkZGBHj16oG7dupg5cyZu3LhREXURVbobd+9h3E/HoNEK9G7sh9e71pG7JCIiKgOjw826detw48YNvPbaa1izZg1CQ0MRFRWFtWvXQqPRVESNRBXuXq4WL644ipTMXDT0d8Vng5vCykohd1lERFQGZepz4+npifHjxyM2Nhb//PMP6tSpgxEjRiAgIAATJ07EpUuXTF0nUYURQuCttSdw5qYKnk62+H5kJBxtreUui4iIyqhcHYoTExOxfft2bN++HUqlEr1798aZM2cQHh6OefPmmapGogr17a7L2HIyEdZWCiwYHomaHo5yl0REROVgdLjRaDRYt24d+vbti5CQEKxZswYTJ05EYmIifvjhB2zfvh0//vgjZsyYURH1EpnU9jNJ+Gy7NLXChwMj8HhYDZkrIiKi8jL62ru/vz90Oh2GDRuGf/75B4899lihdXr27Al3d3cTlEdUcS4kZWDi6uMAgOg2IRjGqRWIiCyC0eFm3rx5GDx4MOzti58V2cPDA/Hx8eUqjKgi3cnKxdgVR5CVq0Xb2p74v77hcpdEREQmYnSzVP/+/ZGdnV1oeVpaGlQqlUmKIqpIGq0Or6z8F9fS7iG4hiO+fbY5bJQcz5KIyFIY/Rt96NChWLVqVaHlv/76K4YOHWqSoogq0oe/n8Xfcalwyp9awcPJVu6SiIjIhIwON4cPH0aXLl0KLe/cuTMOHz5skqKIKsrKw1ex4u+rUCiAL4Y2Qz1fF7lLIiIiEzM63KjVauTl5RVartFocO/ePZMURVQRDselYupvZwAAb/WojyfCfWWuiIiIKoLR4aZly5ZYtGhRoeULFy5EZGSkSYoiMrXrd7Lx8sp/kacT6NvEH690ri13SUREVEGMvlvq448/Rvfu3XHixAl069YNAPDXX3/hyJEj2L59u8kLJCqvLHUexv5wFGlZuYgIdMWnTzeFQsGpFYiILJXRV27atWuHv//+G0FBQfj111+xefNm1KlTBydPnkSHDh0qokaiMtPpBN5acwLnkzLg5WyHRSNawMFWKXdZRERUgco0gc5jjz2GlStXmroWIpP7auclbDudBFulFb4bEYkAdwe5SyIiogpWrtkB7927V2gmcFdX13IVRGQq204l4osd0iSuHz0ZgcgQD5krIiKiymB0s1R2djZee+01+Pj4wNnZGR4eHgYPoqrg7E0VJv16AgAwpl0YnmkRJHNFRERUWYwON2+//TZ27tyJ+fPnw87ODosXL8b06dMREBCAFStWVESNREZJzVTjhRVHcU+jRYe6Xni3dwO5SyIiokpkdLPU5s2bsWLFCnTu3BljxoxBhw4dUKdOHYSEhGDlypV47rnnKqJOolLJzdPh5ZX/4sbdewj1dMQ3w5rDmlMrEBFVK0b/1k9LS0NYWBgAqX9NWloaAKB9+/bYu3evaasjMoIQAlM3ncE/8WlwsbPG4ugWcHO0kbssIiKqZEaHm1q1auHKlSsAgPDwcPz6668ApCs67u7upqyNyCg/HbqKX/5JgEIBfDWsGer4cGoFIqLqyOhwM3r0aJw4IXXUnDJlir7vzcSJE/H222+bvECi0jh4OQXTNp8FAEzu1QBdGvjIXBEREclFIYQQ5dlBQkICjh49itq1a6Np06amqqvCqFQquLm5IT09nbetW4gbd++hz1f7cDdbgyebBWLuMxyBmIjI0hjz+W3UlRuNRoMuXbrg4sWL+mXBwcEYNGiQWQQbsjx5Wh0mrIrF3WwNGge6Ydagxgw2RETVnFHhxsbGBqdPn+aHB1UZX++8jCNX7sDZzhrfPtsc9jacWoGIqLozus/NyJEjsWTJkoqohcgoh+JS8fVOaQTij5+MQLCno8wVERFRVWD0ODe5ublYvHgxYmJi0KJFCzg5ORl8f+7cuSYrjqg4d7JyMWHVcegE8HRkTQx4LFDukoiIqIowOtycPn0azZs3BwCDvjcA2FxFlUIIgXfWnUSSKge1vJwwvX8juUsiIqIqxOhws2vXroqog6jUfjx0FTFnb8FWaYWvhjWDk1255n8lIiILw3HpyaycS1Thoy3nAACToxogItBN5oqIiKiqMfpP3i5dupTY/LRz585yFURUnOzcPLz+Syxy83To2sAHo9uFyl0SERFVQUaHm8cee8zgtUajwfHjx3H69GlER0ebqi6iQmZsPovLyZnwcbHDp083YR8vIiIqktHhZt68eUUunzZtGjIzM8tdEFFRfj95E6uOXINCAXwx5DF4OtvJXRIREVVRJutzM3z4cCxdutRUuyPSu5aWjSnrTwEAXulcG23reMlcERERVWUmCzd///037O3tjd5u/vz5CAsLg729PSIjI7Fv375i1x01ahQUCkWhR6NGvBXYUmm0OryxKhYZOXloHuyOCd3ryV0SERFVcUY3Sw0aNMjgtRACiYmJOHr0KN5//32j9rV69WpMmDAB8+fPR7t27fDdd98hKioKZ8+eRXBwcKH1v/zyS8yePVv/Oi8vD02bNsXgwYONPQwyE1/suIjYhLtwsbfGl0ObwUbJG/yIiKhkRs8KPnr0aIPXVlZW8Pb2RteuXdGjRw+j3rxVq1Zo3rw5FixYoF/WsGFDDBw4ELNmzXrk9hs3bsSgQYMQHx+PkJCQUr0nZwU3Hwcvp+C5JYchBPDts83Rp4m/3CUREZFMjPn8NvrKzbJly8pc2INyc3Nx7NgxTJ482WB5jx49cPDgwVLtY8mSJejevXupgw2Zj9RMNSasPg4hgGGPBzHYEBFRqck2tGtKSgq0Wi18fX0Nlvv6+iIpKemR2ycmJmLbtm34+eefS1xPrVZDrVbrX6tUqrIVTJVGCIG3155EcoYadXyc8UFf9qkiIqLSM7oDg5WVFZRKZbEPYz08VokQolTjlyxfvhzu7u4YOHBgievNmjULbm5u+kdQUJDRNVLlWnbgCnaeT4attRW+ebYZHGyN/3dFRETVl9FXbjZs2GDwWqPRIDY2Fj/88AOmT59e6v14eXlBqVQWukqTnJxc6GrOw4QQWLp0KUaMGAFbW9sS150yZQomTZqkf61SqRhwqrDTN9Ixe9t5AMD7fRqigR/7RRERkXGMDjcDBgwotOzpp59Go0aNsHr1ajz//POl2o+trS0iIyMRExODJ598Ur88JiamyPd40J49e3D58uVSvZednR3s7DjgmznIUudPr6DVoUe4L4a3Zl8qIiIynsnuq23VqhV27Nhh1DaTJk3C4sWLsXTpUpw7dw4TJ05EQkICxo0bB0C66jJy5MhC2y1ZsgStWrVCRESESWqnqmHqpjOIT8mCv5s95nB6BSIiKiOTdCi+d+8evv76a9SsWdOo7YYMGYLU1FTMmDEDiYmJiIiIwNatW/V3PyUmJiIhIcFgm/T0dKxbtw5ffvmlKUqnKuK34zew9th1WOVPr+DuWHJzIxERUXGMHufGw8PD4C9qIQQyMjLg6OiIn376Cf379zd5kabEcW6qnqupWejz1X5kqvMwvltdTHyCoxATEZGhCh3nZt68eQbhpmAQv1atWsHDw8P4aqlay83T4Y1fYpGpzsPjoTXwetc6cpdERERmzuhwM2rUqAoog6qrz7dfwInr6XBzsMEXQx+DNadXICKicjL6k2TZsmVYs2ZNoeVr1qzBDz/8YJKiqHrYe/E2vtsbBwD45KkmCHB3kLkiIiKyBEaHm9mzZ8PLy6vQch8fH8ycOdMkRZHlu52hxqRfTwAAhrcORq8IP5krIiIiS2F0uLl69SrCwsIKLQ8JCSl0ZxNRUXQ6gUm/HkdKphoN/Fzwf33C5S6JiIgsiNHhxsfHBydPniy0/MSJE/D09DRJUWTZFu+Pw75LKbC3scLXw5rB3obTKxARkekYHW6GDh2KN954A7t27YJWq4VWq8XOnTsxfvx4DB06tCJqJAty4tpdzPnjAgDgg76NUNfXReaKiIjI0hh9t9RHH32Eq1evolu3brC2ljbX6XQYOXIk+9xQiTJyNHhjVSzydAK9G/th2OOc44uIiEzP6EH8Cly6dAnHjx+Hg4MDGjdurB9VuKrjIH7yEEJg4urj2Hj8JgLdHbD1jQ5wc7SRuywiIjITFTqIX4G6deuibt26Zd2cqpl1/97AxuM3obRS4KthjzHYEBFRhTG6z83TTz+N2bNnF1r+6aefYvDgwSYpiizL9TvZmPrbaQDAxO51ERlSQ+aKiIjIkhkdbvbs2YM+ffoUWt6rVy/s3bvXJEWR5dDpBN5ZexJZuVq0CPHAy505vQIREVUso8NNZmYmbG0Lz9hsY2MDlUplkqLIcqz8JwEH/0uFvY0VPhvcFEorxaM3IiIiKgejw01ERARWr15daPmqVasQHs7B2Oi+hNRszNp6DgAwuVcDhHo5yVwRERFVB0Z3KH7//ffx1FNP4b///kPXrl0BAH/99Rd+/vlnrF271uQFknnS6QTeXnsC2blatAqrgZFtQuUuiYiIqgmjw03//v2xceNGzJw5E2vXroWDgwOaNm2KnTt38tZq0lvx9xUcjk+Do60Snz7dFFZsjiIiokpSplvB+/Tpo+9UfPfuXaxcuRITJkzAiRMnoNVqTVogmZ8rKVmY/cd5AMCUqAYI9nSUuSIiIqpOjO5zU2Dnzp0YPnw4AgIC8M0336B37944evSoKWsjM6TVCby15gRyNDq0re2J51qZx+CORERkOYy6cnP9+nUsX74cS5cuRVZWFp555hloNBqsW7eOnYkJALDsQDyOXr0DJ1slPnmqCZujiIio0pX6yk3v3r0RHh6Os2fP4uuvv8bNmzfx9ddfV2RtZGb+u52JT/+UJsV8r084gmqwOYqIiCpfqa/cbN++HW+88QZefvllTrtAhRQ0R6nzdOhQ14uTYhIRkWxKfeVm3759yMjIQIsWLdCqVSt88803uH37dkXWRmZk8b44xCbchYudNT55qgkUCjZHERGRPEodbtq0aYPvv/8eiYmJeOmll7Bq1SoEBgZCp9MhJiYGGRkZFVknVWGXbmXg85iLAID3+4YjwN1B5oqIiKg6M/puKUdHR4wZMwb79+/HqVOn8Oabb2L27Nnw8fFB//79K6JGqsLytDq8teYEcvN06FzfG4Nb1JS7JCIiqubKfCs4ANSvXx9z5szB9evX8csvv5iqJjIj3+2Nw4nr6XCxt8bsQWyOIiIi+ZUr3BRQKpUYOHAgNm3aZIrdkZk4n6TCFzuk5qhp/RrBz81e5oqIiIhMFG6o+tHkN0dptALdG/pgUPNAuUsiIiICwHBDZbRg9384fUMFNwcbzHyyMZujiIioymC4IaOduZmOr/66BACYMaARfFzZHEVERFUHww0ZJTdPh7fWnESeTqBnI1/0bxogd0lEREQGGG7IKN/suoxziSp4ONrgo4FsjiIioqqH4YZK7fSNdHy76zIA4MOBEfB2sZO5IiIiosIYbqhU1HlavLXmBLQ6gT6N/dG3CZujiIioamK4oVL5+q/LOJ+UAU8nW8wY0EjucoiIiIrFcEOPdOLaXSzY8x8A4KOBEfB0ZnMUERFVXQw3VKIczf3mqP5NAxDV2F/ukoiIiErEcEMl+mLHJVxKzoSXsx2m92dzFBERVX0MN1SsfxPuYNFeqTlq5pMR8HCylbkiIiKiR2O4oSIVNEfpBDCoWSB6NPKTuyQiIqJSYbihIn2+/QLibmfBx8UOU/uxOYqIiMwHww0VcvRKGhbvjwcAzH6qMdwcbWSuiIiIqPQYbsjAvVypOUoIYHBkTXRt4Ct3SUREREZhuCEDc/48jyup2fB3s8f/9Q2XuxwiIiKjMdyQ3qG4VCw7cAUAMPupJnBzYHMUERGZH4YbAgBkqfPwztqTAIBhjwehUz1vmSsiIiIqG4YbAgB8+ucFJKRlI9DdAe/2bih3OURERGVmLXcBJL/YhDv44e8rAKS7o1zszaw5Sggg6RSguQe4+EkPa85/RURUXTHcVHMarQ5T1p+CyB+sr0NdM2qOSosDTq4BTq4G0v4z/J6DB+Dinx92/AFnX8PXLr6Asx9gzVGXiYgsDcNNNbd4XzzOJ2XAw9EG7/Uxg+ao7DTgzAYp0Fw7fH+5jSPg5AVk3AK0auDeHemRfLbk/Tl63g89zn73r/y4+D8QgnwBpZldzSIiqsYYbqqxq6lZ+PKviwCA9/qEw9O5ijbl5KmBi39Kgebin4BOIy1XWAFhnYAmQ4CGfQE7F6mJ6t4dICMJyEgEMm9JXwteZ9y6/1ynAbJTpcet0yUUoACcfYDHngU6vAXYOVfKYRMRUdkw3FRTQgj838bTyNHo0La2J55qHih3SYaEABIOSYHmzAYg5+797/k2Bpo8AzQeDLj6G26nUACONaSHbwnj9AghXQXKSAQykx4IP0mGj8wkQJcnhaT984CTvwI9PwbCB0rvRUREVQ7DTTX12/Gb2HcpBbbWVvj4ycZQVJUP6tT/gBOrpFBz9+r95S7+UphpOhTwNcFcVwoF4OQpPRBR/Ho6nXRlJ+FvYPv/STWtGSVdMer9GeBdr/y1EBGRSTHcVEN3snLx4e9SX5Q3utZBmJeTvAVlpQKn10mB5sbR+8ttnYGG/aWrNGEdAStl5ddmZQU4ewPh/YG6TwAHvgT2zQXi9wAL2gJtXgE6vsOmKiKiKoThphqaufUcUrNyUc/XGS92rC1PEZoc4OI24MRq4HKM1PQDAAolULur1I+mQW/AVubg9SAbB6DzZKm2P6ZI9R/4Urpjq+fHQKMn2VRFRFQFyD6I3/z58xEWFgZ7e3tERkZi3759Ja6vVqvx3nvvISQkBHZ2dqhduzaWLl1aSdWav4P/pWDNsesAgFmDGsPWuhL/Ceh0wJX9wG+vAZ/VlZp3Lm6Tgo1/U6DnLODN88DwtUCTwVUr2DyoRhjw7Cpg2GrAPQTIuAmsHQ2s6A/cviB3dURE1Z6sV25Wr16NCRMmYP78+WjXrh2+++47REVF4ezZswgODi5ym2eeeQa3bt3CkiVLUKdOHSQnJyMvL6+SKzdPORot3tsg3RU0vHUwIkNqVPybCgHc+Bc49xtwegOQnnD/e641pSanJkMAnwYVX4up1e8F1OokXb3ZPw+I3ys1VbV+Bej0jnT3FhERVTqFEELI9eatWrVC8+bNsWDBAv2yhg0bYuDAgZg1a1ah9f/44w8MHToUcXFxqFGjbB/MKpUKbm5uSE9Ph6ura5lrN0dzt1/AVzsvw8fFDjve7ATXihqJWKeVOuCe2yw9VDfuf8/OFQgfIAWakHZSnxZLkBZ/v6kKkDpA9/wYaDSITVVERCZgzOe3bFducnNzcezYMUyePNlgeY8ePXDw4MEit9m0aRNatGiBOXPm4Mcff4STkxP69++PDz/8EA4ODkVuo1aroVar9a9VKpXpDsKMXLqVgQV7pFF8p/VvZPpgk5crXbk4twk4vwXITrn/PVtnoG4PqVNuvV5S3xVLU9BUdfFPYNs7wJ0rwNoxwLHlQNSn5nlliojITMkWblJSUqDVauHr62uw3NfXF0lJSUVuExcXh/3798Pe3h4bNmxASkoKXnnlFaSlpRXb72bWrFmYPn26yes3JzqdwJT1p6DRCnRv6IOoCD/T7Dg3G/jvL+DsJulDXZ1+/3v27kCDPkDDfkCtLoCNvWnes6qr11O6TfzAl8D+uVLgW9gOaP0y0Ol/bKoiIqoEst8t9fD4KkKIYsdc0el0UCgUWLlyJdzc3AAAc+fOxdNPP41vv/22yKs3U6ZMwaRJk/SvVSoVgoKCTHgEVd+qI9dw9OodONoqMX1ARPnGtMlJBy5ul/rQXNoB5N27/z1nX6BBXynQhLavvlMW2NgDnf8HNM2/q+rCVuDg18CptUCPj4CIpyqnqUqnA9KvAamXpPNmbS9NKGrtcP+5jUP+Mvv7D6XsvxYsg04nDf7o4G6ZVyuJqjDZfot5eXlBqVQWukqTnJxc6GpOAX9/fwQGBuqDDSD10RFC4Pr166hbt26hbezs7GBnV0WnFagEyaoczNp2DgDwVo/6CHQvwy/ZrBTpA/rsJiBu9/3pDwDAPVgai6ZhP6Dm45bTh8YUPEKBYb8YNlWte15qqur9KeBjorm8crOA1MtAyiUg5WL+10vSsgfDZ2kplA+EngfCj439QwHpgeVQABCA0EmdyPXPYbhc6PJfP/j8wW0e/p7IH3DRG3CrCbgGAq4B+c8D5L8SlpMO3Lkqndu7+V/1rxOkec7sXIGu/we0HCvPWE1VjSYHSD4DJJ6U/g01Hlx9/xCiCiNbuLG1tUVkZCRiYmLw5JNP6pfHxMRgwIABRW7Trl07rFmzBpmZmXB2lgZNu3jxIqysrFCzZs1KqdvcTP/9LDJy8tCkphui24aWfsP0G1LfmXObgKsH8j9s8nnVl8JMw37SLdzsMFuygqaqg18B+z4HruwDFrYHWo2Txs0pzQe0ENL0EA+Gl4LnquvFb2dlA3jWBhy9pA/avBzpwyVPLQWfvPxl2twH3ksL5GZKj6rOzg1wyw88roHSwy3Q8Hl5hhTIy5Wuft258kCAeSDM3Lvz6H2oVVK4jf0J6DsPqNmi7PWYG3UGkHQKSDwhhZnEE8Dt89K/sQJHlgBPLwU8QuSrkyyOrHdLrV69GiNGjMDChQvRpk0bLFq0CN9//z3OnDmDkJAQTJkyBTdu3MCKFSsAAJmZmWjYsCFat26N6dOnIyUlBWPHjkWnTp3w/fffl+o9q9PdUjvP38KY5UehtFLgt1fbISLQreQN0uKku5vObjIcKRiQQkzDftJVGu/6FVe0pbtzBfjjXeDCFum1s590V1VBU5UmB0j776EAc1G6ClNS2HD0BLzqAV51Ac+695+7h5SumUmnvR90Ch6agucPBSFNTuH1IKSJTKGQjkOhyH9uVfi5wXql2EZopQlPVTekR/oNQHXTsI9XSezdpGEH9CEo/6qPW6D03NYRuHut6KsvGTcNg32RP3sv6YPZI1T6eXuE3n/t4i+Fmr+mS1d5oAAiRwHdPpDmP7MkWSlSeEk6eT/MpP1X9LqOnoBfY+BmrPRzsXcDBnwr/Y4hKoYxn9+yhhtAGsRvzpw5SExMREREBObNm4eOHTsCAEaNGoUrV65g9+7d+vXPnz+P119/HQcOHICnpyeeeeYZfPTRR8XeLfWw6hJustR56DFvL27cvYcXO9bCu70f0QTy1wzpqoKeAghqlR9o+kq/qMl0LsUAW98G7sRLr33CAU229KGKYv5LKpTSXVmedaXg4lXvfoixtA/K0lBnSCEn/br01SD8FAQgE9wdae1gGFgeDDDuIaWbeiMzGYj5ADjxi/Ta0RN44kNppnlzu/IphPSzNQgyJwyHfHiQa6D0x5FfE+mrfxNpmUIh/XtfO+b+H1OPvwT0+FBqrqrKbhwDDn4jBV9nH8DJR5qmxckn/7W39JV9rUzKrMJNZasu4eaj389i8f54BLo7IGZSRzjalvDX+z/fA1vfkp6HdZJu2W7QF3Ax0V1VVDRNjtTReN/nhn1j7NweCC8PfPUIA6xt5avXHOWo8oNPfgBKv/HQ8xtSqHStmR9eQgD3UMMw4+RtugByZT+w5U2paQYAgtsAfeaWPIO9nHQ6KYAXBJiCMJOdWvT6NWpL4eXBMOPkVfJ7aDXSH1cHv5Je+zcFnl4mNadWNTkqYOdHwD+LUOwfIQ+ydXkg9BQRfh5czvnpHonhpgTVIdycvpGO/t/sh04Ay0a3RJf6PsWvfOEPYNUw6S+Qru8DHd+qvEJJcvcacPWg1EziVc+0H6b0aDpt5Xb01WqAv78F9nwiBSuFUpqAtdPkqvEBJ4Q0COe/PwLnfy/66pdCCXg3uH8lxr8p4BsB2Jfjd+rFP4EN44B7aVIo6P+l1FxbVZzbDGx9R2qqBKSO0IEtgKxk6cpc1m3Dr1p1yft7mI2jYehx8ZN+H/g0kK7s8vcCw01JLD3c5Gl1GDj/AE7fUKFf0wB8PaxZ8SvfjAWW9ZZ+wTYbAfT/utr/5yGqNHevAX9MlgIEIDXV9Jol9WuT4/9hZrLUbPbvCqmPVwFre8C3kWGzkk+jihm7Kv2GdEdhwt/S68hRQK/Z8jbvpF+XQk1BPzmPMKDvXGmC3+IIIYXCzNv3w09mchFBKFlapzR3NTrUkO6w9G4gffVpCHg3BJw8TXOcZoDhpgSWHm4W74vDR1vOwdXeGn+92RneLsW0Xd9NABZ3l8bhqNUFeG4Nb8ckksPFP6X+V3evSq/rPAH0ngPUqFXx763TApf/Av79Abj4hzSJLQDYOAERT0p/9AS2qNyxj7R5wO5Z+X0AhRSkBi8HvOtVXg2A9LM5/J3UDKXJAqysgXYTpKvbpgxbQkg3CzwcetJvSBPx3j4nTe9SXDOYk3d+4AmXrvJ4N5S+OniYrsYqguGmBJYcbq7fycYTc/finkaL2YMaY+jjRU8+int3gaW9pP80Po2AMdukuxWISB6ae8C+ucCBL6Tb8pV2QIc3gXbjK+YKyZ0rQOxK4PhKw47AgS2A5iOBiEHyjyH0305g/YvSB76Nk3S1pOnQynnvm7HA5glA4nHpdVAroN+Xphubyliae9Jdk8nnpMft89LXgkBcFBf/+1d5HvxanqZDmTHclMBSw40QAs//cBQ7zyfj8dAaWPVia1hZFXFpOy8XWPmUNC2Aiz8wdoc0IBoRyS/lktS5P2639LpGbWnAxzrdyr/vPLXUBPbvivv7B6S/8JsOk67SVLWOzRlJwPoXpN9XAPDYc9LPozxjF5VEnQns+hg4vFDqh2jvBnSfDjSPrpoDlKozgZQLQPJ56Y/V5PNS8Em/Vvw2rjWlKzseYVLQsXPJfzz4/KFlVeTuNYabElhquNlyMhGv/vwvbJVW2Dq+Per4FPFXlxDAb69Kf63ZOgOjt0nt50RUdQgBnFkvjYeUmT+Ce6MngZ4zpfF5jHXrrBRoTq4yHHSwVheg+Qjpzsgq8uFVJJ0W2PsZsGe2FDi86kvNVKYOYue3Ss2DBYNiRjwF9JwFuBQ9Yn6VlqO636SVfB5IPiuFnozEsu1PaVs49Ng6Fx2ECh4O7kBYR5MeFsNNCSwx3KTf06D73D24naHG+G51MfGJYtqm98yR/ipRKIFnVwN1n6jcQomo9HJUUt+TgqsIts5Al3elsWAe1QdGnQGcXi+FmgcH5HQJAJoNB5o9Z35jV8XvA9aNlQKftT0QNUdqQitv52vVTWkE6XObpdfuIdLt+XW7l7/mqubeHSn0JJ+V+vTkZkr/VtSq/K8PPcozSrmTN/D25UevZwSGmxJYYrh5d8Mp/Hw4AbW8nbBtfAfYWRdxW+uJVcCGl6TnfecBLcZUbpFEVDaJJ4Etk4DrR6TXvhHSh29wK8P1hJDW+fcH4PQGqRMsIHWErR8lNa3U7mre81tl3gY2jgMu75BeNx4s/T4rS/8gnRY4shj460MgN0P6ObV9Hej4jjRqNUk/I30AynggCBW17KFgZO8q3ahiQgw3JbC0cHPkShoGL5Rum1z9Ymu0qlXEbYHxe4EfB0kTXrYbDzwxo5KrJKJy0emA2B+BHVPvNy01GyH1B4GQ/niJ/fH+4ICANJJ18xFSfxrnEsa6Mjc6HXDwSymUCK3UL2nwMuk29dJKPAlsHg/c/Fd6XbOl1GHYt1HF1EwmwXBTAksKN7l5OvT5ah8uJWdiSIsgfPJ0Ef1nks8DS3pI8/CED5RG/qyKHeOI6NGyUoEdH0jzVQHSaNaabOkPF0CaKqLRk1JzTXBryx63KuGwNHWD6rrUJ6TnTGnm9ZKOOTdLaur7e74UjOxcge5Tgcgx/L1oBhhuSmBJ4ebrvy7h85iL8HK2xY5JneDu+NDQ/Bm3pLFs0hOkWxlHbqqY20qJqHIlHAJ+nwQkn5FeBzTLv4X7qeo1rEN2GrDxFeDiNul1+ACg31dSZ9aHXdwuTX2RnpC/7kAg6hNOM2NGGG5KYCnhJu52Jnp9uQ+5eTp8OfQxDHgs0HCF3CxgeR9pvIYatYDnd1SrkSyJLJ42D4jfIzU5+TWWuxr5CAEcmg/ETJWuYLmHSM1UgZHS9zOSgG3/A85ulF67BQN9PgPq9ZStZCobYz6/K3HYSTIVIQTe3XAKuXk6dKrnjf5NH7o9VKeV7iq4GSsN2f3cWgYbIkujtDbN+DfmTqEA2rwKBLUG1o6WBrZb0hN4Yrp0i/uO6VKH14I5vDpPqbhxcqjKYLgxQ2uOXcehuDTY21jho4ERUDzcxvznu8CFrdIop8N+qZqz6xIRmVLNSOClvcCm14Fzm6TfgwUCmksdhjmuV7XBHlRmJjVTjZlbzwEAJnavh6AaD92yeGiBNC4GADy5UOpUSERUHTi4A8+sAHp/JnUytnUBoj6VRmJnsKlWeOXGzHy05RzuZmsQ7u+K59uHGX7z3O/AH1Ok592nS/PDEBFVJwoF8PgL0uzq1rYWOYEkPRrDjRnZd+k2NsTegJUCmDWoMayVD1x4u35M6mcDAUSOlsazISKqrsxx2gQyGTZLmYl7uVq8t+E0ACC6bSiaBrnf/+adK8AvQ4C8e0CdJ6RLspY8vgUREVEJGG7MxPrY60hIy4a/mz3e7FH//jfu3QFWDgaybku3gw5e9uh5Z4iIiCwYw42Z2HpKms11VNtQONvlh5c8NbBqOJByEXANBJ79tWxzrBAREVkQhhszkJqpxqG4NABAVIS/tFAI4LfXgKv7pTsCnv0VcA0oYS9ERETVA8ONGYg5ewtanUBEoCuCPfNv/d41Ezj1qzQw1TM/AH4R8hZJRERURTDcmIGtp5MAPHDVJvYnYO8c6XnfeRyllIiI6AEMN1VcerYGBy+nAACiIvyA/3YBm/Nv8+7wJhAZLWN1REREVQ/DTRUXc+4W8nQCDfxcUEt3Ffh1JKDLAxoPBrq+L3d5REREVQ7DTRW3Lf8uqeFhWcDKZ6QJ4ILbAgO+5Vg2REREReCAKFWYKkeDQ5eSMF65Ac+d2AToNIBnXWDoSmm2WyIiIiqE4aYK+/fgX1innIIGVtcAHYB6UUC/LwDHGnKXRkREVGUx3FRFudnA7pnoePBbWFnpkG3tDscBnwMRT7EpioiI6BEYbqqaK/uBTa8DaXGwArBR2xbhI+ejXq2wR25KREREDDdVR44K2DEVOLoUAHDP3hevqYYjrkZH7AwLlbc2IiIiM8JwUxVcigE2TwBU16XXzaPx3t2n8NfdTLwS4QcFm6KIiIhKjeFGTtlpwB9TgJOrpNceoUC/r5Bdsx22fbgDANC7sb989REREZkhhhs5CAGc2QBsfRvITgEUVkDrV4Au7wK2TthzKhH3NFoE1XBAowBXuaslIiIyKww3lS0jCdjyJnD+d+m1dwNpQL6aLfSrFMwl1TvCn01SRERERmK4qSxCAMdXAn++C+SkA1bWQPtJQMe3DAbky9FosfPcLQBAFJukiIiIjMZwUxnuXJUmu4zbJb32f0y6WuMXUWjVvRdvIytXiwA3ezSt6Va5dRIREVkAhpuKpNMBR74HdkwHNFmAtT3QeQrQ5jVAWfSPflt+k1QvNkkRERGVCcNNRUm5BPz2GnDtkPQ6uC3Q/2vAq06xm6jztNhxVmqS6t3YrzKqJCIisjgMN6am1QAHvwJ2fwJo1YCtM/DEdCByDGBV8iTsBy6nIEOdBx8XOzQP9qikgomIiCwLw40pJZ6QrtYknZRe1+kO9P0CcA8q1ebbTklNUlERfrCyYpMUERFRWTDcmEr8PmDFAEBoAXt3oNdsoOnQUk90qdHqsP0s75IiIiIqL4YbUwluDfiEA561gN6fAc4+Rm3+93+pSL+ngZezLVqG1qigIomIiCwfw42pKG2A0VsA+7Ldvr3tdCIAoGcjPyjZJEVERFRmJfdwJeOUMdjkaXX480zBXVJskiIiIioPhpsq4J/4NKRl5cLD0QatwtgkRUREVB4MN1XA1geapKyVPCVERETlwU9SmWl1An+c5l1SREREpsJwI7OjV9KQkqmGm4MN2tb2lLscIiIis8dwI7OCuaSeCPeFDZukiIiIyo2fpjLS6YT+FvCoCM4lRUREZAoMNzKKvXYHt1RqONtZo31dL7nLISIisggMNzLamj+XVPeGPrCzVspcDRERkWVguJGJEALbTuU3SfEuKSIiIpORPdzMnz8fYWFhsLe3R2RkJPbt21fsurt374ZCoSj0OH/+fCVWbBonrqfjZnoOHG2V6FTPW+5yiIiILIas4Wb16tWYMGEC3nvvPcTGxqJDhw6IiopCQkJCidtduHABiYmJ+kfdunUrqWLTKbhq07WBD+xt2CRFRERkKrKGm7lz5+L555/H2LFj0bBhQ3zxxRcICgrCggULStzOx8cHfn5++odSaV7hQAihvwWcc0kRERGZlmzhJjc3F8eOHUOPHj0Mlvfo0QMHDx4scdtmzZrB398f3bp1w65du0pcV61WQ6VSGTzkduamCglp2bC3sULn+mySIiIiMiXZwk1KSgq0Wi18fX0Nlvv6+iIpKanIbfz9/bFo0SKsW7cO69evR/369dGtWzfs3bu32PeZNWsW3Nzc9I+goCCTHkdZFIxt06W+DxxtrWWuhoiIyLLI/smqUCgMXgshCi0rUL9+fdSvX1//uk2bNrh27Ro+++wzdOzYschtpkyZgkmTJulfq1QqWQOOEEJ/CzjvkiIiIjI92a7ceHl5QalUFrpKk5ycXOhqTklat26NS5cuFft9Ozs7uLq6GjzkdOFWBuJTsmBrbYWuDXxkrYWIiMgSyRZubG1tERkZiZiYGIPlMTExaNu2ban3ExsbC39/87kCUnDVplM9bzjbyX7hjIiIyOLI+uk6adIkjBgxAi1atECbNm2waNEiJCQkYNy4cQCkJqUbN25gxYoVAIAvvvgCoaGhaNSoEXJzc/HTTz9h3bp1WLdunZyHYZSCW8B7N+ZcUkRERBVB1nAzZMgQpKamYsaMGUhMTERERAS2bt2KkJAQAEBiYqLBmDe5ubl46623cOPGDTg4OKBRo0bYsmULevfuLdchGOXSrQxcSs6EjVKBbg1L3/RGREREpacQQgi5i6hMKpUKbm5uSE9Pr/T+N1/9dQlzYy6iS31vLBv9eKW+NxERkTkz5vNb9ukXqpOtnEuKiIiowjHcVJK425k4n5QBaysFeoSzSYqIiKiiMNxUkoLpFtrU9oS7o63M1RAREVkuhptKUjAqMeeSIiIiqlgMN5UgITUbp2+oYKUAm6SIiIgqGMNNJSi4atO6lic8ne1kroaIiMiyMdxUgq2nOZcUERFRZWG4qWA37t7DiWt3oVAAPRuxSYqIiKiiMdxUsD/yr9q0DK0BHxd7mashIiKyfAw3FUw/l1QE55IiIiKqDAw3FSgpPQdHr94BAPSKYH8bIiKiysBwU4H+PCM1SUWGeMDPjU1SRERElYHhpgLp55JikxQREVGlYbipILcz1PjnShoAoBfDDRERUaVhuKkgf55JghBA05puqOnhKHc5RERE1QbDTQUpGJWYA/cRERFVLoabCpCaqcahOKlJiv1tiIiIKhfDTQWIOXsLWp1AowBXhHg6yV0OERFRtcJwUwEK5pLqzSYpIiKiSsdwY2J3s3Nx8HIKADZJERERyYHhxsRizt5Cnk6ggZ8Lank7y10OERFRtcNwY2Lb8pukojjdAhERkSwYbkxIlaPBvku3AQC9G7NJioiISA4MNyb017lb0GgF6vg4o66vi9zlEBERVUsMNya07VT+XVLsSExERCQbhhsTyVTnYfdFqUmKoxITERHJx1ruAixFQmo2fFzsYKO0QgM/NkkRERHJheHGRMIDXLHvnS64namGQqGQuxwiIqJqi81SJqRQKODjYi93GURERNUaww0RERFZFIYbIiIisigMN0RERGRRGG6IiIjIojDcEBERkUVhuCEiIiKLwnBDREREFoXhhoiIiCwKww0RERFZFIYbIiIisigMN0RERGRRGG6IiIjIojDcEBERkUWxlruAyiaEAACoVCqZKyEiIqLSKvjcLvgcL0m1CzcZGRkAgKCgIJkrISIiImNlZGTAzc2txHUUojQRyILodDrcvHkTLi4uUCgUJt23SqVCUFAQrl27BldXV5Puu6qpTscKVK/j5bFarup0vDxWyyOEQEZGBgICAmBlVXKvmmp35cbKygo1a9as0PdwdXW16H9gD6pOxwpUr+PlsVqu6nS8PFbL8qgrNgXYoZiIiIgsCsMNERERWRSGGxOys7PD1KlTYWdnJ3cpFa46HStQvY6Xx2q5qtPx8lirt2rXoZiIiIgsG6/cEBERkUVhuCEiIiKLwnBDREREFoXhhoiIiCwKw42R5s+fj7CwMNjb2yMyMhL79u0rcf09e/YgMjIS9vb2qFWrFhYuXFhJlZbdrFmz0LJlS7i4uMDHxwcDBw7EhQsXStxm9+7dUCgUhR7nz5+vpKrLbtq0aYXq9vPzK3EbczyvABAaGlrkeXr11VeLXN+czuvevXvRr18/BAQEQKFQYOPGjQbfF0Jg2rRpCAgIgIODAzp37owzZ848cr/r1q1DeHg47OzsEB4ejg0bNlTQERinpOPVaDT43//+h8aNG8PJyQkBAQEYOXIkbt68WeI+ly9fXuT5zsnJqeCjKdmjzu2oUaMK1dy6detH7rcqnttHHWtR50ehUODTTz8tdp9V9bxWJIYbI6xevRoTJkzAe++9h9jYWHTo0AFRUVFISEgocv34+Hj07t0bHTp0QGxsLN5991288cYbWLduXSVXbpw9e/bg1VdfxaFDhxATE4O8vDz06NEDWVlZj9z2woULSExM1D/q1q1bCRWXX6NGjQzqPnXqVLHrmut5BYAjR44YHGdMTAwAYPDgwSVuZw7nNSsrC02bNsU333xT5PfnzJmDuXPn4ptvvsGRI0fg5+eHJ554Qj/fXFH+/vtvDBkyBCNGjMCJEycwYsQIPPPMMzh8+HBFHUaplXS82dnZ+Pfff/H+++/j33//xfr163Hx4kX079//kft1dXU1ONeJiYmwt7eviEMotUedWwDo1auXQc1bt24tcZ9V9dw+6lgfPjdLly6FQqHAU089VeJ+q+J5rVCCSu3xxx8X48aNM1jWoEEDMXny5CLXf+edd0SDBg0Mlr300kuidevWFVZjRUhOThYAxJ49e4pdZ9euXQKAuHPnTuUVZiJTp04VTZs2LfX6lnJehRBi/Pjxonbt2kKn0xX5fXM9rwDEhg0b9K91Op3w8/MTs2fP1i/LyckRbm5uYuHChcXu55lnnhG9evUyWNazZ08xdOhQk9dcHg8fb1H++ecfAUBcvXq12HWWLVsm3NzcTFuciRV1rNHR0WLAgAFG7ccczm1pzuuAAQNE165dS1zHHM6rqfHKTSnl5ubi2LFj6NGjh8HyHj164ODBg0Vu8/fffxdav2fPnjh69Cg0Gk2F1Wpq6enpAIAaNWo8ct1mzZrB398f3bp1w65duyq6NJO5dOkSAgICEBYWhqFDhyIuLq7YdS3lvObm5uKnn37CmDFjHjmJrLme1wLx8fFISkoyOG92dnbo1KlTsf9/geLPdUnbVFXp6elQKBRwd3cvcb3MzEyEhISgZs2a6Nu3L2JjYyunwHLavXs3fHx8UK9ePbzwwgtITk4ucX1LOLe3bt3Cli1b8Pzzzz9yXXM9r2XFcFNKKSkp0Gq18PX1NVju6+uLpKSkIrdJSkoqcv28vDykpKRUWK2mJITApEmT0L59e0RERBS7nr+/PxYtWoR169Zh/fr1qF+/Prp164a9e/dWYrVl06pVK6xYsQJ//vknvv/+eyQlJaFt27ZITU0tcn1LOK8AsHHjRty9exejRo0qdh1zPq8PKvg/asz/34LtjN2mKsrJycHkyZPx7LPPljixYoMGDbB8+XJs2rQJv/zyC+zt7dGuXTtcunSpEqs1XlRUFFauXImdO3fi888/x5EjR9C1a1eo1epit7GEc/vDDz/AxcUFgwYNKnE9cz2v5VHtZgUvr4f/whVClPhXb1HrF7W8qnrttddw8uRJ7N+/v8T16tevj/r16+tft2nTBteuXcNnn32Gjh07VnSZ5RIVFaV/3rhxY7Rp0wa1a9fGDz/8gEmTJhW5jbmfVwBYsmQJoqKiEBAQUOw65nxei2Ls/9+yblOVaDQaDB06FDqdDvPnzy9x3datWxt0xG3Xrh2aN2+Or7/+Gl999VVFl1pmQ4YM0T+PiIhAixYtEBISgi1btpT4wW/u53bp0qV47rnnHtl3xlzPa3nwyk0peXl5QalUFkr1ycnJhdJ/AT8/vyLXt7a2hqenZ4XVaiqvv/46Nm3ahF27dqFmzZpGb9+6dWuz/MvAyckJjRs3LrZ2cz+vAHD16lXs2LEDY8eONXpbczyvBXe/GfP/t2A7Y7epSjQaDZ555hnEx8cjJiamxKs2RbGyskLLli3N7nz7+/sjJCSkxLrN/dzu27cPFy5cKNP/YXM9r8ZguCklW1tbREZG6u8uKRATE4O2bdsWuU2bNm0Krb99+3a0aNECNjY2FVZreQkh8Nprr2H9+vXYuXMnwsLCyrSf2NhY+Pv7m7i6iqdWq3Hu3LliazfX8/qgZcuWwcfHB3369DF6W3M8r2FhYfDz8zM4b7m5udizZ0+x/3+B4s91SdtUFQXB5tKlS9ixY0eZgrcQAsePHze7852amopr166VWLc5n1tAuvIaGRmJpk2bGr2tuZ5Xo8jVk9kcrVq1StjY2IglS5aIs2fPigkTJggnJydx5coVIYQQkydPFiNGjNCvHxcXJxwdHcXEiRPF2bNnxZIlS4SNjY1Yu3atXIdQKi+//LJwc3MTu3fvFomJifpHdna2fp2Hj3XevHliw4YN4uLFi+L06dNi8uTJAoBYt26dHIdglDfffFPs3r1bxMXFiUOHDom+ffsKFxcXizuvBbRarQgODhb/+9//Cn3PnM9rRkaGiI2NFbGxsQKAmDt3roiNjdXfHTR79mzh5uYm1q9fL06dOiWGDRsm/P39hUql0u9jxIgRBnc/HjhwQCiVSjF79mxx7tw5MXv2bGFtbS0OHTpU6cf3sJKOV6PRiP79+4uaNWuK48ePG/w/VqvV+n08fLzTpk0Tf/zxh/jvv/9EbGysGD16tLC2thaHDx+W4xD1SjrWjIwM8eabb4qDBw+K+Ph4sWvXLtGmTRsRGBholuf2Uf+OhRAiPT1dODo6igULFhS5D3M5rxWJ4cZI3377rQgJCRG2traiefPmBrdHR0dHi06dOhmsv3v3btGsWTNha2srQkNDi/3HWJUAKPKxbNky/ToPH+snn3wiateuLezt7YWHh4do37692LJlS+UXXwZDhgwR/v7+wsbGRgQEBIhBgwaJM2fO6L9vKee1wJ9//ikAiAsXLhT6njmf14Lb1h9+REdHCyGk28GnTp0q/Pz8hJ2dnejYsaM4deqUwT46deqkX7/AmjVrRP369YWNjY1o0KBBlQl2JR1vfHx8sf+Pd+3apd/Hw8c7YcIEERwcLGxtbYW3t7fo0aOHOHjwYOUf3ENKOtbs7GzRo0cP4e3tLWxsbERwcLCIjo4WCQkJBvswl3P7qH/HQgjx3XffCQcHB3H37t0i92Eu57UiKYTI7wlJREREZAHY54aIiIgsCsMNERERWRSGGyIiIrIoDDdERERkURhuiIiIyKIw3BAREZFFYbghIiIii8JwQ0QEaRLFjRs3yl0GEZkAww0RyW7UqFFQKBSFHr169ZK7NCIyQ9ZyF0BEBAC9evXCsmXLDJbZ2dnJVA0RmTNeuSGiKsHOzg5+fn4GDw8PDwBSk9GCBQsQFRUFBwcHhIWFYc2aNQbbnzp1Cl27doWDgwM8PT3x4osvIjMz02CdpUuXolGjRrCzs4O/vz9ee+01g++npKTgySefhKOjI+rWrYtNmzZV7EETUYVguCEis/D+++/jqaeewokTJzB8+HAMGzYM586dAwBkZ2ejV69e8PDwwJEjR7BmzRrs2LHDILwsWLAAr776Kl588UWcOnUKmzZtQp06dQzeY/r06XjmmWdw8uRJ9O7dG8899xzS0tIq9TiJyATknrmTiCg6OloolUrh5ORk8JgxY4YQQpqpfty4cQbbtGrVSrz88stCCCEWLVokPDw8RGZmpv77W7ZsEVZWViIpKUkIIURAQIB47733iq0BgPi///s//evMzEyhUCjEtm3bTHacRFQ52OeGiKqELl26YMGCBQbLatSooX/epk0bg++1adMGx48fBwCcO3cOTZs2hZOTk/777dq1g06nw4ULF6BQKHDz5k1069atxBqaNGmif+7k5AQXFxckJyeX9ZCISCYMN0RUJTg5ORVqJnoUhUIBABBC6J8XtY6Dg0Op9mdjY1NoW51OZ1RNRCQ/9rkhIrNw6NChQq8bNGgAAAgPD8fx48eRlZWl//6BAwdgZWWFevXqwcXFBaGhofjrr78qtWYikgev3BBRlaBWq5GUlGSwzNraGl5eXgCANWvWoEWLFmjfvj1WrlyJf/75B0uWLAEAPPfcc5g6dSqio6Mxbdo03L59G6+//jpGjBgBX19fAMC0adMwbtw4+Pj4ICoqChkZGThw4ABef/31yj1QIqpwDDdEVCX88ccf8Pf3N1hWv359nD9/HoB0J9OqVavwyiuvwM/PDytXrkR4eDgAwNHREX/++SfGjx+Pli1bwtHREU899RTmzp2r31d0dDRycnIwb948vPXWW/Dy8sLTTz9deQdIRJVGIYQQchdBRFQShUKBDRs2YODAgXKXQkRmgH1uiIiIyKIw3BAREZFFYZ8bIqry2HpORMbglRsiIiKyKAw3REREZFEYboiIiMiiMNwQERGRRWG4ISIiIovCcENEREQWheGGiIiILArDDREREVkUhhsiIiKyKP8Pb5jYSTCFkHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(result.history[\"accuracy\"])\n",
    "plt.plot(result.history[\"val_accuracy\"])\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"Accuuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Train\", \"Test\"], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6fb9896",
   "metadata": {
    "id": "f6fb9896"
   },
   "outputs": [],
   "source": [
    "model.save(\"cifar10_classification_model_CNN.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d10653a3",
   "metadata": {
    "id": "d10653a3"
   },
   "outputs": [],
   "source": [
    "my_model = load_model(\"cifar10_classification_model_CNN.h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ac3c6f",
   "metadata": {
    "id": "c5ac3c6f"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}