Practical_Project_7.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aduomas/PP7/blob/main/Practical_Project_7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTbD40mf4CQi"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ySRbZEmUVi0"
      },
      "source": [
        "### Dataset\n",
        "https://www.kaggle.com/tawsifurrahman/covid19-radiography-database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4wetbufOCR6f"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sWbKUkD8CT2O"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.upload() # upload kaggle.json (api key)\n",
        "# output cleared"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BU8dYxMUCgtZ"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ne7HvDVwChk-"
      },
      "outputs": [],
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S2iwGptCCmzY"
      },
      "outputs": [],
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D32Vzq5ZCpYa",
        "outputId": "b8da2f98-9f26-4d2c-e3f8-6bd7794ddab9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading covid19-radiography-database.zip to /content\n",
            "100% 744M/745M [00:21<00:00, 51.5MB/s]\n",
            "100% 745M/745M [00:21<00:00, 36.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download 'tawsifurrahman/covid19-radiography-database'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "794WZvPpD3_R",
        "outputId": "095912c2-bfb4-4c9f-e868-e0df74a7c0ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!unzip covid19-radiography-database.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlNtf2qw_P8q"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3mX75ELg37hT"
      },
      "outputs": [],
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.applications.mobilenet_v2 import MobileNetV2\n",
        "from keras.applications.mobilenet_v2 import preprocess_input\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import os "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0o9QHso_SfL"
      },
      "source": [
        "### Path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LngAXp1d_VA4"
      },
      "outputs": [],
      "source": [
        "os.makedirs('./COVID19-DATASET/train/covid19')\n",
        "os.makedirs('./COVID19-DATASET/train/normal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GXD-PrWF_bPR"
      },
      "outputs": [],
      "source": [
        "os.makedirs('./COVID19-DATASET/test/covid19')\n",
        "os.makedirs('./COVID19-DATASET/test/normal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TOsbWndd_e9s"
      },
      "outputs": [],
      "source": [
        "COVID_PATH = './COVID-19_Radiography_Dataset/COVID'\n",
        "NORMAL_PATH = './COVID-19_Radiography_Dataset/Normal'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN-zxgnx_-aR"
      },
      "source": [
        "### Define Params"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XaU1ZkWWABFK"
      },
      "outputs": [],
      "source": [
        "IMAGE_SIZE = [224, 224]\n",
        "\n",
        "# Set the batch size\n",
        "BATCH_SIZE = 32  # try reducing batch size or freeze more layers if your GPU runs out of memory\n",
        "NUM_EPOCHS = 5\n",
        "LEARNING_RATE = 0.0001\n",
        "NUM_CLASSES = 2 # We are aware of it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbPoqTbbUeAp"
      },
      "source": [
        "### Test/Train split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vHD_CRl7MqRE"
      },
      "outputs": [],
      "source": [
        "from os import walk\n",
        "_, _, filenames_covid = next(walk(COVID_PATH), (None, None, []))\n",
        "_, _, filenames_normal = next(walk(NORMAL_PATH), (None, None, []))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTTjXm2APDOk",
        "outputId": "d6d1a44a-e557-4653-9c0d-a958e9776747"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3616"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(filenames_covid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpfU8bVBFYcE"
      },
      "outputs": [],
      "source": [
        "import shutil, os, random\n",
        "fromDirectory= './COVID-19_Radiography_Dataset/COVID/'\n",
        "toDirectory='./COVID19-DATASET/test/covid19/'\n",
        "random.shuffle(filenames_covid)\n",
        "for f in filenames_covid[:700]:\n",
        "   shutil.copy(fromDirectory + f, toDirectory)\n",
        "toDirectory='./COVID19-DATASET/train/covid19/'\n",
        "for f in filenames_covid[700:]:\n",
        "   shutil.copy(fromDirectory + f, toDirectory)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwxCdqqAPv7G",
        "outputId": "c9ea4bd3-f2d9-4427-833a-ee7a916ebbed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10192"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(filenames_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jdjmnNutPqwN"
      },
      "outputs": [],
      "source": [
        "import shutil, os, random\n",
        "fromDirectory= './COVID-19_Radiography_Dataset/Normal/'\n",
        "toDirectory='./COVID19-DATASET/test/normal'\n",
        "random.shuffle(filenames_normal)\n",
        "for f in filenames_normal[:2000]:\n",
        "   shutil.copy(fromDirectory + f, toDirectory)\n",
        "toDirectory='./COVID19-DATASET/train/normal/'\n",
        "for f in filenames_normal[2000:]:\n",
        "   shutil.copy(fromDirectory + f, toDirectory)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kor_6oMhF70Z"
      },
      "outputs": [],
      "source": [
        "train_path = './COVID19-DATASET/train'\n",
        "test_path = './COVID19-DATASET/test'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "59jvDQuAGSkS"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "CLASSES = os.listdir(train_path)\n",
        "NUM_CLASSES = len(CLASSES)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rUbs_Gs0GVVl"
      },
      "source": [
        "### Load Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2KbFZpMQGVGx"
      },
      "outputs": [],
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    shear_range = 0.2,\n",
        "    zoom_range = 0.2,\n",
        "    horizontal_flip = True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU5czxbeGS9h",
        "outputId": "0d22dbe4-7744-4b1d-d92d-7ce81878dfb2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 11108 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "training_set = train_datagen.flow_from_directory(\n",
        "    directory = train_path,\n",
        "    target_size = (224, 224),\n",
        "    batch_size = BATCH_SIZE,\n",
        "    class_mode = 'categorical'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_7iRtuOyGZB4"
      },
      "outputs": [],
      "source": [
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBa7S7qdGbo5",
        "outputId": "19030337-b00e-47f1-9997-e3b08b1d7712"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2700 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "test_set = test_datagen.flow_from_directory(\n",
        "    directory = test_path,\n",
        "    target_size = (224, 224),\n",
        "    batch_size = BATCH_SIZE,\n",
        "    class_mode = 'categorical'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RgD9r2vIrqs"
      },
      "source": [
        "### Model (MobileNetV2) - Keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FRfzkw_Gc4J"
      },
      "outputs": [],
      "source": [
        "mobnet = MobileNetV2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FWkifduNI2dP"
      },
      "outputs": [],
      "source": [
        "for layer in mobnet.layers:\n",
        "  layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dh4lY4pmI6Ex"
      },
      "outputs": [],
      "source": [
        "x = Flatten()(mobnet.output)\n",
        "\n",
        "prediction = Dense(NUM_CLASSES, activation='softmax')(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oEPpoesIJBL-"
      },
      "outputs": [],
      "source": [
        "model = Model(inputs=mobnet.input, outputs=prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSYRO1gSJFES",
        "outputId": "7e1c1547-ad69-46b9-db30-e7a6b370a48c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " bn_Conv1 (BatchNormalization)  (None, 112, 112, 32  128         ['Conv1[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " Conv1_relu (ReLU)              (None, 112, 112, 32  0           ['bn_Conv1[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise (Depth  (None, 112, 112, 32  288        ['Conv1_relu[0][0]']             \n",
            " wiseConv2D)                    )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_BN (Ba  (None, 112, 112, 32  128        ['expanded_conv_depthwise[0][0]']\n",
            " tchNormalization)              )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_relu (  (None, 112, 112, 32  0          ['expanded_conv_depthwise_BN[0][0\n",
            " ReLU)                          )                                ]']                              \n",
            "                                                                                                  \n",
            " expanded_conv_project (Conv2D)  (None, 112, 112, 16  512        ['expanded_conv_depthwise_relu[0]\n",
            "                                )                                [0]']                            \n",
            "                                                                                                  \n",
            " expanded_conv_project_BN (Batc  (None, 112, 112, 16  64         ['expanded_conv_project[0][0]']  \n",
            " hNormalization)                )                                                                 \n",
            "                                                                                                  \n",
            " block_1_expand (Conv2D)        (None, 112, 112, 96  1536        ['expanded_conv_project_BN[0][0]'\n",
            "                                )                                ]                                \n",
            "                                                                                                  \n",
            " block_1_expand_BN (BatchNormal  (None, 112, 112, 96  384        ['block_1_expand[0][0]']         \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " block_1_expand_relu (ReLU)     (None, 112, 112, 96  0           ['block_1_expand_BN[0][0]']      \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block_1_pad (ZeroPadding2D)    (None, 113, 113, 96  0           ['block_1_expand_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block_1_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_1_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_1_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_1_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_1_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_1_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_1_project (Conv2D)       (None, 56, 56, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_1_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_1_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_1_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_2_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_2_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_2_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_2_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_2_depthwise (DepthwiseCo  (None, 56, 56, 144)  1296       ['block_2_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_2_depthwise_BN (BatchNor  (None, 56, 56, 144)  576        ['block_2_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_2_depthwise_relu (ReLU)  (None, 56, 56, 144)  0           ['block_2_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_2_project (Conv2D)       (None, 56, 56, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_2_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_2_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_add (Add)              (None, 56, 56, 24)   0           ['block_1_project_BN[0][0]',     \n",
            "                                                                  'block_2_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_3_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_2_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_3_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_3_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_3_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_3_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_3_pad (ZeroPadding2D)    (None, 57, 57, 144)  0           ['block_3_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_3_depthwise (DepthwiseCo  (None, 28, 28, 144)  1296       ['block_3_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_3_depthwise_BN (BatchNor  (None, 28, 28, 144)  576        ['block_3_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_3_depthwise_relu (ReLU)  (None, 28, 28, 144)  0           ['block_3_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_3_project (Conv2D)       (None, 28, 28, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_3_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_3_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_3_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_4_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_4_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_4_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_4_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_4_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_4_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_4_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_4_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_4_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_4_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_4_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_4_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_4_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_add (Add)              (None, 28, 28, 32)   0           ['block_3_project_BN[0][0]',     \n",
            "                                                                  'block_4_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_5_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_4_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_5_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_5_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_5_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_5_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_5_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_5_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_5_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_5_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_5_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_5_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_5_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_5_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_5_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_5_add (Add)              (None, 28, 28, 32)   0           ['block_4_add[0][0]',            \n",
            "                                                                  'block_5_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_6_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_5_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_6_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_6_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_6_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_6_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_6_pad (ZeroPadding2D)    (None, 29, 29, 192)  0           ['block_6_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_6_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_6_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_6_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_6_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_6_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_6_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_6_project (Conv2D)       (None, 14, 14, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_6_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_6_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_6_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_7_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_7_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_7_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_7_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_7_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_7_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_7_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_7_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_7_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_7_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_7_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_7_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_7_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_add (Add)              (None, 14, 14, 64)   0           ['block_6_project_BN[0][0]',     \n",
            "                                                                  'block_7_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_8_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_7_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_8_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_8_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_8_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_8_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_8_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_8_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_8_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_8_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_8_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_8_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_8_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_8_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_8_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_8_add (Add)              (None, 14, 14, 64)   0           ['block_7_add[0][0]',            \n",
            "                                                                  'block_8_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_9_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_8_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_9_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_9_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_9_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_9_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_9_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_9_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_9_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_9_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_9_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_9_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_9_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_9_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_9_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_9_add (Add)              (None, 14, 14, 64)   0           ['block_8_add[0][0]',            \n",
            "                                                                  'block_9_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_expand (Conv2D)       (None, 14, 14, 384)  24576       ['block_9_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_10_expand_BN (BatchNorma  (None, 14, 14, 384)  1536       ['block_10_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_10_expand_relu (ReLU)    (None, 14, 14, 384)  0           ['block_10_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_depthwise (DepthwiseC  (None, 14, 14, 384)  3456       ['block_10_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_10_depthwise_BN (BatchNo  (None, 14, 14, 384)  1536       ['block_10_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0          ['block_10_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_10_project (Conv2D)      (None, 14, 14, 96)   36864       ['block_10_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_10_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_10_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_10_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_11_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_11_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_11_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_11_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_11_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_11_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_11_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_11_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_11_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_11_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_11_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_11_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_11_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_add (Add)             (None, 14, 14, 96)   0           ['block_10_project_BN[0][0]',    \n",
            "                                                                  'block_11_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_12_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_11_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_12_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_12_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_12_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_12_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_12_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_12_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_12_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_12_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_12_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_12_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_12_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_12_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_12_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_12_add (Add)             (None, 14, 14, 96)   0           ['block_11_add[0][0]',           \n",
            "                                                                  'block_12_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_13_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_12_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_13_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_13_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_13_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_13_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_13_pad (ZeroPadding2D)   (None, 15, 15, 576)  0           ['block_13_expand_relu[0][0]']   \n",
            "                                                                                                  \n",
            " block_13_depthwise (DepthwiseC  (None, 7, 7, 576)   5184        ['block_13_pad[0][0]']           \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_13_depthwise_BN (BatchNo  (None, 7, 7, 576)   2304        ['block_13_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)   0           ['block_13_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_13_project (Conv2D)      (None, 7, 7, 160)    92160       ['block_13_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_13_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_13_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_13_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_14_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_14_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_14_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_14_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_14_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_14_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_14_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_14_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_14_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_14_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_14_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_14_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_14_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_add (Add)             (None, 7, 7, 160)    0           ['block_13_project_BN[0][0]',    \n",
            "                                                                  'block_14_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_15_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_14_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_15_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_15_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_15_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_15_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_15_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_15_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_15_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_15_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_15_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_15_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_15_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_15_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_15_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_15_add (Add)             (None, 7, 7, 160)    0           ['block_14_add[0][0]',           \n",
            "                                                                  'block_15_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_16_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_15_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_16_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_16_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_16_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_16_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_16_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_16_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_16_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_16_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_16_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_16_project (Conv2D)      (None, 7, 7, 320)    307200      ['block_16_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_16_project_BN (BatchNorm  (None, 7, 7, 320)   1280        ['block_16_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " Conv_1 (Conv2D)                (None, 7, 7, 1280)   409600      ['block_16_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)  5120        ['Conv_1[0][0]']                 \n",
            "                                                                                                  \n",
            " out_relu (ReLU)                (None, 7, 7, 1280)   0           ['Conv_1_bn[0][0]']              \n",
            "                                                                                                  \n",
            " flatten (Flatten)              (None, 62720)        0           ['out_relu[0][0]']               \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 2)            125442      ['flatten[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,383,426\n",
            "Trainable params: 125,442\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QXi7zeVVJFxh"
      },
      "outputs": [],
      "source": [
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfls7dPcJMlG",
        "outputId": "39b5dddf-0550-41ee-bdd7-826b0f2af5b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "432/432 [==============================] - 637s 1s/step - loss: 1.1110 - accuracy: 0.8890 - val_loss: 0.2953 - val_accuracy: 0.9635\n",
            "Epoch 2/5\n",
            "432/432 [==============================] - 636s 1s/step - loss: 0.8446 - accuracy: 0.9212 - val_loss: 0.7629 - val_accuracy: 0.9168\n",
            "Epoch 3/5\n",
            "432/432 [==============================] - 658s 2s/step - loss: 0.9072 - accuracy: 0.9300 - val_loss: 2.9941 - val_accuracy: 0.8211\n",
            "Epoch 4/5\n",
            "432/432 [==============================] - 674s 2s/step - loss: 0.8600 - accuracy: 0.9371 - val_loss: 0.7789 - val_accuracy: 0.9367\n",
            "Epoch 5/5\n",
            "432/432 [==============================] - 677s 2s/step - loss: 0.8046 - accuracy: 0.9455 - val_loss: 0.3001 - val_accuracy: 0.9754\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(\n",
        "  training_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=5,\n",
        "  steps_per_epoch=len(training_set),\n",
        "  validation_steps=len(test_set)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toLzSNWCRULo"
      },
      "source": [
        "### Save Model - 95% acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBOAAHnEJNLs",
        "outputId": "c8508890-7f8a-4dcc-812c-ed251981201f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  layer_config = serialize_layer_fn(layer)\n"
          ]
        }
      ],
      "source": [
        "model.save('mobile_net_v2_covid.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ps8tFNCiRVc_"
      },
      "source": [
        "### Loss vs Epoch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "auEZYXTORX0v",
        "outputId": "1a940540-4d81-4765-a5e9-17cf95a3a5ae"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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UjpyZwqJWpRAevqQFG/afYvgHfzDk7d/5cd0BsrK120gpVXyaCMohPz9hbP/m/PZYf166qh0p6VmM/WQ1/V5dyOQlu0nNyHI6RKVUOVJhEkF56+IqqdyfMyTQn+HdGjDnoT5MGNmZ6hHBPDtjIz1fmc9rc7ZxLDndwUiVUuVFhbhYHBISwrFjx4iKqth95cYYjh07RkhIyF9e9/cTBratxcC2tYjdfZwJv+7kzXnbee/XHVwXU4/bezWhUXS4Q1Erpcq6CnGxODMzk/j4eNLS0hyKyntCQkKoV68egYGBhe4XdySZDxfv5JtV+8nMyWFgm1qMubAJHRtU9VKkSqmypLCLxRUiEaiCHUlMY9KS3UxbtofEtCy6Nq7GnRc2oV/LGvjpVNhK+QxNBIrk9Cw+W76Xib/t4sCpNJrXiOCOC5swtEMdggN0SU2lKjpNBOqszOwcZq47yIRfd7DlUBI1IoO5tVdjhndrQKWQwrublFLllyP3EYjIRBE5IiIbCth+nogsFZF0EXnYU3Govwr09+PKjnX56f7eTLm1Ky1qRvLKT1u44OX5/GvmJg6eOu10iEopL/NYi0BELgSSgSnGmLb5bK8BNASuBE4YY/5XlHK1ReB+G/af4v1FO5m5/iACDOlQhzEXNtEpLJSqQBxpERhjFgHHC9l+xBizAsj0VAyqaNrWrcybN3Zk4cN9Gdm9IT+tP8TA1xdzy8fLSUjSexGUqugqzA1lqvTqVwvjuSFtWPpEfx6+pAVLdx5jzNRY0jKznQ5NKeVB5SIRiMgYEYkVkdiEhASnw6nwqoQFMbZ/c8Zd34HVe0/y2NfrfObObaV8UblIBMaY940xMcaYmOrVqzsdjs8Y1K42D1/Sgu/XHGD8gjinw1FKeUiFmGJCec69/ZoRdySZ/83eRtPqEQxqV9vpkJRSbuaxRCAinwJ9gWgRiQeeBQIBjDETRKQWEAtUAnJE5AGgtTEm0VMxqeITEV65pj17jqfy4BdrqF8tjLZ1K5/7jUqpckNvKFNFkpCUztC3fyPHwPdje1KzUsi536SUKjN0YRpVatUjg/lwVBcS0zK5Y0ospzN0JJFSFYUmAlVkretU4o1hHVm//xQPf7WWnJzy1ZpUSuVPE4Eqlotb1+Sxgecxc91B3pi33elwlFJuoKOGVLHdeWET4o4k88a87TStEcGQ8+s4HZJSqhS0RaCKTUT411Vt6dKoKo98uZY1+046HZJSqhQ0EagSCQ7wt+skRwZzx5RYnbVUqXJME4EqsaiIYD4a1YXTGdncPjmW1Iwsp0NSSpWAJgJVKi1rRfLWjR3ZfDCRhz7XkURKlUeaCFT+Eg/A3OchtcCZxM/qd14Nnhzcip83HuLVOVu9EJxSyp101JD6uxN7YMoQOLEbstJh4EvnfMttvRoTdySZ8Qt20KxGBFd1rOf5OJVSbqEtAvVXx3bAx4Ph9Alo3AdWfGhbB+cgIrwwtC3dm1Tjsa/Ws3LPCS8Eq5RyB00E6k9HtsDHgyDrNIz6EYa8BSYHFr9apLcHBfjx7ojO1K4Swp1TY4k/kerhgJVS7qCJQFkH18Gkwfbx6FlQuz1UbQidboaVk213URFUDQ/io1FdSM/K4fbJsSSn60gipco6TQQK4lfC5MshIBRu+QlqnPfntgsfBvGDRf8pcnHNakQwfngnth9J5oHPVpOtI4mUKtM0Efi6PUtgylAIrQq3/gRRTf+6vVId6HIbrPkUjhZ9lbILW1TnmctbM3fzEf7z8xY3B62UcidNBL5sxwKYejVUqm1bAlUa5L9frwchIBh+faVYxd/coyEjuzfgvUU7+SJ2nxsCVkp5giYCX7X1Z/jkBtsCGD3L/vIvSEQN6HYnrP8KjmwuchUiwrNXtKFXs2ie+nY9y3ed+54EpZT3aSLwRRu/g89HQM3WMOoHiKh+7vdc8A8IioAF576nILdAfz/GD+9E/aph3Dk1lr3HdCSRUmWNJgJfs/Zz+OoWqNsZbv4ewqoV7X1h1aDHvbB5BhxYU6wqK4cF8tHoLuQYuG3yCpLSMksQuFLKUzQR+JKVk+HbO6FhTxj5DYQUcxH6HvdASJVitwoAGkeH8+6ITuw6msJ9n64mKzun2GUopTxDE4Gv+OM9+OEf0OwiGPElBEcUv4yQytDzftj+C+xbXuy3X9AsmueHtmHh1gRemqUjiZQqKzyWCERkoogcEZENBWwXEXlTROJEZJ2IdPJULD7vt9fhp0fhvMth2CcQGFrysrrdCeHVYcG/SvT2Ed0aMvqCRkz8fRef/LG35HEopdzGky2CScDAQrYPApq7/sYA73owFt9kDCx4GeY+C22vgesm2WGgpREUboeT7lwIuxaXqIinL2tFnxbVeeb7DSzZcbR08SilSs1jicAYswgobLzgUGCKsZYBVUSktqfi8TnGwJxn7Nj/DiPh6g/AP9A9ZcfcCpG1bavAFP+u4QB/P94a3pFG0eHcPW0Vu46muCcupVSJOHmNoC6Q+y6jeNdrfyMiY0QkVkRiExISvBJcuZaTA7MegSVvQpfb7eRxfv7uKz8w1E49sXcp7JhXoiIqhQTy0agY/MSOJDp1WkcSKeWUcnGx2BjzvjEmxhgTU716Eca8+7KcbHtReMUH0GMsDP4f+Hngn7njzVC5Acx/sUStAoCGUeFMGNmZfcdTGfvJKh1JpJRDnEwE+4H6uZ7Xc72mSio7yw4PXT0VLnwULnkRRDxTV0AQ9H0MDqyGrT+VuJhuTaJ48cq2LN5+lBd+3OTGAJVSReVkIpgB3OwaPdQdOGWMOehgPOVbVgZ8NRrWfwkXPQP9n/JcEjij/TCo1tReK8gp+a/5G7o04I7ejZmydA9Tlu52W3hKqaLx5PDRT4GlQEsRiReR20TkLhG5y7XLLGAnEAd8ANzjqVgqvMw0O2XE5h9g4CvQ+5/eqdc/APo+AYc3wKbvSlXU44NacdF5NXj+h00s3q7XgZTyJjEl7N91SkxMjImNjXU6jLIjIwU+vRF2LYLLx0HMLd6tPycb3u0JJhvuWVaqi9LJ6Vlc884SDpw6zbf39KRZjRLc9KaUypeIrDTGxOS3rVxcLFYFSEuEadfA7sVw1QTvJwGwJ/5+T8LRbbZbqhQiggP4cFQMQf5+3D55BSdTM9wUpFKqMJoIyqvU43ZBmfgVcO1EOH+Yc7G0ugJqtYeFL0N26YaB1q8Wxns3debAyTTunraKTB1JpJTHaSIoj5ITYPIVtm/+hmnQ5ipn4xGB/k/Did2wZnqpi4tpVI2Xr27H0p3HeOb7jZS37kulyhtNBOVN4kGYdBkc2wHDP4eWg5yOyGp+CdTrAr/+x168LqVrOtfj7r5N+XT5Xj7+fXfp41NKFUgTQXlyci98PAgS98PIr6Bpf6cj+tOZVkHiflg12S1FPnJJSy5pXZMXZ25iwdYjbilTKfV3mgjKi+M74ePB9trATd9Bo15OR/R3jftAo96w+FXIKP1KZH5+wrgbOnBerUrc98lqth1OckOQSqm8NBGUBwlbYeIgO1R01Ayo38XpiPInAv2eguTDsOJDtxQZ7hpJFBLoz22TV3A8RUcSKeVumgjKukPrbUvA5MDomVCng9MRFa5hD2h6Efw2DtLd8wu+TpVQPri5M4cT07lr6koysnQkkVLupImgLNu/CiZdbtcQuOUnu9h8edD/KTh9HJZNcFuRHRtU5b/Xtmf57uM89e16HUmklBtpIiir9i6z9wmEVIZbZkF0M6cjKrq6naHlZbDkLTh9wm3FDu1Ql3/0b8aXK+P5YPFOt5WrlK/TRFAW7fwVpl4FETVsS6BqI6cjKr5+T0L6KVg63q3FPjCgBYPb1eLln7Ywd9Nht5atlK/SRFDWbJsN06+zJ/9bfoLK+a7VU/bVamtvdFv2LqS4bzlKPz/h1es60LZOZe7/bDWbDya6rWylfJUmgrJk8w/w2XCocR6M+tG2CMqzvk9AZir8/rpbiw0N8ueDm2OICAng9smxJCSlu7V8pXyNJoKyYv1X8MUoOyro5hkQHuV0RKVXvSW0vwGWfwBJh9xadK3KIXxwcwzHUtK5a9pK0jKz3Vq+Ur5EE0FZsGoqfH07NOgBN30LoVWcjsh9+jwKOVmw+DW3F92+XhVeva4DK/ec4IlvdCSRUiWlicBpyz+AGWOhaT8Y8SUERzodkXtVawIdRsDKj+HkPrcXf1n72jx0cQu+Xb2fdxbucHv5SvkCTQROWvIWzHoYWg6GGz+DoDCnI/KMCx+x/130X48Uf1//Zgw5vw7//WUrP2/Q1U6VKi5NBE4wxs7SOftpO7Lm+in2prGKqkp96HwLrJ5mZ011MxHhP9e2p0P9Kjz4+Vo27D/l9jqUqsg0EXibMTDvebvg+/nD4ZqPwD/Q6ag8r/dD4B9kE6AHhAT68/7NnakaFsgdU2I5klj6qbCV8hWaCLzJGPj5cTsPT8ytMHR8qdb4LVcia0HXO2D9F3YSPQ+oERnCB6NiOJmayR1TdSSRUkXl0UQgIgNFZKuIxInI4/lsbygi80RknYgsFJF6nozHUTk58MP98McE6H4PXPYa+PlYHu75AASG2SUtPaRNncq8PqwDa/ed5JGv1ulIIqWKwGNnIhHxB8YDg4DWwI0iknfWtP8BU4wx7YEXAM+dIZyUnQXf3W0XbOn9T7j0JTtls68Jj4Lud8PGb+2sqh5yaZtaPDqwJT+sPcCb8+I8Vo9SFUWREoGI3C8ilcT6SERWicgl53hbVyDOGLPTGJMBfAYMzbNPa2C+6/GCfLaXf1kZ8PWtsO4zu4LXRc/4ZhI4o8dYO5Hegpc8Ws3dfZpydae6jJu7jR/XHfBoXUqVd0VtEdxqjEkELgGqAjcBr5zjPXWB3APH412v5bYWuNr1+CogUkT+dkutiIwRkVgRiU1ISChiyGVAZhp8cTNs+h4u+defwyh9WWgVuOA+2DoL4ld6rBoR4eWr2xHTsCr//GIta/ed9FhdSpV3RU0EZ37CDgamGmM25nqtNB4G+ojIaqAPsB/42xU+Y8z7xpgYY0xM9erV3VCtF2SkwqfDYNtPcNmrcMFYpyMqO7rdBWFRduSUBwUH+DPhps5ERwRzx5RYDp3SkURK5aeoiWCliMzGJoJfRCQSONcyUfuB+rme13O9dpYx5oAx5mpjTEfgKddr5f+nW3oSTL8Wdv0KQ9+BLrc7HVHZEhxpLxzvmAd7lni0quiIYD4aHUNKeha3T1nB6QwdSaRUXkVNBLcBjwNdjDGpQCBwyzneswJoLiKNRSQIGAbMyL2DiESLyJkYngAmFjnysur0CZhypV1Y5poPoeMIpyMqm7rcDhE1Yf6LdlitB51XqxJv3tiRjQcSeeiLNeTk6EgipXIraiLoAWw1xpwUkZHA00Cht28aY7KAscAvwGbgC2PMRhF5QUSGuHbrC2wVkW1ATcCzfQWelnIUJl8Bh9bBDVOh7TVOR1R2BYVB74dhz++wc6HHq7uoVU2eHNSKnzYcYtzcbR6vT6nyRIoyzlpE1gHnA+2BScCHwPXGmD4ejS4fMTExJjY21tvVnlvSIbu05IndcMN0aD7A6YjKvqx0eLMTVKoNt83x+GgqYwyPfb2OL2LjeWNYB4Z2KKeL/ihVAiKy0hgTk9+2orYIsozNGEOBt40x44EKNk1mKZyKh48H29k1R3ypSaCoAoKhzyMQvwK2z/Z4dSLCi1e2o2vjajzy1TpW7XXfespKlWdFTQRJIvIEdtjoTFe/vg9MkFMEx3fBx4MgJcGuJdD4QqcjKl86jLDLcs5/0d597WFBAX5MGNmZWpVCGDNlJftPnvZ4nUqVdUVNBDcA6dj7CQ5hRwB5Zk7h8uTodtsSSE+CUTOgQTenIyp//APtkpaH1sGWH7xSZbXwID4aFUN6Zja3T44lJT3LK/UqVVYVKRG4Tv7TgcoicjmQZoyZ4tHIyrrDG21LICfTri9cp6PTEZVf7a6D6Bb2buMc7wzvbF4zkreGd2TroUQe+FxHEinfVtQpJq4HlgPXAdcDf4jItZ4MrEw7sAYmXQZ+ATB6FtRq63RE5ZufP/R7EhK2wIZvvFZt35Y1+H+Xt2bOpsP85xfPzIiqVHkQUMT9nsLeQ3AEQESqA3OBrzwVWJm1b2MdNYcAAB7aSURBVDlMuxZCK9tF5qs1djqiiqHVUKjZFha+ZBfr8S/q/5qlM/qCRmw/ksyEX3fQrEYE13auuBPgKlWQol4j8DuTBFyOFeO9FceuxfZmsfAouOUnTQLu5OcH/Z6C4zth7adeq1ZEeH5IGy5oGsUT36xjxe7jXqtbqbKiqCfzn0XkFxEZLSKjgZnALM+FVQbFzbXTRlSpb5NAZf3l6HYtB0GdTvDrv+09Bl4S6O/HOyM6Ua9qGHdOXcm+46leq1upsqCoF4sfAd7H3lDWHnjfGPOYJwMrU7bMhE9vhOjmMHqmXW1LuZ+Inar71D5Y5d2xCFXCgvhwVAxZ2TncPjmWpLRMr9avlJOK3L1jjPnaGPOQ6+9bTwZVpmz42k4lXasdjPoBwqOdjqhia9ofGvSAxa9CpnfH+DetHsE7IzoTl5DM/Z+tIVtHEikfUWgiEJEkEUnM5y9JRBK9FaRj1nwCX98O9brCTd9BaFWnI6r4zrQKkg5CrPfnIOzVPJrnhrRh/pYjvDxrs9frV8oJhQ7NMMb47jQSKz6CmQ9Bk74w7BMICnc6It/RqJc97otfg06jIDjCq9Xf1L0hcYeT+PC3XTSrEcGwrg28Wr9S3uZ7I3+KYuk7Ngk0vxRu/FyTgBP6PQ2pR2H5e45U//8ub03v5tE8/d0Glu445kgMSnmLJoK8Fv0XfnkCWg+FG6ZBYIjTEfmm+l2gxUD4/U1IK3TGc48I8Pfj7eGdaBgVxt3TV7LnWIrXY1DKWzQRnGEMzHvBTn7W/ga4ZiIEBDkdlW/r9ySknbQtNAdUDg3ko1FdALh10goSdSSRqqA0EYBNAr88ZUeqdBoFV07w2p2tqhC1z4dWQ2DpeEh15kavRtHhvDuiM3uOpTL2k9VkZXt+hlSlvE0TQU6OvR6wbLxdVP2KN+xdrqps6PckZCTD7284FkKPplG8eGVbFm1L4MWZOpJIVTy+fcbLyYbv77XDFHs+AANf8fgqWaqYarSys5Mufx+Sj5x7fw8Z1rUBt/VqzKQlu5m6bI9jcSjlCb6bCLIz7T0Caz+Bvk/CgOc0CZRVfR+3U078Ns7RMJ4c3Ip+Lavz3IyN/Lb9qKOxKOVOvpkIstLhi1Gw8Ru4+AXo+5gmgbIsqil0uNHe23Fqv2Nh+PsJb97YkabVw7ln+kp2JiQ7FotS7uR7iSAj1c4btHUmDPov9Lzf6YhUUVz4KJgcWPw/R8OIDLEjiQL8/bhtciynUnUkkSr/PJoIRGSgiGwVkTgReTyf7Q1EZIGIrBaRdSIy2JPxkJ4En1wPO+bDkLeh2xiPVqfcqGpD6DzKTkZ3YrejodSvFsZ7N3Um/kQq93yykkwdSaTKOY8lAhHxB8YDg4DWwI0i0jrPbk8DXxhjOgLDAM8NGD99EqZeDXuWwDUfQqebPFaV8pDeD9tV4X79j9OR0KVRNV66qh2/xx3juRkbMUYnqFPllydbBF2BOGPMTmNMBvAZMDTPPgao5HpcGTjgsWi2z4YDq+G6SdDOd1fZLNcq1YaY2+zCNUfjnI6G62Lqc2efJkz/Yy+Tl+x2OhylSsyTiaAusC/X83jXa7k9B4wUkXjsQjf35VeQiIwRkVgRiU1ISChZNO2vh7EroPWQkr1flQ29HoSAEFj4stORAPDopecxoFVNXvhxEwu3Oje8VanScPr22RuBScaYV0WkBzBVRNoaY/7S6WqMeR+7MA4xMTElb4Pr0pLlX0R1e+Pfb+Og90NQs42j4fj7CW8M68C1E5Zy3yerubhNTSqFBBIRHEBkSAARIQFEhgQSmfd5SADhQQH4++loNeU8TyaC/UD9XM/ruV7L7TZgIIAxZqmIhADRgP60UgW74D5Y8SEseAmGTXc6GsKDA/hwVAwPfr6GP3YeJyktk+T0LIqyrk1EcECBSSMyJICI4MCz2yrlev7na4EEB/ghOvxZlYInE8EKoLmINMYmgGHA8Dz77AUuAiaJSCsgBChh34/yGWHVoMdYWPiSve5Tp6PTEVG3Sihf3Nnj7HNjDKkZ2SSlZZGcnkliWhbJaVlnnyelZeV6zSaOpLQsTp3OJP5E6tl9T2dmn7PuAD/5M5EEB55NGpGFtUyC/2yZnHke4O97o8mV5bFEYIzJEpGxwC+APzDRGLNRRF4AYo0xM4B/Ah+IyIPYC8ejjQ6/UEXR/W74413bKhjxpdPR/I2IEB4cQHhwAPb3TclkZueQ4koSSXmSRlK6fZ6UJ6EkpmVx4GQaSelJZxNKVhGaJ6GB/udsmRTUUjmTdMKC/LV1Ug559BqBMWYW9iJw7teeyfV4E9DTkzGoCiqkkr0ZcO5zsPcPaNDN6Yg8ItDfjyphQVQJK/mU6MYY0rNySPxL0ii4pZI7yRxOTHNts3/n4ifk29o48/xMd1ZEcACVQwPp3TyaqIjgEn825R5OXyxWquS6jrFTVC94EUb94HQ0ZZaIEBLoT0igPzVKsfhsdo4hJSNXyyRP0vgzoWT9JekkJKez62jK2X0zsv4cCxIW5M/NPRox5sImVAvX9T+coolAlV9B4dD7n/Dz47BrETS+0OmIKjR/P6FSSCCVQgKB0BKXk56VTbKr++qDxTt5b9EOpi7dzeiejbijd5NStX5UyUh565KPiYkxsbGxToehyorMNHizI1RpALf+rJMHlkPbDyfx+rztzFp/kPCgAG7p2YjbezWhclig06FVKCKy0hgTk982HSagyrfAELjwYdi3DOLmOR2NKoHmNSMZP7wTP99/IRe2iOat+XH0+vd8XpuzjVOndVI/b9AWgSr/sjLg7c4QWg3GLNRWQTm3+WAib8zdzs8bDxEZEsDtvZpwS69Gri4pVVLaIlAVW0AQ9HkcDq6BLTOdjkaVUqvalZhwU2dm/qMXPZpEMW7uNnq9Mp+35m0nKc2HWwjZmZCR4pGitUWgKobsLHinG/gHwV2/67rTFciG/ad4fe425m4+QpWwQO7o3YRRFzQiIthHxroc22GnX1/zCXS703aFloC2CFTF5x8AfZ+AI5tg07dOR6PcqG3dynw4qgszxvakU4Oq/PeXrfT+93zeWRhHShHubSiXstJh/Vcw+Qp4qxMseRPqdoZ6XTxSnbYIVMWRkwMTetom9D3LbHJQFc6afSd5fe42Fm5NoFp4EGMubMLNPRoSFlQB/r0TtsGqyfbX/+njULkBdLoZOo6ASnVKVXRhLQJNBKpi2fwDfD4SrnwXOuSd2kpVJKv2nuD1udtZtC2B6Igg7rywKSO7NyQ0yN/p0Ion8zRs/M4mgL1L7eJLLQfbFfma9HdbN6cmAuU7jIH3+9gV6cbG2gvJqkJbuec44+Zs57e4o0RHBHNXnyaM7N6QkMAynhAObbAn/7WfQ/opqNYEOo2yP2Aiari9Ok0EyrdsnwPTr4XLx0HMrU5Ho7xkxe7jjJuzjSU7jlE9Mpi7+zRleLcGZSshpCfDxm9g5STYv9IObmg1xP76b9Tbo0OfNREo32IMfHQJJO6H+1bZm86Uz/hj5zHGzd3Gsp3HqREZzL39mnFDl/rOJQRj7HTpqybbC8AZyRDd0p782w+D8CivhKGJQPmenb/ClCEw8N/Q/S6no1EOWLLjKK/P2c7y3cepVSmEe/s15fou9QkO8FJCSDsF67+0v/4PrYeAUGhzlU0A9bt5/cZHTQTKN026HBK2wv1r7AR1yucYY1iy4xjj5mwjds8J6lQO4Z5+zbg+pj5BAR4YPW8MxK+wJ/+N30JmKtRsZ0/+7a6D0Crur7OINBEo37R3GUy8FAY8D70ecDoa5SBjDL/FHWXcnG2s2nuSulVCubdfM67tXM89CSH1OKz7HFZOhoTNEBgO7a6BzqOhTqcyMe2JJgLlu6Zday/K3b/WLmajfJoxhkXbbUJYs+8k9aqGcl//ZlzdqR6BxV2q0xjY87s9+W/6HrLT7Um/8yhoew0El2LxBw/QRKB81/5V8EE/6PcU9HnU6WhUGWGMYeG2BF6fs4218adoUC2Msf2bcXXHuudeuzk5AdZ+Yqd9OBYHwZWg/fV26Gft9t75ACWgiUD5ts9G2IVr7l9rF75XysUYw/wtR3h97nbW7z9Fw6gw7uvfnCs71PlrQsjJgV0L7a//LTMhJ9Ne8O08GlpfCUFhTn2EItNEoHzb4Y3wbk/o/RBc9My591c+xxjD3M1HeH3uNjYeSKRxdDj39W/G0KZ++K+dDqumwsk9EFoVzr/RTvtQo5XTYRdLYYnAo5NziMhA4A3AH/jQGPNKnu3jgH6up2FADWOMc5fVVcVUsw20vRqWTYDu90B4tNMRqTJGRLi4dU0GtKrB7I0H+P3nz4n49gXwXw3kYBr2Qi56Bs67vELel+KxRCAi/sB44GIgHlghIjOMMZvO7GOMeTDX/vcBHT0Vj/JxfZ+ww/l+GweX/svpaFRZdHIfsnoal66eyqXJ+0kPi+JrruKdxAsIONGcf+Q05zL/YMrQfcpu48kWQVcgzhizE0BEPgOGApsK2P9G4FkPxqN8WXRzexfnig+hx1ioVNvpiFRZkJ0J236x4/7j5trXmvaDS18iuOVgrvULJHzDId6Yt41/fLqat+Zt5/4BzRnctjZ+fs4PCXUXj10jEJFrgYHGmNtdz28Cuhljxuazb0NgGVDPGJOdz/YxwBiABg0adN6zZ49HYlYV3PFd8HYMdL4FLvuf09EoJx3f5VrsZTokH4bI2tBxJHS8Cao2/NvuOTmGmesP8sa87cQdSaZlzUjuH9CcgW1qlZuE4Ng1gmIYBnyVXxIAMMa8D7wP9mKxNwNTFUi1xvaLvnIS9PwHVGngdETKm7LS7YiflZNg168gftD8Ejvss/klha5f4ecnXHF+HQa3q82P6w7wxrzt3DN9FefViuSBAc25pHX5SQj58WQi2A/Uz/W8nuu1/AwD7vVgLEpZFz5iF/349T8w9G2no1HecHS7Pfmv/RRSj0Hl+tD3SdsCqFy3WEX5+wlDO9Tl8vZ1+GGtTQh3TVtF69qVeGBAcy5uXRMpA3cRF5cnu4YCgG3ARdgEsAIYbozZmGe/84CfgcamCMHo8FFVaj89Bss/gLErIKqp09EoT8g8DZtm2Bk/9/zuWuxlEHQaba8B+Lnnkm9Wdg7frznAW/O3s/tYKm3rVuKBi1pwUasaZS4hOHYfgYgMBl7HDh+daIz5l4i8AMQaY2a49nkOCDHGPF6UMjURqFJLOgxvnA+th8DV7zsdjXKnwxvtTV/rPrOzf1ZtbMf8dxgBkTU9Vm1Wdg7frt7PW/Pj2Hs8lfb1KvPAgOb0a1l2EoLeUKZUXnOegd/ftGsb1zjP6WhUaWSkwIZv7K//+BV2sZfzLrd3/Tbq7balHosiMzuHb1ft583524k/cZrz61fhgQHN6duiuuMJQROBUnmlHofX20Oz/nD9FKejUSVxYI09+a/7EjKSILqFvfB7/o1eW+ylIJnZOXy9Mp635sex/+RpOjaowoMDWtC7ebRjCUETgVL5mf8vWPQfuHNxmZ4sTOWSlmgXe1k1GQ6uhYAQO9dP59HQoHuZmO45t4ysHL5cuY/x8+M4cCqNzg2r8uCAFvRsFuX1hKCJQKn8nD4Jb7SHBhfA8M+cjkYVxBiIj4VVk2wXUGYq1GhjT/7tr7Pz/5Rx6VnZfBEbzzsL4jh4Ko0ujary4MUtuKCp96Y70USgVEEW/Q/m/x/cPg/q5fsdUU45fQLWfWGHfh7ZZBd7aXu1TQB1O5e5X/9FkZ6Vzecr9jF+QRyHE9Pp1rgaD17cgu5NPN+VpYlAqYKkJ9tWQa32cPN3TkejjIE9S2zXz6bvISsNanewJ/+211SYxYXSMrP5bPlexi/cQUJSOj2aRPHgxS3o2thz06RrIlCqMEvehtlPwehZ0Kin09H4ppSj9oavlZPh2HYIirSLvXQeBbXPdzo6j0nLzGb6H3t5d+EOjian07NZFA8OaEFMI/cnBE0EShUm8zS80QGqNYFbZpXLLodyKScHdi+yXT+bf7SLvdTrak/+ba6CoHCnI/Sa0xnZTP9jDxN+3cHR5Ax6N4/mgQEt6NzQfdc/NBEodS7LP4BZD8NN30LT/k5HU7GlHIXV02wCOLELQqr8udhLzdZOR+eo1Iwspi3bw3u/7uRYSgZ9WlTnwYtb0KF+6Zdp0USg1LlkpcNbnSGihr1wrK0C9zIG9i6D2I9cC71n2NFaMbdAqyEVcrGX0khJz2Lqsj289+sOTqRm0q9ldR4Y0ILzS5EQNBEoVRSrpsCM++DGz+y8NKr00k7ZkT+xE+3In+BK9td/zC3lbqlHJySnZzF5yW4+WLyTk6mZjO3XjIcvbVmisjQRKFUU2ZkwvqsdpnjnIq9OTVDhHFhjf/2v/8qO+6/dAbrcZkf++FDfv7skpWUyecluujSqRrcSDjUtD+sRKOU8/0Do8zh8OwY2z4A2VzodUfmSkQobv4EVH8GBVRAQCu2ugZjboG4np6Mr1yJDAhnbv7nHytdEoFRu7a6Fxa/Cgpeg1RVum664QkvYCrEfw9pPbFdQdEsY+G84fxiElv4ip/I8TQRK5ebnD/2ehC9H2W6N829wOqKyKSsDtvxgE8DuxeAXaKf1jrkNGl6gF9vLGU0ESuXVagjUagcLX7ZTGvgHOh1R2XFijx32uXoqpCTY5T4vetYuARpR3enoVAlpIlAqLz8/6PcUfDrMLmvZeZTTETkrJxu2z7Yjf7bPsb/2WwyEmFuh6UV6Ub0C0ESgVH5aDLQTmy36r+3rDgh2OiLvSzoEq6baFkBiPETUsms+dx4Fles5HZ1yI00ESuVHBPo/DVOvsvcXdL3D6Yi8wxjYtcgO/dwyE3KyoElfGPgStBys3WQVlCYCpQrSpB807GlbBR1GQFCY0xF5TupxO+lb7EQ4Fmfn+O92F3S+BaKbOR2d8jBNBEoVRMReK5g02P5CvuA+pyNyrzMLvsROtOP/s9Kgfjfb/dN6KASGOh2h8hJNBEoVplFP2zL4bZz9dRwc4XREpZeeZJd7XDERDq+HoAjb4om5xY6WUj7Ho5f7RWSgiGwVkTgRebyAfa4XkU0islFEPvFkPEqVSP+nIfUY/DHB6UhK59AG+PEheLUV/Pigfe3ycfDPLXD5a5oEfJjHWgQi4g+MBy4G4oEVIjLDGLMp1z7NgSeAnsaYEyJSw1PxKFVi9WKgxSBY8iZ0ub183S2bmQabvrPTPsQvB/9ge29EzG32c+mNXwrPdg11BeKMMTsBROQzYCiwKdc+dwDjjTEnAIwxRzwYj1Il1+9JeK83LB0P/Z9yOppzO7bD9v2vmW7X/q3WFC75F3QYDmGeWw5RlU+eTAR1gX25nscD3fLs0wJARH4H/IHnjDE/5y1IRMYAYwAaNGjgkWCVKlTt9tD6Slj2jh1NE+75xcaLLTsTts6yCWDnQvALgPMuszd+Ne6jv/5VgZy+WBwANAf6AvWARSLSzhhzMvdOxpj3gffBTkPt7SCVAqDvE3ZRlSVvwMUvOB3Nn07F27V+V02B5ENQqR70exo63QSRtZyOTpUDnkwE+4H6uZ7Xc72WWzzwhzEmE9glItuwiWGFB+NSqmRqnGcXVP/jfeh+L0TWdC6WnBzYMd8Oa932sx0K2vxiiHkdml+is6aqYvFkIlgBNBeRxtgEMAwYnmef74AbgY9FJBrbVbTTgzEpVTp9HrOzkv72Ggz6t/frT06wE76tnAQn90B4dej5gJ32oWoj78ejKgSPJQJjTJaIjAV+wfb/TzTGbBSRF4BYY8wM17ZLRGQTkA08Yow55qmYlCq1qKbQcYTth7/gPu/MuWMM7FniWu93BuRkQqPeMOBZOO8KCAjyfAyqQtOlKpUqrpN74c1ONiFc8Ybn6jl9EtZ9bpNOwhYIrmxH/cTcAtVLtm6t8l26VKVS7lSlAXQeDSs/tt0y1Rq7t/z9q+zJf8PXdr3fup1h6Hhoc3XFnu9IOUYTgVIl0fuftq/+1//AVe+WvryMFHvij50IB1ZDYJhdNjPmVqjTsfTlK1UITQRKlUSl2vYu42XvQK8HoXqLkpVzZIs9+a/9DNJPQfVWMOi/donMkMrujVmpAmgiUKqkej1o1+xd+DJc93HR35eVDpt/sAlgz+/gH2Rn+4y5DRp01xu/lNdpIlCqpMKjoftdsPhV21VUq23h+5/YbRPH6mmQetQO9xzwPHQcactSyiGaCJQqjQvug+Uf2lbBsOl/356d5Vrv9yOIm2d/7bccbEf+NOmv6/2qMkETgVKlEVoVLhgLC/5lR/vU7WRfTzz4541fifshsra9Ga3TzVC5rqMhK5WXJgKlSqvbXbDsXZj/om0hxH4EW2aByYam/e0dyC0Ggb9+3VTZpP9nKlVaIZWg1wMw5xnYMQ9Cq0GPe+29BlFNnY5OqXPSRKCUO3S5w3YH1e0ErYZAYIjTESlVZJoIlHKHoDAY9IrTUShVIjpkQSmlfJwmAqWU8nGaCJRSysdpIlBKKR+niUAppXycJgKllPJxmgiUUsrHaSJQSikfV+7WLBaRBGBPCd8eDRx1YzjuUlbjgrIbm8ZVPBpX8VTEuBoaY6rnt6HcJYLSEJHYghZvdlJZjQvKbmwaV/FoXMXja3Fp15BSSvk4TQRKKeXjfC0RvO90AAUoq3FB2Y1N4yoejat4fCoun7pGoJRS6u98rUWglFIqD00ESinl4ypkIhCRgSKyVUTiROTxfLYHi8jnru1/iEijMhLXaBFJEJE1rr/bvRTXRBE5IiIbCtguIvKmK+51ItKpjMTVV0RO5Tpez3ghpvoiskBENonIRhG5P599vH68ihiX14+Xq94QEVkuImtdsT2fzz5e/04WMS6nvpP+IrJaRH7MZ5v7j5UxpkL9Af7ADqAJEASsBVrn2eceYILr8TDg8zIS12jgbQeO2YVAJ2BDAdsHAz8BAnQH/igjcfUFfvTysaoNdHI9jgS25fPv6PXjVcS4vH68XPUKEOF6HAj8AXTPs48T38mixOXUd/Ih4JP8/r08cawqYougKxBnjNlpjMkAPgOG5tlnKDDZ9fgr4CIRkTIQlyOMMYuA44XsMhSYYqxlQBURqV0G4vI6Y8xBY8wq1+MkYDNQN89uXj9eRYzLEa7jkOx6Guj6yztKxevfySLG5XUiUg+4DPiwgF3cfqwqYiKoC+zL9Tyev38hzu5jjMkCTgFRZSAugGtc3QlfiUh9D8dUVEWN3Qk9XE37n0SkjTcrdjXJO2J/Sebm6PEqJC5w6Hi5ujrWAEeAOcaYAo+ZF7+TRYkLvP+dfB14FMgpYLvbj1VFTATl2Q9AI2NMe2AOf2Z9lb9V2PlTzgfeAr7zVsUiEgF8DTxgjEn0Vr3nco64HDtexphsY0wHoB7QVUTaeqvuwhQhLq9+J0XkcuCIMWalJ+vJqyImgv1A7qxdz/VavvuISABQGTjmdFzGmGPGmHTX0w+Bzh6OqaiKcky9zhiTeKZpb4yZBQSKSLSn6xWRQOzJdrox5pt8dnHkeJ0rLqeOV54YTgILgIF5NjnxnTxnXA58J3sCQ0RkN7b7uL+ITMuzj9uPVUVMBCuA5iLSWESCsBdTZuTZZwYwyvX4WmC+cV15cTKuPP3IQ7D9vGXBDOBm12iY7sApY8xBp4MSkVpn+kZFpCv2/2ePnjxc9X0EbDbGvFbAbl4/XkWJy4nj5aqruohUcT0OBS4GtuTZzevfyaLE5e3vpDHmCWNMPWNMI+w5Yr4xZmSe3dx+rAJK8+ayyBiTJSJjgV+wI3UmGmM2isgLQKwxZgb2CzNVROKwFyOHlZG4/iEiQ4AsV1yjPR0XgIh8ih1REi0i8cCz2AtnGGMmALOwI2HigFTgljIS17XA3SKSBZwGhnkhofcEbgLWu/qWAZ4EGuSKy4njVZS4nDheYEc0TRYRf2zy+cIY86PT38kixuXIdzIvTx8rnWJCKaV8XEXsGlJKKVUMmgiUUsrHaSJQSikfp4lAKaV8nCYCpZTycZoIlPIisTOA/m1GSaWcpIlAKaV8nCYCpfIhIiNdc9WvEZH3XJOTJYvIONfc9fNEpLpr3w4issw1Mdm3IlLV9XozEZnrmuRtlYg0dRUf4ZrAbIuITPfCzLdKFUoTgVJ5iEgr4Aagp2tCsmxgBBCOvbuzDfAr9k5ngCnAY66Jydbnen06MN41ydsFwJlpJjoCDwCtsetT9PT4h1KqEBVuigml3OAi7ORiK1w/1kOx0xTnAJ+79pkGfCMilYEqxphfXa9PBr4UkUigrjHmWwBjTBqAq7zlxph41/M1QCPgN89/LKXyp4lAqb8TYLIx5om/vCjy//LsV9L5WdJzPc5Gv4fKYdo1pNTfzQOuFZEaACJSTUQaYr8v17r2GQ78Zow5BZwQkd6u128CfnWtEhYvIle6yggWkTCvfgqlikh/iSiVhzFmk4g8DcwWET8gE7gXSMEuXvI0tqvoBtdbRgETXCf6nfw52+hNwHuumSMzgeu8+DGUKjKdfVSpIhKRZGNMhNNxKOVu2jWklFI+TlsESinl47RFoJRSPk4TgVJK+ThNBEop5eM0ESillI/TRKCUUj7u/wMF8gOFPt4cVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['loss'], label='Train loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation (Test) loss')\n",
        "plt.title('summarize history for loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFgTTXjcRawo"
      },
      "source": [
        "### Accuracy vs Epoch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "TBONLZ7nRZe5",
        "outputId": "bb045424-82a4-4978-a456-36f0cdf9552c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('summarize history for accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mNogTfZR6-q"
      },
      "source": [
        "### Predict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5urnWN4RdBZ",
        "outputId": "a944d566-82f5-4624-a8ca-e1ad4cd27959"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-12-29 22:29:07--  http://www.itnonline.com/sites/default/files/Chest.jpeg\n",
            "Resolving www.itnonline.com (www.itnonline.com)... 104.18.13.65, 104.18.12.65, 2606:4700::6812:d41, ...\n",
            "Connecting to www.itnonline.com (www.itnonline.com)|104.18.13.65|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://www.itnonline.com/sites/default/files/Chest.jpeg [following]\n",
            "--2021-12-29 22:29:07--  https://www.itnonline.com/sites/default/files/Chest.jpeg\n",
            "Connecting to www.itnonline.com (www.itnonline.com)|104.18.13.65|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1537670 (1.5M) [image/jpeg]\n",
            "Saving to: ‘Chest.jpeg’\n",
            "\n",
            "\rChest.jpeg            0%[                    ]       0  --.-KB/s               \rChest.jpeg          100%[===================>]   1.47M  --.-KB/s    in 0.02s   \n",
            "\n",
            "2021-12-29 22:29:07 (62.6 MB/s) - ‘Chest.jpeg’ saved [1537670/1537670]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://www.itnonline.com/sites/default/files/Chest.jpeg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QUgxwk81SGbD"
      },
      "outputs": [],
      "source": [
        "img_covid = image.load_img('Chest.jpeg', target_size=(224, 224))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1nR53fuQSQ3_"
      },
      "outputs": [],
      "source": [
        "def model_predict(img, actual):\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis = 0)\n",
        "    x_processed = preprocess_input(x)\n",
        "    result = model.predict(x_processed)\n",
        "    if(result[0][0]<.50):\n",
        "        result=\"normal\"\n",
        "    else:\n",
        "        result=\"corona positive\"\n",
        "        \n",
        "    plt.figure()\n",
        "    plt.imshow(img)\n",
        "    plt.title('Actual : {} --> Predicted  : {}'.format(actual, result))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "sHd82JlASirW",
        "outputId": "cad26f6d-a987-4e94-d381-9642139d5dcc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred = model_predict(img_covid, \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion"
      ],
      "metadata": {
        "id": "5LC0hv_CDRUn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have chosen a different dataset that one described in Task's description. The dataset I use is newer and has more images. I was surprised by the effectiveness of Transfer Learning. Right out of the box MobileNetV2 hits 70% accuracy on X ray images classification for Covid. With a bit of training it achieves over 93% accuracy. Transfer Learning saved enourmous amount of time, because I didn't have to make my own model, but just download an existing one. I also learnt how to manage larger amount of data (images) with Computer Vision problem. Because most of CNN are trained on ImageNet dataset (with over 13million images) it seems that these Networks can handle pretty big problems, I am keen of using transfer learning in the future."
      ],
      "metadata": {
        "id": "LWAo_pDNDS5T"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Practical Project 7.ipynb",
      "toc_visible": true,
      "provenance": [],
      "collapsed_sections": [
        "NN-zxgnx_-aR",
        "YbPoqTbbUeAp",
        "rUbs_Gs0GVVl",
        "_RgD9r2vIrqs",
        "toLzSNWCRULo",
        "ps8tFNCiRVc_",
        "gFgTTXjcRawo"
      ],
      "authorship_tag": "ABX9TyNSZGjWRYKG/AbvA5X+byjM",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}