Plant_Pathology_2020_FGVC7.ipynb

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    "colab": {
      "name": "Plant Pathology 2020 - FGVC7.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/MachineLearningHackathons/blob/master/Plant_Pathology_2020_FGVC7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wiAEScieHJ0",
        "colab_type": "text"
      },
      "source": [
        "# Transfer Learning using 5 State of the Art Pre -Trained Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xUrIOoGeHJ2",
        "colab_type": "text"
      },
      "source": [
        "## Accuracy and Loss Achieved"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVs9NH52eHJ3",
        "colab_type": "text"
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      "source": [
        "Model ------------ Score(on Kaggle)\n",
        "\n",
        "InceptionV3 ------ 0.876 \n",
        "\n",
        "ResNet50 --------- 0.935\n",
        "\n",
        "VGG16 ------------ 0.885\n",
        "\n",
        "VGG19 ------------ 0.859\n",
        "\n",
        "Xception --------- 0.878"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1fzZb6geHJ4",
        "colab_type": "text"
      },
      "source": [
        "## Approach"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KZxjgF8eHJ5",
        "colab_type": "text"
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      "source": [
        "Approach\n",
        "\n",
        "Read the train.csv and train images \n",
        "\n",
        "Clean and Pre - process\n",
        "\n",
        "Resize images according to the input of Pre - trained model\n",
        "\n",
        "X --> Load train images and convert them into numpy array for processing\n",
        "\n",
        "y --> Load the labels and convert them into float type integer\n",
        "\n",
        "Split the above data into train and validation\n",
        "\n",
        "Data Augmentation \n",
        "\n",
        "Train and Validation Generator\n",
        "\n",
        "Load Pretrained Network (finetune and addition according to need)\n",
        "\n",
        "Train model with given training set\n",
        "\n",
        "Test the model on test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goUuZBGueHJ5",
        "colab_type": "code",
        "outputId": "4c7f09d5-ea49-4e04-937a-2a84afb3cc6e",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import cv2\n",
        "import numpy as np\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Convolution2D,Dense,MaxPool2D,Activation,Dropout,Flatten\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "from keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model\n",
        "import keras\n",
        "from keras import optimizers"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIjb1q6deHJ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#input data\n",
        "train=pd.read_csv(\"./train.csv\")\n",
        "test=pd.read_csv(\"./test.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArGsLWM6eHKA",
        "colab_type": "code",
        "outputId": "eaaa79be-8d41-4445-bd35-4a117443d826",
        "colab": {}
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>healthy</th>\n",
              "      <th>multiple_diseases</th>\n",
              "      <th>rust</th>\n",
              "      <th>scab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Train_0</td>\n",
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              "      <td>Train_2</td>\n",
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              "      <td>Train_3</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Train_4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "  image_id  healthy  multiple_diseases  rust  scab\n",
              "0  Train_0        0                  0     0     1\n",
              "1  Train_1        0                  1     0     0\n",
              "2  Train_2        1                  0     0     0\n",
              "3  Train_3        0                  0     1     0\n",
              "4  Train_4        1                  0     0     0"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTT7yhuTeHKC",
        "colab_type": "code",
        "outputId": "4ee87756-43ee-4b6d-f53c-ca139cbdfe92",
        "colab": {}
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test_0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test_4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  image_id\n",
              "0   Test_0\n",
              "1   Test_1\n",
              "2   Test_2\n",
              "3   Test_3\n",
              "4   Test_4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-qMFm98eHKF",
        "colab_type": "text"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyPQO2BXeHKG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# making \"image_id\" names to match with input images by adding extension\n",
        "train['image_id']=train['image_id']+'.jpg'\n",
        "test['image_id']=test['image_id']+'.jpg'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_XMhwx0eHKI",
        "colab_type": "code",
        "outputId": "f9698aa5-18d4-4acf-c835-d93995d91c1f",
        "colab": {}
      },
      "source": [
        "print(train.head(5))\n",
        "print(\"---------------------------------------------------------\")\n",
        "print(test.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      image_id  healthy  multiple_diseases  rust  scab\n",
            "0  Train_0.jpg        0                  0     0     1\n",
            "1  Train_1.jpg        0                  1     0     0\n",
            "2  Train_2.jpg        1                  0     0     0\n",
            "3  Train_3.jpg        0                  0     1     0\n",
            "4  Train_4.jpg        1                  0     0     0\n",
            "---------------------------------------------------------\n",
            "     image_id\n",
            "0  Test_0.jpg\n",
            "1  Test_1.jpg\n",
            "2  Test_2.jpg\n",
            "3  Test_3.jpg\n",
            "4  Test_4.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbyBwvRPeHKL",
        "colab_type": "code",
        "outputId": "4d4ffcd1-0b25-4f60-b46f-2cd34d41b352",
        "colab": {}
      },
      "source": [
        "#analyzing dataset\n",
        "train.count()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "image_id             1821\n",
              "healthy              1821\n",
              "multiple_diseases    1821\n",
              "rust                 1821\n",
              "scab                 1821\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gq2c4XuyeHKQ",
        "colab_type": "code",
        "outputId": "e0c0b6e5-5cf9-4472-94ab-5bfde6a4edf3",
        "colab": {}
      },
      "source": [
        "#checking for null values\n",
        "train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "image_id             0\n",
              "healthy              0\n",
              "multiple_diseases    0\n",
              "rust                 0\n",
              "scab                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXGAp5nbeHKS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#no null values!"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kynjzqg6eHKV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#input shape according to pre trainied network\n",
        "# ResNet50 --> 384\n",
        "# VGG16 -- > 224\n",
        "# VGG19 --> 224\n",
        "# Xception --> 224\n",
        "# InceptionV3 --> 224\n",
        "\n",
        "img_size = 384"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lh1yCS6ueHKY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#resizing and shaping images for transfer learning with ResNet50\n",
        "img=[]\n",
        "filename=train.image_id\n",
        "for file in filename:\n",
        "    image=cv2.imread(\"./images/\"+file)\n",
        "    res=cv2.resize(image,(img_size,img_size))\n",
        "    img.append(res)\n",
        "img=np.array(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dThZAbhkeHKd",
        "colab_type": "code",
        "outputId": "430efdb4-da96-41e9-ecf6-92f9d7f17f41",
        "colab": {}
      },
      "source": [
        "img.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1821, 384, 384, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnA3LHXoeHKf",
        "colab_type": "code",
        "outputId": "128d2544-81f7-4721-ff02-6669f64d40aa",
        "colab": {}
      },
      "source": [
        "#converting labels into float\n",
        "train_labels = np.float32(train.loc[:, 'healthy':'scab'].values)\n",
        "train_labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 1.],\n",
              "       [0., 1., 0., 0.],\n",
              "       [1., 0., 0., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0.],\n",
              "       [0., 0., 0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4EhDn4oeHKi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#splitting data for training and validation\n",
        "train, val = train_test_split(train, test_size = 0.15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8E_PJspaeHKk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#data augmentation\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator( horizontal_flip=True,\n",
        "                                    vertical_flip=True,\n",
        "                                    rotation_range=10,\n",
        "                                    width_shift_range=0.1,\n",
        "                                    height_shift_range=0.1,\n",
        "                                    zoom_range=.1,\n",
        "                                    fill_mode='nearest',\n",
        "                                    shear_range=0.1,\n",
        "                                    rescale=1/255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbp-_fRjeHKn",
        "colab_type": "code",
        "outputId": "4035b443-a14a-49df-a7fd-8cf737a0c483",
        "colab": {}
      },
      "source": [
        "train_generator=train_datagen.flow_from_dataframe(train,directory='./images/',\n",
        "                                                      target_size=(img_size,img_size),\n",
        "                                                      x_col=\"image_id\",\n",
        "                                                      y_col=['healthy','multiple_diseases','rust','scab'],\n",
        "                                                      class_mode='raw',\n",
        "                                                      shuffle=False,\n",
        "                                                       subset='training',\n",
        "                                                      batch_size=5)\n",
        "\n",
        "\n",
        "val_generator=train_datagen.flow_from_dataframe(val,directory='./images/',\n",
        "                                                      target_size=(img_size,img_size),\n",
        "                                                      x_col=\"image_id\",\n",
        "                                                      y_col=['healthy','multiple_diseases','rust','scab'],\n",
        "                                                      class_mode='raw',\n",
        "                                                      shuffle=False,\n",
        "                                                      batch_size=5)\n",
        "\n",
        "test_generator=train_datagen.flow_from_dataframe(test,directory='./images/',\n",
        "                                                      target_size=(img_size,img_size),\n",
        "                                                      x_col=\"image_id\",\n",
        "                                                      y_col=None,\n",
        "                                                      class_mode=None,\n",
        "                                                      shuffle=False,\n",
        "                                                      batch_size=5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1547 validated image filenames.\n",
            "Found 274 validated image filenames.\n",
            "Found 1821 validated image filenames.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpVcyFWoeHKq",
        "colab_type": "text"
      },
      "source": [
        "# InceptionV3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bKqyTogeHKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.applications import InceptionV3\n",
        "model_finetuned = InceptionV3(include_top=False, weights='imagenet', input_shape=(img_size,img_size,3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feewMMQMeHKs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = model_finetuned.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(128, activation=\"relu\")(x)\n",
        "x = Dense(64, activation=\"relu\")(x)\n",
        "predictions = Dense(4, activation=\"softmax\")(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJJA55gQeHKv",
        "colab_type": "code",
        "outputId": "00e1e52a-9cad-47ab-d13d-999217893d24",
        "colab": {}
      },
      "source": [
        "adam = keras.optimizers.Adam(learning_rate=0.000005, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "model_finetuned = Model(inputs=model_finetuned.input, outputs=predictions)\n",
        "model_finetuned.compile(optimizer=adam,\n",
        "                  loss = 'categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "model_finetuned.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 111, 111, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 111, 111, 32) 96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 111, 111, 32) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 109, 109, 32) 9216        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 109, 109, 32) 96          conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 109, 109, 32) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 109, 109, 64) 18432       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 109, 109, 64) 192         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 109, 109, 64) 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 54, 54, 64)   0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 54, 54, 80)   5120        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 54, 54, 80)   240         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 54, 54, 80)   0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 52, 52, 192)  138240      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 52, 52, 192)  576         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 52, 52, 192)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 25, 25, 192)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 25, 25, 64)   192         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 25, 25, 64)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 25, 25, 48)   9216        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 25, 25, 96)   55296       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 25, 25, 48)   144         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 25, 25, 96)   288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 25, 25, 48)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 25, 25, 96)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 25, 25, 192)  0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 25, 25, 64)   76800       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 25, 25, 96)   82944       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 25, 25, 32)   6144        average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 25, 25, 64)   192         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 25, 25, 64)   192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 25, 25, 96)   288         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 25, 25, 32)   96          conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 25, 25, 64)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 25, 25, 64)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 25, 25, 96)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 25, 25, 32)   0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 25, 25, 256)  0           activation_6[0][0]               \n",
            "                                                                 activation_8[0][0]               \n",
            "                                                                 activation_11[0][0]              \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 25, 25, 64)   192         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 25, 25, 64)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 25, 25, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 25, 25, 96)   55296       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 25, 25, 48)   144         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 25, 25, 96)   288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 25, 25, 48)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 25, 25, 96)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 25, 25, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 25, 25, 64)   76800       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 25, 25, 96)   82944       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 25, 25, 64)   16384       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 25, 25, 64)   192         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 25, 25, 64)   192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 25, 25, 96)   288         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 25, 25, 64)   192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 25, 25, 64)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 25, 25, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 25, 25, 96)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 25, 25, 64)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 25, 25, 288)  0           activation_13[0][0]              \n",
            "                                                                 activation_15[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 25, 25, 64)   192         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 25, 25, 64)   0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 25, 25, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 25, 25, 96)   55296       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 25, 25, 48)   144         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 25, 25, 96)   288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 25, 25, 48)   0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 25, 25, 96)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 25, 25, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 25, 25, 64)   76800       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 25, 25, 96)   82944       activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 25, 25, 64)   18432       average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 25, 25, 64)   192         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 25, 25, 64)   192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 25, 25, 96)   288         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 25, 25, 64)   192         conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 25, 25, 64)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 25, 25, 64)   0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 25, 25, 96)   0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 25, 25, 64)   0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 25, 25, 288)  0           activation_20[0][0]              \n",
            "                                                                 activation_22[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "                                                                 activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 25, 25, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 25, 25, 64)   192         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 25, 25, 64)   0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 25, 25, 96)   55296       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 25, 25, 96)   288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 25, 25, 96)   0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 12, 12, 384)  995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 12, 12, 96)   82944       activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 12, 12, 384)  1152        conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 12, 12, 96)   288         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 12, 12, 384)  0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 12, 12, 96)   0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 12, 12, 288)  0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 12, 12, 768)  0           activation_27[0][0]              \n",
            "                                                                 activation_30[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 12, 12, 128)  384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 12, 12, 128)  0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 12, 12, 128)  114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 12, 12, 128)  384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 12, 12, 128)  0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 12, 12, 128)  114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 12, 12, 128)  384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 12, 12, 128)  384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 12, 12, 128)  0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 12, 12, 128)  0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 12, 12, 128)  114688      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 12, 12, 128)  114688      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 12, 12, 128)  384         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 12, 12, 128)  384         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 12, 12, 128)  0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 12, 12, 128)  0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 12, 12, 768)  0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 12, 12, 192)  147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 12, 12, 192)  172032      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 12, 12, 192)  172032      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 12, 12, 192)  576         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 12, 12, 192)  576         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 12, 12, 192)  576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 12, 12, 192)  576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 12, 12, 192)  0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 12, 12, 192)  0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 12, 12, 192)  0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 12, 12, 192)  0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 12, 12, 768)  0           activation_31[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "                                                                 activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 12, 12, 160)  480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 12, 12, 160)  0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 12, 12, 160)  179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 12, 12, 160)  480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 12, 12, 160)  0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 12, 12, 160)  179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 12, 12, 160)  480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 12, 12, 160)  480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 12, 12, 160)  0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 12, 12, 160)  0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 12, 12, 160)  179200      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 12, 12, 160)  179200      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 12, 12, 160)  480         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 12, 12, 160)  480         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 12, 12, 160)  0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 12, 12, 160)  0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 12, 12, 768)  0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 12, 12, 192)  147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 12, 12, 192)  215040      activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 12, 12, 192)  215040      activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 12, 12, 192)  576         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 12, 12, 192)  576         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 12, 12, 192)  576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 12, 12, 192)  576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 12, 12, 192)  0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 12, 12, 192)  0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 12, 12, 192)  0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 12, 12, 192)  0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 12, 12, 768)  0           activation_41[0][0]              \n",
            "                                                                 activation_44[0][0]              \n",
            "                                                                 activation_49[0][0]              \n",
            "                                                                 activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 12, 12, 160)  480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 12, 12, 160)  0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 12, 12, 160)  179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 12, 12, 160)  480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 12, 12, 160)  0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 12, 12, 160)  179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 12, 12, 160)  480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 12, 12, 160)  480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 12, 12, 160)  0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 12, 12, 160)  0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 12, 12, 160)  179200      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 12, 12, 160)  179200      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 12, 12, 160)  480         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 12, 12, 160)  480         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 12, 12, 160)  0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 12, 12, 160)  0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, 12, 12, 768)  0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 12, 12, 192)  147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 12, 12, 192)  215040      activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 12, 12, 192)  215040      activation_58[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 12, 12, 192)  576         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 12, 12, 192)  576         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 12, 12, 192)  576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 12, 12, 192)  576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 12, 12, 192)  0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 12, 12, 192)  0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 12, 12, 192)  0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 12, 12, 192)  0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 12, 12, 768)  0           activation_51[0][0]              \n",
            "                                                                 activation_54[0][0]              \n",
            "                                                                 activation_59[0][0]              \n",
            "                                                                 activation_60[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 12, 12, 192)  576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 12, 12, 192)  0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 12, 12, 192)  258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 12, 12, 192)  576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 12, 12, 192)  0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 12, 12, 192)  258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 12, 12, 192)  576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 12, 12, 192)  576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 12, 12, 192)  0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 12, 12, 192)  0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 12, 12, 192)  258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 12, 12, 192)  258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 12, 12, 192)  576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 12, 12, 192)  576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 12, 12, 192)  0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 12, 12, 192)  0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, 12, 12, 768)  0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 12, 12, 192)  258048      activation_63[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 12, 12, 192)  258048      activation_68[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 12, 12, 192)  576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 12, 12, 192)  576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 12, 12, 192)  576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 12, 12, 192)  576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 12, 12, 192)  0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 12, 12, 192)  0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 12, 12, 192)  0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 12, 12, 192)  0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 12, 12, 768)  0           activation_61[0][0]              \n",
            "                                                                 activation_64[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "                                                                 activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 12, 12, 192)  576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 12, 12, 192)  0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 12, 12, 192)  258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 12, 12, 192)  576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 12, 12, 192)  0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 12, 12, 192)  258048      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 12, 12, 192)  576         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 12, 12, 192)  576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 12, 12, 192)  0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 12, 12, 192)  0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, 5, 5, 320)    552960      activation_71[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 5, 5, 192)    331776      activation_75[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 5, 5, 320)    960         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 5, 5, 192)    576         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 5, 5, 320)    0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 5, 5, 192)    0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 5, 5, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 5, 5, 1280)   0           activation_72[0][0]              \n",
            "                                                                 activation_76[0][0]              \n",
            "                                                                 max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 5, 5, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 5, 5, 448)    1344        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 5, 5, 448)    0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 5, 5, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 5, 5, 384)    1548288     activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 5, 5, 384)    1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 5, 5, 384)    1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 5, 5, 384)    0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 5, 5, 384)    0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 5, 5, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 5, 5, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 5, 5, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 5, 5, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, 5, 5, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 5, 5, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 5, 5, 384)    1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 5, 5, 384)    1152        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 5, 5, 384)    1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 5, 5, 384)    1152        conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 5, 5, 192)    245760      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 5, 5, 320)    960         conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 5, 5, 384)    0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 5, 5, 384)    0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 5, 5, 384)    0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 5, 5, 384)    0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 5, 5, 192)    576         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 5, 5, 320)    0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 5, 5, 768)    0           activation_79[0][0]              \n",
            "                                                                 activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 5, 5, 768)    0           activation_83[0][0]              \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 5, 5, 192)    0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 5, 5, 2048)   0           activation_77[0][0]              \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_85[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 5, 5, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 5, 5, 448)    1344        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 5, 5, 448)    0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 5, 5, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 5, 5, 384)    1548288     activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 5, 5, 384)    1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 5, 5, 384)    1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 5, 5, 384)    0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 5, 5, 384)    0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 5, 5, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, 5, 5, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 5, 5, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 5, 5, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_9 (AveragePoo (None, 5, 5, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 5, 5, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 5, 5, 384)    1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 5, 5, 384)    1152        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 5, 5, 384)    1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 5, 5, 384)    1152        conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, 5, 5, 192)    393216      average_pooling2d_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 5, 5, 320)    960         conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 5, 5, 384)    0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 5, 5, 384)    0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 5, 5, 384)    0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 5, 5, 384)    0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_94 (BatchNo (None, 5, 5, 192)    576         conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 5, 5, 320)    0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 5, 5, 768)    0           activation_88[0][0]              \n",
            "                                                                 activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 5, 5, 768)    0           activation_92[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_94 (Activation)      (None, 5, 5, 192)    0           batch_normalization_94[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 5, 5, 2048)   0           activation_86[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_2[0][0]              \n",
            "                                                                 activation_94[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 128)          262272      global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 64)           8256        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 4)            260         dense_2[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 22,073,572\n",
            "Trainable params: 22,039,140\n",
            "Non-trainable params: 34,432\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4t2dkgkeHKz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "from keras.callbacks import ReduceLROnPlateau,ModelCheckpoint\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3,patience=3, min_lr=0.000001)\n",
        "checkpoint = ModelCheckpoint(\"./model.{epoch:02d}-{val_loss:.2f}-{val_accuracy:.2f}.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"auto\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "callbacks = [PlotLossesKeras(),reduce_lr,checkpoint]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmvWgNvFeHK1",
        "colab_type": "code",
        "outputId": "55df43e2-feca-431a-a41a-74f3361ec670",
        "colab": {}
      },
      "source": [
        "history = model_finetuned.fit_generator(train_generator,steps_per_epoch=100,epochs=25,validation_data=val_generator,validation_steps=100,callbacks = callbacks,use_multiprocessing=False,shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\tvalidation       \t (min:    0.365, max:    0.854, cur:    0.841)\n",
            "\ttraining         \t (min:    0.370, max:    0.836, cur:    0.792)\n",
            "Loss\n",
            "\tvalidation       \t (min:    0.070, max:    1.411, cur:    0.423)\n",
            "\ttraining         \t (min:    0.533, max:    1.340, cur:    0.622)\n",
            "lr\n",
            "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.07040\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7QIpZtneHK4",
        "colab_type": "code",
        "outputId": "4c1f2460-79f0-4d1c-8b1a-fcbfec23f364",
        "colab": {}
      },
      "source": [
        "SUB_PATH = \"./sample_submission.csv\"\n",
        "sub = pd.read_csv(SUB_PATH)\n",
        "probs_InceptionV3 = model_finetuned.predict(test_generator, verbose=1)\n",
        "sub.loc[:, 'healthy':] = probs_InceptionV3\n",
        "sub.to_csv('submission_InceptionV3.csv', index=False)\n",
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "365/365 [==============================] - 77s 211ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>healthy</th>\n",
              "      <th>multiple_diseases</th>\n",
              "      <th>rust</th>\n",
              "      <th>scab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test_0</td>\n",
              "      <td>0.432067</td>\n",
              "      <td>0.183365</td>\n",
              "      <td>0.228653</td>\n",
              "      <td>0.155915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test_1</td>\n",
              "      <td>0.033342</td>\n",
              "      <td>0.040654</td>\n",
              "      <td>0.889189</td>\n",
              "      <td>0.036815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test_2</td>\n",
              "      <td>0.031444</td>\n",
              "      <td>0.031660</td>\n",
              "      <td>0.019720</td>\n",
              "      <td>0.917175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test_3</td>\n",
              "      <td>0.664428</td>\n",
              "      <td>0.100164</td>\n",
              "      <td>0.096073</td>\n",
              "      <td>0.139335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test_4</td>\n",
              "      <td>0.000323</td>\n",
              "      <td>0.002236</td>\n",
              "      <td>0.994368</td>\n",
              "      <td>0.003072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  image_id   healthy  multiple_diseases      rust      scab\n",
              "0   Test_0  0.432067           0.183365  0.228653  0.155915\n",
              "1   Test_1  0.033342           0.040654  0.889189  0.036815\n",
              "2   Test_2  0.031444           0.031660  0.019720  0.917175\n",
              "3   Test_3  0.664428           0.100164  0.096073  0.139335\n",
              "4   Test_4  0.000323           0.002236  0.994368  0.003072"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28WWq4HTeHK7",
        "colab_type": "text"
      },
      "source": [
        "# ResNet50"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kw7r0re0eHK7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.applications.resnet50 import ResNet50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEkrOOgTeHK9",
        "colab_type": "code",
        "outputId": "219f02d2-d0c6-4da8-f8ea-ecc567c2f397",
        "colab": {}
      },
      "source": [
        "model_finetuned = ResNet50(include_top=False, weights='imagenet', input_shape=(img_size,img_size,3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/home/arpan/anaconda3/lib/python3.7/site-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
            "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ambVeKcqeHLC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = model_finetuned.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, activation=\"relu\")(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(512, activation=\"relu\")(x)\n",
        "x = Dropout(0.2)(x)\n",
        "predictions = Dense(4, activation=\"softmax\")(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyqF199QeHLE",
        "colab_type": "code",
        "outputId": "533bdb55-8365-42c5-84fd-37261c0f9191",
        "colab": {}
      },
      "source": [
        "adam = keras.optimizers.Adam(learning_rate=0.000005, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "model_finetuned = Model(inputs=model_finetuned.input, outputs=predictions)\n",
        "model_finetuned.compile(optimizer=adam,\n",
        "                  loss = 'categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "model_finetuned.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 384, 384, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 390, 390, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv2D)                  (None, 192, 192, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_conv1 (BatchNormalization)   (None, 192, 192, 64) 256         conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 192, 192, 64) 0           bn_conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 194, 194, 64) 0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 96, 96, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2a (Conv2D)         (None, 96, 96, 64)   4160        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2a (BatchNormalizati (None, 96, 96, 64)   256         res2a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 96, 96, 64)   0           bn2a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2b (Conv2D)         (None, 96, 96, 64)   36928       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2b (BatchNormalizati (None, 96, 96, 64)   256         res2a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 96, 96, 64)   0           bn2a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2c (Conv2D)         (None, 96, 96, 256)  16640       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch1 (Conv2D)          (None, 96, 96, 256)  16640       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2c (BatchNormalizati (None, 96, 96, 256)  1024        res2a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch1 (BatchNormalizatio (None, 96, 96, 256)  1024        res2a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 96, 96, 256)  0           bn2a_branch2c[0][0]              \n",
            "                                                                 bn2a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 96, 96, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2a (Conv2D)         (None, 96, 96, 64)   16448       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2a (BatchNormalizati (None, 96, 96, 64)   256         res2b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 96, 96, 64)   0           bn2b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2b (Conv2D)         (None, 96, 96, 64)   36928       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2b (BatchNormalizati (None, 96, 96, 64)   256         res2b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 96, 96, 64)   0           bn2b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2c (Conv2D)         (None, 96, 96, 256)  16640       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2c (BatchNormalizati (None, 96, 96, 256)  1024        res2b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 96, 96, 256)  0           bn2b_branch2c[0][0]              \n",
            "                                                                 activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 96, 96, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2a (Conv2D)         (None, 96, 96, 64)   16448       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2a (BatchNormalizati (None, 96, 96, 64)   256         res2c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 96, 96, 64)   0           bn2c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2b (Conv2D)         (None, 96, 96, 64)   36928       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2b (BatchNormalizati (None, 96, 96, 64)   256         res2c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 96, 96, 64)   0           bn2c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2c (Conv2D)         (None, 96, 96, 256)  16640       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2c (BatchNormalizati (None, 96, 96, 256)  1024        res2c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 96, 96, 256)  0           bn2c_branch2c[0][0]              \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 96, 96, 256)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2a (Conv2D)         (None, 48, 48, 128)  32896       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2a (BatchNormalizati (None, 48, 48, 128)  512         res3a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 48, 48, 128)  0           bn3a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2b (Conv2D)         (None, 48, 48, 128)  147584      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2b (BatchNormalizati (None, 48, 48, 128)  512         res3a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 48, 48, 128)  0           bn3a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2c (Conv2D)         (None, 48, 48, 512)  66048       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch1 (Conv2D)          (None, 48, 48, 512)  131584      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2c (BatchNormalizati (None, 48, 48, 512)  2048        res3a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch1 (BatchNormalizatio (None, 48, 48, 512)  2048        res3a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 48, 48, 512)  0           bn3a_branch2c[0][0]              \n",
            "                                                                 bn3a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 48, 48, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2a (Conv2D)         (None, 48, 48, 128)  65664       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2a (BatchNormalizati (None, 48, 48, 128)  512         res3b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 48, 48, 128)  0           bn3b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2b (Conv2D)         (None, 48, 48, 128)  147584      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2b (BatchNormalizati (None, 48, 48, 128)  512         res3b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 48, 48, 128)  0           bn3b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2c (Conv2D)         (None, 48, 48, 512)  66048       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2c (BatchNormalizati (None, 48, 48, 512)  2048        res3b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 48, 48, 512)  0           bn3b_branch2c[0][0]              \n",
            "                                                                 activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 48, 48, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2a (Conv2D)         (None, 48, 48, 128)  65664       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2a (BatchNormalizati (None, 48, 48, 128)  512         res3c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 48, 48, 128)  0           bn3c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2b (Conv2D)         (None, 48, 48, 128)  147584      activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2b (BatchNormalizati (None, 48, 48, 128)  512         res3c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 48, 48, 128)  0           bn3c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2c (Conv2D)         (None, 48, 48, 512)  66048       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2c (BatchNormalizati (None, 48, 48, 512)  2048        res3c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 48, 48, 512)  0           bn3c_branch2c[0][0]              \n",
            "                                                                 activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 48, 48, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2a (Conv2D)         (None, 48, 48, 128)  65664       activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2a (BatchNormalizati (None, 48, 48, 128)  512         res3d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 48, 48, 128)  0           bn3d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2b (Conv2D)         (None, 48, 48, 128)  147584      activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2b (BatchNormalizati (None, 48, 48, 128)  512         res3d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 48, 48, 128)  0           bn3d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2c (Conv2D)         (None, 48, 48, 512)  66048       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2c (BatchNormalizati (None, 48, 48, 512)  2048        res3d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 48, 48, 512)  0           bn3d_branch2c[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 48, 48, 512)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2a (Conv2D)         (None, 24, 24, 256)  131328      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2a (BatchNormalizati (None, 24, 24, 256)  1024        res4a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 24, 24, 256)  0           bn4a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2b (Conv2D)         (None, 24, 24, 256)  590080      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2b (BatchNormalizati (None, 24, 24, 256)  1024        res4a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 24, 24, 256)  0           bn4a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch2c (Conv2D)         (None, 24, 24, 1024) 263168      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4a_branch1 (Conv2D)          (None, 24, 24, 1024) 525312      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch2c (BatchNormalizati (None, 24, 24, 1024) 4096        res4a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn4a_branch1 (BatchNormalizatio (None, 24, 24, 1024) 4096        res4a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 24, 24, 1024) 0           bn4a_branch2c[0][0]              \n",
            "                                                                 bn4a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 24, 24, 1024) 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2a (Conv2D)         (None, 24, 24, 256)  262400      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2a (BatchNormalizati (None, 24, 24, 256)  1024        res4b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 24, 24, 256)  0           bn4b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2b (Conv2D)         (None, 24, 24, 256)  590080      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2b (BatchNormalizati (None, 24, 24, 256)  1024        res4b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 24, 24, 256)  0           bn4b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4b_branch2c (Conv2D)         (None, 24, 24, 1024) 263168      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4b_branch2c (BatchNormalizati (None, 24, 24, 1024) 4096        res4b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 24, 24, 1024) 0           bn4b_branch2c[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 24, 24, 1024) 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2a (Conv2D)         (None, 24, 24, 256)  262400      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2a (BatchNormalizati (None, 24, 24, 256)  1024        res4c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 24, 24, 256)  0           bn4c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2b (Conv2D)         (None, 24, 24, 256)  590080      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2b (BatchNormalizati (None, 24, 24, 256)  1024        res4c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 24, 24, 256)  0           bn4c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4c_branch2c (Conv2D)         (None, 24, 24, 1024) 263168      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4c_branch2c (BatchNormalizati (None, 24, 24, 1024) 4096        res4c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 24, 24, 1024) 0           bn4c_branch2c[0][0]              \n",
            "                                                                 activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 24, 24, 1024) 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2a (Conv2D)         (None, 24, 24, 256)  262400      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2a (BatchNormalizati (None, 24, 24, 256)  1024        res4d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 24, 24, 256)  0           bn4d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2b (Conv2D)         (None, 24, 24, 256)  590080      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2b (BatchNormalizati (None, 24, 24, 256)  1024        res4d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 24, 24, 256)  0           bn4d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4d_branch2c (Conv2D)         (None, 24, 24, 1024) 263168      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4d_branch2c (BatchNormalizati (None, 24, 24, 1024) 4096        res4d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 24, 24, 1024) 0           bn4d_branch2c[0][0]              \n",
            "                                                                 activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 24, 24, 1024) 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2a (Conv2D)         (None, 24, 24, 256)  262400      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2a (BatchNormalizati (None, 24, 24, 256)  1024        res4e_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 24, 24, 256)  0           bn4e_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2b (Conv2D)         (None, 24, 24, 256)  590080      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2b (BatchNormalizati (None, 24, 24, 256)  1024        res4e_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 24, 24, 256)  0           bn4e_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4e_branch2c (Conv2D)         (None, 24, 24, 1024) 263168      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4e_branch2c (BatchNormalizati (None, 24, 24, 1024) 4096        res4e_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 24, 24, 1024) 0           bn4e_branch2c[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 24, 24, 1024) 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2a (Conv2D)         (None, 24, 24, 256)  262400      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2a (BatchNormalizati (None, 24, 24, 256)  1024        res4f_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 24, 24, 256)  0           bn4f_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2b (Conv2D)         (None, 24, 24, 256)  590080      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2b (BatchNormalizati (None, 24, 24, 256)  1024        res4f_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 24, 24, 256)  0           bn4f_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res4f_branch2c (Conv2D)         (None, 24, 24, 1024) 263168      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn4f_branch2c (BatchNormalizati (None, 24, 24, 1024) 4096        res4f_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 24, 24, 1024) 0           bn4f_branch2c[0][0]              \n",
            "                                                                 activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 24, 24, 1024) 0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2a (Conv2D)         (None, 12, 12, 512)  524800      activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2a (BatchNormalizati (None, 12, 12, 512)  2048        res5a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 12, 12, 512)  0           bn5a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2b (Conv2D)         (None, 12, 12, 512)  2359808     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2b (BatchNormalizati (None, 12, 12, 512)  2048        res5a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 12, 12, 512)  0           bn5a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch2c (Conv2D)         (None, 12, 12, 2048) 1050624     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5a_branch1 (Conv2D)          (None, 12, 12, 2048) 2099200     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch2c (BatchNormalizati (None, 12, 12, 2048) 8192        res5a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn5a_branch1 (BatchNormalizatio (None, 12, 12, 2048) 8192        res5a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 12, 12, 2048) 0           bn5a_branch2c[0][0]              \n",
            "                                                                 bn5a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 12, 12, 2048) 0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2a (Conv2D)         (None, 12, 12, 512)  1049088     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2a (BatchNormalizati (None, 12, 12, 512)  2048        res5b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 12, 12, 512)  0           bn5b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2b (Conv2D)         (None, 12, 12, 512)  2359808     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2b (BatchNormalizati (None, 12, 12, 512)  2048        res5b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 12, 12, 512)  0           bn5b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5b_branch2c (Conv2D)         (None, 12, 12, 2048) 1050624     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5b_branch2c (BatchNormalizati (None, 12, 12, 2048) 8192        res5b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 12, 12, 2048) 0           bn5b_branch2c[0][0]              \n",
            "                                                                 activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 12, 12, 2048) 0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2a (Conv2D)         (None, 12, 12, 512)  1049088     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2a (BatchNormalizati (None, 12, 12, 512)  2048        res5c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 12, 12, 512)  0           bn5c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2b (Conv2D)         (None, 12, 12, 512)  2359808     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2b (BatchNormalizati (None, 12, 12, 512)  2048        res5c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 12, 12, 512)  0           bn5c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res5c_branch2c (Conv2D)         (None, 12, 12, 2048) 1050624     activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn5c_branch2c (BatchNormalizati (None, 12, 12, 2048) 8192        res5c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, 12, 12, 2048) 0           bn5c_branch2c[0][0]              \n",
            "                                                                 activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 12, 12, 2048) 0           add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 2048)         0           activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 1024)         0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 512)          524800      dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 512)          0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 4)            2052        dropout_2[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 26,212,740\n",
            "Trainable params: 26,159,620\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhv-loQ6eHLG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "from keras.callbacks import ReduceLROnPlateau,ModelCheckpoint\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3,patience=3, min_lr=0.000001)\n",
        "checkpoint = ModelCheckpoint(\"./model.{epoch:02d}-{val_loss:.2f}-{val_accuracy:.2f}.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"auto\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "callbacks = [PlotLossesKeras(),reduce_lr]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIc2MxtreHLI",
        "colab_type": "code",
        "outputId": "09c350b3-2bf8-41d0-ee40-28a6ecf1b018",
        "colab": {}
      },
      "source": [
        "history = model_finetuned.fit_generator(train_generator,steps_per_epoch=100,epochs=25,validation_data=val_generator,validation_steps=100,callbacks = callbacks,use_multiprocessing=False,shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bEsmA4ADWZJdqgqWUUkehCZZS/UBmbilLv95HRk4pmwsraXYbRGBUQgQXTBlKemo06SkxDIsObfeJ9rDoMIZFh3HW+MGt6yrrm9i2t4othRWexKuSZ1fmtLbKiMDCqcP45VmjGRzVc12KwoICOH/y0C4d0+xyU9PgorapmeAAq2tccICj00/zu8IYQ0Ozm5oGK9GKGxDUI9fpDKdDWlv1VP+TV1pLUvSRWxY7EuB0kJ4azZosHYellFJHo3dZpfqwzNwyHvt4B1/sPEBwgIPJSQO5ZfZwTkiNYWpyNFFhx956EBkSyPS0GKanxbSuaxlXtHVvJaMGWWN0fFGA00FUmIMoer71REQICXT6Rze4ujJ460dQsgtGz4exF8DQ461sWfm9/NJaThwee8zHz0iL5bPtxRRXNfSp4jJKKeVtmmAp1Qetzyvjzx/vZPmOYmLDg7j7nDFcc2LKMRWo6IoAp4NRgyI6rAyouqhqP3y9CGpLYNZPIXRgz13rwC54+XIoy4WkGbDicfjyzxCVDGPPh3EXwLDp4PDdcuuqYw3NLvZW1h91bNyRtIzDWptTyjkTh3grNKWU6nM0wVKqD9lUUM6fP9rBsu3FRIcFcse8MVw7M0W7hHmDMbB3A2x/Dwo3QNJ0GH0OJIz1bgtPUz1sfxc2vgK7PgHjAgS+eR0ueRaGTfPetVpkfQaLrgVHAFz3NqTMhNpS2L4UtiyBtf+C1U/AgMHfJlvJJ4FT/7/yF4Xl9RgDyd1IsCYmRhEW5GRNVokmWEopdQR6d1SqD/hmTwV//mgHn2wrYmBYILefPZrrTkplgCZW3dPcANlfWAnP9vehqhDEAdFpsPMD+PQBGJhiJVqj50PKSeA8hm6HxkD+V7DxJfjmDWiogMhEmPUTmHwl1JfD4u/CM2fBGb+Bmbd6L6lb+zQs/RXEj4YrX4boVGt9WAwcf4211FfCjg9g61uw/gUr4QqLhTHnwtgFkDYbAoK8E4/qEXmltQDdasEKdDqYlhLNmmyfnuZLKaVsp9++lPJjmwsreOzjnXy0ZT+RIQH84sxRXD8rlYj+VpmtxjPwPiym+4lHTQns/NBqvdn9KTRWQ2A4HHcajL4HRp4F4bFQtQ92vA/blkLGM7DmHxAcBSPPtJKtkWdCSNSRr1WeBxtfhY0vQ+luCAyzWogmX2klLY4247a+txyW/Bg+/DXkfAEX/sP6fY+Vqxk+uAu+egpGng0Ln4aQDsbMhUTCpEutpbEGdn1stWx98wase876PUfNt1q+IodBVKKVIHZ0PtXrWhKs7rRgAcxIi+FPH+6grKaR6HBNqpVSqj2aYCnlh7btq+Sxj3by/uZ9RIQE8LMzRnHDyan9s+T1+hetxMO4rAQlaphnSfIsw2Cg5zUysf0WppLdVkK1bSnkrwbjhoghMOkyq3Uq9RQIPKQSYsRgmHa9tTTWwO5lVvfBHe/DN4ut7napJ1vHj5oH0SnWcQ3VsHUJbHjJSpQAUk6GU34O4xZAcAfj18Ji4PIXrITow/+DJ0+Ghf+2kpquqiuHxTdYCeTMH8GZ9x+czB1JULgV57gFVnfGrGVWsrV9KWx65eB9g6O+TbaiEg9Ovlr+exz6uaoekV9aS1CAg4RuFqeY4SmS8VVOKWe3qSqqlFLqW5pgKWWDN9YXcO9bm3E4hNBAJ6GeKnOhQVbJ8BDPulDPutb3QQ425Jez9Ot9RAQHcNvpI7nx5LQjTvzaZxkDKx6Dj++D4XOsJKY8HyryoaIA9n0NNcWHHCRW4tSScIVEQc6XcGCHtXnQRDjll1YLVFeq5wWFw9jzrMXtgoIMT7fC9+C9X1nLoAkQexzs/AiaaqxuhnPuhsmXf9st72hEYMb3rPFfr90Az54Lc++Gk3/e+eITJbvhpcuhLAcu+BtM/U7njmtPYIj1WY2eb7WIVe2Fyj3W59/yWrEHKgugcD3UHjj8HGFxcMJNMPeuY49DHVVeSS1J0aHdntds0rAoggMcrMnSBEsppTqiCZZSvaywvI7/9+ZmUmLDmJYS7Znk9duJX2samjlQ3Wi992yra3TR6LLmlxoQHMCPTzuOG09OY2BYP+2i43ZbXeVW/x0mXGJ1l2tvDFBTnfUFvyXpanktz4M966wEbFg6nHAzjJ4HA5O7H5vDCckzrOXM+63qfDves5Kt3BUwcSFMvgqSTzz27oxDj7e6DL79E2scWM6XcPFTMCDhyMdlfW4VsxAHXPsWpM46tuu3xxlgJa4Dkzrep6neSrwq93ybeFXsgdgR3otDtSu/rLbb3QMBggOcTE2OZk22zoellFId0QRLqV5kjOHuN77G5TY8ec20Lg04b3a5qW92E+gUggP8YE6lntLcCG/9EL5+DWb8AM7+XcetN4GhEHectdgl7jiI+zGc9GPvnjckEi55BoafCu/dAf+YBQv/ZbXmtSfjGVh6u9WKduUrEJPm3Xg6IzDESqY0oepVxhjySmqZlhLtlfNNT4vh8U93UlHX1D9bz5VS6ih0QhOletGbG/bw2fZibj97dJereQU4HQwIDujfyVVDtTVX09evwen3wryH+ve8TCLWGLCbP4XQaHjuQvj0Qau7XgtXs5WAvfMzGD4XbvzInuRK2aairomqhmavtGCBNR+WMZCRo9UElVKqPf34m4lSvau4qoHfvL2FqckDue6kVLvD8T81B+C/51vd3BY8YRWF8Ob8U/5s0Hi4ZRlMuRqW/xGeuwAqC61iFi9dBmuehBNvhate1cp+/ZA3SrS3NTU5miCnQ8u1K6VUB7SLoFK95L4lm6lvaOLRs6JxZn9mzbE0fI5WUeuMslx44WJr/NQVL1pFFdTBgsLhwicg7RR45+dWl8GwGKuYxfmPw7Tr7I5Q2cRbJdpbhAQ6mZwUxZosHYellFLt0QRLKW9rboTyXCjNhtIsKMumKHcrPy/cweNBxThfaNN9KzTaKngw7Tprold/Zgy4msDVaC0hUZ0v/X0k+76BFxZCc71VmCH5xO6fsy+bfAUkToPXrreKSXznTSvpUv1Wfmkd4L0WLIAZabH84/PdVDc064TmSil1CP2rqFR31JVZ8xkd2NmaTFFRYM2j5GECwylviqcoOI3UEy6D2OEQM9xKxNY/D1/9E1Y/AckzYep1MP5CqziD3Yq2WbEVbfs2aXI1gavh4ETK1WS1xrmbDj4+PAHGnAvjLrDmkWpv/qmjyVkBL19ptc58931IGOud362vixtpVRlsrrc+O9Wv5ZXWEhMe5NVEaMbwGP62bBcZOaXMGX2U6pVKKdXPaIKlfNe2d62y2YMn2h1J+/LXwuLvQkUehMZYhQOSZsCkK6wEKmY4xKTxq6WF/G9DIW/dPAtnYtTB5xh5BlQXw8aXIPNZePP78P4d1jmmXWeNrelNbrc1+ezqJ6zXgBBITLdao5xBVpLkDLKWgKBvf25dHwjOYKvlqmAtbFoEmf+xWupGnwNjL4ARcyGgE5Odbn3H+nyjU+Ca/x25/Lc6nMOpyZUCrEmGvdl6BTAtJZoAh7AmWxMspZQ6lCZYyjdtfBXeuMX6OfUUOPGH1kSyvlAxzu2GVX+FT+6HyKFw0yfWXErt+GJnMa+t28MP5oxgwqHJVYsB8TDrJ3DSbdZ8RpnPWknJV/+EYSdYrVoTLu7ZL8uNtbDpFVj9D2vS3QGD4bT/g2nfhfDYYz9vU52VqG15y0qYNrwIQRHWnFNjL4DjzoCgdr74ZT5rVb1LnAZXLbLGEimljkleaS2TkwZ69ZxhQQFMHKbjsJRSqj2aYCnfU7QN3vkppMyCUWfDmqfglSutFqEZ37cqpQUPsCe2mgPwxvdh10cwboFVPCC0/S8uNQ3N3Pn61wyPC+cnp488+rlFrLEyaadATYmV8GQ+C0t+BO/fBZMutUpyD5nsvd+nshC++peV0NWVWee+6CkYf1H7E/d2VWCo1U1wzLlWl8jsz61ka9u7Vqn1wDAryRq3AEaeBcERsPxhWPag9f7SZ7UVRvU4EXkGOA8oMsZMaGf77cDVnrcBwFgg3hhTKiI5QBXgApqNMe0/bbFJs8vNnvI6zp88xOvnnpEWy9NfZFHb2ExYkH6dUEqpFvoXUfmWxhp47Trri/fCf0PkEKu89NYlVuvKe7+y5vmZ+h2YfovVfay35HwJr98EtaVw7iOQfuMRy4Q//MF2CivqWPS9mYQEdrHYQ3gszLzVarnLW20lWhtesiaLjRtldR2MGw3xo6zX2OO6Vo1wzzpY/XfY/Aa4XVYCNPNWaxxYT5U+DwiCkWday3mPQe4K67/r1nesV2cQJIyDvRtg8pVwwV+PbdyWUl33LPA34Ln2NhpjHgYeBhCR84GfGWPa1iifa4w50NNBHou9FfW43MZrFQTbmjE8hic/38263HJOHhnn9fMrpZS/0gRL+Q5jrPLSxdvhO29YyRWAM8DqIjfhYijIsBKD1f+wXsecZyUGSTN6LjFwu+CLR+Czh6xWtKsWwZBJRzwkI6eU/67K4doTUzghtRvd20QgZaa1zP+9NaZp1ydQuB42vwmYlh2tZLNt0hU/2krGWlrYXM2w/V1Y9XfIX2111Zv+PZh+c+9PPOsMgOGnWsv8h6HgK9iyBLKWwezbYe6vdY4r1WuMMctFJLWTu18JvNxz0XhXfsscWNHeT7DSU6JxCKzJLtEESyml2vDrBEtE5gF/AZzA08aY3x+yPRn4LzDQs8+dxpilvR6o6pz1z1vd4ubcZRVCaM+wdLjkGTjzfk/Xtmet1o+hx1stXeMWeKdrW4uqffC/myF7OUy8DM571OrGdgT1TS5+9fomhkaFcvu8Md6LJTQaZnzPWsAa31Syy0pID+yEA9uheAdkfWZV+msxYJCVaJXlWgU5BqbA2Q/B8df4xqSzDodVel3LrysfJyJhwDzgR21WG+BDETHAP40xT9kSXAe8PclwWxEhgUxIjGJNlk44rJRSbfltgiUiTuAJ4EygAFgrIkuMMVva7PZ/wCJjzD9EZBywFEjt9WDV0e37Gpbebk28O/v2o+8fNQzO/A2c+ivY+DKsfhL+dxN89P/ghButlq240d0rirHrE3jje9BQDQuesMZ+daJV5a+f7iSruIbnvju9Z+eHCQy1KiweWmXR7bImlz2ww5N8eV5j0mDe76xqft6Yn0qp/ud8YMUh3QNnGWMKRSQB+EhEthljlrd3sIjcAtwCkJyc3PPRYiVYAQ5hSFTPTGg+Iy2G/67Mpb7J1fWu0Eop1Uf5bYIFTAd2GWOyAETkFWAB0DbBMkDLI/oooLBXI1SdU18Ji66DkIFw8dNd+/IfFA4n3GRVu9v9Cax6Aj79rbWEDLS6DibPgKQTIXFq5+aXcjVbRRa+fBTix8J170BC51qivtlTwZOfZ3HJtGHMHhXf+d/DmxxOiB1hLaPn2xODUn3TFRzSPdAYU+h5LRKRN7DuTe0mWJ7WracA0tPTTXv7eFteaS2J0aEEOHumAuuMtFj+9UU2G/LLOXF4NyqOKqVUH+LPCVYikN/mfQEw45B97sPquvFjIBw4o3dCU51mDCz5sdXict3bVsnyY+FwfFtAoTQbclda44zyVsPODzz7BMLQKVZXtCRPl7TwQ8YNVBTA4hutY6deC/P+0H4Z8XY0udz8avEmosOC+L9zdUJcpfoSEYkCTgWuabMuHHAYY6o8P58F3G9TiO3KL6vrkQIXLU5Ii0EE1mSVaoKllFIe/pxgtddX69AnglcCzxpjHhGRmcDzIjLBGOM+6EQ2dNtQHmufhi1vwhn3Qeos75wzJs1ajvdUVa4thfw1kLcK8tbAmn/Cyr9a22KP+zbZCgi2qhS6mqyWtEmXdumyTy3PYsveSp68ZioDw7w4Dkwp1aNE5GVgDhAnIgXAvUAggDHmSc9uFwEfGmNq2hw6CHhDrK7DAcBLxpj3eyvuzsgvreXs8YN77PxRoYGMHRzJmuwSoBPTUSilVD/gzwlWAZDU5v0wDu8CeCPWgGSMMatEJASIA4ra7mRHtw2FVSr8/btg5Nlw0k967jphMVZXuZbuck31VinwPE8L1/alsOEFa9vgSdbcS7EjunSJXUXV/OWTnZwzcTDzJnh/vhmlVM8xxlzZiX2exSrn3nZdFuDFiem8q6q+idKaxh5twVCiauwAACAASURBVAKrXPvLX+XR2OwmKMAHJoNXSimb+XOCtRYYKSJpwB6svvFXHbJPHnA68KyIjAVCgOJejVK1r67Mmu8qYjBc9GT3ilF0VWDIwVXrjLGq8JVmWUU2ujKfFOByG+54fROhgU7uu2C818NVSqljkV9aB9DzCVZaLP9ZkcOmgnLSuzMthVJK9RF++6jJGNOMVSr3A2ArVrXAzSJyv4hc4NntF8DNIrIRa2Dy9cYYbaGymzHw5q1QuddqLQqz+YYsYs0dNXpel5MrgOdX5ZCZW8Y9540jIaJnKnUppVRXtZRo7+kEa3qa9Td8TbaWa1dKKfDvFiw8c1otPWTdPW1+3gJ4aWCP8ppVT1gT3s77vTWvlR/bU17HHz/Yzqmj4rl4aqLd4SilVKuCst5JsGLCgxg9KILVWSXcOve4Hr2WUkr5A79twVJ+Km8NfHwvjD0fZnzf7mi67blVOTQ0u3nwoglIJ+bIUkqp3pJXWktESABRYYE9fq0Zw2PIzC2jyeU++s5KKdXHaYKlek9NCSy+wZokeMETnZq015c1udy8nrmH08YkMCy6Z58QK6VUV+WV1vZ461WL6Wkx1Da6+GZPRa9cTymlfJkmWKp3uN3wxi1QcwAu/S+ERNkdUbd9vr2YA9UNXJaedPSdlVKql/V2ggU6DksppUATLNVbvnwUdn0M839vTfbbByzKyCduQDBzRh/j5MhKKdVD3G5DQWnPTjLcVkJECMPjw1mTVdIr11NKKV+mCZbqWcZA1mew7EGYeClMu8HuiLyiuKqBT7cVsXBqIoFO/WeklPItRVUNNLrcJPVSggVWufaMnDJcbi3Wq5Tq3/y6iqDyEXXlUJ7nWXLb/JwHZbnQWAVxo+C8x/x+3FWLN9YX0Ow2XKrdA5VSPqilRHtvJlgneiYc3lJYycRh/t8NXCmljpUmWKrzirZB1rKDk6myPGg4ZFBz0AAYmALRKZB6MgxMhvEXQ/AAe+L2MmMMizIKmJYSzXEJfeN3Ukr1Lb01B1ZbM9JiAViTXaIJllKqX9MES3XOnnXw7HnQVAOBYVYCNTAZkmdar61LCoRG95mWqvaszy9nV1E1f1g40e5QlFKqXXmltYhA4sDQXrvm4KgQUmLDWJ1Vyk2nDO+16yqllK/RBEsdXWk2vHQZhMXCdSsgOrVPJ1BH81pGPqGBTs6dNNTuUJRSql35pbUMjQolKKB3x4jOSIvhg837cbsNDkf/vU8opfo3HZ2vjqymBF5YCO5muOZ1iEnr18lVbWMzb2/cy7mThjAgWJ9PKKV8U35pLUkxvdd61WJGWiwVdU1s21fV69dWSilfoQmW6lhjrdVyVbkHrnwF4kfZHZHtln69j+qGZp37Sinl0/JKa0myYQL0GcNb5sPScu1Kqf5LEyzVPlczvH4j7MmEhU9D8ol2R+QTFmXkkxYXzgmp0XaHopRS7aprdFFU1dCrBS5aDIsOI3FgKGuyjn3C4a17K8k5UOPFqJRSqndpgqUOZwws/SVsXwrnPAxjz7c7Ip+QfaCGr7JLuTR9GNKPu0kqpXxbQZmngmBs7ydYYLVifZVTijFdmw9rf2U9P1+0gfl/+YJL/7mKsprGHopQKaV6liZY6nBf/Aky/wMn/wym32x3ND5jcWY+DoGFU4fZHYpSSnXIjjmw2joxLZbSmkZ2FlV3av/6JhdPLNvF3D99xjsb93LVjGTKahq5d8nmHo5UKaV6ho7SVwdb/yJ8+luYdDmcfq/d0fiMZpebxZkFzBmdwKDIELvDUUqpDuXbMAdWW9+Owypl1KCIDvczxvDB5v08uHQL+aV1nD1+EHefM5aU2HAGRYTw5493MH/CYOZPHNJboSullFdoC5b61q6P4e3bYPgcuOBv/bpa4KG+2HmA/ZUNXJaurVdKKd+WV1pHaKCT2PAgW66fHBPG4MgQ1mR1XOhi275Krn56Dd9/IZOwwABevGkG//xOOimx4QD8cO4IJiZG8es3v+FAdUNvha6UUl6hCZayFK6HV6+FhLFw2fMQYM+N2VctysgnNjyI08YMsjsUpVQPEJFnRKRIRL7pYPscEakQkQ2e5Z422+aJyHYR2SUid/Ze1O3LK60lOSbMtrGiIsKM4TGsyT58HFZpTSP/9+bXnPOXL9iyt5IHFozn3dtOZtZxcQftF+h08Mhlk6mub+bXb3zd5fFcSillJ02wFJTlwIuXQVgMXPUahETaHZFPKalu4OOt+7no+MRen7RTKdVrngXmHWWfL4wxUzzL/QAi4gSeAOYD44ArRWRcj0Z6FNYcWPZ0D2wxIy2W4qoGsj3VAJtcbv6zIps5Dy/j5a/yuXZmKp/9cg7fmZlKgLP9v6ujBkXw87NG8cHm/by1obA3w1dKqW7RMVj9XctEwq5GuP4diNS+7od6c0MhTS7DpTr3lVJ9ljFmuYikHsOh04FdxpgsABF5BVgAbPFedJ1njCGvtPawFqHe1nYcVn5ZHQ+8s4VdRdWcMjKOe84bx8gjjM1q6+ZThvPh5n3c89Y3zBwRq2NglVJ+QR/H92eNtfDy5VCeD1e9CvGj7Y7I5xhjWLQ2n8lJAxk9uHNfCJRSfdZMEdkoIu+JyHjPukQgv80+BZ51tiipaaSuyUVyTKhdIQAwPC6cuAHB/G7pVq575iuaXW6evjad5747vdPJFYDTIfzp0sk0utzc+fom7SqolPILmmD1V24XvH4TFGToRMJHsKmggu37q7S4hVJqHZBijJkM/BV407O+vYFO7WYBInKLiGSISEZxcXGPBGl3ifYWIsLpYxIwBu4+Zwwf/Gw2Z4wbdEzjwobHD+COeWNYtr2YRRn5Rz9AKaVspglWf2QMLL0dtr8L8/8I4y6wOyKftSgjn+AAB+dPHmp3KEopGxljKo0x1Z6flwKBIhKH1WLVtv/wMKDdAUPGmKeMMenGmPT4+PgeidPuEu1tPXDhBDL+7wxumT2C4ABnt8513cxUThwewwPvbG2dSFkppXyVJlj90aq/Qca/YdZPYMYtdkfjs+oaXSzZUMg5E4cQGRJodzhKKRuJyGDxNL+IyHSs+2cJsBYYKSJpIhIEXAEssSvOvBIr+RgWbX+CFRTgICSwe4lVC4dDePiSyRhj+NXiTbjd2lVQKeW7/DbBOlpZXBH5c5tyujtEpNyOOH1OaRZ88gCMOQ9Ov8/uaHzaB5v3UdXQzGVa3EKpPk9EXgZWAaNFpEBEbhSR74vI9z27XAJ8IyIbgceBK4ylGfgR8AGwFVhkjNlsx+8AkF9WS0JEMKFB3klsfElSTBi/PnccK3eX8MKaXLvDUUqpDvllFcE2ZXHPxOqesVZElhhjWqs2GWN+1mb/HwPH93qgvuj9u8AZCOf8CRx+m1/3ilfX5pMcE8aMtBi7Q1FK9TBjzJVH2f434G8dbFsKLO2JuLqqZQ6svurK6Um8v3kfDy3dxuyR8aTGhdsdklJKHcZfv2G3lsU1xjQCLWVxO3Il8HKvRObLtr8HO96HU+/QcuxHkVdSy6qsEi6dNgyHw57JOpVSqqvyS+tsL3DRk0SEPyycSIBTuH3xRlzaVbBfe25VDh9u3md3GEodxl8TrE6XxRWRFCAN+LQX4vJdTXXw3h0QPwZO/IHd0fi8xZn5iMDCaVo9UCnlHxqb3RRW9O0EC2BIVCj3nT+etTllPPNltt3hKJs0Nrt5aOk2XliTZ3coSh3GXxOsTpfFxRpwvNgY4+rwZL1QOtd2K/4C5blwzsNWF0HVIZfbsDizgFNGxjN0oL1zySilVGftKa/DGN+oINjTLp6ayJnjBvHwh9vZVVRldzjKBpsKyqlrclFc1WB3KEodxl8TrE6XxcVKsI7YPbA3SufaqjQbvngUxl8MabPtjsbnrdh1gMKKep37SinlV3ypRHtPExF+d9FEwoOc/GLRRppdbrtDUr1s5e4SAIqr6m2ORKnD+WuC1amyuCIyGojGqgzVf71/FzgC4OwH7Y7EL7yakc/AsEDOHDfI7lCUUqrT8vpRggUQHxHMAxdOYGNBBU9+vtvucFQvW7n7AAAlNY2aYCuf45cJVkdlcUXkfhFpO2vulcArxpj+Owp2+/uw4z2YcwdE6mS5R1NW08hHm/dz4ZTEbk+MqZRSvSm/tJagAAcJEcF2h9Jrzps0lPMmDeEvn+xkS2Fll46tbWwm+0ANmbllNDbrF3R/Ut/kYl1uOVGhgRhjJVlK+RK/LNMO7ZfFNcbcc8j7+3ozJp/TVA/v3wFxo2GGFrbojLc27KHR5da5r5RSfievtJZh0aH9rvLpAwsmsDqrlF+8tpG3bp2FwVBU2UBRVT37KxvYX9n21VqKKhuoamhuPccpI+N49obpOPvZZ+evMnPLaHS5WTgxkZe/yqe4qoFBkSF2h6VUK79NsFQnrPgLlOXAtUsgIMjuaPzCoowCJiRGMm5opN2hKKVUl/T1ObA6Eh0exEMXT+Tm5zKY+sBHVLdJnFoEOR0kRAYzKDKE0YMjOGVkPIOjQhgUGUx+aR2PfrSDv366k5+eMcqG30B11ardJTgdwjkTh/DyV/kUVdUDUXaHpVQrTbD6qrIc+PJRGH8RDD/V7mj8wjd7Ktiyt5L7F4y3OxSllOqy/NJapqVE2x2GLc4cN4jfXjiBrXsrGRRpJU7Wq7VEhwUi0n7rlDGG3JJa/vLJTqYmRzN7VB8sdtXHrNx9gMnDokjzTDStlQSVr9EEq696/y4QJ5ylhS06a1FGPkEBDhZMbndKNaWU8lkVtU1U1jeTFN3/WrBaXHNiyjEdJyL89sIJbC6s4CevrOfd207RKTp8WHVDMxsLKvj+qcOJG2CNNyyq1ARL+Ra/LHKhjmLHh7B9KZz6K4jSZKEz6ptcvLl+D/PGDyYqTOcJU0r5l5YKgn19kuGeEhrk5O9XT6XJZbj1pXVa9MKHrc0uxeU2nDQijpBAJ1GhgRRXa4KlfIsmWH1NUz289yuIGwUn/tDuaPzGqqwSKuubuXiqJqRKKf/T30q094Th8QP4w8JJrM8r56H3ttodjurAyt0HCHI6WrvDxkcEawuW8jmaYPU1Kx+HsmyY/0ctbNEFmTllOB3CCakxdoeilFJd9m0LlnZt645zJw3hhlmp/GdFDu9u2mt3OKodq7JKmJoykJBAayqVhIhgbcFSPkcTrL6kLBe+eATGXQgj5todjV/JzC1j7JAIwoN1WKJSyv/kl9USEx5ERIh2ce6uu+aPZWryQH61eCO7i6vtDke1UV7byObCSk4aEde6Lj4i2FNFUCnfoQlWX/LB3SAOOFsLW3RFk8vNhvxy0lO09Uop5Z/yS2tJitbWK28ICnDwt6umEhzo5IcvrKOu0WV3SMpjdVYpxsDMEbGt6xIigimuasAYY2NkSh1ME6y+YudHsO0dT2GLYXZH41e27a2irsnF1H5a3lgp5f/ySmu1wIUXDR0YymOXT2FHURW/fvNr/fLuI1btPkBooJPJwwa2rouPCKa+yX3QxNFK2U0TrL6gucEqbBE7Ek681e5o/E5GbikA6ZpgKaX8ULPLzZ6yOi1w4WWzR8Xzk9NH8r91e3hlbb7d4Shg5e4STkiLISjg26+vCREhgM6FpXyLJlh9wcrHoTQLztHCFsciM7eMIVEhOu+JUso/VO2H9+6E5kYA9lbU0+w2mmD1gB+fNpJTRsZx75LNfLOnwu5w+rXiqgZ2FlVzUpvugWC1YIHOhaV8iyZY/q48D5Y/AuMWwIjT7I7GL2XmlrWWe1VKKZ+XuwLW/APe+SkYQ36ZlmjvKU6H8NjlU4gND+IHL2ZSUdtkd0j91qqsEoDDEqwET4KllQSVL9EEy9+9fxeIwNm/szsSv1RYXsfeinpNsJTqx0TkGREpEpFvOth+tYhs8iwrRWRym205IvK1iGwQkYxeCXjCxXDqnbDhRfjiT+TrJMM9KnZAMH+7aip7y+v5xWsbdTyWTVbtPkBESADjh0YdtP7bFiytJKh8hyZY/mzXx1Zhi9m3a2GLY5SRWwagFQSV6t+eBeYdYXs2cKoxZhLwAPDUIdvnGmOmGGPSeyi+w825EyZdDp/+lrAdb+B0CEOiQnrt8v3NtJRo7j5nLB9v3c9Ty7PsDqdfWrm7hBlpsTgdctD6qNBAgpwObcFSPkUTLH+29t8QmQgztbDFsVqXW0ZooJMxQyLsDkUpZRNjzHKg9AjbVxpjyjxvVwP2P9ESgQv+CimzmLfrAeZFZBPg1Ft6T7phVirnThzCHz/YzhpPdzXVO/aU15FbUntY90AAESE+IphiHYOlfIj+NfZXribI/gJGngUBwXZH47cyckuZkjSQQP1iopTqnBuB99q8N8CHIpIpIrf0aiQBwXD5CxQ5Evh90++hZHevXr6/ERF+v3AiKTFh/Ojl9Tq5bS9atdsz/uq4wxMssLoJaguW8iX6rdJf7VkHjVUwfI7dkfitmoZmtu6t0vFXSqlOEZG5WAnWHW1WzzLGTAXmA7eKyOwjHH+LiGSISEZxcbF3ggqL4YfmTkQc8OKlUNthQ5zygoiQQP5+zVSq6pu47eX1NLvcdofUL6zcfYDY8CBGJbTf2yQ+IlirCCqfogmWv8paBgikdXgvV0exMb8cl9swLVUTLKXUkYnIJOBpYIExprV/mDGm0PNaBLwBTO/oHMaYp4wx6caY9Pj4eK/EVd3QzMbaWD6c9AhUFMArV1lzI6oeM2ZwJA9eOJHVWaX8b/0eu8Pp84wxrNpdwonDY3EcMv6qRYK2YCkfowmWv8r6DIZOgTAtznCsMj0FLqYmaYKllOqYiCQD/wO+Y4zZ0WZ9uIhEtPwMnAW0W4mwp7RUEAxKOwku+gfkrYK3bgWtdNejLp6ayMCwQNbnlR19Z9UtOSW17K2oZ2Y7469axEcEU1rTSGOztigq3xBgdwDqGDRUQcFaOOk2uyPxaxm5ZYwaNICosEC7Q1FK2UhEXgbmAHEiUgDcCwQCGGOeBO4BYoG/iwhAs6di4CDgDc+6AOAlY8z7vRl7XmmbObCGLYSyHPjkfohOg9N+3Zuh9CsiwvihkWwurLQ7lD5v5e4DwOHzX7WVEGFV0CypaWBIVGivxKXUkWiC5Y9yVoC7WcdfdYPbbViXV8Z5k4bYHYpSymbGmCuPsv0m4KZ21mcBkw8/ovfklx4yyfDJP4fSbFj+R4hJgylX2Rhd3zZhaBT/WZFDk8uthZJ60KrdJQyODCEtLrzDfb6dC0sTLOUb9C+CP8paBgGhkDTD7kj81s6iaqrqm5mm818ppfxYfmktESEBRIV6WuJF4Lw/Q9qpsOQ2yF5ub4B92LihkTS63OwqqrY7lD6rZfzVzBGxeFqK25XgSbCKq3QclvINmmD5o6zPIGUmBOqkkseqZfyVVhBUSvmzvNJakqLDDv7y6QyEy56D2BHw6jVQvN2+APuwCYlRAHyzp8LmSPquHfurKalpPOL4K2jTgqUJlvIRfttFUETmAX8BnMDTxpjft7PPZcB9WPOUbDTG+H9ficpCKN4GU662OxK/lpFbSmx4EKmxYXaHovqYpqYmCgoKqK/XOXK8LSQkhGHDhhEYqOMmW+SV1jKyvdLVoQPhqkXw9BlW+fabPoEB3qlcqCxpseGEBTnZXFjJpXYH00d1ZvwVQNyA/teCpfeantXd+41fJlgi4gSeAM4ECoC1IrLEGLOlzT4jgbuw5igpE5EEe6L1sqzPrdfhc+yMwu+tyy1jakr0EbscKHUsCgoKiIiIIDU1Vf//8iJjDCUlJRQUFJCWlmZ3OD7B7Tbkl9Vx+thB7e8QnQJXvgLPnguvXAnXvQ2BOj7FWxwOYdyQSDYXagtWT1m5u4TkmDCGRR/5YWhQgIPosMB+Nfmz3mt6jjfuN/7aRXA6sMsYk2WMaQReARYcss/NwBPGmDJonaPE/2Utg7A4GDTB7kj8VnFVAzkltaRr90DVA+rr64mNPfJ4AdV1IkJsbKw+rW2jqKqBxmY3STFH+PI5bBos/BcUZMAb3wO3lrH2pvFDI9lSWInbrWXxvc3lNqzJKjlq61WLhIiQftWCpfeanuON+42/JliJQH6b9wWedW2NAkaJyAoRWe3pUujfjLHGXw2fAw5//U9nv3V5Ov5K9Sy94fUM/VwPll92SAXBjow9H876LWx5Cz65r+cD60fGJ0ZR0+gip6TG7lD6nC2FlVTWNx91/FWL+IjgfjcGS/8m9pzufrb++i29vd/60MdHAcBIrLlNrgSeFpGB7Z5M5BYRyRCRjOLiYq8G6lVFW6F6v3YP7KbM3DKCnI7WAcpK9XcDBgwAoLCwkEsuuaTdfebMmUNGRsYRz/PYY49RW1vb+v6cc86hvLzce4Gqg+SVWJ91UnQnuv3NvBXSvwsr/gK7Pu7hyPqP8UMjAfhG58PyupbxVzOHd7YFK7hftWD5o/50r/HXBKsASGrzfhhQ2M4+bxljmowx2cB2rITrMMaYp4wx6caY9Ph4Hx4EnLXMeh0+x84o/F5mbhkTEiMJCXTaHYpSPmXo0KEsXrz4mI8/9Ka3dOlSBg5s97mW8oK80lpEILEzCZYInP0QxI2Ct39qTVivum1kQgRBToeOw+oBK3eXcFzCABIiO1cxOT4imOLqBozR7pq+rj/ca/w1wVoLjBSRNBEJAq4Alhyyz5vAXAARicPqMpjVq1F6W9ZnEHscDEw66q6qffVNLr4uqCA9Vee/Un3THXfcwd///vfW9/fddx+PPPII1dXVnH766UydOpWJEyfy1ltvHXZsTk4OEyZY4zvr6uq44oormDRpEpdffjl1dXWt+/3gBz8gPT2d8ePHc++99wLw+OOPU1hYyNy5c5k7dy4AqampHDhgPYV+9NFHmTBhAhMmTOCxxx5rvd7YsWO5+eabGT9+PGedddZB11FHll9ay5DIEIIDOvmwKDAELvgrVBTAJw/0bHD9RFCAg1GDB7B5j7ZgeVOTy83anNJOj78CK8FqbHZTWdfcg5GpFnqvOQpjjF8uwDnADmA38GvPuvuBCzw/C/AosAX4GriiM+edNm2a8UlNDcb8dogx7/zC7kj8WkZOiUm54x3z3td77Q5F9VFbtmyx9frr1q0zs2fPbn0/duxYk5uba5qamkxFRYUxxpji4mIzYsQI43a7jTHGhIeHG2OMyc7ONuPHjzfGGPPII4+YG264wRhjzMaNG43T6TRr1641xhhTUlJijDGmubnZnHrqqWbjxo3GGGNSUlJMcXFx67Vb3mdkZJgJEyaY6upqU1VVZcaNG2fWrVtnsrOzjdPpNOvXrzfGGHPppZea559//oi/X3ufL5BhfOC+1JXFG/eahX9fYS57cmXXD3z3dmPujTImd1W3Y1DG3LF4o5nymw9a/z2p7vv2Xl3Y6WPeXF9gUu54x+zcX9mDkfkOvdf07L3GmO7db/yyTDuAMWYpsPSQdfe0+dkAP/cs/q9gLTTVaPfAbsrI0QIXqvf85u3NbPHy2IxxQyO59/zxHW4//vjjKSoqorCwkOLiYqKjo0lOTqapqYm7776b5cuX43A42LNnD/v372fw4MHtnmf58uXcdtttAEyaNIlJkya1blu0aBFPPfUUzc3N7N27ly1bthy0/VBffvklF110EeHh4QBcfPHFfPHFF1xwwQWkpaUxZcoUAKZNm0ZOTk5XP5J+K7+sltkjj6Fb++n3wPalsOTH8L0vdNL6bhqfGMUra/MprKgncaCWwfeGlbtKEIEZaZ1vwUqIsP4/Lqps4Lj25obrw/ReY/Gle43fJlj9TtYyEAeknWJ3JH4tM7eMlNiw1lnfleqLLrnkEhYvXsy+ffu44oorAHjxxRcpLi4mMzOTwMBAUlNTj1qCtr0qStnZ2fzpT39i7dq1REdHc/311x/1POYIYyKCg7/9t+h0OrWLYCfVN7nYX9lw5BLtHQkeAOc/Bi8shOUPw+n/z/sB9iMthS4276nQBMtLVu4uYezgSKLDgzp9TMt9vbhaC130Fr3XdEwTLH+R9RkkToMQrXx3rIwxZOaWcepoHy5kovqUIz3960lXXHEFN998MwcOHODzz63JySsqKkhISCAwMJBly5aRm5t7xHPMnj2bF198kblz5/LNN9+wadMmACorKwkPDycqKor9+/fz3nvvMWfOHAAiIiKoqqoiLi7usHNdf/313HnnnRhjeOONN3j++ee9/4v3IwWdLdHekePOgMlXwYrHYNwCGNLxU2F1ZGMHR+IQq5LgWePbf0qvOq++yUVmXhnXnpjSpeMSIq0v0EWV/S/B0nvNt+fylXuNJlj+oK4c9mTCKb+0OxK/lltSS0lNo3YPVH3e+PHjqaqqIjExkSFDhgBw9dVXc/7555Oens6UKVMYM2bMEc/xgx/8gBtuuIFJkyYxZcoUpk+fDsDkyZM5/vjjGT9+PMOHD2fWrFmtx9xyyy3Mnz+fIUOGsGzZstb1U6dO5frrr289x0033cTxxx+v3QG7Ia/UU6L9WBMsgLMfhF0fwZIfwU2fglO/EhyL0CAnI+IHsEUrCXrFutwyGpvdnHRc57sHAkQEBxAc4NAWrF6k95qOyZGa0/qj9PR0c7T6+71u6zvw6tVw/VJInXX0/VW7FmcW8MvXNvLBT2czenD/6p+tes/WrVsZO3as3WH0We19viKSaYxJtymkY9Lde82zK7K57+0trP31Gd3r8rz5TXjtOjjjN3DyT4/9PP3cz17dwKrdJay++3S7Q/F7j3y4nb9/tpsN95xJREhgl4495Y+fMi05mseuOL6HovMdeq/ped253/hrmfb+JWsZBIbDsBPsjsSvZeaWERESwMiEAXaHopRS3ZJfVkdooJO4AZ0fo9KucQtgzHnw2UNwYJd3guuHxg+NZF9lPQe09aTbVu4uYWJiVJeTK4D4AcHagqV8giZY/iDrM6vlKqCbN9J+LjO3lKnJ0Tgchw+mVEopf5JXWktSTGi7g8O7RATOfQScwfD2beB2eyfAfmb8gNzf1wAAIABJREFUUGt89GYvV3Lrb6obmtmYX96l+a/aSogI6ZdjsJTv0QTL15XnQ8kuGD7X7kj8WkVdEzv2V+v4K6VUn5BfWnvsBS4O9f/Zu/PwKMur8ePfe2aSyTaTPZAESEJYE3YCsilgXVAU1FrXWq2trVbq2/atVttaq76tvm3f/lrrUm1LrbXFKlVZBDcKKgiyL0mAEJIAySRkz2RPZub+/fFkQoAkZJmZZybcn+vKNZntmUMIzHPmvs85luFaPdaJbbDnr5455kUmo6OTYHaJqsMajF1F1ThcknnpcRd+cDfiLWoFS/EPKsHydwVbtMvRi3QMIvDtPanNv8pSCZaiKAFOStmxguWhBAtg+lchbSF89ATUFXvuuBeJyNAgRsWEeXwW0cVm+/Eqgo2GAX8YGm8xU9vUTqvD6eHIFKV/VILl7wo2Q8QwSFCFjIOx90QNRoNg6sgovUNRFMUPCSFWCiHKhRDZPdwvhBDPCSHyhRAHhRAzutx3txDiWMfX3d6OtaqxjaY2p+dWsEDbKnj970E6Yf0PQDXA6rfMJCvZqpPgoGw/XsX0UVGEBhsH9PyEjoYvlQ1tngxLUfpNJVj+zOWCgk+01avB7rO/yO0uqmFiooVws2pDrChKt14FlvRy/zXA2I6vbwEvAQghYoAngEuA2cATQgivLpWfqh7kDKyexKTB5Y/DsQ/g0GrPHvsiMCk5khNVTdhb2vUOJSDVNbWTbatj7gDrr+DMsOFye+8DaRXF21SC5c9OZ0NTpaq/GiSH08X+U7XMHKW2BypDX21tLS+++OKAnnvttddSW1vb62N+9rOf8fHHHw/o+P5MSvkpUN3LQ5YDr0nNDiBKCJEIXA18JKWsllLWAB/Re6I2aB6ZgdWTS74NyVmw8RForPT88Ycwdx2W2iY4MDsKq5CSAddfgdbkAqCiXtVheZt6r+mdSrD8WWf91UJdwwh0h0vraW53MjM1Ru9QFMXrenvTczp7r0vYsGEDUVG9b6N96qmnuOKKKwYcXwBLBk51uV7ccVtPt3vNgjFx/PXrszy/ggVgMMLy56G1Hjb+yPPHH8ImqU6Cg7L9eBUhQQamDWIrf+cKlkqwvE691/ROJVj+rGAzxE8Aa5LekQS0PSe0D6VVB0HlYvDoo49y/Phxpk2bxsMPP8yWLVtYvHgxd9xxB5MnTwbghhtuYObMmWRmZvLKK690Pjc1NZXKykqKioqYOHEi9913H5mZmVx11VU0NzcDcM8997B69erOxz/xxBPMmDGDyZMnc+TIEQAqKiq48sormTFjBt/+9rdJSUmhsjLgV0O626cte7n9/AMI8S0hxG4hxO6KiooBBxIbYWbx+ARCggZWp3JBCRPhsochezUc3eid1xiC4i1mhlnN5KhOggPy+fFKZqXGEGwa+KlpbEQwQqgVLF9Q7zW9UwmWv2pvgRPbVfdAD9h9oobEyBCSo0L1DkVRvO7ZZ58lPT2d/fv38+tf/xqAnTt38otf/ILc3FwAVq5cyZ49e9i9ezfPPfccVVVV5x3n2LFjPPjgg+Tk5BAVFcW///3vbl8vLi6OvXv38sADD/Cb3/wGgCeffJLLL7+cvXv3cuONN3Ly5Ekv/Wl9qhgY2eX6CMDWy+3nkVK+IqXMklJmxcfHey1Qj1jwfUjI0BpetKiEoa8ykyLVCtYAVNS3kne6YVDbAwGCjAZiwoLVCpYPqPea3qmKf3916gtwNKv6Kw/Ye6KGGWr1StHDxkeh7JBnjzl8MlzzbL+eMnv2bNLS0jqvP/fcc7zzzjsAnDp1imPHjhEbe3ZheVpaGtOmTQNg5syZFBUVdXvsm266qfMxb7/9NgBbt27tPP6SJUuIjh4S//7WAiuEEG+gNbSok1KWCiE+AH7ZpbHFVcBjegXpMaZgWPY8/OUKrXX79b/TO6KAMCnJypaj5TS3OQfcCc+TpJTUNbcTFRasdyi92lGgnXgPpsGFW7zFfPGtYKn3Gr97r1ErWP6qYAsYTJA6X+9IApqtthlbXYuaf6Vc1MLDwzu/37JlCx9//DHbt2/nwIEDTJ8+nZaW8ztumc3mzu+NRiMOh6PbY7sf1/UxMgBbfAshVgHbgfFCiGIhxDeEEPcLIe7veMgGoADIB/4EfAdASlkNPA3s6vh6quO2wDdiJsz5jjZ8uPAzvaMJCBlJkbgkHCnzj1WsN3efYs4zm6hp9O+25dvyK7GYTUzqaBQyGFqCpboI6kG915yhVrD8VcFmGDELzBa9Iwloe05oA4ZV/ZWii35++ucJFouF+vr6Hu+vq6sjOjqasLAwjhw5wo4dOzwew4IFC3jzzTf50Y9+xIcffkhNTY3HX8PTpJS3X+B+CTzYw30rgZXeiEt3i38CR9bDu9+Br78HUaP0jsivTUrWEoQcm53pftC59q3dxbS0uzhW3sDsNP9s9HSyqom395Vw3eRETMbBf+6fYAnheHmDByILIOq9xu/ea9QKlj9qqgbbflV/5QF7TtQQGmRkYuLgPxVTlEAQGxvL/PnzmTRpEg8//PB59y9ZsgSHw8GUKVN4/PHHmTNnjsdjeOKJJ/jwww+ZMWMGGzduJDExEYtFfVgUkILD4OaVWh3Wymug8pjeEfm15KhQIkODyPGDgcO22mZ2d3zIWFTZqHM0PXtyXQ5BBsEjSyZ45HjxFjMVDa1+vboxFKj3mt4J9Qt4tqysLLl79259g8h5F966G+79EEZdom8sAe76P2wl3GzkjW/N1TsU5SJx+PBhJk6cqHcYumptbcVoNGIymdi+fTsPPPAA+/fv98ixu/v5CiH2SCmzPPICPuIX7zX9UXoQXr8JpIS73obEqXpH5Lfu/PMO6lscrF2xQNc4/vRpAb/YcBgh4IGF6R5LYDzpo9zT3Pfabn5y7UTuu2y0R465cmshT63PZd/jVxId7t+1Z4Oh3mu8+14Dg3u/UVsE/VHBFgi2QPIMvSMJaI2tDnJL7TywMF3vUBTlonLy5EluueUWXC4XwcHB/OlPf9I7JGWwEqfA19+H15bDq9fDnW/CKM9/Ij0UZCZF8uq2ItqdLoI8sOVtoNYdtDE5OZKGVgdFVf63gtXc5uTna3MYmxDBPfNTPXbcrrOwhnKCpfj3e41KsPxRwWZIuxSMQXpHEtAOFNfidElVf6UoPjZ27Fj27dundxiKp8WNgXvfh7/fAH+/EW59HcZ8Se+o/E5mkpU2p4v88gbdtqefqGrkYHEdP752AtuPV1FY2aRLHL15aUs+JbXNrLpvjkcT0YSOBKuivpXxw/1ju5jiHf78XqNqsPxNdSHUFKn6Kw/YU6TtPZ/hB4XGiqIoQ0LUSPj6RohJh3/eCrlr9I7I72QmRQKQrePA4fUHSwFYOiWJ1LhwTlQ1+lVNUmFlI3/8pIAbpiV5pDV7V2dWsFQnQeWM6sY2mtq671DoDSrB8jcFW7RLNf9q0PacrGFsQgSRYWolUPEtfzqRGUrUz9VPRCTAPeu1bexv3QP7Xtc7Ir+SFhdOWLBR14HD6w7YmJkSTXJUKGlx4TS1Of1m+K6UkifW5hBsMvDjaz1fQ5RgDQG4KGZhqf8T+8bhcmGrbaa6H+MKBvuzDdgESwixRAhxVAiRL4R4tJv77xFCVAgh9nd8fVOPOPutYAtYkiBurN6RBDSXS7L3RA1ZqWr1SvGtkJAQqqqq1Bufh0kpqaqqIiQkRO9QFIDQKLjrHUhbCGsehB0v6R2R3zAaBBMTrbp1Ejx2up4jZfVcPyURgJRYbTZRoZ90Evwg5zSf5lXw/SvHdSZDnhQebCQ0yOg3CaW3qPeavqtrasclJTF9rMnzxPtNQNZgCSGMwAvAlUAxsEsIsVZKmXvOQ/8lpVzh8wAHyuWEwk9g/LUghN7RBLT8igbsLQ61PVDxuREjRlBcXExFRYXeoQw5ISEhjBgxQu8wFLfgcLjjX/Dvb8D7j2qt3Bf+SL1/AZOSrKzeU4zLJTEYfPvzWHewFCHg2slagpXWkWCdqGpkzmjPbsfrr6Y2B0+ty2HCcAt3z03xymsIIUiwmof8CpZ6r+m7cru2XfREfd8TpsG+3wRkggXMBvKllAUAQog3gOXAuQlWYCk7CM01qv7KA9wDhrNS/XOwojJ0BQUFkZaWpncYiuIbJjPc/Cqsewi2PAMtdrj6Fxd9kpWZHMnftp+gqKqR0fERPntdKSXrD9qYkxbbuTqUFBVCkFH4RaOL5/+Tj62uhd/dNt0jQ4V7Eh9hHvI1WOq9pm+yS+q4+29beXJZJgsnpvrsdQN1i2AycKrL9eKO2871ZSHEQSHEaiHESN+ENgjHN2uXoxfpGcWQsLuohtjwYFJjw/QORVEUZWgzmmDZ83DJ/bDjBVi7QtuRcRHLTNK6B/q6Diu31E5BRSPXT03qvM1kNDAyJkz3YcPHKxr402cF3DQjmdlp3v3w82JYwVL6ZtXOk5hNBm6Y1l2a4D2BmmB199HYuZtQ1wGpUsopwMfA33o8mBDfEkLsFkLs1nWptWALJGRqBcTKoOw9WcOMlGjERf4pqqIoik8YDLDkWW2L4L7XYfXXwXHxnuCOTbAQbDSQ7eM6rHUHSjEaBEsmDT/r9rTYcF1nYUkpeWJNDiFBRh67xvvDcbUVrIv390/RNLU5WLPfxtLJiT5veBaoCVYx0HVFagRg6/oAKWWVlNL9r+tPwMyeDialfEVKmSWlzIqPj/d4sH3S3gwnd0C66h44WJUNrRRWNqr5V4qiKL4kBCz+MVz1C619+6rboc0/Giv4WrDJwLjhEeSU+G4Fy709cMGYuPOK+VPjtATL5dKnIcKGQ2Vsza/kh1eN72yj7k0J1hDqWxy0tF/cK6kXu/cOltLQ6uDWWb7fxBaoCdYuYKwQIk0IEQzcBqzt+gAhRGKXq8uAwz6Mr/9OfA7OVrU90AP2uuuvVIKlKIrie/NWwLI/QMFm+PtNWvOLi9CkpEhybHU+6/K2/1QtxTXNZ20PdEuNDaOl3cVpHeqSGlsdPL0+l4xEK3deMsonrxkfcWbYsHLxemPXKUbHh3t9S2p3AjLBklI6gBXAB2iJ05tSyhwhxFNCiGUdD3tICJEjhDgAPATco0+0fVSwBQxBkDJP70gC3p4TNQQbDUxKjtQ7FEVRlIvTjK/BzSuhZDe8thyaqvWOyOcyk6zUNLVjq/NNUrP+YCnBRgNXZQ47777UOK2TYJEOjS6e23SMMnsLT98wyauNLbqKt7qHDasE62J17HQ9e07UcNuskbqUiwRqF0GklBuADefc9rMu3z8GPObruAasYDOMvERre6sMyp4TNUxKthISZNQ7FEVRlItX5o1gCoU3vwavLoW73gXL+Sf/Q1Vmx4d8OSV1JEeFevW1XC5te+DC8fFYQ86vNUntaNVeVNXI3HTftWo/drqev2wt5JasET7dtq9WsJQ3dp0iyCi4aYY+oz0CcgVryGmshLJDkL5I70gCXqvDycGSOlV/pSiK4g/GL4E734SaInj1Wqgr1jsin5k43IpBQLYPOgnuKqrmtL2V66Ykdnt/UlQowUaDTzsJSin52ZocwoKN/GjJBJ+9LmhdBAEq/KBVe5vDxTMbDnParn8sF4tWh5O39xZzZcYw4iK8X/PXHZVg+YOCLdrlaNXgYrC25VfS5nD59BM6RVEUpRejF8Fd70BDOay8BqoL9I7IJ0KDjaTHR5Drg06C6w+WEhJk4IqJ3a8QGg2CUbFhFPowwVp7wMb2gioeXjKBWB+f5MaGmzEI/1jB2nuyhpc/LWDVzpN6h3LR+DDnNDVN7dw2yzc1f91RCZY/OPYhhEZD0nS9Iwl4a/bbiAoLYsEYnbpBKoqiKOcbNQfuXgttDVqSVX5E74h8IjPJSraXOwk6nC42HCrlSxOHEW7uufIj1Yet2utb2vnFe4eZnBzJHbN9f5JrNAhi/aRVe3aJlmBvOarjGKCLzBu7TpIcFcqCMXG6xaASLL052yHvfRh3DRhUzdBgNLU5+DDnNNdOTiTYpH61FUVR/ErSdLjnPUBq2wVLD+odkddNSo6kzN5CZYP3TvS3F1RR1djG9T1sD3RLjQ3jRFWTT1q1//7jY1Q0tPL0DZMwGvSZRxkf4R/Dhg91JFgHimupaWzTOZqh72RVE9vyq7h11kgMOv3ugUqw9Fe0VWthO/E6vSMJeB/lnqa53cnyblrUKoqi9EYIsUQIcVQIkS+EeLSb+/+fEGJ/x1eeEKK2y33OLvetPfe5ShfDMuDrG7XmF3+7Dk7t0jsir8pIsgKQ48U6rPUHSokwm1g0PqHXx6XGhdPqcFHm5Vqgo2X1/PXzIm6bNZJpI6O8+lq9SbD6xwrWoZI6kiJDkBI+y6/UO5wh71+7T2IQ8JUsfZpbuKkES29H1kNQGKRfrnckAW/NfhuJkSHMSvX9vANFUQKXEMIIvABcA2QAtwshMro+Rkr5fSnlNCnlNOAPwNtd7m523yelXIbSu9h0uHcjhMbA32+Aws/0jshrMpM6Ogl6qQ6rzeFiY3YpV2UMu2Dn3LTOVu3e2yYopeTxNdlYQkw8crVvG1ucyx9WsBpaHRRWNvKVrJFEhwWx5Wi5rvEMdQ6ni7d2F7NofAKJkd7t3HkhKsHSk8sFR97TkqsgfX8RAl11Yxuf5lWwbGqSrkvCiqIEpNlAvpSyQErZBrwBLO/l8bcDq3wS2VAVNQrufR8iR8A/boZjH+sdkVdEhgYxKiaMHC/VYW3Nr8De4uC6qb1vD4Qzs7AKvViHtfaAjZ2F1fxoyQSiw4O99jp9kWA1U9nQ6pMtkT3JtdmREqaOjOTSsfF8mlepazxD3eajFZTXt3LbrJF6h6ISLF3Z9kF9KUy8Xu9IAt6GQ6U4XJJl09T2QEVR+i0ZONXlenHHbecRQqQAacB/utwcIoTYLYTYIYS4wXthDjGW4VpNVtw4WHUbHF6nd0RekZlk9doK1roDpUSG9q2xU6I1BLPJu63a1+63kRIbxq1Z+p/gxkeYcbgkNU361T25668mJUeycFw8lQ2t5JZ6v23/xeqNnSeJt5hZPKH37bK+oBIsPR1ZBwYTjLta70gC3tr9NsYmRJCRaNU7FEVRAk93y949fcx8G7BaSunsctsoKWUWcAfwOyFEercvIsS3OhKx3RUVqqMYAOFxcPc6rQHGm3fDgX/17/ntLVB5TFsB270SyrK9E+cgTEqOpKiqCXtLu0eP29Lu5MOcMpZkDu9TYyeDQZASG0ZhZZNH4+gqx2Zn+sgov9hJkmANAdC1Diu7pI4Ei5kESwiXjdOS4E/yhta/fZdL0u506R0GpXXNbD5azldmjiDIqH9603M/T8X7Dq+H1AVai3ZlwEpqm9lZVM0PrxqHEPr/p64oSsApBrp+5D4CsPXw2NuAB7veIKW0dVwWCCG2ANOB4+c+UUr5CvAKQFZWlton5BYapc3JWnUbvPNtaG+CrK9r97lc2k6PmiKoPQE1J87+vv6cvyZDEHzpZzB3BRj0P8mCM40ucm125oz23IzGLUfLaWxzcn0/GjulxIZ7bQWrqqGVMntLZ92Z3uIt7mHDrUy88A5Kr8guqWNycmRnPJOSrXxytIIHF4/RJyAv+N3Heaw/WMqm/16o6znY6t3FuCTc6gfbA0ElWPqpyIOqY3DJt/WOJOCtO6C9wS6b2u2OHkVRlAvZBYwVQqQBJWhJ1B3nPkgIMR6IBrZ3uS0aaJJStgoh4oD5wK98EvVQYo6AO9+CN78G678Hh96C+jKoOwXOrlu8BFiTIToV0hdDVIr2fXQKhMfDxz+Hjx6Hgs1wwx/B0v3gXV+a1NnowrMJ1roDpcRFBDNndN8bO6XFhfNJXgUul/T4KpO7U2Jmkn/sJEnoSLD0WsFqanNwvKKBayefye4Wjovnj58UUNfcTmRokC5xedr7OWUUVDZSXt/KsI5VQ19zuST/2n2KeemxpMSG6xLDuVSCpZcjHXvNJyzVN44hYM1+G9NHRTEqNkzvUBRFCUBSSocQYgXwAWAEVkopc4QQTwG7pZTu1uu3A29IKbuuPk0EXhZCuNC23T8rpcz1ZfxDRlAo3PoP2PgIlO6H4ZO1GuXolDOJVORIMPXSPOGW12DPq/D+Y/DH+XDDSzD2Sl/9CboVbzGTYDGTU+K5OqzGVgebjpzmKzNHYurHdqjU2HDaHC5sdc2MiPbse6a7tijDTxKsritYesi12XFJOlewABaNT+CFzcf5PL+SaybrtKzmQaftLeSdbgC0P69eCda245UU1zTzyBJ9O1d2pRIsvRxeD8kzwaqaMgxG3ul6Dpfa+fn1GRd+sKIoSg+klBuADefc9rNzrv+8m+d9Dkz2anAXE1MwXP+7gT9fCG174ai5sPperUPh3BXatkGT2XNx9tOk5EiPzsL6+PBpWtpd/doeCJAapyVVRZVNHk+wcmx2kqNCiQrTt3ugW1iwiQizifJ678796om7wcXkEWcSrOkjo7CEmPgkr2JIJFjbusz1yrHV6dZc4o2dp4gKC+LqTP1XrN38Y4PyxaauBGx71eqVB6zZX4LRIFg6RSWqiqIoSoeECXDfJph1H2x/Hv5yJVTm6xZOZpKV/IoGWtqdF35wH6w7UMpwawhZKf2r4U7zYqv2HFud36xeucVb9JuFlV1iJy7C3LlVEcBkNLBgTBxbjlZw9kJ4YNqaX0lMeDAjY0J1645Y1dDKh7ll3DR9BGZT77PgfEklWHo42vEh6QTVnn0wpJSs2W9j/pi4zq0AiqIoigJoWw6X/gZu+yfUnoSXL4N9/wAdTmwzkyJxuiRHyuoHfay65nY+zatg6ZTEftdRDbOEEBLk+VbtTW3aQF1/qb9yi48w61aDpTW4sJ7X+GHR+HjKumytC1RSSrblVzIvPZZJSZHkenCFtj/e3ltCu1Ny+2z/aG7hphIsPRxep839iB+ndyQBbe/JWoprmlnezy0SiqIoykVkwlJ44HNIngFrvgP//ia0+PZk0J14ZHugDuvDnDLanP3fHggdrdpjwjnh4RWsw6X1SInfjUqJt5qp1CHBam5zcqy8/qz6Kzd3u/YtR8t9HZZHHa9o4LS9lQVj4shItFJU1URDq8OnMUgpWbXrJDNTohk7zOLT174QlWD5WnMNFG1V2wM9YO3+EswmA1f50Z5bRVEUxQ9Zk+Bra+DyxyHnHfjjAije7bOXHxEdSmRokEfqsNYdLGVkTChTRwysHXpqXBiFHl7Byu0YpJzZTUKhJ71WsHJLtQYXk7r5eSRGhjJ+mCXg52FtPabVX80fE9e5NfSwj7cJ7j5RQ0FFo9+0Zu9KNbnwtbwPQDrV9sBBcjhdrD9YyhUTh2EJGRqtThVFURQvMhjhsh9C2mWw+huw8mpY/BOY/72eZ2a1N2vt4htOd7kshfrT0FgBIVawJIJl+NmXEcO01vMdhBBkJlnJsQ1uBau6sY1t+ZV867LRA545lBoXzuYjFThdEqOHWrXn2OxEhQWRFKlPF7meJFjNNLQ6aGpzEBbsu1Ne999zdwkWaNsEV24rpLHVQbg5ME/Ft+ZXkhobxsiYsM7Bvrk2O7NS+z42YLBW7TxJhNnEdVP8r2FIYP6tBrLD68CSpE2tVwZs2/EqqhrbWDZNbQ9UFEVR+mHkbLj/M1j/fdj0pDYza+xV3SRSp6G1m4TIYNISqPA4qD4O9g3gaD7/cWar9riOpOu7ziD+c9qII7sS07irILj/Xfw2ZpfidEmuH0Rjp7TYcNqcLmy1zYyM8UwnwdxSOxmJ59cb6S0+4kyr9pRY353yHiquIzY8mMQeEs6F4+J5+dMCPj9exZUZgbcLp93pYkdBNcs7zsGGWc3EhAf7tA6rrrmdDYdKuWnGCJ8mz33lfxENZW1NkL8Jpt/pNxPmA9WafSVYQkwsGh+vdyiKoihKoAmNgptXQvrl2tytwk/BFKoNJo4YDgkTYfTiM9c7L4dDaMzZ7+FSQqtdS8zqS7tcnj5z/dQXzLaXMtfYBqtf0xKvBd+HmfdozTj6aN0BG6Pjw5mYOPB6k1R3J8HKRo8kWO1OF0fK6rl7bsqgj+VpCR1zmbQEy3cDaA+V1DEpObLHhDMrNYawYCOf5JUHZIJ1sLiWhlYHC8bEAdoKbUailZxSz816u5C1+0toaXdx+6xRPnvN/lAJli8VbNY+5Zpwnd6RBLTmNicf5JRx3ZQkv2rJqSiKogQQIWDGXZB5I7gcEBKp3TaQ44REal/x43t8WOHper78/zbw3CIDC8v+Bu8/Clt/B5f+N8z4GgT1vr2u3N7CF4XVPHT52EGtFLlbtRdVNXIZg/+Q8nhFA20OF5lJ/lV/BWdWsHxZh9XS7uRYeQNXTOw5cQo2GZiXfqZdu7+t/F3I1mNVCAFz02M7b8tIsvLqtiLana7OLYPeIqVk1c5TZCZZz5oz5k/UMoovHV6v/QecukDvSALapiOnaWxzsny62h6oKIqiDJI5QlvR8vJJblp8BO3BkWxuy4B71sPd6yE2HTY+DH+YAbv+DI6eE4H3DpUiJVw/dXD1JgkWM6FBRooqmwZ1HDf3tjB/a9EOWg0W4NNZWIdL7ThdkknJvf88Fo6Pp7immQIPNxzxhW35lUxOjjxrqHRmkpU2p4vjFd5vP59dYie31M5tftjcwk0lWL7idEDeRhi3BIyqKcNgrNlvY5jVzCVpsRd+sKIoiqL4AaNBMDGxS6OLtEvhnve07oaRI+G9/4Y/zITdfwVH23nPX3+wlAnDLYxJGFw7aiEEKbFhFHXXqt3l0rY2luwF2/4+zQzLsdkxmwydK2P+JCYsGKNBUF7fcvYddhu0t3T/pEHK7kg4e2pw4baoo137J0cDq5tgY6uDvSdrmN+xPdDN3aLfF3VYq3adJCTIwPLpyV5/rYFSWwR95eTnWot2tT1wUOqa2tlytJy756Z6rPuRoiiKovjCpCQrq/cU43JJbUiwEDB6EaQthOP/QW55BrH+e7R/8n+cmvwgR4ctpaICjXObAAAgAElEQVTJxWl7C3tO1PDw1T1vQbwgR1tHTVgpN5t30m4rhvffAXuJdrvdpl26uswyik6DqbfBlFshJq3bw+bY6piQaMXk5W1hA2EwCOIigs+sYNWXwZZnYO9r2jzSW/7u8Zmk2cV1RIcFkRzVe23dyJgwRseHsyWvgnsXdP+z9UdfFFbhcEkuPSfBSosLx2wykGOzc9MM771+Y6uDtfttLJ2chNWPu0gHdIIlhFgC/B4wAn+WUj7bw+NuBt4CZkkpfTf4oqvD68EUAmO+pMvLDxUbs0tpd0qWT/PfTy0URVEUpTuZSZH8bfsJfr/pGO1OF1UNbVQ2tHZ8CSoafsA81z6+V7eaaZ8/itH1W/7gvJF3XQtIjorgxp4+sW+xn0mS7Dao77i0l575vvHMSsk3Oy7lnjCENUmbE5YyH6yJYE3WWs231MHBf8GWZ7WkZNRcLdHKvFHbUolWC5Nrs3PdAIYe+0q8xUxdXS1sfgY+/wM4W2HqHZD3PvxpMSx/XvszeciFGlx0tXBcPP/84iQt7U5CggKjpnzrsSrMJgMzUqLPut1kNDAh0er1Faz3DpXS0Orgttn+uz0QAjjBEkIYgReAK4FiYJcQYq2UMvecx1mAh4AvfB9lBynhyHtat6Jg/1tCDyRr9tsYHRd+wb3NiqIoiuJvZqZGIwT8ftMxjAZBbHgwcRFm4ixm0hMiiI8wExcxgcKIrxFU9zmjc/7Abypf5tcxHyHmfAfyjnckUqVdVp5Koa3+/BcLjdGSJWuiNhrG0pFIWRN5/6SRRz6sYN2Ka0mJizj/uW7T74S6Yjj4Jhx4A9Z/Dzb+CMZfA1NvpzhmLvYWR+f2ML/jdHCL/Ijri/8Gp2ohYzl86Qmt9q2uBN66G966B07tgiufHHQJR0u7k7zT9dw3fnSfHr9ofAJ/3VbEjoIqFo1PGNRr+8q2/Epmp8V0mxBmJFrZcKjUq407/r2nmPT4cLLOSfD8TcAmWMBsIF9KWQAghHgDWA7knvO4p4FfAT/0bXhdlO4HezEs/rFuIQwFZXUt7Cis4r++NLgOSoqiKIqih/T4CL547EuYjAaiQoO0bYI9uhUW3QJHNyA2PwMbOk5jhFFbYbImau3k07909sqTNUm77KUrYYyxGjvbKaxq6j3BAogcAZf+QGsrb9unrWodegty3yXBHMMTpllkBa8AOcrrjUL6TEo4ugE+eoKvVR9jn5hI9DdWw8hZZx4TmQz3bIAPfwo7XoCSPfCVV7Wf5QDlna7H4ZJMvkD9ldslaTGYTQa2HK0IiASrvL6Fo6fruXFG9yupGUlWVu08SUltMyOiPTNjrauWdid7T9Zw74I0vz8PDOQEKxk41eV6MXBJ1wcIIaYDI6WU64UQ+iVYh9eDMGif+CgDtu6ADSlR2wMVRVGUgOWezdQnQsCEpTDuGqjM07bmhceDYXDbyVJjtZPfE1X96CQoBCTP0L6u+h/I/5gTH77CHS2bMK/9AD4fr9VrjbsawmIhJOqCree9ong3fPi4VvseO5Z3J/yaHxxI4lhyFuf91EzBcO2vtOHTa78LL18KN/9Va0AyAIdKtAYmfU2wQoKMzE2P5dO8wGh08Xl+FUDn/KtzuTtJ5trsXkmw9p2spd0pmZ0a4/Fje1ogJ1jdpa6d7W6EEAbg/wH3XPBAQnwL+BbAqFFeGFh25D1tb3OY//9C+LM1B0qYOiLSLzsVKYqiKIrXGAyQMMFjh4u3mAkPNlI40BbhxiAYfw2/2hFHVds3eWdhubaFcNOT2pebKQRCo7VkKzSq4zK6y/dRZ+6PiNe6KYbFDmwlrOo4bHoKct/VktClv4UZd2PfWYxrfw5Vja0kWHpI+CbfDMMy4V93wWvLtG2E8/+r33Fkl9QRGRrEiOi+D49eOC6eJ9flcrKqiVGxnk9KPGlrfiXRYUE9bgmdMNyCEJBbaueqzOEef/1dRdUIAVkp/n8+HcgJVjHQtcJtBGDrct0CTAK2dCwjDgfWCiGWndvoQkr5CvAKQFZW1oV7kvZH1XGoOAwz/9ejh73Y5Jc3kF1i5/HrMvQORVEURVECmtaqPbz7Vu39kGOzMzstCbKWQtbXobpA20bYXKt1Tm6p1b53X9YVw+ls7fvu6sYATKHatsSokdpl5Kizr1uStJUnt8Yq+PRXsOsvWuK38FGYtwLMWjv7BMuZWVg9Jligbbf81mZYswI+fgKKd8ENL2rzS/tIa3Bh7df2tYXudu155dw1N7XPz/M1KSVbj1Uyb0xcj1tbw4JNpMWFe63Rxc7CaiYMtxIZ5r/dA90COcHaBYwVQqQBJcBtwB3uO6WUdUDnGqYQYgvwQ593ETy8TrucsNSnLzvUrD1gQwi4fsrgBiwqiqIoiqK11e6cyTUA1Y1tlNa1nD1gOGa09tUXznatU6E7AWs4DbWnoK7jq/YUlGVDY/k5TxRajVnkCK1e6vhmaGuAGV+DRY+B5eyVk/iOBKu8vpXMC8Vktmh1WDtego8eh1cWaa3ch0+64B+nzeHiaFl9v1uup8WFMyomjE/yKvw6wTpe0UiZvaXH7YFuGYlW9p2s9fjrtztd7DlRw61+PFy4q4BNsKSUDiHECuADtDbtK6WUOUKIp4DdUsq1+kbY4ch7kDhV++RFGRApJWv3lzAvPbZ/e9cVRVEURelWalwY7+eU0e50ETSAGVbu5Cwzqe8rPGcxBkF4nPbVm/Zmrc187UltBcydfNWdgtKDkHYZXP54j1so3atWnbOwLkQImPsdrfPiW/fAn6+A63+n1Zf1Iu90Pe3Ovje4OPNygoXj4vn33mJaHU7MJv9s174tvxLopv7K2Q5tjZ2t+zOTIll/sJS6pnaPrjRll9TR3O5kVgDUX0EAJ1gAUsoNwIZzbvtZD49d5IuYzlJfBsU7YfFPff7SQ8nB4jqKqpr4zqIxeoeiKMoQdaG5ikKIe4Bfo+2YAHheSvnnjvvuBtz/0f+PlPJvPglaUQYhNTYcp0tSXNM8oNpm9zYwr7doDwrV2qrHpg/o6fFdtgj2S8pc+Pan8O9vwDvfhlNfwJJnwWTu9uH9bXDR1cJx8fx9xwl2F9Uw/wIrRHrZml/JqJgwRsZ01IlJCblrtO2UDRWw5Jcw424y3I0uSu3MTY/12OvvKqoGYFaaf7dnd/O/sdtDyZH3tMuJ1+kbR4B7d38JwUYDV0/yfMGkoihKl7mK1wAZwO1CiO4KPv8lpZzW8eVOrmKAJ9C62M4GnhBCBMYZgHJRcydVA63DyrHZSYoMITo8+MIP1lFIkBFLiKn/CRaAZRjc9S7M/x7sXgkrr4ZDq6HskLay1sWhkjosISZGxfS/UcXc9FiCjQY+8dNugg6nix3Hq84kf6d2wl+u0uaIBYVpnSXX/Resuo1MawvAoLafdmdnYTWj48J7r6PzIwG9guX3jryn7UWO91znn4uN0yVZd6CUxRPiiQz1/6JGRVECUl/nKnbnauAjKWV1x3M/ApYAq7wUq6J4REpsR4JV2Qjj+//8HFsdGQPdHuhj8RYz5fUtA3uy0aQNIR4xC9Z8R1vRAkBAdArEjYO4cSQeN3BjXCqipVbrjNgP4WYTs9Ki2XK0nB9fO7H/MUqp1bHVFWtfbY2QfrnHulcfLKmjvtXBlYnN2rbJnHcgYhhc/xxM/yog4Is/wsc/J654ETeHf4PcUs+N1HG5JLuKargmgD5oVwmWt7TUQeGnMOcB/xm8F4C2H6+isqGVG9TsK0VRvOeCcxU7fFkIcRmQB3xfSnmqh+d2+x+W10eCKEo/xEUEE2E2aQlWPzW1OSiobOS6KUleiMzzEizmga1gdTXxOhhzBVTlQ+VRqMjrvJQFn/BdZys0AP/7AwhP0BKv+HEQNx7ixmit6E0h2pbHrpemEDAYWDQugV9sOIyttpmkqHPavDsdUF96pgat7pT2fe2pLknVOV0ZDUEw9kqY/BVtDmtQ31vHn2tX7nF+anqdxR991KVT43fB3GVI9dzvQPpiePs+flP2v7yfvxda/9rZzXEw8srrqWtuD5j6K1AJlvfkfQiudph4vd6RBLQ1+0uwmE0snuD/E84VRQlYvc5V7LAOWCWlbBVC3A/8Dbi8j8/VbvTmSBBF6SchBKlxYRT2Z9hwhyNl9UhJZ72Nv4u3hHCo2AOd7YJCtI6C53QVzC2u5jsvvMOvFoZwiaXyTPJ16N/Q2oetckYz9xpDWGYWhL8SAeEW7bUMJq2e324D6Tz7OaExWifF2HQYvbCjpf1I7Qsg9x1tO+PRDRBsgYxlMOUWSL2074OqHa2w68/c+cUvCTM1IqZ+FRb/ROve2J2EifDN/7B95Q+5suQ15EvzETe9AqPm9O31erCzUKu/mp02wATLXgpFW7Utn2mXDSqWvlIJlrccWa8tnyZn6R1JwGppd/J+dhlXTxpOSJB/dtVRFGVIuNBcRaSUVV2u/glwDzcsBhad89wtHo9QUbwgNTacg8X9r5XJ6WhwkRkgCVaCxUz5YFewepFd2sAJOZyErEXQtWGIlFr7+arjWiv59mZwtHR7aWhv5os9BQwzCC6JC4X2Fu2D+pT558wF67gMvkBjkhEz4YontcTi4JtweC3s/4fW4n7Sl2HKrTB8cve7rLo2sKgpYp9rCnlTH+Eby/uwaGAKpnrOY9y6KoV/Ov9K8F+v0WrYFj129vyyfviisJqkyJC+D3CuPw1Fn2l/9qLPtFVH0FbzVIIVwNpbIP9j7S/SoPqIDNSWo+XUtzpYPi0wtiAoihKwep2rCCCESJRSlnZcXQYc7vj+A+CXXRpbXAU85v2QFWXw0uLC2XColDaHi2BT389Xcm11RIYGkXzuVjY/FW8x09TmpLHVQbjZ86e+2SV2IswmUs5tcCGENpfLcuHaIQFsbz3I+gOl7L35ygG1zj+Pwaitbo1eCEt/A3nvw8G34IuXYfvzWo+AKbdo56tRHduWT+2ED36idcFOyODAopXc9X4Ir0/pbtd09zKSrOyWE1g/bzU3lT8PW38L+R/BTX/SVrn6QUrJzsJq5qXH9jzAuaECTmyFwo6kqvKodrvZCinzYOY92srd8Mn9eu3BUAmWNxRs0T6pUN0DB+XdfTbiIszMHe25Np+Koijn6uNcxYeEEMsAB1AN3NPx3GohxNNoSRrAU+6GF4ri71Jjw3FJKK5pYnR8xIWf0CHXZiczydrzCa+fSegybDjNCwnWoZI6MpOsGAyD+3ksHBfPqp2n2HeyduDb4XoSFAqZN2pfTdWQ+662srXpKe1r1DytKYZ7B9ayP8C0O1m34SjBphNkpfa9cUdKTBhhwUYOVji5afnzWg3Y2ofg5YVwxc/hkvv7vABxoqqJivrWs38ejVVnJ1QVHZ93BVu09vrT7+xIqKZoTUp0oBIsbziyTsuaU32zDDnUHDtdz4tbjvNhbhl3z0vF5IlPcRRFUXpxobmKUsrH6GFlSkq5Eljp1QAVxQtS47QVl6Kqxj4nWA6niyNl9dw1J8WboXlU11lYA5n51RuH08XhUrtHfh7zxsRhMgi2HC33fILVVVgMZN2rfdWcgENvaclW6QFtK9/cFZ0NLLbmVzIrNbpfpRoGg2BiovVMq/YJS7UujGsfgg8e01bSbngJIrvpB9TaoDX0qC+F+jLqcg/zM1Muy/JMkF2h1VPVndQeGxSuJVRTb9XOuROn6pZQncs/ohhKXE44uhHGXjXgvaYXq+ySOl7YnM/7OWWEmIzcOz+N/7pirN5hKYqiKMqQlNrRqr2wsu+NLo5XNNLqcJGZHBj1V0Dn7KQBt2rvxbHyBlodLiaPGHzLemtIEDNSovkkr4JHlvhoxE90Clz2Q7j0v7XrXVYlK+pbOVJWz48GEEtmkpW395bgckltZS8iAW5fBXtfg/cfg5fmwtQ7oLlaS6bsWkJ1bjfEqUC6KZTw2hFac42UuRB3t1ZLlTRd62roh1SC5Wknd0BTldoe2A+7i6p5fnM+W45WYAkxsWLxGL4+P40YPx9eqCiKoiiBLCY8GEtI/1q155ZqqxKZATIDC85ewfK0QyWe/XksHBfPrz84Snl9i2+H6naz3fPz45UALHAPGO6HjEQrr7We4FRNU+fMNYSAmXdD2qXw7oOw+y8dNWqJMCxTa4NvGQ7WpM7br/pLHmlJw3j5rsBqGqcSLE87sh6MZu2XROmRlJJt+VU8v/kYOwqqiQkP5uGrx3PX3BSsIf75aYSiKIqiDCVCCNLiwimq6nuClVNix2wyMNrDW+28KSo0iCCj8EonwZySOsKDjR77eSwaryVYn+ZVcvPMER455kBtPVZJVFjQgNrxu5+Ta7OfSbDcYkbDvRu1boW91PGV1jWTV5PHrfMDrxZfJVieJKWWYI1e5JHBakORlJKPD5fz/OZ8DpyqZZjVzOPXZXD77JGEBatfR0VRFEXxpdTYcPadqunz43NsdiYMtwRUfbTBIIiL8MCw4W5oDS4iB93gwi0j0Uq8xcwneRW6JljaB+GVzEuPxTiAP9u4YRaMBkGOzc41k3uYm3WBJinu+VeXeLMezUvUGa0nlR2C2pNw2cN6R+J3nC7Je4dKeXFzPkfK6hkZE8ovb5zMl2cmYzapGVeKoiiKoofUuHDWH7T1qVW7lJIcWx1LpwTe+BRvzMJyOF3kltq5Y7bnGn4IIbhsbDybjpzG6ZIDSm48obCyEVtdCw8OYHsgQEiQkTHxEeSW2gccw87CaiLMJiYmBk69n5tKsDyp7CAYg2H8tXpH4lfWHrDxu4/yKKhsJD0+nN/eMpVlU5MC6tMvRVEURRmKUmPDcEk4Wd3EmITeOwmW1DZjb3EEzIDhruItZkpqPdvk4nhFIy3tLiZ5uOHHovHx/HtvMQeKa5kxqu/t0T1pW75Wf3XpmPgBHyMjycr241UXfmAPdhZWMzMlWrckczDUGa4nTf8qPFIA4QPL9ocal0vyyw2HeWjVPkKCjLx45ww++v5CbpoxQiVXiqIoiuIHUjtqh/rS6CLHpq1GDKQmR2/xlhAqPNxFMLujwcXkZM82/FgwJg6DgE+OVnj0uP3x2bFKRsaEMio27MIP7kFmkpUyewtVDf1fOaxubONYeYN329V7kTrL9TRVewVAS7uTFav28sqnBXx1zijWrpjPtZMTPbZHWVEURVGUwUvraEDQl0YXOTY7BgEThwdigmWmqrENh9PlsWMeKqkjLNjYryHNfREdHszUkVFsydMnwXI4XWwvqBpQ98CuMjq29g1km+CuosCtvwKVYCleUN3Yxp1//oINh8r4ybUTeXr5JLVipSiKoih+KDo8mMjQIAr7sIKVa7MzOj6C0ODAq51OsJiREqoa2zx2zOySOjISrV7ZwrZoXAIHi2sHtPozWIdK6qhvcTB/kAmWu3Yq19b/BGtnYTXBJoNH5ovpQZ31Kh5VWNnITS9uI7ukjhfvnMF9l41GXKBLjKIoiqIo+kmNC+dE1YWHDefa6jpXJQKNp2dhOV2SHJudSR7eHuh2VeYwpIQXtxz3yvF7466/mpc+uAQrOjyYpMiQAa9gTR8ZFbCN0FSCpXjM7qJqbnpxG/YWB/+8bw7X9tSWU1EURVEUv5EWG3bBFayaxjZsdS0B2eACtBUsgHIP1WEVVjbQ3O70WoI1MdHKV+eM4q/bCtl/qtYrr9GTrfmVZCZZiQkPHvSxMpIiO2v3+qqh1UF2SV3Abg8ElWApHrL+oI07/vwFUWHBvP3APGam6NP1RlEURVGU/kmJDcdW10xLu7PHx7hPkjOTAnPLlqdXsA55qcFFV48smUCCJYRH/32Qdg/WjvWmqc3B3hO1LBjrmYZtGUlWCioaaG7r+XfrXHtO1OCSMDst8AYMu6kESxkUKSV//OQ4K/65jynJkbz9wLzOjkSKoiiKovi/tLhwpIRT1T1vE8wt1RKKQOwgCGcSrHK7hxKsYjshQQbS4713zmMNCeLpGyZxpKyeVz4t8NrrdLWzsJo2p2vQDS7cMhKtuCQcPV3fjxiqMBoE00dFeSQGPagESxkwh9PFT9/N5tmNR7huSiKvf/MSoj2wnKwoiqIoiu+4PxjtbZtgjs1OYmSIR7aN6cFsMhIZGkSFh5pGZJfUMTHR6vUmXldmDGPp5ER+v+kYxysavPpaoNVfBZsMzEr1zPY895bSHFtdn5+zq7CGScmRhJsDd1yvSrCUAWlsdXDfa7v5xxcnuX9hOs/dNp2QoMAsRFQURVGUi1lfWrXn2OwBW3/lFm8xe2QFy+WS5NjqvLo9sKsnlmUQYjLw2NuHcLmkV19ra34VWSnRHjunGxEdiiXE1OdOgi3tTvafqg3o+isI8ARLCLFECHFUCJEvhHi0m/vvF0IcEkLsF0JsFUJk6BHnUHPa3sItL2/n02OV/OLGSTx6zQQ130pRFEVRAlRkWBDRYUEU9dBJsLnNSUFFAxkBWn/llmAxe2QFq7CqkcY27zW4OFeCJYSfLs1gZ2E1b+w65bXXqWxo5XCpfdDt2bsSQpCRaO1zJ8EDp2ppc7o8toKml4BNsIQQRuAF4BogA7i9mwTqn1LKyVLKacCvgN/6OMwh50iZnRtf2EZRZSN/vjuLOy9J0TskRVEURVEGKTUunKIetggeKbPjkgRsi3a3eIvZI10Es33Q4OJcX8kawbz0WJ7ZcJjTds90QjzX58erADxWf+WWmRTJkdJ6nH1YfdtZqA0YnpUa2M3SAjbBAmYD+VLKAillG/AGsLzrA6SUXdPlcMC766pD3Of5lXzlpe04peTN++eyeHyC3iEpiqIoiuIBqbE9J1hnOggGdoKVYDFTUd+KlIM7HTxUXIfZZGBsQoSHIrswIQS/vHEybU4XP1uT7ZXX2HasksjQII+vzGUkWWlud/ZpmPXOomomDLcQFRaYtX5ugZxgJQNd10mLO247ixDiQSHEcbQVrId8FNuQU9PYxgP/2EtiVAjvfGd+wLZpVRRF6U4ftpz/QAiRK4Q4KITYJIRI6XKfs2Mr+n4hxFrfRq4onpEaG46trqXbVu25pXYiQ4MYER2qQ2SeE28x09Luor7VMajjHCqpY4IPGlycKzUunO9fOY4Pck7zfnapR48tpWRrfiXz0mMxerjsw73yeaFtgg6niz0napgd4PVXENgJVnd/++d9JCGlfEFKmQ78CPhptwcS4ltCiN1CiN0VFRUeDnNo+O1HeTS0Onj+jhkkRQX2f7CKoihd9XHL+T4gS0o5BViN9qGdW7OUclrH1zKfBK0oHpYaFwbAiW7qsHJsdjISrQgR2PXWCZYQYHCzsFwuSa7NzuRkfVbzvrkgjcwkK4+vyaGuud1jxy2qaqKkttmj9VduYxIiCDKKCza6yLHZaWpzBnz9FQR2glUMjOxyfQRg6+XxbwA3dHeHlPIVKWWWlDIrPj7egyEODYdL7fzjixPcNSeFccMseoejKIriaX3Zcr5ZSuk+89yB9p6jKENGWg+t2h1OF0dK7QE7/6orT8zCOlHdRH2rw6f1V12ZjAb+98tTqG5s49mNhz1yzDaHi5e25AOer78CCDYZGDfMcsFW7buKtPortYKlr13AWCFEmhAiGLgNOGtrhhBibJerS4FjPoxvSJBS8uS6HCJDg/jeFWMv/ARFUZTA06ct5118A9jY5XpIxy6IHUKIbj/IUxR/556FdeKcVu0FlY20OlwBX38FWg0WMKhOgoc6Glz4qoNgdyYlR/LNBWms2nmK7R2NKQaqsLKRL7/0OW/uLuYbC9JIiQ3zUJRny0i0kmuz91r/9kVhNamxYQyzhnglBl8K2ARLSukAVgAfAIeBN6WUOUKIp4QQ7i0aK4QQOUKI/cAPgLt1CjdgvZ9dxo6Can5w1fiALzhUFEXpQZ+2nAMIIb4KZAG/7nLzKCllFnAH8DshRHoPz1Xb0RW/ZQ0JIjY8+LxZWLmdDS4Cv/b6zArWwLvwZZfUEWw0MDZB3x0937tiHKNiwvjxO4e6rZvri7f3FnPdc59xsrqJl++ayePXZXhtG2hGkpWqxrYet2e6XJJdRdVDYvUKIHBHJANSyg3AhnNu+1mX7//L50ENIS3tTv7nvcNMGG7h9lkjL/wERVGUwNSnLedCiCuAnwALpZSdZwlSSlvHZYEQYgswHTh+7vOllK8ArwBkZWWprraK30mJDTtvi2COrY5gk4H0+HCdovKcyNAggo2GQa1gZZfUMSHRQrBJ3zWK0GAjz9w0mTv//AXPbTrGI0sm9Pm5Da0OHn83m3f2lTA7LYbf3TrN6/X17gQ9p9ROQjcrVMfKG6htah8S9VcQwCtY/qi5zUllQyv1Le20O12DbgOqtz99WkBJbTNPXJ/p8045iqIoPtSXLefTgZeBZVLK8i63RwshzB3fxwHzgVyfRa4oHqTNwjq7yUWOzc6E4ZYhcR4ghCDeYqZigDVYUkqyS+p03R7Y1fwxcXxl5ghe/rTggg0k3A4W17L0uc9Ys7+E718xjlX3zfFJ87IJidqKX09x7uyov7okLdbrsfhCQK9g+Zv1B208vPpg53WDgJAgI2aT4bxL8znXU2LDWLF4LKHBRh3/BGeU1jXz4pbjXDt5OHPTh8Yvu6IoSneklA4hhHvLuRFY6d5yDuyWUq5F2xIYAbzVsYXmZEfHwInAy0IIF9qHls9KKVWCpQSktNhw3t5bQnObk9BgI1JKckvtXDNpuN6heUy8xTzgFayT1U3YW/RrcNGdnyydyOaj5Tz69kHefmBej4mwyyX5y9ZCfvXBEeIjzPzr23N9ulpkDQliVExYzwlWYTXDrSGMjBkanapVguVB00dF8/TyTFodLlranZ2XLe0uWh1nX7a0O6lvcVBR30qrw8V7h0rZdLicV+7KYpSXCgz745kNR3BJyWPXTNQ7FEVRFK/rw5bzK3p43ufAZO9Gpyi+4W50UVTVyMREK7a6Fmqb2skYAvVXbvEWMye7aUXfF50NLvzo5xEVFszPl2Wy4p/7ePXzIr556ejzHjMyakYAACAASURBVFNR38p/v3WAT/MqWJI5nGe/PFmXuvrMJGu3s7CklOwsrGJ2WmzAjwJwUwmWB41JiGDMAKd6f5JXwUOr9nH981v5/W3TWDQ+wcPR9d2uomrWHrDx0OVjGBmjf7KnKIqiKIr3pXXpJDgx0UpOR0LhHhQ7FCRYzOw5UTOg52aX2AkyCsYNH9i5nrcsnZzIuxNL+M2HR7kqY/hZH9R/mlfBD97cT32Lg1/cOIk7Zo/SLYnJSLSyMbuMhlYHEeYzKcjJ6iZO21uHTIMLUDVYfmPhuHjWrVhAUlQoX391F8//5xgul+9ruJwurS17YmQI9y/qthGWoiiKoihDkLtFd2FHHVaOzY4QMDFx6MzAjLeYqW5so83h6vdzs0vqGD/cgtnkH+UcbkIInr5hEiaDgZ+8ewgpJW0OF89sOMzXVu4kJjyYdd9dwJ2XpOi6QuSepXbknFWsnYXu+iuVYCleMCo2jLcfmMeyqUn85sM87n99D/UtnpvS3Rer95wiu8TOo9dMICxYLXAqiqIoysXCEhJEXEQwRR2dBHNL7YyOCx9S5wMJFq2DXVVj3+uwWh1OPsmr4EBxrV/VX3WVGBnKj5aM57NjlTz/n3y+8sfPefnTAr46ZxRrVyxg3DD9k2R3gnXuNsGdhdVEhwUxJt6/VgYHY+j8ixkiQoON/O7WaUwdEcUvNhxm+QvbeOWumYzxwbwFe0s7v/7gKFkp0SybmuT111MURVEUxb+kxoZT2DELK9dmZ2ZKtM4ReZZ7FlZFfSuJkT03VKhsaOU/R8rZdPg0nx2rpKnNSUiQgasy/Lfhx52XpPDufhv/91EekaFB/PGrM1niRw1KhltDiAkPPq/Rxc6iarJSYzAYhkb9FagEyy8JIbh3QRoZSVZW/HMvy5/fxv/dMs3r/0j+sOkYVY1tvPr12UOmyFBRFEVRlL5LjQvn07wKahrbKKlt5q65KXqH5FEJncOGz17BklJypKyeTYdP8/Hhcg4U1yKllhTcMD2ZKyYmMC89jpAg/9oe2JXBIPjtLVNZubWQby1MJ9kH7df7QwhBRqKVnC4J1ml7CyeqmrhrztD6PVMJlh+bMzqWdd9dwAOv7+X+1/fw4OJ0fnDleIxeyPCPVzTw121F3Jo10m/mOyiKoiiK4ltpceGs3lPM7o5GEJlJQ6fBBXRZwWpopaXdyY6CKjYdLuc/R8opqW0GYOqISL73pXF8aWICmUnWgPrQOSU2nCeXT9I7jB5lJFl59fMi2p0ugoyGzvqrodTgAlSC5fcSI0P517fn8PO1Obyw+TiHSuw8d9s0j7fXfHp9LqFBRn549XiPHldRFEVRlMCRGqt1Etx4qBQYWh0EAeIitATrxS35PL0+l6Y2J6FBRhaMjeO7l4/h8gkJJFhDdI5y6MpMstLmcFFQ0cj44RZ2FlYTHmwccr9nKsEKAGaTkWdumsKUEVE8sSaH65/fyh+/OpNMD81h2HyknC1HK/jp0omd//EoiqIoinLxSY3TOgl+lHua4dYQYofYeUGwyUBGopWapjZumpHMlyYOY+7oWL/e+jeUuBOpHFtdZ4I1IyW6xwHJgUolWAHk9tmjmDDcwgOv7+XLL33OszdN4YbpyYM6ZpvDxdPrcxkdH87X5qZ6JlBFURRFUQKSewWrvtUx5LZtub330AKAgNr6N1SkxYVjNhnItdm5fEIbR0/Xc/3URL3D8rihlS5eBKaPimbddxcwdUQU3/vXfh5ZfYDD3UzF7qu/fV5EQWUjj1+XQbBJ/TooiqIoysUs3GzqrFMaavVXbkIIlVzpxGQ0MCHRSm6pnV1FWp3f7LRYnaPyPHVGHYDiLWZe/+Yl3HdpGu/sK+Ga33/Gtb//jJVbC6lq6Ptch4r6Vp7bdIzLJySw+P+3d/9Rdpblwe+/F5PZxNn8yEwIahMgQeKPoBZ1Cq6qLQWV4PvW0CW+Df6iPZxD2xfOqq/LHqBvbStHe95Y+9LTJVqx0BepNlBaSmqrWBV/dSkwERQCRsaAMoQjCRkQkoYQ5jp/7Dt2MrN/TbLJnpn9/aw1K3vf+36u+5rbbZ5c3M9zPy859jnMWJIkzRUryirWqg7diiBNtqoUWLdteYxK32G8ctn8+55ZYM1R/X2H8d//0ypu//038sG3nsyCvuDyz93LaX/yZf73a0f4wj3/X8unlP/pLd9n995n+YP/9LJDlLUkSZrt9t2HNV9XsNRdq37uKB7f9Qz/fPcjnHLconl5/5v3YM1xg9UK5//ics7/xeX84CdP8vcbx7jpzof50n0/YXCgn7f+/M9x7muO4+VL999m9Htjj/N3G8f4P95wIifOoydnS5Kkg/PGlz2f7U/tYdng7HqOkuaHfRtdPPLEbt726mVdzua5YYE1j7z4+Udy2Vtexu+d9RK+ObqdGzeO8bd3PMS13/oRL37+Ebzt1cv4tVctZcmRh/PBf7qXxdUKF59xUrfTliRJs8ibT34Bbz75Bd1OQ/PUy154JBGQOf+ef7WPBdY8tKDvME5/ybGc/pJjeeLfn+Fz39vK328c4//5/PdZ94Xv84qlR/PdsSf4yNteyVEL+7udriRJknrEQGUBK46p8uD2nbz6hMFup/OcsMCa545+Xj/vPO0E3nnaCfxw21P8w3fGuOk7D/MLywc59zXzc1lWkiRJs9fpLz6WB4ae4ojD52cpMj9/K9X1oiVH8HtnvZTfO+ulZKZblEqSJOmQ+8NfXdXtFJ5T7iLYoyyuJEmSpM6zwJIkSZKkDrHAkiT1vIhYHRGbI2I0Ii6t8/nhEXF9+fy2iFg+6bPLSvvmiDjrUOYtSZp95myB1cbJ8H0RcW9EfC8ivhwRJ3QjT0nS7BYRfcCVwNnAKuC8iJh6g8AFwHhmngRcAawrx64C1gInA6uBj5d4kqQeNScLrDZPhncCw5n5SuBG4COHNktJ0hxxKjCamVsycw+wHlgzpc8a4Nry+kbgzKjdzLoGWJ+ZT2fmA8BoiSdJ6lFzssCijZNhZt6ambvK228D7kkuSapnKfDQpPdjpa1un8zcCzwBLG7zWElSD5mrBdZMT2gXAJ9/TjOSJM1V9bZVzTb7tHNsLUDEhRExEhEj27Ztm2GKkqS5Yq4WWDM5ob0LGAb+tGEwT3qS1MvGgOMmvV8GbG3UJyIWAEcDO9o8FoDMvCozhzNzeMmSJR1KXZI028zVAqutE1pEvBH478BbM/PpRsE86UlST7sDWBkRKyKiQm3Tig1T+mwAzi+vzwW+kplZ2teWXQZXACuB2w9R3pKkWShq54e5pfzXwx8AZwIPUzs5viMzN03q8ypqNyKvzsz7ZxB7G/Cjg0jvGGD7QRw/Xzkv0zkn9Tkv0zkn9e2blxMy86D+61hEvAX4c6APuCYzPxwRlwMjmbkhIhYC1wGvorZytTYzt5Rj/zvwvwF7gfdmZstL0j3XPGecl+mck/qcl+mck/omz0tb55s5WWBBWyfDLwGvAB4ph/w4M996CPIayczh53qcucZ5mc45qc95mc45qa+X56WXf/dmnJfpnJP6nJfpnJP6DmReFjxXyTzXMvNfgH+Z0vaHk16/8ZAnJUmSJKmnzdV7sCRJkiRp1rHA6ryrup3ALOW8TOec1Oe8TOec1NfL89LLv3szzst0zkl9zst0zkl9M56XOXsPliRJkiTNNq5gSZIkSVKHWGB1UESsjojNETEaEZd2O5/ZICIejIi7I+KuiBjpdj7dEhHXRMSjEXHPpLahiPjXiLi//DnYzRy7ocG8/HFEPFy+M3eVHUN7RkQcFxG3RsR9EbEpIn63tPfs96XJnPTkd8VzTX2ebzzXNOK5ZjrPNdN18lzjJYIdEhF91J7N9SZqD0K+AzgvM+/tamJdFhEPAsOZ2dPPVYiIXwKeAj6dmS8vbR8BdmTm/yj/SBrMzEu6meeh1mBe/hh4KjM/2s3cuiUiXgi8MDO/ExFHAhuBc4DfoEe/L03m5L/QY98VzzWNeb7xXNOI55rpPNdM18lzjStYnXMqMJqZWzJzD7AeWNPlnDRLZObXqT2cdLI1wLXl9bXU/k/cUxrMS0/LzEcy8zvl9ZPAfcBSevj70mROepHnGjXkuaY+zzXTea6ZrpPnGguszlkKPDTp/Ri9+w+AyRL4YkRsjIgLu53MLPP8zHwEav+nBo7tcj6zycUR8b1yWUfPXJ4wVUQsB14F3IbfF2DanEDvfVc81zTm+aY+/+5orNf+/qjLc810B3uuscDqnKjT5vWX8LrMfDVwNnBRWaaXmvkE8CLgFOAR4M+6m053RMQRwN8D783Mn3Y7n9mgzpz04nfFc01jnm80E73498c0nmum68S5xgKrc8aA4ya9XwZs7VIus0Zmbi1/PgrcRO3yFtX8pFzvu++630e7nM+skJk/ycxnM3MC+BQ9+J2JiH5qf7l/JjP/oTT39Pel3pz06HfFc00Dnm8a6um/Oxrp0b8/9uO5ZrpOnWsssDrnDmBlRKyIiAqwFtjQ5Zy6KiKq5SZBIqIKvBm4p/lRPWUDcH55fT5wcxdzmTX2/cVe/Bo99p2JiACuBu7LzP856aOe/b40mpMe/a54rqnD801TPft3RzM9+vfHz3iuma6T5xp3Eeygsm3jnwN9wDWZ+eEup9RVEXEitf+KCLAA+GyvzklE/C1wOnAM8BPgj4B/BG4Ajgd+DLw9M3vqJtwG83I6tWX4BB4Efmvf9eC9ICJeD3wDuBuYKM2/T+068J78vjSZk/Powe+K55rpPN/UeK6pz3PNdJ5rpuvkucYCS5IkSZI6xEsEJUmSJKlDLLAkSZIkqUMssCRJkiSpQyywJEmSJKlDLLAkSZIkqUMssCQBEBGnR8Tnup2HJGl+83yj+c4CS5IkSZI6xAJLmmMi4l0RcXtE3BURn4yIvoh4KiL+LCK+ExFfjoglpe8pEfHtiPheRNwUEYOl/aSI+FJEfLcc86IS/oiIuDEivh8RnylPNZck9SDPN9KBscCS5pCIeBnw68DrMvMU4FngnUAV+E5mvhr4GrWn1AN8GrgkM19J7cnk+9o/A1yZmT8P/CKw74nkrwLeC6wCTgRe95z/UpKkWcfzjXTgFnQ7AUkzcibwGuCO8h/7ngc8CkwA15c+fwP8Q0QcDSzKzK+V9muBv4uII4GlmXkTQGbuBijxbs/MsfL+LmA58M3n/teSJM0ynm+kA2SBJc0tAVybmZft1xjxgSn9skWMRp6e9PpZ/DtCknqV5xvpAHmJoDS3fBk4NyKOBYiIoYg4gdr/l88tfd4BfDMznwDGI+INpf3dwNcy86fAWEScU2IcHhEDh/S3kCTNdp5vpAPkfy2Q5pDMvDci/gD4YkQcBjwDXATsBE6OiI3AE9Sumwc4H/jLckLbAvxmaX838MmIuLzEePsh/DUkSbOc5xvpwEVms5VdSXNBRDyVmUd0Ow9J0vzm+UZqzUsEJUmSJKlDXMGSJEmSpA5xBUuSJEmSOsQCS5IkSZI6xAJLkiRJkjrEAkuSJEmSOsQCS5IkSZI6xAJLkiRJkjrEAkuSJEmSOsQCS5IkSZI6xAJLkiRJkjrEAkuSJEmSOsQCS5rHIuLBiHhjt/OQJEnqFRZYkiRJktQhFlhSD4qIBd3OQZIkaT6ywJJ6QET8cUTcGBF/ExE/BX6j2zlJkiTNR/5XbKl3rAHeDrwHOLzLuUiSJM1LFlhS7/hWZv5jef3vXc1EkiRpnvISQal3PNTtBCRJkuY7Cyypd2S3E5AkSZrvLLAkSZIkqUMssCRJkiSpQyLTq4YkSZIkqRNcwZIkSZKkDrHAkiRJkqQOscCSJEmSpA6xwJIkSZKkDrHAkiRJkqQOWdDtBGabY445JpcvX97tNCRJbdq4ceP2zFzS7TwkSQILrGmWL1/OyMhIt9OQJLUpIn7U7RwkSdrHSwQlSZIkqUMssCRJkiSpQyywJEmSJKlDLLAkSZIkqUMssCRJkiSpQ9oqsCJidURsjojRiLi0zueHR8T15fPbImL5pM8uK+2bI+KsVjEjYkWJcX+JWWk2RkQsj4h/j4i7ys9fTor1moi4uxzzFxERM58iSZIkSWpPywIrIvqAK4GzgVXAeRGxakq3C4DxzDwJuAJYV45dBawFTgZWAx+PiL4WMdcBV2TmSmC8xG44RvHDzDyl/Pz2pPZPABcCK8vP6la/ryRJkiQdqHaeg3UqMJqZWwAiYj2wBrh3Up81wB+X1zcCHyurRWuA9Zn5NPBARIyWeNSLGRH3AWcA7yh9ri1xP9FkjLoi4oXAUZn5rfL+08A5wOfb+J0PyK49e3l81zPPVfjnxAuOWshhh7mwJ0mSJHVCOwXWUuChSe/HgNMa9cnMvRHxBLC4tH97yrFLy+t6MRcDj2fm3jr9G40BsCIi7gR+CvxBZn6j9B9rMPZz4nPfe4T/68bvPZdDdNxv//KLuPTsl3Y7DUmSJGleaKfAqre8kW32adRe79LEZv2bjfEIcHxmPhYRrwH+MSJObjPvWuCIC6ldSsjxxx9fr0tbhk8YZN3bXnHAxx9q/++X7mfLtqe6nYYkSZI0b7RTYI0Bx016vwzY2qDPWEQsAI4GdrQ4tl77dmBRRCwoq1iT+9cdIzMTeBogMzdGxA+BF5f+y1rkTTnuKuAqgOHh4bpFWDtOXHIEJy454kAPP+RuuvNhxnft6XYakiRJ0rzRzi6CdwAry+5+FWqbVmyY0mcDcH55fS7wlVL4bADWlh0AV1DbaOL2RjHLMbeWGJSYNzcbIyKWlE0ziIgTyxhbMvMR4MmIeG25V+s9k2IJGKpWGJ9j94xJkiRJs1nLFaxyv9PFwC1AH3BNZm6KiMuBkczcAFwNXFc2sdhBrWCi9LuB2oYYe4GLMvNZgHoxy5CXAOsj4kPAnSU2jcYAfgm4PCL2As8Cv52ZO8pnvwP8L+B51Da3eM42uJiLBgcqjO90BUuSJEnqlKgtGmmf4eHhHBkZ6XYah8RHb9nMx786yuiH3+JOgpLmrIjYmJnD3c5DkiRo80HDmp8GqxUmEp7cvbd1Z0mSJEktWWD1sKFqPwA73OhCkiRJ6ggLrB62aKACwA7vw5IkSZI6wgKrhw2VAutxV7AkSZKkjrDA6mFDVVewJEmSpE6ywOphg6XA8mHDkiRJUmdYYPWwaqWP/r7wYcOSJElSh1hg9bCI8GHDkiRJUgdZYPW4oWrFe7AkSZKkDrHA6nGLBvq9B0uSJEnqEAusHjdUrXgPliRJktQhFlg9znuwJEmSpM6xwOpxgwMVxnftYWIiu52KJEmSNOdZYPW4wWqFiYQnd+/tdiqSJEnSnGeB1eOGqv0A7HCjC0mSJOmgWWD1uEUDFQC3apckSZI6wAKrxw2VAutxV7AkSZKkg2aB1eOGqq5gSZIkSZ3SVoEVEasjYnNEjEbEpXU+Pzwiri+f3xYRyyd9dllp3xwRZ7WKGRErSoz7S8xKqzHK58dHxFMR8f5JbQ9GxN0RcVdEjLQ/Lb1jsBRYPmxYkiRJOngtC6yI6AOuBM4GVgHnRcSqKd0uAMYz8yTgCmBdOXYVsBY4GVgNfDwi+lrEXAdckZkrgfESu+EYk1wBfL7Or/ArmXlKZg63+l17UbXSR39f+LBhSZIkqQPaWcE6FRjNzC2ZuQdYD6yZ0mcNcG15fSNwZkREaV+fmU9n5gPAaIlXN2Y55owSgxLznBZjEBHnAFuATe3/6gKICB82LEmSJHVIOwXWUuChSe/HSlvdPpm5F3gCWNzk2Ebti4HHS4ypY9UdIyKqwCXAB+vknsAXI2JjRFzYxu/ak4aqFe/BkiRJkjpgQRt9ok5bttmnUXu9wq5Z/2ZjfJDaJYVPlQWtyV6XmVsj4ljgXyPi+5n59amdSvF1IcDxxx9fZ5j5bdFAv/dgSZIkSR3QzgrWGHDcpPfLgK2N+kTEAuBoYEeTYxu1bwcWlRhTx2o0xmnARyLiQeC9wO9HxMUAmbm1/PkocBO1SxOnycyrMnM4M4eXLFnSfDbmoaFqxXuwJEmSpA5op8C6A1hZdverUNu0YsOUPhuA88vrc4GvZGaW9rVlB8AVwErg9kYxyzG3lhiUmDc3GyMz35CZyzNzOfDnwJ9k5sciohoRRwKUywjfDNzT5rz0FO/BkiRJkjqj5SWCmbm3rAjdAvQB12Tmpoi4HBjJzA3A1cB1ETFKbVVpbTl2U0TcANwL7AUuysxnAerFLENeAqyPiA8Bd5bYNBqjiecDN5XLBhcAn83ML7SckR40OFBhfNceJiaSww6rdyWmJEmSpHZEbdFI+wwPD+fISG89Muvqbz7A//25e/nuH76Zowf6u52OJM1IRGz0URySpNmirQcNa34bqtaKqh1udCFJkiQdFAsssWigAuBW7ZIkSdJBssASQ6XAetwVLEmSJOmgWGCJoaorWJIkSVInWGCJwVJg+bBhSZIk6eBYYIlqpY/+vvBhw5IkSdJBssASEeHDhiVJkqQOsMASULsPy3uwJEmSpINjgSUAFg30ew+WJEmSdJAssATUVrC8B0uSJEk6OBZYAvAeLEmSJKkDLLAElAJr1x4mJrLbqUiSJElzlgWWgNqzsCYSnty9t9upSJIkSXOWBZYAGKr2A7DDjS4kSZKkA2aBJQAWDVQA3KpdkiRJOggWWAJgqBRYj7uCJUmSJB0wCywBtW3awRUsSZIk6WBYYAmobXIB+LBhSZIk6SC0VWBFxOqI2BwRoxFxaZ3PD4+I68vnt0XE8kmfXVbaN0fEWa1iRsSKEuP+ErPSaozy+fER8VREvL/dvPUfqpU++vvChw1LkiRJB6FlgRURfcCVwNnAKuC8iFg1pdsFwHhmngRcAawrx64C1gInA6uBj0dEX4uY64ArMnMlMF5iNxxjkiuAz88wbxUR4cOGJUmSpIPUzgrWqcBoZm7JzD3AemDNlD5rgGvL6xuBMyMiSvv6zHw6Mx8ARku8ujHLMWeUGJSY57QYg4g4B9gCbJph3ppkqFrxHixJkiTpILRTYC0FHpr0fqy01e2TmXuBJ4DFTY5t1L4YeLzEmDpW3TEiogpcAnzwAPIGICIujIiRiBjZtm1bvS49YdFAv/dgSZIkSQehnQIr6rRlm3061d5sjA9Su6TwqTZzmt6YeVVmDmfm8JIlS+p16QlD1Yr3YEmSJEkHYUEbfcaA4ya9XwZsbdBnLCIWAEcDO1ocW699O7AoIhaUVarJ/RuNcRpwbkR8BFgETETEbmBjG3lrEu/BkiRJkg5OOytYdwAry+5+FWqbVmyY0mcDcH55fS7wlczM0r627AC4AlgJ3N4oZjnm1hKDEvPmZmNk5hsyc3lmLgf+HPiTzPxYm3lrksGBCuO79jAxUXehT5IkSVILLVewMnNvRFwM3AL0Addk5qaIuBwYycwNwNXAdRExSm1VaW05dlNE3ADcC+wFLsrMZwHqxSxDXgKsj4gPAXeW2DQaY6Z5tzUrPWqwWmEi4cndezl6oL/b6UiSJElzTtQWjbTP8PBwjoyMdDuNrrjpzjH+2/Xf5db3n86KY6rdTkeS2hIRGzNzuNt5SJIEbT5oWL1h0UAFwK3aJUmSpANkgaWfGSoF1uNu1S5JkiQdEAss/cxQ1RUsSZIk6WBYYOlnBkuB5cOGJUmSpANjgaWfqVb66O8Lduz0YcOSJEnSgbDA0s9EBIMDFe/BkiRJkg6QBZb2M1SteA+WJEmSdIAssLSfRQP93oMlSZIkHSALLO1nqFphfJf3YEmSJEkHwgJL+xkcqDDuJYKSJEnSAbHA0n4GByqM79rDxER2OxVJkiRpzrHA0n4GqxUmEp7cvbfbqUiSJElzjgWW9jNU7QdghxtdSJIkSTNmgaX9DA5UANyqXZIkSToAFljaz74Cy4cNS5IkSTNngaX9DFVdwZIkSZIOlAWW9jNYCiwfNixJkiTNXFsFVkSsjojNETEaEZfW+fzwiLi+fH5bRCyf9NllpX1zRJzVKmZErCgx7i8xK83GiIhTI+Ku8vPdiPi1SbEejIi7y2cjM5+e3lOt9NHfF+zY6cOGJUmSpJlqWWBFRB9wJXA2sAo4LyJWTel2ATCemScBVwDryrGrgLXAycBq4OMR0dci5jrgisxcCYyX2A3HAO4BhjPzlDLGJyNiwaTcfiUzT8nM4bZmpMdFBIMDFe/BkiRJkg5AOytYpwKjmbklM/cA64E1U/qsAa4tr28EzoyIKO3rM/PpzHwAGC3x6sYsx5xRYlBintNsjMzclZn7Htq0EPAJuQdpqFrxHixJkiTpALRTYC0FHpr0fqy01e1Tip0ngMVNjm3Uvhh4fFLBNHmsRmMQEadFxCbgbuC3Jx2fwBcjYmNEXNjG7ypg0UC/92BJkiRJB6CdAivqtE1dJWrUp1PtTfPIzNsy82TgF4DLImJh+fx1mflqapciXhQRv1QnBhFxYUSMRMTItm3b6nXpKUPVCuO7vAdLkiRJmql2Cqwx4LhJ75cBWxv1Kfc/HQ3saHJso/btwKJJ91BNHqvRGD+TmfcBO4GXl/dby5+PAjdRuzRxmsy8KjOHM3N4yZIlDaahdwwOVBj3EkFJkiRpxtopsO4AVpbd/SrUNq3YMKXPBuD88vpc4CuZmaV9bdkBcAWwEri9UcxyzK0lBiXmzc3GKDEWAETECcBLgAcjohoRR5b2KvBmahtiqIXBgQrju/YwMeHtbJIkSdJMLGjVITP3RsTFwC1AH3BNZm6KiMuBkczcAFwNXBcRo9RWldaWYzdFxA3AvcBe4KLMfBagXswy5CXA+oj4EHBniU2jMYDXA5dGxDPABPBfM3N7RJwI3FTbN4MFwGcz8wsHNk29ZbBaYSLhyd17OXqgv9vpSJIkSXNG1BaNtM/w8HCOjPT2I7NuunOM/3b9d7n1/aez4phqt9ORpKYiYqOP4pAkzRZtPWhYvWVwoALgVu2SJEnSDFlgaZp9BZYPG5YkSZJmxgJL0wxVXcGSJEmSDoQFPMVGHwAAGulJREFUlqYZLAWWDxuWJEmSZsYCS9NUK3309wU7dvqwYUmSJGkmLLA0TUQwOFDxHixJkiRphiywVNdQteI9WJIkSdIMWWCprkUD/d6DJUmSJM2QBZbqGqpWGN/lPViSJEnSTFhgqa7BgQrjXiIoSZIkzYgFluoaHKgwvmsPExPZ7VQkSZKkOcMCS3UNVitMJDy5e2+3U5EkSZLmDAss1TVU7QdghxtdSJIkSW1b0O0ENDsNDlQA2LFzDyuOqXY5G0nPpWeeeYaxsTF2797d7VSaWrhwIcuWLaO/v7/bqUiS1JAFluraV2D5sGFp/hsbG+PII49k+fLlRES306krM3nssccYGxtjxYoV3U5HkqSGvERQdQ1V/2MFS9L8tnv3bhYvXjxriyuAiGDx4sWzfpVNkiQLLNU1WAosHzYs9YbZXFztMxdylCTJAkt1VSt99PcFO3b6sGFJz70jjjii2ylIktQRbRVYEbE6IjZHxGhEXFrn88Mj4vry+W0RsXzSZ5eV9s0RcVarmBGxosS4v8SsNBsjIk6NiLvKz3cj4tfazVuNRQSDAxXvwZLUNc8++2y3U5AkacZaFlgR0QdcCZwNrALOi4hVU7pdAIxn5knAFcC6cuwqYC1wMrAa+HhE9LWIuQ64IjNXAuMldsMxgHuA4cw8pYzxyYhY0GbeamKoWvEeLEmH1Fe/+lV+5Vd+hXe84x284hWv6HY6kiTNWDsrWKcCo5m5JTP3AOuBNVP6rAGuLa9vBM6M2sXya4D1mfl0Zj4AjJZ4dWOWY84oMSgxz2k2Rmbuysx9T8NdCOQM8lYTiwb6vQdL0iF3++238+EPf5h7772326lIkjRj7WzTvhR4aNL7MeC0Rn0yc29EPAEsLu3fnnLs0vK6XszFwOOTCqbJ/RuNsT0iTgOuAU4A3l0+bydvNTFUrfCDnzzV7TQkHUIf/KdN3Lv1px2NuernjuKPfvXktvufeuqpbsUuSZqz2lnBqrdtU7bZp1PtTfPIzNsy82TgF4DLImJhm3nXAkdcGBEjETGybdu2el160uBAhXEvEZR0iFWrPtxckjR3tbOCNQYcN+n9MmBrgz5jEbEAOBrY0eLYeu3bgUURsaCsYk3u32iMn8nM+yJiJ/DyNvPed9xVwFUAw8PDdYuwXjQ4UGF81x4mJpLDDnN7ZKkXzGSlSZIkTdfOCtYdwMqyu1+F2qYVG6b02QCcX16fC3wlM7O0ry07AK4AVgK3N4pZjrm1xKDEvLnZGCXGAoCIOAF4CfBgm3mricFqhYmEJ3fvbd1ZkiRJUusVrHI/08XALUAfcE1mboqIy4GRzNwAXA1cFxGj1FaV1pZjN0XEDcC9wF7gosx8FqBezDLkJcD6iPgQcGeJTaMxgNcDl0bEM8AE8F8zc3uLMdSGoWo/ADt27eHogf4uZyNpPnvqqdr9nqeffjqnn356d5ORJOkgtHOJIJn5L8C/TGn7w0mvdwNvb3Dsh4EPtxOztG+htgPg1Pa6Y2TmdcB17eat9g0OVADYsXMPK47xnghJkiSplbYeNKzetK/A8mHDkiRJUnsssNTQUPU/VrAkSZIktWaBpYYGS4Hlw4al+a+2x9DsNhdylCTJAksNVSt99PcFO3Y+0+1UJD2HFi5cyGOPPTarC5jM5LHHHmPhwoXdTkWSpKba2uRCvSkiGByoeA+WNM8tW7aMsbExZvuD1hcuXMiyZcu6nYYkSU1ZYKmpoWrFe7Ckea6/v58VK1Z0Ow1JkuYFLxFUU4sG+r0HS5IkSWqTBZaaGqpWGN/lPViSJElSOyyw1NTgQIVxLxGUJEmS2mKBpaYGByqM79rDxMTs3V1MkiRJmi0ssNTUYLXCRMKTu/d2OxVJkiRp1rPAUlND1X4AdrjRhSRJktSSBZaaGhyoALhVuyRJktQGCyw1ta/AcqMLSZIkqTULLDU1VC0FlpcISpIkSS1ZYKmpQQssSZIkqW0WWGqqWumjvy/YsdOHDUuSJEmtWGCpqYhgcKDC465gSZIkSS21VWBFxOqI2BwRoxFxaZ3PD4+I68vnt0XE8kmfXVbaN0fEWa1iRsSKEuP+ErPSbIyIeFNEbIyIu8ufZ0yK9dUyxl3l59iZT5GGqhV3EZQkSZLa0LLAiog+4ErgbGAVcF5ErJrS7QJgPDNPAq4A1pVjVwFrgZOB1cDHI6KvRcx1wBWZuRIYL7EbjgFsB341M18BnA9cNyW3d2bmKeXn0ZYzomkWDfR7D5YkSZLUhnZWsE4FRjNzS2buAdYDa6b0WQNcW17fCJwZEVHa12fm05n5ADBa4tWNWY45o8SgxDyn2RiZeWdmbi3tm4CFEXF4uxOg1oaqFcZ3eQ+WJEmS1Eo7BdZS4KFJ78dKW90+mbkXeAJY3OTYRu2LgcdLjKljNRpjsrcBd2bm05Pa/rpcHviBUsBphgYHKj4HS5IkSWpDOwVWvaIk2+zTqfaWeUTEydQuG/ytSZ+/s1w6+Iby8+46MYiICyNiJCJGtm3bVq9LT6utYO1hYmLq/+ySJEmSJmunwBoDjpv0fhmwtVGfiFgAHA3saHJso/btwKISY+pYjcYgIpYBNwHvycwf7guamQ+XP58EPkvt0sRpMvOqzBzOzOElS5Y0mYretGigwkTCk7v3tu4sSZIk9bB2Cqw7gJVld78KtU0rNkzps4HaBhMA5wJfycws7WvLDoArgJXA7Y1ilmNuLTEoMW9uNkZELAL+GbgsM/9tX0IRsSAijimv+4H/DNzTxu+rKYaq/QDscKMLSZIkqamWBVa53+li4BbgPuCGzNwUEZdHxFtLt6uBxRExCrwPuLQcuwm4AbgX+AJwUWY+2yhmiXUJ8L4Sa3GJ3XCMEuck4ANTtmM/HLglIr4H3AU8DHxq5lOkwYEKgFu1S5IkSS1EbdFI+wwPD+fIyEi305hVvvvQ46y58t/4q/cM88ZVz+92OpK0n4jYmJnD3c5DkiRo80HD6m1D1doKls/CkiRJkpqzwFJLgxZYkiRJUlsssNRStdJHf1+wY6cPG5YkSZKascBSSxHB4ECFx13BkiRJkpqywFJbhqoVdxGUJEmSWrDAUlsWDfR7D5YkSZLUggWW2jJUrTC+y3uwJEmSpGYssNSWwYEK414iKEmSJDVlgaW21Faw9jAx4YOpJUmSpEYssNSWRQMVJhKe3L2326lIkiRJs5YFltoyVO0HYIcbXUiSJEkNWWCpLYMDFQC3apckSZKasMBSW/YVWG50IUmSJDVmgaW2DFVLgeUlgpIkSVJDFlhqy6AFliRJktSSBZbaUq300d8X7Njpw4YlSZKkRiyw1JaIYHCgwuOuYEmSJEkNWWCpbUPVirsISpIkSU20VWBFxOqI2BwRoxFxaZ3PD4+I68vnt0XE8kmfXVbaN0fEWa1iRsSKEuP+ErPSbIyIeFNEbIyIu8ufZ0yK9ZrSPhoRfxERMfMp0j6LBvq9B0uSJElqomWBFRF9wJXA2cAq4LyIWDWl2wXAeGaeBFwBrCvHrgLWAicDq4GPR0Rfi5jrgCsycyUwXmI3HAPYDvxqZr4COB+4blJenwAuBFaWn9UtZ0QNDVUrjO/yHixJkiSpkXZWsE4FRjNzS2buAdYDa6b0WQNcW17fCJxZVovWAOsz8+nMfAAYLfHqxizHnFFiUGKe02yMzLwzM7eW9k3AwrLa9ULgqMz8VmYm8OlJsXQABgcqPgdLkiRJaqKdAmsp8NCk92OlrW6fzNwLPAEsbnJso/bFwOMlxtSxGo0x2duAOzPz6dJ/rEXemoHaCtYeJiay26lIkiRJs1I7BVa9+5am/gu7UZ9OtbfMIyJOpnbZ4G+103+yiLgwIkYiYmTbtm31ughYNFBhIuHJ3Xtbd5YkSZJ6UDsF1hhw3KT3y4CtjfpExALgaGBHk2MbtW8HFpUYU8dqNAYRsQy4CXhPZv5wUv9lLfIGIDOvyszhzBxesmRJ3UkQDFX7AdjhRheSJElSXe0UWHcAK8vufhVqm1ZsmNJnA7UNJgDOBb5S7nvaAKwt90StoLbRxO2NYpZjbi0xKDFvbjZGRCwC/hm4LDP/bV9CmfkI8GREvLbc2/WeSbF0AAYHKgBu1S5JkiQ10LLAKvc7XQzcAtwH3JCZmyLi8oh4a+l2NbA4IkaB9wGXlmM3ATcA9wJfAC7KzGcbxSyxLgHeV2ItLrEbjlHinAR8ICLuKj/Hls9+B/graptr/BD4/MymR5PtK7Dc6EKSJEmqL2qLRtpneHg4R0ZGup3GrPTQjl284SO38qfnvpK3Dx/X+gBJOgQiYmNmDnc7D0mSoM0HDUsAg9WyguU9WJIkSVJdFlhqW7XSR39fsGOnDxuWJEmS6rHAUtsigsGBCo+7giVJkiTVZYGlGRmqVtxFUJIkSWrAAkszsmig33uwJEmSpAYssDQjQ9UK47u8B0uSJEmqxwJLMzI4UPE5WJIkSVIDFliakdoK1h4mJnx+miRJkjSVBZZmZNFAhYmEJ3fv7XYqkiRJ0qxjgaUZGar2A7DDjS4kSZKkaSywNCODAxUAt2qXJEmS6rDA0ozsK7Dc6EKSJEmazgJLMzJULQWWlwhKkiRJ01hgaUYGLbAkSZKkhiywNCPVSh/9fcGOnT5sWJIkSZrKAkszEhEMDlR43BUsSZIkaRoLLM3YULXiLoKSJElSHRZYmrFFA/3egyVJkiTV0VaBFRGrI2JzRIxGxKV1Pj88Iq4vn98WEcsnfXZZad8cEWe1ihkRK0qM+0vMSrMxImJxRNwaEU9FxMem5PXVMsZd5efYmU2P6hmqVhjf5T1YkiRJ0lQtC6yI6AOuBM4GVgHnRcSqKd0uAMYz8yTgCmBdOXYVsBY4GVgNfDwi+lrEXAdckZkrgfESu+EYwG7gA8D7G/wK78zMU8rPo61+X7U2OFDxOViSJElSHe2sYJ0KjGbmlszcA6wH1kzpswa4try+ETgzIqK0r8/MpzPzAWC0xKsbsxxzRolBiXlOszEyc2dmfpNaoaVDoLaCtYeJiex2KpIkSdKssqCNPkuBhya9HwNOa9QnM/dGxBPA4tL+7SnHLi2v68VcDDyemXvr9G80xvYW+f91RDwL/D3wocy0KjhIiwYqTCT8n+vvZMFhccjGXbroebz3jS+mssBbByVJkjQ7tVNg1fsX9NQipVGfRu31/oXcrH+7eUz1zsx8OCKOpFZgvRv49NROEXEhcCHA8ccf3yKkTl0+xMpjj2DTw08csjEnEm6+ayvPP2oh5//i8kM2riRJkjQT7RRYY8Bxk94vA7Y26DMWEQuAo4EdLY6t174dWBQRC8oq1uT+jcZoKDMfLn8+GRGfpXZp4rQCKzOvAq4CGB4edoWrhVcsO5p/fd8vH9IxM5PzPvVt/uLL9/O21yzjiMPb+epKkiRJh1Y711rdAawsu/tVqG1asWFKnw3A+eX1ucBXyqV4G4C1ZQfAFcBK4PZGMcsxt5YYlJg3txijrohYEBHHlNf9wH8G7mnj99UsFBFcevbLeGznHj719S3dTkeSJEmqq+UyQLnf6WLgFqAPuCYzN0XE5cBIZm4Argaui4hRaqtKa8uxmyLiBuBeYC9wUWY+C1AvZhnyEmB9RHwIuLPEptEYJdaDwFFAJSLOAd4M/Ai4pRRXfcCXgE8dwBxpljjluEW85RUv4K++sYV3vfYElhx5eLdTkiRJkvYT7vmwv+Hh4RwZGel2Gmpgy7aneNMVX+ddpx3PB9e8vNvpSJoFImJjZg53Ow9JkqDNBw1Ls8WJS45g7S8cx2du+zE/emxnt9ORJEmS9mOBpTnnd89cSX/fYXz0iz/odiqSJEnSfiywNOcce9RCLnj9Cv7pu1u5e+zQbRUvSZIktWKBpTnpwl8+kcGBftZ94fvdTkWSJEn6GQsszUlHLezn4jNW8s3R7Xzj/m3dTkeSJEkCLLA0h73rtcezdNHzWPeF7zMx4W6YkiRJ6j4LLM1Zhy/o4/1nvZh7Hv4pn7v7kW6nI0mSJFlgaW5b8/NLeekLjuSjt2xmz96JbqcjSZKkHmeBpTntsMOCS85+KT/esYu/vf3H3U5HkiRJPc4CS3Pe6S9ewmtPHOIvvnw/Tz29t9vpSJIkqYdZYGnOiwguPftlPLZzD5/6+pZupyNJkqQeZoGleeGU4xbxlle8gL/6xha2Pfl0t9ORJElSj7LA0rzx/je/hN17J/jYV+7vdiqSJEnqURZYmjdOXHIEv/4Lx/GZ237Mjx7b2e10JEmS1IMssDSvvPfMlfT3HcZHv/iDbqciSZKkHmSBpXnl2KMWcsHrV/BP393K3WNPdDsdSZIk9RgLLM07F/7yiQwO9LPuC9/vdiqSJEnqMRZYmneOWtjPxWes5Juj2/nG/du6nY4kSZJ6SFsFVkSsjojNETEaEZfW+fzwiLi+fH5bRCyf9NllpX1zRJzVKmZErCgx7i8xK83GiIjFEXFrRDwVER+bktdrIuLucsxfRETMbHo0V73rtcezdNHzWPeF7zMxkd1OR5IkST2iZYEVEX3AlcDZwCrgvIhYNaXbBcB4Zp4EXAGsK8euAtYCJwOrgY9HRF+LmOuAKzJzJTBeYjccA9gNfAB4f530PwFcCKwsP6tb/b6aHw5f0Mf7z3ox9zz8Uz539yPdTkeSJEk9op0VrFOB0czckpl7gPXAmil91gDXltc3AmeW1aI1wPrMfDozHwBGS7y6McsxZ5QYlJjnNBsjM3dm5jepFVo/ExEvBI7KzG9lZgKfnhRLPWDNzy/lpS84ko/espk9eye6nY4kSZJ6wII2+iwFHpr0fgw4rVGfzNwbEU8Ai0v7t6ccu7S8rhdzMfB4Zu6t07/RGNub5D3WYGz1gMMOCy45+6X85l/fwds/+S2Ofl5/t1OSVMebVj2fd7/2hG6nIUlSR7RTYNW7b2nqTS2N+jRqr7dy1qx/u3m0k9P0jhEXUruUkOOPP75JSM01p794Cb/1Sydy2wM7+Om/P9PtdCTVsXvPs91OQZKkjmmnwBoDjpv0fhmwtUGfsYhYABwN7GhxbL327cCiiFhQVrEm9280RrO8l7XIG4DMvAq4CmB4eNgdEeaRiOCyt7ys22lIkiSpR7RzD9YdwMqyu1+F2qYVG6b02QCcX16fC3yl3Pe0AVhbdgBcQW2jidsbxSzH3FpiUGLe3GKMujLzEeDJiHhtubfrPZNiSZIkSVLHtVzBKvc7XQzcAvQB12Tmpoi4HBjJzA3A1cB1ETFKbVVpbTl2U0TcANwL7AUuysxnAerFLENeAqyPiA8Bd5bYNBqjxHoQOAqoRMQ5wJsz817gd4D/BTwP+Hz5kSRJkqTnRDRZBOpJw8PDOTIy0u00JEltioiNmTnc7TwkSYI2HzQsSZIkSWrNAkuSJEmSOsQCS5IkSZI6xAJLkiRJkjrEAkuSJEmSOsQCS5IkSZI6xG3ap4iIbcCPgGOA7V1OZzZzflpzjppzflpzjprbNz8nZOaSbicjSRJYYDUUESM+V6Ux56c156g556c156g550eSNBt5iaAkSZIkdYgFliRJkiR1iAVWY1d1O4FZzvlpzTlqzvlpzTlqzvmRJM063oMlSZIkSR3iCpYkSZIkdYgF1hQRsToiNkfEaERc2u18ZqOIeDAi7o6IuyJipNv5zAYRcU1EPBoR90xqG4qIf42I+8ufg93MsZsazM8fR8TD5Xt0V0S8pZs5dlNEHBcRt0bEfRGxKSJ+t7T7HSqazJHfI0nSrOIlgpNERB/wA+BNwBhwB3BeZt7b1cRmmYh4EBjOTJ/PU0TELwFPAZ/OzJeXto8AOzLzf5RifTAzL+lmnt3SYH7+GHgqMz/azdxmg4h4IfDCzPxORBwJbATOAX4Dv0NA0zn6L/g9kiTNIq5g7e9UYDQzt2TmHmA9sKbLOWkOyMyvAzumNK8Bri2vr6X2j8Ge1GB+VGTmI5n5nfL6SeA+YCl+h36myRxJkjSrWGDtbynw0KT3Y3gCryeBL0bExoi4sNvJzGLPz8xHoPaPQ+DYLuczG10cEd8rlxD27OVvk0XEcuBVwG34HapryhyB3yNJ0ixigbW/qNPmNZTTvS4zXw2cDVxULv+SZuoTwIuAU4BHgD/rbjrdFxFHAH8PvDczf9rtfGajOnPk90iSNKtYYO1vDDhu0vtlwNYu5TJrZebW8uejwE3ULq3UdD8p943su3/k0S7nM6tk5k8y89nMnAA+RY9/jyKin1rh8JnM/IfS7Hdoknpz5PdIkjTbWGDt7w5gZUSsiIgKsBbY0OWcZpWIqJYbzImIKvBm4J7mR/WsDcD55fX5wM1dzGXW2Vc4FL9GD3+PIiKAq4H7MvN/TvrI71DRaI78HkmSZht3EZyibPH750AfcE1mfrjLKc0qEXEitVUrgAXAZ50jiIi/BU4HjgF+AvwR8I/ADcDxwI+Bt2dmT2700GB+Tqd2WVcCDwK/te9+o14TEa8HvgHcDUyU5t+ndo+R3yGaztF5+D2SJM0iFliSJEmS1CFeIihJkiRJHWKBJUmSJEkdYoElSZIkSR1igSVJkiRJHWKBJUmSJEkdYoElCYCIOD0iPtftPCRJkuYyCyxJkiRJ6hALLGmOiYh3RcTtEXFXRHwyIvoi4qmI+LOI+E5EfDkilpS+p0TEtyPiexFxU0QMlvaTIuJLEfHdcsyLSvgjIuLGiPh+RHwmIqJrv6gkSdIcZIElzSER8TLg14HXZeYpwLPAO4Eq8J3MfDXwNeCPyiGfBi7JzFcCd09q/wxwZWb+PPCLwCOl/VXAe4FVwInA657zX0qSJGkeWdDtBCTNyJnAa4A7yuLS84BHgQng+tLnb4B/iIijgUWZ+bXSfi3wdxFxJLA0M28CyMzdACXe7Zk5Vt7fBSwHvvnc/1qSJEnzgwWWNLcEcG1mXrZfY8QHpvTLFjEaeXrS62fx7whJkqQZ8RJBaW75MnBuRBwLEBFDEXECtf8vn1v6vAP4ZmY+AYxHxBtK+7uBr2XmT4GxiDinxDg8IgYO6W8hSZI0T/lfp6U5JDPvjYg/AL4YEYcBzwAXATuBkyNiI/AEtfu0AM4H/rIUUFuA3yzt7wY+GRGXlxhvP4S/hiRJ0rwVmc2uJJI0F0TEU5l5RLfzkCRJ6nVeIihJkiRJHeIKliRJkiR1iCtYkiRJktQhFliSJEmS1CEWWJIkSZLUIRZYkiRJktQhFliSJEmS1CEWWJIkSZLUIf8/FB4huNyVI0gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\tvalidation       \t (min:    0.227, max:    0.896, cur:    0.882)\n",
            "\ttraining         \t (min:    0.342, max:    0.868, cur:    0.849)\n",
            "Loss\n",
            "\tvalidation       \t (min:    0.071, max:    2.004, cur:    0.905)\n",
            "\ttraining         \t (min:    0.439, max:    1.292, cur:    0.483)\n",
            "lr\n",
            "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Qk2S7SgeHLK",
        "colab_type": "code",
        "outputId": "e6ca562b-c7c3-4f39-fa7a-7ed611ad6503",
        "colab": {}
      },
      "source": [
        "SUB_PATH = \"./sample_submission.csv\"\n",
        "sub = pd.read_csv(SUB_PATH)\n",
        "probs_RESNET = model_finetuned.predict(test_generator, verbose=1)\n",
        "sub.loc[:, 'healthy':] = probs_RESNET\n",
        "sub.to_csv('submission_RESNET.csv', index=False)\n",
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "365/365 [==============================] - 115s 315ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>healthy</th>\n",
              "      <th>multiple_diseases</th>\n",
              "      <th>rust</th>\n",
              "      <th>scab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test_0</td>\n",
              "      <td>0.081055</td>\n",
              "      <td>0.043995</td>\n",
              "      <td>0.813451</td>\n",
              "      <td>0.061499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test_1</td>\n",
              "      <td>0.006444</td>\n",
              "      <td>0.046127</td>\n",
              "      <td>0.923563</td>\n",
              "      <td>0.023866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test_2</td>\n",
              "      <td>0.020700</td>\n",
              "      <td>0.032261</td>\n",
              "      <td>0.005576</td>\n",
              "      <td>0.941462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test_3</td>\n",
              "      <td>0.969971</td>\n",
              "      <td>0.003665</td>\n",
              "      <td>0.016411</td>\n",
              "      <td>0.009952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test_4</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.001266</td>\n",
              "      <td>0.998639</td>\n",
              "      <td>0.000078</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  image_id   healthy  multiple_diseases      rust      scab\n",
              "0   Test_0  0.081055           0.043995  0.813451  0.061499\n",
              "1   Test_1  0.006444           0.046127  0.923563  0.023866\n",
              "2   Test_2  0.020700           0.032261  0.005576  0.941462\n",
              "3   Test_3  0.969971           0.003665  0.016411  0.009952\n",
              "4   Test_4  0.000017           0.001266  0.998639  0.000078"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNpqjCQkeHLM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euY2FP96eHLQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9b3MICgeHLS",
        "colab_type": "text"
      },
      "source": [
        "# VGG16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7RhcLHqeHLT",
        "colab_type": "code",
        "outputId": "f9198a4b-d28b-4d9c-8d1f-4039bd66aa61",
        "colab": {}
      },
      "source": [
        "#load model\n",
        "model_finetuned = VGG16(include_top=False, weights='imagenet', input_shape=(img_size,img_size,3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60lxyObReHLV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = model_finetuned.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(256, activation=\"relu\")(x)\n",
        "x = Dense(128, activation=\"relu\")(x)\n",
        "x = Dense(64, activation=\"relu\")(x)\n",
        "predictions = Dense(4, activation=\"softmax\")(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQqsVY_meHLX",
        "colab_type": "code",
        "outputId": "ff996bbb-5082-4411-df7c-9e761095c90b",
        "colab": {}
      },
      "source": [
        "adam = keras.optimizers.Adam(learning_rate=0.000005, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "model_finetuned = Model(inputs=model_finetuned.input, outputs=predictions)\n",
        "model_finetuned.compile(optimizer=adam,\n",
        "                  loss = 'categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "model_finetuned.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 4)                 260       \n",
            "=================================================================\n",
            "Total params: 14,788,868\n",
            "Trainable params: 14,788,868\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4H8HZpbeHLa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "from keras.callbacks import ReduceLROnPlateau,ModelCheckpoint\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3,patience=3, min_lr=0.000001)\n",
        "checkpoint = ModelCheckpoint(\"./model.{epoch:02d}-{val_loss:.2f}-{val_accuracy:.2f}.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"auto\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "callbacks = [PlotLossesKeras(),reduce_lr,checkpoint]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYKKZxmFeHLb",
        "colab_type": "code",
        "outputId": "e53f4a39-b133-423a-fc96-5f62dd9c00b4",
        "colab": {}
      },
      "source": [
        "history_vgg16 = model_finetuned.fit_generator(train_generator,steps_per_epoch=100,epochs=25,validation_data=val_generator,validation_steps=100,callbacks = callbacks,use_multiprocessing=False,shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\tvalidation       \t (min:    0.411, max:    0.853, cur:    0.833)\n",
            "\ttraining         \t (min:    0.374, max:    0.887, cur:    0.887)\n",
            "Loss\n",
            "\tvalidation       \t (min:    0.069, max:    1.764, cur:    0.158)\n",
            "\ttraining         \t (min:    0.331, max:    1.250, cur:    0.332)\n",
            "lr\n",
            "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.06871\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3hcb22ueHLd",
        "colab_type": "code",
        "outputId": "70937acd-eee6-4ea7-bcea-e1694fabfc67",
        "colab": {}
      },
      "source": [
        "SUB_PATH = \"./sample_submission.csv\"\n",
        "sub = pd.read_csv(SUB_PATH)\n",
        "probs_VGG16 = model_finetuned.predict(test_generator, verbose=1)\n",
        "sub.loc[:, 'healthy':] = probs_VGG16\n",
        "sub.to_csv('submission_VGG16.csv', index=False)\n",
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "365/365 [==============================] - 66s 181ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>healthy</th>\n",
              "      <th>multiple_diseases</th>\n",
              "      <th>rust</th>\n",
              "      <th>scab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test_0</td>\n",
              "      <td>0.009542</td>\n",
              "      <td>0.067860</td>\n",
              "      <td>0.784495</td>\n",
              "      <td>0.138104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test_1</td>\n",
              "      <td>0.001551</td>\n",
              "      <td>0.055775</td>\n",
              "      <td>0.901524</td>\n",
              "      <td>0.041150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test_2</td>\n",
              "      <td>0.001745</td>\n",
              "      <td>0.013011</td>\n",
              "      <td>0.001145</td>\n",
              "      <td>0.984099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test_3</td>\n",
              "      <td>0.990908</td>\n",
              "      <td>0.001069</td>\n",
              "      <td>0.005947</td>\n",
              "      <td>0.002076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test_4</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>0.000691</td>\n",
              "      <td>0.999039</td>\n",
              "      <td>0.000229</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  image_id   healthy  multiple_diseases      rust      scab\n",
              "0   Test_0  0.009542           0.067860  0.784495  0.138104\n",
              "1   Test_1  0.001551           0.055775  0.901524  0.041150\n",
              "2   Test_2  0.001745           0.013011  0.001145  0.984099\n",
              "3   Test_3  0.990908           0.001069  0.005947  0.002076\n",
              "4   Test_4  0.000041           0.000691  0.999039  0.000229"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdYzvL7DeHLg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-Qiwfq6eHLj",
        "colab_type": "text"
      },
      "source": [
        "# VGG19"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EEx1KZzeHLl",
        "colab_type": "code",
        "outputId": "e70c42e3-bfe1-4495-a91c-570f0942f0cb",
        "colab": {}
      },
      "source": [
        "from keras.applications.vgg19 import VGG19\n",
        "model_finetuned = VGG19(include_top=False, weights='imagenet', input_shape=(img_size,img_size,3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdkjEqxceHLo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = model_finetuned.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(256, activation=\"relu\")(x)\n",
        "x = Dense(256, activation=\"relu\")(x)\n",
        "x = Dense(128, activation=\"relu\")(x)\n",
        "x = Dense(64, activation=\"relu\")(x)\n",
        "predictions = Dense(4, activation=\"softmax\")(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRLJi7x6eHLq",
        "colab_type": "code",
        "outputId": "e513c943-a517-4d3d-a752-5053f687c8bc",
        "colab": {}
      },
      "source": [
        "adam = keras.optimizers.Adam(learning_rate=0.000005, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "model_finetuned = Model(inputs=model_finetuned.input, outputs=predictions)\n",
        "model_finetuned.compile(optimizer=adam,\n",
        "                  loss = 'categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "model_finetuned.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               65792     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 4)                 260       \n",
            "=================================================================\n",
            "Total params: 20,262,916\n",
            "Trainable params: 20,262,916\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6yDFQA2eHLs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "from keras.callbacks import ReduceLROnPlateau,ModelCheckpoint\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3,patience=3, min_lr=0.000001)\n",
        "checkpoint = ModelCheckpoint(\"./model.{epoch:02d}-{val_loss:.2f}-{val_accuracy:.2f}.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"auto\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "callbacks = [PlotLossesKeras(),reduce_lr,checkpoint]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SaI6wDBUeHL1",
        "colab_type": "code",
        "outputId": "7f02f300-c937-44ce-d3cf-2b0ac39e6173",
        "colab": {}
      },
      "source": [
        "history_vgg19 = model_finetuned.fit_generator(train_generator,steps_per_epoch=100,epochs=25,validation_data=val_generator,validation_steps=100,callbacks = callbacks,use_multiprocessing=False,shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\tvalidation       \t (min:    0.455, max:    0.878, cur:    0.878)\n",
            "\ttraining         \t (min:    0.360, max:    0.863, cur:    0.863)\n",
            "Loss\n",
            "\tvalidation       \t (min:    0.086, max:    1.575, cur:    0.558)\n",
            "\ttraining         \t (min:    0.429, max:    1.296, cur:    0.449)\n",
            "lr\n",
            "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.08576\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7UGwcm8eHL7",
        "colab_type": "code",
        "outputId": "5bf487f7-e34f-4331-9541-7cff3bfd2513",
        "colab": {}
      },
      "source": [
        "SUB_PATH = \"./sample_submission.csv\"\n",
        "sub = pd.read_csv(SUB_PATH)\n",
        "probs_VGG19 = model_finetuned.predict(test_generator, verbose=1)\n",
        "sub.loc[:, 'healthy':] = probs_VGG19\n",
        "sub.to_csv('submission_VGG19.csv', index=False)\n",
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "365/365 [==============================] - 70s 193ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>healthy</th>\n",
              "      <th>multiple_diseases</th>\n",
              "      <th>rust</th>\n",
              "      <th>scab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test_0</td>\n",
              "      <td>0.046945</td>\n",
              "      <td>0.115182</td>\n",
              "      <td>0.744790</td>\n",
              "      <td>0.093083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test_1</td>\n",
              "      <td>0.032540</td>\n",
              "      <td>0.158993</td>\n",
              "      <td>0.601060</td>\n",
              "      <td>0.207407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test_2</td>\n",
              "      <td>0.019010</td>\n",
              "      <td>0.037376</td>\n",
              "      <td>0.008835</td>\n",
              "      <td>0.934779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test_3</td>\n",
              "      <td>0.983426</td>\n",
              "      <td>0.004792</td>\n",
              "      <td>0.004148</td>\n",
              "      <td>0.007635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test_4</td>\n",
              "      <td>0.003130</td>\n",
              "      <td>0.008352</td>\n",
              "      <td>0.987062</td>\n",
              "      <td>0.001455</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  image_id   healthy  multiple_diseases      rust      scab\n",
              "0   Test_0  0.046945           0.115182  0.744790  0.093083\n",
              "1   Test_1  0.032540           0.158993  0.601060  0.207407\n",
              "2   Test_2  0.019010           0.037376  0.008835  0.934779\n",
              "3   Test_3  0.983426           0.004792  0.004148  0.007635\n",
              "4   Test_4  0.003130           0.008352  0.987062  0.001455"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drWYbZbdeHL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Klwdcyj9eHMF",
        "colab_type": "code",
        "outputId": "c5ada387-9e5e-4f41-a187-a5e7c723f1d2",
        "colab": {}
      },
      "source": [
        "from keras.applications import Xception\n",
        "model_finetuned = Xception(include_top=False, weights='imagenet', input_shape=(img_size,img_size,3))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /home/arpan/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-IO9OS9eHMI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = model_finetuned.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(128, activation=\"relu\")(x)\n",
        "x = Dense(64, activation=\"relu\")(x)\n",
        "predictions = Dense(4, activation=\"softmax\")(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-CwlPN0eHMK",
        "colab_type": "code",
        "outputId": "99e8cbda-c56e-45c9-c42f-3a6cb0c9c176",
        "colab": {}
      },
      "source": [
        "adam = keras.optimizers.Adam(learning_rate=0.000005, beta_1=0.9, beta_2=0.999, amsgrad=False)\n",
        "model_finetuned = Model(inputs=model_finetuned.input, outputs=predictions)\n",
        "model_finetuned.compile(optimizer=adam,\n",
        "                  loss = 'categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "model_finetuned.summary()\n",
        "from livelossplot import PlotLossesKeras\n",
        "from keras.callbacks import ReduceLROnPlateau,ModelCheckpoint\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3,patience=3, min_lr=0.000001)\n",
        "checkpoint = ModelCheckpoint(\"./model.{epoch:02d}-{val_loss:.2f}-{val_accuracy:.2f}.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"auto\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "callbacks = [PlotLossesKeras(),reduce_lr,checkpoint]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1 (Conv2D)           (None, 111, 111, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1_bn (BatchNormaliza (None, 111, 111, 32) 128         block1_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1_act (Activation)   (None, 111, 111, 32) 0           block1_conv1_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2 (Conv2D)           (None, 109, 109, 64) 18432       block1_conv1_act[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2_bn (BatchNormaliza (None, 109, 109, 64) 256         block1_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2_act (Activation)   (None, 109, 109, 64) 0           block1_conv2_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv1 (SeparableConv2 (None, 109, 109, 128 8768        block1_conv2_act[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv1_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv2_act (Activation (None, 109, 109, 128 0           block2_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv2 (SeparableConv2 (None, 109, 109, 128 17536       block2_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2_sepconv2_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 55, 55, 128)  8192        block1_conv2_act[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block2_pool (MaxPooling2D)      (None, 55, 55, 128)  0           block2_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 55, 55, 128)  512         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 55, 55, 128)  0           block2_pool[0][0]                \n",
            "                                                                 batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv1_act (Activation (None, 55, 55, 128)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv1 (SeparableConv2 (None, 55, 55, 256)  33920       block3_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv1_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv2_act (Activation (None, 55, 55, 256)  0           block3_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv2 (SeparableConv2 (None, 55, 55, 256)  67840       block3_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3_sepconv2_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 28, 28, 256)  32768       add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 28, 28, 256)  1024        conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 28, 28, 256)  0           block3_pool[0][0]                \n",
            "                                                                 batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv1_act (Activation (None, 28, 28, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv1 (SeparableConv2 (None, 28, 28, 728)  188672      block4_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv1_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv2_act (Activation (None, 28, 28, 728)  0           block4_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv2 (SeparableConv2 (None, 28, 28, 728)  536536      block4_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4_sepconv2_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 14, 14, 728)  186368      add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block4_pool (MaxPooling2D)      (None, 14, 14, 728)  0           block4_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 14, 14, 728)  2912        conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 14, 14, 728)  0           block4_pool[0][0]                \n",
            "                                                                 batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv1_act (Activation (None, 14, 14, 728)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv2_act (Activation (None, 14, 14, 728)  0           block5_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv3_act (Activation (None, 14, 14, 728)  0           block5_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 14, 14, 728)  0           block5_sepconv3_bn[0][0]         \n",
            "                                                                 add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv1_act (Activation (None, 14, 14, 728)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv2_act (Activation (None, 14, 14, 728)  0           block6_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv3_act (Activation (None, 14, 14, 728)  0           block6_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 14, 14, 728)  0           block6_sepconv3_bn[0][0]         \n",
            "                                                                 add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv1_act (Activation (None, 14, 14, 728)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv2_act (Activation (None, 14, 14, 728)  0           block7_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv3_act (Activation (None, 14, 14, 728)  0           block7_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 14, 14, 728)  0           block7_sepconv3_bn[0][0]         \n",
            "                                                                 add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv1_act (Activation (None, 14, 14, 728)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv2_act (Activation (None, 14, 14, 728)  0           block8_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv3_act (Activation (None, 14, 14, 728)  0           block8_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block8_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 14, 14, 728)  0           block8_sepconv3_bn[0][0]         \n",
            "                                                                 add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv1_act (Activation (None, 14, 14, 728)  0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv1_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv2_act (Activation (None, 14, 14, 728)  0           block9_sepconv1_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv2_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv3_act (Activation (None, 14, 14, 728)  0           block9_sepconv2_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv3_act[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block9_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 14, 14, 728)  0           block9_sepconv3_bn[0][0]         \n",
            "                                                                 add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv2_act (Activatio (None, 14, 14, 728)  0           block10_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv3_act (Activatio (None, 14, 14, 728)  0           block10_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv3_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block10_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 14, 14, 728)  0           block10_sepconv3_bn[0][0]        \n",
            "                                                                 add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv2_act (Activatio (None, 14, 14, 728)  0           block11_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv3_act (Activatio (None, 14, 14, 728)  0           block11_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv3_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block11_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 14, 14, 728)  0           block11_sepconv3_bn[0][0]        \n",
            "                                                                 add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv2_act (Activatio (None, 14, 14, 728)  0           block12_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv3_act (Activatio (None, 14, 14, 728)  0           block12_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv3_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block12_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 14, 14, 728)  0           block12_sepconv3_bn[0][0]        \n",
            "                                                                 add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block13_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block13_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv2_act (Activatio (None, 14, 14, 728)  0           block13_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv2 (SeparableConv (None, 14, 14, 1024) 752024      block13_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block13_sepconv2_bn (BatchNorma (None, 14, 14, 1024) 4096        block13_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 7, 7, 1024)   745472      add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block13_pool (MaxPooling2D)     (None, 7, 7, 1024)   0           block13_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 7, 7, 1024)   4096        conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 7, 7, 1024)   0           block13_pool[0][0]               \n",
            "                                                                 batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv1 (SeparableConv (None, 7, 7, 1536)   1582080     add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv1_bn (BatchNorma (None, 7, 7, 1536)   6144        block14_sepconv1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv1_act (Activatio (None, 7, 7, 1536)   0           block14_sepconv1_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv2 (SeparableConv (None, 7, 7, 2048)   3159552     block14_sepconv1_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv2_bn (BatchNorma (None, 7, 7, 2048)   8192        block14_sepconv2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block14_sepconv2_act (Activatio (None, 7, 7, 2048)   0           block14_sepconv2_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 2048)         0           block14_sepconv2_act[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 128)          262272      global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 64)           8256        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 4)            260         dense_2[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 21,132,268\n",
            "Trainable params: 21,077,740\n",
            "Non-trainable params: 54,528\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNmfuJfbeHMM",
        "colab_type": "code",
        "outputId": "bce1d285-75e6-46e2-dab1-2cdf26bc5eea",
        "colab": {}
      },
      "source": [
        "history_vgg19 = model_finetuned.fit_generator(train_generator,steps_per_epoch=100,epochs=25,validation_data=val_generator,validation_steps=100,callbacks = callbacks,use_multiprocessing=False,shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "\tvalidation       \t (min:    0.391, max:    0.849, cur:    0.835)\n",
            "\ttraining         \t (min:    0.334, max:    0.854, cur:    0.794)\n",
            "Loss\n",
            "\tvalidation       \t (min:    0.208, max:    1.387, cur:    0.818)\n",
            "\ttraining         \t (min:    0.510, max:    1.353, cur:    0.636)\n",
            "lr\n",
            "\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.20812\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9eRPorgeHMO",
        "colab_type": "code",
        "outputId": "e26cc838-408c-451a-882d-33831fe74b27",
        "colab": {}
      },
      "source": [
        "SUB_PATH = \"./sample_submission.csv\"\n",
        "sub = pd.read_csv(SUB_PATH)\n",
        "probs_Xception = model_finetuned.predict(test_generator, verbose=1)\n",
        "sub.loc[:, 'healthy':] = probs_Xception\n",
        "sub.to_csv('submission_Xception.csv', index=False)\n",
        "sub.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "365/365 [==============================] - 103s 282ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>healthy</th>\n",
              "      <th>multiple_diseases</th>\n",
              "      <th>rust</th>\n",
              "      <th>scab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Test_0</td>\n",
              "      <td>0.046703</td>\n",
              "      <td>0.038921</td>\n",
              "      <td>0.870548</td>\n",
              "      <td>0.043829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Test_1</td>\n",
              "      <td>0.083707</td>\n",
              "      <td>0.047295</td>\n",
              "      <td>0.800505</td>\n",
              "      <td>0.068493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Test_2</td>\n",
              "      <td>0.064407</td>\n",
              "      <td>0.054447</td>\n",
              "      <td>0.040115</td>\n",
              "      <td>0.841031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Test_3</td>\n",
              "      <td>0.935912</td>\n",
              "      <td>0.009721</td>\n",
              "      <td>0.033996</td>\n",
              "      <td>0.020372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Test_4</td>\n",
              "      <td>0.012939</td>\n",
              "      <td>0.002830</td>\n",
              "      <td>0.977683</td>\n",
              "      <td>0.006547</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  image_id   healthy  multiple_diseases      rust      scab\n",
              "0   Test_0  0.046703           0.038921  0.870548  0.043829\n",
              "1   Test_1  0.083707           0.047295  0.800505  0.068493\n",
              "2   Test_2  0.064407           0.054447  0.040115  0.841031\n",
              "3   Test_3  0.935912           0.009721  0.033996  0.020372\n",
              "4   Test_4  0.012939           0.002830  0.977683  0.006547"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMB3eRj9eHMS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}