cap9_2_first_cnns.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "cap9_2_first_cnns.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPS5K+2VFbUWwI6mDec4x/W",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusrpb/cic0193_machinelearning/blob/main/cap9_2_first_cnns.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qh0ZWibZtXyZ"
      },
      "source": [
        "# Capítulo 9\n",
        "\n",
        "### 9.2. Criando Redes Neurais Convolucionais\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sko_fRbqI4ap"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,MaxPooling2D,Activation,Flatten,Conv2D,BatchNormalization,Dropout\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FctMSCS4wxqu"
      },
      "source": [
        "Carregando o conjunto de dados de folhas que utilizaremos como exemplo:\n",
        "\n",
        "Link para download: https://drive.google.com/file/d/1cP75d6lJ6SLt9W4ykz9IFVThpcfo3CTZ/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whvCeVL6IvDr"
      },
      "source": [
        ""
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1s3MtjUD_58Q"
      },
      "source": [
        "### 9.2.2. Utilizando nossos próprios dados\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APEjn_A6Bton",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8eaaa124-f3a8-497b-ce25-597dd554ce66"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jf12eShWBsI4"
      },
      "source": [
        "!cp -r \"/content/drive/My Drive/leafs\" \"leafs\""
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmcabwAgllZI"
      },
      "source": [
        "Deve-se considerar uma semente para garantir que os dados sejam \"bagunçados\" e selecionados na mesma ordem em diferentes execuções desse script.\n",
        "\n",
        "Observe que existem três DataGenerators separados para os dados de treinamento, validação (desenvolvimento) e teste. Assim, pode-se garantir que a Data Augmentation seja executada apenas no treinamento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZ353JtFkj5c",
        "outputId": "42f07232-9546-49da-8efb-5c827222f861"
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1./255.,\n",
        "                                   rotation_range =20,\n",
        "                                   fill_mode='nearest',\n",
        "                                   width_shift_range=0.2,\n",
        "                                   height_shift_range=0.2,\n",
        "                                   horizontal_flip=True,\n",
        "                                   vertical_flip=True,\n",
        "                                   validation_split=0.2)\n",
        "\n",
        "validation_datagen = ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    validation_split=0.2)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255.)\n",
        "\n",
        "train_set = train_datagen.flow_from_directory(\"leafs/train\",\n",
        "                                              subset='training',\n",
        "                                              seed=42,\n",
        "                                              target_size=(128,128),\n",
        "                                              class_mode=\"categorical\",\n",
        "                                              shuffle=True,\n",
        "                                              batch_size=32)\n",
        "\n",
        "validation_set = validation_datagen.flow_from_directory(\"leafs/train\",\n",
        "                                              subset='validation',\n",
        "                                              target_size=(128,128),\n",
        "                                              seed=42,\n",
        "                                              shuffle=True,\n",
        "                                              class_mode=\"categorical\",\n",
        "                                              batch_size=32)\n",
        "\n",
        "test_set = test_datagen.flow_from_directory(\"leafs/test\",\n",
        "                                            target_size=(128,128),\n",
        "                                            class_mode=\"categorical\",\n",
        "                                            batch_size=32)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 160 images belonging to 4 classes.\n",
            "Found 40 images belonging to 4 classes.\n",
            "Found 100 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGMwj8XfEgb3"
      },
      "source": [
        "kernel_dimensions = (5,5)\n",
        "tam_strides = (1,1)\n",
        "number_filters = 100\n",
        "number_of_neurons = 30\n",
        "\n",
        "num_classes = 4"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3F8Sfh4xD8c6"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=number_filters,padding=\"same\",kernel_size=kernel_dimensions,input_shape=(128,128,3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(filters=50,padding=\"same\",kernel_size=kernel_dimensions))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(filters=25,padding=\"same\",kernel_size=kernel_dimensions))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Conv2D(filters=15,padding=\"same\",kernel_size=kernel_dimensions))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(2,2))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(number_of_neurons))\n",
        "model.add(Activation(\"relu\"))\n",
        "#model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation(\"softmax\"))\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEzt4ZLQSP9M",
        "outputId": "af3d56ce-4a57-4b32-cece-addc92300b4f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_10 (Conv2D)           (None, 128, 128, 100)     7600      \n",
            "_________________________________________________________________\n",
            "activation_16 (Activation)   (None, 128, 128, 100)     0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 64, 64, 100)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 64, 64, 50)        125050    \n",
            "_________________________________________________________________\n",
            "activation_17 (Activation)   (None, 64, 64, 50)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 32, 32, 50)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 32, 32, 25)        31275     \n",
            "_________________________________________________________________\n",
            "activation_18 (Activation)   (None, 32, 32, 25)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 16, 16, 25)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 16, 16, 15)        9390      \n",
            "_________________________________________________________________\n",
            "activation_19 (Activation)   (None, 16, 16, 15)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 8, 8, 15)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 960)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 30)                28830     \n",
            "_________________________________________________________________\n",
            "activation_20 (Activation)   (None, 30)                0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 4)                 124       \n",
            "_________________________________________________________________\n",
            "activation_21 (Activation)   (None, 4)                 0         \n",
            "=================================================================\n",
            "Total params: 202,269\n",
            "Trainable params: 202,269\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzJqrABxSigg"
      },
      "source": [
        "sgd = SGD(learning_rate=0.1)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPxBRdttTOWd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98ec54ad-eb9a-470a-e7ac-ee5efea935c2"
      },
      "source": [
        "history = model.fit(train_set,validation_data=validation_set,epochs=20)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 17s 3s/step - loss: 1.3913 - accuracy: 0.2500 - val_loss: 1.3863 - val_accuracy: 0.2500\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3878 - accuracy: 0.2438 - val_loss: 1.3913 - val_accuracy: 0.3250\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3948 - accuracy: 0.2438 - val_loss: 1.3879 - val_accuracy: 0.2500\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3893 - accuracy: 0.2250 - val_loss: 1.3863 - val_accuracy: 0.2500\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3876 - accuracy: 0.2438 - val_loss: 1.3854 - val_accuracy: 0.2500\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3877 - accuracy: 0.2250 - val_loss: 1.3848 - val_accuracy: 0.2500\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3890 - accuracy: 0.2313 - val_loss: 1.3863 - val_accuracy: 0.3250\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3900 - accuracy: 0.1937 - val_loss: 1.3851 - val_accuracy: 0.4000\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3868 - accuracy: 0.2438 - val_loss: 1.3841 - val_accuracy: 0.2750\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3868 - accuracy: 0.2937 - val_loss: 1.3843 - val_accuracy: 0.2500\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3857 - accuracy: 0.3125 - val_loss: 1.3819 - val_accuracy: 0.4000\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3853 - accuracy: 0.2937 - val_loss: 1.3821 - val_accuracy: 0.2250\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3872 - accuracy: 0.2562 - val_loss: 1.3784 - val_accuracy: 0.4750\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3857 - accuracy: 0.3000 - val_loss: 1.3858 - val_accuracy: 0.2500\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3797 - accuracy: 0.3000 - val_loss: 1.3871 - val_accuracy: 0.2750\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3929 - accuracy: 0.1813 - val_loss: 1.3835 - val_accuracy: 0.2250\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3846 - accuracy: 0.2625 - val_loss: 1.3790 - val_accuracy: 0.4000\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3816 - accuracy: 0.3187 - val_loss: 1.3703 - val_accuracy: 0.2750\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3756 - accuracy: 0.3250 - val_loss: 1.3505 - val_accuracy: 0.3000\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 13s 3s/step - loss: 1.3870 - accuracy: 0.2688 - val_loss: 1.3527 - val_accuracy: 0.3500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pvVeobUXmzs8",
        "outputId": "8c84176c-9b05-4a31-e0d8-67250c6beb47"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['training set', 'validation set'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OdUmaDu70vck111zDsmXLOHHiBNdddx2gm+AVFRWxbds2QkNDSU1NtdvmuzNycnJ49NFH2bJlC3FxcSxZssSt41hxpq34M888w+bNm/nwww+ZNm0a27ZtQynFE088wcUXX9xm2zVr1rgti6EbqC7QAWor0cnGgjC0wWsWhFKqCbgb+ATIAJYqpfaKyEMisriT3c8BdonIDmAZcLtSqtRbsnqT6667jrfeeotly5a1Dt+pqKhgwIABhIaG8tVXX3H48OEOj3HOOee0Bnv37NnDrl27AKisrCQyMpKYmBgKCgpYvXp16z6OWnLPnTuX5cuXc/LkSWpqanj//feZO9duiMcu2dnZzJw5k4ceeoj+/ftz9OhRLr74Yp5++mkaGxsByMrKoqamxrQF93eqCnSA2kpUsrEgDG3wagxCKfUR8FG7ZX90sO08m+fvAu96U7buYvz48VRVVZGSkkJyss4UufHGG1m0aBETJ05k+vTpjBkzpsNj3HHHHfzwhz9k7NixjB07lmnTpgEwadIkpkyZwpgxYxgyZAhz5sxp3ee2225jwYIFDBo0iK+++qp1+dSpU1myZAkzZswA4NZbb2XKlCkOp9S157777uPAgQMopTj//POZNGkS6enp5ObmMnXqVJRS9O/fn+XLl5Oenk5wcDCTJk1iyZIl/PKXv3TlozN4E6XsWBCDtNupuRGCQ30nm8FvMO2+DT7D/H98yMlS+FsaXPwwzL5TL9v6b1j1C/jlXogZ7Fv5DN1GR+2+TS8mg6E3Yttmw0prqqtxMxk0RkEYDL2R1ipqGwXRWixnAtUGTY9XED3FhdbTMP8XH1NlVRA2QWpjQRja0aMVRHh4OCUlJeZi5GcopSgpKSE8PNzXovRebPswWYlIgOAwY0EYWunR7b4HDx5MXl4e/tiGo7cTHh7O4MEmEOozqgsgpC/0iTq1TASikowFYWilRyuI0NBQ0tLSfC2GweB/VBfoAHX79i5mcJDBhh7tYjIYDA6oLmgboLYSnQyV+acvN/RKjIIwGHoj1YVt4w9WolO0i8nE7QwYBWEw9E6qTrTNYLISlQxNtXpWtaHXYxSEwdDbaKrXCsCRiwlMoNoAGAVhMPQ+7FVRW2mdTW1SXQ1GQRgMvY/qQv1oLAhDJxgFYTD0NuwVyVkxs6kNNhgFYTD0NqpO6Ed7QeqQPrqi2gwOMmAUhMHQ+6guBETPo7aHKZYzWDAKwmDobVSfsPRdcjAUyIweNVgwCsJg6G1UF+qeS44wo0cNFoyCMBh6G+1HjbYnehDUFEFTQ/fJZPBLjIIwGHobVQX2A9RWTCaTwYJREAZDb0IpJyyIFP1oFESvxygIg6E3UVsGLY32i+SstBbLmUB1b8coCIOhN9FRmw0rxsVksGAUhMHQm2itou5AQfSNg5BwY0EYjIIwGHoVVVYF0UGQWsSkuhoALysIEVkgIvtF5KCI3N/BdleJiBKR6TbLfmPZb7+IXOxNOQ2GXkNHfZhsiR5kGvYZvKcgRCQYeBK4BBgH3CAi4+xsFwX8HNhss2wccD0wHlgAPGU5nsFg6ArVBRDSF/pEdbxdVLJp+W3wqgUxAziolDqklGoA3gIus7Pdn4H/Aepsll0GvKWUqldK5QAHLcczGNrS0gxPzYZNT/taEvd5eSGs/Uf3nKu6QAeoRTreLjq594wefe+nsOqXvpbCL/GmgkgBjtq8zrMsa0VEpgJDlFIfurqvZf/bRGSriGwtKiryjNSGwOLoZijcBwc/97Uk7tFwEnLX6b/uoLqg4wC1lahB0Fyv02J7OjlfQ9YnvpbCL/FZkFpEgoDHgF+5ewyl1HNKqelKqen9+/f3nHCGwCFjlX48vsu3crhLyUFAQWlO95yvurDz+AP0nlqIxlodjK/Mh5oSX0vjd3hTQeQDQ2xeD7YssxIFTADWiEguMAtYYQlUd7avwaDdHxkrQYKgpvDUnINAojhLP1YcheYm75+v6kTHGUxWWkeP9vBAddnhU89P7PSdHH6KNxXEFmCUiKSJSBg66LzCulIpVaGUSlRKpSqlUoFNwGKl1FbLdteLSB8RSQNGAd96UVZDIHJiF1QcgfTrLa93+1Yed7AqiJYmqMzz7rma6qGu3DkXU7RFQVT28PuyMhvLLRC/P17GawpCKdUE3A18AmQAS5VSe0XkIRFZ3Mm+e4GlwD7gY+AupVSzt2Q1BCgZq7T1cO59+vXxALwDLNp/6rm33UzOVFFbiUoCpOenulo/8z4xgeum9CIh3jy4Uuoj4KN2y/7oYNt57V7/Ffir14QzBD6Zq2DobIgfDnGp2qIINIqzYOBEKNhtuZud771zVRfqR2csiOBQiOzf81Ndy3IgLApS5wTm98fLmEpqQ2BSkq2zl8Yu0q+T0gPvDrClWQeph58LwWHdZ0E4E6SGU6muPZnSHIhP1d+f4gPQUONrifwKoyAMgUnGSv045lL9mJyu7wbrKn0nk6uU5UJzA/QfA7HD2vrDvYE1iO9MkBp6x2zqshyIS9PfHxQU7PO1RH6FURCGwCRzFSRPgtih+nXSJP1YsMd3MrmKNUDdfzTEp2mF4U2qCwGByETntu/ps6lbmqH8iP7skybqZSaTqQ1GQRgCj8rjkLcFxiw6tcz6Aw8kN5NVQSSO0nexpbnerVyuLoCIBB1fcIaoQVBbCo11nW8biFQe0xZcXBrEDIHw2MD6/nQDRkEYAo9MS3HcWBsFEZWkg6qBFGgsyoLIAbq9dnwaNFTBSS8Wa1UXWLKTnCS6h8+FsLr04tN065Hk9MD6/nQDRkEYAo/MVZAwUrtmrIjoQGMg/cCLs069h7hU/ejNQHVno0bb09MHB1k/a+tnn5SuYxDdUbAYIBgFYQgsast036IxC09vOJecDoWZ0NTgG9lcQSko3g+JZ+jXcWn60ZuB6qoC5wPUYFMs10PjEGU5EBQC0YP166R03X/K6vozGAVhCDCyPtFVx2Pt1FomTdTzlosyul8uV6kuhLoKGwUxTD96K1CtlLEg2lOWq5Mcgi3lYMnp+jGQrFAvYxSEIbDIWKmDp4OmnL7OmskUCC0TWjOYLAoitK9+X95yMdWWaeXpTJGclfAYCI3oubUQpTmnLDeAhFF61KoJVLdiFIQhcGg4CQe/0LUPQXa+uvHDIaxfYPzAiy0tNqwWBFhSXb2kIFxps2FFxDJZrof2YyrL0Z+5leAQGDjeWBA2GAVhCByyv4CmWhi70P76oCAYOCEwfuBFWVqZRduMOYlL854F0VpF7YKCgJ47m/pkqXbxWQPUVpIm6u9PbxiU5ARGQRgCh4xVOiV02BzH2yRN1C6mlpbuk8sdirN0/YNtoD0uFapPaEvJ01S5qSB66mxqq6Vm62ICHaiuq9AFdAajIAwBQnMjZK2GMy7puNArOR0aqr3ftqKrFGe1dS/BKXeHNwLVXbUg/F3hukqpTQ2ELcnWOFYAWKHdgFEQhsAgd62+s3PkXrKSFACZKPVV2q/fXkHEeVlBhPSFPlGu7Rc9SAe3vVnA5wusn3F7F9OAcbqFfCAkOnQDRkEYAoOMVTqjZsR5HW83YKzObffnQHXxAf1oW+gHNhaEF6yf6gIdoG5fO9IZramuPawWoixHW1NhkW2Xh0Voxe3P359uxCgIg//T0gKZH8LIC3Q6aEeE9IH+Y/3bgmjtwdTOgugbpwfXeCNQXV3gunsJbIrlelgcojT39PiDFWug2mAUhCEAyN+qg7e2vZc6whqo9leKs7SVEz+87XIRPZvAKxZEoWtFclZ6sgXR3r1kJSlduwBrephbzQ2MgjD4Pxkr9QV11EXObZ+cru+YrZk7/kbRfn33ai/YHpfqHQui6oRrbTas9BuoffI9yYJorNPtQ9oHqK2YiupWjIIw+DdK6eZ8aedA31jn9vH3QLVtk772xKXpFMsWD45gb6qHunL3XEzBIbrjbE+yIMoPA6oDF5Off3+6EaMgDP5N4T4oPeS8ewkgaYJ+PO6Hw1+aG/X7aR9/sBKfprOGPFm97Oqo0fb0tNGj1gwmRxZERLxu4GcC1UZBGPycjFWAwOhLnd8nPEa7avwxDlGao5sNOlIQ1rtaT7qZqgv1oyuzIGzpaaNHSx0UydliZkMARkEY/J3MlTBkhms9hMB/Z0O0b9LXHm+kunbZguhh/ZjKciA0suPRq0npOh25oab75PJDnFIQIvKeiFwqIkahGLqPslxtBbjiXrKSnK5dOXWVHherS9hr0mdLdAoEhXrWgqg6oR/dCVKDdjHVVXinBYgvKM05NUXOEcnpgNIDhHoxzl7wnwK+DxwQkUdExEGEzWDwIBmW0aJjOqmetoe19XfBHs/J4wmKsrTLxlFFc1CwnlHgUQuiEJCO75g7IspSC9FT3Ewdpbhasc44P+GHcaxuxCkFoZT6XCl1IzAVyAU+F5ENIvJDEXHYGEdEFojIfhE5KCL321l/u4jsFpEdIrJORMZZlqeKSK1l+Q4Reca9t2cIaDJX6e6sjoKJHdH6A/ezOERxlmP3kpV4D3d1rS6AiISOe1h1hHU2dU+YLNfSAmWHO/9OxQyB8NheH6h22mUkIgnAEuBWYDvwf2iF8ZmD7YOBJ4FLgHHADVYFYMMbSqmJSqnJwN+Ax2zWZSulJlv+bndWTkMPoboQjmxyz3oAHZCN7O9fP3CltF/bkXvJSlyadq95quV0dYH7AWroWRZE1XE9VrSjADVo91Nyuv/dYHQzzsYg3gfWAhHAIqXUYqXU20qpe4B+DnabARxUSh1SSjUAbwGX2W6glLJ1EEcCpgm7QbP/I0C5F38A/QNPSvcvF0HlMWio6lxBxKdBfaWeAucJXB012p6eZEGUOejiao+kdJ1m3dzkXZm6yoHPIGetV2ZYOGtB/FMpNU4p9bBSqs1thFJquoN9UoCjNq/zLMvaICJ3iUg22oL4mc2qNBHZLiJfi8hceycQkdtEZKuIbC0qKnLyrRgCgoyV2k88cLz7x0hOh8JMaGrwmFhdojWDqZMQnqdTXavc7MNkpU8UhEX1DAuiNcU1tfNtk9Khqe7U/80faW6Cj+6Dz/7glcM7qyDGiUhrGauIxInInZ4QQCn1pFJqBPBfwO8ti48DQ5VSU4B7gTdEJNrOvs8ppaYrpab379/fE+IY/IG6Cjj0tXYvudp91JakibrorCjTc7J1BUdN+tpjvXh5IlCtlPuN+myJTu45FoQE6xhDZwRCy419y/V7mvurrv1WHOCsgviJUqrc+kIpVQb8pJN98gHb/8JgyzJHvAVcbjl+vVKqxPJ8G5ANdPKrMvQYDnymL+zuupesJPnZ8JfiLN2ttbOLtVVBeMKCqC3Tn2VXFURPGT1amgOxQ5wL2CeMgpBw/41DKAVrH4PE0a4VkrqAswoiWOSUerIEoMM62WcLMEpE0kQkDLgeWGG7gYiMsnl5KXDAsry/5RyIyHBgFHDISVkNgU7GSn1BGzyja8eJH67nPvtLoLpo/+ljRu0RFqFrFjwxOMhaJOdqoWF7esro0bLczgPUVoJDtIvTH1u2AGR9AoV74exf6nnsXsDZo34MvC0i54vI+cCblmUOUUo1AXcDnwAZwFKl1F4ReUhEFls2u1tE9orIDrQr6QeW5ecAuyzLlwG3K6VKXXpnhsCksVZbEKO/1/UvfVCQTpP1Jwuis/iDlfg0z7iY3B012p6oZN1yPdBHj5bluJY2bZ0N4YUAcJdQCtY+CjFDYeLVXjtNiJPb/RfwU+AOy+vPgBc620kp9RHwUbtlf7R5/nMH+70LvOukbIaexKE10FjT+WhRZ0maCDvf0hc2L91lOUVtub5YdxZ/sBKXpj+LrmLtw9TlGMQg3UOqpqjr1oivqC3XLjdnAtRWktJh28u6w27cMG9J5jq56yBvC3zvUffrW5zA2UK5FqXU00qpqy1/zyqlPNiP2GCwkLFK++lTz/HM8ZLTdWqpN4bwuIJ1zKizCiI+TbfYbqzt2nlb22x4QEFAYPdkKnOiSV97kq1xLD+LQ6z9h27DPuUmr57G2TqIUSKyTET2icgh659XJTP0PpqbdP3DGRdDSGchLifxl97+1h5MzrqYWjOZDnftvNUFENLXcWsPZ2mdLBfAcYhSF2ogrAwYpwcm+fr7Y0v+Njj0Fen/7e8AACAASURBVMy+q/MRvF3EWZv738DTQBMwH3gVeM1bQhl6KUc2QG2p59xLAAPG6ml0vg5UF2dBcBjEOummiPNQV1drkVxXUyBbLYgATnUtc6EGwkpYhM5m8vX3x5a1j+mW9tN/5PVTOasg+iqlvgBEKXVYKfUgOuvIYPAcGat0WuHICzx3zJA+0H+M710ERVkQP0JnxjhDa9vv3K6dt6ttNqxE9tf1A4FsQZTl6vfhqjXlT7MhCjN1j7IZP4Xw00rDPI6zCqLe0ur7gIjcLSJX4LjFhsHgOkpB5ocw4nwIi/Tssf1hNkTx/s6b9NkSkaCrl7taC1Fd2LU2G1aCgrWiCeRU19Ic1+IPVpLSdeylpsTzMrnK+schNAJmdk97OmcVxM/RfZh+BkwDbuJUSqrB0HWObYfKPM+6l6wkp+s76aoCzx/bGZrq9d2rswFq0C6h+NSuu5iqTrg/B6I9UcmBPZu6LNc195IVf6moLjsMu5bCtCUQmdAtp+xUQVgK1q5TSlUrpfKUUj9USl2llNrUDfIZegsZK7UL44wFnj+2rwPVJdmgWnTFqyvEdbHtd1M91JV3PYPJSiDPpm6qh4o8N1vH+4mC2PBPHTCffXe3nbJTBWFJZz27G2Qx9GYyV0HqHD0w3tMkTdCPvvqBdzZm1BFxqVB+GFrczCjv6qjR9gTybOryI4Byz8UUEQ/Rg30bqK4qgO/+A5NvgJjTep56DWcL5baLyArgHaB1SKtS6j2vSGXoXRRl6YvojNu8c/zwGH2x9dUP3KogEka6tl98GjQ36MyhWCeay7XHWiTniSA1aAuivhLqq6FPgIUgrcF+dywI8P1siE1P6p5ac37Rrad1NgYRDpQA5wGLLH9ecBYbeiWZK/XjGC8mxvkyUF20X7dEcDX4HtfFTCZvWBAQmFZEqRtFcrYkpUPJAd/M5a4tgy0vwvgrIGFEt57aKQtCKfVDbwsSsCgF3z6vL27daPr1KDJWQsq0U7n23iA5HTJWQF1lt6QHtsGZMaP2iLephUizOxKlY1qrqD1oQYC2aBJHdbytv1GWo7N/3FWWyek6jlSwF4ac6RGRmlsU+45VsiG7mDOSopg/2oFs3z4PDdVw9r0eOa8rOKUgROTf2Jn2ppTyfqWGv3NsO6y+T99dfO/vvpYm8KjI05/h+Q949zzWQGPBXhg227vnsqWlRbfZSHUjjBc9WBf5uRuori4EBCIT3dv/NHksN0CBakHEpbpfMNg643yn2wpCKUVOcQ3rs0tYf6CYjYdKqKhtBCA2IpRNvzmf8NDgtjs11MCmp3XyhjWW1o04G4NYZfM8HLgCCOB8Nw+SafloMj+ES/7mlaEdPZrMD/Xj2MUdb9dVbDNRulNBVByFplrXUlytBIdA7FD3U12rC3Q9haeauVnbbQRiP6ayHF2o6C4xQyA81uU4REFlHesPFrP+YAkbsos5XlEHQEpsXy4aN5A5IxMJDQ7irje+Y8WOY1x7ZrtY07ZXdHeBub9yX/Yu4KyLqU1nVRF5E1jnFYkCjYyVuvq3Mh+OfaddJQbnyVipK50TXQzgukpUkq6i7e5AtatN+toTl9oFC8JDVdRWwiJ0wD/QUl1bWnQcpysV+iLazdTJ96eitpHNh0q0Usgu4WBhNaAthLNGJHDXiETOHpnIsIQIrCN2lFKMHhjFyxtyuWb64NblNNXDhidg2NkwpIuzUdzEWQuiPaMAD0W+Ahhr9s3838GaR0750g3OUVMCh9d3j29VxBKo7ubhL6426WtPXBrkf+fevtY+TJ4kEFNdqwv0bGl3iuRsSUqHLS/oppI2LVMOl9Tw9pajrM8uYXdeOS0K+oYGc2ZaPNdOH8xZIxIZlxxNUJB974KIcMtZw/jd+3vYdriM6amWVO+db+nCxMv+1TW5u4CzMYgq2sYgTqBnRPRurNk3k2/UF7qMVXDBg76UKLDIWq0Df96onrZH0kTY+CQ0NXiuW2xnFGdB33j34wDxabrYrbYM+sa5tm+VC/MnnCUQZ1OXudHF1R5J6VrRFGfBwHEAtLQobnnpW/LLapk8JJa7zxvFnBEJTBkaR1iI8/NHLp+cwiOrM3ll42GtIJqbYN3/QvJkGHFe1+TuAs66mLrYK7iHkrFKWwwxKTBmIXz0a53S6O7dYm8jY5X27SZP7p7zJafrXPKizFPtE7xNUVbXLtLWtMzSHEhxQUEoZbEgPDzcJ2oQFGZ49pjepqsprlZsW25YFMSarEIOl5zkX9+fwsJ097PwIvuEcO30IbyyIZfCS8cy4PAqrdiu/Y9P45rOzoO4QkRibF7Hisjl3hMrAKjI0zGHMZa7X2sOf8ZK38kUSNRXQ/aX+vPrrh9AknX4SzfGIVxt0tce21RXV6gt08rQ0woiOlkrnuYmzx7Xm5Tl6BYVMW4UG9qSMErHG20C1a9sOMzA6D5cPL7rsZ6bZw2jqUXx+qbDuqV34hmnri8+wlkb6AGlVIX1hVKqHPByXqKf05p9s0g/Rg+ClOlGQTjLwc+gub773EsA8cMhNLL7AtU1JXCypNMeTFV1jTQ2O5j1bPWbuxqothbJeXo8aFSydgvWFHr2uN6kNAdiBnfdrRgcAgPHw3Edx8opruHrrCJunDmM0OCuj7NNTYxk3uj+HNn8PhTu1bE5X47JxXkFYW87dwPcPYPW7BubgqGxi+D4Dig/6ju5AoWMVToFc2g3ppwGBelc8u5qmWBtsdGBi+nzfQWc9fCXLHpiHYdLak7fICxSj5Z01YJoraL2tAVhHRwUQIHqMjfbfNsjaaK2QJXi1Y25hAYL18/oomViww9mD+OmxmWcjBgEE6/22HHdxVkFsVVEHhOREZa/x4Bt3hTMr6kpgcMbTjf/rNaE1bow2KepAQ58CqO/p+cMdCdJlp46LQ7u2D1JawbT6QqiuUXx2Kf7ufXVraTE9eV4RR2LnljHV5l27szj01wfPWrtw+TxGIR19GgABarLcrseoLaSlA51FZwszGHZ1jwunZjMgKhwzxwbODcsi2lBB3g9+HLP1a90AWcVxD1AA/A28BZQB9zlLaH8nqzVoJpPd48kjID+Y08Vzxnsk/ONbvpmVajdSXI6NFR1fc6CMxQf0D7rdr7v8pMN/PiVLfzzy4NcPW0wy++aw6p7zmZwXAQ/emULj3+eRUuLTdKgO22/W9ts9HILoq5Su/k8ZUEk6zjWt5u+pqq+iVvOSvXMcS0ErX+M2rB4Hi2awZ78is538DJOKQilVI1S6n6l1HSl1JlKqd8qpezYw72EjrJvxi7UKa81xd0vV6CQsQLC+kHaud1/7u7s7V+0Xwc2baykfccqWfSvdaw/WMxfLp/A369OJzw0mCHxEbx7x1lcMTmFxz8/wE9e3drahoH4NF2I2VTv/LmrCyCkr+vjNTsjIhGCQgPHgnBnDnVHDBiHkiCO7tvExJQYpgyJ9cxxQde7ZH8Js+8mKLQvr2zI9dyx3cTZLKbPRCTW5nWciHziPbH8mM6yb8Yu0kG8/au7X7ZAoKUZ9n8Eoy6EUM+Z5k4zYKzub9QdcYh2Tfre357HlU+vp6Gphbd/OpubZg07VTUL9A0L5h/XTuKhy8bzdVYRi/+1jswTlZa7X+Wam8laJOfpDLGgIO1mChQLotRDNRBWwiKojR5O0sksfnBWapv/X5dZ9xj0iaHv7J9wxdQUPth5jLKaBs8d3w2cdTElWjKXAFBKldFbK6kPft5x9k1Sum7tbNxM9jn6LdQU+ca9BBDSRycXeDuTqbFWD6lJPIPG5hYeXLGXX769k/TBsay6Zy5Th9qvaRARbpmdylu3zaK2oZkrntzANyWW2QuuuMU83WbDlujkwOnHVOahGggb9jQPY2LwYRamJ3vsmBRm6sSXmbdBeDQ/mJ2qbyS2+jbhxVkF0SIiQ60vRCQVO91d2yMiC0Rkv4gcFJH77ay/XUR2i8gOEVknIuNs1v3Gst9+EbnYSTm9T8bKjrNvRLTyyP4K6qu6V7ZAIHMVBIfByAt9J0N3zIYoPgAoKiLT+P7zm3h5Qy4/PjuN12+dSf+oPp3uPj01nlX3nM2ElGju/VT7optLDjl//upCz7fZsBKVHDjtNspy9e/VQy3e88tr+bx8IEmUEN5Q3vkOzrL+cd2OfOYdAIxOimLW8Hj+s/EwzS2dXmq9hrMK4nfAOhH5j4i8BnwN/KajHSyzrJ8ELgHGATfYKgALbyilJiqlJgN/Ax6z7DsOuB4YDywAnrIcz7e0Zt9c0nH2zZiF2so48Fn3yRYIKKXjD8Pndf9MBluS0/UddlWB985hSXG945Mq9uRX8n/XT+YPC8e5lC8/IDqcN34yi4Wz06lRffh47SaKqpyMQ1Sd8NwciPZED9IuJuW7C5fTlHowxRV4fdNh9rWk6heeuskoOwy7lsK0JRCZ0Lr4B7NTyS+v5YsML35PO8HZVhsfi8h04DZgO7AcqO1ktxnAQaXUIQAReQu4DNhnc9xKm+0jOWWVXAa8pZSqB3JE5KDleBudkddrtGbfdNKaeugsHczLXAUTruwe2QKBE7u122Xur30rR2tv/92eLyRDd+fcuf1bJiqhKGwI7986mzFJ7inE0OAgHrxsAhXZw4ioOMLCJ9by9E3THLqoAB3Mriv3fAaTlahkaKzRv4XwGJ79Ops1+4u6dMhrpg/myqmDPSSgDWU5MNgznVDrGpt5a8tR5p0xHXLRCmLE/K4dVCnd6FOCYPbdbVZdOG4gyTHhvLrxMBd5oFLbHZwNUt8KfAH8Cvg18B/gwU52SwFsHWh5lmXtj32XiGSjLYifubjvbSKyVUS2FhV17QvqFJkrncu+CQqGMd+DrE9dyzzp6WSu0j+E0d/zrRy2w188TG1DM79aupO8AzsoDk1m2T3nua0cbIkZdAZnxVURFhLEdc9u5D+bDqMc3cF7etRoe2xSXbcfKePh1ZkUV9fT3KLc+ttfUMVL672QdtzUoFvieChAvWrXcUprGrj67El6mJMn4lhf/w/sfANm33XaRMqQ4CBumjWMdQeLOVjoG3e1s9XQPwfOBDYppeaLyBjgvz0hgFLqSeBJEfk+8HvgBy7s+xzwHMD06dO9a++2NOsCOGezb8Ysgu9e1VbHKB/62/2JjFU6dtOvv2/lCI/RaY8eDlQfKTnJT1/bRuaJSn4bV0JCykSkr4eKneLT6HPgM1beexY/f3sXf1i+h51Hy/nL5RNOn0JmLZLzVpDaUiynKo/x8BdlJPYL4/275tCvj3vNFR5encFL63Kob2qmT4gHPckVR3VGoQdcTEopXtmQy6gB/Zg9IkG7KbuaCbf+n7DmYd0N2sFExevOHML/fX6AVzce5qHLun+inLMO0TqlVB2AiPRRSmUCnbUszQdsK4QGW5Y54i3A2gDQ1X29jzX7xtnmWcPPhbAo7XM3QEm27i/j4+ZjrXg4UL1mfyGL/rWO/LKTvHTzVBLrjyJdadLXnrg0aK4ntqmEl5acyc/OG8mybXlc/cwGjpaebLut1y0IrSAysvbzbU4pPz9/lNvKASA9JZbGZkXWiWpPSajxYIrrjqPl7M6v4BZramtSuh4z3HCy853tseUF+OwPMP4KWPyEw55Lif36sHBSMu9uy6OqrrEL78A9nFUQeZY6iOXAZyLyAdBZUvYWYJSIpIlIGDro3OZqKSK2k88vBSzjt1gBXC8ifUQkDT2g6FsnZfUO1uybURc5t31IHzjjIsj8SFsfvR1r2q+1662vSUqH0kMeyTR7/ptD/PDlLSTHhLPynrOZn1SrkxQ6adLnEtZCr7JcgoOEey8azQu3TOdw8UkW/Wsd3+aUntrWW1XUViwWxOade0hLjOT6GUM72aFjJqboRtG78j2YFQQeTXF9ZUMuUX1CuHKKxQ2UNFFbJwV7XT/Yzrfgw1/pOdNXPNdpu5kfzE6lpqGZd7fluSF513C2kvoKpVS5UupB4A/Ai5y623e0TxNwN/AJkAEsVUrtFZGHRMQa5b1bRPaKyA7gXizuJaXUXmApOqD9MXCXUsp3V1mldHrr8HmuZd+MWQgni+HoZm9JFjhkrNIX5bhhvpZE09rbf0+XDpNTXMPDqzO4cOxA3rvzLIYlROoZEODZYT122n5fMG4gK+45m7iIMO558zsqrXeY1YWA6BGr3iC0L/WhMYTUnOD/XTy6y51Mh8T3JaZvqOdbS5Tl6lYnXXS1FVXV8+Hu41w1bTCRVkup9fvjYhxr3wew/A5IOweuecWpDrOThsQyaUgsr2483LYFSzfg8n9WKfW1UmqFUqrTEj+l1EdKqTOUUiOUUn+1LPujUmqF5fnPlVLjlVKTlVLzLYrBuu9fLfuNVkr5tiz5xG4oP+y6e2TUhRDcR18cezNVJyDv286zv7oTD7XcePbrbEKDg/jrFROJCLNcPKxN+mw7/XaVmCEgwaf1ZEpLjOTx6yZTWFXP3z7O1AurC3Tuv5eavdU2NHOkKYbREdUsmND1OIeIMDElht2eVhClOdry6mK181vfHqGxWXHLbJubm5ghEB7rWhziwGew7Mcw+Ey4/k2XOgksOWsYh4prWJ/dvS18fNtsPFBwN/umT5S2OjJWBkbOuLewupe6c/ZDZ0Ql6TvsLgSqj1fU8u53eVx35pC2xW/FWfrYEfEeENRCcCjEDrFbTT1pSCxLzkrltU1H2Jpb6t0qauCl9TnkN8UxPqrGY60mJqTEsP9EFfVNHnQUeKDNd2NzC69tPsw5Z/RneP9+p1aIaCvC2e9Pzlp4+ybd6uX7S6FPv873seF7E5NJiAzr9v5MRkE4Q1eyb8Yugooj3TvFzN/IWAXxI3SLC39B5FRvfzd5/psclILbzhnedkVRlmfjD1Y66Or664tGkxLbl9+8t5uWqhNeC1CX1jTwzJpsgmJSiKz33NCgiSkxNDYr9p/wUDqnUh5p8/3p3gIKKuv5wWw7rtGkdCjc1/l0vbyt8Ob12pq5eTn0db3BX5+QYG6YMZQvMgtPT0rwIkZBdEZXs29GX6Ktj97qZqotg9y12nrw4WxduySl6/nKTa43RCutaeDNb49w2eQUBsdFnFqhVNfHjDoiPs1hP6bIPiH8+fLxHCispqYk32sB6ie+PEBNQxPjx4zWsY5mz2TWWAPVHnMzVRdC48kuWxCvbMxlaHwE80bbUbhJ6dBUd2owlD1O7IbXrtQW5c3L21RKu8qNs4YSJMJrm1ycDdIFjILojK5m30QmwtCzem/zvqxPoaXJv+IPVpLT9dzmokyXd315fQ51Tc3cMa+d9VBTBHUVng1QW4lL1Qq31n62z3ljBrJwYhJ96oopC/Kge8vCkZKTvLbpMNdOH0JCciqgTmVMdRFroHp3nocURFnXU1wzjlfybU4pN88aRnCQnZub1kC1gzhEURa8erkurv3Bitb0YHdJjunLxeMH8taWo9Q2dE/OjlEQneGJ7JuxC7UpWpLtObkChYwVOi1y0FRfS3I6SXr4i6tupqq6Rl7ekMvF45IYOaDdvIUia4DaGwrCmsmU63CTBy4cRJg088HBJo9nvDz66X6Cg4RfXngGRFmqqT3UtM/jgerSrqe4vroxl/DQIK6Z7qAFSMIonSVl7/tTmgOvLtbeg1tWQGzXUoGt3DI7lYraRlbs7J6yMKMgOsJT2TdW91TGyq7LFEg0nISDX2jry8fD1+0SPxxCI12uiH198xEq65q4c/6I01e2jhn1QgzCTqpre/qjrYttJaEebRW9K6+cFTuPcevZwxkYHX7qbrjSc4ODJqTEkFVQRV2jB+6Oy3IA0YF9Nyg/2cD72/O5YkoKsREOUlGDQ2DAODjeLtW18hi8eplu+X7Lckgc6ZYM9piZFs+YpChe2dBBqxUP4oe/Wj/COlu6q9k3sZbpc73NzZT9JTTV+k/1dHuCgiBpgkuZTHWNzbywNoe5oxJJH2wn2Fh8QCud6NNah3Uda7FcR+NHLVXUCUlD+e+PMiisrOvyaZVSPLI6k/jIMH56rsWl5mELAiB9sAcD1aU5EDNYF6y6wTtb86hrbOHmWakdb5hsqci3Xqyri7RyOFkKN78HA8e7dX5HWOeF7DteydbDZR49tj2MguiIjJWey74ZuxDytgTOJC5PkLlK54qnnu1rSRyTZOmp09Li1ObvbD1KcXU9d813cFdYtF/XP3gjIN8nSgc7OxocZOnD9OMFM6lvauFPK/c53tZJvs4qYkN2CfecN5KocEttRUS8rvHxoAXh0UB1WY7bY0abWxT/2XSYGanxjBvUSWFsUrqOOVUc1fGh/1wB5UfhxqWQMs2t83fG5VMGER0e0i0pr0ZBOMLT2TdWN1VvsSKaG/Vo0dGXeK1gyyMkp0NDlVPT2hqbW3jm60NMGxbHzDQHQeDiA96JP1iJS+3YgrAEjYcMHc7PzhvJh7uP8/k+9+cJNLdo62FofAQ3zrSJw4noWgsPWhCD4zxYUd2FFNc1+ws5UnqSH5yV2vnGyZY41uGN8NrV2sV4/Wsw7Cy3zu0MEWEhXDt9CB/vOUGBByzEjjAKwhGezr7pP1oHtXqLgshdp++s/NW9ZCWpk0wUG1bsOEZ+eS13zR9hv0Csvhoq87yT4molLq3j2dTVBRDSF/pEcds5Ixg9MIo/fLCH6vpOcvUd8P72fDJPVHHfxaMJC2l3ubAODvIQIkL64Bh2dTWTqb5KZ5O5GaB+ZeNhBkb34aLxTqQKDxinA9ErfwbHtsPV/4aRF7h1Xle4adYwmpXi9c1HvHoeoyAckbnS89k3YxfqC2et932HPidzlb5QjTjP15J0zICxEBQCWR93WO3e0qJ4+utsxiRFMd9eTjycyof3RpGclfg0rYQc1W5UF+giORHCQoL47ysncqKyjkc/2e/yqeoam3ns0/2kD47h0ol2UjSjkqGqiy6mhpPw9s3w1cOAhwLV1iwvN1xMh4qq+SariBtnDnOux1RYhL7xa6qHK57ttm4BqYmRzDujP29sPkJDk3PuUXcwCsIeDSfhwOeez74Zs0hbJVmfeO6Y/khLi2V2xgX6B+TPhPSBmbfDzjfh67853OzTfQUcLKzmzvkjHbeXKLY0I/aqiylNdxEtd3Dn2K7NxrRhcdw8axivbMxl+xHXbkxe3pDLsYo67r9kDEH26gC6Onq0qR6W3qxTodf+AyrymZgSQ1NLFwPVXWjz/erGw4QGCze40qH24r/Cda9B+jUun68r/OCsVIqr61m9x3txTaMg7OGt7JtBU3R2S09Pd83fpn3TYxb5WhLnuPDPemjLmv+GDU+ctlopxVNrDpKaEGH/TtpK8X7dUC9+uONtukpnqa7Vhae12bjv4tEMjArnN+/tprHZubvN8pMNPPXVQeaP7s9ZIxLtbxQ9SP9O6txo093cBMt+BAc/h3m/BRRs/JdN6+8uuJncbPNdXd/Eu9vyuHRictveWp0x6kKf9Bk7Z1R/UhMivBqsNgrCHt7KvgkK0lbJwS/cHzQSCGSu1G6bM5ycneFrgoJg0T9h3OXw6e9h60ttVq87WMyuvApuP3eE/YpaK0X7tXJwooWz21gveo4C1VUnTmuzERUeykOXjSfzRBXPfXPIqdM8+dVBquqb+K9LOsjgi7LWQrh4B9vSAh/cqX9nC/4H5v0XTLwWtr3M4LAaYiNC2dOVOERpDvSNc7nn0fvb86mqb+IWZ4LTfkBQkHDz7FS+O1LuuQr09ufwylEDmeZG2L/ae9k3Yxbqu67sLzx/bH9AKV19njpX/0gDheAQuPJ5GHUxrLoXdr7duurJrw6SFB3OFVM7qW3wdgYTaOsgNMJ+NXVTvb6b73d6J9eLxidxyYQk/u+LA+QU13R4iqOlJ3llw2Gunjq443narbOpXYhDKAUf3gu73obz/gCzbtfLz/4FNNYi3z7b9YrqslyXrQelFK9uyCV9cAxThrjeTM9XXD1tMBFhwbyyMdcrxzcKoj256/SPzFvZN8Pm6AtnT23eV5QJpdn+1drbWULC4NpXIG2uHuqybwXbDpey6VApPzlneMfzkpsb9fv2ZgYT6PTSuFT7LibrLGoHnVwfXDyePiFB/O793R1W4T72WRYicO9FnbwXqwXhbKBaKW2hbfs3nH0vnPPrU+v6j9bfmc3PMW1gcNcC1WU5LscfNmaXcKCwmltmp3qshXl3ENM3lGunD0HAK5XVRkG0x9vZN8Eheq5E1mqPdcL0KzJWAuL/6a2OCO2rh7mkTINlP+KbD98kLiKUG2Z00rKhLFcnIHjbggDHbb+ts6gdzIIYGB3O/ZeMYUN2CcscjK/ce6yC5Tvy+eGcNJJj+nYsh6supjWPwMZ/wYyfwvl/PH392fdCfQWX1H1EU4si051AdXOjLlRzMYPplY25xEeGsTC9aw31fMEDi8bx92smeUWxGQVhS3dl34xZqGsEctd67xy+ImOlnpjlxYE1XqdPP7jxHWrjRnNHwQP8fnzpqWlxjmht0ufFFFcr8WlaIbW/Y7QqiA5mQdxw5lDOTI3jrx9lUFxdf9r6R1ZnEtM3lDvm2ekz1Z6QMIhIdM6CWP9/8PUjMOUmWPCI/eLTlKkw4jxGZr9CHxrcczNVHAXV7JKLKa/sJJ/tK+D6M4cQHtrxfGh/xJsWj1EQtnRX9s2I+bpfT09zM5Ud1n1pAtG91J6+sTwU+xfyGcCVmffqoS8d4Y0xo46IS9VxrPattq2vO5gFERQkPHzlRE7WN/PnVW3bcKw9UMTaA8XcPX8kMX2djL9FJ3duQWx5AT77I4y/UicDdJQ6PvdXBNcWs6TvWnbnuZEd5UaKq7XY7MZZfjIv3Y8wCsKW7sq+Ce0LI8/X1oqTPYACAmtzw0B1L9mQW1zD2/tO8tHUZ5B+/fXQl46qrYsPaJdLeCe9ezyBo1TX6kJAdL+mDhg5IIo754/ggx3H+Gq/jlu0WFpqDI7ry832pqc5ImpQxxbEjjfhw1/BGZfAlc9BUCd36MPmwJCZ/CRoJfvyVZuKaQAAHiBJREFUSp2Xw0prkZxzCkIpxQfb85k/egApsZ241HohRkFY6e7sm7GLofoE5HdyZxpIZKyEAeMhwQn3hJ/z7DfZhAQHcd15M3Q//7B+evhLkYPpYUX7uyf+AI7nQlQXQESCU9l3d8wbwcgB/fj9+3s42dDEip3H2HuskvsuHt1xML49HVkQ+z7Q6axp58I1LzuXFSgCc39FYnMhY4o/cT1QXZajmwhGORdLyDxRxbGKOufaavRCjIKw0pp9003FXWdcBEGhPadorroIjmzsEe6lExV1LNuWx3XThzAgKlwPi7plhe658+plp1+YldIWhDdmQNgjZoiWpX2gurrA6VGjfUKCefjKieSX1/LI6kz+/sl+JqREsyh9kGuyRA2Ck8U6xdaWrE9h2Y9h8Ay44U0IDXf+mKMuojJmNLcHfUDmcRfjEKU5+v/lZAeELzO1BeWwfUovxygIK63ZN26OFnWV8BhIO0dnTXXD4A+vs/8jQPUI99Lzaw/RouC2c2wqohNH6uEvjSfhlcVtc/+rjuuOsN1lQYSE6VkHp7mYCiDK+TvhM1Pj+f7Moby68TD55bXcv2Cs/ZYaHWEdHGQbD8n5RrfQGDhOt70Oi3TtmCI0nvVLRgYdo/y791zb18UaiK8yC5mQEs2AaBcUWC/CKAgrvsi+GbsQSg9BYUb3ndNbZK7SYxWTJvpaki5RWtPAG5uPcNnkQQyJb5fJNnC8HgJzslRbEtVFerk3x4w6wl6qa5XzFoSV/1owhkEx4Zw3ZgBnj3LQUqMj2g8OOroF3rheB9Jvel/fCLlB/PRrOEISIzOfc/4GSin9mTgZoC6raeC7I2WcZ6wHh3hVQYjIAhHZLyIHReR+O+vvFZF9IrJLRL4QkWE265pFZIflb4U35fRZ9s3oSwEJfDdTXSUcWqPjKgFUZGSPl9fnUNfUzJ2O0jxTpsH339a59q9doTvzdkeTvvbEp7W1IJRyycVkJaZvKJ/eey7P3uzmcBvbaurju+D1q3Sa7S0fQGSCe8cEJDiEz+JvYHDdft0bzRlqiqCxxmkL4psDRbQomD/GKAhHeE1BiEgw8CRwCTAOuEFExrXbbDswXSmVDiwDbNtp1iqlJlv+PDSUwQG+yr6JGghDZursqUDmwKfQ3BDw7qWqukZe3pDLReMGMnJAlOMNU+fooTBF++H1a+DYd9Anunutz7g0OFmilTNoRdXS6LKCAOjXJ8S51tb2sLqYcr7W09TCouAHKzzyWVSMupLjKp6Wb/7h3A7W2JCTFsSXmYUkRIYxyd7oWAMAnVT/dIkZwEGl1CEAEXkLuAxoTb5WSn1ls/0m4CYvyuOQut3L6TNgHOKL7JuxC3X7gWPbnc688Dv2vq9TK4fM8LUkXeL1zUeorGviznlODJkfeYEeDrP0Fj1KNmV691pP1krhslw9Fc+JIjmvEB6rOw9sexkiB2jlEOtCq+wOGDekP8+vu5Q/HvkPHNkEQ2d1vIPV5eaEBdHcovg6q4jzxgxwPe7Si/CmgkgBjtq8zgNmdrD9j4HVNq/DRWQr0AQ8opRa7nkRoawwn5j8b3kh+Gr2vLWdOSMSmTMqsftyosdYFMRz87rnfN5i2pLOc9z9mLrGZl5Ym8PcUYlMcrZZ29iFekjMez+BAR6YW+4KtrUQtgqiuyvYRSB2iHbv3PKBR1OcJw6O5ZfN8/l/ESsJX/uYDnh3RFkOIE4pqO1Hyig/2ch5xr3UId5UEE4jIjcB04FzbRYPU0rli8hw4EsR2a2Uym63323AbQBDh7p31xIaHsG29AcpqBnB+oMlfLBDZ6ekJkRw1shE5oxIZPaIBOIjvdTCOT4Nvv+OnhIWqEiQJZ4SuLyz9SjF1fXcOW+KazumX6MziuK6uQq3fdvv1kZ9Psjnv/J56BPl8fqXQTHh9I2MZk3cVSw48KIuVOwoCaI0R8dEnEip/TKzkOAgYe6ojosKezveVBD5gG2Hs8GWZW0QkQuA3wHnKqVak6mVUvmWx0MisgaYArRREEqp54DnAKZPn+5Wrmi/6DjOvOoXnAn8TimyCqpZf7CYDdnFrNhxjDc2H0EExiVHM2dkImeNSGBGWnznvXlcIVDmJvRQGptbePabQ0wdGsus4fGuH2DYbM8L1Rnh0boozhqodqLNhtcYNNkrhxURJqTE8EL5BSwIWwrr/heufsnxDmU5Tgeov8wsZPqwOOdbivRSvKkgtgCjRCQNrRiuB75vu4GITAGeBRYopQptlscBJ5VS9SKSCMyhbQDbK4gIo5OiGJ0UxY/OTqOpuYWdeRVsOFjM+uxiXl6fy3PfHCI0WJgyJI45IxOZMzKBSUNi3Q/yGXzOyp3HyCur5U+LxwdUq+c2qa7VBToW0KeD4HoAMjElmmcOFtN07o8I2fQEzP+dY0ulNMepm61j5bVknqjiNx0NQzIAXlQQSqkmEbkb+AQIBl5SSu0VkYeArUqpFcDfgX7AO5Yf5hFLxtJY4FkRaUFnWj2ilNpn90ReJCQ4iGnD4pg2LI57zh9FbUMzWw+Xsv5gCesPFvP4F1n87+fQNzS4S3ciwUHCjbOGcse5I7r9ApVVUMVv3tvNOaP6c895I3tdwK6lRfHUmmzGJEUFnj86Pg2OfqufVxfoAHUgKTgnmJgSS3OLYl/qTaRveRbWPw6LTx8LS0MN1BQ6ZUFY+08F3P/bB3g1BqGU+gj4qN2yP9o8v8DBfhsAv6u46hsWzNxR/Vv9luUnG9h0qITNOaWcrHdzuAmQX17L3z7ez44j5fzj2klEhXeP2btq1zH+37JdKAXbDpex42gZj183hZiI3mN2f7qvgIOF1fzf9ZMDy3oAncm05z09A6G6ILBbrDtg4mBdaLejNIz0KTfrbKlz74eYdtP9XEhx/SqzkMFxfRk5oJ9nhe2B+EWQOlCJjQhjwYRkFkzoWnqqUooX1+Xw8OpMLntyPc/eNI1RA73nKmhqbuFvn+znOYvf/embpvHZvgL+tHIvi/61jmdvnsbY5G7oSupjlFI8teYgwxIiuHRiAKYYx6Xp2QflR3SQujtajXczg2LCiY8M0zOXL7hHzwvf+C9Y8HDbDVtTXFM7PF5dYzPrD5ZwzfTBgXdD4AOM49wPEBFunTuc12+dSWVtI5c/uZ7Vu10cBO8kxdX13Pzitzz3zSFunjWMt26bzcDocG6yPK9vauaKp9bzwY7T8gl6HOsOFrMrr4Lbzx1BSCDGkGxTXatO+CZA7WVE5NSM6rhhkH6ttiJqittuWOZcDcSmQyXUNjab6mknCcBfRc9l1vAEVt5zNmckRXHH69/x8OoMmpo9Ny9ix9FyFj2xju+OlPHoNZP48+X/v707D4+qvBc4/v1lJyEZEpJATNjCviSsopKAuDyIXlzw4VqseFGpK/aprbbF2mprr72t2t5bvRTQarXWViuIgsWrKAFFZVUIWwJJZAlEJiGRGPaQ9/5xTsgYZrJNZsnk93meeTJzznvm/HJyZn4573ve9x1BVETDKTC2TyLLv59LdkY3fvDaFn61fAdn2nH/wWZ+XhE9EqK5cUx684WDUf2XYXmhNY9619CrYgLISnewx1ljDf2d+0M4cwLWL/x2ocovrXGfYpu+Cy2vwElMZBiXZLZ9GJDORBNEkElzdOG1uy7mlot6s2hNCbP/soEjbqaGbK1/bNjPTQs/IzxMWHLvBGaMzXBbLjU+hle/dxF35PTjL5/s5Zbn1+P85qTX+w82m/dVsq6kkjsnZrZu/oNgEt/TunPpwHrrtb97UfvJiHSH1VBdVm0NqT50Gqx/rmGYEWjRKK7GGFYVOsnpn9whpxYNBE0QQSg6Ipwnpmfx5IxsNu6t4tpn15LflukXsepc5y3J5+E3t3FRZhLL789lRHrTI2xGhofx6LXD+OPMUeQftK46Nu+ratP+g9Wf8opJjI3k5vHtMyxEQIhYde777QQRgo3UANl2Q/W2UntuiNwfwamjVntEvarmR3EtLq/hQOUJrV5qBU0QQeymcb1Ycs8ERIQZCz/jnxsPNL+Ri0Nfn+A7iz7jtY0HmHtZf166fTyJregRfv2odJbel0NMZDgzn/uMV9btw4TA3BW7yqr5sMDJ7Tn9iIvu4PdpJPa1ZiaEkL2CSHPE0D0uymqHAEgfA/0vh8/mW9VNZ2uthvpmGqjPTQ6kCaLFNEEEuawMB8u/n8v4vkn8xL4SOFXb/C21nxZVMO3ZtRSXH2PRrWP58VVDCG9DH4ehaQksm5tL7oBkfvHWdh56I7/100B6YIyh8KtveGXdPvYfOd4u79kSC1YXExcVzuxL+vptnz7j+l9zCDZSQ0OP6u0HXWaXm/ig1e/hi79Zw9TU1TZbxbSqwMmQnvE693QrdPB/nzqHpLgoXr5jPE+/X8iC1cXsLKtm4awxpDnOP9GNMTz/cQm/fbeAzJSuLLp1LP1TvLvf2xEbyQuzL+SZVXv4nw/2UPBVNQtnjT1/Qp0WOFB5nE+LK/ik6AifFh+hwm5fGX5BAm/PzfH53UR7K47xTv4h7pyUGRr9Pc59KYo1om6Iys5wMD+vnBOnz9IlKhz65FhD5X/yTMOVQxNVTNUnz7BpbxV3us4SqJqlCaKDCA8Tfjp1CCMzHDz4z61Me2Ytz353NBP6N8wCVnOqlp8uzudf28q4JqsnT84YSdd2qkIJCxMeuHIQWekOHnh9C9f+71qemTmaSYOa/lKqPHbaJSFUsM++UkjuGk3OgO7kDEjmzNk6Hlm6nRc/+ZK7Jvl2yPVFHxUTER7GnNyWT0sZ1Oq/FGO7Q3gIJDwPRqQ7qDOws6yasX0SrfaXiQ/C32+Cj562CjVxBfHx7gpq64z2nm4lTRAdzNQRaQxI7crdr2zm1hc2MG/qEL43sR8lFce4+5XNlJTX8PDVQ7hrUqZPOgJdMbQHy+/P5Z6/bWb2Xzbw0JTB3De5YYiQY6dq2bC30hq/quiIdecJEB8dwUWZ3bltQl9yBiQzMLXruW2MMeQVlPOHlbuZOjyN3t1bf2XSEl8dPcnizaV858JepMaHyBzE9V+KIVq9VC/LvrFi+8GjVoIAGDgFeoyAA+sgPKphdjs3VhU4cXSJZHRLh3JXgCaIDmlAajxvzc3hoTe28sSKXXxcVMHn+6qIigjjlTkXkTOgDXMLt0Lf5DjevG8C85Zs46n3Ctl64GuGXZDAp0VH+OJAFWfOGqLscawemjKICQOSyU53eKw+EhF+fcNwrvz9Gh55axt/vWO8T5Lb8x+XUGfgbh9fpfhVt96AWLMThrA0RwzJXaPIL3VphxCx+kUsmQPd+nicj6SuzrBmt5NLB6V0zA6RAaQJooOKj4lk4ayxLFxTwlPvFTAi3cGCWWP91gAXGxXBH2eOYmSvbvxmxS5W7jpMVrqDObmZ5A5IZlzfxFbda57m6MJPpg7hsWU7eHvLIW4Y3b6d1yqPnebv6/dz/cgL2tR2ErQioiB1KHRvwSx4HZjbhmqA4dNhze+sY+BB/sGjVNSc1uqlNtAE0YGJCPdO7s+1I9NIjY/5Vq9of+1/Tm4/pmWnERMR7nWj76yL+/DWloM8/s5OJg1KaddJml765EtOnDnLvZND6Oqh3m3/gogQqTJrQla6g492uzRUg3XVcMd7Tba/rCpwEiZwaTPtZep8er0VAjISY/2eHFz1SIhplzuCwsOE/7oxi+oTZ3jiX7vaITLLNyfP8NKne7lqeA+fDoIYMLFJEBVCV0UeZJ1rqG50FRGb1OQ8GHkFTkb3TmxVHyBl0QShgsqQngncc2l/lnxeyto9Fc1v0AKvrt9P9cla7psc2tUwoS6rcY/qFnBWn2TbwaNavdRGmiBU0Ln/8gH0S47jZ0u3ceK0d53yTp45y58//pKJA5MZqXewdGg9E6yG6m0Hq5svbFtdWA7AZYM1QbSFJggVdGIiw/nN9Cz2Vx7njx/u8eq93thcSkXNqdBse+hkGob+bvm4ZKsKnKQ5YhiaFoJVi36gCUIFpUv6d+emcRk8/3EJOw+1/D9GV2fO1rFoTTGje3fT4Z1DRFa6gyJnDcdP1zZb9nRtHWuLKpg8OFUnB2ojTRAqaP3smqEkxkby8Jv5nK1r/SCBy7ceorTqBHMnD9AviBBR36N6V1nz/zRs3FtJzalabX/wgiYIFbS6xUbx6LXD2Vp6lJc/3duqbevqDH9aXcyQnvH6BRFCsjOsdqT8FjRUrypwEhURRs4AvXpsK00QKqhdm53GZYNTePr9QkqrWj7i6/s7D1PkrOHeyf0Ja8Motio49UiIJrlrdMPQ303IK3BycWZ3YqO0u1dbaYJQQc0ahmMEAI++vaNF81EYY/jT6iL6dI/l37LSfB2i8iOroTrh/B7VjeytOEZJxTEuH6yd47yhCUIFvYzEWB6cMphVBU7eyS9rtvzaogryS49yz6X9deydEJSV0a3Zhur6yYEuHxLaY1T5mn56VIdw24S+ZGc4+NXyHRw9fqbJsvPziuiREM2NY9p3PCcVHM71qG7i7ra8Qif9U+J8NjJwZ6EJQnUI9cNwVB0/w29WeB6GY/O+KtaVVHLnxEyiI3Ri+lBUP/S3p3aIY6dqWV9SqTcntAOfJggRmSoihSJSJCLz3Kz/kYjsFJF8EflQRPq4rJstInvsx2xfxqk6huEXOPjexH68vukAnxUfcVtmweoiusVGcvP43n6OTvnLuYZqD3cyrS2q4PTZOp17uh34LEGISDgwH7gaGAbcLCLDGhX7AhhnjMkGFgNP2tsmAY8BFwHjgcdEJNFXsaqO44ErBtE7KZZHlm47b27sXWXVfLDLye0T+hHXTjPpqeAjImRnODxeQeQVOImPjuDCvkl+jiz0+PIKYjxQZIwpMcacBl4DrnctYIzJM8bU37u4Dsiwn18FrDTGVBpjqoCVwFQfxqo6iC5R4TwxfQQlFceYn1f0rXULVhcTFxXO7Al9PGytQsWIdAfF5ec3VBtjyCt0MnFQMpF6g4LXfHkE04EDLq9L7WWezAHebc22InKXiGwSkU3l5eVehqs6iokDU7hxdDoLVhdT+NU3gHVb4zv5h5h1cR+6xeqwzqHOU0P1jkPVHK4+pYPztZOgSLEiMgsYBzzVmu2MMc8ZY8YZY8alpOj9zp3Jz6cNIz4mgnlv5lNXZ1j0UTER4WHMyfU8cb0KHdn20N+Ne1Tn2be3TtYE0S58mSAOAr1cXmfYy75FRK4EHgGuM8acas22qvNKioviF9OG8cX+r/n9ykIWby7lpnEZpCaE/sxqypqkKiU++rwOc6sKnYzMcJASHx2gyEKLLxPERmCgiPQTkShgJrDMtYCIjAYWYSUHp8uq94ApIpJoN05PsZcpdc700elMHJjM/Lxi6gzcPUmH9O5MrKG/GxLEkZpTbDnwtd691I58liCMMbXA/Vhf7LuAfxpjdojI4yJynV3sKaAr8IaIbBGRZfa2lcCvsZLMRuBxe5lS54gIT9yQRZfIcKaPTqdXknaK6kyy0h0Ulddw7JTVUL1mdznGoP0f2pFP7wU0xqwAVjRa9qjL8yub2PZF4EXfRadCQe/usaz58WSdb7gTykp3YAzsLKvmwr5JrCpwktw1mhEXOAIdWsgIikZqpbyRmhCjtzR2Qq5zVNeereOj3eVcNjhFR+9tR/qpUkp1SD0SYkiNt4b+3ryviuqTOjlQe9PupkqpDqu+oTo1IZrIcCF3YHKgQwopmiCUUh3WiHQHqwqdnK6t48K+ScTHRAY6pJCiVUxKqQ4rO8NqqN5feVyrl3xAE4RSqsOqH/ob0P4PPqBVTEqpDivVbqjuEhVOZnJcoMMJOZoglFId2sPXDCEuKgIRvb21vWmCUEp1aNNHZzRfSLWJtkEopZRySxOEUkoptzRBKKWUcksThFJKKbc0QSillHJLE4RSSim3NEEopZRySxOEUkopt8QYE+gY2oWIlAP7vHiLZKCincLxBY3POxqfdzQ+7wRzfH2MMSnuVoRMgvCWiGwyxowLdByeaHze0fi8o/F5J9jj80SrmJRSSrmlCUIppZRbmiAaPBfoAJqh8XlH4/OOxuedYI/PLW2DUEop5ZZeQSillHJLE4RSSim3OlWCEJGpIlIoIkUiMs/N+mgRed1ev15E+voxtl4ikiciO0Vkh4j8wE2ZySJyVES22I9H/RWfSwx7RWSbvf9NbtaLiDxjH8N8ERnjx9gGuxybLSJSLSIPNCrj12MoIi+KiFNEtrssSxKRlSKyx/6Z6GHb2XaZPSIy24/xPSUiBfbfb6mIdPOwbZPngg/j+6WIHHT5G17jYdsmP+8+jO91l9j2isgWD9v6/Ph5zRjTKR5AOFAMZAJRwFZgWKMy9wEL7eczgdf9GF8aMMZ+Hg/sdhPfZOCdAB/HvUByE+uvAd4FBLgYWB/Av/dXWJ2AAnYMgUnAGGC7y7IngXn283nA79xslwSU2D8T7eeJfopvChBhP/+du/haci74ML5fAg+14O/f5OfdV/E1Wv974NFAHT9vH53pCmI8UGSMKTHGnAZeA65vVOZ64GX7+WLgCvHTRLfGmDJjzOf282+AXUC6P/bdzq4H/mos64BuIpIWgDiuAIqNMd70rveaMeYjoLLRYtfz7GXgBjebXgWsNMZUGmOqgJXAVH/EZ4x53xhTa79cBwRsTk8Px68lWvJ591pT8dnfHTcB/2jv/fpLZ0oQ6cABl9elnP8FfK6M/QE5CnT3S3Qu7Kqt0cB6N6svEZGtIvKuiAz3a2AWA7wvIptF5C4361tynP1hJp4/mIE+hj2MMWX286+AHm7KBMtxvAPritCd5s4FX7rfrgJ70UMVXTAcv4nAYWPMHg/rA3n8WqQzJYgOQUS6AkuAB4wx1Y1Wf45VZTISeBZ4y9/xAbnGmDHA1cBcEZkUgBiaJCJRwHXAG25WB8MxPMdYdQ1Bea+5iDwC1AKveigSqHNhAdAfGAWUYVXjBKObafrqIeg/S50pQRwEerm8zrCXuS0jIhGAAzjil+isfUZiJYdXjTFvNl5vjKk2xtTYz1cAkSKS7K/47P0etH86gaVYl/KuWnKcfe1q4HNjzOHGK4LhGAKH66vd7J9ON2UCehxF5DZgGnCLncTO04JzwSeMMYeNMWeNMXXA8x72G+jjFwHcCLzuqUygjl9rdKYEsREYKCL97P8wZwLLGpVZBtTfLTIDWOXpw9He7PrKF4Bdxpg/eCjTs75NRETGY/39/JnA4kQkvv45VmPm9kbFlgH/Yd/NdDFw1KU6xV88/ucW6GNocz3PZgNvuynzHjBFRBLtKpQp9jKfE5GpwE+A64wxxz2Uacm54Kv4XNu0pnvYb0s+7750JVBgjCl1tzKQx69VAt1K7s8H1h02u7HubnjEXvY41gcBIAarWqII2ABk+jG2XKyqhnxgi/24BrgHuMcucz+wA+uOjHXABD8fv0x731vtOOqPoWuMAsy3j/E2YJyfY4zD+sJ3uCwL2DHESlRlwBmsevA5WO1aHwJ7gA+AJLvsOODPLtveYZ+LRcDtfoyvCKv+vv48rL+z7wJgRVPngp/ie8U+t/KxvvTTGsdnvz7v8+6P+OzlL9Wfcy5l/X78vH3oUBtKKaXc6kxVTEoppVpBE4RSSim3NEEopZRySxOEUkoptzRBKKWUcksThFIBZI8u+06g41DKHU0QSiml3NIEoVQLiMgsEdlgj92/SETCRaRGRP5brPk7PhSRFLvsKBFZ5zKfQqK9fICIfGAPFPi5iPS3376riCy252B41aWn92/Fmh8kX0SeDtCvrjoxTRBKNUNEhgLfAXKMMaOAs8AtWL22NxljhgNrgMfsTf4K/NQYk43V47d++avAfGMNFDgBqwcuWCP3PgAMw+phmyMi3bGGkRhuv89/+va3VOp8miCUat4VwFhgoz072BVYX+R1NAzG9jcgV0QcQDdjzBp7+cvAJHvcnXRjzFIAY8xJ0zDO0QZjTKmxBp/bAvTFGmr+JPCCiNwIuB0TSSlf0gShVPMEeNkYM8p+DDbG/NJNubaOW3PK5flZrNncarFG91yMNarq/7XxvZVqM00QSjXvQ2CGiKTCuTml+2B9fmbYZb4LrDXGHAWqRGSivfxWYI2xZgksFZEb7PeIFpFYTzu05wVxGGtI8h8CI33xiynVlIhAB6BUsDPG7BSRn2PN/hWGNXLnXOAYMN5e58RqpwBrCO+FdgIoAW63l98KLBKRx+33+PcmdhsPvC0iMVhXMD9q519LqWbpaK5KtZGI1BhjugY6DqV8RauYlFJKuaVXEEoppdzSKwillFJuaYJQSinlliYIpZRSbmmCUEop5ZYmCKWUUm79P2KeZOosmqnLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "WSdMbX-nnRdj",
        "outputId": "3f6b1c55-eb84-431b-bcca-d4074c9f624f"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['training set', 'validation set'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1pPWRN0lbwdW",
        "outputId": "475ae201-3a66-4ccb-86e4-4df6f24fb3b6"
      },
      "source": [
        "y_prob = model.predict(test_set)\n",
        "y_pred = np.argmax(y_prob,axis=1)\n",
        "\n",
        "print(classification_report(test_set.classes,y_pred))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.43      0.12      0.19        25\n",
            "           1       0.20      0.04      0.07        25\n",
            "           2       0.00      0.00      0.00        25\n",
            "           3       0.26      0.92      0.41        25\n",
            "\n",
            "    accuracy                           0.27       100\n",
            "   macro avg       0.22      0.27      0.17       100\n",
            "weighted avg       0.22      0.27      0.17       100\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-8gdjTbBegx"
      },
      "source": [
        "## Jargões\n",
        "\n",
        "Stride: pulo da evolução no processo de convolução entre uma imagem e um fitlro. O ideal é utilizarmos stride 1 ou 2 (caso a imagem seja grande). No stride 1, cada pixel da imagem é considerado na convolução, enquanto que no stride 2, o filtro é passado sobre a imagem a cada 2 pixels.\n",
        "\n",
        "Stride horizontal Stride vertical"
      ]
    }
  ]
}