Brain_TumorF.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zrztdzDKp9H8"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNAxXA8LqcHC",
        "outputId": "532f31be-a4ed-4ed0-96bc-33bf5d59ec7f",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/archive.zip\n",
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          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/archive.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o3_TMF7Zqo1Y"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import shutil\n",
        "import glob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taxJJs7Qqt7m",
        "outputId": "84fdcefb-a621-4fb1-e385-51886a1ffb52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('no', 98), ('yes', 155)])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#count no of img 1-brain tumor and 0-healthy\n",
        "ROOT_DIR=\"/content/brain_tumor_dataset\"\n",
        "number_of_images={}\n",
        "\n",
        "for dir in os.listdir(ROOT_DIR):\n",
        "  number_of_images[dir]=len(os.listdir(os.path.join(ROOT_DIR,dir)) )\n",
        "\n",
        "number_of_images.items()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KR5TTc1rCW3"
      },
      "source": [
        "Splitting of Data\n",
        "\n",
        "\n",
        "*   70% for training\n",
        "*   15% for validation\n",
        "\n",
        "*   15% for testing\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GelGSv3q3kd"
      },
      "outputs": [],
      "source": [
        "def dataFolder(p, split=0.7):\n",
        "    # Check if the target folder exists\n",
        "    if not os.path.exists(\"./\" + p):\n",
        "        os.mkdir(\"./\" + p)\n",
        "\n",
        "        for dir in os.listdir(ROOT_DIR):\n",
        "            # Create subdirectory in the new folder\n",
        "            os.makedirs(os.path.join(\"./\" + p, dir))\n",
        "\n",
        "            # Select a random subset of images\n",
        "            number_of_images_in_dir = number_of_images[dir]\n",
        "            selected_images = np.random.choice(\n",
        "                a=os.listdir(os.path.join(ROOT_DIR, dir)),\n",
        "                size=(math.floor(split * number_of_images_in_dir) - 2),\n",
        "                replace=False\n",
        "            )\n",
        "\n",
        "            # Copy selected images to the new location and delete originals\n",
        "            for img in selected_images:\n",
        "                original_path = os.path.join(ROOT_DIR, dir, img) # Original path\n",
        "                destination_dir = os.path.join(\"./\" + p, dir)   # Destination folder\n",
        "                shutil.copy(original_path, destination_dir)     # Copy image\n",
        "                os.remove(original_path)                        # Remove original image\n",
        "    else:\n",
        "        print(f\"{p} folder already exists\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0mxRqi0ErQkR"
      },
      "outputs": [],
      "source": [
        "dataFolder(\"train\",0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ITvlAFbrTd5"
      },
      "outputs": [],
      "source": [
        "dataFolder(\"val\",0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KPjp4xHorWLg"
      },
      "outputs": [],
      "source": [
        "dataFolder(\"test\",0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0q4YgInr0bq"
      },
      "source": [
        "preparing data using  imagegenrator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "slTd1fXprY79"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator # Import the ImageData\n",
        "from keras.applications.mobilenet import preprocess_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bo8pyfjrslkM"
      },
      "outputs": [],
      "source": [
        "def preprocessingImages1(path):\n",
        "  \"\"\"\n",
        "  input:path\n",
        "  output:pre processed images\n",
        "  \"\"\"\n",
        "  image_data=ImageDataGenerator(\n",
        "      preprocessing_function=preprocess_input,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True\n",
        "  )\n",
        "  image=image_data.flow_from_directory(\n",
        "      directory=path,\n",
        "      target_size=(224,224),\n",
        "      batch_size=32,\n",
        "      class_mode='binary'\n",
        "  )\n",
        "  return image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46y4Imlxr-Gx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6509fc61-665f-4456-f6e0-cb4f35f9b07e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 198 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "path=\"/content/train\"\n",
        "train_data=preprocessingImages1(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bOuSerO1s1Ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee408e02-9c6b-4bad-89d2-826be38c9a84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'no': 0, 'yes': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train_data.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Nl18GF6sBf3"
      },
      "outputs": [],
      "source": [
        "def preprocessingImages2(path):\n",
        "  \"\"\"\n",
        "  input:path\n",
        "  output:pre processed images\n",
        "  \"\"\"\n",
        "  image_data=ImageDataGenerator(\n",
        "      preprocessing_function=preprocess_input\n",
        "  )\n",
        "  image=image_data.flow_from_directory(\n",
        "      directory=path,\n",
        "      target_size=(224,224),\n",
        "      batch_size=32,\n",
        "      class_mode='binary'\n",
        "  )\n",
        "  return image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wBJgd8sMswF1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb2f8baa-6a16-4246-ec1b-7870e01fb186"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 20 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "path=\"/content/test\"\n",
        "test_data=preprocessingImages2(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cVnwxk-dsxyy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d558483-26f1-4a46-df46-c2a43a4997c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 20 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "path=\"/content/val\"\n",
        "val_data=preprocessingImages2(path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSMpum5AtE-Y"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JNIXq1KotC-j"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Flatten,Dense\n",
        "from keras.models import Model,load_model\n",
        "from keras.applications.mobilenet import MobileNet,preprocess_input\n",
        "import keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c4Q-GfVMt9uC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3898e6bb-057c-4ab5-ffcc-5e501e6b8dfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5\n",
            "\u001b[1m17225924/17225924\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "base_model=MobileNet(input_shape=(224,224,3),include_top=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92g06V6MusQz"
      },
      "outputs": [],
      "source": [
        "for layer in base_model.layers:\n",
        "  layer.trainable=False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "O2n03uk0uzCq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3a314478-a08d-4f94-a793-f4fbd14c10c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv1 (\u001b[38;5;33mConv2D\u001b[0m)                       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m864\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv1_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m128\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv1_relu (\u001b[38;5;33mReLU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_1 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m288\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_1_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m128\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_1_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_1 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_1_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │             \u001b[38;5;34m256\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_1_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_2 (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m113\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_2 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m576\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_2_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_2_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_2 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m8,192\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_2_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_2_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_3 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,152\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_3_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_3_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_3 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m16,384\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_3_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_3_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_4 (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m57\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_4 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │           \u001b[38;5;34m1,152\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_4_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │             \u001b[38;5;34m512\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_4_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_4 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │          \u001b[38;5;34m32,768\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_4_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_4_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_5 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m2,304\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_5_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_5_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_5 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │          \u001b[38;5;34m65,536\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_5_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_5_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_6 (\u001b[38;5;33mZeroPadding2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_6 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m2,304\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_6_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_6_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_6 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │         \u001b[38;5;34m131,072\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_6_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_6_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_7 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m4,608\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_7_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_7_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_7 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │         \u001b[38;5;34m262,144\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_7_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_7_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_8 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m4,608\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_8_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_8_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_8 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │         \u001b[38;5;34m262,144\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_8_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_8_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_9 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m4,608\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_9_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_9_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_9 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │         \u001b[38;5;34m262,144\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_9_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_9_relu (\u001b[38;5;33mReLU\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_10 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m4,608\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_10_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_10_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_10 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │         \u001b[38;5;34m262,144\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_10_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_10_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_11 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m4,608\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_11_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_11_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_11 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │         \u001b[38;5;34m262,144\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_11_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_11_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_12 (\u001b[38;5;33mZeroPadding2D\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_12 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m4,608\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_12_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_12_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_12 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │         \u001b[38;5;34m524,288\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_12_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │           \u001b[38;5;34m4,096\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_12_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_13 (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │           \u001b[38;5;34m9,216\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_13_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │           \u001b[38;5;34m4,096\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_13_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_13 (\u001b[38;5;33mConv2D\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │       \u001b[38;5;34m1,048,576\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_13_bn (\u001b[38;5;33mBatchNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │           \u001b[38;5;34m4,096\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_13_relu (\u001b[38;5;33mReLU\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m1024\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50176\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │          \u001b[38;5;34m50,177\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv1_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_1_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_1_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_1_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">113</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">113</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">576</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_2_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_2_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,192</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_2_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_2_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_3_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_3_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,384</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_3_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_3_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">57</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">57</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_4_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_4_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">32,768</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_4_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_4_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_5_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_5_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">65,536</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_5_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_5_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,304</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_6_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_6_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">131,072</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_6_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_6_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_7_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_7_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_7_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_7_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_8_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_8_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_8_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_8_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_9_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_9_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_9_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_9_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_10_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_10_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_10_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_10_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_11_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_11_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,144</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_11_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_11_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pad_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,608</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_12_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_12_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │         <span style=\"color: #00af00; text-decoration-color: #00af00\">524,288</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_12_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_12_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,216</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_13_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_dw_13_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,048,576</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_13_bn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv_pw_13_relu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50176</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │          <span style=\"color: #00af00; text-decoration-color: #00af00\">50,177</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,279,041\u001b[0m (12.51 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,279,041</span> (12.51 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,177\u001b[0m (196.00 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">50,177</span> (196.00 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,228,864\u001b[0m (12.32 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,228,864</span> (12.32 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "X=Flatten()(base_model.output)\n",
        "X=Dense(units=1,activation='sigmoid')(X)\n",
        "\n",
        "model=Model(base_model.input,X)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5EDeOHZ4u6IH"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) # Changed loss to 'binary_crossentropy'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FNiGhwKwvxPc"
      },
      "outputs": [],
      "source": [
        "##call back\n",
        "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
        "mc=ModelCheckpoint(filepath=\"./best_model.keras\",monitor='val_accuracy',verbose=1,save_best_only=True) # Changed the file extension to .keras\n",
        "es=EarlyStopping(monitor='val_accuracy',min_delta=0.01,patience=3,verbose=1)\n",
        "cb=[mc,es]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQEvAqa_wH5j",
        "outputId": "dc16c25f-fe9d-49af-ace6-9d958379f30b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 743ms/step - accuracy: 0.6209 - loss: 2.0209\n",
            "Epoch 1: val_accuracy improved from -inf to 0.75000, saving model to ./best_model.keras\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 1s/step - accuracy: 0.6254 - loss: 2.0275 - val_accuracy: 0.7500 - val_loss: 1.6821\n",
            "Epoch 2/30\n",
            "\u001b[1m5/8\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.8283 - loss: 1.1049\n",
            "Epoch 2: val_accuracy improved from 0.75000 to 0.90000, saving model to ./best_model.keras\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 78ms/step - accuracy: 0.8347 - loss: 1.1324 - val_accuracy: 0.9000 - val_loss: 0.3928\n",
            "Epoch 3/30\n",
            "\u001b[1m6/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.8831 - loss: 0.7878\n",
            "Epoch 3: val_accuracy improved from 0.90000 to 0.95000, saving model to ./best_model.keras\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 69ms/step - accuracy: 0.8858 - loss: 0.7658 - val_accuracy: 0.9500 - val_loss: 0.6109\n",
            "Epoch 4/30\n",
            "\u001b[1m5/8\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.8685 - loss: 0.6152\n",
            "Epoch 4: val_accuracy did not improve from 0.95000\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - accuracy: 0.8742 - loss: 0.6215 - val_accuracy: 0.9000 - val_loss: 0.5589\n",
            "Epoch 5/30\n",
            "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 0.9278 - loss: 0.4664\n",
            "Epoch 5: val_accuracy did not improve from 0.95000\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - accuracy: 0.9254 - loss: 0.4645 - val_accuracy: 0.9000 - val_loss: 0.4966\n",
            "Epoch 6/30\n",
            "\u001b[1m7/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - accuracy: 0.9317 - loss: 0.3408\n",
            "Epoch 6: val_accuracy did not improve from 0.95000\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 41ms/step - accuracy: 0.9308 - loss: 0.3403 - val_accuracy: 0.9000 - val_loss: 0.5945\n",
            "Epoch 6: early stopping\n",
            "best model saved to: ./best_model.keras\n"
          ]
        }
      ],
      "source": [
        "hist=model.fit(train_data,\n",
        "                         steps_per_epoch=8,\n",
        "                         epochs=30,\n",
        "                         validation_data=val_data,\n",
        "                         verbose=1,\n",
        "                         validation_steps=16,\n",
        "                         callbacks=cb)\n",
        "print(\"best model saved to:\",mc.filepath)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rk8dpfXTxre1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8025e4d7-92bb-4117-a1d8-c1fe601b3deb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.8500 - loss: 1.6034\n",
            "the model Accuracy is =  85.00000238418579\n"
          ]
        }
      ],
      "source": [
        "acc=model.evaluate(test_data)[1]\n",
        "print(\"the model Accuracy is = \",acc*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-SuGszSx-SE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0b4fca4-a0f3-445d-cf4d-1c3514b2b8a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# model graphical representation\n",
        "h=hist.history\n",
        "h.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T9Q7n7KOyBKf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "399b40c6-7be4-4b06-9808-7b8a4e296e93"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(h['accuracy'])\n",
        "plt.plot(h['val_accuracy'],c='red') # Changed 'val_accuracy' to 'accuracy'\n",
        "plt.title(\"acc vs val_acc\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSdDhI3cyJQq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "c9e6c1da-365a-4799-95a3-0e68ffdeebc4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UIjY2VjvmdtXV1SgtLdV5GFpHL0eMifIHwMaIREREre2eA4tGo8Hs2bPRt29fdOvW7Y7jcnNz4eXlpXPNy8sLubm52q/XX7vTmNstWrQISqVS+/D397/Xj3FfZg/uCGsLOQ5dKkbKqXxJaiAiImoL7jmwzJgxA8ePH8e6detasp5mmTdvHlQqlfaRnZ1t8BoAwEdpiyl9gwGwMSIREVFruqfAMnPmTPz888/4448/4Ofnd9ex3t7eyMvL07mWl5cHb29v7dfrr91pzO2sra3h5OSk85DK9IfaQ2lriXP55fgh7bJkdRAREZkzvQKLIAiYOXMmNm/ejB07diA4OLjJ1/Tu3RspKSk615KTk9G7d28AQHBwMLy9vXXGlJaW4sCBA9oxxkxpZ4kZA9sDAD5kY0QiIqJWoVdgmTFjBr799lusWbMGjo6OyM3NRW5uLq5fv64dM2nSJMybN0/7+5deeglJSUn44IMPcPr0aSxcuBCHDx/GzJkzAYjnmsyePRtvv/02fvzxRxw7dgyTJk2Cr68vRo0a1TKfspVN6h0EX6UNclRVWL33ktTlEBERmR29Asvnn38OlUqFAQMGwMfHR/tYv369dkxWVhZycnK0v+/Tpw/WrFmD//73vwgPD8fGjRuxZcsWnYW6r776KmbNmoXnn38e0dHRKC8vR1JSEmxsbFrgI7Y+G0sF/nGjMeLSP85DVcnGiERERC3pvs5hMRZSnMNyO7VGwLCPd+NsXjleeCgE84Z1lqQOIiIiU2Gwc1joJoVchtduNEb8as8lXC253sQriIiIqLkYWFrQw2GeiAlyRQ0bIxIREbUoBpYWJJPJ8NqNxogbUy/jbB4bIxIREbUEBpYWFhXogviubIxIRETUkhhYWsEr8WGQy4Dtp/Jw6FKR1OUQERGZPAaWVtDB0wEJ0WyMSERE1FIYWFrJS4M6wcZSjtTMYiSfzGv6BURERHRHDCytxFtpg6k3GiO+u+0M6tQaiSsiIiIyXQwsrehvA9rD2c4S59kYkYiI6L4wsLQiJxtLzBzYAQDwYfI5XK9hY0QiIqJ7wcDSyib2DkQ7Z1vkllZhFRsjEhER3RMGllZmbaHAy0PExoif7TyPksoaiSsiIiIyPQwsBjAyoh3CvB1RVlWHz3ZekLocIiIik8PAYgAK+c0j+1ftvYQrbIxIRESkFwYWAxnQyQMPhIiNET9MZmNEIiIifTCwGIhMJsPcYZ0BAD+kXcbp3FKJKyIiIjIdDCwGFOHvjEe6e0MQgPfYGJGIiKjZGFgMbM6QUCjkMqSczseBi4VSl0NERGQSGFgMLMTDAePqGyMmsTEiERFRczCwSOClQR1ha6nAkawSbDvBxohERERNYWCRgKeTDaY9WN8Y8TQbIxIRETWBgUUiz/cPgYudJS4WVOD7VDZGJCIiuhsGFok42lhi1sMdAQAfJp9lY0QiIqK7YGCR0IQHAuDnYov8smqs3JMhdTlERERGi4FFQtYWCswZEgoAWLbzAoor2BiRiIioMQwsEnss3BedfZxQVl2HpX+cl7ocIiIio8TAIjG5XIa5Nxojfr0vE5eLKyWuiIiIyPgwsBiB/h3d0ae9G2rUGvyHjRGJiIgaYGAxAjKZDK8NFe+ybD5yBady2BiRiIjoVgwsRiLc3xnDe/hAEIB3k05LXQ4REZFRYWAxInOGhMJCLsMfZwqw7wIbIxIREdVjYDEiwe72GB8TAICNEYmIiG7FwGJkZg3qADsrBf7KLkHS8VypyyEiIjIKDCxGxtPRBtMeDAEAvLftDGrZGJGIiIiBxRg992Aw3OytcPFaBTYczpa6HCIiIskxsBghsTFiBwDAR9vPobKmTuKKiIiIpMXAYqSeig2Ev6stCsqqseJ/bIxIRERtm96BZffu3RgxYgR8fX0hk8mwZcuWu45/5plnIJPJGjy6du2qHbNw4cIGXw8LC9P7w5gTKwu5tjHiF7svorC8WuKKiIiIpKN3YKmoqEB4eDiWLl3arPEff/wxcnJytI/s7Gy4urpizJgxOuO6du2qM+7PP//UtzSzM6KHL7r6OqG8ug5L2BiRiIjaMAt9XzBs2DAMGzas2eOVSiWUSqX291u2bEFxcTGmTJmiW4iFBby9vfUtx6zVN0acuOIgvt2fial9g+Hvaid1WURERAZn8DUsK1asQFxcHAIDA3Wunzt3Dr6+vggJCcGECROQlZV1x/eorq5GaWmpzsNcPdjRA/06uKNWLeCD389IXQ4REZEkDBpYrl69it9++w3Tpk3TuR4bG4tVq1YhKSkJn3/+OTIyMvDggw+irKys0fdZtGiR9s6NUqmEv7+/IcqXTH1jxC3pV3H8ikriaoiIiAzPoIFl9erVcHZ2xqhRo3SuDxs2DGPGjEGPHj0QHx+PX3/9FSUlJdiwYUOj7zNv3jyoVCrtIzvbvM8q6e6nxIhwXwDAu9t4l4WIiNoegwUWQRCwcuVKTJw4EVZWVncd6+zsjE6dOuH8+cYXmlpbW8PJyUnnYe7mDOkEC7kMu88WYM/5a1KXQ0REZFAGCyy7du3C+fPn8eyzzzY5try8HBcuXICPj48BKjMNgW72mBB7ozHib6eh0bAxIhERtR16B5by8nKkp6cjPT0dAJCRkYH09HTtItl58+Zh0qRJDV63YsUKxMbGolu3bg2+NmfOHOzatQuXLl3C3r17MXr0aCgUCowfP17f8szarEEdYW+lwLErKvx6PEfqcoiIiAxG78By+PBhREZGIjIyEgCQmJiIyMhIzJ8/HwCQk5PTYIePSqXCDz/8cMe7K5cvX8b48eMRGhqKsWPHws3NDfv374eHh4e+5Zk1dwdrPNefjRGJiKjtkQmCYPJzC6WlpVAqlVCpVGa/nqW8ug4D3vsD18pr8NbIrpjYO0jqkoiIiO6JPj+/2UvIxDhYW+DvgzoCAD5OOYeKajZGJCIi88fAYoLGRQcg0M0O18pr8CUbIxIRURvAwGKCbm2M+N/dF3CNjRGJiMjMMbCYqOHdfdC9nRIVNWos2cHGiEREZN4YWJpDY3y7ceobIwLAdwcykVlYIXFFRERErYeB5W4uXwYmTACiowEj3EzVt4M7HuxY3xjxrNTlEBERtRoGlrtxcgI2bwbS0oDDh6WuplH1jRF//IuNEYmIyHwxsNyNkxMwcqT4/Ntvpa3lDrq1U2JkhNgY8Z2k0xJXQ0RE1DoYWJoycaL469q1QG2ttLXcwcuDQ2GpkOF/567hf+cKpC6HiIioxTGwNGXwYMDDAygoAJKTpa6mUQFudpgQGwhAvMvCxohERGRuGFiaYmkJ1Ddh/OYbaWu5i1kPd4CDtQWOXynFz8fYGJGIiMwLA0tz1E8LbdkClJZKWsqduDlY4/kbjRHf33YGNXXGtxWbiIjoXjGwNEdUFBAaClRVAZs2SV3NHT3bLxjuDtbIKqrE2oNZTb+AiIjIRDCwNIdMdvMuixFPC9lbW+ClOLEx4icp51DOxohERGQmGFiaa8IE8dc//hAPlDNS46L9Eexuj8KKGizffVHqcoiIiFoEA0tzBQUBDz4onni7Zo3U1dyRpUKOV+LFxojL/3cRBWVsjEhERKaPgUUfTz8t/mqkh8jVG9bNG+H+zqisUePTHeekLoeIiOi+MbDoY8wYwMoKOHYM+Osvqau5I5lMhrk3juxfcyALl66xMSIREZk2BhZ9uLgAI0aIz4148S0A9G7vhgGhHqjTCHj/9zNSl0NERHRfGFj0VT8ttGYNoFZLW0sTXo0Pg0wG/Hw0B0cvl0hdDhER0T1jYNHXI48Arq5ATg6wY4fU1dxVF18njI5oBwBY/NtpCAKP7CciItPEwKIvKytg7FjxuZEvvgWAfwzuBCuFHHsvFOJ/565JXQ4REdE9YWC5F/WHyP3wA1Bh3Ata/V3tMLG32Bhx8W9sjEhERKaJgeVe9O4NhISIYWXrVqmradKMgR3gaG2Bkzml+OnoVanLISIi0hsDy72QyW4uvjXy3UIA4Gpvhb8NaA8AeG/bGVTXGfdiYSIiotsxsNyr+sDy++9Abq60tTTDlL5B8HS0xuXi61hzgI0RiYjItDCw3KuOHYHYWECjAdatk7qaJtlZWWB2XCcAwKc7zqOsqlbiioiIiJqPgeV+mEAH51uN7eWHEHd7FLExIhERmRgGlvuRkABYWABpacDJk1JX0yQLhRyvDq1vjJiB/LIqiSsiIiJqHgaW++HuDgwbJj43gTNZACC+qzci/J1xvVaNT1LYGJGIiEwDA8v9ql98+9134noWIyeTyTB3mNgYce3BbFwsKJe4IiIioqYxsNyvESMAJycgKwv43/+krqZZHghxw8NhnlBrBHzw+1mpyyEiImoSA8v9srUFxowRn5vI4lsAeHVoKGQy4JdjOUjPLpG6HCIiortiYGkJ9dNC338PVJnGQtYwbyc8HukHAFj82yk2RiQiIqPGwNIS+vcH/P2B0lLgp5+krqbZEod0gpWFHPsvFmHX2QKpyyEiIrojBpaWIJcDEyaIz01ktxAAtHO2xWQ2RiQiIhPAwNJS6g+R+/VX4No1aWvRw4sDOsDRxgKnc8uw9a8rUpdDRETUKAaWltKlCxAZCdTVAevXS11Ns7nYW2H6jcaI7287y8aIRERklPQOLLt378aIESPg6+sLmUyGLVu23HX8zp07IZPJGjxyb2sYuHTpUgQFBcHGxgaxsbE4ePCgvqVJr/4uiwlNCwHAlD7B8HKyxpWS6/h2PxsjEhGR8dE7sFRUVCA8PBxLly7V63VnzpxBTk6O9uHp6an92vr165GYmIgFCxYgLS0N4eHhiI+PR35+vr7lSWv8eHE9y/79wDnTOUXW1kqBf9xojLhkxzmUsjEiEREZGb0Dy7Bhw/D2229j9OjRer3O09MT3t7e2odcfvOP/s9//oPnnnsOU6ZMQZcuXbBs2TLY2dlh5cqV+pYnLW9vYPBg8fl330lbi56ejPJDew97FFfW4otdF6Quh4iISIfB1rBERETAx8cHgwcPxp49e7TXa2pqkJqairi4uJtFyeWIi4vDvn37Gn2v6upqlJaW6jyMxq3TQiZ0tonYGFE8sn/FnxnIKzWN82SIiKhtaPXA4uPjg2XLluGHH37ADz/8AH9/fwwYMABpaWkAgGvXrkGtVsPLy0vndV5eXg3WudRbtGgRlEql9uHv79/aH6P5Ro0C7O2BCxfEqSETMqSLF3oGOKOqVoOPtpvOlBYREZm/Vg8soaGheOGFFxAVFYU+ffpg5cqV6NOnDz788MN7fs958+ZBpVJpH9nZ2S1Y8X2ytwcef1x8bkJH9QP1jRE7AwA2HM7G+Xw2RiQiIuMgybbmmJgYnD9/HgDg7u4OhUKBvLw8nTF5eXnw9vZu9PXW1tZwcnLSeRiV+qP6168HamqkrUVPMcGuiOssNkZ8f9sZqcshIiICIFFgSU9Ph4+PDwDAysoKUVFRSElJ0X5do9EgJSUFvXv3lqK8+zdoEODjAxQVAb/9JnU1enslPgxyGZB0IhdpWcVSl0NERKR/YCkvL0d6ejrS09MBABkZGUhPT0dWlnh+x7x58zBp0iTt+I8++ghbt27F+fPncfz4ccyePRs7duzAjBkztGMSExOxfPlyrF69GqdOncL06dNRUVGBKVOm3OfHk4hCATz1lPjcxKaFACDU2xFP9LzRGPHX02yMSEREkrPQ9wWHDx/GwIEDtb9PTEwEAEyePBmrVq1CTk6ONrwA4i6gl19+GVeuXIGdnR169OiB7du367xHQkICCgoKMH/+fOTm5iIiIgJJSUkNFuKalKefBj74QGyGWFICODtLXZFe/jG4E7b+dRUHLxXhjzP5eDjMhP+3ICIikycTzOCfz6WlpVAqlVCpVMaznkUQgB49gOPHgf/+F3juOakr0tuiX0/hi90XEerliF9fehAKuUzqkoiIyIzo8/ObvYRai0x2c/GtiR3VX2/6gPZwsrHAmbwybD7CxohERCQdBpbW9NRTYnDZvRu4dEnqavTmbGeFFwd2AAD85/czqKplY0QiIpIGA0tr8vcHBgwQn5vYUf31nukTBG8nG1xVVeGbfZlSl0NERG0UA0trM9Gj+uvZWCqQOPhGY8Q/zkN1nY0RiYjI8BhYWtsTTwA2NsDp00BqqtTV3JPHe7ZDR08HqK7XYhkbIxIRkQQYWFqbkxMwcqT43EQX397aGHHlnxnIVbExIhERGRYDiyHUTwutXQvU1Ulbyz2K6+yJXoEuqK7T4KPtZ6Uuh4iI2hgGFkMYMgTw8ADy84HkZKmruSdiY0TxLovYGLFM4oqIiKgtYWAxBEtLYNw48bkJHtVfr1eQKwZ38YJGAN5NYmNEIiIyHAYWQ6mfFtqyBSgz3bsTr8aHQi4Dfj+Zh9TMIqnLISKiNoKBxVB69QI6dQKuXwc2bZK6mnvW0csRY6L8AQCLf2NjRCIiMgwGFkORyW7eZTHhaSEAmD24I6wt5Dh0qRgpp/KlLoeIiNoABhZDmjBB/HXHDuCK6fbm8VHaYkrfYADAO0mnodbwLgsREbUuBhZDCg4G+vUTT7xds0bqau7L9IfaQ2lriXP55fgh7bLU5RARkZljYDG0+g7OJj4tpLSzxIyB7QEAHyafZWNEIiJqVQwshjZ2LGBlBRw7Bhw9KnU192VS7yD4Km2Qo6rC6r2XpC6HiIjMGAOLobm4AI8+Kj438bssNpYK/ONGY8Slf5yHqpKNEYmIqHUwsEihflpozRpAbdpTKY/39EMnLweUVtXhs13npS6HiIjMFAOLFB55RLzTcvUq8McfUldzXxRyGV670Rjxqz2XcLXkusQVERGROWJgkYK1tbiWBTDZDs63ejjMEzHBrqhhY0QiImolDCxSqT9E7ocfgMpKaWu5T7c2RtyYehln80y39QARERknBhap9OkjnstSXi72FzJxPQNcMLSrNxsjEhFRq2BgkYpMdnPxrRlMCwHAK0NDoZDLsP1UHg5dYmNEIiJqOQwsUqoPLL//DuTlSVtLC2jv4YCxvdgYkYiIWh4Di5Q6dQJiYsStzevWSV1Ni5gd1xE2lnKkZhYj+aTphzAiIjIODCxSM5MOzvW8nGzwbD+xMeK7286gTq2RuCIiIjIHDCxSS0gALCyA1FTg1Cmpq2kRLzzUHs52ljjPxohERNRCGFik5uEBDB0qPjeTxbdONpaYObADAODD5HO4XmPap/kSEZH0GFiMwa27hTTmMYUysXcg2jnbIre0CqvYGJGIiO4TA4sxeOwxwMkJyMoC/vxT6mpahLWFAi8PERsjfrbzPEoqaySuiIiITBkDizGwtQWefFJ8biaLbwFgZEQ7hHk7oqyqDp/tvCB1OUREZMIYWIxF/bTQ998DVVXS1tJCFHIZXrtxZP+qvZdwhY0RiYjoHjGwGIuHHgL8/ACVCvj5Z6mraTEDOnnggRCxMeKHyWyMSERE94aBxVjI5cCECeJzM9ktBNQ3RuwMAPgh7TJO55ZKXBEREZkiBhZjUn+I3K+/AoWF0tbSgiL8nfFId28IAvAeGyMSEdE9YGAxJl27AhERQG0tsH691NW0qDlDxMaIKafzceCi+YQxIiIyDAYWY1N/l8WMpoUAIMTDAeOibzRGTGJjRCIi0o/egWX37t0YMWIEfH19IZPJsGXLlruO37RpEwYPHgwPDw84OTmhd+/e2LZtm86YhQsXQiaT6TzCwsL0Lc08jB8vrmfZtw84f17qalrUS4M6wtZSgSNZJdh2go0RiYio+fQOLBUVFQgPD8fSpUubNX737t0YPHgwfv31V6SmpmLgwIEYMWIEjhw5ojOua9euyMnJ0T7+NJMD1PTm4wPExYnPv/tO2lpamKeTDaY9WN8Y8TQbIxIRUbNZ6PuCYcOGYdiwYc0e/9FHH+n8/t///je2bt2Kn376CZGRkTcLsbCAt7e3vuWYp4kTgd9/Fw+Rmz8fkMmkrqjFPN8/BN/uz8TFggp8n3oZ42MCpC6JiIhMgMHXsGg0GpSVlcHV1VXn+rlz5+Dr64uQkBBMmDABWVlZd3yP6upqlJaW6jzMyqhRgJ0dcOECcOCA1NW0KEcbS8x6uCMA4MPks2yMSEREzWLwwPL++++jvLwcY8eO1V6LjY3FqlWrkJSUhM8//xwZGRl48MEHUVZW1uh7LFq0CEqlUvvw9/c3VPmG4eAAPP64+NyMjuqvN+GBAPi52CK/rBor92RIXQ4REZkAgwaWNWvW4I033sCGDRvg6empvT5s2DCMGTMGPXr0QHx8PH799VeUlJRgw4YNjb7PvHnzoFKptI/s7GxDfQTDqT+qf906oMa8GgdaWygwZ0goAGDZzgsoqjCvz0dERC3PYIFl3bp1mDZtGjZs2IC4+kWld+Ds7IxOnTrh/B12yVhbW8PJyUnnYXYGDQK8vYGiIiApSepqWtxj4b7o7OOEsuo6LP3DvHZDERFRyzNIYFm7di2mTJmCtWvXYvjw4U2OLy8vx4ULF+Dj42OA6oyUhQXw1FPiczOcFpLLZZh7ozHiN/sykV1UKXFFRERkzPQOLOXl5UhPT0d6ejoAICMjA+np6dpFsvPmzcOkSZO049esWYNJkybhgw8+QGxsLHJzc5GbmwuVSqUdM2fOHOzatQuXLl3C3r17MXr0aCgUCowfP/4+P56Jq58W+uknoKRE0lJaQ/+O7ujT3g01ajZGJCKiu9M7sBw+fBiRkZHaLcmJiYmIjIzE/PnzAQA5OTk6O3z++9//oq6uDjNmzICPj4/28dJLL2nHXL58GePHj0doaCjGjh0LNzc37N+/Hx4eHvf7+UxbRIR4XH91NbBxo9TVtDiZTIbXhop3WTanX8HJq2a224uIiFqMTDCDM9JLS0uhVCqhUqnMbz3L4sXAvHnAQw8BO3dKXU2rmLEmDb8czcGAUA+smhIjdTlERGQg+vz8Zi8hYzdhgvjrrl1AZqa0tbSSOUNCYSGXYeeZAqw7eOfzd4iIqO1iYDF2/v7AgAHiczM7qr9esLs9ZgzsAAB4ffMxJB3PlbgiIiIyNgwspqC+g/M33wCmP4PXqNlxHZHQyx8aAfj7uiPYd6FQ6pKIiMiIMLCYgieeAGxsgNOngbQ0qatpFTKZDP8a3Q1Dunihpk6D574+jONXVE2/kIiI2gQGFlOgVAKPPSY+//ZbaWtpRRYKOT4ZH4nYYFeUV9fhma8O4tK1CqnLIiIiI8DAYirqp4XWrgXq6qStpRXZWCqwfHIvdPFxwrXyGkxceQD5pVVSl0VERBJjYDEV8fGAuzuQlwckJ0tdTatysrHE6qkxCHSzQ3bRdUxaeRCq67VSl0VERBJiYDEVlpbAuHHiczOeFqrn4WiNb6bGwsPRGqdzyzBt9SFU1aqlLouIiCTCwGJK6o/q37wZKCuTthYDCHCzw9dTY+BoY4FDl4oxc00a6tQaqcsiIiIJMLCYkpgYoGNH4Pp1MbS0AZ19nLBicjSsLeTYfiofczcdgxkczkxERHpiYDElMpnumSxtREywK5Y81RMKuQwbUy9j8W+npS6JiIgMjIHF1NQf1Z+SAly9Km0tBjS4ixcWP94dAPDF7ov4YtcFiSsiIiJDYmAxNSEhQN++4om3a9ZIXY1Bjenlj3nDxO7Oi347je8PZ0tcERERGQoDiymqX3zbhqaF6r3wUHs83z8EADB30zEkn8yTuCIiIjIEBhZTNHasuM356FHx0cbMGxaGJ6P8oNYImLkmDQcziqQuiYiIWhkDiylydQWGDxeft4EzWW4nk8mw+PHuGBTmieo6DZ5dfQgnr5ZKXRYREbUiBhZTVb9baM0aQN32DlSzUMixdEJPRAe5oKyqDpO/OoiswkqpyyIiolbCwGKqhg8HnJ2BK1eAnTulrkYSNpYKfDk5GmHejigoq8bElQdQUFYtdVlERNQKGFhMlbW1uJYFaJPTQvWUtpb4emoM/FxskVlYickrD6K0in2HiIjMDQOLKaufFtq4Eahsu9Mhnk42+PbZWLg7WOFkTimeW32YfYeIiMwMA4sp69MHCAoCysuBrVulrkZSQe72WDUlBg7WFjiQUYS/rz3CvkNERGaEgcWUyeU3z2Rpw9NC9bq1U2L5pF6wUsjx+8k8/N/m4+w7RERkJhhYTF19YNm2DcjjIWq927vhk/ERkMuA9Yez8d62M1KXRERELYCBxdSFhgLR0eLW5vXrpa7GKAzt5oN/jRb7Dn228wK+/N9FiSsiIqL7xcBiDtpgB+emjI8JwCvxoQCAt385hU1plyWuiIiI7gcDizlISAAUCuDwYeD0aamrMRovDmiPqX2DAQCvbDyKHac5ZUZEZKoYWMyBpycwdKj4nItvtWQyGf7f8M4YHdkOao2AF79LQ2om+w4REZkiBhZzcetuIQ2389aTy2V498keGBDqgapaDaZ8dQhncsukLouIiPTEwGIuRo4EHB2BzExgzx6pqzEqlgo5PpvQEz0DnFFaVYdJKw8gu6jtHrRHRGSKGFjMha0t8OST4nMuvm3AzsoCK5+JRicvB+SVVmPSyoO4Vs6+Q0REpoKBxZzUTwtt2ABUVUlbixFytrPC11Nj0c7ZFhnXKjDlq0Mor66TuiwiImoGBhZzMmAA4OcHqFTAL79IXY1R8lba4OtnY+Bqb4VjV1R4/uvDqK5j3yEiImPHwGJO5HLgqafE59wtdEftPRywako07K0U2HuhEP9Ynw61hkf4ExEZMwYWc1N/iNwvvwCFhdLWYsR6+Dnjvzf6Dv16LBf/3Mq+Q0RExoyBxdx06waEhwO1teJaFrqjvh3c8WFCBGQyYM2BLHyYfFbqkoiI6A4YWMxR/V0WTgs1aXgPH7w1shsA4JMd57FqT4bEFRERUWMYWMzR+PHiepa9e4ELF6Suxug9/UAgEgd3AgAs/OkktqZfkbgiIiK6nd6BZffu3RgxYgR8fX0hk8mwZcuWJl+zc+dO9OzZE9bW1ujQoQNWrVrVYMzSpUsRFBQEGxsbxMbG4uDBg/qWRvV8fYFBg8Tn330nbS0mYtbDHTC5dyAA4OUNf2HnmXyJKyIiolvpHVgqKioQHh6OpUuXNmt8RkYGhg8fjoEDByI9PR2zZ8/GtGnTsG3bNu2Y9evXIzExEQsWLEBaWhrCw8MRHx+P/Hz+0Lhnt3Zw5mLSJslkMiwY0RUjwn1RpxEw/ds0HMkqlrosIiK6QSbcx9YImUyGzZs3Y9SoUXcc89prr+GXX37B8ePHtdfGjRuHkpISJCUlAQBiY2MRHR2NJUuWAAA0Gg38/f0xa9YszJ07t8k6SktLoVQqoVKp4OTkdK8fx7yUlwNeXkBlJbB/PxAbK3VFJqGmToNnVx/C/85dg7OdJb5/oTc6ejlKXRYRkVnS5+d3q69h2bdvH+Li4nSuxcfHY9++fQCAmpoapKam6oyRy+WIi4vTjrlddXU1SktLdR50GwcHYPRo8TmP6m82Kws5lj0dhXB/Z5RU1mLSyoO4UnJd6rKIiNq8Vg8subm58PLy0rnm5eWF0tJSXL9+HdeuXYNarW50TG5ubqPvuWjRIiiVSu3D39+/1eo3afVH9a9bB9TUSFuLCbG3tsBXz0SjvYc9clRVmLTiAIoq+P0jIpKSSe4SmjdvHlQqlfaRnZ0tdUnGKS5OnBYqLARuWTNETXO1t8I3z8bCR2mDCwUVmLLqECrYd4iISDKtHli8vb2Rl5ency0vLw9OTk6wtbWFu7s7FApFo2O8vb0bfU9ra2s4OTnpPKgRFhY3j+rntJDefJ1t8c2zMXC2s8Rf2SX427epqKnTSF0WEVGb1OqBpXfv3khJSdG5lpycjN69ewMArKysEBUVpTNGo9EgJSVFO4buQ/200I8/ik0RSS8dPB3x1TPRsLNS4H/nriFxQzo07DtERGRwegeW8vJypKenIz09HYC4bTk9PR1ZWVkAxOmaSZMmacf/7W9/w8WLF/Hqq6/i9OnT+Oyzz7Bhwwb84x//0I5JTEzE8uXLsXr1apw6dQrTp09HRUUFpkyZcp8fjxAZCXTpAlRXAxs3Sl2NSYoMcMGyp6NgqZDh56M5WPjTCfYdIiIyML0Dy+HDhxEZGYnIyEgAYtiIjIzE/PnzAQA5OTna8AIAwcHB+OWXX5CcnIzw8HB88MEH+PLLLxEfH68dk5CQgPfffx/z589HREQE0tPTkZSU1GAhLt0DmezmXRZOC92z/p088P6YcMhkwNf7MvFJynmpSyIialPu6xwWY8FzWJqQlQUEiqe4IjMTCAiQth4TtnrvJSz48QQA4K1R3TDxgUCJKyIiMl1GdQ4LGYGAAOChh8TnPKr/vkzuE4S/D+oIAJi/9Th+OZojcUVERG0DA0tbwaP6W8w/4jpiQmwABAGYvf4I/jx3TeqSiIjMHgNLW/Hkk4C1NXDqFHDkiNTVmDSZTIY3R3bDI929UasW8Pw3h/FXdonUZRERmTUGlrZCqQQee0x8/u230tZiBhRyGT5MiEDfDm6orFFjyqpDuFBQLnVZRERmi4GlLamfFlqzBqjjqa33y9pCgS8m9kL3dkoUVdRg0oqDyFGx7xARUWtgYGlL4uMBNzcgLw/Yvl3qasyCg7UFVk2JRoi7Pa6UXMekFQdRUsm+Q0RELY2BpS2xsgLGjROfc1qoxbg5WOPrZ2Pg5WSNc/nlmLrqECpreAeLiKglMbC0NfWHyG3eDJRzzUVL8XOxwzfPxkJpa4m0rBK8+F0aatXsO0RE1FIYWNqa2FigY0egslIMLdRiOnk5YuUzvWBjKcfOMwV45fu/2HeIiKiFMLC0NTyqv1VFBbri86ejYCGXYUv6Vbz1y0n2HSIiagEMLG3RhAnirykpwNWr0tZihgaGeuK9MT0AAF/tuYTPdl6QuCIiItPHwNIWtW8P9OkDaDTA2rVSV2OWRkf64Z+PdgEAvLftDNYezGriFUREdDcMLG0Vp4Va3bP9gjFjYHsAwP9tPoak4+w7RER0rxhY2qqxYwFLS+Cvv4Bjx6SuxmzNGRKKcdH+0AjA39emY+8F9h0iIroXDCxtlZsb8Mgj4nOeydJqZDIZ3h7VDfFdvVCj1uD5r1Nx/IpK6rKIiEwOA0tbVn9U/3ffietZqFVYKOT4eFwkHghxRXl1HSavPIiMaxVSl0VEZFIYWNqy4cMBZ2fgyhVg506pqzFrNpYKLJ/UC119nVBYUYOJKw4gr7RK6rKIiEwGA0tbZmMDjBkjPue0UKtztLHEqikxCHKzw+Vise+QqrJW6rKIiEwCA0tbVz8ttHGjePottSoPR2t882wsPBytcSavDM+uPoTrNWqpyyIiMnoMLG1d375AYCBQVgb8+KPU1bQJ/q52+HpqDBxtLHA4sxgz17DvEBFRUxhY2jq5/OaZLJwWMpjOPk5YMTka1hZypJzOx2s/HGXfISKiu2BgoZuBJSkJyM+XtpY2JCbYFUuf6gmFXIZNaVewOOm01CURERktBhYCwsKAXr0AtRpYv17qatqUuC5eeOcJse/Qf3dfxLJd7DtERNQYBhYS1S++5VH9BvdklB9efyQMALD4t9PYcChb4oqIiIwPAwuJxo0DFArg0CHgzBmpq2lznu/fHi88FAIAmLvpKJJP5klcERGRcWFgIZGnJxAfLz7n4ltJzB0ahjFRftAIwIw1aThwsVDqkoiIjAYDC910624hHtVvcDKZDIse7464zl6oqdNg2urDOHm1VOqyiIiMAgML3TRyJODgAFy6BOzdK3U1bZKFQo4lT0UiJsgVZdV1mLTyILIKeaAfEREDC91kZwc88YT4nItvJWNjqcDyyb0Q5u2Ia+XVeHrFAeSXse8QEbVtDCykq3630IYNQHW1tLW0YUpbS3w9NQYBrnbIKqrE5JWHUFrFvkNE1HYxsJCuAQOAdu2AkhLgl1+krqZN83SywTfPxsDdwRqnckoxbfVhVNWy7xARtU0MLKRLoQCeekp8zmkhyQW62WPVlGg4WlvgYEYRZq09gjr2HSKiNoiBhRqqnxb65RegqEjaWgjd2imxfHIvWFnIkXwyD69vPgZBYN8hImpbGFiooe7dgR49gNpacS0LSe6BEDd8Oj4Schmw4fBlvLuNh/sRUdvCwEKNq7/LwkPkjEZ8V28serw7AODznRfw5f8uSlwREZHhMLBQ48aPB2QyYM8e4CJ/MBqLhOgAvDo0FADw9i+n8EPqZYkrIiIyDAYWaly7dsCgQeLz776TthbSMf2h9pjWLxgA8OoPR7HjNPsOEZH5Y2ChO7u1gzMXeRoNmUyG1x/pjMcj20GtEfDid2k4fImLo4nIvN1TYFm6dCmCgoJgY2OD2NhYHDx48I5jBwwYAJlM1uAxfPhw7ZhnnnmmwdeHDh16L6VRSxo9GrC1Bc6dA+7yvzEZnlwuwztP9sDDYZ6oqtVg6qpDOJ3LvkNEZL70Dizr169HYmIiFixYgLS0NISHhyM+Ph75+fmNjt+0aRNycnK0j+PHj0OhUGDMmDE644YOHaozbu3atff2iajlODqKoQXg4lsjZKmQY+lTPREV6ILSqjpMWnEQ2UXsO0RE5knvwPKf//wHzz33HKZMmYIuXbpg2bJlsLOzw8qVKxsd7+rqCm9vb+0jOTkZdnZ2DQKLtbW1zjgXF5d7+0TUsuo7OK9bJ25zJqNia6XAysnRCPVyRH5ZNSauOIBr5WypQETmR6/AUlNTg9TUVMTFxd18A7kccXFx2LdvX7PeY8WKFRg3bhzs7e11ru/cuROenp4IDQ3F9OnTUVhYeMf3qK6uRmlpqc6DWsngwYCnJ3DtGrBtm9TVUCOUdpZYPTUG7ZxtcamwEs98dRBl7DtERGZGr8By7do1qNVqeHl56Vz38vJCbm5uk68/ePAgjh8/jmnTpulcHzp0KL7++mukpKTgnXfewa5duzBs2DCo1Y33TVm0aBGUSqX24e/vr8/HIH1YWIhbnAEe1W/EvJVi3yE3eyscv1KK579OZd8hIjIrBt0ltGLFCnTv3h0xMTE618eNG4fHHnsM3bt3x6hRo/Dzzz/j0KFD2LlzZ6PvM2/ePKhUKu0jOzvbANW3YfW7hX78EVCppK2F7ijEwwGrpsTA3kqBfRcLMXtdOtQa7u4iIvOgV2Bxd3eHQqFAXp7uuQ95eXnw9va+62srKiqwbt06PPvss03+OSEhIXB3d8f58+cb/bq1tTWcnJx0HtSKevYEOncGqqqAH36Quhq6i+5+Siyf1AtWCjmSTuTi/205zr5DRGQW9AosVlZWiIqKQkpKivaaRqNBSkoKevfufdfXfv/996iursbT9Ys47+Ly5csoLCyEj4+PPuVRa5HJbi6+5bSQ0evTwR0fj4uATAasPZiF/ySflbokIqL7pveUUGJiIpYvX47Vq1fj1KlTmD59OioqKjBlyhQAwKRJkzBv3rwGr1uxYgVGjRoFNzc3nevl5eV45ZVXsH//fly6dAkpKSkYOXIkOnTogPj4+Hv8WNTiJkwQf925E8jKkrQUatqw7j54e1Q3AMCnO87jqz0ZEldERHR/LPR9QUJCAgoKCjB//nzk5uYiIiICSUlJ2oW4WVlZkMt1c9CZM2fw559/4vfff2/wfgqFAkePHsXq1atRUlICX19fDBkyBG+99Rasra3v8WNRiwsMBPr3B3bvBtasAebOlboiasKE2EAUldfgg+SzeOOnk3Cxs8KoyHZSl0VEdE9kghlMcJeWlkKpVEKlUnE9S2v68kvgueeALl2A48fFqSIyaoIg4I2fTmLV3kuwkMvw5eReGBDqKXVZREQA9Pv5zV5C1HxPPglYWwMnTwLp6VJXQ80gk8kw/9EueCzcF3UaAdO/TUNqZrHUZRER6Y2BhZrP2RkYMUJ8zqP6TYZcLsP7Y8LRv5MHrteqMXXVIbz980n8eiwHeaVVUpdHRNQsnBIi/fz4IzByJODtDWRniwfLkUmorKnDhC8P4EhWic71ds626Bnogp4BzogKdEFnHydYKvhvGSJqffr8/GZgIf3U1AC+vkBhIZCUBHAnl0mpqlXjt+M5SM0sRmpmCc7kluL2s+VsLOXo4eeMngEuiLoRZNwcuACeiFoeAwu1rhkzgM8+E89m4bksJq28ug5/ZZcgLbMYqVnFOJJVAtX1hn2IAt3sEBXggshAF0QFuCDU2xEKORddE9H9YWCh1rVvH9CnD2BnB+TlAQ4OUldELUSjEXDxWgXSMouRllWM1MxinMsvbzDO3kqBiADxLkzPQBf09HeB0s5SgoqJyJQxsFDrEgSgUyfg/HnxDkszTi8m06WqrMWR7GKkZZXgyI27MOXVdQ3GtfewvzGFJE4ltfdwgJx3YYjoLhhYqPW98QawcCEwZAiwbZvU1ZABqTUCzuWXITWzGGmZJUjLKkbGtYoG45xsLBAZ4HLjLowzIvyd4WjDuzBEdBMDC7W+8+eBjh0BuRy4fBlg36c2rbC8GkeySrTTSEcvq3C9Vq0zRiYDQr0cb+xIEu/CBLnZQcYDCInaLAYWMow+fcT1LB98ACQmSl0NGZFatQZncm/chbkRYi4XX28wztXeCj0DnLV3YsL9lbCz4lZ5oraCgYUM47PPxB1DERHAkSNSV0NGLr+0CmlZ4lqY1MxiHLuiQk2dRmeMQi5DZx9HRNUv5g1wgZ+LLe/CEJkpBhYyjMJC8QC5ujrg2DGgWzepKyITUl2nxsmrpUjNFBfypmYWI7eRk3c9HK21h9r1DHBBt3ZK2FgqJKiYiFoaAwsZzsiR4um3r70GLF4sdTVk4q6WXNdOI6VlleDEFRXqbjvZzlIhQ1dfpTbA9Ax0ho/SVqKKieh+MLCQ4WzcCIwZA/j5AZmZ4iJcohZSVavGsSuqGzuSxCBzrbymwThfpY32ULuegS7o4uMEKwv+f5HI2DGwkOFUVYnTQioVsGMHMHCg1BWRGRMEAdlF15GaVaTdUn0qp2F7AWsLOXr4KbXrYHoGuMDDke0FiIwNAwsZ1nPPAV9+CUydCqxYIXU11MZUVNfhr8slN+7AiCGmpLJhe4EAVzvtWpjIABeEeTvCgk0eiSTFwEKGtXs38NBDgJMTkJsL2HI9AUlHEMT2AuJi3pvtBW7/m87OSoFwvxuLeQOdEenvAhd7K2mKJmqjGFjIsDQaIDgYyMoC1q0DEhKkrohIR2lVLdJv7ERKyypGelYJyhppLxDiYX9Ll2oXdPRkewGi1sTAQob3f/8H/PvfwKOPAj/9JHU11BhBAFJSxIe7u7hQ2t9f/NXXF7BoOwe2qTUCzueX39yRlFmMi420F3C0sUCEv7M2xEQEOMOJ7QWIWgwDCxneqVNAly7iD72rVwEPD6kronpqNbB5s7jtPDW18TFyubh4uj7ANParj49Zh5riihocyS7W9khKzy5ptL1AJ09H9Ay82ak6xN2eB9sR3SMGFpJGr17iD8RPPwVmzpS6GqquBr7+GnjvPeDcOfGara24Db22VuwBlZ0NXLki/r4pcrkYWm4PMrc+9/Y2m1BTp9bgdG6Z9g5MWlYJsooqG4xzsbNE5I07MJEBzgj3c4a9tXl8D4haGwMLSePjj4HZs4GYGODAAamrabtKS4Fly4CPPgJycsRrLi7ArFniw91dd7xGA+Tn3wwwjf2qT6jx9b3zXRo/PzH0KEzzpNr8siqxyeONqaS/LjfeXiDM2xFRgS7o39ED/Tq682ReMl2CAFy6BBw6BBQUiO1YWhADC0kjLw9o106cgjh9GggNlbqitiUvTwyNn30mnosDiAHh5ZeBadMAB4d7f+/6UHOnQFN/p6au4ULWBhQKMbTcbfrJ29skQk1NnQYnc0q1a2GOZBbjqkq3vYCtpQL9O7ljcBdvDArz5E4kMm4FBWI4OXjw5q/Xrolfs7cX/25pwf82GVhIOo88Avz2G/DPfwJvvil1NW3DxYvA++8DK1eK00AAEBYmtkt46inAykA/INXqpkPN1avNDzVN3akx0lCTo7qOtMwS7L9YiO2n8pBzS4CRy4DoIFcM6eqNIV284O9qJ2Gl1OZVVABpaWIoqQ8oGRkNx1laAuHh4t3zRYvEIyxaCAMLSWfNGmDCBHGb84UL4ipFah3p6cA77wAbNoh3QAAgNhaYOxd47DHjbJOgVot3gpqaflKrm36v+lBztzs1Xl6ShhpBEHDiail+P5mH30/k4nRumc7Xw7wdMaSLFwZ38Ua3dk5cvEutp7YWOHHiZjg5eFD8vUbTcGxoqBhO6h/h4YB165wUzcBC0qmsFH9IlJcDf/4J9O0rdUXmRRCAXbvEHT/btt28PnSoGFT69zf9kFgfapq6U9OcUGNh0fSdGgOGmuyiSiSfzMPvJ3Nx6FIx1Lf0FPBR2mBwFy8M7uKF2GA39kKieycI4p3XW8NJWprYSuV27doB0dE3w0lUFODsbLBSGVhIWpMni7tTXnhBXPxJ90+jEbtiL158c0GzXC4e0vfqq0BEhKTlGZxaLZ6qfHuQuT3UNPavx9vVh5qm7tS08B2r4ooa7Didj+STedh1tkBnC7WjjQUGhnpicBcvDAj1gCPPfqG7ycu7ud6kfmqnqKjhOKVSDCf1ASU6WgwsEmJgIWlt3w4MHizuTMnJabVbiW1CTQ3w3XfAu++KC5kB8fs5dSowZw4QEiJtfcasrq7xUHPrr/qEmnbtmr5Tc4+hpqpWjT3nryH5ZB62n8rT6UhtqZChd3t38e5LZy94K23u6c8gM1FWJh4fcWtAycpqOM7KCoiMvBlMYmKAjh2NbqqYgYWkpVYDAQHiD4NNm4DRo6WuyPSUlwPLlwP/+Y/4gxUQ/3U0Ywbw97+LPxzp/tWHmrtNP+Xk6Bdqbg0ywcFiqGzfXvxvohkLoNUaAenZxfj9ZB6ST+Q1OIE33E95Y+rIG528HLjuxZzV1ADHjunu2Dl5Eg0aY8lkQOfON6d1oqOBHj0Mt+D+PjCwkPReeUXcuTJ6tBhaqHkKCsSD95YsAYqLxWs+PsA//iFOsfH/34ZXVyeGlrvdqWlOqJHLxRATEnIzxNQ/DwkBXF0bXX90Pr8cySfzkHwyF0eyS3R+VgW62WFwZ3HdS68gVyjY98h0aTTA+fO60zpHjtzc+Xcrf3/dcBIVZbJ/NzCwkPSOHhVXlltZiX+Zu7pKXZFxy8wEPvgA+PJL4Pp18VrHjuL6lIkTOa1m7Gprb96pqQ8xWVniFtGLF8VH/f+ud6JU6gaYWwNNQABgaYn8siqknMrH7ydysedCoc6hda72Vng4TFz30r+jB2ytjG/LN90iJ0d3Uezhw0BJScNxzs66O3aio8Ut/WaCgYWMQ48e4u3MZcvEuwPU0PHj4tbktWtv7nqJihJ3/IwebZTnjNA9EAQx0NSHl4sXxW3/9c/rTyS+E7lcDC23hJgq/0CkWrjil3Jb/HK5CqrrN08itrGUo18HDwzp6oVBYZ5wc2DglZRKJa47uTWgXLnScJyNzc11J/WP9u1Nf+ffXTCwkHF47z3xDkG/fsD//id1Ncblzz/FoPLzzzevxcWJQeXhh836LyhqRGWl7t2YW0NNRkbj21FvITg7o6JdADKV3kizdMVJG3dkOXsjy9kbuUoPRAS7Y0gXbwzu4oUgd3sDfag2qroa+Osv3UWx9QvmbyWXA1276m4p7tZNPKStDWFgIeNw+bL4r8L6MwGCg6WuSFoaDfDrr+LW5D17xGsyGfDkk+KptFFR0tZHxkmjuXl35ta7MvWP3Ny7vrxOJscVpSeylN7IdvZGhV8gPCM6I6xPBDo+0ANyVxcDfRAzpNEAZ87ohpP09Mb7bgUF6YaTnj3vr12GmWBgIeMRFwekpABvvQX8v/8ndTXSqK0F1q8X76gcPy5es7ISz6uZMwfo1Ena+si0VVTo3p25NdRkZDS+aPMWlQ5K1AUFw75zRyjqp5zqf/XzM5vu2/dNEMRpnFt37Bw+LDYbvZ2bm+524uhowNPT8DWbAAYWMh6rVwPPPCP+UD59um1NdVRWAitWiLul6s9JcHQE/vY3sau1r6+k5VEboNGI62NuhJiqM+dQ8Ncp1J47D6ecbLhXlNz99RYWQGBgwx1N9aHGnP++LS4WA8mtAaWxtUa2tuLd0VsDSnBw2/q77j4wsJDxKCsTzwy5fl08oTUmRuqKWl9REbB0KfDJJze7nHp6iiFl+nSDHntNdCfVdWocOJqFI7vTkH3oOJQ52QhQ5SKgJBcBJXkIKM2DZV0jUxu3cnO78zZtPz/TWTReVSVO5dy6KPbcuYbjFApxncmtO3a6duVdqPvQ6oFl6dKleO+995Cbm4vw8HB8+umniLnDD6JVq1ZhypQpOtesra1RdcsiMkEQsGDBAixfvhwlJSXo27cvPv/8c3Ts2LFZ9TCwGLmnnhJ3wcyaJf4QN1eXL4sHvf33v+JtekD8i/uVV8TpH1tbaesjugONRsBfl0tu9DnKw/n8csg1aniVFyGgJBe9ZSr0gQqhlQVwysmG7OJF8cygu7G0FNdt3GmrtqOjQT5bA2q1eLf31nBy9GjjXcRDQnR37ERGAnbssN2SWjWwrF+/HpMmTcKyZcsQGxuLjz76CN9//z3OnDkDz0bm6FatWoWXXnoJZ86cufmHymTwuuWkznfeeQeLFi3C6tWrERwcjH/+8584duwYTp48CRubpo+hZmAxcr/+CgwfDnh4iHPA5rYK/tQpcUfUt9/eXGwXHi7u+HnySf7ri0xOxrUKJJ/Mxe8n8pCaVaxzWJ2fiy0Gd/HC0AB7RKmLYZF5qeH6mUuXGl94eit39ztPNfn6tszdGUEQp2NvXRSbmiqeJH07Dw/dcNKrl1gjtapWDSyxsbGIjo7GkiVLAAAajQb+/v6YNWsW5s6d22D8qlWrMHv2bJQ0diAOxLsrvr6+ePnllzFnzhwAgEqlgpeXF1atWoVx48Y1WRMDi5GrqxP/AiooELfxDh8udUUt48ABccfPli03rw0YIO74iY/nHDaZhYKyauw4nYfkk3n437lrqL7lsDpnO0s8HOqJIV290L+TB+ysboRztVr8x0ljO5suXAAKC+/+h1pZ3bw701ioudPumsJCMZzcGlDy8xuOs7e/ue6k/hEQwP9mJaDPz2+9/ulXU1OD1NRUzJs3T3tNLpcjLi4O+/btu+PrysvLERgYCI1Gg549e+Lf//43unbtCgDIyMhAbm4u4uLitOOVSiViY2Oxb9++RgNLdXU1qm9Z+V7a2CptMh4WFsD48eJ00DffmHZgEQRg2zZxx8/OnTevjxolBpUHHpCqMqJW4eFojYToACREB6Cypg67z4pNGneczkNxZS02HbmCTUeuwMpCjgc7iE0aB3X2gkdAgBgCBgxo+KYq1Z13Nl26JPbQOXtWfDTG01P3FODMTDGcXLjQcKyFhXiI5a1bijt3Np31NaSlV2C5du0a1Gq1znQOAHh5eeF0YwfjAAgNDcXKlSvRo0cPqFQqvP/+++jTpw9OnDgBPz8/5N44Q6Cx98y9w/kCixYtwhtvvKFP6SS1iRPFwLJ1q7gN0NTuhNXVARs3indU/vpLvGZhIX6uV14R/wIkMnN2VhYY2s0bQ7t5o06tweHM4ht9jvKQVVSJlNP5SDmdD5nsGHoGuNxo0uiF9h633RFRKoGICPFxu7o6cT1YY4foXbwoLmrPzxcf+/c3fH3Hjro7diIiuH7MTLT65Hrv3r3Ru3dv7e/79OmDzp0744svvsBbb711T+85b948JCYman9fWloKf3//+66VWlFUFBAWJi52++EH4LaF2Ebr+nVxa/Z774l/WQLi7eTnnxcbEvL/d9RGWSjkeCDEDQ+EuOH/De+MM3llSD6Rh+RTeTh6WYXUzGKkZhZj8W+n0d7DHoNvnLQb6e8M+d2aNFpYiNNBQUHiqc+3KykR787UB5jMTLFBaP26E/YtM1t6BRZ3d3coFArk5eXpXM/Ly4N3M5sxWVpaIjIyEufPnwcA7evy8vLg4+Oj854RjaVviLuMrNkMzrTIZMDTT4uHx33zjfEHlpIS4PPPgY8+ujkH7uYGvPQSMGMG/1IkuoVMJkOYtxPCvJ0wa1BH5KiuY/uNHUf7LhTiQkEFLuy6gGW7LsDD0RpxncUmjX3au8PGUs+pGWdncbdOZGSrfBYyXnJ9BltZWSEqKgopKSnaaxqNBikpKTp3Ue5GrVbj2LFj2nASHBwMb29vnfcsLS3FgQMHmv2eZCImTBB/3blT7GhrjK5eFfsfBQQAr78uhpWAAHE6KzMT+Oc/GVaImuCjtMXE3kH45tlYpM0fjE/GR+LRHj5wtLZAQVk11h7MxtRVh9HzrWRM/zYVm9Iuo6SyRuqyycjpPSWUmJiIyZMno1evXoiJicFHH32EiooK7VkrkyZNQrt27bBo0SIAwJtvvokHHngAHTp0QElJCd577z1kZmZi2rRpAMRkPnv2bLz99tvo2LGjdluzr68vRo0a1XKflKQXFAQ8+KDYCHHNGnGRqrE4d06c9lm9WlzwB4gHQs2dCyQkmN9WbCIDcbKxxGPhvngs3Bc1dRrsv1iI30/mYvvJfOSWVuG347n47XguFHIZYoJctete/F153gnp0juwJCQkoKCgAPPnz0dubi4iIiKQlJSkXTSblZUFufzmjZvi4mI899xzyM3NhYuLC6KiorB371506dJFO+bVV19FRUUFnn/+eZSUlKBfv35ISkpq1hksZGImThQDyzffiHcypN5GmJoq7vjZuBHawyb69hWDyiOPiB1ViahFWFnI0b+TB/p38sCbjwk4dkWlXbR7Jq8M+y4WYt/FQrz580l09nHC4C5eGNLFC119nSCT+u8KkhyP5ifDKi4GvL3FuxhHjjS+S6C1CQKwY4e442f79pvXH31UvOvTr5/hayJq4zILK7Qn7R6+VATNLT+Z2jnbau+8xAS7wlLBf0iYC/YSIuP25JPiTqGXXxYbAxqKWi0e8rZ4sdjUDBDPYnjqKfFuT7duhquFiO6oqKIGKafEOy+7zxWgqvbmYXVONhZ4OMwTg7t446FQDzhY8yRpU8bAQsZt61bxoDUfH3HxbWsf4FRdLU5BvfvuzYZmtrbAtGlAYqK4toaIjNL1GjX+PH8NySdzsf1UPooqbi7OtVLI0aeDG/p39EBsiCvCvJ2guNuWaTI6DCxk3GpqxLBSVCSeGjtkSOv8OaWlwBdfAB9+eLMtvIsLMHOm2IjRw6N1/lwiahVqjYC0LPGwut9P5OJSYaXO1x1tLBAT5IqYYFfEhrihq68Tp4+MHAMLGb8XXxTPOZk4Efj665Z977w8cRvy0qXiEeAA0K6dOAX13HN37kNCRCZDEASczy/H9lP52H+xEIcvFaGiRq0zxs5KgahAF8TeCDA9/JSwtuCR/MaEgYWM39694m4ce3sxYNjb3/97XrwIfPABsHIlUFUlXgsLExfSPvWU2FCNiMxSnVqDkzmlOHCxCAcyinDoUhFU13U7RltbyBEZ4IyYYDc8EOyKyAAX2FoxwEiJgYWMnyAAHTqIIePbb28eKncv/vpL3Jq8fj2gubE4LyYGmDcPeOwxbk0maoM0GgFn8spw4GIhDl4qwoGLRSis0D2czlIhQw8/Z3EKKdgVUYEucLThmUuGxMBCpmHBAuDNN4H4eCApSb/XCgKwe7e44+fW18bHi2eoPPSQ9Ge8EJHREAQBFwoqcCCjEAczxACTW1qlM0YuA7q1UyImSJxCig5ygbMd78y2JgYWMg3nzgGdOol3QC5fFhfiNkWjAX76SQwq9Z1a5XJg7FhxazL7ixBRMwiCgOyi69ifUYgDF4tw8FIhsouu64yRyYBQL0ftGpjoIFd4OLKPXUtiYCHT0bu3GDz+8x+x+/Gd1NSIx/m/+y5w6pR4zdoamDpVXEzbvr1h6iUis3W15Lp49yWjCAcyCnGxoKLBmPYe9ogNcRNDTLAbvJU8kf1+MLCQ6Vi6VNxmHBkJpKU1/Hp5OfDll+Ji2suXxWtOTmLH5JdeAm60hCAiamkFZdU3Aow4jXQ6t6zBmABXO8QGi1upHwhxg5+LLdsI6IGBhUzHtWviVFBdHXD8uNhwsP76p5+Kj+Ji8Zq3t3gX5oUXAKVSupqJqE0qrqjBoUviHZiDGUU4cVWl00IAAHyUNjcCjBtiQ1wR4m7PAHMXDCxkWh57TFyXMncu8Le/idNDy5cD12/MJ3foIK5PmTgRYENMIjISpVW1SM0sFtfAZBTi6GUV6m5LMO4O1to7MLEhrujk6Qg5T+PVYmAh0/L99+KiWXt78Rj9ujrxelSUGGJGj2794/uJiO5TZU0djmSV4MDFQhzIKMKR7BLU1Gl0xjjbWSI6yFW7BqazjyMs2vBpvAwsZFquXxene0pLxd8PGiQGlUGDuDWZiExWVa0aRy+rtGfBHL5UjOu1uqfxOlhboFeQy42zYNzQvZ0SVhZtJ8AwsJDp2bIFSE4GpkwBevWSuhoiohZXq9bg+BWVdg3MoYwilFXX6YyxtVSgZ6AzYoLENTAR/s6wsTTfO8wMLEREREZOrRFwKqf0RoARdyIVV+q2E7BSyBHh76xdA9MzwAX21hYSVdzyGFiIiIhMjEYj4HxBuXYNzIGMIhSUVeuMsZDL0K2d8sZhdq7oFeQKJxNuJ8DAQkREZOIEQcClwkpxDcyNAHOlpOFpvF18nBAb7IaYG7uRXO1Np50AAwsREZEZulxceWMbtXig3aXCygZjOnk5aANMbLArPJ2M9zgIBhYiIqI2IK+0SrsG5sDFIpzLL28wJtjd/pazYNzQztlWgkobx8BCRETUBhWWV2tP4z1wsQincktx+0/5ds62iA25eRZMoJudZKfxMrAQERERVNdrcfiSOIW0P6MIx6+ooL7tNF4vJ2vE3JhCeiDYFR08HQwWYBhYiIiIqIGK6jqkZhZr18D8la1CjVr3NF5XeyvEBN1sJxDm7QRFK7UTYGAhIiKiJlXVqnEkq0QbYNKyilFVqxtgnGwsEH0jwEx4IBAOLXgODAMLERER6a2mToNjV0q0a2AOXypCRY3YTsDKQo6jC4a06Mm7+vz8Np/j8oiIiOi+WFnIERXoiqhAV7w4AKhTa3Ayp/TGKbw1krYJYGAhIiKiRlko5Ojh54wefs5Sl4K20xKSiIiITBYDCxERERk9BhYiIiIyegwsREREZPQYWIiIiMjoMbAQERGR0WNgISIiIqPHwEJERERGj4GFiIiIjB4DCxERERm9ewosS5cuRVBQEGxsbBAbG4uDBw/ecezy5cvx4IMPwsXFBS4uLoiLi2sw/plnnoFMJtN5DB069F5KIyIiIjOkd2BZv349EhMTsWDBAqSlpSE8PBzx8fHIz89vdPzOnTsxfvx4/PHHH9i3bx/8/f0xZMgQXLlyRWfc0KFDkZOTo32sXbv23j4RERERmR2ZIAiCPi+IjY1FdHQ0lixZAgDQaDTw9/fHrFmzMHfu3CZfr1ar4eLigiVLlmDSpEkAxDssJSUl2LJli/6fAPq1pyYiIiLjoM/Pb726NdfU1CA1NRXz5s3TXpPL5YiLi8O+ffua9R6VlZWora2Fq6urzvWdO3fC09MTLi4uePjhh/H222/Dzc2t0feorq5GdXW19vcqlQqA+MGJiIjINNT/3G7OvRO9Asu1a9egVqvh5eWlc93LywunT59u1nu89tpr8PX1RVxcnPba0KFD8fjjjyM4OBgXLlzA66+/jmHDhmHfvn1QKBQN3mPRokV44403Glz39/fX5+MQERGRESgrK4NSqbzrGL0Cy/1avHgx1q1bh507d8LGxkZ7fdy4cdrn3bt3R48ePdC+fXvs3LkTgwYNavA+8+bNQ2Jiovb3Go0GRUVFcHNzg0wma9GaS0tL4e/vj+zsbE43tSJ+nw2D32fD4ffaMPh9NozW+j4LgoCysjL4+vo2OVavwOLu7g6FQoG8vDyd63l5efD29r7ra99//30sXrwY27dvR48ePe46NiQkBO7u7jh//nyjgcXa2hrW1tY615ydnZv3Ie6Rk5MT/2MwAH6fDYPfZ8Ph99ow+H02jNb4Pjd1Z6WeXruErKysEBUVhZSUFO01jUaDlJQU9O7d+46ve/fdd/HWW28hKSkJvXr1avLPuXz5MgoLC+Hj46NPeURERGSm9N7WnJiYiOXLl2P16tU4deoUpk+fjoqKCkyZMgUAMGnSJJ1Fue+88w7++c9/YuXKlQgKCkJubi5yc3NRXl4OACgvL8crr7yC/fv349KlS0hJScHIkSPRoUMHxMfHt9DHJCIiIlOm9xqWhIQEFBQUYP78+cjNzUVERASSkpK0C3GzsrIgl9/MQZ9//jlqamrw5JNP6rzPggULsHDhQigUChw9ehSrV69GSUkJfH19MWTIELz11lsNpn2kYG1tjQULFhhFLeaM32fD4PfZcPi9Ngx+nw3DGL7Pep/DQkRERGRo7CVERERERo+BhYiIiIweAwsREREZPQYWIiIiMnoMLERERGT0GFiasHTpUgQFBcHGxgaxsbE4ePCg1CWZld27d2PEiBHw9fWFTCa7547ddHeLFi1CdHQ0HB0d4enpiVGjRuHMmTNSl2V2Pv/8c/To0UN7Gmjv3r3x22+/SV2W2Vu8eDFkMhlmz54tdSlmZ+HChZDJZDqPsLAwSWphYLmL9evXIzExEQsWLEBaWhrCw8MRHx+P/Px8qUszGxUVFQgPD8fSpUulLsWs7dq1CzNmzMD+/fuRnJyM2tpaDBkyBBUVFVKXZlb8/PywePFipKam4vDhw3j44YcxcuRInDhxQurSzNahQ4fwxRdfNNnyhe5d165dkZOTo338+eefktTBc1juIjY2FtHR0ViyZAkAsQ2Bv78/Zs2ahblz50pcnfmRyWTYvHkzRo0aJXUpZq+goACenp7YtWsX+vfvL3U5Zs3V1RXvvfcenn32WalLMTvl5eXo2bMnPvvsM7z99tuIiIjARx99JHVZZmXhwoXYsmUL0tPTpS6Fd1jupKamBqmpqYiLi9Nek8vliIuLw759+ySsjOj+qVQqAOIPU2odarUa69atQ0VFxV17rdG9mzFjBoYPH67z9zS1vHPnzsHX1xchISGYMGECsrKyJKlD76P524pr165BrVZrWw7U8/LywunTpyWqiuj+aTQazJ49G3379kW3bt2kLsfsHDt2DL1790ZVVRUcHBywefNmdOnSReqyzM66deuQlpaGQ4cOSV2KWYuNjcWqVasQGhqKnJwcvPHGG3jwwQdx/PhxODo6GrQWBhaiNmbGjBk4fvy4ZPPQ5i40NBTp6elQqVTYuHEjJk+ejF27djG0tKDs7Gy89NJLSE5Oho2NjdTlmLVhw4Zpn/fo0QOxsbEIDAzEhg0bDD7NycByB+7u7lAoFMjLy9O5npeXB29vb4mqIro/M2fOxM8//4zdu3fDz89P6nLMkpWVFTp06AAAiIqKwqFDh/Dxxx/jiy++kLgy85Gamor8/Hz07NlTe02tVmP37t1YsmQJqquroVAoJKzQfDk7O6NTp044f/68wf9srmG5AysrK0RFRSElJUV7TaPRICUlhfPRZHIEQcDMmTOxefNm7NixA8HBwVKX1GZoNBpUV1dLXYZZGTRoEI4dO4b09HTto1evXpgwYQLS09MZVlpReXk5Lly4AB8fH4P/2bzDcheJiYmYPHkyevXqhZiYGHz00UeoqKjAlClTpC7NbJSXl+sk9YyMDKSnp8PV1RUBAQESVmZeZsyYgTVr1mDr1q1wdHREbm4uAECpVMLW1lbi6szHvHnzMGzYMAQEBKCsrAxr1qzBzp07sW3bNqlLMyuOjo4N1l/Z29vDzc2N67Ja2Jw5czBixAgEBgbi6tWrWLBgARQKBcaPH2/wWhhY7iIhIQEFBQWYP38+cnNzERERgaSkpAYLceneHT58GAMHDtT+PjExEQAwefJkrFq1SqKqzM/nn38OABgwYIDO9a+++grPPPOM4QsyU/n5+Zg0aRJycnKgVCrRo0cPbNu2DYMHD5a6NKJ7cvnyZYwfPx6FhYXw8PBAv379sH//fnh4eBi8Fp7DQkREREaPa1iIiIjI6DGwEBERkdFjYCEiIiKjx8BCRERERo+BhYiIiIweAwsREREZPQYWIiIiMnoMLERERGT0GFiIiIjI6DGwEBERkdFjYCEiIiKj9/8B0RGntWr8tLIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(h['loss'])\n",
        "\n",
        "# Check if 'val_loss' exists in h before plotting\n",
        "if 'val_loss' in h:\n",
        "    plt.plot(h['val_loss'],c='red')\n",
        "plt.title('loss vs val_loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nUkj4_aAyte9"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing import image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CNNTEadkyL_8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "6d8f10c0-afa3-4a2b-d61f-9ccfc61ce874"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input shape: (224, 224, 3)\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n",
            "Predicted probability: 1.0\n",
            "Brain Tumor Detected in MRI\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "\n",
        "# path to the image\n",
        "path = \"/content/test/yes/Y153.jpg\"\n",
        "# Load the image and resize it to 224x224\n",
        "img = load_img(path, target_size=(224, 224))\n",
        "input_arr = img_to_array(img) / 255.0\n",
        "\n",
        "# Plot of img\n",
        "plt.imshow(input_arr)\n",
        "plt.show()\n",
        "\n",
        "print(\"Input shape:\", input_arr.shape)\n",
        "\n",
        "# Expand the dimensions to match the model input\n",
        "input_arr = np.expand_dims(input_arr, axis=0)\n",
        "\n",
        "# Assuming `model` is preloaded and compiled, predict the result\n",
        "pred = model.predict(input_arr)[0][0]\n",
        "\n",
        "# Interpret the prediction\n",
        "print(f\"Predicted probability: {pred}\")\n",
        "\n",
        "# Use 0.5 as the threshold for classification\n",
        "# for threshold 0 it didn't work\n",
        "if pred < 0.5:\n",
        "    print(\"Brain Tumor not Detected in MRI\")\n",
        "else:\n",
        "    print(\"Brain Tumor Detected in MRI\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('brain_best.keras')"
      ],
      "metadata": {
        "id": "bBFeK5YxgmIH"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "mount_file_id": "https://github.com/11PRIMUS/Early-Stage-Cancer-Prediction/blob/main/Brain%20Tumor.ipynb",
      "authorship_tag": "ABX9TyPtspisxbWHYc2fW265hBR1"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}