Image_Classfication_Models.ipynb

{
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "801aa_xvWTnG"
      },
      "outputs": [],
      "source": [
        "#script to download data from the google drive\n",
        "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: en-US,en;q=0.9,fa;q=0.8\" --header=\"Cookie: SID=g.a000gAih6wfkjbcbADSz62FJPjIAu0M9JFQvPctI__Kec1ykJV_0UKFwxW_kYqkWSVnwLNbCdgACgYKAWwSAQASFQHGX2MiPA83fXJNYO5R44lOJHFU7hoVAUF8yKrYo9Jjp_WQrUAO_eP-00570076; __Secure-1PSID=g.a000gAih6wfkjbcbADSz62FJPjIAu0M9JFQvPctI__Kec1ykJV_0EaA3rzo3JlioS0jL98KYPQACgYKAccSAQASFQHGX2Miy8vlaewbNEPEU8dFD-DPNBoVAUF8yKo1GeLaYOqiGsvuYVQmR0Gj0076; __Secure-3PSID=g.a000gAih6wfkjbcbADSz62FJPjIAu0M9JFQvPctI__Kec1ykJV_0daBOqj8Sl6aWULeHb8tTFAACgYKASoSAQASFQHGX2MiU_Z8GO_YZyp2xk9ipkG_qRoVAUF8yKqwUJ6U4fkIpUEvqydw10-C0076; HSID=Abda-Gu80HucPyRdw; SSID=AYc-aPVVRZHjx9zPm; APISID=woK740gzd8Mq8kes/Aad25S2XAz6880nUJ; SAPISID=ULq-o5cD2WNC6_GS/AqprLuiDBmBUYwwbH; __Secure-1PAPISID=ULq-o5cD2WNC6_GS/AqprLuiDBmBUYwwbH; __Secure-3PAPISID=ULq-o5cD2WNC6_GS/AqprLuiDBmBUYwwbH; AEC=Ae3NU9MGppRzqPBz29a_V94PJiE37Upgo3fiOY7BQf6fUQy0fifpTrmNUA; __Secure-ENID=17.SE=mSlGvO_Zx1CoU0XyDrk5A9MsubxTHPSq69XI7GooDN15PPT5s7kXg9Fnnp9MhlE7jiJXQgKeD95KnKc7TDO7DIdGebg4F9fT-IO4KVAcsPmWo0ASzhd4e_tgvfR8PungZt9jURyXcjUDpoHJNdF-Ola5RdaKaCAm0pfoZIxIX-VQ2lJ3WOXmhoxKoyF6OcAaDUQGqTrXCG4Ul2uLZe1PVf9Duui7gpnLfKBwsgkaSdEYTK8kBeKc06y9K6HLvvyUhKEiD9GLLrjRIP6e51hgg9aeLUGcD8Hk-A4o961nJi8rHfPALwgkkhvSewckxgzBXjmkzOnIFeW4vzwfNR6TpZp-cC0tZ0oeOIWJjF7TtIH1sm_siprks0spbE76QiKtMJrwo61OGR2PJok; AMP_MKTG_630874745a=JTdCJTdE; AMP_630874745a=JTdCJTIyZGV2aWNlSWQlMjIlM0ElMjI0ODU1MGQ2NC1kMmM2LTQwNjMtYmQyMy1mNjM2YTg1NDkxOGIlMjIlMkMlMjJ1c2VySWQlMjIlM0ElMjJobTIxMDBtaCU0MGdtYWlsLmNvbSUyMiUyQyUyMnNlc3Npb25JZCUyMiUzQTE3MDc2NDk3MjE3MjElMkMlMjJvcHRPdXQlMjIlM0FmYWxzZSUyQyUyMmxhc3RFdmVudFRpbWUlMjIlM0ExNzA3NjQ5NzIxOTMyJTJDJTIybGFzdEV2ZW50SWQlMjIlM0EzJTdE; NID=511=YT1iftDi-fBjdwnvc3CoI6x5mPzJbtEWlZf1jrTWPISJQztb-YbJcuRp3X7f6ZolaO_l29b_unlLbZ8vNFZKGM2Q2aAnYrLVDzwM5ftxqX7ThxCVQ6cQrWqIT-CwxkETn4ivK1_rKBlPL_oLTX96W72LLqjFVUnJNf_XXREZ-oEzT8-XHjPgdTaCP1BqeH36N8RS2wqsXvIgHD60tSJ0OXtdygH_2AgKi9eVZxTmJQSw2MRNoTSVDYl2LmRb_liylBHzblVdLP4kJ0Kjj3_v8QOIKZRg8Dd79yu3KHbWU0Dd2lbCsuq79t0tdmCVUiVr2odUxoJ1WMya82QNJEJ_zWLKrA2EbaeVtqVcPJUbKsWJA5OVJKcNIUXnP0CCIA7y05uMTDiQojryVmQJz58kgr7XgxyknnXpOiTJRlc_rqurtUOf9BOX4IJVhT_9PkWD4If9cl4n8ZMCY5JA4tz15bEzEuhY0RlammGJR-7d0nbRnm9fCofANemqIj-m6dDC2jP2_Hs7jWmnSphH4He80GoCFHwpifjXvLDEe9NN_haWutR3rtjua0aAFryK09cEAROYSFgfmfawe2E10jV6iXKlwF1gzLma9jUTiOrgsC49VLGTGFcSh-8C0nO7QWICsup4DRug89wSoNzbxe-VkViVLXptK7DCU8REv4SCujqhoexC50WHD197hV73vlBa3vq_NXqc; __Secure-1PSIDTS=sidts-CjEBPVxjSiPBKn40DYo4nGcMQp1z758lZH6I_Hc-ZgXqILnbgFHs3tqG8vn_bv9zUhrLEAA; __Secure-3PSIDTS=sidts-CjEBPVxjSiPBKn40DYo4nGcMQp1z758lZH6I_Hc-ZgXqILnbgFHs3tqG8vn_bv9zUhrLEAA; ph_phc_cjuWI21zkiw3B30S2vdrLd2HtfmqRWuzZMldJrXEpPw_posthog=%7B%22distinct_id%22%3A%2218d987a092756c-0cf9e32b2aae19-1e525637-fa000-18d987a0928f50%22%2C%22%24device_id%22%3A%2218d987a092756c-0cf9e32b2aae19-1e525637-fa000-18d987a0928f50%22%2C%22%24user_state%22%3A%22anonymous%22%2C%22%24referrer%22%3A%22%24direct%22%2C%22%24referring_domain%22%3A%22%24direct%22%2C%22%24sesid%22%3A%5B1707660162905%2C%2218d987a092f2e7-0cd80a666118e5-1e525637-fa000-18d987a093036f%22%2C1707660151087%5D%2C%22%24session_recording_enabled_server_side%22%3Afalse%2C%22%24active_feature_flags%22%3A%5B%5D%2C%22%24enabled_feature_flags%22%3A%7B%7D%2C%22%24feature_flag_payloads%22%3A%7B%7D%7D; 1P_JAR=2024-02-11-14; mp_851392464b60e8cc1948a193642f793b_mixpanel=%7B%22distinct_id%22%3A%20%22%24device%3A18d9774c207714-093e713ddbba6f-1e525637-fa000-18d9774c207714%22%2C%22%24device_id%22%3A%20%2218d9774c207714-093e713ddbba6f-1e525637-fa000-18d9774c207714%22%2C%22%24initial_referrer%22%3A%20%22%24direct%22%2C%22%24initial_referring_domain%22%3A%20%22%24direct%22%7D; SIDCC=ABTWhQGUo_c7QMzJlJ7B3MDwdlxCSzVEGzdnCZ2vTNYf7jPYvq5f87wepPlk8GZSZi-XzOJ_HRKQ;__Secure-1PSIDCC=ABTWhQHvLXW1lnu41w0U9AXYAOP4JPcogay1dxjKeHVVrg6S4wHEoj-nuMfcIOQBc6beobZei1I; __Secure-3PSIDCC=ABTWhQFw_jZxYIo4ZvYRabvxKWJg3MBGwNQ0i5FSiV-tskZHup5Y9QKUAScdVHFFZNrYcPYFZI0\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1P4E6cTn4X6rgK9SyOsMA344kLNyISPHQ&export=download&authuser=0&confirm=t&uuid=cd6bed3e-4a7c-47ba-a27e-b581a55f3623&at=APZUnTVMN7FsFLBDI4_D7CAJCR7d%3A1707660525023\" -c -O 'Phase1.zip'\n",
        "!unzip Phase1.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4uMifr__aDc"
      },
      "source": [
        "#### **1.1. Import Required Libraries**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "58da9001"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import glob\n",
        "import random\n",
        "import shutil\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing import image\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Model,load_model\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
        "from keras.layers import Convolution2D,Dense,MaxPooling2D,Activation,Dropout,Flatten\n",
        "from keras.layers import Input, Add, ZeroPadding2D, BatchNormalization, Conv2D, AveragePooling2D, GlobalMaxPooling2D\n",
        "\n",
        "!mkdir Train\n",
        "!mkdir Test\n",
        "!mkdir Validation\n",
        "!mv /content/DataSet1/Defected /content/Train\n",
        "!mv /content/DataSet1/OK /content/Train\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6tmATL4_ecG"
      },
      "source": [
        "#### **1.2. Test-Train Data**\n",
        "**Split the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "907a8041",
        "outputId": "fd9b9143-b200-4343-d037-d16e5924022e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images split successfully!\n"
          ]
        }
      ],
      "source": [
        "\n",
        "Train_dir = '/content/Train'\n",
        "Test_dir = '/content/Test'\n",
        "Val_dir = '/content/Validation'\n",
        "\n",
        "os.makedirs(os.path.join(Test_dir, 'OK'), exist_ok=True)\n",
        "os.makedirs(os.path.join(Test_dir, 'Defected'), exist_ok=True)\n",
        "os.makedirs(os.path.join(Val_dir, 'OK'), exist_ok=True)\n",
        "os.makedirs(os.path.join(Val_dir, 'Defected'), exist_ok=True)\n",
        "\n",
        "for category in ['OK', 'Defected']:\n",
        "    images = os.listdir(os.path.join(Train_dir, category))\n",
        "    num_images_to_move = int(0.15 * len(images))  # Randomly select 15% of images to move\n",
        "    images_to_move = random.sample(images, num_images_to_move)\n",
        "\n",
        "    # Move the selected images to Test and Validation directories\n",
        "    for image in images_to_move:\n",
        "        source_path = os.path.join(Train_dir, category, image)\n",
        "        if random.random() < 0.5:  # 50% chance to move to Test directory\n",
        "            dest_path = os.path.join(Test_dir, category, image)\n",
        "        else:\n",
        "            dest_path = os.path.join(Val_dir, category, image)\n",
        "        shutil.move(source_path, dest_path)\n",
        "\n",
        "print(\"Images split successfully!\")\n",
        "\n",
        "def get_files(directory):\n",
        "    if not os.path.exists(directory):\n",
        "        return 0\n",
        "    count = 0\n",
        "    # crawls inside folders\n",
        "    for current_path, dirs, files in os.walk(directory):\n",
        "        for dr in dirs:\n",
        "            count += len(glob.glob(os.path.join(current_path, dr + \"/*\")))\n",
        "    return count\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a07bcaa8",
        "outputId": "cb5e7a99-e20f-4df0-ae87-b923e2f0365a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 Classes\n",
            "1063 Train images\n",
            "85 Test images\n",
            "85 Val images\n"
          ]
        }
      ],
      "source": [
        "\n",
        "train_samples =get_files(Train_dir)     #train file image count\n",
        "test_samples=get_files(Test_dir)  #test file image count\n",
        "val_samples = get_files(Val_dir)\n",
        "num_classes=len(glob.glob(Train_dir+\"/*\"))\n",
        "\n",
        "print(num_classes,\"Classes\")\n",
        "print(train_samples,\"Train images\")\n",
        "print(test_samples,\"Test images\")\n",
        "print(test_samples,\"Val images\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuGK0vnQ_jRe"
      },
      "source": [
        "#### **1.3. ImageDataGenerator**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aa4DsLKmxl6w"
      },
      "source": [
        "**ImageDataGenerator**,Data augmentation is used to increase the size of training set. Through Data augmentation we want to prevent overfitting ,this refers to randomly changing the images in ways that shouldn’t impact their interpretation, such as horizontal flipping, zooming, and rotating\n",
        "* **Rescale:** One of the many magnification parameters adjusts the pixel values of our image.\n",
        "* **Shear_range:** counterclockwise shear angle in degrees\n",
        "* **Zoom_range:** zoom\n",
        "* **Horizontal_flip:** flip image horizontally"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d3d73be1"
      },
      "outputs": [],
      "source": [
        "train_datagen=ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "\n",
        "test_datagen=ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-U7ZGKPT8ChD"
      },
      "source": [
        "**flow_from_directory() -->** Another method to read images into TensorFlow environment is to use the .\n",
        "\n",
        "**Parameters:**\n",
        "* **directory:** The path of the target directory. It must contain one subdirectory per class. Any PNG, JPG, BMP or TIF formatted images found in each of the subdirectories will be included in the generator.\n",
        "* **target_size:** A tuple of integers, (height, width), by default (256,256). All found images will be resized.\n",
        "* **batch_size:** The size of the data chunks (default: 32).\n",
        "* **shuffle:** Decides whether to shuffle data (default: True). If set to false, it sorts the data in alphanumeric order.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwD6Vmh4mrDl",
        "outputId": "14a6e015-86f8-4201-edea-aa2f0dbce7c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-ca8a9f717433>:9: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
            "  img_resized = img.resize(target_size, Image.ANTIALIAS)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images resized successfully!\n"
          ]
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "def resize_images(folder_path, target_size=(224, 224)):  #resizing fusnction\n",
        "    for filename in os.listdir(folder_path):\n",
        "        file_path = os.path.join(folder_path, filename)\n",
        "        if os.path.isfile(file_path):\n",
        "            img = Image.open(file_path)\n",
        "            img_resized = img.resize(target_size, Image.ANTIALIAS)\n",
        "            img_resized.save(file_path)\n",
        "\n",
        "#Defected Casting paths for the Train and Test directories\n",
        "resize_images(Train_dir+\"/Defected\")\n",
        "resize_images(Test_dir+\"/Defected\")\n",
        "resize_images(Val_dir+\"/Defected\")\n",
        "#Non defected Casting paths for the Train and Test directories\n",
        "resize_images(Train_dir+\"/OK\")\n",
        "resize_images(Test_dir+\"/OK\")\n",
        "resize_images(Val_dir+\"/OK\")\n",
        "\n",
        "\n",
        "print(\"Images resized successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WpHBoue9d3M",
        "outputId": "9d36f5ef-d3b3-4a0e-be4c-6dbbaeb756e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".ipynb_checkpoints folder does not exist.\n",
            "Found 1063 images belonging to 2 classes.\n",
            "Found 85 images belonging to 2 classes.\n",
            "Found 102 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "checkpoint_folder = os.path.join('/content/Train', \".ipynb_checkpoints\")\n",
        "\n",
        "if os.path.exists(checkpoint_folder):\n",
        "    os.rmdir(checkpoint_folder)\n",
        "    print(f\"Deleted .ipynb_checkpoints folder successfully.\")\n",
        "else:\n",
        "    print(\".ipynb_checkpoints folder does not exist.\")\n",
        "\n",
        "input_shape=(224,224,3)\n",
        "Train_generator =train_datagen.flow_from_directory(Train_dir,target_size=(224,224),batch_size=32)\n",
        "Test_generator=test_datagen.flow_from_directory(Test_dir,shuffle=True,target_size=(224,224),batch_size=32)\n",
        "Val_generator=val_datagen.flow_from_directory(Val_dir,shuffle=True,target_size=(224,224),batch_size=32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHhlQnxs_SXv"
      },
      "source": [
        "#### **1.4. CNN Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUSM1725Ey4Y"
      },
      "source": [
        "A Convolutional Neural Network (ConvNet) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c7b4d1a",
        "outputId": "260444ba-8836-445a-aa42-46c71e1a7d97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_1 (Conv2D)           (None, 220, 220, 32)      2432      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 73, 73, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 71, 71, 128)       36992     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 35, 35, 128)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 33, 33, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 16, 16, 256)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 65536)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               33554944  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               65664     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 33963586 (129.56 MB)\n",
            "Trainable params: 33963586 (129.56 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, (5, 5), input_shape=input_shape, activation='relu', name=\"conv2d_1\"))\n",
        "model.add(MaxPooling2D(pool_size=(3, 3), name=\"max_pooling2d_1\"))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', name=\"conv2d_2\"))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), name=\"max_pooling2d_2\"))\n",
        "model.add(Conv2D(256, (3, 3), activation='relu', name=\"conv2d_3\"))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), name=\"max_pooling2d_3\"))\n",
        "model.add(Flatten(name=\"flatten_1\"))\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Yd5dldbH2xn"
      },
      "source": [
        "When compiling the model, we provide **objective function (loss)**, **optimization method (adam)** and **accuracy** that we will follow."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0IXz69bROKNH"
      },
      "source": [
        "'fit()' parameters:\n",
        "* **train:** training data,\n",
        "* **validation_data:** validation set,\n",
        "* **shuffle:** change of location of data in each epoch,\n",
        "* **verbose:** to be able to see the outputs during the training (0-> does not show, 1-> does)\n",
        "* **epoch:** determines how many times the dataset will be trained by traversing the model\n",
        "* **callbacks:** An object that can perform actions at various stages of training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8d3b4edf",
        "outputId": "2e3db1be-4a4d-489d-edbd-db004db93e25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - 27s 686ms/step - loss: 0.6396 - accuracy: 0.5986 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 22s 707ms/step - loss: 0.6164 - accuracy: 0.6086 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 22s 695ms/step - loss: 0.6145 - accuracy: 0.6577 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 22s 693ms/step - loss: 0.5754 - accuracy: 0.6957 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 22s 689ms/step - loss: 0.5509 - accuracy: 0.7307 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 21s 676ms/step - loss: 0.5486 - accuracy: 0.7247 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 23s 724ms/step - loss: 0.5219 - accuracy: 0.7347 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 20s 642ms/step - loss: 0.5273 - accuracy: 0.7477 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 23s 718ms/step - loss: 0.4780 - accuracy: 0.7646 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 20s 641ms/step - loss: 0.4985 - accuracy: 0.7608 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 24s 774ms/step - loss: 0.4415 - accuracy: 0.7928 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 21s 678ms/step - loss: 0.4338 - accuracy: 0.7888 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 22s 694ms/step - loss: 0.3746 - accuracy: 0.8228 - lr: 0.0010\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 22s 690ms/step - loss: 0.3553 - accuracy: 0.8318 - lr: 0.0010\n",
            "Epoch 15/15\n",
            "32/32 [==============================] - 21s 655ms/step - loss: 0.3850 - accuracy: 0.8308 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model_checkpoint = ModelCheckpoint('best_CNN_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "history = model.fit(\n",
        "    Train_generator,\n",
        "    steps_per_epoch=32,\n",
        "    epochs=15,\n",
        "    validation_data=Val_generator,\n",
        "    validation_steps=4,\n",
        "    verbose=1,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3, patience=3, min_lr=0.000001), early_stopping, model_checkpoint],\n",
        "    shuffle=True\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cal_sensitivity(conf_matrix):\n",
        "    TP = conf_matrix[1, 1]\n",
        "    FN = conf_matrix[1, 0]\n",
        "    sensitivity = TP / (TP + FN)\n",
        "    return sensitivity\n",
        "\n",
        "def cal_specificity(conf_matrix):\n",
        "    TN = conf_matrix[0, 0]\n",
        "    FP = conf_matrix[0, 1]\n",
        "    specificity = TN / (TN + FP)\n",
        "    return specificity"
      ],
      "metadata": {
        "id": "NyHSzU3hvoU0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a616bb60",
        "outputId": "d1e15f3f-142e-44e8-baa6-542e88c0213d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 30ms/step - loss: 0.4214 - accuracy: 0.7957\n",
            "Test Accuracy: 79.56989407539368\n",
            "3/3 [==============================] - 0s 45ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[35 21]\n",
            " [28  9]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.56      0.62      0.59        56\n",
            "          OK       0.30      0.24      0.27        37\n",
            "\n",
            "    accuracy                           0.47        93\n",
            "   macro avg       0.43      0.43      0.43        93\n",
            "weighted avg       0.45      0.47      0.46        93\n",
            "\n",
            "Sensitivity: 0.24324324324324326\n",
            "Specificity: 0.625\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the saved model\n",
        "model = load_model('/content/best_CNN_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnyAcVHQCmUG"
      },
      "source": [
        "#### **1.5. VGG16 Model**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_CAE0q1c4Tg"
      },
      "source": [
        "**VGG16** Architecture consists of 16 layers.\n",
        "\n",
        "* **include_top :** Whether to include 3 layers fully connected to the top of the network\n",
        "* **weight:** checkpoint from which model is initialized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GND3gR2Cr9K",
        "outputId": "a0a49c8a-fb89-4795-bcea-3418a089d590"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (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",
            "=================================================================\n",
            "Total params: 14714688 (56.13 MB)\n",
            "Trainable params: 0 (0.00 Byte)\n",
            "Non-trainable params: 14714688 (56.13 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def create_Base_model_using_VGG16():\n",
        "    model = VGG16(\n",
        "        weights = \"imagenet\",\n",
        "        include_top=False,\n",
        "        input_shape = (224,224, 3) # goruntu boyutu\n",
        "        )\n",
        "    for layer in model.layers:\n",
        "      layer.trainable = False\n",
        "    return model\n",
        "create_Base_model_using_VGG16().summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wy5A0gDCC-2j",
        "outputId": "ab9292eb-313a-4e3c-95bb-5a11892f0f99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (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",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               3211392   \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 256)               33024     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18000386 (68.67 MB)\n",
            "Trainable params: 3285698 (12.53 MB)\n",
            "Non-trainable params: 14714688 (56.13 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def add_new_layers():\n",
        "    model = create_Base_model_using_VGG16()\n",
        "    x = model.output\n",
        "    x = tf.keras.layers.Flatten()(x)\n",
        "    x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(256, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(64, activation=\"relu\")(x)\n",
        "    predictions = tf.keras.layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "\n",
        "    final_model = tf.keras.models.Model(\n",
        "        inputs = model.input,\n",
        "        outputs = predictions)\n",
        "\n",
        "    final_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return final_model\n",
        "\n",
        "add_new_layers().summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9u0Ny_CC-_H",
        "outputId": "ce9b5538-9651-4c5a-c6d9-3445172bd0ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.7273 - accuracy: 0.6186"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 4 batches). You may need to use the repeat() function when building your dataset.\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/32 [==============================] - 30s 674ms/step - loss: 0.7273 - accuracy: 0.6186 - val_loss: 0.5394 - val_accuracy: 0.7234 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 13s 407ms/step - loss: 0.5286 - accuracy: 0.7327 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 28s 883ms/step - loss: 0.4855 - accuracy: 0.7578 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 20s 637ms/step - loss: 0.4796 - accuracy: 0.7608 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 18s 550ms/step - loss: 0.4107 - accuracy: 0.8027 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 13s 415ms/step - loss: 0.2980 - accuracy: 0.8679 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 13s 392ms/step - loss: 0.3079 - accuracy: 0.8699 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 13s 411ms/step - loss: 0.2822 - accuracy: 0.8819 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 14s 429ms/step - loss: 0.2376 - accuracy: 0.9014 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 14s 426ms/step - loss: 0.1797 - accuracy: 0.9259 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 13s 392ms/step - loss: 0.2311 - accuracy: 0.9059 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 13s 401ms/step - loss: 0.1746 - accuracy: 0.9249 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 13s 407ms/step - loss: 0.1355 - accuracy: 0.9459 - lr: 0.0010\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 13s 389ms/step - loss: 0.1493 - accuracy: 0.9429 - lr: 0.0010\n",
            "Epoch 15/15\n",
            "32/32 [==============================] - 13s 403ms/step - loss: 0.1382 - accuracy: 0.9443 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "model_from_vgg16 = add_new_layers()\n",
        "\n",
        "model_checkpoint = ModelCheckpoint('best_VGG16_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "history2 = model_from_vgg16.fit(\n",
        "    Train_generator,\n",
        "    steps_per_epoch=32,\n",
        "    epochs=15,\n",
        "    validation_data=Val_generator,\n",
        "    validation_steps=4,\n",
        "    verbose=1,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3,patience=3, min_lr=0.000001),early_stopping, model_checkpoint],\n",
        "    use_multiprocessing=False,\n",
        "    shuffle=True\n",
        "    )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "93fUErbvC_C6",
        "outputId": "5599f082-7447-4286-bb2b-089c145fe531",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 132ms/step - loss: 0.0985 - accuracy: 0.9462\n",
            "Test Accuracy: 94.62365508079529\n",
            "3/3 [==============================] - 1s 125ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[32 24]\n",
            " [23 14]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.58      0.57      0.58        56\n",
            "          OK       0.37      0.38      0.37        37\n",
            "\n",
            "    accuracy                           0.49        93\n",
            "   macro avg       0.48      0.47      0.47        93\n",
            "weighted avg       0.50      0.49      0.50        93\n",
            "\n",
            "Sensitivity: 0.3783783783783784\n",
            "Specificity: 0.5714285714285714\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load the saved model\n",
        "model = load_model('/content/best_VGG16_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history2.history['accuracy'])\n",
        "plt.plot(history2.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9uxSFjxMY9w"
      },
      "source": [
        "#### **1.7. InceptionV3 Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MISclavSt04"
      },
      "source": [
        " Assigning the weight parameter to the imagenet will enable the weights of the imagenet model to be used. If we want to train something using the Inception mesh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "76v_E9vjMeTp",
        "outputId": "400c065e-e02d-431a-a56b-234340794b56",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87910968/87910968 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "IMAGE_SIZE = [224, 224]\n",
        "inception = InceptionV3(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
        "\n",
        "for layer in inception.layers:\n",
        "    layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZfyn8-HMeXE",
        "outputId": "25f3fc4a-d8fc-49bb-d6e6-750818052067"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 111, 111, 32)         864       ['input_2[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 111, 111, 32)         96        ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 111, 111, 32)         0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 109, 109, 32)         9216      ['activation[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 109, 109, 32)         96        ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)   (None, 109, 109, 32)         0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 109, 109, 64)         18432     ['activation_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 109, 109, 64)         192       ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_2 (Activation)   (None, 109, 109, 64)         0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPoolin  (None, 54, 54, 64)           0         ['activation_2[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 54, 54, 80)           5120      ['max_pooling2d_3[0][0]']     \n",
            "                                                                                                  \n",
            " batch_normalization_10 (Ba  (None, 54, 54, 80)           240       ['conv2d_8[0][0]']            \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_3 (Activation)   (None, 54, 54, 80)           0         ['batch_normalization_10[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 52, 52, 192)          138240    ['activation_3[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_11 (Ba  (None, 52, 52, 192)          576       ['conv2d_9[0][0]']            \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_4 (Activation)   (None, 52, 52, 192)          0         ['batch_normalization_11[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_4 (MaxPoolin  (None, 25, 25, 192)          0         ['activation_4[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 25, 25, 64)           12288     ['max_pooling2d_4[0][0]']     \n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 25, 25, 64)           192       ['conv2d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_8 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 25, 25, 48)           9216      ['max_pooling2d_4[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_8[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 25, 25, 48)           144       ['conv2d_11[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 25, 25, 96)           288       ['conv2d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_6 (Activation)   (None, 25, 25, 48)           0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_9 (Activation)   (None, 25, 25, 96)           0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 25, 25, 192)          0         ['max_pooling2d_4[0][0]']     \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 25, 25, 64)           12288     ['max_pooling2d_4[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_6[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_9[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 25, 25, 32)           6144      ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 25, 25, 64)           192       ['conv2d_10[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 25, 25, 64)           192       ['conv2d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 25, 25, 96)           288       ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 25, 25, 32)           96        ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_5 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_7 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_10 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_11 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)        (None, 25, 25, 256)          0         ['activation_5[0][0]',        \n",
            "                                                                     'activation_7[0][0]',        \n",
            "                                                                     'activation_10[0][0]',       \n",
            "                                                                     'activation_11[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 25, 25, 64)           192       ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_15 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 25, 25, 48)           12288     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_15[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 25, 25, 48)           144       ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 25, 25, 96)           288       ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_13 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_16 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (Avera  (None, 25, 25, 256)          0         ['mixed0[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_13[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_16[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 25, 25, 64)           16384     ['average_pooling2d_1[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 25, 25, 64)           192       ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 25, 25, 64)           192       ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 25, 25, 96)           288       ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 25, 25, 64)           192       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_12 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_14 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_17 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_18 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)        (None, 25, 25, 288)          0         ['activation_12[0][0]',       \n",
            "                                                                     'activation_14[0][0]',       \n",
            "                                                                     'activation_17[0][0]',       \n",
            "                                                                     'activation_18[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 25, 25, 64)           192       ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_22 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 25, 25, 48)           13824     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_22[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 25, 25, 48)           144       ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 25, 25, 96)           288       ['conv2d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_20 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_23 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (Avera  (None, 25, 25, 288)          0         ['mixed1[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_20[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_23[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 25, 25, 64)           18432     ['average_pooling2d_2[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 25, 25, 64)           192       ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 25, 25, 64)           192       ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 25, 25, 96)           288       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_32 (Ba  (None, 25, 25, 64)           192       ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_19 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_21 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_24 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_25 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_32[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)        (None, 25, 25, 288)          0         ['activation_19[0][0]',       \n",
            "                                                                     'activation_21[0][0]',       \n",
            "                                                                     'activation_24[0][0]',       \n",
            "                                                                     'activation_25[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_34 (Ba  (None, 25, 25, 64)           192       ['conv2d_32[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_27 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_34[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_27[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_35 (Ba  (None, 25, 25, 96)           288       ['conv2d_33[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_28 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_35[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)          (None, 12, 12, 384)          995328    ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)          (None, 12, 12, 96)           82944     ['activation_28[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_33 (Ba  (None, 12, 12, 384)          1152      ['conv2d_31[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_36 (Ba  (None, 12, 12, 96)           288       ['conv2d_34[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_26 (Activation)  (None, 12, 12, 384)          0         ['batch_normalization_33[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_29 (Activation)  (None, 12, 12, 96)           0         ['batch_normalization_36[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_5 (MaxPoolin  (None, 12, 12, 288)          0         ['mixed2[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)        (None, 12, 12, 768)          0         ['activation_26[0][0]',       \n",
            "                                                                     'activation_29[0][0]',       \n",
            "                                                                     'max_pooling2d_5[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_41 (Ba  (None, 12, 12, 128)          384       ['conv2d_39[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_34 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_41[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_34[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_42 (Ba  (None, 12, 12, 128)          384       ['conv2d_40[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_35 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_42[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_35[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_38 (Ba  (None, 12, 12, 128)          384       ['conv2d_36[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_43 (Ba  (None, 12, 12, 128)          384       ['conv2d_41[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_31 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_38[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_36 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_43[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_31[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_36[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_39 (Ba  (None, 12, 12, 128)          384       ['conv2d_37[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_44 (Ba  (None, 12, 12, 128)          384       ['conv2d_42[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_32 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_39[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_37 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_44[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (Avera  (None, 12, 12, 768)          0         ['mixed3[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_32[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_37[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_3[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_37 (Ba  (None, 12, 12, 192)          576       ['conv2d_35[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_40 (Ba  (None, 12, 12, 192)          576       ['conv2d_38[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_45 (Ba  (None, 12, 12, 192)          576       ['conv2d_43[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_46 (Ba  (None, 12, 12, 192)          576       ['conv2d_44[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_30 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_37[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_33 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_40[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_38 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_45[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_39 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_46[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)        (None, 12, 12, 768)          0         ['activation_30[0][0]',       \n",
            "                                                                     'activation_33[0][0]',       \n",
            "                                                                     'activation_38[0][0]',       \n",
            "                                                                     'activation_39[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_51 (Ba  (None, 12, 12, 160)          480       ['conv2d_49[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_44 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_51[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_44[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_52 (Ba  (None, 12, 12, 160)          480       ['conv2d_50[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_45 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_52[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_45[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_48 (Ba  (None, 12, 12, 160)          480       ['conv2d_46[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_53 (Ba  (None, 12, 12, 160)          480       ['conv2d_51[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_41 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_48[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_46 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_53[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_41[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_46[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_49 (Ba  (None, 12, 12, 160)          480       ['conv2d_47[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_54 (Ba  (None, 12, 12, 160)          480       ['conv2d_52[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_42 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_49[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_47 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_54[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (Avera  (None, 12, 12, 768)          0         ['mixed4[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_42[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_47[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_4[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_47 (Ba  (None, 12, 12, 192)          576       ['conv2d_45[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_50 (Ba  (None, 12, 12, 192)          576       ['conv2d_48[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_55 (Ba  (None, 12, 12, 192)          576       ['conv2d_53[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_56 (Ba  (None, 12, 12, 192)          576       ['conv2d_54[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_40 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_47[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_43 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_50[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_48 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_55[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_49 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_56[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)        (None, 12, 12, 768)          0         ['activation_40[0][0]',       \n",
            "                                                                     'activation_43[0][0]',       \n",
            "                                                                     'activation_48[0][0]',       \n",
            "                                                                     'activation_49[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_61 (Ba  (None, 12, 12, 160)          480       ['conv2d_59[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_54 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_61[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_54[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_62 (Ba  (None, 12, 12, 160)          480       ['conv2d_60[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_55 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_62[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_55[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_58 (Ba  (None, 12, 12, 160)          480       ['conv2d_56[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_63 (Ba  (None, 12, 12, 160)          480       ['conv2d_61[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_51 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_58[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_56 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_63[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_51[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_56[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_59 (Ba  (None, 12, 12, 160)          480       ['conv2d_57[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_64 (Ba  (None, 12, 12, 160)          480       ['conv2d_62[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_52 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_59[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_57 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_64[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (Avera  (None, 12, 12, 768)          0         ['mixed5[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_52[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_57[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_5[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_57 (Ba  (None, 12, 12, 192)          576       ['conv2d_55[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_60 (Ba  (None, 12, 12, 192)          576       ['conv2d_58[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_65 (Ba  (None, 12, 12, 192)          576       ['conv2d_63[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_66 (Ba  (None, 12, 12, 192)          576       ['conv2d_64[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_50 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_57[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_53 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_60[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_58 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_65[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_59 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_66[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)        (None, 12, 12, 768)          0         ['activation_50[0][0]',       \n",
            "                                                                     'activation_53[0][0]',       \n",
            "                                                                     'activation_58[0][0]',       \n",
            "                                                                     'activation_59[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_71 (Ba  (None, 12, 12, 192)          576       ['conv2d_69[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_64 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_71[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_64[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_72 (Ba  (None, 12, 12, 192)          576       ['conv2d_70[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_65 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_72[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_65[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_68 (Ba  (None, 12, 12, 192)          576       ['conv2d_66[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_73 (Ba  (None, 12, 12, 192)          576       ['conv2d_71[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_61 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_68[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_66 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_73[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_61[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_66[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_69 (Ba  (None, 12, 12, 192)          576       ['conv2d_67[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_74 (Ba  (None, 12, 12, 192)          576       ['conv2d_72[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_62 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_69[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_67 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_74[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (Avera  (None, 12, 12, 768)          0         ['mixed6[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_62[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_67[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_6[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_67 (Ba  (None, 12, 12, 192)          576       ['conv2d_65[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_70 (Ba  (None, 12, 12, 192)          576       ['conv2d_68[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_75 (Ba  (None, 12, 12, 192)          576       ['conv2d_73[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_76 (Ba  (None, 12, 12, 192)          576       ['conv2d_74[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_60 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_67[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_63 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_70[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_68 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_75[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_69 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_76[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)        (None, 12, 12, 768)          0         ['activation_60[0][0]',       \n",
            "                                                                     'activation_63[0][0]',       \n",
            "                                                                     'activation_68[0][0]',       \n",
            "                                                                     'activation_69[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_79 (Ba  (None, 12, 12, 192)          576       ['conv2d_77[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_72 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_79[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_72[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_80 (Ba  (None, 12, 12, 192)          576       ['conv2d_78[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_73 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_80[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_73[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_77 (Ba  (None, 12, 12, 192)          576       ['conv2d_75[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_81 (Ba  (None, 12, 12, 192)          576       ['conv2d_79[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_70 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_77[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_74 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_81[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)          (None, 5, 5, 320)            552960    ['activation_70[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)          (None, 5, 5, 192)            331776    ['activation_74[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_78 (Ba  (None, 5, 5, 320)            960       ['conv2d_76[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_82 (Ba  (None, 5, 5, 192)            576       ['conv2d_80[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_71 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_78[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_75 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_82[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_6 (MaxPoolin  (None, 5, 5, 768)            0         ['mixed7[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)        (None, 5, 5, 1280)           0         ['activation_71[0][0]',       \n",
            "                                                                     'activation_75[0][0]',       \n",
            "                                                                     'max_pooling2d_6[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)          (None, 5, 5, 448)            573440    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_87 (Ba  (None, 5, 5, 448)            1344      ['conv2d_85[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_80 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_87[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)          (None, 5, 5, 384)            491520    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_80[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_84 (Ba  (None, 5, 5, 384)            1152      ['conv2d_82[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_88 (Ba  (None, 5, 5, 384)            1152      ['conv2d_86[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_77 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_84[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_81 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_88[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (Avera  (None, 5, 5, 1280)           0         ['mixed8[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)          (None, 5, 5, 320)            409600    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_85 (Ba  (None, 5, 5, 384)            1152      ['conv2d_83[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_86 (Ba  (None, 5, 5, 384)            1152      ['conv2d_84[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_89 (Ba  (None, 5, 5, 384)            1152      ['conv2d_87[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_90 (Ba  (None, 5, 5, 384)            1152      ['conv2d_88[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)          (None, 5, 5, 192)            245760    ['average_pooling2d_7[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_83 (Ba  (None, 5, 5, 320)            960       ['conv2d_81[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_78 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_85[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_79 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_86[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_82 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_89[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_83 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_90[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_91 (Ba  (None, 5, 5, 192)            576       ['conv2d_89[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_76 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_83[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)      (None, 5, 5, 768)            0         ['activation_78[0][0]',       \n",
            "                                                                     'activation_79[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 5, 5, 768)            0         ['activation_82[0][0]',       \n",
            "                                                                     'activation_83[0][0]']       \n",
            "                                                                                                  \n",
            " activation_84 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_91[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)        (None, 5, 5, 2048)           0         ['activation_76[0][0]',       \n",
            "                                                                     'mixed9_0[0][0]',            \n",
            "                                                                     'concatenate[0][0]',         \n",
            "                                                                     'activation_84[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_94 (Conv2D)          (None, 5, 5, 448)            917504    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_96 (Ba  (None, 5, 5, 448)            1344      ['conv2d_94[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_89 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_96[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)          (None, 5, 5, 384)            786432    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_95 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_89[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_93 (Ba  (None, 5, 5, 384)            1152      ['conv2d_91[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_97 (Ba  (None, 5, 5, 384)            1152      ['conv2d_95[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_86 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_93[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_90 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_97[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_96 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_97 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (Avera  (None, 5, 5, 2048)           0         ['mixed9[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)          (None, 5, 5, 320)            655360    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_94 (Ba  (None, 5, 5, 384)            1152      ['conv2d_92[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_95 (Ba  (None, 5, 5, 384)            1152      ['conv2d_93[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_98 (Ba  (None, 5, 5, 384)            1152      ['conv2d_96[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_99 (Ba  (None, 5, 5, 384)            1152      ['conv2d_97[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_98 (Conv2D)          (None, 5, 5, 192)            393216    ['average_pooling2d_8[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_92 (Ba  (None, 5, 5, 320)            960       ['conv2d_90[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_87 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_94[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_88 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_95[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_91 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_98[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_92 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_99[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_100 (B  (None, 5, 5, 192)            576       ['conv2d_98[0][0]']           \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_85 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_92[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)      (None, 5, 5, 768)            0         ['activation_87[0][0]',       \n",
            "                                                                     'activation_88[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 5, 5, 768)            0         ['activation_91[0][0]',       \n",
            " )                                                                   'activation_92[0][0]']       \n",
            "                                                                                                  \n",
            " activation_93 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_100[0][0\n",
            "                                                                    ]']                           \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)       (None, 5, 5, 2048)           0         ['activation_85[0][0]',       \n",
            "                                                                     'mixed9_1[0][0]',            \n",
            "                                                                     'concatenate_1[0][0]',       \n",
            "                                                                     'activation_93[0][0]']       \n",
            "                                                                                                  \n",
            " dense_11 (Dense)            (None, 5, 5, 128)            262272    ['mixed10[0][0]']             \n",
            "                                                                                                  \n",
            " dense_12 (Dense)            (None, 5, 5, 512)            66048     ['dense_11[0][0]']            \n",
            "                                                                                                  \n",
            " dense_13 (Dense)            (None, 5, 5, 256)            131328    ['dense_12[0][0]']            \n",
            "                                                                                                  \n",
            " dense_14 (Dense)            (None, 5, 5, 128)            32896     ['dense_13[0][0]']            \n",
            "                                                                                                  \n",
            " dense_15 (Dense)            (None, 5, 5, 64)             8256      ['dense_14[0][0]']            \n",
            "                                                                                                  \n",
            " flatten_1 (Flatten)         (None, 1600)                 0         ['dense_15[0][0]']            \n",
            "                                                                                                  \n",
            " dense_16 (Dense)            (None, 2)                    3202      ['flatten_1[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 22306786 (85.09 MB)\n",
            "Trainable params: 504002 (1.92 MB)\n",
            "Non-trainable params: 21802784 (83.17 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "x = (inception.output)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dense(256, activation='relu')(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Flatten()(Dense(64, activation='relu')(x))\n",
        "prediction = Dense(num_classes, activation='softmax')(x)\n",
        "model = Model(inputs=inception.input, outputs=prediction)# Create a model object\n",
        "\n",
        "model.summary()# View the structure of the model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I_7g_QOKMyFH",
        "outputId": "a82b646e-945e-4c9d-dc6e-3bc0c40fc1bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.5951 - accuracy: 0.6537"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/32 [==============================] - 27s 590ms/step - loss: 0.5951 - accuracy: 0.6537 - val_loss: 0.7033 - val_accuracy: 0.6569 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 15s 479ms/step - loss: 0.5319 - accuracy: 0.7227 - val_loss: 0.4951 - val_accuracy: 0.7157 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 15s 467ms/step - loss: 0.4426 - accuracy: 0.7988 - val_loss: 0.4764 - val_accuracy: 0.7157 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 15s 458ms/step - loss: 0.3752 - accuracy: 0.8218 - val_loss: 0.3996 - val_accuracy: 0.7745 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 25s 801ms/step - loss: 0.3479 - accuracy: 0.8328 - val_loss: 0.5097 - val_accuracy: 0.7353 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 15s 455ms/step - loss: 0.3147 - accuracy: 0.8539 - val_loss: 0.4332 - val_accuracy: 0.7941 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 15s 461ms/step - loss: 0.3015 - accuracy: 0.8689 - val_loss: 0.4400 - val_accuracy: 0.7843 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 14s 454ms/step - loss: 0.2516 - accuracy: 0.9039 - val_loss: 0.9689 - val_accuracy: 0.6471 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 14s 435ms/step - loss: 0.3215 - accuracy: 0.8709 - val_loss: 0.4785 - val_accuracy: 0.7647 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 15s 446ms/step - loss: 0.2160 - accuracy: 0.9169 - val_loss: 0.3645 - val_accuracy: 0.8529 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 14s 427ms/step - loss: 0.2357 - accuracy: 0.8929 - val_loss: 0.4994 - val_accuracy: 0.7843 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 16s 511ms/step - loss: 0.2114 - accuracy: 0.9129 - val_loss: 0.4097 - val_accuracy: 0.8431 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 14s 427ms/step - loss: 0.2542 - accuracy: 0.8889 - val_loss: 0.4206 - val_accuracy: 0.7843 - lr: 0.0010\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 15s 453ms/step - loss: 0.2034 - accuracy: 0.9109 - val_loss: 0.5627 - val_accuracy: 0.8235 - lr: 0.0010\n",
            "Epoch 15/15\n",
            "32/32 [==============================] - 14s 423ms/step - loss: 0.2132 - accuracy: 0.9129 - val_loss: 0.4618 - val_accuracy: 0.8039 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "# Defining the cost and model optimization method to use\n",
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "model_checkpoint = ModelCheckpoint('best_InceptionV3_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "history3 = model.fit(\n",
        "    Train_generator,\n",
        "    steps_per_epoch=32,\n",
        "    epochs=15,\n",
        "    validation_data=Val_generator,\n",
        "    validation_steps=4,\n",
        "    verbose=1,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3,patience=3, min_lr=0.000001),early_stopping, model_checkpoint],\n",
        "    shuffle=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mwdq-24FOFvT",
        "outputId": "a3540a3c-ec02-44df-d91b-82cccea31a84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 4s 1s/step - loss: 0.3794 - accuracy: 0.8471\n",
            "Test Accuracy: 84.70588326454163\n",
            "3/3 [==============================] - 2s 77ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[28 24]\n",
            " [15 18]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.65      0.54      0.59        52\n",
            "          OK       0.43      0.55      0.48        33\n",
            "\n",
            "    accuracy                           0.54        85\n",
            "   macro avg       0.54      0.54      0.53        85\n",
            "weighted avg       0.56      0.54      0.55        85\n",
            "\n",
            "Sensitivity: 0.5454545454545454\n",
            "Specificity: 0.5384615384615384\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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pU6cyYcIEqlevTuXKlZk1axa9e/cGwOFw0L59e5KSkvj666+pVKkS27Zty5iPZsOGDbRq1YpHHnmEDz/8EDc3N5YvX47dbs3vR6Sg2HT4NM9/v5HdcWcAiE1M4X8r9/O/lfupGOhLp/rhdKoXTsWgYtc4UuEXl3ielHQHESU1pUdeMQyDVLuD1HQHKen//qed1HQHXu6u1C4dYFmNCjfXcC7NTs3hiy0597aRbfHxyJ1f0ciRI2nTpk3G65IlS1KvXr2M12+++SZz5sxh/vz5DB48+KrH6devHw899BAAo0aN4qOPPmLNmjW0a9fumjUsXbqUs2fP0rZtWwB69erF559/nhFuli5dypo1a9i+fTtVq1YFoGLFihmfHzNmDI0aNeKTTz7J2FarVq3ruXwRp5Sa7mD8st188ute7A6DwGKe/LdLLVxsNuZtPMrSbbHsO57MuKW7Gbd0N3XLBNCpXjgd64UT4u9ldfm5wu4w2HDoFMt2xLFsRzzbjyUCUDPMn871zWsNL+5tcZX550xKOusPnuJsajopF0LHv4PH5a9T0hyk2h2X/nnFPpfte9nra2lUrgSzBjbLhyvOmsJNEdGoUaNMr8+cOcOIESP46aefOHbsGOnp6Zw7d47o6Ohsj1O3bt2M576+vvj7+2csaXAtU6ZMoUePHhn9fR566CFefPFF9u7dS6VKldiwYQNlypTJCDb/tmHDBrp163Zd5xJxdluOJPDCzI3siEkCoGO9cEZ2qkUJXw8A7q4VypmUdH7ZGsO8DUdZuec4mw4nsOlwAm/9vJ2mFUvRqV447WuHEeDjbuWl5Njps6n8tiue5Tvi+G1XPKfOXpqU02YDV5uNbccS2XYskdELd9CkQkk61w/nntphGT8fZ3L6bCpLtsWyeGsMK3Yfv67wkZs8XF3wcDMfnhf+GRJgbXhWuLkGb3dXto1sa9m5c4uvr2+m1y+88AJLlizhvffeo3Llynh7e9O1a1dSU1OzPY67e+Y/gjabDYfj2v8hnTx5kjlz5pCWlsbEiRMzttvtdqZMmcJbb72VsTzC1VzrfZGiIM3uYMLyPXy8bA/pDoOSvh78t0tt7qlz5XppxTzduP+WMtx/SxmOn0nh583HmLfhKOsOnmLV3hOs2nuCYfO2cFe1YDrXD6dV9RC8PQresiSGYbAzNollO+JYviOOdQdP4bisK5G/lxt3VA2iZfVg7qwahIvNxsItMczbcIS/9p9kzYXH6/O2ckfVIDrXD6d1jRB8PQvvV2Bc0nl+2RrLoi0xrN53AvtlP5CIkt6E+Hnh6e6Ch6sLnm6umYLHla8vPq693xXbXV1wKYCd1y3/zU6YMIF3332XmJgY6tWrx/jx42nSpEmW+6alpTF69GimTZvGkSNHqFatGu+888513RK5UTabLdduDRUkf/zxB/369eO+++4DzJacAwcO5Nn5vvnmG8qUKcPcuXMzbf/ll194//33GTlyJHXr1uXw4cPs2rUry9abunXrEhUVxRtvvJFndYoUZDtiEnn++41sPWreemlfO5Q3u9QmsJjnNT8bWMyTPk3L06dpeQ6dPMuPm44yf8NRdsQksWRbLEu2xeLr4UrbWqF0qh/ObZUDcXO1bszJuVQ7q/Yezwg0RxPOZ3q/akgxWlQPpmW1YBqWK3FFrQ9HluXhyLIcPX2OBZuOMm/DUbYeTbxw+yoOb3dX2tQMoXP9cG6vEoSHW8EfX3Po5FkWb41h0ZYY1kWf4vK+4tVD/WhXO5R2tUOpFuJX5Gdjt/Rbe8aMGQwZMoRJkyYRGRnJuHHjaNu2LTt37iQ4OPiK/V977TW+/vprJk+eTPXq1Vm8eDH33Xcfq1atokGDBhZcQeFVpUoVZs+eTceOHbHZbAwbNuy6WmBu1Oeff07Xrl2pXbt2pu0REREMHTqURYsW0aFDB+644w4eeOABxo4dS+XKldmxYwc2m4127doxdOhQ6tSpw5NPPskTTzyBh4cHy5cvp1u3bgQGBuZZ7VI4/BN9inFLd7M3/gyd64fzSPMKlLqOL/3CIN3u4NMV+xi3dBdpdoPiPu6M7FybjnXDbuhLLKKkD0/eVZkn76rMjphE5m8wv/yPnD7H7H+OMPufI5Ty9aBD3TA61w/nlrIl8uXL8tDJsxnhY/W+E5lur3i6udC8ciAtqgXRonowZUpcX4fh8OLePH5HJR6/oxJ74pLMa914lIMnzjJ/41HmbzxKcR937qkTRud64TQuX7JAtUTsjT/Doi1moNl8JCHTe/UiitOulhloKgT6XuUIRZOl4Wbs2LE89thj9O/fH4BJkybx008/MWXKFF5++eUr9v/qq6949dVXueeeewAYOHAgS5cu5f333+frr7/O19oLu7Fjx/LII4/QrFkzAgMDeemll0hMTMyTc61bt46NGzcyefLkK94LCAigVatWfP7553To0IEffviBF154gYceeojk5GQqV67M22+/DUDVqlX55ZdfeOWVV2jSpAne3t5ERkZmdHCWomnLkQTGLtnFsh2X+n5NWL6XKSsP0DOyLI/dUbFQd57dE5fE899vZONh84utdY0QRt1fm2C/3Lmm6qH+VG/nz4ttq7E++hTzNhxlwaZjnEhO5cvVB/ly9UHKlPCmU71wOtcvTbVQv1w5L5i32P4+cIrlO81As+fCaK+LShf3pmX1YFpWD6ZppVJ43eSt+srBfgy5uxrPtanKxsMJzNtwhAWbjhGflML0v6KZ/lc0YQFedKoXTqf64dQM88/3FhDDMNh2LJHFW2JYuCUmYwQcgIsNGpcvSbvaobStFVqkOkrnlM2waBKE1NRUfHx8mDVrFl26dMnY3rdvX06fPs28efOu+EypUqUYM2YMAwYMyNjWq1cvVq5cedVbKikpKaSkpGS8TkxMJCIigoSEBPz9/TPte/78efbv30+FChXw8iq8fwzl6vQ7dh47YhL5YMkuFm+NBcw//PffUobmlUsxZeWBjP/L9XB1oXvjMvznjkqFaniw3WHwv9/38f6SXaSmO/D3cmNEp1rc16B0nn/hptkd/LHnOPM3HGXx1hiSUy9Nt1AtxC9jaPmN/DyPn0nh151mZ+AVu+NJOp+e8Z6ri42G5UpkBJoqwcXy/FrtDoM/951g3oYjLNwSk6meysHFzKBTL5zyedgy4nAYbDh8OqOFJvrk2Yz33F1tNKsUSLvaobSpGXJdtyCdVWJiIgEBAVl+f/+bZeHm6NGjlC5dmlWrVtG0adOM7f/3f//Hb7/9xl9//XXFZx5++GE2btzI3LlzqVSpElFRUXTu3Bm73Z4pwFxuxIgRWfbRULgpmvQ7Lvz2xp9h3NLdLNh0FMMwR8d0qhfOM62qZMzjYhgGv+2K5+Nle/j7oDnJo5uLjS4NSvPkXZUK/Hwv++LP8MLMjayPPg3AXdWCePv+uoRaMALlXKqdqB2xzN9wlF93xpNqv3SrqGG5EuYopDphV/3SdTgMthxNYPmOeJbtjGPT4dOZ+oqU9PXgrqrmraY7qgRZOnLrfJqdX3fGM3/jEZZuj8t0W6xeRHE61wvn3nphudJqlm53sObASRZtiWHx1hhiEy99h3m6uXBn1SDa1wmlZfUQArwL12i2vOK04SY+Pp7HHnuMH3/8EZvNRqVKlWjdujVTpky5Yqbbi9Rykz+eeOKJq94a7NWrF5MmTcrnirKm33HhdfBEMh9G7WbuP0cyRsrcUyeUZ1tXpWpI1rdKDMPgr/0n+XjZHlbuOQ6YLTwd6oYzqEUlqodm/wcyvzkcBl+sOsCYxTs4n+agmKcbw++tSbdGZQpEB9GEs2ks2mqOuFq970RGSHF1sXFb5UA61w/n7lqhGIbByt1mZ+Bfd8UTn5T5fz5rhfvTsnowLaoHU69M8QK5VETS+TQWb41l3oYj/LHneMa/cy42aFYpkE71w2lXOxR/r+sPHinpdlbtOcGiLTEs2R7LyeRLo1OLebrRsnow7WuHcme1IKccyHKzCkW4uZHbUhedP3+eEydOEB4ezssvv8yCBQvYunXrdZ03ux+OvvhuXFxc3FX77Pj7+2fZQdwK+h0XPodPneXjZXuYue5wxnDX1jVCeK5NFWqFX/8MqP9En2LC8j0s3X6pb06bmiEMblGZehHFc7vsHDt4IpkXZ25izYGTANxWOZB3utaldAHtVxGbeJ4fL3TI3XT4UkdXTzcXHIZBmv3SV4uvhyu3VQmkZfVg7qoWXOj6QMUnXRxGfySjNQ3Aw82FlheG0beoHpxln6Czqen8tjOeRVtjWLY9jqSUS7e9Svi406ZmCO1qh9K8ciCebgVvGH5BUijCDUBkZCRNmjRh/PjxgDn9ftmyZRk8eHCWHYr/LS0tjRo1atC9e3dGjRp1XedUuCna9DsuPGITz/Pxsj18tzY644vyjqpBDGlTlfo3EUa2HU1kwq97+HnzsYyWh9urBPJUyyo0qXDtZURym8Nh8M1fBxn18w7Opdnx8XDl1Q41eLhJ2QLRWnM99sWfMUcebTjKvuPJAFQI9KVFNbPvTOMKJZzmizv6hDmMfu4/RzJ19vXzdKNt7VA61w+ndngAv+2KZ+GWY/y2K57zaZdubwX7eZpDtmuF0qRCSUuH2xc2hSbczJgxg759+/Lpp5/SpEkTxo0bx/fff8+OHTsICQmhT58+lC5dmtGjRwPw119/ceTIEerXr8+RI0cYMWIE+/fvZ/369RQvXvy6zqlwU7Tpd1zwHT+TwsRf9/L1nwdJudDnoWnFUjx/d1Ualc+98LEn7gyf/LqHeRuOZrQINalQksEtKnN7lcB8G/r80g+bWLX3BAC3VizJu13rFaqOz5czDIPdcWfwcHXJ0w64BYFhGOyISWLehqP8uNEcRn81ZUp40752KO1qh9EgoniBGmpemOQk3Fh6U69Hjx7Ex8czfPhwYmJiqF+/PosWLSIkJASA6OhoXFwupdrz58/z2muvsW/fPooVK8Y999zDV199dd3BRkQKrlPJqXy6Yh/TVh3IWKy2UbkSDLm7Ks0q5f48RpWDizG2e32ea12Vib/tZdbfh1mz/yR99q+hXpkABresQqvqwXnyRWQYBt+uOcRbP20jOdWOt7srL7evTu9byxXqLz6bzXbV/k/OxmazUSPMnxph/vxf22qsiz7F/A1H+WnzMU4mp1I5uBjtLwzZrhWe/0PKizpLW26soJabok2/44In4Vwan/++jyl/HODMhf4I9coEMOTuatyRTy0oADEJ5/lsxT6mrzmYcRuheqgfT7aoTIc6YbnW6fXo6XO89MMmft9tdnBuVK4E73Wr5/QtHUVFmt3B6bNpBPkV3SHbeaXQ3JaygsJN0abfccFxJiWdL/7Yz2cr9pF4YW6RmmH+DGlTlVY1gi37P93jZ1KYsnI/X64+mBG2Kgb6MvCuSnRpUBr3G+wjYRgGM9cd5s0ft5GUko6nmwsvtq1G/+YVCuRoIZGCRuEmGwo3RZt+x9Y7l2rny9UHmPTb3ozVnKsEF2NIm6q0rRVaYG7LJJxN44tVB5i6aj+nL9RZurg3T9xViW4Ny+RottzYxPMMnb05YxblBmWL8163elQq4PPtiBQkCjfZULi5fuXLl+fZZ5/l2WeftbqUXKPfsXXOp9mZ/lc0n/y6l+NnzHlPKgT68mzrKtxbN7zAtl6cSUnnmz8PMvn3fRw/Y85LEuznyeN3VOThyLLZzkdiGAZzNxzh9XlbSTyfjoerC0Purspjt1cssNcrUlAp3GTDGcPNtZrvX3/9dUaMGJHj48bHx+Pr64uPz82P3Dhw4AAVKlTgn3/+oX79+jd9vBtVWH/HhVlquoMZfx9iwrI9xCSaKztHlPTm6ZZVuK9B6UIzFPZ8mp0Zaw8x6be9HLuwQnVJXw8G3FaB3k3LXTGZW3xSCq/O2cwv28zlIeqUDuD97vWKTIdbkdxWaEZLSe44duxYxvMZM2YwfPhwdu7cmbGtWLFLTd+GYWC323Fzu/avPigoKHcLlSIlze5g9vrDfBS1J2OYbFiAF0+1rELXhmXwcCscoeYiL3dX+jYrz0NNyjJ7/WEm/raXgyfO8u7inUz6bS/9mpWnf/MKlPT14MeNRxk+bwunzqbh7mrjmVZV+M+dlW64v46I5Iz+S3MCoaGhGY+AgABsNlvG6x07duDn58fChQtp2LAhnp6erFy5kr1799K5c2dCQkIoVqwYjRs3ZunSpZmOW758ecaNG5fx2maz8b///Y/77rsPHx8fqlSpwvz583PlGlJSUnj66acJDg7Gy8uL2267jbVr12a8f+rUKXr27ElQUBDe3t5UqVKFqVOnAuZs14MHDyYsLAwvLy/KlSuXMTeS5D+7w2DOP4dpM/Y3XvphM0dOnyPIz5MRHWuy/IW7eDiybKELNpfzcHPhwSZliRpyJx8+WJ8qwcVIOp/O+GV7uO2dZXSftJqnvv2HU2fTqBnmz/zBtzG4ZRUFG5F8pJabazEMSDt77f3ygruPuSpgLnj55Zd57733qFixIiVKlODQoUPcc889vPXWW3h6evLll1/SsWNHdu7cSdmyZa96nDfeeIMxY8bw7rvvMn78eHr27MnBgwcpWfLmJlf7v//7P3744QemTZtGuXLlGDNmDG3btmXPnj2ULFmSYcOGsW3bNhYuXEhgYCB79uzJWE/so48+Yv78+Xz//feULVuWQ4cOcejQoZuqR3LG7jDYHZfE2gOnmLbqAHsuzNxa0teDgXdWotet5fD2cI4Zai9yc3Whc/3SdKwbzi/bYvh4+R62HElkzYGTuLnYGNSiMoNaVC7UQU6ksFK4uZa0szAq3Jpzv3IUPHJn7ouRI0fSpk2bjNclS5akXr16Ga/ffPNN5syZw/z58xk8ePBVj9OvXz8eeughAEaNGsVHH33EmjVraNeu3Q3XlpyczMSJE/niiy9o3749AJMnT2bJkiV8/vnnvPjii0RHR9OgQQMaNWoEmK1KF0VHR1OlShVuu+02bDYb5cqVu+Fa5PqcOJPCP9Gn+efQKf6JPs3GQ6dJTrVnvB/g7c7jd1SkX7Py+Ho6958ZFxcb7WqH0bZWKL/uimfZ9jh6NI6gdunrX/dKRHKXc//VkQwXQ8FFZ86cYcSIEfz0008cO3aM9PR0zp07R3R0dLbHqVu3bsZzX19f/P39iYuLy+YT17Z3717S0tJo3rx5xjZ3d3eaNGnC9u3bARg4cCAPPPAA69ev5+6776ZLly40a9YMMANXmzZtqFatGu3atePee+/l7rvvvqma5JI0u4PtxxLNMBN9in8OnebgiStbM309XKkXUZzmlQOz7GDr7Gw2Gy2qBdOiWsFYJFakKFO4uRZ3H7MFxapz5xJf38wtQC+88AJLlizhvffeo3Llynh7e9O1a1dSU1OzL8k98xeWzWbD4XBcZe/c0759ew4ePMjPP//MkiVLaNWqFYMGDeK9997jlltuYf/+/SxcuJClS5fSvXt3WrduzaxZs/K8LmcUk3A+I8T8E32KTYcTMtZ4ulzl4GI0iChOg7IluKVccaoE+2l4s4gUCAo312Kz5dqtoYLkjz/+oF+/ftx3332A2ZJz4MABS2qpVKkSHh4e/PHHHxm3lNLS0li7dm2mOXaCgoLo27cvffv25fbbb+fFF1/kvffeA8Df358ePXrQo0cPunbtSrt27Th58uRN9wVydufT7Gw9mnChVeY066NPZQxzvpy/lxsNypagQdni3FK2BPUiihPgXbRaZkSk8FC4KaKqVKnC7Nmz6dixIzabjWHDhuVLC8zlQ9QvqlWrFgMHDuTFF1+kZMmSlC1bljFjxnD27FkGDBgAwPDhw2nYsCG1atUiJSWFBQsWUKNGDQDGjh1LWFgYDRo0wMXFhZkzZxIaGqoFVf/FMAwOnzrH+uhTF/rLnGbb0QTS7JmnunKxQbVQf24pWzwj0FQo5VtgZg4WEbkWhZsiauzYsTzyyCM0a9aMwMBAXnrpJRITE/P8vA8++OAV2w4dOsTbb7+Nw+Ggd+/eJCUl0ahRIxYvXkyJEiUA8PDwYOjQoRw4cABvb29uv/12vvvuOwD8/PwYM2YMu3fvxtXVlcaNG/Pzzz9nWlG+KEpOSWfT4YSMTr//RJ/OmBn4coHFPDJCTIOIEtQtE+D0nYBFxLlphuLLaPZa5+fMv+NTyan8tiuetQdOsj76NDtjEnH8679ud1cbNcMDLvSVMW8xlSnhbdkilSIi10szFIsUEQdPJLNkWyxLtsXy98FT2P+VZsIDvC61ypQtTq3wgBwt+CgiUhgp3EiueOKJJ/j666+zfK9Xr15MmjQpnytyTg6HwcbDp1m63Qw0u2LPZHq/eqgft1cJ5JayJWhQtgShAc7VOiUicj0UbiRXjBw5khdeeCHL967VfCjZO59mZ9Xe4yzZFkfU9ljiki71m3F1sRFZoSRtaobQukYIESVzb/oAEZHCSuFGckVwcDDBwZq8LLecSk5l2Y44lmyLZcXueM5eNvtvMU837qwWxN01Q7irajABPhqSLSJyOYUbkQLiwPFklm6P5Zdtsfx94GSmzsBhAV60rhFC65oh3FqxJJ5u6jcjInI1CjdZyI/5XsQaBel363AYbDh8mqUXOgTvjsvcf6ZGmD9taobQpkYItUv7a0STiMh1Uri5jIeHBy4uLhw9epSgoCA8PDz0heIkDMMgNTWV+Ph4XFxc8PDwsKSO82l2/thznKXbY1m6PY74y/rPuLnYiKxYkjY1Qmil/jMiIjdM4eYyLi4uVKhQgWPHjnH0qEXrSUme8vHxoWzZsvk6wd/JjP4zMazYdZxzaZf6z/hd6D/TpmYId1UL1pIGIiK5QOHmXzw8PChbtizp6enY7fZrf0AKDVdXV9zc3PKlNW7/8eSM201/H7yy/8zF0U23ViyFh1vRnklZRCS3KdxkwWaz4e7ufsUK2CJZOZdqZ2/8GfbEnWH7sUSidsSx51/9Z2pe7D9TM4Ra4eo/IyKSlxRuRK7T6bOp7Ik7c+lxIdAcPnXuin3dXGzcWrEUbWqG0KpGMGVKqP+MiEh+UbgRuYxhGMQlpWQEmN1xSReeJ2e56ORFJXzcqRLsR6XgYjStVIo7qwap/4yIiEUUbqRIsjsMDp86m2VLTNL59Kt+LizAi8rBxS49gsx/lirmmY/Vi4hIdhRuxKmlpjs4cCLZbIWJvRRg9sWfISU96zlvXGxQrpQvlS4ElyoXgkyl4GIU89R/MiIiBZ3+UovTiEs6zx97jpsh5kJrzMGTZ69YKfsiDzcXKgb6ZmqJqRLsR/lAH80ALCJSiCncSKF2/EwKC7fE8NOmo/y1/yRGFjmmmKcblS5rgbl4KymipA+uLhq1JCLibBRupNA5mZzKoi0x/LT5KKv3nsg0h0zdMgHUKR1wIcj4UTm4GCH+nhp6LSJShCjcSKFwKjmVX7bFsGDTMVbtPZHpVlPdMgF0qBPGPXXCtGSBiIgo3EjBlXA2jcXbYvhp0zH+2HOc9MsCTe3S/nSoE06HOmGULaVAIyIilyjcSIGSeD6NJVtj+WnzMX7fHU+a/VKgqRHmz711zRaaCoG+FlYpIiIFmcKNWC7pfBpLt8fy06ZjrNh1nFT7pSHa1UP9zFtOdcOoFFTMwipFRKSwULgRS5xJSSfqQqD5dVc8qZfNOVM5uBj31g2jQ50wqoT4WViliIgURpaHmwkTJvDuu+8SExNDvXr1GD9+PE2aNLnq/uPGjWPixIlER0cTGBhI165dGT16NF5eXvlYtdyIs6npRG2P46dNx1i+My7TJHoVg3y5t24499YNo6oCjYiI3ARLw82MGTMYMmQIkyZNIjIyknHjxtG2bVt27txJcHDwFftPnz6dl19+mSlTptCsWTN27dpFv379sNlsjB071oIrkGs5l2pn+U4z0ETtiOV82qVAU76UD/fWDadD3TCqh/ppuLaIiOQKm2FkNe1Z/oiMjKRx48Z8/PHHADgcDiIiInjqqad4+eWXr9h/8ODBbN++naioqIxtzz//PH/99RcrV668rnMmJiYSEBBAQkIC/v7+uXMhksn5NDu/7oznp83HiNoey9lUe8Z7ZUv6mLec6oZRM8xfgUZERK5LTr6/LWu5SU1NZd26dQwdOjRjm4uLC61bt2b16tVZfqZZs2Z8/fXXrFmzhiZNmrBv3z5+/vlnevfufdXzpKSkkJJyaTXnxMTE3LsIyWTr0QQ+W7GPpdtiSb4s0JQp4U2HumHcWyec2qUVaEREJG9ZFm6OHz+O3W4nJCQk0/aQkBB27NiR5Wcefvhhjh8/zm233YZhGKSnp/PEE0/wyiuvXPU8o0eP5o033sjV2uVKW48m0OPTPzmTYq6oHR7gZQaauuHULROgQCMiIvnGxeoCcuLXX39l1KhRfPLJJ6xfv57Zs2fz008/8eabb171M0OHDiUhISHjcejQoXysuGg4dPIs/aau5UxKOo3KlWD2k8344+WWvNqhJvUiiivYiIhIvrKs5SYwMBBXV1diY2MzbY+NjSU0NDTLzwwbNozevXvz6KOPAlCnTh2Sk5N5/PHHefXVV3FxuTKreXp64unpmfsXIIC5zlPfKWuIT0qhWogfn/drTIC3u9VliYhIEWZZy42HhwcNGzbM1DnY4XAQFRVF06ZNs/zM2bNnrwgwrq6uAFjYL7rIOpuaziNfrGXf8WRKF/dm2iNNFGxERMRylg4FHzJkCH379qVRo0Y0adKEcePGkZycTP/+/QHo06cPpUuXZvTo0QB07NiRsWPH0qBBAyIjI9mzZw/Dhg2jY8eOGSFH8kea3cGgb9az4dBpivu4M+2RxoQGaK4hERGxnqXhpkePHsTHxzN8+HBiYmKoX78+ixYtyuhkHB0dnaml5rXXXsNms/Haa69x5MgRgoKC6NixI2+99ZZVl1AkGYbBK7M3s3xnPF7uLnzetzGVgzXxnoiIFAyWznNjBc1zc/PeXbyDCcv34mKDz3o3onXNkGt/SERE5Cbk5Pu7UI2WEutNW3WACcv3AjDqvjoKNiIiUuAo3Mh1+3nzMUb8uBWAIW2q8mCTshZXJCIiciWFG7kuq/ee4NnvNmAY0DOyLE+1rGx1SSIiIllSuJFr2n4skce//JtUu4N2tUIZ2bm2JuYTEZECS+FGsnX41Fn6TV1DUko6TcqXZNyD9XF1UbAREZGCS+FGrupUcip9pqwhNjGFqiHFmNynEV7umk9IREQKNoUbydK5VDuPTFvLvvhkwgK8zNmHfTT7sIiIFHwKN3KFdLuDwdPX80/0aQK83fnykSaEBXhbXZaIiMh1UbiRTAzD4NU5W4jaEYenmwuf921ElRDNPiwiIoWHwo1k8sGSXcz4+xAuNhj/UAMalS9pdUkiIiI5onAjGb768yAfLdsDwH+71OHuWqEWVyQiIpJzCjcCwKItxxg+bwsAz7auwsORmn1YREQKJ4Ub4a99J3j6wuzDDzUpyzOtqlhdkoiIyA1TuCnidsQk8uiXf5Oa7qBNzRDe7FxLsw+LiEihpnBThB05fY5+U9aSdD6dRuVKMP6hBri56l8JEREp3PRNVkSdPptK3ylriEk8T5XgYvyvr2YfFhER56BwUwSdS7UzYNrf7Ik7Q6i/OftwcR8Pq8sSERHJFQo3RUy63cFT3/7DuoOn8Pdy48sBTQgvrtmHRUTEeSjcFCGGYTBs3haWbo/Fw82F//VtTFXNPiwiIk5G4aYIGbd0N9+uuTT7cJMKmn1YREScj8JNEfHNXwf5MGo3ACM716atZh8WEREnpXBTBCzeGsOwuebsw0+3qkKvW8tZXJGIiEjeUbhxcmsPnOTpb//BYcCDjSN4rrVmHxYREeemcOPEdsUmMeCLtaSkO2hdI4T/dqmt2YdFRMTpKdw4qaOnz9F3yhoSz6fTULMPi4hIEaJvOyeUcDaNvlPWcCzhPJWDi/F530Z4e2j2YRERKRoUbpzM+TQ7j365lt1xZwjx99TswyIiUuQo3DgRu8Pg6W//Ye2BU/h5uTHtkSaU1uzDIiJSxCjcOImLsw//ss2cfXhyn0ZUD/W3uiwREZF8p3DjBAzD4I0ftzH9r2hsNviwR31urVjK6rJEREQsoXBTyBmGwcgF2/hi1QEA3rm/Lu3rhFlblIiIiIUUbgoxwzB466ftTP3jAABv31+H7o0jrC1KRETEYgo3hZRhGIxeuIP/rdwPwKj76vBgk7IWVyUiImI9hZtCyDAM3lm0k89W7APgv11q83Ckgo2IiAgo3BQ6hmHw7uKdTPptLwAjO9fSQpgiIiKXUbgpRAzDYOySXXzyqxlsRnSsSZ+m5a0tSkREpIApEOFmwoQJlC9fHi8vLyIjI1mzZs1V973rrruw2WxXPDp06JCPFVtj3NLdjF+2B4Dh99akX/MKFlckIiJS8FgebmbMmMGQIUN4/fXXWb9+PfXq1aNt27bExcVluf/s2bM5duxYxmPLli24urrSrVu3fK48f324dDcfRu0G4LUONXjkNgUbERGRrFgebsaOHctjjz1G//79qVmzJpMmTcLHx4cpU6ZkuX/JkiUJDQ3NeCxZsgQfHx+nDjfjo3bzwdJdALxyT3Uevb2ixRWJiIgUXJaGm9TUVNatW0fr1q0ztrm4uNC6dWtWr159Xcf4/PPPefDBB/H19c3y/ZSUFBITEzM9CpMJy/fw/hIz2LzUrjqP31HJ4opEREQKNkvDzfHjx7Hb7YSEhGTaHhISQkxMzDU/v2bNGrZs2cKjjz561X1Gjx5NQEBAxiMiovBMcjfx1728u3gnAC+2rcbAuxRsRERErsXy21I34/PPP6dOnTo0adLkqvsMHTqUhISEjMehQ4fyscIb99mKvbyzaAcAz7epyqAWlS2uSEREpHBws/LkgYGBuLq6Ehsbm2l7bGwsoaGh2X42OTmZ7777jpEjR2a7n6enJ56enjdda3763+/7GPWzGWyea12Vp1pVsbgiERGRwsPSlhsPDw8aNmxIVFRUxjaHw0FUVBRNmzbN9rMzZ84kJSWFXr165XWZ+erzlfv570/bAXi6VRWeaa1gIyIikhOWttwADBkyhL59+9KoUSOaNGnCuHHjSE5Opn///gD06dOH0qVLM3r06Eyf+/zzz+nSpQulSpWyouw88cUf+3lzwTYAnmpZmecUbERERHLM8nDTo0cP4uPjGT58ODExMdSvX59FixZldDKOjo7GxSVzA9POnTtZuXIlv/zyixUl54kvVx9gxI9msHnyrkoMaVMVm81mcVUiIiKFj80wDMPqIvJTYmIiAQEBJCQk4O/vb3U5AHz150GGzd0CwBN3VuKldtUUbERERC6Tk+/vQj1ayhlM/ys6I9g8fkdFBRsREZGbpHBjoe/WRPPKnM0APHpbBYa2r65gIyIicpMUbizy/dpDvDzbDDb9m5fn1Q41FGxERERygcKNBWb+fYiXZm8CoF+z8gy/t6aCjYiISC5RuMlnP6w7zP/9sAnDgD5Ny/F6RwUbERGR3KRwk4/m/HOYF2ZtxDCg161leaNTLQUbERGRXKZwk0/mbTjC89+bweahJmUZ2am2go2IiEgeULjJBz9uPMpzMzbgMODBxhG81aU2Li4KNiIiInlB4SaP/bTpGM9eCDbdG5Vh1H11FGxERETykMJNHlq4+RhPf/cPdodB14ZlePv+ugo2IiIieUzhJo8s2hLDU9+aweb+BqV55wEFGxERkfygcJMHftkaw+Dp60l3GHSpH8673erhqmAjIiKSLxRuctnSbbEMuhBsOtUL5/3u9RVsRERE8pHCTS6K2h7LwG/WkWY3uLduGGO7q8VGREQkvync5JLfdsUz8Ov1pNkNOtQJY1yP+ri56scrIiKS39ysLsBZRJTwpoSvO7eULcG4BxVsRERErKJwk0sqBhVj9pPNCfbzxF3BRkRExDIKN7modHFvq0sQEREp8tTEICIiIk5F4UZEREScisKNiIiIOBWFGxEREXEqCjciIiLiVBRuRERExKko3IiIiIhTUbgRERERp6JwIyIiIk5F4UZEREScisKNiIiIOBWFGxEREXEqOQ435cuXZ+TIkURHR+dFPSIiIiI3Jcfh5tlnn2X27NlUrFiRNm3a8N1335GSkpIXtYmIiIjk2A2Fmw0bNrBmzRpq1KjBU089RVhYGIMHD2b9+vV5UaOIiIjIdbMZhmHczAHS0tL45JNPeOmll0hLS6NOnTo8/fTT9O/fH5vNllt15prExEQCAgJISEjA39/f6nJERETkOuTk+9vtRk+SlpbGnDlzmDp1KkuWLOHWW29lwIABHD58mFdeeYWlS5cyffr0Gz28iIiIyA3JcbhZv349U6dO5dtvv8XFxYU+ffrwwQcfUL169Yx97rvvPho3bpyrhYqIiIhcjxz3uWncuDG7d+9m4sSJHDlyhPfeey9TsAGoUKECDz744HUdb8KECZQvXx4vLy8iIyNZs2ZNtvufPn2aQYMGERYWhqenJ1WrVuXnn3/O6WWIiIiIk8pxy82+ffsoV65ctvv4+voyderUax5rxowZDBkyhEmTJhEZGcm4ceNo27YtO3fuJDg4+Ir9U1NTadOmDcHBwcyaNYvSpUtz8OBBihcvntPLEBERESeV4w7Fa9euxeFwEBkZmWn7X3/9haurK40aNbruY0VGRtK4cWM+/vhjABwOBxERETz11FO8/PLLV+w/adIk3n33XXbs2IG7u3tOys6gDsUiIiKFT06+v3N8W2rQoEEcOnToiu1Hjhxh0KBB132c1NRU1q1bR+vWrS8V4+JC69atWb16dZafmT9/Pk2bNmXQoEGEhIRQu3ZtRo0ahd1uv+p5UlJSSExMzPQQERER55XjcLNt2zZuueWWK7Y3aNCAbdu2Xfdxjh8/jt1uJyQkJNP2kJAQYmJisvzMvn37mDVrFna7nZ9//plhw4bx/vvv89///veq5xk9ejQBAQEZj4iIiOuuMcdublS9iIiI5IIchxtPT09iY2Ov2H7s2DHc3G54ZPl1cTgcBAcH89lnn9GwYUN69OjBq6++yqRJk676maFDh5KQkJDxyKrVKVfY0+D7PrB5Vt4cX0RERK5LjtPI3XffzdChQ5k3bx4BAQGAOYLplVdeoU2bNtd9nMDAQFxdXa8ISrGxsYSGhmb5mbCwMNzd3XF1dc3YVqNGDWJiYkhNTcXDw+OKz3h6euLp6Xnddd2wf76C7fNh+49w/jQ0fjTvzykiIiJXyHHLzXvvvcehQ4coV64cLVq0oEWLFlSoUIGYmBjef//96z6Oh4cHDRs2JCoqKmObw+EgKiqKpk2bZvmZ5s2bs2fPHhwOR8a2Xbt2ERYWlmWwyVe39LsQaAz46Xn4bYxuU4mIiFggx+GmdOnSbNq0iTFjxlCzZk0aNmzIhx9+yObNm3Pcn2XIkCFMnjyZadOmsX37dgYOHEhycjL9+/cHoE+fPgwdOjRj/4EDB3Ly5EmeeeYZdu3axU8//cSoUaNy1JE5z7i4wD3vwZ0vma+XvwWLhsJlQUxERETy3g11kvH19eXxxx+/6ZP36NGD+Ph4hg8fTkxMDPXr12fRokUZnYyjo6NxcbmUvyIiIli8eDHPPfccdevWpXTp0jzzzDO89NJLN11LrrDZoMUr4F0SFr0Ef02Ec6eg88fgemND10VERCRnbnjhzG3bthEdHU1qamqm7Z06dcqVwvJKvs1zs3EGzB0Ihh2qtoduU8HdO+/OJyIi4sTydOHMffv2cd9997F582ZsNhsXs9HFFcCzm3OmSKnXA7wCYGZf2LUQvrofHv7O3CYiIiJ5Jsd9bp555hkqVKhAXFwcPj4+bN26lRUrVtCoUSN+/fXXPCixEKvWDnrNBk9/iF4FX3SAM3FWVyUiIuLUchxuVq9ezciRIwkMDMTFxQUXFxduu+02Ro8ezdNPP50XNRZu5ZtDv5/ANwhiNsOUtnDqoNVViYiIOK0chxu73Y6fnx9gzlVz9OhRAMqVK8fOnTtztzpnEVYXHlkMAWXh5D4z4MRtt7oqERERp5TjcFO7dm02btwImAtfjhkzhj/++IORI0dSsWLFXC/QaZSqBAMWQ1ANSDoGU9rBobVWVyUiIuJ0chxuXnvttYxJ9EaOHMn+/fu5/fbb+fnnn/noo49yvUCn4h8O/X+G0o3MWYy/7AR7oq75MREREbl+NzwU/HInT56kRIkSGSOmCrJ8GwqenZQz8H1v2LsMXNzh/s+g9v3W1CIiIlII5OT7O0ctN2lpabi5ubFly5ZM20uWLFkogk2B4VkMHpoBte4DRxrMegT+nmJ1VSIiIk4hR+HG3d2dsmXLai6b3ODmAQ98Dg37AwYseA5WvKf1qERERG5SjvvcvPrqq7zyyiucPHkyL+opWlxc4d4P4PYXzNfL3oRfXtN6VCIiIjchx31uGjRowJ49e0hLS6NcuXL4+vpmen/9+vW5WmBuKxB9brKyegIsfsV8Xu9h6DQeXG9o6S8RERGnk6fLL3Tp0uVG65LsNB0E3iVg3mDYOB3OJ0DXKeDuZXVlIiIihUqujJYqTApsy81FO36Gmf3AngLlboOHvgWvAliniIhIPsqz0VKSD6rfA71ng4cfHFwJ0+6FM/FWVyUiIlJo5DjcuLi44OrqetWH5ILyt0G/BeATCMc2wtR2cDra6qpEREQKhRz3uZkzZ06m12lpafzzzz9MmzaNN954I9cKK/LC65vrUX3VBU7sMZdr6D0HgqpZXZmIiEiBlmt9bqZPn86MGTOYN29ebhwuzxT4Pjf/lnAEvroPju8E75LQcxaUaWh1VSIiIvnKkj43t956K1FRWicp1wWUhv4LIfwWOHcSpnWEvcutrkpERKTAypVwc+7cOT766CNKly6dG4eTf/MtBX3nQ8W7IC0ZpneHbQW7hUxERMQqOe5z8+8FMg3DICkpCR8fH77++utcLU4u4+kHD38Psx8zg83Mfubsxg37WV2ZiIhIgZLjcPPBBx9kCjcuLi4EBQURGRlJiRIlcrU4+Rc3T+g61VyHav00+PEZOHcKbnvO6spEREQKDE3iVxgZBkS9ASs/MF83exrajAStzC4iIk4qTzsUT506lZkzZ16xfebMmUybNi2nh5MbYbNB6xHQ5k3z9aqPYP5gsKdbWpaIiEhBkONwM3r0aAIDA6/YHhwczKhRo3KlKLlOzZ+GzhPA5gL/fA0z+0LaeaurEhERsVSOw010dDQVKlS4Ynu5cuWIjtYsuvmuQS/o/iW4esCOBfBtD0hPtboqERERy+Q43AQHB7Np06Yrtm/cuJFSpUrlSlGSQzU6Qq8fwKMY7PsVlr5udUUiIiKWyXG4eeihh3j66adZvnw5drsdu93OsmXLeOaZZ3jwwQfzoka5HhXugPsnm8///AS2zrW0HBEREavkONy8+eabREZG0qpVK7y9vfH29ubuu++mZcuW6nNjter3QPNnzOfzBsPxPdbWIyIiYoEbHgq+e/duNmzYgLe3N3Xq1KFcuXK5XVuecIqh4Nmxp8OXneDgHxBcCx5dCh4+VlclIiJyU3Ly/a15bpxRUgxMuh2S46Dew9DlE82BIyIihVqeznPzwAMP8M4771yxfcyYMXTr1i2nh5O84BcKXT83h4hvnA7/fGV1RSIiIvkmx+FmxYoV3HPPPVdsb9++PStWrMiVoiQXVLgDWr5mPv/pBTi20dp6RERE8kmOw82ZM2fw8PC4Yru7uzuJiYm5UpTkkubPQdV2YE+B7/vAudNWVyQiIpLnchxu6tSpw4wZM67Y/t1331GzZs1cKUpyiYsLdJkIxcvCqQMw90lzXSoREREnluNVwYcNG8b999/P3r17admyJQBRUVFMnz6dWbNm5XqBcpN8SkK3aTClLez8CVaNN5dtEBERcVI5brnp2LEjc+fOZc+ePTz55JM8//zzHDlyhGXLllG5cuW8qFFuVulboN3b5vOlI+DgKkvLERERyUs5DjcAHTp04I8//iA5OZl9+/bRvXt3XnjhBerVq3dDRUyYMIHy5cvj5eVFZGQka9asueq+X3zxBTabLdPDy8vrhs5bpDR6BOp0B8MOM/vDmTirKxIREckTNxRuwBw11bdvX8LDw3n//fdp2bIlf/75Z46PM2PGDIYMGcLrr7/O+vXrqVevHm3btiUu7upfvv7+/hw7dizjcfDgwRu9jKLDZoN7P4Cg6nAmBmY9Ag671VWJiIjkuhyFm5iYGN5++22qVKlCt27d8Pf3JyUlhblz5/L222/TuHHjHBcwduxYHnvsMfr370/NmjWZNGkSPj4+TJky5aqfsdlshIaGZjxCQkJyfN4iybOYuYK4uy8c+B2Wa7kMERFxPtcdbjp27Ei1atXYtGkT48aN4+jRo4wfP/6mTp6amsq6deto3br1pYJcXGjdujWrV6++6ufOnDlDuXLliIiIoHPnzmzduvWq+6akpJCYmJjpUaQFVYNOH5nPf38Pdi22th4REZFcdt3hZuHChQwYMIA33niDDh064OrqetMnP378OHa7/YqWl5CQEGJiYrL8TLVq1ZgyZQrz5s3j66+/xuFw0KxZMw4fPpzl/qNHjyYgICDjERERcdN1F3p1ukLjx8znsx+HU7qtJyIizuO6w83KlStJSkqiYcOGREZG8vHHH3P8+PG8rC1LTZs2pU+fPtSvX58777yT2bNnExQUxKeffprl/kOHDiUhISHjcejQoXyuuIBq+xaE3wLnT8PMvpCeYnVFIiIiueK6w82tt97K5MmTOXbsGP/5z3/47rvvCA8Px+FwsGTJEpKSknJ88sDAQFxdXYmNjc20PTY2ltDQ0Os6hru7Ow0aNGDPnj1Zvu/p6Ym/v3+mhwBuntB9GniXgKP/wOJXrK5IREQkV+R4tJSvry+PPPIIK1euZPPmzTz//PO8/fbbBAcH06lTpxwdy8PDg4YNGxIVFZWxzeFwEBUVRdOmTa/rGHa7nc2bNxMWFpajcwvmzMX3Tzafr/0fbJppbT0iIiK54IaHgoPZ/2XMmDEcPnyYb7/99oaOMWTIECZPnsy0adPYvn07AwcOJDk5mf79+wPQp08fhg4dmrH/yJEj+eWXX9i3bx/r16+nV69eHDx4kEcfffRmLqXoqtIG7njRfP7jMxC3w9p6REREblKOl1/IiqurK126dKFLly45/myPHj2Ij49n+PDhxMTEUL9+fRYtWpTRyTg6OhoXl0sZ7NSpUzz22GPExMRQokQJGjZsyKpVq7Su1c24aygc+gv2rzAX2HxsmTlsXEREpBCyGUbRWkkxMTGRgIAAEhIS1P/mcmfi4dPbIekY1O4KD/zPnPhPRESkAMjJ9/dN3ZYSJ1IsCLpOBZsrbJkFf39udUUiIiI3ROFGLinXFNq8YT5fNBSOrLO2HhERkRugcCOZNR0M1e8Feyp83xfOnrS6IhERkRxRuJHMbDboPAFKVICEQzDnP+BwWF2ViIjIdVO4kSt5FzcX2HTzgt2/wMqxVlckIiJy3RRuJGthdeGe98zny9+Cfb9ZW4+IiMh1UriRq7ulN9TvBYYDfhgAicesrkhEROSaFG4ke/e8CyG1ITkeZvUHe5rVFYmIiGRL4Uay5+Fj9r/x8IPo1RA10uqKREREsqVwI9dWqhJ0mWA+X/URbF9gbT0iIiLZULiR61OzM9w6yHw+dyCc3GdtPSIiIlehcCPXr80bEBEJKYnmAptp56yuSERE5AoKN3L9XN3N9ad8SkHMZlj4f1ZXJCIicgWFG8mZgNLmiuHYYP2X8M83VlckIiKSicKN5FyllnDXUPP5T89DzBZr6xEREbmMwo3cmDtehEqtIP2c2f/mfKLVFYmIiAAKN3KjXFzg/sngXwZO7oX5g8EwrK5KRERE4UZugm8p6PYFuLjDtnnw50SrKxIREVG4kZsU0RjavmU+XzIMov+yth4RESnyFG7k5jV5HGrdB450mNkPEo9aXZGIiBRhCjdy82w26DQeSlWBpKPwRQdIOGJ1VSIiUkQp3Eju8PSD3rOheDlzaYYv7oHTh6yuSkREiiCFG8k9xctCv5+gRHk4dcAMOKcOWl2ViIgUMQo3kruKR0C/n6FkRTgdbd6iOrnf6qpERKQIUbiR3BdQ2gw4pSpDwiH44l6tIi4iIvlG4Ubyhn+YeYsqsCokHoapHeDEXqurEhGRIkDhRvKOX6gZcIKqm6Oopt4Dx3dbXZWIiDg5hRvJW8WCoe8CCK4JZ2LMPjjxO62uSkREnJjCjeS9YkFmwAmpDWdizYATt93qqkRExEkp3Ej+8C0FfX+E0DqQHG92Mo7danVVIiLihBRuJP/4lIQ+8yGsPpw9bgacmM1WVyUiIk5G4Ubyl09J6DMXwm+BcydhWkc4ttHqqkRExIko3Ej+8y5hBpzSjeDcKTPgHP3H6qpERMRJKNyINbwCoPcciIiE8wkwrTMcXmd1VSIi4gQUbsQ6Xv7Q6wco2xRSEuCrLnBordVViYhIIadwI9by9IOes6Bcc0hJhK/ug+i/rK5KREQKsQIRbiZMmED58uXx8vIiMjKSNWvWXNfnvvvuO2w2G126dMnbAiVveRaDnjOh/O2QmgRf3w8HV1tdlYiIFFKWh5sZM2YwZMgQXn/9ddavX0+9evVo27YtcXFx2X7uwIEDvPDCC9x+++35VKnkKQ9fePh7qHAnpJ6Brx+AAyutrkpERAohy8PN2LFjeeyxx+jfvz81a9Zk0qRJ+Pj4MGXKlKt+xm6307NnT9544w0qVqyYj9VKnvLwgYdnQKWWkJYM33SD/SusrkpERAoZS8NNamoq69ato3Xr1hnbXFxcaN26NatXX/22xMiRIwkODmbAgAHXPEdKSgqJiYmZHlKAuXvDg99C5daQdha+6Q57l1tdlYiIFCKWhpvjx49jt9sJCQnJtD0kJISYmJgsP7Ny5Uo+//xzJk+efF3nGD16NAEBARmPiIiIm65b8pi7F/T4Bqq0hfRz8O2DsCfK6qpERKSQsPy2VE4kJSXRu3dvJk+eTGBg4HV9ZujQoSQkJGQ8Dh06lMdVSq5w94IeX0HV9pB+Hr59CHYvsboqEREpBNysPHlgYCCurq7ExsZm2h4bG0toaOgV++/du5cDBw7QsWPHjG0OhwMANzc3du7cSaVKlTJ9xtPTE09PzzyoXvKcmyd0/xJm9YcdC+C7h6H7V1CtndWViYhIAWZpy42HhwcNGzYkKurSLQeHw0FUVBRNmza9Yv/q1auzefNmNmzYkPHo1KkTLVq0YMOGDbrl5IzcPKDbF1CjE9hTYUYv2PGz1VWJiEgBZmnLDcCQIUPo27cvjRo1okmTJowbN47k5GT69+8PQJ8+fShdujSjR4/Gy8uL2rVrZ/p88eLFAa7YLk7E1R26ToHZj8HWOfB97wuBp+M1PyoiIkWP5eGmR48exMfHM3z4cGJiYqhfvz6LFi3K6GQcHR2Ni0uh6hokecHVHe7/H9hcYcssmNkPHvgcanWxujIRESlgbIZhGFYXkZ8SExMJCAggISEBf39/q8uRnLKnw7wnYdMMM+g8MBlqP2B1VSIiksdy8v2tJhEpXFzdoMtEqPcwGHb44VHYNNPqqkREpABRuJHCx8UVOn8M9XuB4YA5j8PGGVZXJSIiBYTCjRROLq7QaTzc0udCwPkPbJhudVUiIlIAKNxI4eXiAvd+CI0eAQyY+ySs/8rqqkRExGIKN1K4ubhAh7HQ+DHAgPmD4fs+sHcZXJjgUUREihbLh4KL3DSbDe55F1w94M8JsG2e+ShRHm7pC/V7gl/INQ8jIiLOQUPBxbnEbIH108wOxikJ5jYXN6h2DzTsBxVbmK09IiJSqOTk+1vhRpxT6lnYNhf+ngqH11zaXrwcNOxrjrRSa46ISKGhcJMNhZsiKHYrrJsGG7/7V2tO+wutOS3VmiMiUsAp3GRD4aYIu9ias+4LOPTXpe3Fy5p9cxr0Ar8rV6MXERHrKdxkQ+FGAIjddqFvzrdw/kJrjs31QmtOf6ik1hwRkYJE4SYbCjeSSepZc2TVui/g0J+XtgeUhYZ9zL45/mGWlSciIiaFm2wo3MhVxW03Q06WrTn9LrTmuFpZoYhIkaVwkw2FG7mmtHOXWnOiV1/aHlDWXO6hgVpzRETym8JNNhRuJEfidph9czZMh/OnzW02V6jaDhr1V2uOiEg+UbjJhsKN3JC0c7Bt/oXWnFWXtgdEXNaaE25ZeSIizk7hJhsKN3LT4neaISer1pyG/aByK7XmiIjkMoWbbCjcSK5JOw/bL7TmHPzj0vZSVeD256FOV3B1t6w8ERFnonCTDYUbyRPxO81ZkDd8c6k1p3g5uO05qP8wuHlaWp6ISGGncJMNhRvJUylJsPZzWP0xJMeb2/zCofkzZt8cDx9r6xMRKaQUbrKhcCP5IvUsrP8S/vgQko6a23yDoNlT0OgR8PSztj4RkUJG4SYbCjeSr9JTzFtVKz+A09HmNu8ScOuT0ORx8C5uaXkiIoWFwk02FG7EEvY02DwTfn8fTuwxt3n6Q5PHzKDjG2htfSIiBZzCTTYUbsRSDru5MvmK9yBum7nN3ce8VdXsKa1KLiJyFQo32VC4kQLB4YCdP8OKd+HYBnObq6fZ6bj5M1A8wtLyREQKGoWbbCjcSIFiGLAnClaMgUN/mdtc3KDeQ+Yw8lKVrK1PRKSAULjJhsKNFEiGAQd+N1ty9q8wt9lcoE43uG0IBFe3tj4REYsp3GRD4UYKvOi/4Pf3YPcvFzbYoGYnuP0FCKtraWkiIlbJyfe3Sz7VJCLXq2wk9JwJj/8GNToCBmybB5/eDtN7wOG/ra5QRKRAU7gRKajC60OPr2Hgaqjd1bxNtWsR/K8VfNkZDqy0ukL5t0Nr4IfHIG671ZWIFGm6LSVSWBzfY04GuOk7cKSb28o2gztegEotwWaztr6iLvpP+Op+SEuGwKrwn9/B3cvqqkSchm5LiTijwMrQZQI8tR4aDQBXD4heBV/fD5Nbwo6fzY7Jkv8OrYWvu5rBBuD4LrPflIhYQi03IoVV4lFYNR7+ngrp58xtIbWhQW/zllZIbfAsZmmJRcKR9fBlF0hJgPK3mz//OY+bQ/of/xVC61hdoYhT0GipbCjciNM5Ew9/ToA1kyH1zGVv2Mx5ckLrQlg9c6RVaD3wLWVZqU7n2EaY1gnOnzZvEfaaBR6+MKMXbP8RwurDo1Hg6mZ1pSKFnsJNNhRuxGmdPQnrp5l9P45turQa+b/5l7kQdC4LPf6l1Wcnp2K3whf3wrmTUKYJ9J59abX3pBiY0ATOJ0Cbkeas0yJyUxRusqFwI0XGmXiI2WgGnWMbIWYTnNyX9b7eJc2QE1bvUugpWQlc1C0vS3E74IsOcPY4lG4IveeAV0Dmff75GuYNAjcvGLhKs02L3KRCF24mTJjAu+++S0xMDPXq1WP8+PE0adIky31nz57NqFGj2LNnD2lpaVSpUoXnn3+e3r17X9e5FG6kSDufCLFbzLBzbJMZeOK2g2G/cl+PYma/nctDT1B1cPPI/7oLkuO7zWBzJtb8ufSZB94lrtzPMOCrLrDvVyh3G/T9UWFR5CYUqnAzY8YM+vTpw6RJk4iMjGTcuHHMnDmTnTt3EhwcfMX+v/76K6dOnaJ69ep4eHiwYMECnn/+eX766Sfatm17zfMp3Ij8S9p5c4XymE2XWnlit17qpHw5Vw8z4ITVuxR4Qmub/UyKghN7zWCTdMwMfn1/BJ+SV9//1AH4pCmknYV7PzBXfxeRG1Kowk1kZCSNGzfm448/BsDhcBAREcFTTz3Fyy+/fF3HuOWWW+jQoQNvvvnmNfdVuBG5DvZ0OLHnQuDZeOm21vmELHa2QWCVC7ez6kLFu8zg42xOHYCpHSDxMATVgH4LwDfw2p/7cyIsehk8/eHJPyGgdJ6XKuKMcvL9bWkX/tTUVNatW8fQoUMztrm4uNC6dWtWr159zc8bhsGyZcvYuXMn77zzTpb7pKSkkJKSkvE6MTHx5gsXcXaubuZincHVoW53c5thwOmDl25nXWzlORNjzutyfBdsmQXYzA60LV8DV3dLLyPXnD4E0zqawSawKvSdf33BBqDJ47DlBzi8Fn4aAg99p87bInnM0nBz/Phx7HY7ISEhmbaHhISwY8eOq34uISGB0qVLk5KSgqurK5988glt2rTJct/Ro0fzxhtv5GrdIkWSzQYlypuPmp0ubT8TdyHwbDQX/dy9GP4YZy4P0fVzc//CLPGoGWxOR5udrPvMh2JX3jK/KhdX6PSxuTbYrkVm0KnTNe/qFZHCOUOxn58fGzZsYO3atbz11lsMGTKEX3/9Nct9hw4dSkJCQsbj0KFD+VusiLMrFgxVWsPtz0PP76H7l+bIoSN/w6TbYfMsqyu8cUkxZrA5td8MaX1/BP+wnB8nuDrc8aL5fOH/QfKJXC1TRDKzNNwEBgbi6upKbGxspu2xsbGEhoZe9XMuLi5UrlyZ+vXr8/zzz9O1a1dGjx6d5b6enp74+/tneohIHqrZGZ5YCRG3Qkoi/DDAHBKdmmx1ZTlzJt6coO/EHgiIMIPNzfSXaf4sBNeEsyfMPjgikmcsDTceHh40bNiQqKiojG0Oh4OoqCiaNm163cdxOByZ+tWIiMWKl4V+P8Ed/wfYzDlfPr3TvH1VGCSfMFdeP77TnOCw74/mNd0MNw/z9pTNBTZ/D7sW506tInIFy29LDRkyhMmTJzNt2jS2b9/OwIEDSU5Opn///gD06dMnU4fj0aNHs2TJEvbt28f27dt5//33+eqrr+jVq5dVlyAiWXF1g5avmsHALwxO7Ib/tYK/Pi3YC3yePQlfdYa4rVAs1Ky/ZIXcOXaZhnDrk+bzBc+Z8w6JSK6zfMGTHj16EB8fz/Dhw4mJiaF+/fosWrQoo5NxdHQ0LpdNfJWcnMyTTz7J4cOH8fb2pnr16nz99df06NHDqksQkexUuB2e+MO8NbVrodnnZN+v0HlC9nPEWOHcafjqPojZDL5BZrDJ7ZmFW7wKOxaYQ8uXjoB7x+bu8UXE+nlu8pvmuRGxiGGYrTZLhoE9FfzC4YHJUP42qysznU80g82Rv8GnlHlbLbhG3pxr/wqzozJAv5+hfPO8OY+IE8nJ97flt6VEpIiw2eDWJ+DRpVCqsrmw57SOsHyUOWmglVLOwDfdzGDjXcJcUiGvgg1AhTvglr7m8/lPQVoWs0GLyA1TuBGR/BVWDx7/Der3AsMBv70D0+41J8qzQmoyTO8Bh/40h7D3nguhdfL+vG1Gmn2RTu6FX9/O+/OJFCEKNyKS/zyLQZcJ8MDn4OEH0ath0m2w/cf8rSPtHHz7EBxcaS6P0HsOhNfPn3N7F4cOF/rbrBoPRzfkz3lFigCFGxGxTp2u8MQKCL8Fzp+GGb1gwZD8uU2Tdh6+6wn7fzNXQO85C0o3zPvzXq76PVDrfnNV9vmDwZ6Wv+cXcVIKNyJirZIV4ZHF5npUAH9/DpNbQtz2vDtneip83wf2RoG7D/ScCWUj8+582Wk/xuznE7MZVn1kTQ0iTkbhRkSs5+Zh9kHpNRt8gyFuG3zWAv6emvtz4tjTYFZ/cw0sNy94eAaUa5a758iJYkHQ7kKfm1/fgfhd1tUi4iQUbkSk4KjcCgb+AZVaQvo5WPAszOxrzj+TG+zp8MOj5jwzrp7w4HRz5JLV6vaAyq3BnmKOnnI4rK5IpFBTuBGRgqVYMPT8wWzJcXGDbfPMBTij/7q54zrsMPcJ2DYXXD2gx9dmmCoIbDa49wOz78+hP81bcyJywxRuRKTgcXEx++AM+MVcjTshGqa2hxXvmiElpxwOc4bkzTPNwNRtGlS9O9fLvinFy0LrEebzpSPgdLSV1YgUago3IlJwlW4I//kd6nQzRxQt+6+5oGXises/hsMBPz4NG78Fmyt0nWKOUiqIGg0wV1NPPWOuPVW0JpAXyTUKNyJSsHn5w/2ToctEcPeFA7/DxGawc9G1P2sY8PPz8M9X5mrcD0yGmp3zvuYb5eICnT82+wPtWQqbZlhdkUihpHAjIgWfzQb1H4b//AahdeHcSfi2Byx8GdJTsv6MYcCil+HvKYANukyC2g/ka9k3JLAK3PWS+XzRy3Amztp6RAohhRsRKTwCq5hrU0UONF//NRH+1xqO78m8n2HAL6/BX5PM150/hno98rfWm9HsaXMJiHOnzFXURSRHFG5EpHBx84T2b8NDM8C7JMRsgk/vgA3TzVBjGBD1Bqz+2Nz/3nHQoJelJeeYqzt0+tjsI7R1Duz4yeqKJCc0lN9yCjciUjhVa2fOiVP+dkhLhrkDYfbjZrBZ+YG5zz3vQaP+1tZ5o8LrQ7OnzOc/PZ97c/1I3jm5D766H0aXgT8nqUO4hWyGUbR++omJiQQEBJCQkIC/v7/V5YjIzXLYYeVYWD7aHFF1UdvR0PRJ6+rKDWnnYGJzc+XwW/pCJy3PUCClp5pLZ6x4F9LPX9pe/V7zlqh3CetqcyI5+f5Wy42IFG4urnDHi9B/IQREmNtav1H4gw2Auzd0Gm8+Xz8N9v1mbT1ypYOr4dPbYdmbZrCpeBe0HGZOFLljAUy6Aw6ttbrKIkctNyLiPFLPQtIxKFXJ6kpy14Ih5qzFJcrDwNXg4WN1RXL2pDnZ4vpp5mufQGg32pyTyWaDo//AzH5w6oA5cWSr16HpYHO4v9wQtdyISNHk4eN8wQbMmYv9S5tflMvfsrqaos0wYNNMmNDkUrC5pQ8MXgt1u5vBBiC8AfxnBdS6DxzpsGSYOX1B8gnrai9CFG5ERAo6L39z7SmAPz+Bw+usraeoOrkPvroPZj8KyfEQWM28HdppPPiUvHJ/rwDoOtUcsefqCbt/gUm3wYE/8r30okbhRkSkMKjaFup0B8MB8webnVglf6Snwor34JOmsG+5GVRavgZPrIRyzbL/rM1mjth7bBmUqgJJR2HavfDbDa6TJtdF4UZEpLBo9zb4lIK4bZeGu0veyqrD8JOrzU7sbh7Xf5zQ2vD4r1D3QTOgLv8vfH0/JMXmVeVFmsKNiEhh4VsK2o8xn694F+K2W1vPv6Wdh/2/w7FNhX+Ol3OnYP7TMLUdxO8wOwzfPxl6z73xfl2exeD+Ty+sk+YD+341b1Pt+zUXCxfQaCmryxERyRnDgG8fhF2LoHQjGPCLORzeKqcOwp4lsHsJ7F8BaWfN7aWqmB1s63SFkhWtqy+nDAM2z4LFQ81+NWB2GG79Rtb9am5U/E5zNFXcNsBmtgTd+RK4uuXeOZxMTr6/FW5ERAqbhCMwIRJSk8xbVbcOzL9zp6fCoT/NzrG7l5itGpcrFgrnT2eezK50IzPo1LofigXlX605dXKfOex+33LzdWA16Dju2v1qblTaOVj40qVRV+WawwP/A//wvDlfIadwkw2FGxFxCn9PgQXPmbc3nlxtzoGTVxKPmkFm9y/mLZTUM5fes7lCRCRUaQNV7oaQWub72xfA5u/N/Q3HpX0rtTA7RlfvYN6mKQj+PcOwqyfc+SI0eyZn/Wpu1OZZ8OMz5s/Np5S5gn3Vu/P+vIWMwk02FG5ExCk4HDCtIxxcaXZy7T330hwrN8ueDofXXGidWQqxmzO/7xsElduYgaZSi+yXF0iKha2zYdP3cHT9pe3uPlDtHnPSu8qtzMVCrXBwNSx49lILVIU7zWH3+T1f0om95m2qmE3m62ZPQ6vh1v1cCiCFm2wo3IiI0zixFyY2M1sbOk+4udXPz8TBnqVmoNm7DM4nXPamDco0MltmqrSB0Ho3NtPu8T2weabZonNy36Xt3iXNye7qdjdbgXIrpGXn3ClY8vrVZxi2QnoK/DIM1nxqvi7TGLpOgeJlramngFG4yYbCjYg4lT8+hCXDzQnjBq0Bv9Dr+5zDDkfWX2id+QWObcj8vncJqNzaDDSVWpkjtXKLYZitOJtmwpYfIDnu0nvFy5oBo053CK6ee+e8/Nz50WH4Zmybb85ldD7B/L12/gRq3Gt1VZZTuMmGwo2IOBV7OvyvlRlOanSEHl9ffd/kE7A3yuw/s2cpnDuZ+f2w+hdaZ+6G0rfkzygsezrs/81s0dn+Y+b+PCF1oG43qN0VAkrf/Lnyu8PwzTh1EGb1hyMXZqOOfALajAQ3T2vrspDCTTYUbkTE6cRshs/uMtcw6v4l1Oxsbnc4IGbjpc7Ah/8GLvuT7xlg9pmpcrfZSuMXYkX1l6SehV0LzZaV3UvAkXbhDRuUv81s0anZKfs+PlnJqsPwHS9C83zqMHyj0lNh2UhYdWFl+LB65nIOzrh+2nVQuMmGwo2IOKVl/zW/vH2Doe1b5iil3Usy3/IBCKl9aWRTmcYFt8Pq2ZOwba556yp61aXtrh5m7XW6QdV24O6V/XEKSofhm7FrMcx5wmxp8/CDTh9B7futrirfKdxkQ+FGRJxSeoo52+3xXZm3exQzR1NVaWOOcMqN2zv57XS02ZqzeeaFSe8u8PSHGp3MW1flb898G+2KDsOloO3ozCt3FyYJR+CHARC92nzdsL/ZAdrd29q68pHCTTYUbkTEaR3+G75+AIqFXGqdKdu0YN96yanYreaw8s2zIPHwpe3FQqH2A2bQObEXFr18qcNwg95mf5WC0mH4RtnT4dfR8Pv7gAHBtaDbFxBU1dqaTh80+zOd3Gf+7E/uhVKVof07uXoqhZtsKNyIiDgBh8Nsxdj8PWyda86K/G+BVeHecVC+eT4Xl8f2LoPZj5vhzd0X7h0L9R7Mu/PZ0yEhGk7sM4PL5SHmdLTZ1+vfQurAwJW5WobCTTYUbkREnEx6ijn6a/NM2LnQHO59x4vQ/GnnHV2UFAuzHzXX8wKo3xPueRc8fG/seBcDzMl9V4aY0wezDjAXuXmZ64eVrGj2ZSpZEYKqQ9lbb6yWqyh04WbChAm8++67xMTEUK9ePcaPH0+TJk2y3Hfy5Ml8+eWXbNmyBYCGDRsyatSoq+7/bwo3IiJOLDXZXObhWh2NnYHDbt6i+nW0ucRFYDXoNtVcAuNq+5+O/tctpAtB5tTBy0anZeHyAHN5iClZCfzCbmxSxxwqVOFmxowZ9OnTh0mTJhEZGcm4ceOYOXMmO3fuJDg4+Ir9e/bsSfPmzWnWrBleXl688847zJkzh61bt1K69LU7yinciIiIUzmwEn54FJKOmSHk7v+awePfIebUgWsHmBIVLgSXCmZwuRhi/MLzJcBkp1CFm8jISBo3bszHH38MgMPhICIigqeeeoqXX375mp+32+2UKFGCjz/+mD59+lxzf4UbERFxOsnHYc5/zNtz2XH1vCy4VLzU+lKqUoEIMNnJyfe3Wz7VlKXU1FTWrVvH0KFDM7a5uLjQunVrVq9efV3HOHv2LGlpaZQsWch7wYuIiNwo30B4eCasHg9/TgIv/6xvIfmXLtABJrdYGm6OHz+O3W4nJCTzrJghISHs2LHjuo7x0ksvER4eTuvWrbN8PyUlhZSUlIzXiYmJN16wiIhIQeXiYs663PwZqyuxXKGOb2+//Tbfffcdc+bMwcsr685jo0ePJiAgIOMRERGRz1WKiIhIfrI03AQGBuLq6kpsbGym7bGxsYSGZr+y7Xvvvcfbb7/NL7/8Qt26da+639ChQ0lISMh4HDp0KFdqFxERkYLJ0nDj4eFBw4YNiYqKytjmcDiIioqiadOmV/3cmDFjePPNN1m0aBGNGjXK9hyenp74+/tneoiIiIjzsrTPDcCQIUPo27cvjRo1okmTJowbN47k5GT69+8PQJ8+fShdujSjR48G4J133mH48OFMnz6d8uXLExMTA0CxYsUoVqyYZdchIiIiBYPl4aZHjx7Ex8czfPhwYmJiqF+/PosWLcroZBwdHY3LZT27J06cSGpqKl27ds10nNdff50RI0bkZ+kiIiJSAFk+z01+0zw3IiIihU9Ovr8L9WgpERERkX9TuBERERGnonAjIiIiTkXhRkRERJyKwo2IiIg4FYUbERERcSoKNyIiIuJUFG5ERETEqVg+Q3F+uzhnYWJiosWViIiIyPW6+L19PXMPF7lwk5SUBEBERITFlYiIiEhOJSUlERAQkO0+RW75BYfDwdGjR/Hz88Nms+XqsRMTE4mIiODQoUNFcmmHon79oJ+Brr9oXz/oZ1DUrx/y7mdgGAZJSUmEh4dnWnMyK0Wu5cbFxYUyZcrk6Tn8/f2L7L/UoOsH/Qx0/UX7+kE/g6J+/ZA3P4NrtdhcpA7FIiIi4lQUbkRERMSpKNzkIk9PT15//XU8PT2tLsUSRf36QT8DXX/Rvn7Qz6CoXz8UjJ9BketQLCIiIs5NLTciIiLiVBRuRERExKko3IiIiIhTUbgRERERp6Jwk0smTJhA+fLl8fLyIjIykjVr1lhdUr4ZPXo0jRs3xs/Pj+DgYLp06cLOnTutLssyb7/9NjabjWeffdbqUvLVkSNH6NWrF6VKlcLb25s6derw999/W11WvrDb7QwbNowKFSrg7e1NpUqVePPNN69rDZzCasWKFXTs2JHw8HBsNhtz587N9L5hGAwfPpywsDC8vb1p3bo1u3fvtqbYPJDd9aelpfHSSy9Rp04dfH19CQ8Pp0+fPhw9etS6gnPZtX7/l3viiSew2WyMGzcu3+pTuMkFM2bMYMiQIbz++uusX7+eevXq0bZtW+Li4qwuLV/89ttvDBo0iD///JMlS5aQlpbG3XffTXJystWl5bu1a9fy6aefUrduXatLyVenTp2iefPmuLu7s3DhQrZt28b7779PiRIlrC4tX7zzzjtMnDiRjz/+mO3bt/POO+8wZswYxo8fb3VpeSY5OZl69eoxYcKELN8fM2YMH330EZMmTeKvv/7C19eXtm3bcv78+XyuNG9kd/1nz55l/fr1DBs2jPXr1zN79mx27txJp06dLKg0b1zr93/RnDlz+PPPPwkPD8+nyi4w5KY1adLEGDRoUMZru91uhIeHG6NHj7awKuvExcUZgPHbb79ZXUq+SkpKMqpUqWIsWbLEuPPOO41nnnnG6pLyzUsvvWTcdtttVpdhmQ4dOhiPPPJIpm3333+/0bNnT4sqyl+AMWfOnIzXDofDCA0NNd59992MbadPnzY8PT2Nb7/91oIK89a/rz8ra9asMQDj4MGD+VNUPrra9R8+fNgoXbq0sWXLFqNcuXLGBx98kG81qeXmJqWmprJu3Tpat26dsc3FxYXWrVuzevVqCyuzTkJCAgAlS5a0uJL8NWjQIDp06JDp34WiYv78+TRq1Ihu3boRHBxMgwYNmDx5stVl5ZtmzZoRFRXFrl27ANi4cSMrV66kffv2Fldmjf379xMTE5Ppv4WAgAAiIyOL9N9Fm81G8eLFrS4lXzgcDnr37s2LL75IrVq18v38RW7hzNx2/Phx7HY7ISEhmbaHhISwY8cOi6qyjsPh4Nlnn6V58+bUrl3b6nLyzXfffcf69etZu3at1aVYYt++fUycOJEhQ4bwyiuvsHbtWp5++mk8PDzo27ev1eXluZdffpnExESqV6+Oq6srdrudt956i549e1pdmiViYmIAsvy7ePG9ouT8+fO89NJLPPTQQ0VmMc133nkHNzc3nn76aUvOr3AjuWrQoEFs2bKFlStXWl1Kvjl06BDPPPMMS5YswcvLy+pyLOFwOGjUqBGjRo0CoEGDBmzZsoVJkyYViXDz/fff88033zB9+nRq1arFhg0bePbZZwkPDy8S1y9Xl5aWRvfu3TEMg4kTJ1pdTr5Yt24dH374IevXr8dms1lSg25L3aTAwEBcXV2JjY3NtD02NpbQ0FCLqrLG4MGDWbBgAcuXL6dMmTJWl5Nv1q1bR1xcHLfccgtubm64ubnx22+/8dFHH+Hm5obdbre6xDwXFhZGzZo1M22rUaMG0dHRFlWUv1588UVefvllHnzwQerUqUPv3r157rnnGD16tNWlWeLi376i/nfxYrA5ePAgS5YsKTKtNr///jtxcXGULVs242/iwYMHef755ylfvny+1KBwc5M8PDxo2LAhUVFRGdscDgdRUVE0bdrUwsryj2EYDB48mDlz5rBs2TIqVKhgdUn5qlWrVmzevJkNGzZkPBo1akTPnj3ZsGEDrq6uVpeY55o3b37F8P9du3ZRrlw5iyrKX2fPnsXFJfOfU1dXVxwOh0UVWatChQqEhoZm+ruYmJjIX3/9VWT+Ll4MNrt372bp0qWUKlXK6pLyTe/evdm0aVOmv4nh4eG8+OKLLF68OF9q0G2pXDBkyBD69u1Lo0aNaNKkCePGjSM5OZn+/ftbXVq+GDRoENOnT2fevHn4+fll3FMPCAjA29vb4urynp+f3xX9i3x9fSlVqlSR6Xf03HPP0axZM0aNGkX37t1Zs2YNn332GZ999pnVpeWLjh078tZbb1G2bFlq1arFP//8w9ixY3nkkUesLi3PnDlzhj179mS83r9/Pxs2bKBkyZKULVuWZ599lv/+979UqVKFChUqMGzYMMLDw+nSpYt1Reei7K4/LCyMrl27sn79ehYsWIDdbs/4u1iyZEk8PDysKjvXXOv3/+8w5+7uTmhoKNWqVcufAvNtXJaTGz9+vFG2bFnDw8PDaNKkifHnn39aXVK+AbJ8TJ061erSLFPUhoIbhmH8+OOPRu3atQ1PT0+jevXqxmeffWZ1SfkmMTHReOaZZ4yyZcsaXl5eRsWKFY1XX33VSElJsbq0PLN8+fIs/7vv27evYRjmcPBhw4YZISEhhqenp9GqVStj586d1hadi7K7/v3791/17+Ly5cutLj1XXOv3/2/5PRTcZhhOPIWmiIiIFDnqcyMiIiJOReFGREREnIrCjYiIiDgVhRsRERFxKgo3IiIi4lQUbkRERMSpKNyIiIiIU1G4EZEiz2azMXfuXKvLEJFconAjIpbq168fNpvtike7du2sLk1ECimtLSUilmvXrh1Tp07NtM3T09OiakSksFPLjYhYztPTk9DQ0EyPEiVKAOYto4kTJ9K+fXu8vb2pWLEis2bNyvT5zZs307JlS7y9vSlVqhSPP/44Z86cybTPlClTqFWrFp6enoSFhTF48OBM7x8/fpz77rsPHx8fqlSpwvz58/P2okUkzyjciEiBN2zYMB544AE2btxIz549efDBB9m+fTsAycnJtG3blhIlSrB27VpmzpzJ0qVLM4WXiRMnMmjQIB5//HE2b97M/PnzqVy5cqZzvPHGG3Tv3p1NmzZxzz330LNnT06ePJmv1ykiuSTflugUEclC3759DVdXV8PX1zfT46233jIMw1x1/oknnsj0mcjISGPgwIGGYRjGZ599ZpQoUcI4c+ZMxvs//fST4eLiYsTExBiGYRjh4eHGq6++etUaAOO1117LeH3mzBkDMBYuXJhr1yki+Ud9bkTEci1atGDixImZtpUsWTLjedOmTTO917RpUzZs2ADA9u3bqVevHr6+vhnvN2/eHIfDwc6dO7HZbBw9epRWrVplW0PdunUznvv6+uLv709cXNyNXpKIWEjhRkQs5+vre8Vtotzi7e19Xfu5u7tnem2z2XA4HHlRkojkMfW5EZEC788//7zidY0aNQCoUaMGGzduJDk5OeP9P/74AxcXF6pVq4afnx/ly5cnKioqX2sWEeuo5UZELJeSkkJMTEymbW5ubgQGBgIwc+ZMGjVqxG233cY333zDmjVr+PzzzwHo2bMnr7/+On379mXEiBHEx8fz1FNP0bt3b0JCQgAYMWIETzzxBMHBwbRv356kpCT++OMPnnrqqfy9UBHJFwo3ImK5RYsWERYWlmlbtWrV2LFjB2COZPruu+948sknCQsL49tvv6VmzZoA+Pj4sHjxYp555hkaN26Mj48PDzzwAGPHjs04Vt++fTl//jwffPABL7zwAoGBgXTt2jX/LlBE8pXNMAzD6iJERK7GZrMxZ84cunTpYnUpIlJIqM+NiIiIOBWFGxEREXEq6nMjIgWa7pyLSE6p5UZEREScisKNiIiIOBWFGxEREXEqCjciIiLiVBRuRERExKko3IiIiIhTUbgRERERp6JwIyIiIk5F4UZEREScyv8Dh/spWMw2ZvkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load the saved model\n",
        "model = load_model('/content/best_InceptionV3_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history3.history['accuracy'])\n",
        "plt.plot(history3.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgwxOoX0EQhl"
      },
      "source": [
        "#### **1.8. AlexNet**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xerOG713Kkc7"
      },
      "source": [
        "This deep convolutional neural network consisting of 25 layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oX_DgSpHScMN",
        "outputId": "37b81354-5148-4b49-be96-6a2fa1f1e0bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 224, 224, 96)      2688      \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 112, 112, 96)      0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 112, 112, 96)      384       \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 112, 112, 256)     221440    \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 56, 56, 256)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 56, 56, 256)       1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 56, 56, 384)       885120    \n",
            "                                                                 \n",
            " batch_normalization_2 (Bat  (None, 56, 56, 384)       1536      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 56, 56, 384)       1327488   \n",
            "                                                                 \n",
            " batch_normalization_3 (Bat  (None, 56, 56, 384)       1536      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 56, 56, 256)       884992    \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 28, 28, 256)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 28, 28, 256)       1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 200704)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               102760960 \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " batch_normalization_5 (Bat  (None, 512)               2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1024)              525312    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 1024)              0         \n",
            "                                                                 \n",
            " batch_normalization_6 (Bat  (None, 1024)              4096      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 2)                 2050      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 106621698 (406.73 MB)\n",
            "Trainable params: 106615874 (406.71 MB)\n",
            "Non-trainable params: 5824 (22.75 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "\n",
        "# Convolutional layers\n",
        "model.add(Convolution2D(96, 3, strides=(1, 1), padding='same', input_shape=(224, 224, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Convolution2D(256, 3, strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Convolution2D(384, 3, strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Convolution2D(384, 3, strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Convolution2D(256, 3, strides=(1, 1), padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Fully connected layers\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Dense(units=1024, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Output layer\n",
        "model.add(Dense(units=num_classes, activation='softmax'))\n",
        "\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYV4cGQacYhn",
        "outputId": "573621a7-c48c-453a-bc48-b876a1b72759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.8528 - accuracy: 0.6607"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/32 [==============================] - 66s 1s/step - loss: 0.8528 - accuracy: 0.6607 - val_loss: 2.3847 - val_accuracy: 0.4216 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 28s 883ms/step - loss: 0.6882 - accuracy: 0.7257 - val_loss: 1.7952 - val_accuracy: 0.4118 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 29s 926ms/step - loss: 0.6765 - accuracy: 0.7287 - val_loss: 0.6131 - val_accuracy: 0.6471 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 30s 944ms/step - loss: 0.5532 - accuracy: 0.7518 - val_loss: 1.0086 - val_accuracy: 0.4902 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 26s 829ms/step - loss: 0.5456 - accuracy: 0.7598 - val_loss: 0.8478 - val_accuracy: 0.5392 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 30s 950ms/step - loss: 0.5336 - accuracy: 0.7648 - val_loss: 0.7055 - val_accuracy: 0.6765 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 30s 945ms/step - loss: 0.4830 - accuracy: 0.7898 - val_loss: 1.1032 - val_accuracy: 0.4804 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 17s 530ms/step - loss: 0.4889 - accuracy: 0.7578 - val_loss: 0.9055 - val_accuracy: 0.4804 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 18s 540ms/step - loss: 0.5032 - accuracy: 0.7798 - val_loss: 0.7028 - val_accuracy: 0.7647 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 29s 897ms/step - loss: 0.4338 - accuracy: 0.8028 - val_loss: 0.5867 - val_accuracy: 0.7745 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 18s 545ms/step - loss: 0.4448 - accuracy: 0.8058 - val_loss: 0.7515 - val_accuracy: 0.7353 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 29s 919ms/step - loss: 0.4233 - accuracy: 0.7948 - val_loss: 0.5398 - val_accuracy: 0.7843 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 30s 957ms/step - loss: 0.3938 - accuracy: 0.8258 - val_loss: 0.5901 - val_accuracy: 0.7843 - lr: 0.0010\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 29s 899ms/step - loss: 0.3883 - accuracy: 0.8178 - val_loss: 0.5587 - val_accuracy: 0.8039 - lr: 0.0010\n",
            "Epoch 15/15\n",
            "32/32 [==============================] - 17s 530ms/step - loss: 0.3920 - accuracy: 0.8198 - val_loss: 1.0043 - val_accuracy: 0.5784 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "model_checkpoint = ModelCheckpoint('best_AlexNet_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "history4 = model.fit(\n",
        "    Train_generator,\n",
        "    steps_per_epoch=32,\n",
        "    epochs=15,\n",
        "    validation_data=Val_generator,\n",
        "    validation_steps=4,\n",
        "    verbose=1,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3,patience=3, min_lr=0.000001),model_checkpoint,early_stopping],\n",
        "    shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NnPc-QEh0vTL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a9b2e413-b1b3-4d3f-d645-ea52339e519d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_test_function.<locals>.test_function at 0x7e6d722dd750> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 112ms/step - loss: 0.4020 - accuracy: 0.8118\n",
            "Test Accuracy: 81.17647171020508\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7e6d722deb00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 96ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[22 30]\n",
            " [18 15]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.55      0.42      0.48        52\n",
            "          OK       0.33      0.45      0.38        33\n",
            "\n",
            "    accuracy                           0.44        85\n",
            "   macro avg       0.44      0.44      0.43        85\n",
            "weighted avg       0.47      0.44      0.44        85\n",
            "\n",
            "Sensitivity: 0.45454545454545453\n",
            "Specificity: 0.4230769230769231\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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z53BwcFDNeC6eNjMlsEdEIjx48KDAi4REZFiSkpLg6Oio3cnsEVFJy1+v55v1RyEzt9RxNFQalk3nOjzlhZCXjew/12u8Lleh8mffFntOGcREVEblD8fJzC2ZiMoJiZHma/iQYeCw+ytMRERE+ohVc0REpFN8RkRERDrFHhEREemUAfWIymZ6JCKidyuFFVrDwsLQpEkTWFlZwdbWFt26dSuw3H1ycjL+85//wM7ODhYWFmjYsCF27twpqh0mIiIiKlRsbCwCAwNx5swZREdHIycnB35+fsjIyFAdM2jQICQkJGDv3r24cuUKevTogd69e+PixYsat8OhOSIifVQKQ3NRUVFqn8PDw2Fra4sLFy7A29sbAHDq1CksX74cH3/8MQDgv//9LxYsWIALFy6gQYMGGrXDHhERkR6SSCRabcCrVV5f37KysjRqMzU1FQBgY2Oj2te8eXNs3boVKSkpUCqV2LJlCzIzM+Hj46PxvTARERHpoaIkIicnJygUCtUWFhb23vaUSiVGjRqFFi1aoE6dOqr927ZtQ05ODipVqgSZTIbhw4cjIiICrq6uGt8Lh+aIiPSR5P83sefg1Rx3crlctVsmk7331MDAQFy9ehUnTpxQ2z9lyhQ8e/YMhw4dwgcffIDdu3ejd+/eOH78OOrWratRWExERER66PUejoiTAAByuVwtEb1PUFAQIiMjcezYMbVJWm/evIkff/wRV69exUcffQQA8PT0xPHjx7F06VKsWLFCo+szERERUaEEQcCIESMQERGBo0ePonr16mrfv3jxAgAKLGVhZGQEpVKpcTtMREREeqgoPSJNBQYGYvPmzdizZw+srKyQnJwMAFAoFDAzM0Pt2rXh6uqK4cOHY+7cuahUqRJ2796N6OhoREZGatwOixWIiPRQUYoVNLV8+XKkpqbCx8cH9vb2qm3r1q0AAGNjY/z222+oXLkyPvvsM9SrVw8bNmzA+vXr0bFjR43bYY+IiEgPlUaPSBCE9x7z4Ycfip5J4U1MRERE+qgIVXNlDRMREZEeKo0eUWnhMyIiItIp9oiIiPTQq6nmxPaISiaWomIiIiLSQxJoMTRXRjMRExERkR4ypGdETERERPqIVXNERKRTWvSIhDLaI2LVHBER6RR7REREekibZ0TiixtKBxMREZEeYiIiIiLdYrECERHpEntERESkU4aUiFg1R0REOsUeERGRHjKkHhETERGRHmIiIiIi3WLVHBER6RJ7REREpFOGlIhYNUdERDrFHhERkR4ypB4RExERkT5isQIREekSe0RERKRTTERERKRTEmiRiMro2Byr5oiISKfYIyIi0kMcmiMiIt1i1RwREemSIfWI+IyIiEgP5ScisZsYYWFhaNKkCaysrGBra4tu3bohISGhwHGnT5/GJ598AgsLC8jlcnh7e+Ply5cat8NEREREhYqNjUVgYCDOnDmD6Oho5OTkwM/PDxkZGapjTp8+jfbt28PPzw/nzp3D77//jqCgIEilmqcXDs0REekhieTVJvYcMaKiotQ+h4eHw9bWFhcuXIC3tzcAYPTo0Rg5ciQmTpyoOs7NzU1UO+wRERHpoVeJSOzQ3Ktz09LS1LasrCyN2kxNTQUA2NjYAAAeP36Ms2fPwtbWFs2bN0eVKlXQunVrnDhxQtS9MBEREekjyf96RZpu+VVzTk5OUCgUqi0sLOy9zSmVSowaNQotWrRAnTp1AAC3bt0CAISEhOCLL75AVFQUGjZsiE8//RQ3btzQ+FY4NEdEpIeKUjWXlJQEuVyu2i+Tyd57bmBgIK5evarW21EqlQCA4cOHIyAgAADQoEEDHD58GGvXrtUowQFMREREeqkoz4jkcrlaInqfoKAgREZG4tixY3B0dFTtt7e3BwB4eHioHe/u7o67d+9qfH0OzRERUaEEQUBQUBAiIiIQExOD6tWrq31frVo1ODg4FCjp/uuvv+Di4qJxO+wRERHpIalUAqlUXJdIEHl8YGAgNm/ejD179sDKygrJyckAAIVCATMzM0gkEowfPx7Tpk2Dp6cn6tevj/Xr1+P69evYsWOH5vciKqoybNWqVXBycoJUKsXChQt1HY6a8PBwWFtb6zoMIjIgYgsVtBnKW758OVJTU+Hj4wN7e3vVtnXrVtUxo0aNwqRJkzB69Gh4enri8OHDiI6ORs2aNTVuR6eJaPDgwaoHbsbGxqhSpQratm2LtWvXqh6CaSItLQ1BQUH49ttvcf/+fXz55ZdFjo3Jg4jKstKYWUEQhEK3wYMHqx03ceJEJCUlISMjA6dOnULLli1FtaPzHlH79u3x8OFD3LlzB/v374evry+Cg4PRuXNn5ObmanSNu3fvIicnB506dYK9vT3Mzc1LOGoiIt0qjR5RadF5IpLJZLCzs0PVqlXRsGFDTJ48GXv27MH+/fsRHh4OAHj27BmGDRuGypUrQy6X45NPPsGlS5cAvOq51K1bFwBQo0YNSCQS3LlzBwCwZ88eNGzYEKampqhRowZCQ0PVktuzZ88wfPhwVKlSBaampqhTpw4iIyNx9OhRBAQEIDU1VfV/ESEhIQCArKwsjBs3DlWrVoWFhQWaNm2Ko0ePqt1TeHg4nJ2dYW5uju7du+Pp06cl+jMkovKnNHpEpUXniagwn3zyCTw9PbFr1y4AwOeff47Hjx9j//79uHDhguqFqZSUFPTp0weHDh0CAJw7dw4PHz6Ek5MTjh8/jkGDBiE4OBh//vknVq5cifDwcMyaNQvAq/r3Dh064OTJk/j555/x559/4ocffoCRkRGaN2+OhQsXQi6X4+HDh3j48CHGjRsH4FUZ4+nTp7FlyxZcvnwZn3/+Odq3b696eevs2bMYOnQogoKCEBcXB19fX8ycOfO995yVlVXgbWciovKgzFbN1a5dG5cvX8aJEydw7tw5PH78WPXS1dy5c7F7927s2LEDX375JSpVqgQAqFy5Muzs7AAAoaGhmDhxIvz9/QG86i3NmDEDEyZMwLRp03Do0CGcO3cO8fHxqFWrluqYfAqFAhKJRHU94NUQ4Lp163D37l04ODgAAMaNG4eoqCisW7cO33//PRYtWoT27dtjwoQJAIBatWrh1KlTBeZselNYWBhCQ0OL40dHROWAIS0DUWYTkSAIkEgkuHTpEtLT01XJJt/Lly9x8+bNt55/6dIlnDx5UtUDAoC8vDxkZmbixYsXiIuLg6OjoyoJaeLKlSvIy8srcE5WVpYqvvj4eHTv3l3tey8vr/cmokmTJmHMmDGqz2lpaXByctI4NiIqX0pj0tPSUmYTUXx8PKpXr4709HTY29sXeA4D4J1Vbenp6QgNDUWPHj0KfGdqagozMzPRMaWnp8PIyAgXLlyAkZGR2neWlpair/c6mUym0TQbREQAIIEWPaIyukRrmUxEMTExuHLlCkaPHg1HR0ckJyejQoUKqFatmsbXaNiwIRISEuDq6lro9/Xq1cO9e/fw119/FdorMjExQV5entq+Bg0aIC8vD48fP0arVq0Kva67uzvOnj2rtu/MmTMax01EpAn2iIpRVlYWkpOTkZeXh0ePHiEqKgphYWHo3LkzBg0aBKlUCi8vL3Tr1g2zZ89GrVq18ODBA/z666/o3r07GjduXOh1p06dis6dO8PZ2Rm9evWCVCrFpUuXcPXqVcycOROtW7eGt7c3evbsifnz58PV1RXXr1+HRCJB+/btUa1aNaSnp+Pw4cPw9PSEubk5atWqhQEDBmDQoEGYN28eGjRogCdPnuDw4cOoV68eOnXqhJEjR6JFixaYO3cuunbtigMHDrx3WI6ISCxDekak86q5qKgo2Nvbo1q1amjfvj2OHDmCxYsXY8+ePTAyMoJEIsFvv/0Gb29vBAQEoFatWujbty/+/vtvVKlS5a3XbdeuHSIjI3Hw4EE0adIEzZo1w4IFC9TmP9q5cyeaNGmCfv36wcPDAxMmTFD1gpo3b46vvvoKffr0QeXKlTF79mwAwLp16zBo0CCMHTsWbm5u6NatG37//Xc4OzsDAJo1a4bVq1dj0aJF8PT0xMGDB/Hf//63BH+CRET6TSIIgqDrIKigtLQ0KBQKjN5+HjLzoj1/Iv2w8Lslug6BSomQl42sK6uRmpoqahZs4H+/G+p/tw9Gphaizs3LzEDcrM+0arck6XxojoiIxDOkoTkmIiIiPcRiBSIi0in2iIiISLe0mcS0bOYh3VfNERFR+cYeERGRHuLQHBER6RSLFYiISKfYIyIiIp1ij4iIiHTKkHpErJojIiKdYo+IiEgPGVKPiImIiEgP8RkRERHpFHtERESkU+wRERGRThlSj4hVc0REpFPsERER6SEJtBiaK5FIio6JiIhID0klEkhFZiKxx5cWJiIiIj1kSMUKfEZERKSH8osVxG5ihIWFoUmTJrCysoKtrS26deuGhISEQo8VBAEdOnSARCLB7t27RbXDREREpIekEu02MWJjYxEYGIgzZ84gOjoaOTk58PPzQ0ZGRoFjFy5cqHVVHofmiIioUFFRUWqfw8PDYWtriwsXLsDb21u1Py4uDvPmzcP58+dhb28vuh2NEtHevXs1vmCXLl1EB0FERCJJtHgv6P8PT0tLU9stk8kgk8nee3pqaioAwMbGRrXvxYsX6N+/P5YuXQo7Oztx8fw/jRJRt27dNLqYRCJBXl6eVoEQEZHmilKs4OTkpLZ/2rRpCAkJeee5SqUSo0aNQosWLVCnTh3V/tGjR6N58+bo2rWruGBeo1EiUiqVWjdARETFT/L/f8SeAwBJSUmQy+Wq/Zr0hgIDA3H16lWcOHFCtW/v3r2IiYnBxYsXRcXxpiIVK2RmZhapcSIi0k5RihXkcrna9r5EFBQUhMjISBw5cgSOjo6q/TExMbh58yasra1RoUIFVKjwqm/Ts2dP+Pj4aH4vYm8+Ly8PM2bMQNWqVWFpaYlbt24BAKZMmYI1a9aIvRwREWmhNMq3BUFAUFAQIiIiEBMTg+rVq6t9P3HiRFy+fBlxcXGqDQAWLFiAdevWadyO6EQ0a9YshIeHY/bs2TAxMVHtr1OnDn766SexlyMiojIqMDAQP//8MzZv3gwrKyskJycjOTkZL1++BADY2dmhTp06ahsAODs7F0ha7yI6EW3YsAGrVq3CgAEDYGRkpNrv6emJ69evi70cERFpIb9YQewmxvLly5GamgofHx/Y29urtq1btxbrvYh+j+j+/ftwdXUtsF+pVCInJ6dYgiIioncrjbnmBEEQdby254juEXl4eOD48eMF9u/YsQMNGjQQHQAREYlXGj2i0iK6RzR16lT4+/vj/v37UCqV2LVrFxISErBhwwZERkaWRIxERPSGcr0wXteuXbFv3z4cOnQIFhYWmDp1KuLj47Fv3z60bdu2JGIkIqI3lOseEQC0atUK0dHRxR0LERGVQ1pPenr+/HnEx8cDePXcqFGjRsUWFBERvVu5Xhjv3r176NevH06ePAlra2sAwLNnz9C8eXNs2bJF7a1bIiIqGRKIX/q7bKYhLZ4RDRs2DDk5OYiPj0dKSgpSUlIQHx8PpVKJYcOGlUSMRET0htKYWaG0iO4RxcbG4tSpU3Bzc1Ptc3Nzw5IlS9CqVatiDY6IiAqnzUJ3Yo8vLaITkZOTU6Evrubl5cHBwaFYgiIioncr1+Xbc+bMwYgRI3D+/HnVvvPnzyM4OBhz584t1uCIiMjwadQjqlixolomzcjIQNOmTVVTfufm5qJChQoYMmSIxovoERFR0ZTRDo5oGiWihQsXlnAYREQkhiENzWmUiPz9/Us6DiIiEqFcFyu8LjMzE9nZ2Wr7Xl9+loiISoYh9YhEFytkZGQgKCgItra2sLCwQMWKFdU2IiIqeRItt7JIdCKaMGECYmJisHz5cshkMvz0008IDQ2Fg4MDNmzYUBIxEhGRARM9NLdv3z5s2LABPj4+CAgIQKtWreDq6goXFxds2rQJAwYMKIk4iYjoNYY015zoHlFKSgpq1KgB4NXzoJSUFABAy5YtcezYseKNjoiICmVIy0CITkQ1atTA7du3AQC1a9fGtm3bALzqKeVPgkpERCXLkOaaE52IAgICcOnSJQDAxIkTsXTpUpiammL06NEYP358sQdIREQFGVKPSPQzotGjR6v+uU2bNrh+/TouXLgAV1dX1KtXr1iDIyKiwhnSM6IivUcEAC4uLnBxcSmOWIiIqBzSKBEtXrxY4wuOHDlS62CIiEgz2gy1ldEOkWaJaMGCBRpdTCKRMBEVsx7udrC04mwV5cG033/UdQhUStLS0lCl0uoiXcOQZlbQKBHlV8kREVHZIIX4ajPR1WmlpMjPiIiIqPQZUo+orCZIIiIqJ9gjIiLSQxItloEoox0iJiIiIn3E9YiIiEinyv0zouPHj2PgwIHw8vLC/fv3AQAbN27EiRMnijU4IiIqXH6PSOwmRlhYGJo0aQIrKyvY2tqiW7duSEhIUH2fkpKCESNGwM3NDWZmZnB2dsbIkSORmpoq7l7EhQXs3LkT7dq1g5mZGS5evIisrCwAQGpqKr7//nuxlyMiIi2UxlxzsbGxCAwMxJkzZxAdHY2cnBz4+fkhIyMDAPDgwQM8ePAAc+fOxdWrVxEeHo6oqCgMHTpU3L0IgiCIOaFBgwYYPXo0Bg0aBCsrK1y6dAk1atTAxYsX0aFDByQnJ4sKgAqXlpYGhUKB41fv8YXWcqK2g5WuQ6BS8uqFVgVSU1Mhl4v77zv/d8PIbechM7cUdW7Wi3Qs7t1Yq3YB4MmTJ7C1tUVsbCy8vb0LPWb79u0YOHAgMjIyUKGCZk9/RD8jSkhIKDQAhUKBZ8+eib0cERFpoSiTnqalpantl8lkkMlk7z0/f8jNxsbmncfI5XKNkxCgxdCcnZ0dEhMTC+w/ceKEasE8IiIqWVItNwBwcnKCQqFQbWFhYe9tT6lUYtSoUWjRogXq1KlT6DH//PMPZsyYgS+//FLUvYjuEX3xxRcIDg7G2rVrIZFI8ODBA5w+fRrjxo3DlClTxF6OiIi0UJRJT5OSktSG5jTpDQUGBuLq1atvLUpLS0tDp06d4OHhgZCQEFFxiU5EEydOhFKpxKeffooXL17A29sbMpkM48aNw4gRI8RejoiItCCFFkNzeHW8XC4X9YwoKCgIkZGROHbsGBwdHQt8//z5c7Rv3x5WVlaIiIiAsbGxqLhEJyKJRILvvvsO48ePR2JiItLT0+Hh4QFLS3EPzYiISHulsQyEIAgYMWIEIiIicPToUVSvXr3AMWlpaWjXrh1kMhn27t0LU1NTcY2gCC+0mpiYwMPDQ9vTiYiojAsMDMTmzZuxZ88eWFlZqaqiFQoFzMzMkJaWBj8/P7x48QI///wz0tLSVIUQlStXhpGRkUbtiE5Evr6+73w7NyYmRuwliYhIpNKY4mf58uUAAB8fH7X969atw+DBg/HHH3/g7NmzAABXV1e1Y27fvo1q1app1I7oRFS/fn21zzk5OYiLi8PVq1fh7+8v9nJERKSFV5Oeip3iR1wb73vN1MfH573HaEJ0Inrbaq0hISFIT08vckBERPR+hrRUeLGtRzRw4ECsXbu2uC5HRETvUBpzzZWWYpt9+/Tp01pVSxARkXiS//8j9pyySHQi6tGjh9pnQRDw8OFDnD9/ni+0EhGRaKITkUKhUPsslUrh5uaG6dOnw8/Pr9gCIyKityu3C+Pl5eUhICAAdevWRcWKFUsqJiIieg9DSkSiihWMjIzg5+fHWbaJiHQsf4VWsVtZJLpqrk6dOrh161ZJxEJERBoypKo50Ylo5syZGDduHCIjI/Hw4UPVlA6vT+1AREQlqzRWaC0tGj8jmj59OsaOHYuOHTsCALp06aLWzRMEARKJBHl5ecUfJRERGSyNE1FoaCi++uorHDlypCTjISIiDRRlhdayRuNElD+fUOvWrUssGCIi0owhVc2JKt8uqxUXRETljjbPfMror3BRiahWrVrvTUYpKSlFCoiIiN5PColqxVUx55RFohJRaGhogZkViIio9BnS7NuiElHfvn1ha2tbUrEQEVE5pHEi4vMhIqKyo1wWKxTHKnxERFQ8ymX5tlKpLMk4iIhIhHL7jIiIiMoGKbToERlC1RwREZUNhtQjEj3pKRERUXFij4iISA9JIb4nUVZ7HkxERER6SJuF7srqazhMREREekgC8VPHlc00xERERKSXyuV7REREVLaUzbQiXll9dkVEROUEe0RERHrIkN4jYiIiItJDhlQ1x6E5IiI9JNVyEyMsLAxNmjSBlZUVbG1t0a1bNyQkJKgdk5mZicDAQFSqVAmWlpbo2bMnHj16JPpeiIhIz+T3iMRuYsTGxiIwMBBnzpxBdHQ0cnJy4Ofnh4yMDNUxo0ePxr59+7B9+3bExsbiwYMH6NGjh6h2ODRHRKSHSuM9oqioKLXP4eHhsLW1xYULF+Dt7Y3U1FSsWbMGmzdvxieffAIAWLduHdzd3XHmzBk0a9ZMo3bYIyIiKmfS0tLUtqysLI3OS01NBQDY2NgAAC5cuICcnBy0adNGdUzt2rXh7OyM06dPaxwPExERkR4qytCck5MTFAqFagsLC3tve0qlEqNGjUKLFi1Qp04dAEBycjJMTExgbW2tdmyVKlWQnJys8b1waI6ISA8VZdLTpKQkyOVy1X6ZTPbecwMDA3H16lWcOHFCZKvvx0RERKSHilK+LZfL1RLR+wQFBSEyMhLHjh2Do6Ojar+dnR2ys7Px7NkztV7Ro0ePYGdnp/H1OTRHRKSHJFpuYgiCgKCgIERERCAmJgbVq1dX+75Ro0YwNjbG4cOHVfsSEhJw9+5deHl5adwOe0RERHqoNGZWCAwMxObNm7Fnzx5YWVmpnvsoFAqYmZlBoVBg6NChGDNmDGxsbCCXyzFixAh4eXlpXDEHMBEREdFbLF++HADg4+Ojtn/dunUYPHgwAGDBggWQSqXo2bMnsrKy0K5dOyxbtkxUO0xERER6SAoJpCIH28QeLwjCe48xNTXF0qVLsXTpUlHXfh2fEYmUlJSEIUOGwMHBASYmJnBxcUFwcDCePn2qOsbHxwejRo1SO2/RokWQyWTYsmVLKUdMRIYof2hO7FYWMRGJcOvWLTRu3Bg3btzAL7/8gsTERKxYsQKHDx+Gl5cXUlJSCj1v2rRpmDx5Mvbs2YO+ffuWctREZIgkWv4pizg0J0JgYCBMTExw8OBBmJmZAQCcnZ3RoEED1KxZE999951qTBV41a0dOXIkfv75Z0RHR6N58+a6Cp2IDIwhLQPBHpGGUlJScODAAXzzzTeqJJTPzs4OAwYMwNatW1Vjqrm5uRg4cCB27NiB2NjY9yahrKysAtNuEBG9jeT/nxGJ2dgj0nM3btyAIAhwd3cv9Ht3d3f8+++/ePLkCQBg9erVAIBLly6hdu3a771+WFgYQkNDiy9gIiI9wR6RSJpUkQBAy5YtYWlpiSlTpiA3N/e9x0+aNAmpqamqLSkpqaihEpEBY7FCOeTq6gqJRIL4+PhCv4+Pj0fFihVRuXJlAEDdunVx+PBhHDlyBH369HlvMpLJZKppN8ROv0FE5Q8TUTlUqVIltG3bFsuWLcPLly/VvktOTsamTZvQp08ftbmf6tevj8OHD+PYsWPo3bs3cnJySjtsIjJQhlQ1x0Qkwo8//qh6c/jYsWNISkpCVFQU2rZti6pVq2LWrFkFzvH09ERMTAxOnDjBZERExUYq0W4ri5iIRPjwww9x/vx51KhRA71790bNmjXx5ZdfwtfXF6dPn1YtFvWmunXrIiYmBqdOncLnn3+O7OzsUo6ciAyNIfWIWDUnkouLC8LDw995zNGjRwvsq1OnDh49elQyQRER6TEmIiIiPWRIL7QyERER6aFX6wuJXBivZEIpMiYiIiI9pE3xQVktVmAiIiLSQ9oUH7BYgYiIio0hPSNi+TYREekUe0RERHpIAvHFB2W0Q8RERESkj6SQQCpyrE3sUuGlhYmIiEgPsUdERES6ZUCZiMUKRESkU+wRERHpIb5HREREuqXNQndlMw8xERER6SMDekTEREREpJcMKBMxERER6SFDekbEqjkiItIp9oiIiPSQIU16ykRERKSHDOgREYfmiIj0kkTLTaRjx47hs88+g4ODAyQSCXbv3q32fXp6OoKCguDo6AgzMzN4eHhgxYoVotpgIiIi0kMSLf+IlZGRAU9PTyxdurTQ78eMGYOoqCj8/PPPiI+Px6hRoxAUFIS9e/dq3AaH5oiI9FBpPSPq0KEDOnTo8NbvT506BX9/f/j4+AAAvvzyS6xcuRLnzp1Dly5dNGqDPSIionImLS1NbcvKytL6Ws2bN8fevXtx//59CIKAI0eO4K+//oKfn5/G12AiIiLSQ0V5ROTk5ASFQqHawsLCtI5jyZIl8PDwgKOjI0xMTNC+fXssXboU3t7eGl+DQ3NERPqoCGVzSUlJkMvlqt0ymUzrMJYsWYIzZ85g7969cHFxwbFjxxAYGAgHBwe0adNGo2swERER6aGizKwgl8vVEpG2Xr58icmTJyMiIgKdOnUCANSrVw9xcXGYO3cuExERkSErCy+05uTkICcnB1Kp+lMeIyMjKJVKja/DREREpIdK64XW9PR0JCYmqj7fvn0bcXFxsLGxgbOzM1q3bo3x48fDzMwMLi4uiI2NxYYNGzB//nyN22AiIiKitzp//jx8fX1Vn8eMGQMA8Pf3R3h4OLZs2YJJkyZhwIABSElJgYuLC2bNmoWvvvpK4zaYiIiI9FEpdYl8fHwgCMJbv7ezs8O6devEX/g1TERERHrIkJaBYCIiItJDZaFYobgwERER6SFDmn2biYiISB8ZUCbiFD9ERKRT7BEREekhFisQEZFOsViBiIh0yoAeETERERHpJQPKRExERER6yJCeEbFqjoiIdIo9IiIifaRFsUIZ7RAxERER6SMDekTEREREpJcMKBMxERER6SFDKlZgIiIi0kOG9EIrq+aIiEin2CMiItJDBvSIiImIiEgvGVAmYiIiItJDLFYgIiKdkkCLYoUSiaTomIiIiPSQAY3MsWqOiIh0iz0iIiI9ZEjvETERlVGCIAAAMtKf6zgSKi1paYKuQ6BS8jwtDcD//jvXjuEMzjERlVHPn79KQO2bues4EiIqKc+fP4dCodDqXPaIqMQ5ODggKSkJVlZWkJTVf3tKQFpaGpycnJCUlAS5XK7rcKiElde/b0EQ8Pz5czg4OGh9DcPpDzERlVlSqRSOjo66DkNn5HJ5ufrFVN6Vx79vbXtC+QypR8SqOSIi0in2iIiI9JAhzazAHhGVKTKZDNOmTYNMJtN1KFQK+PddBBItN5GOHTuGzz77DA4ODpBIJNi9e3eBY+Lj49GlSxcoFApYWFigSZMmuHv3rsZtMBFRmSKTyRASEsJfTOUE/761V0p5CBkZGfD09MTSpUsL/f7mzZto2bIlateujaNHj+Ly5cuYMmUKTE1NNb8XoWiF7EREVIrS0tKgUCiQeO8fWIks8HielgZXxw+QmpqqVXGIRCJBREQEunXrptrXt29fGBsbY+PGjaKvl489IiIiPSTR8k9xUiqV+PXXX1GrVi20a9cOtra2aNq0aaHDd+/CREREVM6kpaWpbVlZWVpd5/Hjx0hPT8cPP/yA9u3b4+DBg+jevTt69OiB2NhYja/DREREpI+K8JDIyckJCoVCtYWFhWkVglKpBAB07doVo0ePRv369TFx4kR07twZK1as0Pg6LN8mIp1TKpWQSvn/xWIUZWaFN2ey0LZY5IMPPkCFChXg4eGhtt/d3R0nTpzQ+Dr8mycinbp27RpWrVqF3NxcXYeiV/JnVhC7Af+bySJ/0zYRmZiYoEmTJkhISFDb/9dff8HFxUXj67BHRAZj7ty5MDc3xzfffKPrUEhDSqUS48aNg7u7OypU4K8jcbQpPhBfrJCeno7ExETV59u3byMuLg42NjZwdnbG+PHj0adPH3h7e8PX1xdRUVHYt28fjh49qnEb/Jsng5Ceno6///4bP/30E8zNzTF48GBdh0QakEqleP78OSpWrKjrUPROac01d/78efj6+qo+jxkzBgDg7++P8PBwdO/eHStWrEBYWBhGjhwJNzc37Ny5Ey1bttS4DSYiMgiWlpb49ttvYWlpieDgYOTl5WHo0KG6Dos0UKFCBVStWlXXYdBb+Pj4vHfdpCFDhmDIkCFat8FERHov/0G3o6Mj+vTpg+zsbHz99dewsrJC7969dR0eveHu3bs4dOgQ+vXrBzMzM/z777+qZxSCIKiWPXn9n8mwMRGR3suvttq9ezfmzZsHGxsbAMCgQYOQkZGBgIAAXYZHrxEEAWFhYYiNjUVOTg6++OILCIKg+j/u1xMPk9C7GdIyEExEZBD++OMP9O3bFwsXLkS3bt1w584drFu3DiNHjgQAJqMyQiKRYNasWRg7dixWr16N9PR05Obm4smTJ9i7dy8EQUCFChWQm5sLQRCQkpKCmjVronXr1roOvcwxpNm3mYjIIMTHx8Pd3R0BAQGQyWSws7NDlSpVkJubi6+++gpWVlbo1auXrsMst16+fAmpVIr79++jRo0aWLFiBb755huEh4fj+vXrGD9+PGrWrImnT58iJycHpqamMDY2Rl5enqg39MsTQ+oR8T0iMggKhQI3b97EjRs3VPuqV6+O//znP8jNzUXv3r2xYcMGHUZYfsXHx2PgwIFo3Lgx3NzcULduXSxatAhLly5Fy5YtUbduXYwdOxZnz57F48ePkZCQgJs3b+L27duIj49HrVq1dH0LZVJpzb5dGpiISO8UVsHz0UcfoXbt2ti4caPaOihOTk7o0qULZsyYgaZNm5ZmmATgypUr8PLygr29PUaNGoVt27ahZs2amDx5MoYNG4bQ0FA0adIER44cwebNm6FUKmFnZwdzc3MYGxvD2tpa17dApYBDc6RX8iupTpw4gStXriAxMRF9+vRB48aNMXz4cMyePRu5ubno168fXF1dsWbNGqSlpSEwMJC/1ErZkydP4O/vj6+//lptLrOWLVti27ZtGDt2LKysrLB69WoMHToUv/zyC16+fIlRo0bByMhIh5HriaLM8VPGMBGRXpFIJNi1axeGDBmCzz77DHfu3MHhw4fRvHlzLFu2DM+fP8euXbuwdOlSuLq64v79+zhy5AiTkA7cu3cPOTk5GDBgAPLy8mBkZASlUonKlStj4MCBePbsGaZPn64qMhk4cCAOHjyIIUOG8AVXDbBYgUhH4uPjMW7cOMybNw9Dhw5FcnIynJ2d8dlnnwEARo0ahX79+uHq1avIzMxE3bp14ezsrOOoy6dLly4hMTERderUAfCqN5tfaq9QKNC/f3/MmTMHp0+fRuvWrfHzzz8jPT2dSUhDhlSswEREZdqbLzU+evQI1tbWGDp0KG7cuIG2bdti8ODBmDFjBoBXzyRq1aqFTz/9VFch0/9zdXUFAOzcuRM9e/Ys8F5Q9erVUaNGDTx69AjA/ybiJM0Y0MgcixWo7Mlf4wT430uNDx8+BABkZmZCLpfjn3/+QZs2beDn56da9+TYsWPYsGEDnjx5UvpBUwHVqlWDXC7Hhg0b8Pfff6v25//9/vvvvzAzM0OjRo10FaJ+M6CyOSYiKnOkUikSExMxe/ZsAMD27dsREBCAp0+folmzZkhISICtrS169eqFVatWqYZ79uzZg4sXL8Lc3FyX4dP/c3R0xPLlyxEVFYUpU6bg2rVrAP43E8b8+fPx4MEDtGrVSpdhUhnAoTkqc/Ly8hAVFYWJEyfi0qVL+OWXX7Bu3TpUqlQJALB69WoMHz4cKSkpuHfvHpKTk7F9+3asWbMGJ06cUE3xQ7rXtWtXLF68GEFBQTh37hxatGgBe3t73L59G/v378fhw4dFrVtD/2NIxQoS4X3TqhLpQHZ2NoYOHYpNmzahR48e2LFjh+q7tLQ07N+/H2PGjIEgCFAoFLCwsMBPP/2E+vXr6y5oequzZ89i9uzZSEhIgLW1NTw9PTFixAjUrl1b16HpnbS0NCgUCty4nST6mVpaWho+rO6E1NTUMvU8jomIypT84oTs7GxMnToViYmJiImJQXBwMKZNm6Z2bFpaGi5fvoyKFSuiSpUq+OCDD3QUNWkiLy8PUqkUEomES4MXQWZmJqpXr47k5GStzrezs8Pt27dhampazJFpj0NzVGbkJ6GzZ8/i0aNHCA4Oho2NDZYsWYKZM2cCgFoyevbsmajFt0i38pMQwJm1i8LU1BS3b99Gdna2VuebmJiUqSQEMBFRGZGfhHbu3ImhQ4di7NixcHNzg729PQICAiCRSDBz5kwIgoCQkBCEhITgwoUL2LRpU5kaYqC34xIPxcfU1LTMJZOi4NAclRlnzpxBx44dMWfOHAwcOFC1WBoAPH36FJs2bcLYsWPh4eGhmlGhcePGOoyYiIoDExGVGXPmzMGBAwewf/9+GBsbA4BqahjgVa/pjz/+wNmzZ9G+fXvUqFFDl+ESUTHh0BzpXP6D68TERAiCAGNjY9UM2/lJKC4uDk5OTmjUqBFfgCQyMCxbIZ3Lr55q3LgxTp48iTNnzkAikaieIzx//hybNm3ChQsXCl0Cgoj0G4fmqNTlFyYkJibi0aNHEAQBDRs2hLGxMXr06IFbt25h9erVaN68OTIyMvDDDz9gzZo1OHXqFKpVq6br8ImomDERUanJ/1ctfymHyZMnQxAE2NjYICMjA4cPH8bff/+NBQsWYPv27aqXU+/evYv9+/ejQYMGOoyeiEoKExGVuD/++AMfffSRqgru+PHj6Nixo2oph4MHD6JTp05YuHAhRo4ciWfPnuHw4cO4du0abG1t4efnx8IEIgPGREQlKigoCFeuXMGePXtUi9PNmzcPiYmJWL58OZKSktCiRQt06dIFP/74IwAgKytLrXSbiAwbixWoxJw6dQo7d+7EjBkzYG1tjdzcXADAnTt3kJmZiXv37qF58+bo0KEDlixZAuDV2jWLFi1CXl6eLkMnolLEREQlJi0tDTk5OahXrx62bNmCQYMGIS8vDx9//DGSkpLQtGlTtGvXDitXrgQA5ObmIiYmBvfu3dN6+hIi0j9MRFRi2rdvD09PTzRo0AD9+/fHJ598AiMjI3To0AEZGRnIzMzEoEGDAADp6emYOnUqdu3ahW+++QZmZmY6jp6ISgufEVGJyH9JNX/tIAcHB1y7dg0KhQLAqxVXW7duDQsLC6SmpqJWrVq4cuUKIiMjWR1HVM4wEVGJEAQBmZmZmDNnDipWrIitW7fin3/+wcGDB+Hs7Azg1fxxR44cwdWrV+Hh4YGPP/6Y7wkRlUNMRFSs8l9WfdP9+/fRvXt3PH/+HAcPHoSTk5MOoiOisoiJiIpNfhI6dOgQIiMj8eeff6JXr15o3rw56tSpg/v376Nnz55ITU1FdHQ0HB0d35q4iKj8YLECFRuJRIKIiAj06NEDWVlZaNasGaZPn44JEybgzp07qFq1Knbs2IFKlSqhUaNGuH//PpMQETERUfFJSkpCSEgIZs+ejeXLlyM0NBRpaWmoW7eu6tmPo6MjNm/ejHr16iEzM1O3ARNRmcChORLtbcNpSUlJ6Nq1K44fP44HDx7A19cXHTt2xKpVqwAA586dg7u7O6ysrNTWGSKi8o09IhJFqVRCIpHgxYsX+Oeff3DkyBHcv38fqampkEqlePz4Mc6dO4cOHTqgY8eOWLFiBQDg8uXLWLBgAW7cuAEATEJEpMJERBrLfzfor7/+wtdff41WrVqhY8eO+Oijj/DNN9/g33//xYABA/Dpp5+iQYMGWLVqlWqtoS1btuDmzZuwt7fX8V0QUVnDoTnSSH4Sunz5Mtq3b4+uXbuiWbNmaNq0KcLDw7Fjxw4YGxtj6NChuHLlCk6dOoXly5cjNTUVJ0+exE8//YTjx4/D09NT17dCRGUMExG91+tJyMvLC8HBwZg+fToqVPjfSvNbtmzBggULIJFIMGzYMJw6dQq7du2Cs7MzqlSpgnnz5qFevXo6vAsiKquYiEgjSUlJaNiwIXx9fbFt2zYAr4oW8vLyVAlp5cqV+O677xAWFoYvvvgCiYmJsLe3h1KphJWVlS7DJ6IyjM+ISCN5eXmoXr06srKycOLECQCv3huqUKGCauXV4cOHw93dHfv37wcAVK9eHRYWFkxCRPROTESkkWrVqmHTpk3Izs7GzJkzVcnoTRUqVIC5uTkAVsYRkWaYiEhjH374IRYvXgyJRIKZM2fi5MmTAF71jJRKJe7duwczMzO0bdsWAMBRXyLSBBMRifJ6MpoxY4aqZySVSvHjjz/iwYMH+PTTTwGA0/cQkUZYrEBauXHjBkaOHAlBEBAWFobo6GhVYmKJNhGJwUREWrtx4wbGjBmDc+fO4d9//8Xp06fRqFEjXYdFRHqGQ3OktQ8//BBz585Fs2bNcPHiRSYhItIKe0RUZDk5OTA2NtZ1GESkp5iIiIhIpzg0R0REOsVEREREOsVEREREOsVEREREOsVEREREOsVERKShwYMHo1u3bqrPPj4+GDVqVKnHcfToUUgkEjx79uytx0gkEuzevVvja4aEhKB+/fpFiuvOnTuQSCSIi4sr0nWo/GEiIr02ePBgSCQSSCQSmJiYwNXVFdOnT0dubm6Jt71r1y7MmDFDo2M1SR5E5VWF9x9CVLa1b98e69atQ1ZWFn777TcEBgbC2NgYkyZNKnBsdnY2TExMiqVdGxubYrkOUXnHHhHpPZlMBjs7O7i4uODrr79GmzZtsHfvXgD/G06bNWsWHBwc4ObmBuDVirO9e/eGtbU1bGxs0LVrV9y5c0d1zby8PIwZMwbW1taoVKkSJkyYUGBZizeH5rKysvDtt9/CyckJMpkMrq6uWLNmDe7cuQNfX18AQMWKFSGRSDB48GAAr5ZhDwsLQ/Xq1WFmZgZPT0/s2LFDrZ3ffvsNtWrVgpmZGXx9fdXi1NS3336LWrVqwdzcHDVq1MCUKVOQk5NT4LiVK1fCyckJ5ubm6N27N1JTU9W+/+mnn+Du7g5TU1PUrl0by5YtEx0L0ZuYiMjgmJmZITs7W/X58OHDSEhIQHR0NCIjI5GTk4N27drBysoKx48fx8mTJ2FpaYn27durzps3bx7Cw8Oxdu1anDhxAikpKYiIiHhnu4MGDcIvv/yCxYsXIz4+HitXroSlpSWcnJywc+dOAEBCQgIePnyIRYsWAQDCwsKwYcMGrFixAteuXcPo0aMxcOBAxMbGAniVMHv06IHPPvsMcXFxGDZsGCZOnCj6Z2JlZYXw8HD8+eefWLRoEVavXo0FCxaoHZOYmIht27Zh3759iIqKwsWLF/HNN9+ovt+0aROmTp2KWbNmIT4+Ht9//z2mTJmC9evXi46HSI1ApMf8/f2Frl27CoIgCEqlUoiOjhZkMpkwbtw41fdVqlQRsrKyVOds3LhRcHNzE5RKpWpfVlaWYGZmJhw4cEAQBEGwt7cXZs+erfo+JydHcHR0VLUlCILQunVrITg4WBAEQUhISBAACNHR0YXGeeTIEQGA8O+//6r2ZWZmCubm5sKpU6fUjh06dKjQr18/QRAEYdKkSYKHh4fa999++22Ba70JgBAREfHW7+fMmSM0atRI9XnatGmCkZGRcO/ePdW+/fv3C1KpVHj48KEgCIJQs2ZNYfPmzWrXmTFjhuDl5SUIgiDcvn1bACBcvHjxre0SFYbPiEjvRUZGwtLSEjk5OVAqlejfvz9CQkJU39etW1ftudClS5eQmJgIKysrtetkZmbi5s2bSE1NxcOHD9G0aVPVdxUqVEDjxo3fuupsXFwcjIyM0Lp1a43jTkxMxIsXL1Qr2ubLzs5GgwYNAADx8fFqcQCAl5eXxm3k27p1KxYvXoybN28iPT0dubm5kMvlasc4OzujatWqau0olUokJCTAysoKN2/exNChQ/HFF1+ojsnNzYVCoRAdD9HrmIhI7/n6+mL58uUwMTGBg4MDKlRQ/9fawsJC7XN6ejoaNWqETZs2FbhW5cqVtYrBzMxM9Dnp6ekAgF9//VUtAQCvnnsVl9OnT2PAgAEIDQ1Fu3btoFAosGXLFsybN090rKtXry6QGI2MjIotViqfmIhI71lYWMDV1VXj4xs2bIitW7fC1ta2QK8gn729Pc6ePQtvb28Ar/7P/8KFC2jYsGGhx9etWxdKpRKxsbFo06ZNge/ze2R5eXmqfR4eHpDJZLh79+5be1Lu7u6qwot8Z86cef9NvubUqVNwcXHBd999p9r3999/Fzju7t27ePDgARwcHFTtSKVSuLm5oUqVKnBwcMCtW7cwYMAAUe0TvQ+LFajcGTBgAD744AN07doVx48fx+3bt3H06FGMHDkS9+7dAwAEBwfjhx9+wO7du3H9+nV8880373wHqFq1avD398eQIUOwe/du1TW3bdsGAHBxcYFEIkFkZCSePHmC9PR0WFlZYdy4cRg9ejTWr1+Pmzdv4o8//sCSJUtUBQBfffUVbty4gfHjxyMhIQGbN29GeHi4qPv98MMPcffuXWzZsgU3b97E4sWLCy28MDU1hb+/Py5duoTjx49j5MiR6N27N+zs7AAAoaGhCAsLw+LFi/HXX3/hypUrWLduHebPny8qHqICdP2QiqgoXi9WEPP9w4cPhUGDBgkffPCBIJPJhBo1aghffPGFkJqaKgjCq+KE4OBgQS6XC9bW1sKYMWOEQYMGvbVYQRAE4eXLl8Lo0aMFe3t7wcTERHB1dRXWrl2r+n769OmCnZ2dIJFIBH9/f0EQXhVYLFy4UHBzcxOMjY2FypUrC+3atRNiY2NV5+3bt09wdXUVZDKZ0KpVK2Ht2rWiixXGjx8vVKpUSbC0tBT69OkjLFiwQFAoFKrvp02bJnh6egrLli0THBwcBFNTU6FXr15CSkqK2nU3bdok1K9fXzAxMREqVqwoeHt7C7t27RIEgcUKpD0ujEdERDrFoTkiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItIpJiIiItKp/wOulfqkYO2oMwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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YkHr16jFnzpzC151OJ7169WLZsmV88sknbNmyhVdeeaVwPpr169fTrVs3GjduzIoVK1i6dCl9+vTB4XBc/g9GRERcjrqlLuJknoPG434q5rsSS+Szt4zvia9nyfwRjR8/nh49ehQ+DwoKokWLFoXPX3rpJb755hvmzZvHyJEjz3ucu+++m8GDBwMwYcIEpk2bxurVq7nxxhsvWsOiRYvIysqiZ8+eANx5553MmDGDu+66q/D11atXs3XrVurXrw9ATExM4fsnTZpEmzZt+Pe//124rUmTJpdy+iIiUomo5aaSaNOmTZHnJ06cYMyYMTRq1IiqVatSpUoVtm7detGWm+bNmxc+9vPzIyAgoHBJg4uZOXMmgwYNKhzvM3jwYJYtW8bu3bsBs2WmZs2ahcHmrwpabkRERC5ELTcX4ePhxpbxPS9t5+x0OLYH3DwhpCFc4YRxPh4ltzyAn59fkedjxoxh4cKFTJ48mXr16uHj40P//v3Jzc294HE8PDyKPLfZbDidzot+fmpqKt988w15eXlMnz69cLvD4WDmzJm8/PLLhcsjnM/FXhcREQGFm4uy2WyX3jXkHggn3IB8cHOAu3ep1nYlli1bxt13381tt90GmC05e/fuLbXP+/TTT6lZsyZz584tsn3BggW8/vrrjB8/nubNm3PgwAF27Nhxztab5s2bExcXx4svvlhqdYqISMWnbqmSZHcDz1MtJDkZ1tZyEbGxsXz99desX7+eDRs2cMcdd1xSC8zlmjFjBv3796dp06ZFbvfeey8pKSn8+OOPXHvttVxzzTX069ePhQsXsmfPHubPn8+PP/4IwDPPPMOaNWt4+OGH2bhxI9u2bWP69OmkpKSUWt0iIlLxKNyUNC9/876ch5spU6ZQrVo1OnbsSJ8+fejZsydXXXVVqXzW2rVr2bBhA/369TvrtcDAQLp168aMGTMA+Oqrr2jbti2DBw+mcePG/OMf/yi8Gqp+/fosWLCADRs20K5dOzp06MD//ve/S5qzR0REKg+bUZLXG1cA6enpBAYGkpaWRkBAQJHXsrOz2bNnD9HR0Xh7X2aXUm4mpOwAmxuEN7vicTdSskrkz1hERMrchb6//0otNyXNw9cMNoYD8rKsrkZERKTSUbgpaTbbGV1T6dbWUoYefPBBqlSpcs7bgw8+aHV5IiJSiWiwQmnw8ofs45CdAf4RVldTJsaPH8+YMWPO+drFmg9FRERKksJNaShoucnLBKfDvIrKxYWGhhIaGmp1GSIiIuqWKhXuXuDmZT7OOWFtLSIiIpWMwk1pqYTjbkRERMoDhZvSUkHmuxEREXE1CjelxauKee/IgfwLr9ckIiIiJUfhprTY3cGjYCkGdU2JiIiUFYWb0lTBu6bq1KnD1KlTrS5DRESkWBRuStOZ4aYUV7mw2WwXvL3wwguXddw1a9bwwAMPlEiNe/fuxWazsX79+hI5nohIefT73lReX7CdL3/fz6YDaWTnOawuqVLSPDelydMXbPZTSzGcNJ+XgsOHDxc+njVrFuPGjWP79u2F26pUqVL42DAMHA7HJS02GRISUrKFioi4qJx8B68v2MH7v8UX+b+s3QbRwX40jAigUbg/DcMDaBjhT42qPti09mCpUctNabLZwbP0LwkPDw8vvAUGBmKz2Qqfb9u2DX9/f+bPn0/r1q3x8vJi6dKl7N69m1tvvZWwsDCqVKlC27ZtWbRoUZHj/rVbymaz8cEHH3Dbbbfh6+tLbGws8+bNK5FzyMnJ4dFHHyU0NBRvb286d+7MmjVrCl8/duwYQ4YMISQkBB8fH2JjY/nwww8ByM3NZeTIkURERODt7U3t2rWZOHFiidQlInIxWw+nc+vby3hviRlsujcKpVO96gT5eeI0YPeRTL7feJjJC3Zw339/p/Ori2n+wgIGvLucsXM38+mqfazdd4wTOflWn4rLUMvNxRjGlS2AaXMzW21OJJ/uprpUHr4ltqr4008/zeTJk4mJiaFatWrs37+fm266iZdffhkvLy/++9//0qdPH7Zv306tWrXOe5wXX3yRSZMm8dprr/HWW28xZMgQ9u3bR1BQ0BXV949//IOvvvqKjz/+mNq1azNp0iR69uzJrl27CAoKYuzYsWzZsoX58+cTHBzMrl27OHnyJADTpk1j3rx5fPnll9SqVYv9+/ezf//+K6pHRORinE6DD5bGM/mnHeQ6nFT38+SVfs3p0TgMMFvKj5zIYdvhDLYlprPtcAZbEzPYlZxBRk4+a/YeY83eY0WOWSvIl4bh/qdbeiICqBXki5tdrTzFoXBzMXlZMCHSms/+5yHw9CuRQ40fP54ePXoUPg8KCqJFixaFz1966SW++eYb5s2bx8iRI897nLvvvpvBgwcDMGHCBKZNm8bq1au58cYbL7u2zMxMpk+fzkcffUSvXr0AeP/991m4cCEzZszgySefJCEhgVatWtGmTRvAbFUqkJCQQGxsLJ07d8Zms1G7du3LrkVE5FIcOJbFmNkbWBmfCkC3hqG80q85If5ehfvYbDZC/b0J9ffmmvqnu/nzHE7ij2SyLTGdrWcEn8T0bBJSs0hIzWLBlqTC/X083Kgf7n+qW8sMPA3D/anq61l2J3weTqfByTwHWbkOsnLzC++93N1oWiPQsroUbiqJglBQ4MSJE7zwwgt8//33HD58mPz8fE6ePElCQsIFj9O8efPCx35+fgQEBJCcnHxFte3evZu8vDw6depUuM3Dw4N27dqxdetWAB566CH69evHunXruOGGG+jbty8dO3YEzMDVo0cPGjRowI033sjNN9/MDTfccEU1iYici2EYzF1/kHFz/yQjJx9fTzfG3dyYQW2jLnkMjYebnQbh/jQI9+fWlqe3H8vMZVvi6bCzLTGd7UkZnMxzsGH/cTbsP17kOBGB3kXCTqOIAKKD/fBwO3vESb7DSVaeg5O5DjJzCkLImYHkjMenXs/MdXAyN//UvYPM3Pyi9zkOTp5nwHTbOtWY/WDHS/2xljiFm4vx8DVbUK5E2n7ISgW/EAgoRiuQR8kNQPbzK9oCNGbMGBYuXMjkyZOpV68ePj4+9O/fn9zcC0846OHhUeS5zWbD6XSWWJ3n06tXL/bt28cPP/zAwoUL6datGyNGjGDy5MlcddVV7Nmzh/nz57No0SIGDhxI9+7dmTNnTqnXJSKVx7HMXJ6bu5nvN5kXcbSqVZU3BrakTnDJtLBX8/OkQ93qdKhbvXCbw2mw92hmYdgpaOk5cOwkh9OyOZyWzeLtRwr393SzUyfYF8OgMLBk5jrIzS/939O+nm74errj6+lGaIB3qX/ehSjcXIzNduVdQ1XCzHE3zvwS62a6UsuWLePuu+/mtttuA8yWnL1791pSS926dfH09GTZsmWFXUp5eXmsWbOGxx57rHC/kJAQhg0bxrBhw+jSpQtPPvkkkydPBiAgIIBBgwYxaNAg+vfvz4033khqauoVjwUSEQFYsuMIY2ZvIDkjB3e7jce6x/LgtXVxP0crSUlys9uoG1KFuiFV6N08onB7enYeOxLNMTzbDqebLT6H08nMdbAj6fwLNrvZbfh6uOHrdTqInBlKCu+93PD1cMfPyw0fTzf8PN2L3p963TyOG97ubtjL0bgghZuyUHDFVH42OPLAzePC+5eB2NhYvv76a/r06YPNZmPs2LFl0gJz5iXqBZo0acJDDz3Ek08+SVBQELVq1WLSpElkZWVx7733AjBu3Dhat25NkyZNyMnJ4bvvvqNRo0YATJkyhYiICFq1aoXdbmf27NmEh4dTtWrVUj8fEXFtJ3MdvDJ/Kx+v2AdATIgfUwe1pHnNqpbWFeDtQZs6QbSpc/o/cE6nwcHjJ4lPycTDzYavpzt+nkXDiZe7vVJcgq5wUxbc3MHDx2y9yckAX+tbE6ZMmcI999xDx44dCQ4O5qmnniI9vfSXibj99tvP2rZ//35eeeUVnE4nd911FxkZGbRp04affvqJatWqAeDp6ckzzzzD3r178fHxoUuXLnzxxRcA+Pv7M2nSJHbu3Imbmxtt27blhx9+wG7XTAcicvk2HUjjsVl/sPtIJgDDOtTm6V6N8PF0s7iyc7PbbUQF+RIVVDpzqlUkNsMoxalzy6H09HQCAwNJS0sjICCgyGvZ2dns2bOH6OhovL1LuL8w/RCcSAKfIKimq3msUqp/xiLiEvIdTqb/sps343aS7zQI9ffitQEtuLa+Jja10oW+v/9KLTdlxcvfDDc56ebcOZWgWVBEpKLZdzSTx2etZ13CcQB6N4vgX32bUs3P+suu5dKp3b6sePoBdnNQcX621dWUuAcffJAqVaqc8/bggw9aXZ6IyAUZhsHnqxPo9eZvrEs4jr+XO1MGtuDtO1op2FRAarkpKzY7ePmZY25yMswxOC5k/PjxjBkz5pyvXaz5UETESikncnj6q00s2mpOnNc+OojXB7agZjWNXamoFG7KklfAqXCTDlVCra6mRIWGhhIa6lrnJCLF43AaZGTn4evpjqd7xegYWLgliae/2sjRzFw83eyM6Vmf+zrHlKvLmqX4FG7KUsHaUjmZ4HSCruYRkXLG6TTIyMknLSuPtJOnb8dP5hY+Tj+Zx/G/vJ6WlUfGqYUfvT3stK5djfbR1WkfHUTLWlXxci9fVxhl5uTz0ndb+GKNuQ5dw3B/3hjUkkYRaml2BZaHm3feeYfXXnuNxMREWrRowVtvvUW7du3Ou//UqVOZPn06CQkJBAcH079/fyZOnFiiV76U2nwv7t5gdzfH3eRlFn8hTbliZTGXj4jVDMMgM9fB8awLB5Ljp7YXPs/KIyM7D+cVXkObnedk2a6jLNt1FAAvdzutalU1w05MEFfVqoa3h3VhZ+2+Yzw+az0JqVnYbHB/lxhG96hvaU1SsiwNN7NmzWL06NG8++67tG/fnqlTp9KzZ0+2b99+zi6Ozz77jKeffpqZM2fSsWNHduzYwd13343NZmPKlClXXI+npyd2u51Dhw4REhKCp6dnyU92ZPOF/DTIOAaG9ZP5VRaGYZCbm8uRI0ew2+14emqAoLiW7DwHn6zcx0fL95KYlk3+FSYUbw87gT4eBPp4UNXHk4CCx74ehdsDfTwIPON5VR8P/L092Hs0k1XxR1m5J5VV8UdJOZHLyvhUc5HJOHOJgJZRVWkfE8TVMdW5qla1Mpk7Js/hZFrcTt5ZvAunATWq+jB5QIsiyx2Ia7B0npv27dvTtm1b3n77bcD8X3VUVBSPPPIITz/99Fn7jxw5kq1btxIXF1e47YknnmDVqlUsXbr0kj7zYtfJ5+bmcvjwYbKysi7zrC4iNxOyjoKbJ/iHl85nyHn5+voSERGhcCMuIzffyazf9/P2zztJSs8p8pqnm/1UKHGnqq9n0VByxu2vgSXAx6PEWjEMw2D3kUxW7TnKyngz7CRnFK3Tw81G85pVuTomiPbR1Wlduxp+XiX7f+9dyRk8PmsDmw6mAfC3VjV44dYmBHjrP5kVRYWY5yY3N5e1a9fyzDPPFG6z2+10796dFStWnPM9HTt25JNPPmH16tW0a9eO+Ph4fvjhB+66667zfk5OTg45Oaf/IV1sFl5PT09q1apFfn4+Dse5Vzu9IplH4MO7ARvcswB8q5X8Z8g5ubm54e7uXimmHhfXl+9w8s0fB3kzbicHjp0EIDLQm0e6xXJdgxACfTzw8XCz/O+7zWajXmgV6oVWYUj72hiGwd6jWayMP8qq+KOs2pPK4bRs1u47xtp9x3hn8W7c7Taa1Qws7MZqU7sa/pcZQgzD4L8r9jHhh63k5Dup6uvBy32bFVmnSVyPZeEmJSUFh8NBWFhYke1hYWFs27btnO+54447SElJoXPnzhiGQX5+Pg8++CD//Oc/z/s5EydO5MUXXyxWbTabDQ8Pj7NWwC4R3lHgGwDJf8Kh5dC0X8l/hoi4LKfT4PtNh3lj0Q7iTy0LEFzFi5Fd6zK4fa1yN3D3r2w2G9HBfkQH+zG4XS0Mw2B/6klWxh9l5Z6jrIpP5eDxk/yRcJw/Eo7z7q+7cbPbaBoZQPsYc4By2+igS2pxSUrPZszsDfy2MwWALrHBTB7QgjCLV6yW0mf5gOLi+OWXX5gwYQL//ve/ad++Pbt27WLUqFG89NJLjB079pzveeaZZxg9enTh8/T0dKKiosqq5HOr29UMN7sXK9yIyCUxDINFW5N5fcF2tiVmAFDV14OHrq3L0A51yu16Rxdjs9moVd2XWtV9GdjW/N28PzWLVafG66zcc5T9qSfZcCCNDQfSeG9JPHYbNI4MoH10da6OqU67OkEE+hYNO99vPMw/v9lE2sk8vNztPNu7EXddXdvyliwpG5aFm+DgYNzc3EhKSiqyPSkpifDwc49FGTt2LHfddRf33XcfAM2aNSMzM5MHHniAZ5999pwLJXp5eeHl5VXyJ3AlYrrCirch/hctxSAiF2QYBkt3pTB5wQ427D8OgL+XO/d1ieGeznUuu7umPCtY/LF/65oAHDp+klWnWnVWxh9l79EsNh9MZ/PBdGYs3YPNBg3DA2gfHUT76CAWbEnimz8OAtCsRiBvDGpJvdAqVp6SlDHLwo2npyetW7cmLi6Ovn37AuaA4ri4OEaOHHnO92RlZZ0VYNzczP+tVKj1P2t3NAcUp+2Ho7sgONbqikSkHFq9J5XJC7azek8qAD4ebtzdqQ5/vyaGqr6VZ1B8ZFUfbmtVk9tamWEnMS379ADlPUeJP5LJ1sPpbD2czkfL9wJgt8GIrvV4tFssHm6aU6yysbRbavTo0QwbNow2bdrQrl07pk6dSmZmJsOHDwdg6NCh1KhRg4kTJwLQp08fpkyZQqtWrQq7pcaOHUufPn0KQ06F4OkLta6GPUvMrimFGxE5w4b9x5m8YHvhWBFPdzt3tq/NQ9fVJcS/nLVEWyA80JtbW9bg1pY1AEjOyGbVqaCzek8qvp7ujL25Ma1r64KNysrScDNo0CCOHDnCuHHjSExMpGXLlvz444+Fg4wTEhKKtNQ899xz2Gw2nnvuOQ4ePEhISAh9+vTh5ZdftuoULl9MVzPcxC+G9g9YXY2IlANbD6czZeEOFm4xu+vd7TYGto1iZNd6RFZ1rfXoSlKovzd9WkTSp0Wk1aVIOWHpPDdWKM518qXq0B/w3nXg6Q9P7QE31+s3l8rFMAx++jOJ7DwHt7SI1No8xbD7yAmmLtrJdxsPYRhml0rfVjV4rFt9alXX4o0iUEHmuan0wluATxCcTIUDv0PtDlZXJHLZft+bykvfby0c8PrVugO8PrAFof665PZC9qdmMS1uJ1+tO1C45EHv5hE83j2WeqFankXkcincWMVuh5jr4M+vza4phRupgBKOZvHqj9v4ftNhAHw93XA4DX7bmUKvqb/x2oDmXN8w7CJHqXyS0rN56+edzFqznzyHmWq6Nwrl8R71aRIZaHF1IhWfwo2V6nY1w83uxdD1/BMRipQ36dl5vPPzLj5ctpdchxObDQa1iWJ0j/qknczjkc//YFtiBvd89Dt3d6zD070aalFC4OiJHKb/spv/W7mPnHxzEdfO9YIZfUN9rqqlwa8iJUXhxkoxXc37g2shOw289T82Kd/yHU4+X53AG4t2kpqZC0CnetV59qbGNI40+8BDA7yZO6ITr/64jQ+X7eWj5XtZGX+UaYNbUT+scna1pGXl8f5v8cxctoesXHNZlza1q/HEDQ20aKNIKdCAYqu91dqc62bQp9DoZqurETknwzD4ZfsRXv5hK7uSTwBQN8SPZ3s3omuD0PPO+rp4WzJjZm/gaGYuXu52nru5MXe2r1VpZok9kZPPR8v28N6SeNKz8wFzUrknbqjPtfVDKs3PQaQkaEBxRVL3ejPc7P5Z4UbKpa2H05nww9bCOVeC/Dx5vHsst7erddHJ0bo2DGX+Y10YM3sjS3YcYezczfy6/QiT+jcnyM91J6HLznPwfyv2Mf3X3YUtXA3C/Bl9Q31uaBymUCNSytRyY7VtP8AXgyEoBh79w+pqRAolZ2QzZcEOvvx9P04DPN3sDO9Uh4e71iPQp3hTFzidBh8u38ur87eR63AS6u/FG4Na0qlecClVb42cfAdfrtnPWz/vIjkjB4DoYD8e6x7Lzc0jcdPl8SKXTS03FUmdzmBzg9R4OLYPqtW2uiKp5LLzHHzwWzzTf9lN5qnxIb2bRfDUjQ0ve84Vu93GvZ2jaR8dxKNf/EH8kUzunLGKv19Tl9E96uPpXrGnx8/OczD79/1M/2U3h9KyAahR1YdR3WL521U1cNf0/yJlSi035cGMnrB/JfR5E1rfbXU1Ukk5nQbzNhxi0o/bCr+gW0RVZWzvRrSpE1Rin5OVm89L323l89UJADSvGcibt7ciOtivxD6jrGTnOfhidQLv/hpPYrr5Mwv192Lk9fUY1DYKL3ddISZSUtRyU9HUvd4MN7t/VrgRS6zZm8q/vtvChgNpgNnq8I8bG9CnecnPNOzr6c7EvzXjmthgnv56ExsPpNF72m+8eEsT+reuWSHGo2TnOfh0VQL/+XV3YfdTRKA3D11Xl4FtonTZu4jF1HJTHuxfDTN6gHdV+Ec82PWLUcrGvqOZvDJ/G/M3JwLg5+nGw13rcW/n6DL5gj50/CSPz1rPqlOrXt/cPIKXb2tW7DE9ZSUrN5/PVpktNSknzFATGejNw13rMaBNTbXUiJQitdxUNJFXgVcgZB+Hw+uhRmurKxIXl3Yyj3cW7+KjU5Pw2W0wqG0tRveoX6arTkdW9eGz+6/m3V93M2XhDr7beJg/Eo7z5u0tS7Qr7Epl5uTzycp9vLcknqOnrn6qUdWHEV3r0b91zQo/ZkjE1SjclAdu7hDdBbZ9Z85WrHAjpSTP4eSzVQlMXbSDY1l5AHSJDea53o1pEG7NBHtudhsjutajY93qjPpiPQmpWQz8zwoe7RbLyK71LB2MeyInn/+u2MsHv+0pvKQ7KsiHkV3r8beral70UngRsYa6pcqLNR/A909A7c4w/HurqxEXYxgGP29L5uUfthJ/JBOA2NAqPNu7Edc1CLW4utMysvN4/n9/8vUfBwFzFt+pt7ekZrWyXRk7IzuPj5fv5YOlezh+KgTWru7LyK716NuqhkKNiAWK8/2tcFNeHN0Nb10Fdg94ai94VbG6InERWw6l8/IPW1i26ygA1f08ebxHfW5vG1VuL1Ge+8dBnpu7mRM5+fh7uzPhtmb0aRFZ6p+bdtIMNTOW7iHtpBlqYoL9GHl9PW5pEVluf14ilYHG3FREQTFQtRYcT4B9y6H+DVZXJBVccno2ry/YwZdr92OcmoTvns7RPNy1LgHe5XPAboG+rWpwVa1qjJr1B38kHOeRz//g1x1HeOGWJlTxKvlfW2lZecxctoeZy/aQcWqZhLohfjzaTZPviVRECjflhc1mLqS57mOIX6xwI5ftZK6D93+L591fdxcu0nhzc3MSvqigsu3euRK1qvvy5d878FbcTt5evIs5aw/w+95U3ry9FS2iqpbIZxzPymXG0j18tGwvGTlmqIkNrcIj3WLp3SxCoUakglK4KU/qXm+Gm90/W12JVCBZufkcTsvm8PFsdiVnFJlQrlWtqjzXuzGta1ezuMrL4+FmZ/QNDehUL5jHZq1n79Es+k1fzpieDXigS8xlz8GTmpnLB7/F8/HyvYWzMDcI8+fRbrH0ahpe4nP7iEjZ0pib8iQrFSbFAAaM3goBpT/GQMq37DyHGVzSTnL4uHl/KC2bxLRsDh0/yeG07MKxIWeqUdWHp3s15ObmERViUrxLkZaVxzPfbOSHTeacPJ3qVWfKwJaEBXhf8jGOnsjhvd/i+b8V+wpbtRpFBDCqWz1uaKxQI1KeaUDxBZTrcAPwXlc4tA76ToeWd1hdjZSinHwHSWk5HEo7aYaVMwKMGWiyCy8/vpgqXu5EBHoTHuhNl9hghnao45Kz5BqGwZe/7+eFeVs4meegmq8Hk/q3oEfjsAu+70hGDu+fCjUn88xQ0yQygFHdYumhVbpFKgQNKK7I6p4KN7sXK9xUYHkOJ0npZkA5dNwML4fPaG05nJZdOMPtxfh4uBFR1ZvIQB/CA72JDPQmoqoPEYHeRAT6EFHVu9wPEC4pNpuNQW1r0aZOEI9+/gd/Hkrn/v/+zl1X1+bZ3o3OCnTJGdn859d4Pl21j+w8J2CuZTWqWyzXNwxVqBFxUWq5KW/2LoWPeoNfCDyxA+y69LQ8S8vKY1tiOtsSM9iWmM72xAwOHDvJkRM5XMq/LC93e5GQUvA4suqpbYHeBPp46Ev4HHLyHUz+aTvv/7YHMAcCTxvcikYRASSlZzP9l918vjqBnHwz1LSMqsqo7rFcVz9EP0+RCkjdUhdQ7sNNfi68WgfyMuHBpRDezOqKBMh3ONmTksnWxAy2HT4VZg6nF66efS4ebjbCC8LKX1tbAr2JrOpDNV8Flyu1ZMcRRn+5gZQTOXi62+nROIyFW5LIPRVqrqpVlVHd63NNbLB+1iIVmLqlKjJ3T6jTCXYuMLumFG7KXMqJHLYdNltitp6635l8ovDL8q9qVPWhUYQ/DcMDaBDuT+3qvkQE+lDdz1MDVMvANfVD+PGxLvxjzkZ+3pbM9xsPA9C2TjVGdatPp3rVFWpEKhmFm/Ko7vVmuIlfDJ0etboal5WT72B3cmZht9LWw2aYOd9YGD9PNxqE+9MwIoBGp+4bhPtXmvEu5VlwFS9mDGvDp6sSWBF/lCHta9EhRqFGpLJSuCmPYrqa9/uWQ142eFz6pa5yNsMwSErPYWtiemGLzLbDGew+coJ859m9sjYb1KnuR8NwszWmYYQ/jcIDqFnNRy0x5ZjNZuPOq2tz59W1rS5FRCymcFMehTQA/wjIOAwJK8wrqOSSnMx1sCOpaJfStsSMwsUP/yrQx4OG4f40iggww0xEAPXDquDrqX8aIiIVlX6Dl0cFSzFs+MzsmlK4uag9KZlMXbSD7zYexnGO1hg3u426IX5FWmIaRvgTHuCtrgsRERejcFNe1b3eDDe7F0MPq4spvw4cy+KtuF3MWXegMNQEV/E83RJzKsTUDanikpPaiYjI2RRuyquY68z7xI1w4ghUCbG0nPImOT2btxfv4vPVCeQ5zFBzfcNQRveoT9MagRZXJyIiVlK4Ka+qhEBYM0jaBHt+hWb9ra6oXEjNzOXdX3fz8fK9hZOzdapXndE9GlTYxSFFRKRkKdyUZ3WvM8PN7sWVPtykncxjxm/xzFi6p3AV56tqVWVMzwZ0rBtscXUiIlKeKNyUZ3Wvh+VvmYOKDcMcaFzJZObk89Hyvby3JL5w9esmkQGMuaEB1zXQNPoiInI2hZvyrFYHcPOC9IOQssO8RLySyM5z8OmqBKb/souUE+bK2LGhVXjihvr0bBKuUCMiIuelcFOeefhA7Q4Q/4vZNVUJwk1uvpMvf9/P2z/vIjHdXLepdnVfHu9enz4tInHTJHoiInIRCjflXUxXM9zEL4arH7S6mlLjcBp888dB3ozbwf7UkwBEBnrzaLdY+rWuiYebVkcXEZFLo3BT3tW9HhY9D3uXmiuGu3taXVGJcjoNfth8mDcW7mD3kUwAQvy9GNm1Hre3i8LLXXPTiIhI8SjclHdhTcE3GLJS4MAac8VwF2AYBnFbk3l94Q62Hk4HoKqvBw9dW5ehHerg46lQIyIil0fhpryz280J/TbPMbumKni4MQyDpbtSmLxgBxv2HwfA38ud+7rEcE/nOvhrhW0REblCCjcVQd2uZrjZvRiuf87qai7bmr2pvPbTdlbvSQXAx8ONuzvV4e/XxFDV17W620RExDoKNxVBzKmFMw+tg5PHwKdizcS78cBxJi/YwZIdRwDwdLdzZ/vaPHRdXUL8vSyuTkREXI3CTUUQWAOCG0DKdtizBBrfanVFl2RbYjpTFuxgwZYkANztNga2jWJk13pEVvWxuDoREXFVCjcVRd2uZrjZvbjch5v4IyeYumgn3248hGGA3QZ9W9VgVLdYalf3s7o8ERFxcQo3FUVMV1j1rjmouJzIznOQfjKP4yfzSDuZR1pWHgu2JPLVuoM4nOZK3b2bRfB4j1jqhfpbXK2IiFQWCjcVRZ3OYPeAY3shNR6CYkrksLn5TjOYnLqZYSWXtKw80k7mk3bqefoZ+xzPMu8LVuU+l24NQxl9Q32aRAaWSJ0iIiKXSuGmovCqAlHtYN8ys2vqHOEmO8/BtsSMUwHk3IHkr7esUytsXy67DQJ9PApvNav5cm+XaK6qVbEGPYuIiOtQuKlIYrqa4SZ+MbS9t8hLqZm59H1nGQmpWZd1aH9vd6r6ehQJKoE+noWPz37Ng0BfD6p4umPXek8iIlKOKNxUJHW7wuJ/mVdMOR1gN2fxNQyD5+ZuIiE1C38vd2oG+RLo435GMDFDSoCPB1X/ElCq+nrg7+2hBSlFRMRlKNxUJJGtwDsQstPg0B9Qsw0A8zYc4odNibjbbXx2/9U0q6lxLiIiUnlpqeWKxO4G0deaj3f/DEBiWjZj524G4JHrYxVsRESk0lO4qWjqnpqtePdiDMPgqa82kp6dT/OagTzcta61tYmIiJQDCjcVTcFSDAdW8+Xyrfy64wie7namDGyBh5v+OEVERPRtWNEERUO1aHDm88tP3wDwj54NNEmeiIjIKQo3FZDzVOtNO+cG2kcHcU+naIsrEhERKT8UbiqgRTmNALjWbTOTB7TQPDMiIiJnULipYLYnZvDMH0E4DBsxtoNEuR2zuiQREZFyReGmAsnNdzL6y/Ucdfiwx7uhubEcLaQpIiJSHijcVCBv/byTPw+lU9XXg4iWvcyNp+a7EREREZPCTQWxfv9x/v3LbgBe7tsMv8Y9zBfifwHn+VfnFhERqWwUbiqA7DwHo79cj8NpcEuLSHo3j4CabcGzCmQdhaRNVpcoIiJSbijcVACv/riN+COZhPp7Mf7WJuZGNw+o08V8vFvjbkRERAqUi3DzzjvvUKdOHby9vWnfvj2rV68+777XXXcdNpvtrFvv3r3LsOKys3x3Ch8u2wvAq/2bU9XX8/SLhUsxaNyNiIhIAcvDzaxZsxg9ejTPP/8869ato0WLFvTs2ZPk5ORz7v/1119z+PDhwtvmzZtxc3NjwIABZVx56UvPzuPJ2RsBuKN9Lbo2CC26Q8FSDAkrIe9kGVcnIiJSPlkebqZMmcL999/P8OHDady4Me+++y6+vr7MnDnznPsHBQURHh5eeFu4cCG+vr4uGW5e+nYLB4+fpFaQL8/e1OjsHYJjIaAGOHJg3/KyL1BERKQcsjTc5ObmsnbtWrp37164zW630717d1asWHFJx5gxYwa33347fn5+53w9JyeH9PT0IreKYNGWJGavPYDNBpMHtMDPy/3snWy2011Tmu9GREQEsDjcpKSk4HA4CAsLK7I9LCyMxMTEi75/9erVbN68mfvuu++8+0ycOJHAwMDCW1RU1BXXXdqOnsjh6a/N7qj7u8TQLjro/DsXdE1pULGIiAhQDrqlrsSMGTNo1qwZ7dq1O+8+zzzzDGlpaYW3/fv3l2GFxWcYBs/N3UzKiVzqh1VhdI/6F35DzHXmfdJmOHHucUoiIiKViaXhJjg4GDc3N5KSkopsT0pKIjw8/ILvzczM5IsvvuDee++94H5eXl4EBAQUuZVn/1t/iPmbE3G325gysCXeHm4XfoNfMIQ3Nx/H/1Lq9YmIiJR3loYbT09PWrduTVxcXOE2p9NJXFwcHTp0uOB7Z8+eTU5ODnfeeWdpl1lmEtOyGfe/zQA82i2WpjUCL+2Nda8379U1JSIiYn231OjRo3n//ff5+OOP2bp1Kw899BCZmZkMHz4cgKFDh/LMM8+c9b4ZM2bQt29fqlevXtYllwrDMPjHVxtJz86nRc1AHr6u7qW/+cz5bgyjdAoUERGpIM5xCU7ZGjRoEEeOHGHcuHEkJibSsmVLfvzxx8JBxgkJCdjtRTPY9u3bWbp0KQsWLLCi5FLx6aoEluw4gpe7ndcHtsTdrRi5M+pqcPeGE4lwZBuEnuOycRERkUrCZhiV67/66enpBAYGkpaWVm7G3+xNyaTXm79xMs/B2Jsbc2/n6OIf5P9uM1tuek6EDg+XfJEiIiIWKs73t+XdUpWdw2kwZvYGTuY5uDomiOEd61zegQrG3Wi+GxERqeQUbiz2/m/x/L7vGFW83Jk8oAV2u+3yDhRzxribT/rD8rcgcRM4nSVXrIiISAVg+ZibymxbYjpTFuwAYNzNjalZzffyDxbWBGq2gwOrYddC8wbgWx2ir4Hoa805cYIuo8tLRESkAlG4sUhuvpPRszaQ63DSrWEoA9rUvLID2mxwz0+Q/Kc53038r+Z6U1lH4c9vzBtA1Vqng070tVAl5EpPRUREpFzRgGKLTP5pO28v3kU1Xw9+evwaQv29S/5D8nPh4O9m0NnzKxxYA878ovuENoGYU2Gndkfw8i/5OkRERK5Qcb6/FW4s8EfCMfpNX47TgH8PuYqbmkWUzQfnnICEFadbdpI2FX3d7g41Wp9u1anZFtw9y6Y2ERGRC1C4uQCrw83JXAe9p/1GfEomt7aM5M3bW5V5DYUyU8wWnYKWnWN7i77u4Qu1OphhJ+ZaCGsGdo1BFxGRslec72+NuSljr/64jfiUTMICvBh/S1Nri/ELhqb9zBuY4aYg6MT/ClkpsDvOvAH4BJmDk2OuNVt2gmLMsT4iIiLliFpuytCyXSkM+WAVAB8Nb8t1DULL9POLxemE5C2ng86+ZZB7oug+gVGngs51ZujxD7OiUhERqQTULXUBVoWb9Ow8bnxjCYfSsrmjfS0m3NaszD67RDjy4OA6c7zOnl9h/2pw5hXdJ7Qx1O8JXZ8FNw9LyhQREdekbqlyaPy3WziUlk2tIF+evakCrv3k5gG12pu3656C3EzYtwL2/GK27CRuMlt6krdASENocbvVFYuISCWlcFMGFm5JYs7aA9hs8PrAFvh5ucCP3dMPYrubN4DMo7D4Zfh9BmycpXAjIiKW0aUvpezoiRye+XojAA90iaFtnSCLKyolftWh4yPm4/hfICPR0nJERKTyUrgpRYZh8Ow3m0k5kUv9sCo83qO+1SWVrqBoiGoPhhM2zbG6GhERqaQUbkrR3PUH+fHPRNztNqYMbIm3h5vVJZW+5oPM+41fWFuHiIhUWgo3peRw2knG/e9PAEZ1i6VpjUCLKyojTW4Du4c5wDhpi9XViIhIJaRwUwoMw+AfczaSkZ1Pi6iqPHRdXatLKju+QRB7g/l405fW1iIiIpWSwk0p+GTlPn7bmYKXu53XB7TA3a2S/ZhbFHRNzTYnAxQRESlDlexbt/TtTclkwg/bAHjqxobUC61icUUWiO0JXoGQfsCc2VhERKQMKdyUIIfT4InZGziZ56BDTHXu7ljH6pKs4eENTfqajzWwWEREypjCTQl6b0k8a/cdo4qXO68NaI7dXokXlSy4amrLPMg7aW0tIiJSqSjclJCth9N5Y+EOAMb1aUzNar4WV2SxWh0gsBbkpMP2+VZXIyIilUixw02dOnUYP348CQkJpVFPhZV2Mo9AXw+6NwplQOuaVpdjPbsdmg8wH2/UVVMiIlJ2ih1uHnvsMb7++mtiYmLo0aMHX3zxBTk5OaVRW4VydUx1Fjx2Da/2a47NVom7o85U0DW1a6G59pSIiEgZuKxws379elavXk2jRo145JFHiIiIYOTIkaxbt640aqwwqvl5Ur2Kl9VllB8hDSCiJTjz4c+vra5GREQqicsec3PVVVcxbdo0Dh06xPPPP88HH3xA27ZtadmyJTNnzsQwjJKsUyqqwuUYZllbh4iIVBqXHW7y8vL48ssvueWWW3jiiSdo06YNH3zwAf369eOf//wnQ4YMKck6paJq1h9sbnBgDRzdbXU1IiJSCbgX9w3r1q3jww8/5PPPP8dutzN06FDeeOMNGjZsWLjPbbfdRtu2bUu0UKmgqoRC3a6wa5E5sLjrM1ZXJCIiLq7YLTdt27Zl586dTJ8+nYMHDzJ58uQiwQYgOjqa22+/vcSKlAqu+am/CxtngborRUSklBW75SY+Pp7atWtfcB8/Pz8+/PDDyy5KXEzDm8DDD47tMbunotpZXZGIiLiwYrfcJCcns2rVqrO2r1q1it9//71EihIX4+kHjfqYjzWwWERESlmxw82IESPYv3//WdsPHjzIiBEjSqQocUEFK4Vv/gryc62tRUREXFqxw82WLVu46qqrztreqlUrtmzZUiJFiQuKvhaqhMPJY+bgYhERkVJS7HDj5eVFUlLSWdsPHz6Mu3uxh/BIZWF3My8LB60ULiIiparY4eaGG27gmWeeIS0trXDb8ePH+ec//0mPHj1KtDhxMQUT+m3/EU4et7QUERFxXcUON5MnT2b//v3Url2brl270rVrV6Kjo0lMTOT1118vjRrFVYQ3g9DG4MiBLf+zuhoREXFRxQ43NWrUYOPGjUyaNInGjRvTunVr3nzzTTZt2kRUVFRp1CiuwmaD5gPNx1opXERESonNqGSLQKWnpxMYGEhaWhoBAQFWl1P5pB2AN5oCBjy2GaoqEIuIyMUV5/v7skcAb9myhYSEBHJzi17We8stt1zuIaUyCKwJdTrD3t9g05fQ5QmrKxIRERdzWTMU33bbbWzatAmbzVa4+rfNZgPA4XCUbIXiepoPMsPNhlnQebTZXSUiIlJCij3mZtSoUURHR5OcnIyvry9//vknS5YsoU2bNvzyyy+lUKK4nMa3gLs3pGyHwxusrkZERFxMscPNihUrGD9+PMHBwdjtdux2O507d2bixIk8+uijpVGjuBrvQGjQy3ysgcUiIlLCih1uHA4H/v7+AAQHB3Po0CEAateuzfbt20u2OnFdBSuFb5oNjnxraxEREZdS7DE3TZs2ZcOGDURHR9O+fXsmTZqEp6cn7733HjExMaVRo7iiet3AJwgyk2HPL1Cvu9UViYiIiyh2y81zzz2H0+kEYPz48ezZs4cuXbrwww8/MG3atBIvUFyUmwc07Wc+VteUiIiUoBKZ5yY1NZVq1aoVXjFVnmmem3LkwO/wQTfw8IUxO8GritUViYhIOVWc7+9itdzk5eXh7u7O5s2bi2wPCgqqEMFGypkarSGoLuRlwbbvra5GRERcRLHCjYeHB7Vq1dJcNlIybLbTi2lqpXARESkhxR5z8+yzz/LPf/6T1NTU0qhHKpvmA8z7+F8gI9HSUkRExDUU+2qpt99+m127dhEZGUnt2rXx8/Mr8vq6detKrDipBIJiIKo97F8Fm+ZAx5FWVyQiIhVcscNN3759S6EMqdSaDzTDzcZZCjciInLFtCq4WC8rFSbXB2cePLwSQhtZXZGIiJQzpXa1lEip8A2C2BvMxxtnWVuLiIhUeMUON3a7HTc3t/PeRC5L84Hm/cbZcGqSSBERkctR7DE333zzTZHneXl5/PHHH3z88ce8+OKLJVaYVDL1bwSvQEg/APuWQXQXqysSEZEKqtjh5tZbbz1rW//+/WnSpAmzZs3i3nvvLZHCpJLx8IYmt8K6/5pdUwo3IiJymUpszM3VV19NXFxcSR1OKqOClcK3/A/yTlpbi4iIVFglEm5OnjzJtGnTqFGjRkkcTiqrWh0gMApy0mHHj1ZXIyIiFVSxu6X+ukCmYRhkZGTg6+vLJ598UqLFSSVjt0OzAbB0irlSeJPbrK5IREQqoGKHmzfeeKNIuLHb7YSEhNC+fXuqVatWosVJJdR8kBludi6AzKPgV93qikREpIIpdri5++67S6EMkVNCG0JECzi8Af78Gtrdb3VFIiJSwRR7zM2HH37I7Nmzz9o+e/ZsPv744xIpSiq5goHFmtBPREQuQ7HDzcSJEwkODj5re2hoKBMmTCh2Ae+88w516tTB29ub9u3bs3r16gvuf/z4cUaMGEFERAReXl7Ur1+fH374odifK+VY035gs8OBNXB0t9XViIhIBVPscJOQkEB0dPRZ22vXrk1CQkKxjjVr1ixGjx7N888/z7p162jRogU9e/YkOTn5nPvn5ubSo0cP9u7dy5w5c9i+fTvvv/++rtJyNf5hUPd68/Gms1sJRURELqTY4SY0NJSNGzeetX3Dhg1Ur168wZ9Tpkzh/vvvZ/jw4TRu3Jh3330XX19fZs6cec79Z86cSWpqKnPnzqVTp07UqVOHa6+9lhYtWhT3NKS8az7IvN/wBVSutV1FROQKFTvcDB48mEcffZTFixfjcDhwOBz8/PPPjBo1ittvv/2Sj5Obm8vatWvp3r376WLsdrp3786KFSvO+Z558+bRoUMHRowYQVhYGE2bNmXChAk4HI7zfk5OTg7p6elFblIBNOwNHn5wbA8c+N3qakREpAIpdrh56aWXaN++Pd26dcPHxwcfHx9uuOEGrr/++mKNuUlJScHhcBAWFlZke1hYGImJied8T3x8PHPmzMHhcPDDDz8wduxYXn/9df71r3+d93MmTpxIYGBg4S0qKuqSaxQLefpBoz7m441fWFuLiIhUKDbDuLw2/507d7J+/Xp8fHxo1qwZtWvXLtb7Dx06RI0aNVi+fDkdOnQo3P6Pf/yDX3/9lVWrVp31nvr165Odnc2ePXsKVyCfMmUKr732GocPHz7n5+Tk5JCTk1P4PD09naioKNLS0ggICChWzVLGdsXBJ38DnyB4Yju4e1pdkYiIWCQ9PZ3AwMBL+v4u9jw3BWJjY4mNjb3ctxMcHIybmxtJSUlFticlJREeHn7O90RERODh4VEYbAAaNWpEYmIiubm5eHqe/eXn5eWFl5fXZdcpFoq5DqqEwYkk2LUIGt5kdUUiIlIBFLtbql+/frz66qtnbZ80aRIDBgy45ON4enrSunXrIottOp1O4uLiirTknKlTp07s2rULp9NZuG3Hjh1EREScM9hIBWd3M5djAM15IyIil6zY4WbJkiXcdNPZ/4Pu1asXS5YsKdaxRo8ezfvvv8/HH3/M1q1beeihh8jMzGT48OEADB06lGeeeaZw/4ceeojU1FRGjRrFjh07+P7775kwYQIjRowo7mlIRVFw1dT2+ZCdZm0tIiJSIRS7W+rEiRPnbCXx8PAo9pVIgwYN4siRI4wbN47ExERatmzJjz/+WDjIOCEhAbv9dP6Kiorip59+4vHHH6d58+bUqFGDUaNG8dRTTxX3NKSiCG8GIY3gyFbY8j+4aqjVFYmISDlX7AHF7dq14+abb2bcuHFFtr/wwgt8++23rF27tkQLLGnFGZAk5cRvUyDuRajTBe7+zupqRETEAqU6oHjs2LH87W9/Y/fu3Vx/vTmLbFxcHJ999hlz5sy5vIpFLqT5QDPc7P0Nju+HqrqcX0REzq/YY2769OnD3Llz2bVrFw8//DBPPPEEBw8e5Oeff6ZevXqlUaNUdoE1zVYb0HIMIiJyUcUONwC9e/dm2bJlZGZmEh8fz8CBAxkzZoyWQZDSUzCweOMsLccgIiIXdFnhBsyrpoYNG0ZkZCSvv/46119/PStXrizJ2kROa3wLuHvDkW2QePbaZiIiIgWKNeYmMTGRjz76iBkzZpCens7AgQPJyclh7ty5NG7cuLRqFAHvQGjQC/78BjZ+CRFqJRQRkXO75JabPn360KBBAzZu3MjUqVM5dOgQb731VmnWJlJUQdfUptngyLe2FhERKbcuueVm/vz5PProozz00ENXtOyCyGWr281cZ+pEEuz5Fep1s7oiEREphy655Wbp0qVkZGTQunVr2rdvz9tvv01KSkpp1iZSlLsnNO1nPtZyDCIich6XHG6uvvpq3n//fQ4fPszf//53vvjiCyIjI3E6nSxcuJCMjIzSrFPEVNA1tfVbyDlhbS0iIlIuFftqKT8/P+655x6WLl3Kpk2beOKJJ3jllVcIDQ3llltuKY0aRU6r2QaCYiAvC7Z9b3U1IiJSDl32peAADRo0YNKkSRw4cIDPP/+8pGoSOT+breicNyIiIn9xReGmgJubG3379mXevHklcTiRC2s+0LyPXwwZSdbWIiIi5U6JhBuRMhUUAzXbgeGEzVrPTEREilK4kYqpoPVGXVMiIvIXCjdSMTXtB3Z3OLwBkrdZXY2IiJQjCjdSMfkGQewN5mO13oiIyBkUbqTiOnM5BqfT2lpERKTcULiRiqv+jeAVCGn7IWG51dWIiEg5oXAjFZeHNzS51XysrikRETlF4UYqtoKuqT//B3nZ1tYiIiLlgsKNVGy1OkJgFOSkwY4fra5GRETKAYUbqdjsdmg2wHysrikREUHhRlxBQdfUzgWQedTaWkRExHIKN1LxhTaEiBbgzIc/v7a6GhERsZjCjbiGwpXCv7S2DhERsZzCjbiGpv3BZocDq2HnQshOt7oiERGxiLvVBYiUCP8wiOkKu+Pg0/7mtsAoCG106tbYvA+uDx4+1tYqIiKlSuFGXEf3F8Bmg6Q/IeOwOXNx2n5zoHEBmx2CYooGntDG5jY3D8tKFxGRkmMzDMOwuoiylJ6eTmBgIGlpaQQEBFhdjpSWrFQ4sg2St5irhidvheQ/4eSxc+/v5mm26oQ0LBp8qtY2LzcXERFLFef7Wy034pp8g6B2R/NWwDDgRPKpwLP19P2RbZB7ApI2m7czefieCjyNi3Zx+YebrUQiIlLuKNxI5WGzmWNz/MOgbtfT251Os/vqzMCTvBVStkNeFhxaZ97O5F317MAT2sgMVSIiYil1S4mcjyMfUuPhyNaiwefoLjCc535PlXCo1d4c3BxzHQRFl2nJIiKuqjjf3wo3IsWVlw1Hd/6lpWcLHE84e99qdcygU7crRF8DPtXKvFwREVegcHMBCjdSanIyIHEz7FkC8YvhwBpz1uQCNjtEtjoddmq2A3dP6+oVEalAFG4uQOFGykxOBuxdCrsXm2EnZUfR1z38oE6n02EnpKEGKYuInIfCzQUo3Ihl0g5C/C9m0In/BTKPFH29Srg5TqfuqfE6/uFlX6OISDmlcHMBCjdSLjid5rw7Ba06+5ZDfnbRfUIbn27Vqd0RPP2sqVVEpBxQuLkAhRspl/KyYf/K02Hn8EbgjH+adg+Iag91r4OY6yGyJdjdLCpWRKTsKdxcgMKNVAiZR2HPL6fCzi/mPDxn8q5qXn1Vt6vZuqNLzkXExSncXIDCjVQ4hmHOt7P7ZzPo7FkCOX9Z9fzMS87rdVcXloi4HIWbC1C4kQrPkW/OmFzQhfXXS86r14N7fgK/YOtqFBEpYQo3F6BwIy7nzEvO//wGMpOhRhsY9i14+lpdnYhIiSjO97eWOxap6Lz8oUEvuGkS3P29OR7n4O/w1X3gdFhdnYhImVO4EXElIfVh8Bfg5gXbv4f5T5ljdkREKhGFGxFXU7sD/O0/gA3WvA/Lp1ldkYhImVK4EXFFTW6Dni+bjxeOg01zrK1HRKQMKdyIuKoOI6D9Q+bjuQ+Zg45FRCoBhRsRV9bzZWjUBxy58MUdkLzN6opEREqdwo2IK7O7wd/eN5duyE6DT/tD+mGrqxIRKVUKNyKuzsMHbv/cnNwvbT98NsCcG0dExEUp3IhUBn7VYcgc8AuBxE3w5TBw5FldlYhIqVC4EaksgqLhjlng4Qu74+C7xzQHjoi4JIUbkcqkRmvo/yHY7PDHJ/DrJKsrEhEpcQo3IpVNgxuh9+vm418mmCFHRMSFKNyIVEZt7oHOo83H346CXXHW1iMiUoIUbkQqq27joNlAcObDl0Ph8EarKxIRKREKNyKVlc0Gt74DdbpA7gn4dAAc3291VSIiV0zhRqQyc/eEQZ9AaGM4kWhO8nfymNVViYhcEYUbkcrOpyoMmQ3+kXBkG3xxJ+TnWF2ViMhlU7gREQisaQYcT3/YtxTmPgxOp9VViYhcFoUbETGFN4VB/wW7O2yeA3EvWl2RiMhlKRfh5p133qFOnTp4e3vTvn17Vq9efd59P/roI2w2W5Gbt7d3GVYr4sLqXg+3vGU+XjYV1nxgaTkVTm6WedWZZn4WsZTl4WbWrFmMHj2a559/nnXr1tGiRQt69uxJcnLyed8TEBDA4cOHC2/79u0rw4pFXFzLO6Drc+bjH56EbT9YW095l50GG2fDrDthUgz8p4tavUQsZnm4mTJlCvfffz/Dhw+ncePGvPvuu/j6+jJz5szzvsdmsxEeHl54CwsLK8OKRSqBa8bAVUPBcMKce+DA71ZXVL5kHoV1/2dePv9aPfj6Ptj6LeSfNF9f9iYcWm9piSKVmaXhJjc3l7Vr19K9e/fCbXa7ne7du7NixYrzvu/EiRPUrl2bqKgobr31Vv7888/z7puTk0N6enqRm4hchM0Gvd+Aej3ML+zPBkFqvNVVWSv9MKx+Hz7uA5NjYd5I2LkAHLkQXB+6jIEHfoWm/cxQOG+kVl4XsYi7lR+ekpKCw+E4q+UlLCyMbdu2nfM9DRo0YObMmTRv3py0tDQmT55Mx44d+fPPP6lZs+ZZ+0+cOJEXX1QTsUixubnDgI/go5vg8Ab4pD/cuxD8qltdWdk5ttdskdkyDw78ZSxgeHNodAs0vgVCGpzefuOrsPtnSNwEK96Gzo+XackiAjbDsG7k26FDh6hRowbLly+nQ4cOhdv/8Y9/8Ouvv7Jq1aqLHiMvL49GjRoxePBgXnrppbNez8nJISfn9Jwd6enpREVFkZaWRkBAQMmciIgry0iCGd3heALUbAtD54Gnr9VVlZ4j280ws3UeJP5lSYqa7cww0/BmCIo+/zHWfw5zHwR3b3hoOVSvW7o1i1QC6enpBAYGXtL3t6UtN8HBwbi5uZGUlFRke1JSEuHh4Zd0DA8PD1q1asWuXbvO+bqXlxdeXl5XXKtIpeUfBkO+ghk94MAa+Pp+GPhfsLtZXVnJMAwzxBQEmpQdp1+z2aF2J2h8qxloAiIu7ZgtboeNsyB+sbkw6bBvza4+ESkTlo658fT0pHXr1sTFnV6R2Ol0EhcXV6Ql50IcDgebNm0iIuISf+mISPGF1IfBn4ObF2z7Dn58pmJf7ux0QsIq+OlZeLMF/Oca+G2yGWzsHhB7g3lJ/JhdcPd30O7+Sw82YAaZPlPBwxf2/gZ//F+pnYqInM3SlhuA0aNHM2zYMNq0aUO7du2YOnUqmZmZDB8+HIChQ4dSo0YNJk6cCMD48eO5+uqrqVevHsePH+e1115j37593HfffVaehojrq90RbnsX5gyH1f+BqlHQ8RGrq7p0jnzYt8xsndn6nbmWVgF3H4jtDo1uhfo3gHfglX9etTrQ9VlY8CwseM4MTP6X1iItIlfG8nAzaNAgjhw5wrhx40hMTKRly5b8+OOPhYOMExISsNtPNzAdO3aM+++/n8TERKpVq0br1q1Zvnw5jRs3tuoURCqPpn+D9EOnv7ADapjbyqv8HIj/xQw0236Ak6mnX/MKgPo9zUHB9bqXzjii9g+asz0f+sOcM2iQWnBEyoKlA4qtUJwBSSJyDoYBPz4Nq94FN0+4ay7U6WR1VaflZsKuReYYmh0/QW7G6dd8gqBhbzPQxFwL7mUwHi9xE7x3HTjzzRXYG/Up/c8UcUEVZkCxiFRANhv0nABpB8zxN18MNi8RP/Ny6NLmdEDGYfNS7WN7IXXP6cdJf56eTA/AP8IcDNz4FqjV0bzEvSyFN4NOo+C31+H7MVCni7kSu4iUGrXciMjlyTsJH99izv8SWAvuW1iyY0pyMuDYvtOh5dgZAeZ4gjl53vlUrXVqDppboUYbsFs8GXteNrzbCY7ugtbDzcHGIlIsxfn+VrgRkcuXedS8RDx1N0S0gLt/AK8ql/ZepxMyDp0RXvYWbYXJSrnw++0eZoipVqfoLTgWQhqWv0uv9y6Fj3qbj+/+oXx15YlUAAo3F6BwI1LCUuPhgx5mGKnXHQZ/AW4e5ms5J84OLwWtMBdrfQFzjExQ9NkBplodczBzRZtr59tRsPYjqF4PHlwGHt5WVyRSYSjcXIDCjUgpOLDWbJXIP2nOYgxmiMk8cuH32d1Ptb6cK8DULplLssuTk8fhnfbmZehdnoBu46yuSKTCULi5AIUbkVKyfT58cYe5aOSZfIJOB5a/tsJUxNaXK7X1O5g1xAx2D/xiDjgWkYtSuLkAhRuRUrT7Z0jc7NqtLyVh1l3m3DuRreC+uMoX8EQugy4FFxFr1L3evMmF3fQaxP9qTu636l3oMMLqikRcisXXR4qIVEL+4XDDS+bjn/9ljk8SkRKjcCMiYoWrhpoT+uVlwbePVeyFSEXKGYUbEREr2GzQ501w94b4xbDhC6srEnEZCjciIlapXheue9p8/NMzcOIil86LyCVRuBERsVKHkebl4CePmQuSisgVU7gREbGSmwfc8hbY7LB5jrmSuYhcEYUbERGrRbY6fTn4d6PNRUNF5LIp3IiIlAfX/dOc+DD9AMSNt7oakQpN4UZEpDzw9DWvngJY/T4krLK2HpEKTOFGRKS8iLkOWt4JGDDvEcjPsboikQpJ4UZEpDy54SXwC4GU7bD0DaurEamQFG5ERMoT3yDoNcl8vGQyJG+1th6RCkjhRkSkvGlyG9TvBc48s3vK6bC6IpEKReFGRKS8sdmg9+vg6Q8H1sCaGVZXJFKhKNyIiJRHgTWgxwvm47gX4fh+S8sRqUgUbkREyqvW90DU1ZB7Ar4frZXDRS6Rwo2ISHllt8Mt08DNE3YugM1fWV2RSIWgcCMiUp6FNIBrnjQfz38KslKtrUekAlC4EREp7zo9BqGNISsFfvqn1dWIlHsKNyIi5Z27p7lyODbY8DnsirO6IpFyTeFGRKQiqNkG2j9oPv7uMcjNtLQckfJM4UZEpKK4/jkIjILjCbB4gtXViJRbCjciIhWFVxW4+dR6Uyv/DQfXWluPSDmlcCMiUpHE9oBmA8Fwwv8eAUee1RWJlDsKNyIiFc2Nr4BvdUj+E5a9aW0thgEpu2DtR/DV/fBmC/jwJtj+oyYdFMvYDKNy/e1LT08nMDCQtLQ0AgICrC5HROTybPwSvr4f3LzgoWUQHFs2n2sYcGQ77FsKe5fBvmVwIunc+4Y1g2uegEa3gN2tbOoTl1Wc72+FGxGRisgw4NP+sGsR1O4Ew74zZzQuaU6n2UK0d5kZaPYth6yjRfdx8zKv5qrdCaLaw94l5mKfuSfM16vHQpfR0GwAuHmUfI1SKSjcXIDCjYi4jOMJ8M7VkJdpDjRuc8+VH9ORD4kbzRCzb5l5n3286D7uPhDVDup0NgNNjdbg4V10n6xUWPUfWPXu6fdXrWVOSNhyyNn7i1yEws0FKNyIiEtZOR1+fBq8AmDEKgiILN77HXlwaP3pbqaElZCbUXQfzypQ62qo3RFqd4bIVubEgpciJ8NsxVnxNmQeMbdVCYdOj0Lru8HTr3j1SqWlcHMBCjci4lKcDphxAxz8HRr0hts/BZvt/Pvn55iXkBeMl9m/2mz5OZNXINTuYLbK1OkE4S3Azf3K6szNgj/+zxwAnX7Q3OZbHa5+CNo9AN6BV3Z8cXkKNxegcCMiLidpC/ynCzjzYeB/ofGtp1/LOwkH1pwOMwfWQH520ff7BJmtMnU6m/dhTUtvAHB+rrmExNI34Ngec5tXILS7H65+GPyql87nSoWncHMBCjci4pJ+fhmWTIIqYdBnGhxYbQaag2vB+Ze5cPxCTrXKnBozE9KwdAYjX4gjH/78Bn6bDEe2mds8fM1xQx1GQkBE2dYj5Z7CzQUo3IiIS8rPgXc7Q8qOs1/zjzS7lwrGzATHXrjrqiw5nbD9e1jyGhzeYG5z84RWd5qDj6vVtrQ8KT8Ubi5A4UZEXNb+1fB/fwOfaqfCzKkxM9Wiy0+YOR/DMFc7/20yJKwwt9ncoPkg8zLysprHR8othZsLULgREZdmGOU/yFzM3mVmS0784lMbbNCkL3R5AsKbWVmZWEjh5gIUbkREKogDa82WnO0/nN5WvxdcM8acNFAqFYWbC1C4ERGpYJL+hN9eh81fA6e+sqKvhWueNAdFV/SWKrkkCjcXoHAjIlJBpewyLyHf+IV52TuYyz10GWOulq6Q49IUbi5A4UZEpII7nmBOBrju/8CRY24Lb252VzXsU/aXtUuZULi5AIUbEREXkZFoLuuwZubpWZaDG8C1/4Cm/dSS42KK8/2teCsiIhWTfzjc8C94fDNc8w9zCYeU7fDVvfBJP0g7YHWFYhGFGxERqdh8g+D6Z+GxzdD1OXD3ht1x5orpaz8yL4+XSkXhRkREXIN3AFz7JDy41BxonJsB346C/+trjtORSkPhRkREXEtwLAyfDz0ngLsPxP8C/+4Aaz4wl3sQl6dwIyIirsfuBh1GwEPLoFZHyD0B3z8B/70FUvdYXZ2UMoUbERFxXdXrwt3fQ69J5qrje3+D6R1h1XtqxXFhCjciIuLa7HZo/3ezFadOF8jLgvlPwsc3w9HdVlcnpUDhRkREKoegGBg6D26aDB5+sG8ZTO8EK/4NTofV1UkJUrgREZHKw26HdvfDwysg+hrIPwk/PQMf3mQu7yAuQeFGREQqn2q1zVacm6eCpz/sXwnvdoLlb6kVxwUo3IiISOVks0Gb4WYrTt3rIT8bFjwHM3vCke1WVydXQOFGREQqt6pRcOfXcMvb4BUAB9bAu13MFcgd+VZXJ5dB4UZERMRmg6vugodXQr0e5mrji16AGT0geavV1UkxlYtw884771CnTh28vb1p3749q1evvqT3ffHFF9hsNvr27Vu6BYqISOUQWAOGzIa+082FOA+tg/9cA0teA0ee1dXJJbI83MyaNYvRo0fz/PPPs27dOlq0aEHPnj1JTk6+4Pv27t3LmDFj6NKlSxlVKiIilYLNBi3vgIdXQf0bwZELP/8LPugGiZutrk4ugeXhZsqUKdx///0MHz6cxo0b8+677+Lr68vMmTPP+x6Hw8GQIUN48cUXiYmJKcNqRUSk0giIgMFfwG3vgXdVOLwB3rsOfnlVrTjlnKXhJjc3l7Vr19K9e/fCbXa7ne7du7NixYrzvm/8+PGEhoZy7733XvQzcnJySE9PL3ITERG5JDYbtBgEI1ZDw5vBmQe/TID3usLhjVZXJ+dhabhJSUnB4XAQFhZWZHtYWBiJiYnnfM/SpUuZMWMG77///iV9xsSJEwkMDCy8RUVFXXHdIiJSyfiHwaBPoN8M8AmCpE3wflf4+WXIz7W6OvkLy7uliiMjI4O77rqL999/n+Dg4Et6zzPPPENaWlrhbf/+/aVcpYiIuCSbDZr1hxGroPGt4MyHJZPMrqpDf1hdnZzB3coPDw4Oxs3NjaSkpCLbk5KSCA8PP2v/3bt3s3fvXvr06VO4zXlqVVd3d3e2b99O3bp1i7zHy8sLLy+vUqheREQqpSqhMPC/8Oc38P0YSP4T3u8GnR+Da58Cd33nWM3SlhtPT09at25NXFxc4Tan00lcXBwdOnQ4a/+GDRuyadMm1q9fX3i75ZZb6Nq1K+vXr1eXk4iIlJ0mt5mtOE37geGA3143LxvfvxpO/cdbrGFpyw3A6NGjGTZsGG3atKFdu3ZMnTqVzMxMhg8fDsDQoUOpUaMGEydOxNvbm6ZNmxZ5f9WqVQHO2i4iIlLq/IKh/0xo3Be+Hw1HtpkT/7l5mTMfV60Fgafuq9Y+va1KuLmIp5QKy8PNoEGDOHLkCOPGjSMxMZGWLVvy448/Fg4yTkhIwK6/ACIiUp41vgXqdIYfn4FNs80Zjo/uMm/n4uYJgTXPHXyq1gL/CLC7le05XApHPpxMhcwUyEo5dX/07OfB9eHmKZaVaTMMw7Ds0y2Qnp5OYGAgaWlpBAQEWF2OiIi4GkcepB+E4wmnbvtPP05LgLSDZjfWhdjdIaDGuYNPYJT5mlsJtE/k51w8qBQ8zzoKJ49d2nEjW8EDv1x5fWcozve35S03IiIiLsXNA6rVMW/n4siHjEPnDj7HEyDtgHkl1vF95o3fzj6Gze1U+PlL6Clo9cnNgMyjZ4SUlHM/z824jBO0gW8Q+Aab3XK+1U/dn/E8sOZlHLfkKNyIiIiUJTf304HkXJwOyEg8o+XnjOBTEH4cuadagRJg37Irq8fubgYS32Dwq35GSDnX82DwqVY+u8zOoHAjIiJSntjdzAU8A2tA7bOvHMbphBNJp4LO/lMtPGe0AmUkgnfAuVtUijw/FV68q5pz+LgQhRsREZGKxG43170KiADaW11NuaTLkERERMSlKNyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCjYiIiLgUhRsRERFxKQo3IiIi4lIUbkRERMSlKNyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCjYiIiLgUd6sLKGuGYQCQnp5ucSUiIiJyqQq+twu+xy+k0oWbjIwMAKKioiyuRERERIorIyODwMDAC+5jMy4lArkQp9PJoUOH8Pf3x2azleix09PTiYqKYv/+/QQEBJTosSuCyn7+oJ+Bzr9ynz/oZ1DZzx9K72dgGAYZGRlERkZit194VE2la7mx2+3UrFmzVD8jICCg0v6lBp0/6Geg86/c5w/6GVT284fS+RlcrMWmgAYUi4iIiEtRuBERERGXonBTgry8vHj++efx8vKyuhRLVPbzB/0MdP6V+/xBP4PKfv5QPn4GlW5AsYiIiLg2tdyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCTQl55513qFOnDt7e3rRv357Vq1dbXVKZmThxIm3btsXf35/Q0FD69u3L9u3brS7LMq+88go2m43HHnvM6lLK1MGDB7nzzjupXr06Pj4+NGvWjN9//93qssqEw+Fg7NixREdH4+PjQ926dXnppZcuaQ2cimrJkiX06dOHyMhIbDYbc+fOLfK6YRiMGzeOiIgIfHx86N69Ozt37rSm2FJwofPPy8vjqaeeolmzZvj5+REZGcnQoUM5dOiQdQWXsIv9+Z/pwQcfxGazMXXq1DKrT+GmBMyaNYvRo0fz/PPPs27dOlq0aEHPnj1JTk62urQy8euvvzJixAhWrlzJwoULycvL44YbbiAzM9Pq0srcmjVr+M9//kPz5s2tLqVMHTt2jE6dOuHh4cH8+fPZsmULr7/+OtWqVbO6tDLx6quvMn36dN5++222bt3Kq6++yqRJk3jrrbesLq3UZGZm0qJFC955551zvj5p0iSmTZvGu+++y6pVq/Dz86Nnz55kZ2eXcaWl40Lnn5WVxbp16xg7dizr1q3j66+/Zvv27dxyyy0WVFo6LvbnX+Cbb75h5cqVREZGllFlpxhyxdq1a2eMGDGi8LnD4TAiIyONiRMnWliVdZKTkw3A+PXXX60upUxlZGQYsbGxxsKFC41rr73WGDVqlNUllZmnnnrK6Ny5s9VlWKZ3797GPffcU2Tb3/72N2PIkCEWVVS2AOObb74pfO50Oo3w8HDjtddeK9x2/Phxw8vLy/j8888tqLB0/fX8z2X16tUGYOzbt69siipD5zv/AwcOGDVq1DA2b95s1K5d23jjjTfKrCa13Fyh3Nxc1q5dS/fu3Qu32e12unfvzooVKyyszDppaWkABAUFWVxJ2RoxYgS9e/cu8nehspg3bx5t2rRhwIABhIaG0qpVK95//32ryyozHTt2JC4ujh07dgCwYcMGli5dSq9evSyuzBp79uwhMTGxyL+FwMBA2rdvX6l/L9psNqpWrWp1KWXC6XRy11138eSTT9KkSZMy//xKt3BmSUtJScHhcBAWFlZke1hYGNu2bbOoKus4nU4ee+wxOnXqRNOmTa0up8x88cUXrFu3jjVr1lhdiiXi4+OZPn06o0eP5p///Cdr1qzh0UcfxdPTk2HDhlldXql7+umnSU9Pp2HDhri5ueFwOHj55ZcZMmSI1aVZIjExEeCcvxcLXqtMsrOzeeqppxg8eHClWUzz1Vdfxd3dnUcffdSSz1e4kRI1YsQINm/ezNKlS60upczs37+fUaNGsXDhQry9va0uxxJOp5M2bdowYcIEAFq1asXmzZt59913K0W4+fLLL/n000/57LPPaNKkCevXr+exxx4jMjKyUpy/nF9eXh4DBw7EMAymT59udTllYu3atbz55pusW7cOm81mSQ3qlrpCwcHBuLm5kZSUVGR7UlIS4eHhFlVljZEjR/Ldd9+xePFiatasaXU5ZWbt2rUkJydz1VVX4e7ujru7O7/++ivTpk3D3d0dh8NhdYmlLiIigsaNGxfZ1qhRIxISEiyqqGw9+eSTPP3009x+++00a9aMu+66i8cff5yJEydaXZolCn73VfbfiwXBZt++fSxcuLDStNr89ttvJCcnU6tWrcLfifv27eOJJ56gTp06ZVKDws0V8vT0pHXr1sTFxRVuczqdxMXF0aFDBwsrKzuGYTBy5Ei++eYbfv75Z6Kjo60uqUx169aNTZs2sX79+sJbmzZtGDJkCOvXr8fNzc3qEktdp06dzrr8f8eOHdSuXduiispWVlYWdnvRX6dubm44nU6LKrJWdHQ04eHhRX4vpqens2rVqkrze7Eg2OzcuZNFixZRvXp1q0sqM3fddRcbN24s8jsxMjKSJ598kp9++qlMalC3VAkYPXo0w4YNo02bNrRr146pU6eSmZnJ8OHDrS6tTIwYMYLPPvuM//3vf/j7+xf2qQcGBuLj42NxdaXP39//rPFFfn5+VK9evdKMO3r88cfp2LEjEyZMYODAgaxevZr33nuP9957z+rSykSfPn14+eWXqVWrFk2aNOGPP/5gypQp3HPPPVaXVmpOnDjBrl27Cp/v2bOH9evXExQURK1atXjsscf417/+RWxsLNHR0YwdO5bIyEj69u1rXdEl6ELnHxERQf/+/Vm3bh3fffcdDoej8PdiUFAQnp6eVpVdYi725//XMOfh4UF4eDgNGjQomwLL7LosF/fWW28ZtWrVMjw9PY127doZK1eutLqkMgOc8/bhhx9aXZplKtul4IZhGN9++63RtGlTw8vLy2jYsKHx3nvvWV1SmUlPTzdGjRpl1KpVy/D29jZiYmKMZ5991sjJybG6tFKzePHic/67HzZsmGEY5uXgY8eONcLCwgwvLy+jW7duxvbt260tugRd6Pz37Nlz3t+Lixcvtrr0EnGxP/+/KutLwW2G4cJTaIqIiEilozE3IiIi4lIUbkRERMSlKNyIiIiIS1G4EREREZeicCMiIiIuReFGREREXIrCjYiIiLgUhRsRqfRsNhtz5861ugwRKSEKNyJiqbvvvhubzXbW7cYbb7S6NBGpoLS2lIhY7sYbb+TDDz8sss3Ly8uiakSkolPLjYhYzsvLi/Dw8CK3atWqAWaX0fTp0+nVqxc+Pj7ExMQwZ86cIu/ftGkT119/PT4+PlSvXp0HHniAEydOFNln5syZNGnSBC8vLyIiIhg5cmSR11NSUrjtttvw9fUlNjaWefPmle5Ji0ipUbgRkXJv7Nix9OvXjw0bNjBkyBBuv/12tm7dCkBmZiY9e/akWrVqrFmzhtmzZ7No0aIi4WX69OmMGDGCBx54gE2bNjFv3jzq1atX5DNefPFFBg4cyMaNG7npppsYMmQIqampZXqeIlJCymyJThGRcxg2bJjh5uZm+Pn5Fbm9/PLLhmGYq84/+OCDRd7Tvn1746GHHjIMwzDee+89o1q1asaJEycKX//+++8Nu91uJCYmGoZhGJGRkcazzz573hoA47nnnit8fuLECQMw5s+fX2LnKSJlR2NuRMRyXbt2Zfr06UW2BQUFFT7u0KFDkdc6dOjA+vXrAdi6dSstWrTAz8+v8PVOnTrhdDrZvn07NpuNQ4cO0a1btwvW0Lx588LHfn5+BAQEkJycfLmnJCIWUrgREcv5+fmd1U1UUnx8fC5pPw8PjyLPbTYbTqezNEoSkVKmMTciUu6tXLnyrOeNGjUCoFGjRmzYsIHMzMzC15ctW4bdbqdBgwb4+/tTp04d4uLiyrRmEbGOWm5ExHI5OTkkJiYW2ebu7k5wcDAAs2fPpk2bNnTu3JlPP/2U1atXM2PGDACGDBnC888/z7Bhw3jhhRc4cuQIjzzyCHfddRdhYWEAvPDCCzz44IOEhobSq1cvMjIyWLZsGY888kjZnqiIlAmFGxGx3I8//khERESRbQ0aNGDbtm2AeSXTF198wcMPP0xERASff/45jRs3BsDX15effvqJUaNG0bZtW3x9fenXrx9TpkwpPNawYcPIzs7mjTfeYMyYMQQHB9O/f/+yO0ERKVM2wzAMq4sQETkfm83GN998Q9++fa0uRUQqCI25EREREZeicCMiIiIuRWNuRKRcU8+5iBSXWm5ERETEpSjciIiIiEtRuBERERGXonAjIiIiLkXhRkRERFyKwo2IiIi4FIUbERERcSkKNyIiIuJSFG5ERETEpfw/8Q0cwtzbkdYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
        "# Load the saved model\n",
        "model = load_model('/content/best_AlexNet_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history4.history['accuracy'])\n",
        "plt.plot(history4.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_ogK7rSCKCj"
      },
      "source": [
        "#### **1.9. ResNet50 Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GUuLyWtfCKCl"
      },
      "outputs": [],
      "source": [
        "from keras.applications.resnet50 import ResNet50\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "pretrained_model = ResNet50(\n",
        "    input_shape=(224,224, 3),\n",
        "    include_top=False,\n",
        "    weights='imagenet',\n",
        "    pooling='avg'\n",
        ")\n",
        "pretrained_model.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EgAlKJT74fL8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a846a079-9396-414e-f0b5-e79c58ec87ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)   (None, 230, 230, 3)          0         ['input_3[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)         (None, 112, 112, 64)         9472      ['conv1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalizati  (None, 112, 112, 64)         256       ['conv1_conv[0][0]']          \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)     (None, 112, 112, 64)         0         ['conv1_bn[0][0]']            \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)   (None, 114, 114, 64)         0         ['conv1_relu[0][0]']          \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)   (None, 56, 56, 64)           0         ['pool1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2  (None, 56, 56, 64)           4160      ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2  (None, 56, 56, 256)          16640     ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
            "                                                                     'conv2_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activati  (None, 56, 56, 256)          0         ['conv2_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)      (None, 56, 56, 256)          0         ['conv2_block1_out[0][0]',    \n",
            "                                                                     'conv2_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activati  (None, 56, 56, 256)          0         ['conv2_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2  (None, 56, 56, 64)           16448     ['conv2_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2  (None, 56, 56, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNo  (None, 56, 56, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activ  (None, 56, 56, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2  (None, 56, 56, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNo  (None, 56, 56, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)      (None, 56, 56, 256)          0         ['conv2_block2_out[0][0]',    \n",
            "                                                                     'conv2_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activati  (None, 56, 56, 256)          0         ['conv2_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2  (None, 28, 28, 128)          32896     ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2  (None, 28, 28, 512)          131584    ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
            "                                                                     'conv3_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activati  (None, 28, 28, 512)          0         ['conv3_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)      (None, 28, 28, 512)          0         ['conv3_block1_out[0][0]',    \n",
            "                                                                     'conv3_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activati  (None, 28, 28, 512)          0         ['conv3_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)      (None, 28, 28, 512)          0         ['conv3_block2_out[0][0]',    \n",
            "                                                                     'conv3_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activati  (None, 28, 28, 512)          0         ['conv3_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2  (None, 28, 28, 128)          65664     ['conv3_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2  (None, 28, 28, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNo  (None, 28, 28, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activ  (None, 28, 28, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2  (None, 28, 28, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNo  (None, 28, 28, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)      (None, 28, 28, 512)          0         ['conv3_block3_out[0][0]',    \n",
            "                                                                     'conv3_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activati  (None, 28, 28, 512)          0         ['conv3_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2  (None, 14, 14, 256)          131328    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2  (None, 14, 14, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
            "                                                                     'conv4_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block1_out[0][0]',    \n",
            "                                                                     'conv4_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block2_out[0][0]',    \n",
            "                                                                     'conv4_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block3_out[0][0]',    \n",
            "                                                                     'conv4_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block4_out[0][0]',    \n",
            "                                                                     'conv4_block5_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block5_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2  (None, 14, 14, 256)          262400    ['conv4_block5_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2  (None, 14, 14, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNo  (None, 14, 14, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activ  (None, 14, 14, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2  (None, 14, 14, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNo  (None, 14, 14, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)      (None, 14, 14, 1024)         0         ['conv4_block5_out[0][0]',    \n",
            "                                                                     'conv4_block6_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activati  (None, 14, 14, 1024)         0         ['conv4_block6_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2  (None, 7, 7, 512)            524800    ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2  (None, 7, 7, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
            "                                                                     'conv5_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block1_out[0][0]',    \n",
            "                                                                     'conv5_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2  (None, 7, 7, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2  (None, 7, 7, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNo  (None, 7, 7, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activ  (None, 7, 7, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2  (None, 7, 7, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNo  (None, 7, 7, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)      (None, 7, 7, 2048)           0         ['conv5_block2_out[0][0]',    \n",
            "                                                                     'conv5_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activati  (None, 7, 7, 2048)           0         ['conv5_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePoo  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
            " ling2D)                                                                                          \n",
            "                                                                                                  \n",
            " dense_17 (Dense)            (None, 64)                   131136    ['avg_pool[0][0]']            \n",
            "                                                                                                  \n",
            " dense_18 (Dense)            (None, 128)                  8320      ['dense_17[0][0]']            \n",
            "                                                                                                  \n",
            " dense_19 (Dense)            (None, 512)                  66048     ['dense_18[0][0]']            \n",
            "                                                                                                  \n",
            " dense_20 (Dense)            (None, 512)                  262656    ['dense_19[0][0]']            \n",
            "                                                                                                  \n",
            " dense_21 (Dense)            (None, 256)                  131328    ['dense_20[0][0]']            \n",
            "                                                                                                  \n",
            " dense_22 (Dense)            (None, 128)                  32896     ['dense_21[0][0]']            \n",
            "                                                                                                  \n",
            " dense_23 (Dense)            (None, 64)                   8256      ['dense_22[0][0]']            \n",
            "                                                                                                  \n",
            " dense_24 (Dense)            (None, 2)                    130       ['dense_23[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 24228482 (92.42 MB)\n",
            "Trainable params: 640770 (2.44 MB)\n",
            "Non-trainable params: 23587712 (89.98 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "inputs = pretrained_model.input\n",
        "\n",
        "x = Dense(64, activation='relu')(pretrained_model.output)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dense(512, 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",
        "\n",
        "\n",
        "outputs = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gQBMDngK4jW2"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMSdVG1KLhZm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d562bc63-1ec4-4f3e-9d2d-6c379bd25455"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.6693 - accuracy: 0.5996"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/32 [==============================] - 18s 547ms/step - loss: 0.6693 - accuracy: 0.5996 - val_loss: 0.6788 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 16s 491ms/step - loss: 0.6745 - accuracy: 0.5876 - val_loss: 0.6667 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 15s 458ms/step - loss: 0.6660 - accuracy: 0.6036 - val_loss: 0.6599 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 15s 454ms/step - loss: 0.6349 - accuracy: 0.6056 - val_loss: 0.6743 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 14s 426ms/step - loss: 0.6412 - accuracy: 0.5916 - val_loss: 0.6845 - val_accuracy: 0.5098 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 14s 438ms/step - loss: 0.6749 - accuracy: 0.5928 - val_loss: 0.6631 - val_accuracy: 0.5686 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 15s 472ms/step - loss: 0.6246 - accuracy: 0.6486 - val_loss: 0.6872 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 14s 428ms/step - loss: 0.6313 - accuracy: 0.6306 - val_loss: 0.6718 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 15s 478ms/step - loss: 0.6182 - accuracy: 0.6537 - val_loss: 0.6602 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 13s 414ms/step - loss: 0.6374 - accuracy: 0.5906 - val_loss: 0.6649 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 13s 415ms/step - loss: 0.6211 - accuracy: 0.6396 - val_loss: 0.6576 - val_accuracy: 0.5686 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 13s 420ms/step - loss: 0.6481 - accuracy: 0.5996 - val_loss: 0.6828 - val_accuracy: 0.5882 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 13s 418ms/step - loss: 0.6307 - accuracy: 0.5936 - val_loss: 0.6568 - val_accuracy: 0.5882 - lr: 3.0000e-04\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 15s 460ms/step - loss: 0.6157 - accuracy: 0.5966 - val_loss: 0.6587 - val_accuracy: 0.5882 - lr: 3.0000e-04\n",
            "Epoch 15/15\n",
            "32/32 [==============================] - 14s 442ms/step - loss: 0.6127 - accuracy: 0.6196 - val_loss: 0.6570 - val_accuracy: 0.6176 - lr: 3.0000e-04\n"
          ]
        }
      ],
      "source": [
        "model_checkpoint = ModelCheckpoint('best_ResNet50_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "history5 = model.fit(\n",
        "    Train_generator,\n",
        "    validation_data=Val_generator,\n",
        "    epochs=15,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3,patience=3, min_lr=0.000001),model_checkpoint,early_stopping],\n",
        "    verbose=1,\n",
        "    steps_per_epoch=32,\n",
        "    validation_steps=4\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xrxc-tSGYShB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d0297555-16af-4348-827a-cfce3895c43b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 3s 925ms/step - loss: 0.6130 - accuracy: 0.6824\n",
            "Test Accuracy: 68.23529601097107\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7e6d2ebf4790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 100ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[39 13]\n",
            " [24  9]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.62      0.75      0.68        52\n",
            "          OK       0.41      0.27      0.33        33\n",
            "\n",
            "    accuracy                           0.56        85\n",
            "   macro avg       0.51      0.51      0.50        85\n",
            "weighted avg       0.54      0.56      0.54        85\n",
            "\n",
            "Sensitivity: 0.2727272727272727\n",
            "Specificity: 0.75\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
        "# Load the saved model\n",
        "model = load_model('/content/best_ResNet50_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history5.history['accuracy'])\n",
        "plt.plot(history5.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cDV-AlrZ2UlM"
      },
      "outputs": [],
      "source": [
        "pd.DataFrame(history.history)[['accuracy','val_accuracy']].plot()\n",
        "plt.title(\"Accuracy\")\n",
        "plt.show()\n",
        "\n",
        "pd.DataFrame(history.history)[['loss','val_loss']].plot()\n",
        "plt.title(\"Loss\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "models = ['CNN', 'VGG16', 'InceptionV3', 'AlexNet', 'ResNet50']\n",
        "total_params = [33963586, 18000386, 22306786, 106621698, 24228482]\n",
        "sizes = [129.56, 68.67, 85.09, 406.73, 92.42]\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax1.set_xlabel('Models')\n",
        "ax1.set_ylabel('Total Params', color=color)\n",
        "ax1.bar(models, total_params, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:green'\n",
        "ax2.set_ylabel('Size (MB)', color=color)\n",
        "ax2.plot(models, sizes, color=color, marker='o')\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "plt.title('Total Params and Size of Each Model')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZRnQlVGeYe5-",
        "outputId": "54bb4201-c404-45e5-a50c-935f86ae82af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "models = ['VGG16', 'ResNet50', 'InceptionV3']\n",
        "total_params = [16000000, 23000000, 25000000]\n",
        "sizes = [68.67, 406.73, 85.09]\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax1.set_xlabel('Models')\n",
        "ax1.set_ylabel('Total Params (Millions)', color=color)\n",
        "ax1.bar(models, total_params, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:pink'\n",
        "ax2.set_ylabel('Size (MB)', color=color)\n",
        "ax2.plot(models, sizes, color=color, marker='o')\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "plt.title('Total Params and Size of Each Model')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZrvPan-ydaVh",
        "outputId": "a20d29b3-1653-4871-e563-23ce576840fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YybBG0FDCCMQ"
      },
      "source": [
        "#### **Predict CNN Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "68VIb5WoAIou"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.models import load_model\n",
        "from keras.preprocessing import image\n",
        "model_cnn=load_model('/content/best_CNN_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bc186b0d"
      },
      "outputs": [],
      "source": [
        "classes=list(train_generator.class_indices.keys())\n",
        "def prepare(img_path):\n",
        "    img = image.load_img(img_path, target_size=(224,224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = x/255\n",
        "    return np.expand_dims(x, axis=0)\n",
        "\n",
        "img_url='/content/Test/test.jpg'\n",
        "result_cnn = model_cnn.predict([prepare(img_url)])\n",
        "casting_img=image.load_img(img_url)\n",
        "plt.imshow(casting_img)\n",
        "\n",
        "classresult=np.argmax(result_cnn,axis=1)\n",
        "print(classes[classresult[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euXbHlTReseg"
      },
      "source": [
        "#### **Predict VGG16 Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rMjk-ShKesej"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.models import load_model\n",
        "from keras.preprocessing import image\n",
        "model_vgg16=load_model('/content/best_VGG16_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cmjrf-Tt8BlK"
      },
      "outputs": [],
      "source": [
        "classes=list(train_generator.class_indices.keys())\n",
        "def prepare(img_path):\n",
        "    img = image.load_img(img_path, target_size=(224,224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = x/255\n",
        "    return np.expand_dims(x, axis=0)\n",
        "\n",
        "img_url='/content/Test/test.jpg'\n",
        "result_vgg16 = model_vgg16.predict([prepare(img_url)])\n",
        "casting_img=image.load_img(img_url)\n",
        "plt.imshow(casting_img)\n",
        "\n",
        "classresult=np.argmax(result_vgg16,axis=1)\n",
        "print(classes[classresult[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XluDawQiSBbc"
      },
      "source": [
        "#### **Predict Inception Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jQW-fvESHAu"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.models import load_model\n",
        "from keras.preprocessing import image\n",
        "model_inception=load_model('/content/best_InceptionV3_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xcHJA-r77zKc"
      },
      "outputs": [],
      "source": [
        "classes=list(train_generator.class_indices.keys())\n",
        "def prepare(img_path):\n",
        "    img = image.load_img(img_path, target_size=(224,224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = x/255\n",
        "    return np.expand_dims(x, axis=0)\n",
        "\n",
        "img_url='/content/Test/test.jpg'\n",
        "result_inception = model_inception.predict([prepare(img_url)])\n",
        "casting_img=image.load_img(img_url)\n",
        "plt.imshow(casting_img)\n",
        "\n",
        "classresult=np.argmax(result_inception,axis=1)\n",
        "print(classes[classresult[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq8erty27Ip2"
      },
      "source": [
        "#### **Predict AlexNet Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Wf0Gmxe7Ip2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.models import load_model\n",
        "from keras.preprocessing import image\n",
        "model_alexnet=load_model('/content/best_AlexNet_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0fvmPuln7Ip2"
      },
      "outputs": [],
      "source": [
        "classes=list(train_generator.class_indices.keys())\n",
        "def prepare(img_path):\n",
        "    img = image.load_img(img_path, target_size=(224,224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = x/255\n",
        "    return np.expand_dims(x, axis=0)\n",
        "\n",
        "img_url='/content/Test/test.jpg'\n",
        "result_alexnet = model_alexnet.predict([prepare(img_url)])\n",
        "casting_img=image.load_img(img_url)\n",
        "plt.imshow(casting_img)\n",
        "\n",
        "classresult=np.argmax(result_alexnet,axis=1)\n",
        "print(classes[classresult[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LS9_tKl-e1He"
      },
      "source": [
        "#### **Predict ResNet50 Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iL2dRYB8e1Hh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from keras.models import load_model\n",
        "from keras.preprocessing import image\n",
        "model_alexnet=load_model('/content/best_ResNet50_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2WlPXLWge1Hi"
      },
      "outputs": [],
      "source": [
        "classes=list(train_generator.class_indices.keys())\n",
        "# Pre-Processing test data same as train data.\n",
        "def prepare(img_path):\n",
        "    img = image.load_img(img_path, target_size=(224,224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = x/255\n",
        "    return np.expand_dims(x, axis=0)\n",
        "\n",
        "img_url='/content/Test/test.jpg'\n",
        "result_alexnet = model_alexnet.predict([prepare(img_url)])\n",
        "casting_img=image.load_img(img_url)\n",
        "plt.imshow(casting_img)\n",
        "\n",
        "classresult=np.argmax(result_alexnet,axis=1)\n",
        "print(classes[classresult[0]])"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "euXbHlTReseg",
        "XluDawQiSBbc",
        "aq8erty27Ip2",
        "LS9_tKl-e1He"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}