13_mnist_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 \n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ece7314e",
   "metadata": {
    "id": "ece7314e"
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26b3f8b2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "26b3f8b2",
    "outputId": "68112e85-4dea-4122-b63e-99d1137cf0cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "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": "c30b1af9-65c2-43bf-a83c-b914c591804a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "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": "2d57caae-6918-4268-ec73-8dadade8772a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "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": "389a293c-7b78-49e8-cef4-6729b0d9f47b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "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": "9f1d16e8-361a-473c-cd41-64404c0d6cf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "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": "07c52ecb-dca1-4666-9a94-24747a54edcb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 6)         156       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 6)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1176)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               602624    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               65664     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 669,734\n",
      "Trainable params: 669,734\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Input(shape=(28, 28, 1)))\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": "0a4a6401-19dc-463f-cdd0-a2ef9ec60ba4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1875/1875 [==============================] - 20s 7ms/step - loss: 0.1482 - accuracy: 0.9546 - val_loss: 0.0673 - val_accuracy: 0.9787\n",
      "Epoch 2/50\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0522 - accuracy: 0.9836 - val_loss: 0.0447 - val_accuracy: 0.9854\n",
      "Epoch 3/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0325 - accuracy: 0.9900 - val_loss: 0.0404 - val_accuracy: 0.9865\n",
      "Epoch 4/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0239 - accuracy: 0.9923 - val_loss: 0.0518 - val_accuracy: 0.9834\n",
      "Epoch 5/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0178 - accuracy: 0.9939 - val_loss: 0.0642 - val_accuracy: 0.9811\n",
      "Epoch 6/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0133 - accuracy: 0.9956 - val_loss: 0.0512 - val_accuracy: 0.9852\n",
      "Epoch 7/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0119 - accuracy: 0.9962 - val_loss: 0.0552 - val_accuracy: 0.9860\n",
      "Epoch 8/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0604 - val_accuracy: 0.9851\n",
      "Epoch 9/50\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0079 - accuracy: 0.9973 - val_loss: 0.0570 - val_accuracy: 0.9876\n",
      "Epoch 10/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.0633 - val_accuracy: 0.9868\n",
      "Epoch 11/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0077 - accuracy: 0.9979 - val_loss: 0.0655 - val_accuracy: 0.9866\n",
      "Epoch 12/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0063 - accuracy: 0.9978 - val_loss: 0.0717 - val_accuracy: 0.9849\n",
      "Epoch 13/50\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0054 - accuracy: 0.9983 - val_loss: 0.0663 - val_accuracy: 0.9881\n",
      "Epoch 14/50\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.0558 - val_accuracy: 0.9885\n",
      "Epoch 15/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0064 - accuracy: 0.9984 - val_loss: 0.0636 - val_accuracy: 0.9875\n",
      "Epoch 16/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0037 - accuracy: 0.9987 - val_loss: 0.0622 - val_accuracy: 0.9884\n",
      "Epoch 17/50\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.0639 - val_accuracy: 0.9868\n",
      "Epoch 18/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0057 - accuracy: 0.9984 - val_loss: 0.0767 - val_accuracy: 0.9873\n",
      "Epoch 19/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 0.0783 - val_accuracy: 0.9871\n",
      "Epoch 20/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0062 - accuracy: 0.9983 - val_loss: 0.0871 - val_accuracy: 0.9855\n",
      "Epoch 21/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0880 - val_accuracy: 0.9870\n",
      "Epoch 22/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.1019 - val_accuracy: 0.9871\n",
      "Epoch 23/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 0.1078 - val_accuracy: 0.9865\n",
      "Epoch 24/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.0906 - val_accuracy: 0.9869\n",
      "Epoch 25/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.0911 - val_accuracy: 0.9883\n",
      "Epoch 26/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0850 - val_accuracy: 0.9879\n",
      "Epoch 27/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.0979 - val_accuracy: 0.9887\n",
      "Epoch 28/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0061 - accuracy: 0.9987 - val_loss: 0.1005 - val_accuracy: 0.9863\n",
      "Epoch 29/50\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.1027 - val_accuracy: 0.9853\n",
      "Epoch 30/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.1144 - val_accuracy: 0.9875\n",
      "Epoch 31/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.0959 - val_accuracy: 0.9871\n",
      "Epoch 32/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.1186 - val_accuracy: 0.9846\n",
      "Epoch 33/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.1235 - val_accuracy: 0.9843\n",
      "Epoch 34/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0037 - accuracy: 0.9991 - val_loss: 0.1083 - val_accuracy: 0.9877\n",
      "Epoch 35/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0038 - accuracy: 0.9991 - val_loss: 0.1044 - val_accuracy: 0.9872\n",
      "Epoch 36/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.1113 - val_accuracy: 0.9870\n",
      "Epoch 37/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0051 - accuracy: 0.9990 - val_loss: 0.0985 - val_accuracy: 0.9868\n",
      "Epoch 38/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.1147 - val_accuracy: 0.9862\n",
      "Epoch 39/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.1032 - val_accuracy: 0.9870\n",
      "Epoch 40/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0036 - accuracy: 0.9992 - val_loss: 0.1108 - val_accuracy: 0.9895\n",
      "Epoch 41/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.1316 - val_accuracy: 0.9875\n",
      "Epoch 42/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1384 - val_accuracy: 0.9883\n",
      "Epoch 43/50\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0026 - accuracy: 0.9995 - val_loss: 0.1329 - val_accuracy: 0.9863\n",
      "Epoch 44/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0044 - accuracy: 0.9991 - val_loss: 0.1829 - val_accuracy: 0.9842\n",
      "Epoch 45/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.1536 - val_accuracy: 0.9863\n",
      "Epoch 46/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.1373 - val_accuracy: 0.9868\n",
      "Epoch 47/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0020 - accuracy: 0.9996 - val_loss: 0.1654 - val_accuracy: 0.9845\n",
      "Epoch 48/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0049 - accuracy: 0.9991 - val_loss: 0.1730 - val_accuracy: 0.9870\n",
      "Epoch 49/50\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1401 - val_accuracy: 0.9878\n",
      "Epoch 50/50\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 8.6221e-04 - accuracy: 0.9998 - val_loss: 0.1611 - val_accuracy: 0.9864\n"
     ]
    }
   ],
   "source": [
    "result = model.fit(X_train, y_train, verbose=1, epochs=50, 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": "b1aedfc2-dd38-4cb4-e681-0c2b38b728be"
   },
   "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": "acc76a59-e94e-4cc9-95b4-b6ee9ea8af9c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lSYJWq4WNzcPDinr1PFGkk1Co1cFWJV8/TK3qATKXd955B+np6YZbXFyc3E0iIiKSla+vr+Hm6uoKhUJh+D46OhrOzs7YsmULOnToALVajX379iH60mX0ebY/vLx94OjkhLB2HbBq4++4nJyJ6IQMnI1PR2BQEN778BMkpucBED1N//3vfzFw4EA4ODggJCQEv/76q9nfX63qAfL19UVSUlKpbUlJSXBxcYG9vT1UKhVUKlW5+/j6+lZ4XLVaDbVabZY2E1mL5Mw8zPnjAkL9XDCme0OobUxTrp7KSs7Mw/ZzSdh7OQX1nNR4sqkXujXxhJO6Zv9Lz8wrxMZjN/H76QQE1XPE5KeaINjT0Wyvl5ZdgIuJmbidnY/OwR7wdtaY7bWMJUkScgu1sry2xkYJrQ7IL9Iiv0iHgiId8ot0UCkVcHOwhZPaptIzraZPn47PP/8c/vUbQGfniLPXrqPjo09h3JR3YKdW47cNP2H8yCH4Zfdh+AUEljzxvsPPnj0bn376KT777DMsWrQII0eOxI0bN+Dh4WHCd15azf5ruU/Xrl3x559/ltoWGRmJrl27AhD1FTp06ICoqCg8//zzAMS6K1FRUZg8ebKlm0tkNdJzCxGx/AguJGQAJ2/hp8OxmNG/BZ4K9Xn4k2uBs/HpWL4/Bi4aW0x9uilc7W0t3ob4u7nYdjYRW88m4siNNEhSyWM/HoqFrUqBDkHueLKZN55o6oVQX+caM+U6JjUb3/99HRuO3URWfhEA4OiNO9h8Mh4vtAvAGz1DEOhR9eKAeYVaXE7KQnRiBi4mZuJiUiaiEzORkplv2EehADoFeaB3K1/0buULfzf7ar+v++UWaHHw2m3E3clBToEWuQVa5BVqkVOghQ2KEF5fAbs7OVDYFCG/UIc+C/eavA2VsWFiV9jZlD8AdCenALYqJdwd7ODuYAu17YM/yEx/dwYat+1q+Lk2bd4KbcPaQm2jhI1KgS5t/4O9kX/izP6deHTSJNioFLBVKeHnoin1Mx89ejRGjBgBAJgzZw4WLlyIw4cPo3fv3iZ612XJGgBlZWXhypUrhu9jYmJw8uRJeHh4oEGDBnjnnXcQHx+P//3vfwCAiRMnYvHixXj77bcxduxY7Ny5E+vWrcMff/xhOMa0adMQERGBjh07onPnzliwYAGys7MxZswYi78/ImuQV6jF+P8dxYWEDHg62UGpUOD67RyMXXkUPUO9MaN/CwTVM9+n/IqkZRdg0/Gb+PvqbbQOcMUL7QOMaockSTh4LQ1f776KPZdSDNu3nE3AnIGt0bO5+YO766nZ2HI2EVvPJuDUzfRSj4XVd8XTLXyQnJmPvy6mIDYtBwevpeHgtTR8vCUaPi5qPNHUC08280YzX2fYKBVQKhSwUSmgUiigUoqbUqmAjVIBO5USNibMx5AkCXsvp2Ll39ex62KyIWBr5OWIEZ0a4MC129gZnYz1x27i5xPxGNIxEK8/1aRSgcndnALsuZyK3RdTcCL2Dq7fzoZOKn/fQA97OKltcSEhA4evp+Hw9TR88Pt5hNV3Re9WfujTyhcNq9ELdetuLnZGJ2NndDL2X0lFflH563wFOKvQzdsbWflFUGiVyJOp9wcAdJIEBRSws1HAzkYFtY0SdjZK5BdqcTe3EIVaHZIz85CcmQdHOxu4O9pBd88J1ukkpOeKBV09g5uL9wQFXO1toVEU4NM5s/DHH38gISEBRUVFyM3NRXJiPBzv6aG8Pzhv06aN4b6joyNcXFzMXr5G1gDo6NGj6NGjh+H7adOmAQAiIiKwcuVKJCQkIDY21vB4cHAw/vjjD0ydOhVffvkl6tevj//+97/o1auXYZ9hw4YhJSUFM2bMQGJiItq2bYutW7eWSYwmohI6nQSl0vjegiKtDpN/PIHDMWlwVtvg+7GdEVTPEYuiLuO7fTGIik7G3supmPB4I7zWozEc7B7+L0erk3A5ORPZ+Vq09HeB5iGfQO9/H/uupGLtkThsP5+IQq34p70zOhlfRl1G+wZuGNi+Pvq38YObQ/kVeXU6CTsuJOHr3VdxIvYuAEClVKBvaz+cjU9HTGo2Xvn+KAa2C8DM/i0qPE5VSJKEc7cysP1cIrafT0J0YqbhsXt7MHq18kXAfYHC9dRs/HUxGbsvpeDAtdtIysjHuqM3se7ozUq/vqu9LTwc7Qy3eo52cC/+6uFoB3cHO9jbqeBgp4K9rQoaW5Xhe42NCkqlAtn5Rdh0Ih4r98fgakq24dhPhXpjdLeGeLSJJ5RKBcY/3gjHY+/gi8hL2Hs5FWsOx2LjsZsY0TkQk3o0gbdLyXCVVifhTHy64f2dirtbJuDxcLRDMx9nNPMtuTX1cTYMC97fg3bqZjpO3UzHJ1ujEerrjF4tfdHY20m8Zwc71HMSX+/vKdHqJJyMu4ud0UmIupBc6mcEAAFu9mgd4AoHdcl5srdVwV2jgJtDPnxcNNBoNMgv0uK31x9FbkFRqefb2SjhrLaFs70NHO1U0OokFGh1KCySDENVBTodCot0KNQ+eFFVBQBblRK2NkrYqZRQ26hgZyMCFbWtCspyegj9XCVk5BXiTk4hMvMKkV1QhOyCItxKz4UkAQnpuUjLLkBqlgiAnJwc4eWsRj1HNexslJg4cRoiIyPx+eefo0mTJrC3t8fgwYNRUFDwwLba2pbuVVUoFGZfNFYhSVIFcbP1ysjIgKurK9LT0+Hi4iJ3c4jMJjoxA3P+jMbR62l4/akQTHi8EVSVDIQkScLbG05j/bGbUNso8b+xndGlUT3D41eSszD7t3PYezkVAODvqsG7/Vqgb+vSqzgnZ+ThRNxdnIi9i5Nxd3DmZjqyC8SnY1uVAq0CXNGhgTs6NnRH+yD3cvM4bt3NxfqjN7HuaBzi7+Yatrep74peLX1xKCYN+y6nGC6atioFejTzxgvtA9Aj1BtqGxUKtTr8evIWlu6+isvJWQDExWhox/qY8FhjNKjngLxCLeZHXsJ/916DTgI8ndT48PlW6N2q4hzDhyko0uFQzG1Enk/CjvNJuFWcGAoANkoFujauh96tfPFMC194OVcuVzGvUIvDMWnYfSkFey6lIDEjDzqdBK0kQasTt4p6TKpDbaOEVPyeAMBJbYPBHeojolvDB+b6HI5Jw7ztF3EoJs1wnJcfCUJzPxfsvpSCvZdTcCen9LIHzXyc8UQzL3RrXA8t/F3g5aSu9JCfPodq27lE/H31NrQPOBnOaht4OIkA0EVjizPx6UjLLrmYKxVA+wbueKq5N3qG+qCpj1O57cjLy0NMTAyCg4Oh0ZT8DhdqdcjMK0RGbhEy84tw7yVZAQUkPPgHpVQoYGcIcERPjv57WxtluUFOZRUU6XA3pwB3cgqx7sdV+Gz2O9h37gYA4OTh/YgY9CxSb6ehnoe74TmtW7fG0KFD8f777wMQIz3169fH6NGjDbV/GjZsiClTpmDKlCnifSoU+Pnnnw2pK4BY8X3BggUYPXp0mXZVdC4B467ftSoHiIhMIzUrH/O2X8LaI7GGC+EnW6MRdSEJ84aGVWqo6OMt0Vh/7CZUSgUWv9i+VPADAE28nfC/sZ2x7VwS/vP7ecTfzcWkH4+jW+N6eKKpF07dvIuTsXdLXfD1HO1Ez0JqVgFOxIrg6L/7YgAADTwc0DFIBEMu9rbYdPwm9lwqCW5cNDYY2C4AQzsFoqW/KwBgUg8RaP166hY2HY/H+YQMbD+fhO3nk+Bqb4uezb1x6FqaIXhyVtvgpa5BGNs9uFTQobFV4d99m6NPK1+8teE0riRnYeIPx9CvjR8+eK4l6jlVLkDJzCvEXxdTEHk+CbsuJiMzr6QXwMFOhcdDvPB0Cx/0bO5dpR4mja0Kjzf1wuNNvSrcR9IHQ8Vfcwu0uJNTgNtZBUjLLkBaTgHSsgpwO7sAd3LEtjs5Bcgtzm3JLRS3vMKST+n64Z9gT0dEdA3CoA714ax5eL5U52AP/DThEfx99Tbmbb+I4/f8vPWc1TZ4NMQTTzT1whPNvODnWvUcHm9nDV56JAgvPRKEuzkFiDyfhL2XU5GUkWd4r2nZBdBJQGa+CExu3M4xPN9FY4MnmnmjZ6jIt3J3rHovoK1KCQ9HNTwc1dDqJGTli2AoI6/QEJjZqkRAY3dfgGNno4SNUmG2XC87GyW8XTTwclbD21kNBRRw0djC3dEOt91F/s79H5hCQkKwadMm9O/fHwqFAu+//77Ze3KqigEQkRXJK9Rixf7rWLLriiFpsU8rX3QO9sC87Zdw9MYd9PlyL/7dtzlGdmlQ4T/WZbuvYtmeawCAj19ojadblD/ErFAo0LuVL55s5oWv/7qKpbuv4u+rt/H31duGfZQKoKmPM9oGuqFdAze0DXRHE28nKBVAXFoujsWm4ej1Ozh24w4uJmUiNi0HsWk52HQivtRrPdLIA8M7NUDvVr7lDpt5u2gw7rFGGPdYI1xMzMSmEzex+UQ8kjLysem4OJankx3GPhqMlx4JgssDLtztGrjj99cfxcKoy1i25xr+OJ2AA1dvY/ZzLfFsGz8oFArkFWpx804uYtOyEXs7BzfSchCXloMbt3Nw/Xa2YXhOvK4a4c298UxLH3Rr7GnUsF9VKYrzgfQXAQc7G9RzUqOJt3HH0ekk5BVpDUm/RToJQR4ORg+pKhQKdG/iiW6N6+GvSylYtvsqcgq0eCzEE0809Ua7Bm5mqRnj5mCHIR0DMaRjYKntIs+lUASC2SIwvJtTgIaejugQ5G6WtqiUCrja28HV3g6SJOrk2CiVVRqeNiWFQgG1rQoKBQz5UhX9b5g/fz7Gjh2Lbt26wdPTE//3f/9XY4sLcwisHBwCI0vLK9TiTHy64UJ/Jv4u3Ozt0NTXGaG+zobchgA3+yr9M5QkCX+cScDHW6Jx847o5Wgd4Ir3+jU39NzEpeXgX+tPGYYhnmjqhU8GtYGva+ku5nVH4/D2htMAgHf6hOIfTzSudDvi0nKweOcVpOcWIizQDW0D3dCmvmup5MgHycgrxMnYuzh64w6O3UhDUkY+nm7hg6EdA6s0nVqrk3CwOBk32NMRgzvUNzr4OHMzHW9tOGXIBQn1dcbdnEIkZpTt2bpXIy9HPNPCF0+38EG7QDfZL3JkXg8atiHjmGoIjAFQORgAkbmlZObj2I00HLtxB0dv3MHZ+PRSPQIVcbRTlQqKAtwd4KhWwVltCyeNDZzUNnDW2EBtozR8QjsVdxf/+f08jt64AwDwcVHj7V6hGNguoMxFV6eTsOLv6/hkazQKinRwtbfFf55vhefC/AEA288lYuIPx6CTgH883gjv9G1u4jNTOxUU6bB41xV8tesKiu7JJ3G0U6FBPUcEeTigQT0HBHo4IMjDAcGejtWa9k21DwMg02EAZEYMgMjUtDoJx27cwZazCdgVnYzr9+QT6Hk6qdExyB0dgtzRroEbMvOKEJ2YiYuJGYhOzMTVlKxKBUmA6Ep3UouASJ/XorFV4h+PN8Y/nmj00NlYl5MyMW3dKZyJF1Ov+7Xxw3Nh/nh9zQkUFOkwpEN9fDq4TY2pM1NTXEvJwvmEDAS42aOBhwM8HO14jggAAyBTYgBkRgyA6F7xd3Ox5UwCUrLyEerrjJb+rmjk6fjQmimFWh0OXruNrWcTse1cElKzShdla+bjjPZB7ugY5I6OQR4I9LB/4MWyUKvD9dTs4qBIFHtLycxHVn4RsvKKkJUvpquW9xf9QvsAvNWrmVGJo4VaHZbsuoJFO6+UmiUT3twHS19qb9KaMUR1HQMg0+EsMCIzSkjPxZ9nEvHH6Vs4XlwL5l52NkqE+jqjhZ8LWvq7oIW/C0J9XaBSKrDvciq2nkvEjgtJuHvP1F1njQ2ebu6DXq188UijekZXE7ZVKRHi44wQH2f0Dyt/H51OQk6htjggKkRmXhHqOarRoJ7xwy22KiWmhDfFU6HemLr2JK6mZKNzsAcWv9iOwQ8R1XoMgIiKJWXkYcuZBPx+OsGQLwMUF6Br6IGmPk64mJiJ87cykF2gxemb6Th9T3VehQKwUylLVYKt52iHZ1r6oHcrP3RtVK/C8vOmorxn6AswzafMNvXd8Mcbj+FwTBo6B3tYZIYSEZG5MQCiOievUItTcXdxOCYNJ+LuoqBIV6puRnl1NI7euIMj10uvr9SpoTv6tfZDn9Z+8LmnKq1OJyE2LQfnEzJw/lYGzt1Kx/mEDCRl5CO/SAcfFzV6t/RF71Z+6BzsUenCgjWZvq4MEVFdwQCIar3s/CIcu3EHh2PScDgmDSfj7qLgISXiK9IhSAQ9fVv7lZn+radUKtDQ0xENPR3Rt7WfYXtqVj7uZBegsZcTpzQTEdVwDICoVpEkCXFpuTgdf1f08lwXU8jvL2Xv5axG52APdApyh5uDnVhDR6tDQdE9N60WhVqxvk6ghwP6VHOFaE8nNTwrWQmYiIjkxQCIaixJkhB/NxdnbqbjdHw6ztxMx5n4dKTnFpbZt767PToHe6BLsAc6B9dDw3oOnH5MREQVYgBENYpWJ+GXk/HYfPIWzty8W2YBREAkGof6OaN1gCs6NfRAp2CPMitjExERPQgDIKoRJEnCXxdT8MnWaMOSAoBYDVsf7LQOEMsmNPVxNvtsKiIia/ewXvSZM2di1qxZVT72/SvAWxoDIJLdidg7+HhLtGENKmeNDcY/1ghPNPVCM19nTrsmIpJBQkKC4f7atWsxY8YMXLx40bDNyclJjmaZDD9Gk2yupmTh1R+OYeBXf+NQTBrsbJSY8Hgj7H27B97oGYKwQDcGP0REMvH19TXcXF1doVAoSm376aef0Lx5c2g0GoSGhuKrr74yPLegoACTJ0+Gn58fNBoNgoKCMHfuXABAw4YNAQADBw6EQqEwfG9p7AEii0vKyMOCHZex7mgctDoJSgUwqH19THm6KXN5iMg6SBJQWHZNQIuwdRCVW6th9erVmDFjBhYvXox27drhxIkTGD9+PBwdHREREYGFCxfi119/xbp169CgQQPExcUhLi4OAHDkyBF4e3tjxYoV6N27N1QqeT7oMgAii7mYmIm1R+Lw4+EbyCsUdXrCm3vjrV6haObrLHPriIgsqDAHmOMvz2v/+xZg51itQ8ycORPz5s3DCy+8AAAIDg7G+fPnsWzZMkRERCA2NhYhISF49NFHoVAoEBQUZHiul5coqurm5gZfX99qtaM6GACRWd3NKcCvp25h/dGbhpXFAaB9AzdM79McnYM9ZGwdEREZKzs7G1evXsUrr7yC8ePHG7YXFRXB1dUVADB69Gg8/fTTaNasGXr37o1nn30WzzzzjFxNLhcDIDK5Iq0Oe6+kYsPRm4g8n2SoymyjVKBnc28M79QATzbzYp0eIrJetg6iJ0au166GrKwsAMC3336LLl26lHpMP5zVvn17xMTEYMuWLdixYweGDh2K8PBwbNiwoVqvbUoMgMhkrqVkYd3Rm/j5xE0kZeQbtjf3c8GQDvUxoK0/6rFSMhFR8erJ1RuGkouPjw/8/f1x7do1jBw5ssL9XFxcMGzYMAwbNgyDBw9G7969kZaWBg8PD9ja2kKr1Vqw1WUxAKJqOxl3F1/tuoLt55MM29wdbDGgbQCGdKyPlv6uMraOiIhMbfbs2XjjjTfg6uqK3r17Iz8/H0ePHsWdO3cwbdo0zJ8/H35+fmjXrh2USiXWr18PX19fuLm5ARAzwaKiotC9e3eo1Wq4u7tb/D0wAKIqkSQJ+66k4uu/ruLvq7cBiA80PZp5Y2jH+ngq1IfFComI6qhx48bBwcEBn332Gd566y04OjqidevWmDJlCgDA2dkZn376KS5fvgyVSoVOnTrhzz//hFIprgvz5s3DtGnT8O233yIgIADXr1+3+HtQSJIkPXw365KRkQFXV1ekp6fDxcVF7ubUKFqdhO3nEvHVX1cNSc02SgWebxeAiU80QhNvzuYiIrpfXl4eYmJiEBwcDI1GI3dzarUHnUtjrt/sAaJKKSjSYfOJeCzdcxXXUrIBABpbJYZ3aoDxjzdi/R4iIqpVGADRQ11NycKo7w4j/m4uAMBFY4PR3RpidPdgeDjaydw6IiIi4zEAogfKK9Ri0urjiL+bC29nNcY9FowXuwTBSc1fHSIiqr14FaMHmv3beUQnZsLTyQ6/v/EovJ05dk1ERLUfp+lQhX49dQtrDsdCoQAWDGvH4IeIqJo476j6THUOGQBRua6nZuPfm84AACY92QSPhnjK3CIiotpLXyG5oKBA5pbUfjk5YhFZW1vbah2HQ2BURn6RFpPXHEdWfhE6N/TAlPAQuZtERFSr2djYwMHBASkpKbC1tTXUw6HKkyQJOTk5SE5OhpubW7VXkWcARGXM/TMaZ+Mz4O5giy9HtIWNin+oRETVoVAo4Ofnh5iYGNy4cUPu5tRqplpFngEQlbL1bCJW/n0dADBvaBj8XFnfh4jIFOzs7BASEsJhsGqwtbWtds+PHgMgMohLy8HbG04BACY83ghPhfrI3CIiorpFqVSyEnQNwbENAgAUanV4fc0JZOQVoW2gG/71TDO5m0RERGQ2DIAIAPD5tos4GXcXzhobLBrRjguZEhFRncarHGFXdDKW7bkGAPhscBsEejjI3CIiIiLzYgBk5RLSczFt3UkAQETXIPRu5Sdvg4iIiCyAAZAVK9Lq8MaaE7iTU4hWAS74d7/mcjeJiIjIIhgAWbH5kZdw5PodOKltsHhEe6htTDO1kIiIqKZjAGSl/rqYjK/+ugoA+HhQazT0dJS5RURERJbDAMgKibwfUe/n5UeC8Gwbf5lbREREZFkMgKyMPu8nLbsALf1d8C7zfoiIyAoxALIy9+b9LHmxPTS2zPshIiLrwwDIijDvh4iISGAAZCWY90NERFSCAZAVuDfvp1UA836IiIgYAFkBfd6PM/N+iIiIADAAqvNK5/20QVA95v0QERExAKrD7s37GdU1CP3acJ0vIiIigAFQnTZ94xlD3s+/+zLvh4iISI8BUB21/0oqdl9Kga1KgYXD2zHvh4iI6B4MgOognU7Cx1uiAQAjuwShkZeTzC0iIiKqWRgA1UF/nk3Amfh0ONqpMPmpJnI3h4iIqMZhAFTHFGp1+HzbRQDAhMcbw9NJLXOLiIiIah4GQHXMT0ficP12Djyd7DDusWC5m0NERFQjMQCqQ7Lzi/DljssAgDd7hsBRbSNzi4iIiGomBkB1yHf7YpCalY+geg4Y3rmB3M0hIiKqsRgA1RG3s/KxbLeo+PyvZ5rBVsUfLRERUUV4lawjFu28guwCLVoHuKJfa1Z8JiIiehAGQHVAXFoOVh+6AQCY3icUSqVC5hYRERHVbAyA6oB52y+iUCvhsRBPdG/iKXdziIiIajwGQLXc2fh0bD55CwDwf71DZW4NERFR7SB7ALRkyRI0bNgQGo0GXbp0weHDhyvct7CwEB988AEaN24MjUaDsLAwbN26tdQ+mZmZmDJlCoKCgmBvb49u3brhyJEj5n4bsvm0uOjhc2H+aBXgKnNriIiIagdZA6C1a9di2rRpmDlzJo4fP46wsDD06tULycnJ5e7/3nvvYdmyZVi0aBHOnz+PiRMnYuDAgThx4oRhn3HjxiEyMhKrVq3CmTNn8MwzzyA8PBzx8fGWelsW8/eVVOwpXvD0X880k7s5REREtYZCkiRJrhfv0qULOnXqhMWLFwMAdDodAgMD8frrr2P69Oll9vf398e7776LSZMmGbYNGjQI9vb2+OGHH5CbmwtnZ2f88ssv6Nevn2GfDh06oE+fPvjwww8r1a6MjAy4uroiPT0dLi4u1XyX5iFJEgYs2Y/TN9MxultDzHqupdxNIiIikpUx12/ZeoAKCgpw7NgxhIeHlzRGqUR4eDgOHDhQ7nPy8/Oh0WhKbbO3t8e+ffsAAEVFRdBqtQ/cp67480wiTt/kgqdERERVIVsAlJqaCq1WCx8fn1LbfXx8kJiYWO5zevXqhfnz5+Py5cvQ6XSIjIzEpk2bkJCQAABwdnZG165d8Z///Ae3bt2CVqvFDz/8gAMHDhj2KU9+fj4yMjJK3Wq6BTsuAQDGP96IC54SEREZSfYkaGN8+eWXCAkJQWhoKOzs7DB58mSMGTMGSmXJ21i1ahUkSUJAQADUajUWLlyIESNGlNrnfnPnzoWrq6vhFhgYaIm3U2W3s/JxOTkLADCmOxc8JSIiMpZsAZCnpydUKhWSkpJKbU9KSoKvr2+5z/Hy8sLmzZuRnZ2NGzduIDo6Gk5OTmjUqJFhn8aNG2P37t3IyspCXFwcDh8+jMLCwlL73O+dd95Benq64RYXF2eaN2km526JHqpgT0e42tvK3BoiIqLaR7YAyM7ODh06dEBUVJRhm06nQ1RUFLp27frA52o0GgQEBKCoqAgbN27EgAEDyuzj6OgIPz8/3LlzB9u2bSt3Hz21Wg0XF5dSt5pMHwC19K/Z7SQiqhXyM4G984G7NfvDL5mWjZwvPm3aNERERKBjx47o3LkzFixYgOzsbIwZMwYAMGrUKAQEBGDu3LkAgEOHDiE+Ph5t27ZFfHw8Zs2aBZ1Oh7fffttwzG3btkGSJDRr1gxXrlzBW2+9hdDQUMMx64Kzt9IBAC39WfeHiKjaImcCR78Dzv0MjN8FqGS9NJKFyPpTHjZsGFJSUjBjxgwkJiaibdu22Lp1qyExOjY2tlTuTl5eHt577z1cu3YNTk5O6Nu3L1atWgU3NzfDPunp6XjnnXdw8+ZNeHh4YNCgQfjoo49ga1t3horOF/cAtQpgDxARUbWkxwMnVon7iaeBw98AXV+Tt01kEbLWAaqpanIdoMy8QrSetR0AcPz9p+HhaCdzi4iIarE/3xJBj707kHsHsHMCJh0GXAPkbhlVQa2oA0RVcyEhEwDg76ph8ENEVB0ZCcCx78X9wSuA+p2Bgixga9lCvFT3MACqZc7Gi/yfFsz/ISKqnv0LAG0+0KAr0OhJ4NkvAIUKuPArcGmb3K0jM2MAVMucY/4PkeUV5QOHvgHObZa7JWQqmYnAsZXi/hP/BygUgG8roGvxUkt//AsoyJGteWR+DIBqmXOcAVZz6XRAXrrcrSBTiz0ILH0U2PIWsH40kHRe7haRKexfCBTliWGvRk+WbH9yOuAaCKTHArs/ka15ZH4MgGqRvEKtoQI0e4BqCG0RcO0v4PdpwPxQ4JNgUU+kOnMLdDrg6i4g+7bJmklVkJ8pEmSX9wZSLxVvlIC/5sjaLDKBrGTg6HJx/8ni3h89O0eg72fi/oHFDHjrMAZAtcjFxExodRI8HO3g66J5+BPIPIryRX7A5knA502A/w0QNUSykgBJC0TNBjaMAQqyjT92ZhLwwwvAqueB//YEcu+auvXW5W4csOX/gOOrgJy0yj/vciTwVVcxOwgS0O4lYOw2AArgwm/ArRPmajFZwv4vgaJcIKAj0Lhn2ceb9QFCnwV0RcAf08SHksoozAMOfCV+36jGY7WnWuTeCtCKez+xkPkV5gGXt4mL36VtQP49C+Y61ANC+wHNBwB3r4sL7rmfgdQrwPDVgHtQ5V7jciTw80QgJ1V8fycG2PwqMGw18IC17KgC2bdFcJp2VXz/25tAw0eB5v3FzbmcJXeybwPb3gFOrxXfuwUB/b8EGvcQ37cZKh7bNQcYud4y74NMKyvlnt6f6aV7f+7V5xPRExt7ADj5A9B+1IOPe+MA8OvrwO3L4nsXf6BJOcEV1Rj8r1qLsAK0TCQJ+GEQsG4UcGa9CH6c/YDOE4CI34B/XgKeWwSEhAOdxgERvwOOXkDSGeCbJ4Frux98/KJ8YNu7wOrBIvjxaQUM+g5Q2QEX/wT+/tIib7NOKcgB1gwXwY+zP+DbWvTOxewG/vwXMC8U+K4X8Pdi4M4N8TM+swFY0lkEOAol8Mgk4LUDJcEPUJwsqwIubwdiD8n3/qjqDiwCCnMA//ZAk/CK93OtD/T4t7gfOQPITi1/v/xMkTC9orcIfpTF/Qp//BMozDVt28mkGADVIpwBJpO4Q8CNfYCNBug6GXglEph6XuQJBD9etmx+UFdgwl+AX1sgNw1YNRA4+HX5eUGpV4Dvnha5BoAIqsZFAa0Hl+QhRH0AxOwx5zusW3RaYNN44OZhQOMKjNoMTNwHvHECePoDMewBCYg7CGx/F/iyDbCgDbDxFRGAejUXP+Pec0Q+yL3qNQbajRT3d31o6XdG1ZV9Gzj8X3H/if+ruPdHr8tEwKe1KJC4/f2yj1/aDix5BDjyrfi+3cvAGydF0H0nRuQDUo3FAKiWKNLqEJ2gHwJjD5BF6cfzWw0Cen0EBHZ++JCUa31g7FagzXDR87B1OrD5NTGUBohg6OQaYNnjQMIpUYV2+BoR9NgW53e1jwDajgQkHbBhLJBxy3zvsa6QJDEEGf276EEb8RPg1Uw85tEI6P4mMD5KBLB9PgWCHhW9PemxgNIWePLfwD/2APU7Vvwaj78tjh2z5+G9e3WJTldxL0htcWARUJgtPpw07fXw/VU2QP8FABTAqR+BmL1ie/ZtYON44MchQMZNwL0hMOoXYMBiwC0Q6POx2G/fF0DKpQoOXgcV5gI7ZgFfdasVH9oYANUSV1OykV+kg5PaBkEeDnI3x7yyU0WuTVG+3C0R3dvnfhb3271s3HNt7YGBS4Fec8RF9tSPwMq+QMpFYNMEYPNE8c+44WPAq38DoX1LP1+hAPrNE59As1PEFGxtoUneVp21/8viT+MK4IVvgKBu5e/nGgB0+Qcw5g8xhDn0f8CkQ2JGkM1DKqy7BQIdRov7uz6q3oy/2iRqNvBZYxG0H10B5GfJ3SLj5KQBh4t7airT+6NXvyPQcay4/8c04NRaYEkn4Mw68XfddTLw6oHSU+mbPweE9AJ0heI51vA7ErMX+LqbCPqSzwFrXgQSz8jdqgdiAFRLGCpA+7lAqazDCdCSBPw4FFj7ErD0MfnzLM5uEkFKvRCgwSPGP1+hEIXVXtoEaNyA+GMiz+TMOpFL8tR74pOji3/5z7e1B4b9D1C7iqG4yBnVejt12un1wI6Z4n6vOUDLgZV7npMX0GKAGN6qrMf+CdjYi5/JlR3Gt/Vh0m8Cv0wGDiwB7saa/vjGKswTQQ8geix/nyLyqH6fWuMvcgYHlohlLnzbiFlexug5Q+T1pV4Cfp4A5NwGvFsC43aIXmG7+z6UKhRA30/F78j1vSVJ9XVRXrqYYPD9s0DaNZEf6d8eKMgEVg8RMzFrKAZAtYRhBlhdz/+5HCmCBABIvQgs7wX8+bZ8nzb1q0S3e6nynxjL07iHyAvybiG+d20AjNkCPP4WoFQ9+LkejURPEgAc/EoEZZWVlwHcvlr3e45i9ogZc4BIXjb3at7OvkDn8eL+zv+Y/hP+b2+K371t/wYWtBbJ9Hvni5+lHC5tAfLTAZf6wDMfAh6NxQXu6HJRJPLbnsCJ1TW3cnJOGnBombhvTO+Pnr0b0Lt4WEtlB/R4V/w9B3So+DnuDYEn3hb3t71rXBmG2iL6T2BJl5KK2h1Gi57Ul38WuXSZCWJyR+4dOVtZIa4GX46auBr80GUHcDgmDZ8PCcPgDvXlbo55SJKofRN/TPwhaYvE9FNAVGZ9doGYaWUpydHAV11ET820C4CzT/WPWZAtptYGPyYSdI2xYzawbz5g6whM2FWS23I/SQJuHgWOrRDBUlGuyG+p1xjwbAp4hYrnejYFPENEL1NtlnROFCvMzwBaPC8WtbRE2YDs2yKBuiALGPaDmFpvCpd3AKsHiZ9Z/U5iGjbu+Tft3UIMsbR4Tty3REmMH4cBl7aKnq+eM8TvWMwe8Tt24Xcx1AOI3+k2w0W5Aa9mInhX2Zq/fQ+z8yNgz6dihuU/9lb99+P6ftFb6xFcuf21haInO+WCyOl7bmHVXremyUoBtrwNnCv+MObRSMyEbfhoyT7pN4H/hosgKOhR4OVNgI3a7E0z5vrNAKgcNS0A0ukkhM3ejsz8Imyd8hhCfeVvk1no//Hb2ANTTgNO3sDVneLTsH4YoM1woPdcwMHD/O3Z9q6YndWsHzDiR/O/3sNoi4AfBooLj2dTYPxOQO1c8nhehuhqP7YSSDpbsl1lB2gLKjioQtQpCugouvLLq41jrMJckceVkyqGCrJvi685qcXbb4t/mI+8WvHQX2Wlxxf/k70FNOgmPnnqk8gtQX9h9WoOvLr/4b15D6MtEj0qKRdEbkmvj0TV4ug/xAKdMXtEcT49j8bAM/8RdajMJTsVmNdMvO6kI4BX09KPZyUDJ34Qv3d3b5R+TGkj2ujVFPBsVhx8NxVDyvcPG5lL7l3Ri5afIXK9WgywzOvq3TggpsgDwNjtQIMuln19U5Ik8T9m63TRq6NQAd0mA0++U/4HqcSz4sNJQSbQ8gVR3sPMH04YAFVTTQuArqdm48nP/4KdjRLnZveCraoOjlxKkpgOfvNIyT9+vYJsYOeHYio5JMDBU4yvt3zBfJ9+iwrE0hY5t8VMImNzBswlKwVY9pj4VNXyBWDwclGV+NgK4MxGka8EiCn7LV8AOo4RwU1GvEi+Tr0ovqZcBFKigby7Jceu3xkY/cfDk4ArkngGWPuymP5bGSo7Mcvt0SliuMBYuXeAFX2B5PPi4jp2q2UC41JtuCt6gfLSxT/31oOrd7yjy0Vejb27mLZv737f690BLm4VwdCVKLGSuZ2z+MBgrvd+aJn4tO/fTgz7VESnA67tEr2OyedFvkxBRUPXCpFM7hVatlfS3s10bc9LB36bInoqvFsAE/fLU1T0l8liSNO7hZhlWBN6xari96klRSR9WgMDFonfiwe59hfww2DRS9jtdTGEakYMgKqppgVAf5xOwKQfjyOsvit+mfzow59QG13dKerl2GiAN0+XP9wUd0RUWk25IL5v2gdoM0T886zXxLTdq+d/EYUPnXyBqefK1vqRU+whMZtMVwS4B5cOODybiaAnbHjZi+f9JEnMLks4LabZ56cDXV4tmcJrjKwU4NseQHpxwqPSFnD0FMGqYz1RLdvBU3zVuIrzG/u32FehAtoME8Mrnk0e/Dq5d8VQzIXfRPJxUZ74GY2LBNwaGN9uU9jzucgD8mgMTDpc9d+VvAxgYTvRU9bnUzFL7UHys0SOXNJZMTX/qXer9roP800P4NZxoPcnwCMTK/88SSoJvO8PvnMfkA/j5Ct6ibxCxSy+5s9VrWct+k8xAyszQXw/Yi3QrLfxxzGFnDRgcUfxgerpD0Q5Bku+9sU/Rb6Sd/OqHyfxLLC0OwCFmLzR/c3KB3Kn14naXIDxv0dGYgBUTTUtAPpkazS+/usqRnRugLkvtJa7OaYnSaKbNO7gwy/ARQUiD2bP5yV5B4CYjure8J5PlM1KPlHeO0xUWT8MBq5EAo9OBcJnGf98czu4FNj6f+K+yk5063cYIy4YVekVi/4D+OlFcX/wclHzqLKK8oHvnxM/P4/GwJg/ASefh7fj+n5gz2ei1wAAoBAztx7/F+DTsmS/7FRR1+fCb6Luzr0/93ohor1+bSrfXlPLzxK9QDm3gecWA+2NLJegt2OWmEJcrwnw2sHKXVz0gbraRfQCPSzoNVbKJTHlW6EC/nlRzJgzhayUkoAo9ZLojUy5JIYy7+fRGHhsmgiSK3NOspKL81N+Lnn+cwtL56fI4eSPIlHf1kEkCpszYJckIPag6K05/4voKXT0Fr2KaqeqHXPty6LnseULwJAVxj9/73xRSgEKYOj3ZhuKZABUTTUtABq1/DD2XErBRwNbYWSX4nWlctJE97Jcn3pN6dpfYs0mlRp48xTg4vfw5yRfAA4tFV9TokVXd0W6vyk+dVVWejywoJUoQPj6ceOmR1uKJAFH/iuSLNsME70s1aW/AD8syfr+dvwyWSSrq11FkUHPEONe9+ZREdBe2lKyrVk/Ecxd2grc2C9+FnpeoSVJwD6tLJME/DB/LxZVpV0DgdePGd8beecGsLiTuFAZM+Sq04lP5cnnRR7Gk9ONb/uDRH0A7J0HNO0NvGiBqdx56UDqZREYJZ0TtbP0M4hcG4jh0rYjy8/zkiTg1E9iLTdDfsrr4pzUhER/SQJWPiuqyjftLX7Opv7dzb0j6hQdWyH+L+opbcUHhx7vAU+8Zfxxk84DX3cV9187WLWeJEkSy4Mc/U78rx/1i6iab2IMgKqpJgVAkiSh44c7cDu7AJsndUfbQDcg7rCYlVGYC0w+IsbSaytJEnkcsX8Dnf8hcnuqcoysZPEHb/g0WfzpMjtZ7GNMr8buz8QyB0HdRW+GtdAWiVXor+8tP8m6PAeWiKnaCqVYHPRBays9TMJpcbE9/wtKzXoCAL8wEfQ0f65sEm5NUJgLfNkWyEoE+n5eMkW+sjaMBc5uFEurjPrVuAvjuZ9FkUyNKzDljPGzCyui04merfQ4MbOu1QumOa4x8rNEL8bfi0r+lp39gG5viJmi+kTqu7Ei1+dqlPjet7XojfNva/k2P0jKReDr7iIYMdXMwfJmfQKip6nVIDEknhYjlnqxcwbePCmGp42xfrT4PWsxQCSSV5VOK3qSLv4h6qK9Emnyv2cGQNVUkwKgxPQ8PDI3CiqlAudm94Lm6lbxz7KoeEkFc479W0LMXlFAS2Un1tBxDTDt8Xd+KIZZ1C7AxL0PT7bV6YCFbcVslueXAm1HmLY9NV1Wsqj0e2+SdUUX4ys7RKEzSQf0mmu62jspF4H9C0X+SJNwoPmzVUuStrTD34qFVp18RaJrZcsmxB0WEwCgEM8zdjhPpxOfzlOiRX0afe2Z6rq+D1jZT/zt/OuSvL0ohbliSZr9C8TvBSByyrpOEu2K+o+YAKBSix6fbq/X3ETjqP8Aez8HXAJEzlhVh6QAUXhw/WhRnFLPu6UIetoMLQmGdTrg2yfFfsbm+SVHA189AkASFevvHZ6uioIc4Pv+QPxR0daJ+0yamG7M9bsOTieqW/QVoJt4OUFzcoWokFyUJ8a1ATGzQFv0gCPUcLs/EV/bjzJ98AMAT0wHAruIKbAbxz28IOD1vSL4UbtYfrpsTeDkDQxZKaYvn9tUUjzufimXgPVjRfDT7iUxpd1UvJoBzy8Ri5h2m1w7gh9A/A67BYleoG+eLCno+SCSJHrQALHIalVymZRKUVATED1yeRnGH6M8p34SX1sMkH8IydYe6DJBfEjqv1D8TuSkipySrdNF8BPUXVygH5tWc4MfQOS4uTcUgdyez6p+HEkCfnldBDU2GiBshJhm/+p+0QN5b0+gUgmEzxb3j/wXuHO98q+z51MAkuitqm7wA4heuxfXitpAA5fKMyuvGAOgGk5UgJbwL9u1YvxU0omCWhP3iRk1mQnA5e1yN1MEFse+By5uefi+etf3i4BDaSuSjc1BZQMM+q/IT7l5BNg158H76ys/txpkuTolNU2DR0qmqm5/t+xyJLl3gDXDxayxwEeAfvNrRh6O3GzUog6RZ1ORzLu8j1jw9kHObRK/l7aOwFPlrDZeWS0HitfNuwsc/qbqx9ErzC0eioSYUVhT2NgBHSKAyceAgd+IWY9qV/E7GPH7w2cR1gS29iVVpQ8sAVKvVO045zaJfCIbjcjLGbhU1Biq6G+xcQ+gUQ8x/Paw/4N6KZdKKs8/bqKeRUAMwY3+Xd7JC2AAVONdiL+NebZL8XRqcUXkJ/8N9P9SXJzbFs/aOVaFjHxTunVSTIH+7Q1xYfx5olhE9GF2F/8TaP+yWD3dXNwalFRg3feFSLouT+4d4PyvJW2yZl0miouqrghYHyFm7QCit3H9aCDtqkj4HfaDRaq71hr1GgPjokSJBm2+WPB26zvl99IW5gGRs8T9R6dWrwilUlVygTqwuHJ/fw9y8U/Ra+raQBSYrGlUNkDYMDGb6u1rQKdXZO1JMFrT3kCTp0UwsnW68UupFGQD24sD5kenVb4ytX5G6+l1IufuYfZ8BkASkxJMHazUgA9Nteg3xgrlZ2LMjf/DINVeSAqVKDX+5D3r2LQfLb5ejpRnwbnCXCByJvDtU6IIntq1eNXzNcCyJ0SBvorcOCCq2pqz9+deLZ8vXsFbAjb9Q0ytvt+ZDeKi5d1SLOZnzRQK8fvm2VT0Mm4YIy7i298VAaStAzBijemmRdclGhdg+I8lAcnBr0QF7/vXgjr0NZAeK3JBuk6q/uu2ekFMoc+9U7LqeVWdKp7x1WZozQ4sFIqaVaOrshQK0QuktBXlNi5tNe75e+eLITS3BkD3Nyr/PP+2xZNBpOIp6Q+QegU4u0HcN1VeWQ1Tg3+zrVxmEoq+64MuupPIkdTIHfyDyDG4l2cToOFjAKSSoRtLub5PzGbYvwCQtCJh9vVjopKwS33RQ/Dfp8X0YJ2u7PP1uT9tX7TcVP5ec8UU6qxEUY/j/k9dx4tnN7R/uUZ8OpGd2hkYukoMz1zfKxIXDxUvyvrCN2KmDZVPqRSTE/TnL2YP8M0TopgcIHrU9swT93vONM1wq1J1Ty7Q4qovIJyVUrLCfU0a/qprPJuUBL5bp4sewcpIuwb8Xdyj3Wuu8flZT70ncvyu7BC/lxXZ85lIuWjap+bNpjMRBkA1Uepl4Ltw2CSfQarkgqn2H8KhZd/y9+0wWnw9bqFkaH1p+ZX9RJDj7AcMXyMKYzl5idotE/cCoc+K7t3t7wI/DhGzi/TiDovid0obUf3XUuwcxKwmlVrkTekv5oBIJEw8LWajtRlmuTbVdN6hJcOH+srNPd413cKfdV2L54BxO0TS691YMdvr3M/AX3PE+kj+7YDWQ0z3eq0Gi3XWcm6LeitVcXaD+FDj3974mk5knMffEv9D71wHDiyq3HO2/lus7deoR9XWgPNoBHQcK+5Hzix/+O32VeDMOnG/jvb+AAyAap78LFEX524s0u0D8ULBbNgEdqx4/+b9AXsPkXR5JdK8bbu4BVjySEnOUYcxYgw+9L7gzMFD5Ib0my8S9K7sEL1FV3eKx/8qzv0JGyEW4rQkn5Yl64xFziiZPnq8uAct9FnLrydV07UeDDxS/Em11aCSXgaqHJ8WwPhd4oJVmCNyqI4W/w31mmPaISaVTcnPZ/9CkStiLP3sL/b+mJ/aCXj6P+L+3vliBfUHuRwpCoYqbcRyKVXtqX78LdEzeet4SbL7vfbOE70/Ic8AAXU3HYABUE0Tf1QU/HLywcf+ixAr+aBlwANqGdioS5Khj5opGbqoANjwikhwzrwlPkGM/gPov6DiomsKhUhMHL9LrJSdnSzW+to4XhQrU6gs2/tzr07jRKCjLRA1lbJvl3zasfbk54r0+khUxR70HYcHq8LBAxi5QSz0C0BMK35O9JiaWuuhJdPE9QtXVlbKRSDhpLjAGrMcClVd68Ei0bwwB9j+XsX7FeUDW4qXv+kysXoFBJ28Ra0kQFT7vrc8SNq1kiD4CRNXFq9hGADVNPocgcDOOJQkLjQt/R9S2bXDGPH1ipmSoU+tEd3iCpVYVuLVvyu/ro5PC7GsQsdXxPf6QCNseOVnLpiaPsHXJQC4fQVY/owY2nNtAAQ/KU+bajqFQsxwYvBTdSobEUgOWQmEvSg+wZvrdR77l7i/f6EoPFdZ+gtfk6eNrxZMVaNQiAr4CqUYHq0oL+fgVyLtwMkHeOL/qv+63SaLYpJpV0vyH4Hi3h+tKEJav0P1X6cGYwBU0ySeAQDke7ZEzG3Rfd3S/yHVqPXJ0JIOOPGD6dt0sXg5iCf+T6ypZWzSna098Ox8MSymcRPl2OXq/dFz8ABe+Fb807ldXIej3ciaPeOF6oaWA4GBX1duzbuqChsuijJmJwPHVlbuOTqdmB4NiCnmZDm+rUvycrb8X9l8zoxbYokeQBQ01JhghQK1c0kgtfsTMVx657rV9P4ADIBqniTRAxRr2wiSBPi5auDpVIk6K/pkaFNXhi7IKambU5WEu3s17w9MPQe8UUMWGG3Y/Z7iXgqxyCJRXaCyLfmQsX+BKFnxMDf2ARk3RTmLppVcjJVMp8e7gL27WNj2/gT2yBmi2nX9zqadpNFhtBguzUoSPUx754vaX42fAgI7me51aigGQDVJUYEYgwdwskAUBnxo74+ePhk6I960ydDX/hJLb7gGmqYMutpJjD/XFI+/JYb1+nxauxeVJbpf2Ajxd5uVJKq0P4y+9k/LAeWvtk7m5eBRUg1810cltcpuHADOrAegAPp+Ztpeahu7ktfctwA4uVrcN8UQWy3AAKgmSYkWU8c1rjiU5gigEvk/evcmQ1e2y7syLhUvbdG0d93M/1DZiGG9LhPkbgmRadnYiXWxALHe2Dc9gG3vAtF/li3KWJBzz9IXVrYAcE3SYTTg20bkJEbNFqunbyme1dchwjz1eFq+IF6zIEv0/gQ/IZbDsQK1sIRmHVY8/AWf1jiXIErZV7oHCBB/PAcWixo36Terv7yETgdc2ibuN2OXOFGt0/YlsbzLtV1iyvOt4+J/BCAqngd1E7fsVFGXyK2BWN+N5KFUiV6e5b1EaQ6FUuSFalyrt1bcA19TCTw9W8zSBYAn637ujx4DoJqkeAZYkXcLXL4sAqBWAZXsAQJE0bKgR8VY/okfqv+LnHBCdJ/bOVV+1hcR1Rw2dsCozWJ2aOwB4MZ+4MbfQOolIPmcuB25Z9mMNsM4EUBuDR4RP4fTa0t683u8Z95ZeY2fEvWIFArzlGaooRgA1SRJYgZYoiYERToJ7g628HM1ciy+w2gRAB3/n8hvUaqq3h79yu6Nn+KCl0S1mVuguLUZKr7PSi4OiP4WQVHiWbG+GycC1AxPfwBE/yGGpbxblswQMydj1hSrIxgA1RSSZJgCf1YXCECHVgGuUBibd3NvMvTlSKBZ76q36WLxAn3NKliGg4hqJydvoMUAcQOA3LuijAaroNcMzr5A38+BffOBAYtq54KvtQD7OmuKjFtiFWeFCgczxQrbLYzJ/9Gz1ZgmGfpunOiRUihFOXQiqrvs3Rj81DRtRwCTjwABdbsYoZwYANUU+gRoz6Y4mZAPAGhV2Rlg92sfIb5e3gakx1ftGJeKe3/qdwYc61XtGERERDUUA6CaIvE0AEDn0xLRiRkAjJwBdi+vpkBQ9+pVhtbn/3D2FxER1UEMgGqK4hlg6a6hyCvUQWOrRMN6jlU/nn59sOP/E7UkjJGfCVzfK+4zACIiojqIAVBNUTwElu4SCgBw0dhCqaxG4cHm/UVZ9YybYjaBMa7uFCuluwcDntVYcZiIiKiGYgBUExRkA7evAgDuuIiAw8GuGtPXAZEMrZ86uesj49YHu3f2V12s/kxERFaPAVBNkHQegAQ4eiNDJWZi2NuZYNpjtzdEL1BKNHBqTeWeo9OK5GmgelPoiYiIajAGQDVBcQFE+LZGboHoqal2DxAgprY+9i9xf9ecyq0IffMokHNbrAjdoGv120BERFQDMQCqCYoToOHbCrmFImHZJAEQAHQaB7jUBzJvAYeWPXz/i3+KryFPAypb07SBiIiohmEAVBMUV4CGT2vkFIgAyN7WRAGQrQZ46l1xf998UWzxQfT1fzj7i4iI6jAGQHLT6YCkc+K+b2vkFpi4BwgQC+t5twDy0oF9X1S8X9o1kS+kUAFNepru9YmIiGoYBkByuxMDFGYDKjVQr0lJD5ApAyClCgifJe4fXAqk3yx/P/3sr6BuInmaiIiojmIAJDf98Jd3c0Blc88QmIkXvwt5RlSH1uYDf80tf59LrP5MRETWgQGQ3JJKEqABmHYW2L0UCiB8trh/8kcgObr047l3gRt/i/tNOf2diIjqNgZAcjPMAGsDAOYZAtML7ASEPivWCIv6oPRjV3YAuiJR+bleY9O/NhERUQ3CAEhu+h4gH9EDlGPqafD36zkTUCiBi38AsQdLtnP2FxERWREGQHLKSQPS48R9n5YAYJ5ZYPfyagq0e1ncj5wJSJJYJuNypNjWlAEQERHVfUYHQBEREdizZ4852mJ99NPf3RqIqs0AcopzgEyyFEZFnpwO2NgDcQeBi1vE17y7gL0HENjZfK9LRERUQxgdAKWnpyM8PBwhISGYM2cO4uPjzdEu62AY/mpt2GToATJVIcTyuPgDj0wU96NmAxd+F/eb9hJT5omIiOo4owOgzZs3Iz4+Hq+++irWrl2Lhg0bok+fPtiwYQMKCwvN0ca6Sz8FvngGGFCSBG22ITC97lMAjZsofHjkW7GNs7+IiMhKVCkHyMvLC9OmTcOpU6dw6NAhNGnSBC+//DL8/f0xdepUXL582dTtrJsMS2CUDYDMMgvsXvZuwOPFC6XqigClLdD4KfO+JhERUQ1RrSTohIQEREZGIjIyEiqVCn379sWZM2fQokULfPHFA5ZcIEBbKHpfAMD3niEwwywwM+YA6XUaLxZKBYDgxwCNi/lfk4iIqAYwOgAqLCzExo0b8eyzzyIoKAjr16/HlClTcOvWLXz//ffYsWMH1q1bhw8++ODhB7NmqZcAbQFg5wy4BRk255irEGJ5bDXAs/NFEPTIJPO/HhERUQ1hdDeDn58fdDodRowYgcOHD6Nt27Zl9unRowfc3NxM0Lw6TF8A0acloBRxqE4nIa9QB8ACQ2B6TXsB085Z5rWIiIhqCKMDoC+++AJDhgyBRqOpcB83NzfExMRUq2F1XpI+Abpk+CuvSGu4b5EeICIiIitl9BDYc889h5ycnDLb09LSkJGRYZJGWYXE0muAASUJ0ACgsWEAREREZC5GB0DDhw/HTz/9VGb7unXrMHz4cKMbsGTJEjRs2BAajQZdunTB4cOHK9y3sLAQH3zwARo3bgyNRoOwsDBs3bq11D5arRbvv/8+goODYW9vj8aNG+M///kPJEkyum1mI0n3zAArWwNIY6uEUqmQo2VERERWwegA6NChQ+jRo0eZ7U8++SQOHTpk1LHWrl2LadOmYebMmTh+/DjCwsLQq1cvJCcnl7v/e++9h2XLlmHRokU4f/48Jk6ciIEDB+LEiROGfT755BN8/fXXWLx4MS5cuIBPPvkEn376KRYtWmTcGzWnrCQgJ1WsyeXd3LC5pAaQBWaAERERWTGjA6D8/HwUFRWV2V5YWIjc3FyjjjV//nyMHz8eY8aMQYsWLbB06VI4ODhg+fLl5e6/atUq/Pvf/0bfvn3RqFEjvPrqq+jbty/mzZtn2Ofvv//GgAED0K9fPzRs2BCDBw/GM88888CeJYvTD3/VawLYORg2G5bBMGcVaCIiIjI+AOrcuTO++eabMtuXLl2KDh06VPo4BQUFOHbsGMLDw0sao1QiPDwcBw4cKPc5+fn5ZZKv7e3tsW/fPsP33bp1Q1RUFC5dugQAOHXqFPbt24c+fSpe5DM/Px8ZGRmlbmaVeFp8vacAImCBhVCJiIgIQBVmgX344YcIDw/HqVOn0LNnTwBAVFQUjhw5gu3bt1f6OKmpqdBqtfDx8Sm13cfHB9HR0eU+p1evXpg/fz4ef/xxNG7cGFFRUdi0aRO02pLk4enTpyMjIwOhoaFQqVTQarX46KOPMHLkyArbMnfuXMyePbvSba+2pLIJ0IAFl8EgIiKyckb3AHXv3h0HDhxAYGAg1q1bh99++w1NmjTB6dOn8dhjj5mjjQZffvklQkJCEBoaCjs7O0yePBljxoyBUlnyNtatW4fVq1fjxx9/xPHjx/H999/j888/x/fff1/hcd955x2kp6cbbnFxcWZ9HyUzwNqU2pxTaKFlMIiIiKxclbJt27Zti9WrV1frhT09PaFSqZCUlFRqe1JSEnx9fct9jpeXFzZv3oy8vDzcvn0b/v7+mD59Oho1amTY56233sL06dMNM9Jat26NGzduYO7cuYiIiCj3uGq1Gmq1ulrvp9IKc4HbxWullRkC01eBZhI0ERGROVVrLbC8vLwq587Y2dmhQ4cOiIqKMmzT6XSIiopC165dH/hcjUaDgIAAFBUVYePGjRgwYIDhsZycnFI9QgCgUqmg0+kq3TazSj4PSDrAoR7gXDrQs9hCqERERFbO6K6GnJwcvP3221i3bh1u375d5vF783EeZtq0aYiIiEDHjh3RuXNnLFiwANnZ2RgzZgwAYNSoUQgICMDcuXMBiCn48fHxaNu2LeLj4zFr1izodDq8/fbbhmP2798fH330ERo0aICWLVvixIkTmD9/PsaOHWvsWzUPw/BXa0BRutaPIQeIs8CIiIjMyugA6K233sKuXbvw9ddf4+WXX8aSJUsQHx+PZcuW4eOPPzbqWMOGDUNKSgpmzJiBxMREtG3bFlu3bjUkRsfGxpbqzcnLy8N7772Ha9euwcnJCX379sWqVatKrTu2aNEivP/++3jttdeQnJwMf39//OMf/8CMGTOMfavmoU+Avm/4C+AsMCIiIktRSEaWSG7QoAH+97//4cknn4SLiwuOHz+OJk2aYNWqVVizZg3+/PNPc7XVYjIyMuDq6or09HS4uLiY9uDLewOxB4CBy4Cw0pWzP/jtPJbvj8HEJxpjep9Q074uERFRHWfM9dvoHKC0tDRD0rGLiwvS0tIAAI8++ij27NlTheZaEUkCkopXXi+vB6hQnwTNHiAiIiJzMjoAatSokWGl99DQUKxbtw4A8Ntvv5UaiqJy3L0B5GcAKjvAs2mZhzkERkREZBlGB0BjxozBqVOnAIiig0uWLIFGo8HUqVPx1ltvmbyBdYp+AVSvZoCNXZmHOQuMiIjIMoxOgp46darhfnh4OKKjo3Hs2DE0adIEbdq0ecAzCQoV4N9O3MqRW8geICIiIkswKgAqLCxE7969sXTpUoSEhAAAgoKCEBQUZJbG1TmhfcWtAoYeIE6DJyIiMiujhsBsbW1x+vRpc7XF6pUMgbESNBERkTkZnQP00ksv4bvvvjNHW6xeyVIY7AEiIiIyJ6O7GoqKirB8+XLs2LEDHTp0gKOjY6nH58+fb7LGWRsOgREREVmG0QHQ2bNn0b59ewDApUuXSj2muG9pBzIOp8ETERFZhtEB0K5du8zRDqsnSRJyDLPAmANERERkTtVaDZ5Mp0Crg1YnViVhHSAiIiLzMrqroUePHg8c6tq5c2e1GmSt9MNfAIfAiIiIzM3oAKht27alvi8sLMTJkydx9uxZREREmKpdVkefAG2rUsBWxY45IiIiczI6APriiy/K3T5r1ixkZWVVu0HWijPAiIiILMdkXQ0vvfQSli9fbqrDWZ2SGWBMgCYiIjI3kwVABw4cgEajMdXhrE4OiyASERFZjNHdDS+88EKp7yVJQkJCAo4ePYr333/fZA2zNvqFUDkDjIiIyPyMDoBcXV1Lfa9UKtGsWTN88MEHeOaZZ0zWMGvDIohERESWY3QAtGLFCnO0w+pxIVQiIiLL4XzrGkJfBdrelj8SIiIiczO6u0GpVD6wEKJWq63wMapYyUrw7AEiIiIyN6Ovtj///HOp7wsLC3HixAl8//33mD17tskaZm1KhsCYA0RERGRuRgdAAwYMKLNt8ODBaNmyJdauXYtXXnnFJA2zNoYkaBZCJCIiMjuTJZw88sgjiIqKMtXhrE4OZ4ERERFZjEkCoNzcXCxcuBABAQGmOJxV4iwwIiIiyzH6auvu7l4qCVqSJGRmZsLBwQE//PCDSRtnTXILWQmaiIjIUqq0GOq9AZBSqYSXlxe6dOkCd3d3kzbOmjAJmoiIyHKMDoBGjx5thmYQc4CIiIgsx+gcoBUrVmD9+vVltq9fvx7ff/+9SRpljbgUBhERkeUYHQDNnTsXnp6eZbZ7e3tjzpw5JmmUNdKvBm9vyyRoIiIiczM6AIqNjUVwcHCZ7UFBQYiNjTVJo6wRe4CIiIgsx+gAyNvbG6dPny6z/dSpU6hXr55JGmWNcgsZABEREVmK0QHQiBEj8MYbb2DXrl3QarXQarXYuXMn3nzzTQwfPtwcbbQKnAVGRERkOUYnnPznP//B9evX0bNnT9jYiKfrdDqMGjWKOUBVpNVJyC/SAeBiqERERJZg9NXWzs4Oa9euxYcffoiTJ0/C3t4erVu3RlBQkDnaZxX0w18AYM+1wIiIiMyuyt0NISEhCAkJMWVbrJZ+BphCAWhsTbY8GxEREVXA6KvtoEGD8Mknn5TZ/umnn2LIkCEmaZS10c8As7dVlaqyTUREROZhdAC0Z88e9O3bt8z2Pn36YM+ePSZplLVhFWgiIiLLMjoAysrKgp2dXZnttra2yMjIMEmjrA1ngBEREVmW0QFQ69atsXbt2jLbf/rpJ7Ro0cIkjbI2hiKIrAJNRERkEUZfcd9//3288MILuHr1Kp566ikAQFRUFH788Uds2LDB5A20BoZlMNgDREREZBFGB0D9+/fH5s2bMWfOHGzYsAH29vYICwvDzp074eHhYY421nmsAk1ERGRZVRpz6devH/r16wcAyMjIwJo1a/Cvf/0Lx44dg1arfciz6X5MgiYiIrKsKhed2bNnDyIiIuDv74958+bhqaeewsGDB03ZNqtRkgTNHCAiIiJLMOqKm5iYiJUrV+K7775DRkYGhg4divz8fGzevJkJ0NWQW5wD5MAq0ERERBZR6R6g/v37o1mzZjh9+jQWLFiAW7duYdGiReZsm9XgNHgiIiLLqnQP0JYtW/DGG2/g1Vdf5RIYJsYkaCIiIsuqdA/Qvn37kJmZiQ4dOqBLly5YvHgxUlNTzdk2q5HLJGgiIiKLqnQA9Mgjj+Dbb79FQkIC/vGPf+Cnn36Cv78/dDodIiMjkZmZac521mn6ITANc4CIiIgswuhZYI6Ojhg7diz27duHM2fO4J///Cc+/vhjeHt747nnnjNHG+u8kmnwnAVGRERkCVWeBg8AzZo1w6effoqbN29izZo1pmqT1cktLJ4FxiEwIiIii6hWAKSnUqnw/PPP49dffzXF4awOZ4ERERFZlkkCIKoeJkETERFZFgOgGoBLYRAREVkWA6AawDAEZsskaCIiIktgAFQDGJbCYA8QERGRRTAAkpkkSchhJWgiIiKLYgAks/wiHSRJ3OcsMCIiIstgACQzff4PwEKIRERElsIASGY5xfk/djZKqJQKmVtDRERkHRgAyYw1gIiIiCyPAZDMcvUJ0FwIlYiIyGIYAMmMy2AQERFZXo0IgJYsWYKGDRtCo9GgS5cuOHz4cIX7FhYW4oMPPkDjxo2h0WgQFhaGrVu3ltqnYcOGUCgUZW6TJk0y91sxWi4DICIiIouTPQBau3Ytpk2bhpkzZ+L48eMICwtDr169kJycXO7+7733HpYtW4ZFixbh/PnzmDhxIgYOHIgTJ04Y9jly5AgSEhIMt8jISADAkCFDLPKejGFYBoNVoImIiCxG9gBo/vz5GD9+PMaMGYMWLVpg6dKlcHBwwPLly8vdf9WqVfj3v/+Nvn37olGjRnj11VfRt29fzJs3z7CPl5cXfH19Dbfff/8djRs3xhNPPGGpt1Vp+llg7AEiIiKyHFkDoIKCAhw7dgzh4eGGbUqlEuHh4Thw4EC5z8nPz4dGoym1zd7eHvv27avwNX744QeMHTsWCkX508zz8/ORkZFR6mYpuawCTUREZHGyBkCpqanQarXw8fEptd3HxweJiYnlPqdXr16YP38+Ll++DJ1Oh8jISGzatAkJCQnl7r9582bcvXsXo0ePrrAdc+fOhaurq+EWGBhY5fdkLCZBExERWZ7sQ2DG+vLLLxESEoLQ0FDY2dlh8uTJGDNmDJTK8t/Kd999hz59+sDf37/CY77zzjtIT0833OLi4szV/DJyWAeIiIjI4mQNgDw9PaFSqZCUlFRqe1JSEnx9fct9jpeXFzZv3ozs7GzcuHED0dHRcHJyQqNGjcrse+PGDezYsQPjxo17YDvUajVcXFxK3SylZCV4JkETERFZiqwBkJ2dHTp06ICoqCjDNp1Oh6ioKHTt2vWBz9VoNAgICEBRURE2btyIAQMGlNlnxYoV8Pb2Rr9+/UzedlMxDIGxECIREZHFyN7tMG3aNERERKBjx47o3LkzFixYgOzsbIwZMwYAMGrUKAQEBGDu3LkAgEOHDiE+Ph5t27ZFfHw8Zs2aBZ1Oh7fffrvUcXU6HVasWIGIiAjY2Mj+NivEpTCIiIgsT/bIYNiwYUhJScGMGTOQmJiItm3bYuvWrYbE6NjY2FL5PXl5eXjvvfdw7do1ODk5oW/fvli1ahXc3NxKHXfHjh2IjY3F2LFjLfl2jMYcICIiIstTSJIkyd2ImiYjIwOurq5IT083ez7QqOWHsedSCj4fEobBHeqb9bWIiIjqMmOu37VuFlhdU5IEzR4gIiIiS2EAJDPWASIiIrI8BkAyM1SC5iwwIiIii2EAJLOSWWCy56MTERFZDQZAMisZAuOPgoiIyFJ41ZVZriEAYg8QERGRpTAAklGRVocCrQ4Ac4CIiIgsiQGQjHKKE6ABzgIjIiKyJAZAMtIPfykVgNqGPwoiIiJL4VVXRjn3zABTKBQyt4aIiMh6MACSUU5xFWgOfxEREVkWAyAZcSV4IiIieTAAkpGhBhBngBEREVkUAyAZ5bAHiIiISBYMgGSUW6hfCZ5FEImIiCyJAZCMuBI8ERGRPBgAyYhJ0ERERPJgACQjBkBERETyYAAkI/1SGPa2zAEiIiKyJAZAMipZCZ4/BiIiIkvilVdG+krQnAVGRERkWQyAZMRCiERERPJgACQjJkETERHJgwGQjFgHiIiISB4MgGSknwXGHCAiIiLLYgAko1xDEjR7gIiIiCyJAZCMOARGREQkDwZAMmISNBERkTwYAMlI3wPkwErQREREFsUASCY6nYTcQg6BERERyYEBkEzyirSG+xwCIyIisiwGQDLRD38BrARNRERkaQyAZKJPgNbYKqFUKmRuDRERkXVhACSTXBZBJCIikg0DIJlwIVQiIiL5MACSSU5xFWjOACMiIrI8BkAyYRFEIiIi+TAAkgmHwIiIiOTDAEgm7AEiIiKSDwMgmeQYVoLnLDAiIiJLYwAkkxwug0FERCQbBkAy4RAYERGRfBgAycSQBM0AiIiIyOIYAMlEHwA52DIHiIiIyNIYAMkk15AEzR4gIiIiS2MAJBMOgREREcmHAZBMShZDZQBERERkaQyAZMJZYERERPJhACSTkiEwJkETERFZGgMgmeiHwLgWGBERkeUxAJJJDmeBERERyYYBkEw4C4yIiEg+DIBkwiRoIiIi+TAAkkFBkQ5FOgkAK0ETERHJgQGQDPS9PwCHwIiIiOTAAEgGOYUiAdpGqYCdDX8ERERElsarrwyYAE1ERCQvBkAyYAI0ERGRvBgAySDHEAAxAZqIiEgODIBkoC+CyCrQRERE8mAAJAMOgREREcmLAZAMmARNREQkLwZAMtAvhMoeICIiInnIHgAtWbIEDRs2hEajQZcuXXD48OEK9y0sLMQHH3yAxo0bQ6PRICwsDFu3bi2zX3x8PF566SXUq1cP9vb2aN26NY4ePWrOt2GUXCZBExERyUrWAGjt2rWYNm0aZs6ciePHjyMsLAy9evVCcnJyufu/9957WLZsGRYtWoTz589j4sSJGDhwIE6cOGHY586dO+jevTtsbW2xZcsWnD9/HvPmzYO7u7ul3tZD6YfANEyCJiIikoVCkiRJrhfv0qULOnXqhMWLFwMAdDodAgMD8frrr2P69Oll9vf398e7776LSZMmGbYNGjQI9vb2+OGHHwAA06dPx/79+7F3794qtysjIwOurq5IT0+Hi4tLlY9TkblbLmDZ7mt45dFgvP9sC5Mfn4iIyBoZc/2WrQeooKAAx44dQ3h4eEljlEqEh4fjwIED5T4nPz8fGo2m1DZ7e3vs27fP8P2vv/6Kjh07YsiQIfD29ka7du3w7bffmudNVBFngREREclLtgAoNTUVWq0WPj4+pbb7+PggMTGx3Of06tUL8+fPx+XLl6HT6RAZGYlNmzYhISHBsM+1a9fw9ddfIyQkBNu2bcOrr76KN954A99//32FbcnPz0dGRkapmzlxFhgREZG8ZE+CNsaXX36JkJAQhIaGws7ODpMnT8aYMWOgVJa8DZ1Oh/bt22POnDlo164dJkyYgPHjx2Pp0qUVHnfu3LlwdXU13AIDA836Pgw9QMwBIiIikoVsAZCnpydUKhWSkpJKbU9KSoKvr2+5z/Hy8sLmzZuRnZ2NGzduIDo6Gk5OTmjUqJFhHz8/P7RoUTqvpnnz5oiNja2wLe+88w7S09MNt7i4uGq8s4fTV4LmLDAiIiJ5yBYA2dnZoUOHDoiKijJs0+l0iIqKQteuXR/4XI1Gg4CAABQVFWHjxo0YMGCA4bHu3bvj4sWLpfa/dOkSgoKCKjyeWq2Gi4tLqZs5cQiMiIhIXrJ2QUybNg0RERHo2LEjOnfujAULFiA7OxtjxowBAIwaNQoBAQGYO3cuAODQoUOIj49H27ZtER8fj1mzZkGn0+Htt982HHPq1Kno1q0b5syZg6FDh+Lw4cP45ptv8M0338jyHsvDQohERETykjUAGjZsGFJSUjBjxgwkJiaibdu22Lp1qyExOjY2tlR+T15eHt577z1cu3YNTk5O6Nu3L1atWgU3NzfDPp06dcLPP/+Md955Bx988AGCg4OxYMECjBw50tJvr0LsASIiIpKXrHWAaipz1wHq/vFOxN/NxeZJ3dE20M3kxyciIrJGtaIOkDUrSYJmDxAREZEcGADJwDAExmnwREREsmAAZGFanYT8Ih0A9gARERHJhQGQheUVzwADWAeIiIhILgyALEw//AUAahuefiIiIjnwCmxhuffk/yiVCplbQ0REZJ0YAFlYTiFngBEREcmNAZCFsQgiERGR/BgAWZhhJXgGQERERLJhAGRhJT1AnAFGREQkFwZAFmaoAs0iiERERLJhAGRhHAIjIiKSHwMgC2MSNBERkfwYAFlYbiF7gIiIiOTGAMjCSlaCZxI0ERGRXBgAWRiHwIiIiOTHAMjCDEnQnAVGREQkGwZAFsYeICIiIvkxALKwkiRo5gARERHJhQGQhRlWg7fjqSciIpILr8IWpp8FZm/LHiAiIiK5MACysBxWgiYiIpIdAyALYyFEIiIi+TEAsjDOAiMiIpIfAyALK1kMlTlAREREcmEAZEGSJN2zFAZ7gIiIiOTCAMiC8ot00EniPofAiIiI5MMAyIL0w18Al8IgIiKSEwMgC8opngFmp1LCRsVTT0REJBdehS0oV18EkcNfREREsmIAZEEsgkhERFQzMACyINYAIiIiqhkYAFlQLnuAiIiIagQGQBZkWAaDC6ESERHJigGQBRXpJNjbquCgZg8QERGRnNgVYUHPhfnjuTB/SJIkd1OIiIisGnuAZKBQKORuAhERkVVjAERERERWhwEQERERWR0GQERERGR1GAARERGR1WEARERERFaHARARERFZHQZAREREZHUYABEREZHVYQBEREREVocBEBEREVkdBkBERERkdRgAERERkdVhAERERERWx0buBtREkiQBADIyMmRuCREREVWW/rqtv44/CAOgcmRmZgIAAgMDZW4JERERGSszMxOurq4P3EchVSZMsjI6nQ63bt2Cs7MzFAqFSY+dkZGBwMBAxMXFwcXFxaTHprJ4vi2L59uyeL4ti+fbsqpyviVJQmZmJvz9/aFUPjjLhz1A5VAqlahfv75ZX8PFxYV/QBbE821ZPN+WxfNtWTzflmXs+X5Yz48ek6CJiIjI6jAAIiIiIqvDAMjC1Go1Zs6cCbVaLXdTrALPt2XxfFsWz7dl8XxblrnPN5OgiYiIyOqwB4iIiIisDgMgIiIisjoMgIiIiMjqMAAiIiIiq8MAyIKWLFmChg0bQqPRoEuXLjh8+LDcTaoT9uzZg/79+8Pf3x8KhQKbN28u9bgkSZgxYwb8/Pxgb2+P8PBwXL58WZ7G1gFz585Fp06d4OzsDG9vbzz//PO4ePFiqX3y8vIwadIk1KtXD05OThg0aBCSkpJkanHt9vXXX6NNmzaGYnBdu3bFli1bDI/zXJvXxx9/DIVCgSlTphi28ZybzqxZs6BQKErdQkNDDY+b81wzALKQtWvXYtq0aZg5cyaOHz+OsLAw9OrVC8nJyXI3rdbLzs5GWFgYlixZUu7jn376KRYuXIilS5fi0KFDcHR0RK9evZCXl2fhltYNu3fvxqRJk3Dw4EFERkaisLAQzzzzDLKzsw37TJ06Fb/99hvWr1+P3bt349atW3jhhRdkbHXtVb9+fXz88cc4duwYjh49iqeeegoDBgzAuXPnAPBcm9ORI0ewbNkytGnTptR2nnPTatmyJRISEgy3ffv2GR4z67mWyCI6d+4sTZo0yfC9VquV/P39pblz58rYqroHgPTzzz8bvtfpdJKvr6/02WefGbbdvXtXUqvV0po1a2RoYd2TnJwsAZB2794tSZI4v7a2ttL69esN+1y4cEECIB04cECuZtYp7u7u0n//+1+eazPKzMyUQkJCpMjISOmJJ56Q3nzzTUmS+PttajNnzpTCwsLKfczc55o9QBZQUFCAY8eOITw83LBNqVQiPDwcBw4ckLFldV9MTAwSExNLnXtXV1d06dKF595E0tPTAQAeHh4AgGPHjqGwsLDUOQ8NDUWDBg14zqtJq9Xip59+QnZ2Nrp27cpzbUaTJk1Cv379Sp1bgL/f5nD58mX4+/ujUaNGGDlyJGJjYwGY/1xzMVQLSE1NhVarhY+PT6ntPj4+iI6OlqlV1iExMREAyj33+seo6nQ6HaZMmYLu3bujVatWAMQ5t7Ozg5ubW6l9ec6r7syZM+jatSvy8vLg5OSEn3/+GS1atMDJkyd5rs3gp59+wvHjx3HkyJEyj/H327S6dOmClStXolmzZkhISMDs2bPx2GOP4ezZs2Y/1wyAiKjKJk2ahLNnz5YasyfTa9asGU6ePIn09HRs2LABERER2L17t9zNqpPi4uLw5ptvIjIyEhqNRu7m1Hl9+vQx3G/Tpg26dOmCoKAgrFu3Dvb29mZ9bQ6BWYCnpydUKlWZzPWkpCT4+vrK1CrroD+/PPemN3nyZPz+++/YtWsX6tevb9ju6+uLgoIC3L17t9T+POdVZ2dnhyZNmqBDhw6YO3cuwsLC8OWXX/Jcm8GxY8eQnJyM9u3bw8bGBjY2Nti9ezcWLlwIGxsb+Pj48JybkZubG5o2bYorV66Y/febAZAF2NnZoUOHDoiKijJs0+l0iIqKQteuXWVsWd0XHBwMX1/fUuc+IyMDhw4d4rmvIkmSMHnyZPz888/YuXMngoODSz3eoUMH2NraljrnFy9eRGxsLM+5ieh0OuTn5/Ncm0HPnj1x5swZnDx50nDr2LEjRo4cabjPc24+WVlZuHr1Kvz8/Mz/+13tNGqqlJ9++klSq9XSypUrpfPnz0sTJkyQ3NzcpMTERLmbVutlZmZKJ06ckE6cOCEBkObPny+dOHFCunHjhiRJkvTxxx9Lbm5u0i+//CKdPn1aGjBggBQcHCzl5ubK3PLa6dVXX5VcXV2lv/76S0pISDDccnJyDPtMnDhRatCggbRz507p6NGjUteuXaWuXbvK2Oraa/r06dLu3bulmJgY6fTp09L06dMlhUIhbd++XZIknmtLuHcWmCTxnJvSP//5T+mvv/6SYmJipP3790vh4eGSp6enlJycLEmSec81AyALWrRokdSgQQPJzs5O6ty5s3Tw4EG5m1Qn7Nq1SwJQ5hYRESFJkpgK//7770s+Pj6SWq2WevbsKV28eFHeRtdi5Z1rANKKFSsM++Tm5kqvvfaa5O7uLjk4OEgDBw6UEhIS5Gt0LTZ27FgpKChIsrOzk7y8vKSePXsagh9J4rm2hPsDIJ5z0xk2bJjk5+cn2dnZSQEBAdKwYcOkK1euGB4357lWSJIkVb8fiYiIiKj2YA4QERERWR0GQERERGR1GAARERGR1WEARERERFaHARARERFZHQZAREREZHUYABEREZHVYQBERFQJCoUCmzdvlrsZRGQiDICIqMYbPXo0FApFmVvv3r3lbhoR1VI2cjeAiKgyevfujRUrVpTaplarZWoNEdV27AEiolpBrVbD19e31M3d3R2AGJ76+uuv0adPH9jb26NRo0bYsGFDqeefOXMGTz31FOzt7VGvXj1MmDABWVlZpfZZvnw5WrZsCbVaDT8/P0yePLnU46mpqRg4cCAcHBwQEhKCX3/91bxvmojMhgEQEdUJ77//PgYNGoRTp05h5MiRGD58OC5cuAAAyM7ORq9eveDu7o4jR45g/fr12LFjR6kA5+uvv8akSZMwYcIEnDlzBr/++iuaNGlS6jVmz56NoUOH4vTp0+jbty9GjhyJtLQ0i75PIjIRkyypSkRkRhEREZJKpZIcHR1L3T766CNJksQK9RMnTiz1nC5dukivvvqqJEmS9M0330ju7u5SVlaW4fE//vhDUiqVUmJioiRJkuTv7y+9++67FbYBgPTee+8Zvs/KypIASFu2bDHZ+yQiy2EOEBHVCj169MDXX39dapuHh4fhfteuXUs91rVrV5w8eRIAcOHCBYSFhcHR0dHwePfu3aHT6XDx4kUoFArcunULPXv2fGAb2rRpY7jv6OgIFxcXJCcnV/UtEZGMGAARUa3g6OhYZkjKVOzt7Su1n62tbanvFQoFdDqdOZpERGbGHCAiqhMOHjxY5vvmzZsDAJo3b45Tp04hOzvb8Pj+/fuhVCrRrFkzODs7o2HDhoiKirJom4lIPuwBIqJaIT8/H4mJiaW22djYwNPTEwCwfv16dOzYEY8++ihWr16Nw4cP47vvvgMAjBw5EjNnzkRERARmzZqFlJQUvP7663j55Zfh4+MDAJg1axYmTpwIb29v9OnTB5mZmdi/fz9ef/11y75RIrIIBkBEVCts3boVfn5+pbY1a9YM0dHRAMQMrZ9++gmvvfYa/Pz8sGbNGrRo0QIA4ODggG3btuHNN99Ep06d4ODggEGDBmH+/PmGY0VERCAvLw9ffPEF/vWvf8HT0xODBw+23BskIotSSJIkyd0IIqLqUCgU+Pnnn/H888/L3RQiqiWYA0RERERWhwEQERERWR3mABFRrceRfCIyFnuAiIiIyOowACIiIiKrwwCIiIiIrA4DICIiIrI6DICIiIjI6jAAIiIiIqvDAIiIiIisDgMgIiIisjoMgIiIiMjq/D8u4rdQoVM3UgAAAABJRU5ErkJggg==\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(\"mnist_classification_model_CNN.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d10653a3",
   "metadata": {
    "id": "d10653a3"
   },
   "outputs": [],
   "source": [
    "my_model = load_model(\"mnist_classification_model_CNN.h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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
}