stacking-approach.ipynb

{
  "cells": [
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      "source": [
        "pip install opendatasets"
      ],
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        "id": "0oLRhsTQi7UB",
        "outputId": "48739f66-2bc3-433c-9218-5118ace812b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting opendatasets\n",
            "  Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.1)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.5.16)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.31.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.4)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.22\n"
          ]
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "import opendatasets as od\n",
        "od.download(\"https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pa-w8gpki-ct",
        "outputId": "f9c1d242-2930-41f2-a2cd-068d4c119c5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: emeryntumba\n",
            "Your Kaggle Key: ··········\n",
            "Downloading ocular-disease-recognition-odir5k.zip to ./ocular-disease-recognition-odir5k\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1.62G/1.62G [00:42<00:00, 41.1MB/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "\n"
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        }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ghSp48nfz-Zc"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 501
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        "id": "NolJLrv90Aek",
        "outputId": "2acf7614-ed09-4bba-dced-ca6653967830"
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              "   ID  Patient Age Patient Sex Left-Fundus Right-Fundus  \\\n",
              "0   0           69      Female  0_left.jpg  0_right.jpg   \n",
              "1   1           57        Male  1_left.jpg  1_right.jpg   \n",
              "2   2           42        Male  2_left.jpg  2_right.jpg   \n",
              "3   4           53        Male  4_left.jpg  4_right.jpg   \n",
              "4   5           50      Female  5_left.jpg  5_right.jpg   \n",
              "\n",
              "                            Left-Diagnostic Keywords  \\\n",
              "0                                           cataract   \n",
              "1                                      normal fundus   \n",
              "2  laser spot，moderate non proliferative retinopathy   \n",
              "3                        macular epiretinal membrane   \n",
              "4             moderate non proliferative retinopathy   \n",
              "\n",
              "                Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \\\n",
              "0                           normal fundus  0  0  0  1  0  0  0  0   \n",
              "1                           normal fundus  1  0  0  0  0  0  0  0   \n",
              "2  moderate non proliferative retinopathy  0  1  0  0  0  0  0  1   \n",
              "3       mild nonproliferative retinopathy  0  1  0  0  0  0  0  1   \n",
              "4  moderate non proliferative retinopathy  0  1  0  0  0  0  0  0   \n",
              "\n",
              "                                            filepath labels  \\\n",
              "0  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
              "1  ../input/ocular-disease-recognition-odir5k/ODI...  ['N']   \n",
              "2  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "3  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "4  ../input/ocular-disease-recognition-odir5k/ODI...  ['D']   \n",
              "\n",
              "                     target     filename  \n",
              "0  [1, 0, 0, 0, 0, 0, 0, 0]  0_right.jpg  \n",
              "1  [1, 0, 0, 0, 0, 0, 0, 0]  1_right.jpg  \n",
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              "4  [0, 1, 0, 0, 0, 0, 0, 0]  5_right.jpg  "
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              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6d123c67-5c04-481e-96b5-5925e8b90c5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv('ocular-disease-recognition-odir5k/full_df.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aOQfvh5g0E81"
      },
      "outputs": [],
      "source": [
        "def has_cataract(text):\n",
        "    if \"cataract\" in text:\n",
        "        return 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cI5XDyEt0G4Y"
      },
      "outputs": [],
      "source": [
        "def is_normal(text):\n",
        "    if \"normal fundus\" in text:\n",
        "        return 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ToUU9g6H0I75"
      },
      "outputs": [],
      "source": [
        "df[\"left_cataract\"] = df[\"Left-Diagnostic Keywords\"].apply(lambda x: has_cataract(x))\n",
        "df[\"right_cataract\"] = df[\"Right-Diagnostic Keywords\"].apply(lambda x: has_cataract(x))\n",
        "left_cataract = df.loc[(df.C == 1) & (df['left_cataract'] == 1)][\"Left-Fundus\"].values\n",
        "right_cataract = df.loc[(df.C == 1) & (df['right_cataract'] == 1)][\"Right-Fundus\"].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LptSXLKQ0LUQ"
      },
      "outputs": [],
      "source": [
        "df[\"left_normal\"] = df[\"Left-Diagnostic Keywords\"].apply(lambda x: is_normal(x))\n",
        "df[\"right_normal\"] = df[\"Right-Diagnostic Keywords\"].apply(lambda x: is_normal(x))\n",
        "left_normal = df.loc[(df.N ==1) & (df[\"left_normal\"] == 1)][\"Left-Fundus\"].sample(300, random_state=42).values\n",
        "right_normal = df.loc[(df.N ==1) & (df['right_normal'] == 1)][\"Right-Fundus\"].sample(300, random_state=42).values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6Fk4zCx0OKO",
        "outputId": "1f36ab33-8366-4fc0-ab03-4e19622cbc6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "594\n",
            "600\n"
          ]
        }
      ],
      "source": [
        "cataract = np.concatenate((left_cataract,right_cataract),axis=0)\n",
        "normal = np.concatenate((left_normal,right_normal),axis=0)\n",
        "\n",
        "\n",
        "print(len(cataract))\n",
        "print(len(normal))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q24oYYPL0Qla"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "import cv2\n",
        "import os, random\n",
        "\n",
        "dataset_dir = \"ocular-disease-recognition-odir5k/preprocessed_images\"\n",
        "labels = []\n",
        "dataset = []\n",
        "image_size=224\n",
        "\n",
        "def create_dataset(image_category, label):\n",
        "    processed_images = []  # Stocke les images traitées\n",
        "    for img in tqdm(image_category):\n",
        "        image_path = os.path.join(dataset_dir, img)\n",
        "        try:\n",
        "            # Charger l'image\n",
        "            original_image = cv2.imread(image_path)\n",
        "\n",
        "            # Convertir l'image en niveaux de gris\n",
        "            gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "            # Ajuster la luminosité à zéro\n",
        "            zero_brightness_image = np.zeros_like(original_image)\n",
        "\n",
        "            # Appliquer une vignette à 20%\n",
        "            mask = np.zeros_like(gray_image)\n",
        "            cv2.circle(mask, (gray_image.shape[1] // 2, gray_image.shape[0] // 2), int(min(gray_image.shape) // 2 * 0.8), 255, -1)\n",
        "            vignette_image = cv2.merge([gray_image * (mask / 255.0)] * 3)\n",
        "\n",
        "            # Ajuster la clarté à 100%\n",
        "            clarity_image = cv2.detailEnhance(vignette_image, sigma_s=10, sigma_r=0.15)\n",
        "\n",
        "            # Redimensionner l'image\n",
        "            resized_image = cv2.resize(clarity_image, (image_size, image_size))\n",
        "\n",
        "            # Normaliser les pixels entre 0 et 1\n",
        "            normalized_image = resized_image / 255.0\n",
        "\n",
        "            # Ajout de l'image normalisée et l'étiquette à la liste du dataset\n",
        "            processed_images.append([np.array(normalized_image), np.array(label)])\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"Erreur lors du traitement de l'image {img}: {str(e)}\")\n",
        "            continue\n",
        "\n",
        "    return processed_images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc57z7xjo-NK",
        "outputId": "621ce77b-ab9d-459b-f75a-81737f6f56ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  8%|▊         | 48/594 [00:03<00:33, 16.13it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2102_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 23%|██▎       | 137/594 [00:11<00:38, 11.87it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2231_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 25%|██▍       | 147/594 [00:12<00:51,  8.73it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2244_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 25%|██▌       | 151/594 [00:13<00:48,  9.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2251_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 80%|████████  | 476/594 [00:51<00:10, 11.76it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 1456_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 98%|█████████▊| 584/594 [00:59<00:00, 16.91it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2229_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 594/594 [01:00<00:00,  9.86it/s]\n",
            "  2%|▎         | 15/600 [00:00<00:33, 17.43it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2851_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 23%|██▎       | 136/600 [00:10<00:27, 16.86it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 3320_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 35%|███▍      | 209/600 [00:15<00:23, 16.70it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2629_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 38%|███▊      | 226/600 [00:16<00:22, 16.66it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2571_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 49%|████▉     | 294/600 [00:21<00:25, 11.85it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 3383_left.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 56%|█████▌    | 333/600 [00:24<00:16, 16.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 3369_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 83%|████████▎ | 498/600 [00:36<00:08, 11.80it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2546_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 84%|████████▍ | 505/600 [00:37<00:06, 15.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2727_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 94%|█████████▍| 563/600 [00:42<00:04,  7.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 2377_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 98%|█████████▊| 589/600 [00:46<00:01,  7.08it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erreur lors du traitement de l'image 3292_right.jpg: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 600/600 [00:50<00:00, 11.84it/s]\n"
          ]
        }
      ],
      "source": [
        "# Créer les datasets pour chaque catégorie\n",
        "dataset_cataract = create_dataset(cataract, 1)\n",
        "dataset_normal = create_dataset(normal, 0)\n",
        "\n",
        "# Exclure les éléments avec des images vides\n",
        "dataset_cataract = [item for item in dataset_cataract if item[0] is not None]\n",
        "dataset_normal = [item for item in dataset_normal if item[0] is not None]\n",
        "# Concaténer les deux datasets\n",
        "dataset = dataset_cataract + dataset_normal\n",
        "\n",
        "# Mélanger le dataset\n",
        "random.shuffle(dataset)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tahwP0hb3RyI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "b490bee0-378d-4ef5-88dd-c58b8d7b3140"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12,7))\n",
        "for i in range(10):\n",
        "    sample = random.choice(range(len(dataset)))\n",
        "    image = dataset[sample][0]\n",
        "    category = dataset[sample][1]\n",
        "    if category== 0:\n",
        "        label = \"Normal\"\n",
        "    elif category == 1:\n",
        "        label = \"Cataract\"\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(image)\n",
        "    plt.xlabel(label)\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ij6ibEgWiozw",
        "outputId": "5f1748b4-6648-4519-fec9-66c6ea0164db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1178"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "len(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D8YO24Gc07YP"
      },
      "outputs": [],
      "source": [
        "# Séparer en features (x) et labels (y)\n",
        "x = []\n",
        "y = []\n",
        "for item in dataset:\n",
        "    image = item[0]\n",
        "    label = item[1]\n",
        "    x.append(image)\n",
        "    y.append(label)\n",
        "\n",
        "# Convertir les listes en tableaux NumPy\n",
        "x = np.array(x)\n",
        "y = np.array(y)\n",
        "\n",
        "# Ajouter une dimension pour le canal\n",
        "x = np.expand_dims(x, axis=-1)\n",
        "\n",
        "# Convertir les labels en entiers\n",
        "y = y.astype(int)\n",
        "\n",
        "# Supprimer la dernière dimension\n",
        "x = np.squeeze(x, axis=-1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTANZAryiozx",
        "outputId": "3f03ee9f-ae86-4105-e9ec-33d77f99ff96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1178, 224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "x.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O0UcEfFg09pV"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#Diviser les données en ensembles d'entraînement et de test\n",
        "x_train, x_temp, y_train, y_temp = train_test_split(x, y, test_size=0.5, random_state=42)\n",
        "\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_temp, y_temp, test_size=0.5, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnLmS_RAiozy",
        "outputId": "2a9dd1ce-39e0-430e-ad83-0f8ae6f0d144"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "589"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "len(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IQytG3Lv1AIy"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "\n",
        "batch_size = 32\n",
        "epochs = 25\n",
        "\n",
        "# Calculer le nombre initial d'images\n",
        "initial_images = len(x_train)\n",
        "\n",
        "# Calculer le nombre d'itérations par époque\n",
        "steps_per_epoch = len(x_train) // batch_size\n",
        "\n",
        "# Calculer le facteur d'augmentation total\n",
        "augmentation_factor = steps_per_epoch * epochs\n",
        "\n",
        "# Calculer le nombre total d'images après l'augmentation\n",
        "total_generated_images = initial_images * augmentation_factor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F0U2D3oX2VWG"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.applications import VGG19\n",
        "from tensorflow.keras.applications import InceptionResNetV2\n",
        "\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, roc_curve, auc\n",
        "import tensorflow as tf\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E4_UalBF5hkW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "accd8e79-d0cf-46bc-87fb-a67694b23cc6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87910968/87910968 [==============================] - 5s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 111, 111, 32)         864       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 111, 111, 32)         96        ['conv2d[0][0]']              \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 111, 111, 32)         0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 32)         9216      ['activation[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_1 (Bat  (None, 109, 109, 32)         96        ['conv2d_1[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)   (None, 109, 109, 32)         0         ['batch_normalization_1[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 109, 109, 64)         18432     ['activation_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_2 (Bat  (None, 109, 109, 64)         192       ['conv2d_2[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_2 (Activation)   (None, 109, 109, 64)         0         ['batch_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2  (None, 54, 54, 64)           0         ['activation_2[0][0]']        \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)           (None, 54, 54, 80)           5120      ['max_pooling2d[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_3 (Bat  (None, 54, 54, 80)           240       ['conv2d_3[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_3 (Activation)   (None, 54, 54, 80)           0         ['batch_normalization_3[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)           (None, 52, 52, 192)          138240    ['activation_3[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_4 (Bat  (None, 52, 52, 192)          576       ['conv2d_4[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_4 (Activation)   (None, 52, 52, 192)          0         ['batch_normalization_4[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPoolin  (None, 25, 25, 192)          0         ['activation_4[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 25, 25, 64)           192       ['conv2d_8[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_8 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 25, 25, 48)           9216      ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 25, 25, 96)           55296     ['activation_8[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (Bat  (None, 25, 25, 48)           144       ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 25, 25, 96)           288       ['conv2d_9[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_6 (Activation)   (None, 25, 25, 48)           0         ['batch_normalization_6[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_9 (Activation)   (None, 25, 25, 96)           0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 25, 25, 192)          0         ['max_pooling2d_1[0][0]']     \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 25, 25, 64)           76800     ['activation_6[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_9[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 25, 25, 32)           6144      ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_5 (Bat  (None, 25, 25, 64)           192       ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 25, 25, 64)           192       ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_10 (Ba  (None, 25, 25, 96)           288       ['conv2d_10[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_11 (Ba  (None, 25, 25, 32)           96        ['conv2d_11[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_5 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_7 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_10 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_10[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_11 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_11[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)        (None, 25, 25, 256)          0         ['activation_5[0][0]',        \n",
            "                                                                     'activation_7[0][0]',        \n",
            "                                                                     'activation_10[0][0]',       \n",
            "                                                                     'activation_11[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 25, 25, 64)           192       ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_15 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 25, 25, 48)           12288     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_15[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 25, 25, 48)           144       ['conv2d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 25, 25, 96)           288       ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_13 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_16 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (Avera  (None, 25, 25, 256)          0         ['mixed0[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_13[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_16[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 25, 25, 64)           16384     ['average_pooling2d_1[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 25, 25, 64)           192       ['conv2d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 25, 25, 64)           192       ['conv2d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 25, 25, 96)           288       ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 25, 25, 64)           192       ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_12 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_14 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_17 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_18 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)        (None, 25, 25, 288)          0         ['activation_12[0][0]',       \n",
            "                                                                     'activation_14[0][0]',       \n",
            "                                                                     'activation_17[0][0]',       \n",
            "                                                                     'activation_18[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 25, 25, 64)           192       ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_22 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 25, 25, 48)           13824     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_22[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 25, 25, 48)           144       ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 25, 25, 96)           288       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_20 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_23 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (Avera  (None, 25, 25, 288)          0         ['mixed1[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_20[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_23[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 25, 25, 64)           18432     ['average_pooling2d_2[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 25, 25, 64)           192       ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 25, 25, 64)           192       ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 25, 25, 96)           288       ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 25, 25, 64)           192       ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_19 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_21 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_24 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_25 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)        (None, 25, 25, 288)          0         ['activation_19[0][0]',       \n",
            "                                                                     'activation_21[0][0]',       \n",
            "                                                                     'activation_24[0][0]',       \n",
            "                                                                     'activation_25[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 25, 25, 64)           192       ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_27 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_27[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 25, 25, 96)           288       ['conv2d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_28 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 12, 12, 384)          995328    ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 12, 12, 96)           82944     ['activation_28[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 12, 12, 384)          1152      ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 12, 12, 96)           288       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_26 (Activation)  (None, 12, 12, 384)          0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_29 (Activation)  (None, 12, 12, 96)           0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPoolin  (None, 12, 12, 288)          0         ['mixed2[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)        (None, 12, 12, 768)          0         ['activation_26[0][0]',       \n",
            "                                                                     'activation_29[0][0]',       \n",
            "                                                                     'max_pooling2d_2[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_34 (Ba  (None, 12, 12, 128)          384       ['conv2d_34[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_34 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_34[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_34[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_35 (Ba  (None, 12, 12, 128)          384       ['conv2d_35[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_35 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_35[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_35[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 12, 12, 128)          384       ['conv2d_31[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_36 (Ba  (None, 12, 12, 128)          384       ['conv2d_36[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_31 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_36 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_36[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_31[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_36[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_32 (Ba  (None, 12, 12, 128)          384       ['conv2d_32[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_37 (Ba  (None, 12, 12, 128)          384       ['conv2d_37[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_32 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_32[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_37 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_37[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (Avera  (None, 12, 12, 768)          0         ['mixed3[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_32[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_37[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_3[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 12, 12, 192)          576       ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_33 (Ba  (None, 12, 12, 192)          576       ['conv2d_33[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_38 (Ba  (None, 12, 12, 192)          576       ['conv2d_38[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_39 (Ba  (None, 12, 12, 192)          576       ['conv2d_39[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_30 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_33 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_33[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_38 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_38[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_39 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_39[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)        (None, 12, 12, 768)          0         ['activation_30[0][0]',       \n",
            "                                                                     'activation_33[0][0]',       \n",
            "                                                                     'activation_38[0][0]',       \n",
            "                                                                     'activation_39[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_44 (Ba  (None, 12, 12, 160)          480       ['conv2d_44[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_44 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_44[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_44[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_45 (Ba  (None, 12, 12, 160)          480       ['conv2d_45[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_45 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_45[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_45[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_41 (Ba  (None, 12, 12, 160)          480       ['conv2d_41[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_46 (Ba  (None, 12, 12, 160)          480       ['conv2d_46[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_41 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_41[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_46 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_46[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_41[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_46[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_42 (Ba  (None, 12, 12, 160)          480       ['conv2d_42[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_47 (Ba  (None, 12, 12, 160)          480       ['conv2d_47[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_42 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_42[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_47 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_47[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (Avera  (None, 12, 12, 768)          0         ['mixed4[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_42[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_47[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_4[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_40 (Ba  (None, 12, 12, 192)          576       ['conv2d_40[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_43 (Ba  (None, 12, 12, 192)          576       ['conv2d_43[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_48 (Ba  (None, 12, 12, 192)          576       ['conv2d_48[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_49 (Ba  (None, 12, 12, 192)          576       ['conv2d_49[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_40 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_40[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_43 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_43[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_48 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_48[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_49 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_49[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)        (None, 12, 12, 768)          0         ['activation_40[0][0]',       \n",
            "                                                                     'activation_43[0][0]',       \n",
            "                                                                     'activation_48[0][0]',       \n",
            "                                                                     'activation_49[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_54 (Ba  (None, 12, 12, 160)          480       ['conv2d_54[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_54 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_54[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_54[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_55 (Ba  (None, 12, 12, 160)          480       ['conv2d_55[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_55 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_55[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_55[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_51 (Ba  (None, 12, 12, 160)          480       ['conv2d_51[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_56 (Ba  (None, 12, 12, 160)          480       ['conv2d_56[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_51 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_51[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_56 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_56[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_51[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_56[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_52 (Ba  (None, 12, 12, 160)          480       ['conv2d_52[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_57 (Ba  (None, 12, 12, 160)          480       ['conv2d_57[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_52 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_52[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_57 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_57[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (Avera  (None, 12, 12, 768)          0         ['mixed5[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_52[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_57[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_5[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_50 (Ba  (None, 12, 12, 192)          576       ['conv2d_50[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_53 (Ba  (None, 12, 12, 192)          576       ['conv2d_53[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_58 (Ba  (None, 12, 12, 192)          576       ['conv2d_58[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_59 (Ba  (None, 12, 12, 192)          576       ['conv2d_59[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_50 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_50[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_53 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_53[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_58 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_58[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_59 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_59[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)        (None, 12, 12, 768)          0         ['activation_50[0][0]',       \n",
            "                                                                     'activation_53[0][0]',       \n",
            "                                                                     'activation_58[0][0]',       \n",
            "                                                                     'activation_59[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_64 (Ba  (None, 12, 12, 192)          576       ['conv2d_64[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_64 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_64[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_64[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_65 (Ba  (None, 12, 12, 192)          576       ['conv2d_65[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_65 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_65[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_65[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_61 (Ba  (None, 12, 12, 192)          576       ['conv2d_61[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_66 (Ba  (None, 12, 12, 192)          576       ['conv2d_66[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_61 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_61[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_66 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_66[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_61[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_66[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_62 (Ba  (None, 12, 12, 192)          576       ['conv2d_62[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_67 (Ba  (None, 12, 12, 192)          576       ['conv2d_67[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_62 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_62[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_67 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_67[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (Avera  (None, 12, 12, 768)          0         ['mixed6[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_62[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_67[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_6[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_60 (Ba  (None, 12, 12, 192)          576       ['conv2d_60[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_63 (Ba  (None, 12, 12, 192)          576       ['conv2d_63[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_68 (Ba  (None, 12, 12, 192)          576       ['conv2d_68[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_69 (Ba  (None, 12, 12, 192)          576       ['conv2d_69[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_60 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_60[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_63 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_63[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_68 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_68[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_69 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_69[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)        (None, 12, 12, 768)          0         ['activation_60[0][0]',       \n",
            "                                                                     'activation_63[0][0]',       \n",
            "                                                                     'activation_68[0][0]',       \n",
            "                                                                     'activation_69[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_72 (Ba  (None, 12, 12, 192)          576       ['conv2d_72[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_72 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_72[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_72[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_73 (Ba  (None, 12, 12, 192)          576       ['conv2d_73[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_73 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_73[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_73[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_70 (Ba  (None, 12, 12, 192)          576       ['conv2d_70[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_74 (Ba  (None, 12, 12, 192)          576       ['conv2d_74[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_70 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_70[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_74 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_74[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)          (None, 5, 5, 320)            552960    ['activation_70[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)          (None, 5, 5, 192)            331776    ['activation_74[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_71 (Ba  (None, 5, 5, 320)            960       ['conv2d_71[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_75 (Ba  (None, 5, 5, 192)            576       ['conv2d_75[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_71 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_71[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_75 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_75[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPoolin  (None, 5, 5, 768)            0         ['mixed7[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)        (None, 5, 5, 1280)           0         ['activation_71[0][0]',       \n",
            "                                                                     'activation_75[0][0]',       \n",
            "                                                                     'max_pooling2d_3[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)          (None, 5, 5, 448)            573440    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_80 (Ba  (None, 5, 5, 448)            1344      ['conv2d_80[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_80 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_80[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)          (None, 5, 5, 384)            491520    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_80[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_77 (Ba  (None, 5, 5, 384)            1152      ['conv2d_77[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_81 (Ba  (None, 5, 5, 384)            1152      ['conv2d_81[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_77 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_77[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_81 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_81[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (Avera  (None, 5, 5, 1280)           0         ['mixed8[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)          (None, 5, 5, 320)            409600    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_78 (Ba  (None, 5, 5, 384)            1152      ['conv2d_78[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_79 (Ba  (None, 5, 5, 384)            1152      ['conv2d_79[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_82 (Ba  (None, 5, 5, 384)            1152      ['conv2d_82[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_83 (Ba  (None, 5, 5, 384)            1152      ['conv2d_83[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)          (None, 5, 5, 192)            245760    ['average_pooling2d_7[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_76 (Ba  (None, 5, 5, 320)            960       ['conv2d_76[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_78 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_78[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_79 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_79[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_82 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_82[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_83 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_83[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_84 (Ba  (None, 5, 5, 192)            576       ['conv2d_84[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_76 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_76[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)      (None, 5, 5, 768)            0         ['activation_78[0][0]',       \n",
            "                                                                     'activation_79[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 5, 5, 768)            0         ['activation_82[0][0]',       \n",
            "                                                                     'activation_83[0][0]']       \n",
            "                                                                                                  \n",
            " activation_84 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_84[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)        (None, 5, 5, 2048)           0         ['activation_76[0][0]',       \n",
            "                                                                     'mixed9_0[0][0]',            \n",
            "                                                                     'concatenate[0][0]',         \n",
            "                                                                     'activation_84[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)          (None, 5, 5, 448)            917504    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_89 (Ba  (None, 5, 5, 448)            1344      ['conv2d_89[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_89 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_89[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)          (None, 5, 5, 384)            786432    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_89[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_86 (Ba  (None, 5, 5, 384)            1152      ['conv2d_86[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_90 (Ba  (None, 5, 5, 384)            1152      ['conv2d_90[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_86 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_86[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_90 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_90[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (Avera  (None, 5, 5, 2048)           0         ['mixed9[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)          (None, 5, 5, 320)            655360    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_87 (Ba  (None, 5, 5, 384)            1152      ['conv2d_87[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_88 (Ba  (None, 5, 5, 384)            1152      ['conv2d_88[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_91 (Ba  (None, 5, 5, 384)            1152      ['conv2d_91[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_92 (Ba  (None, 5, 5, 384)            1152      ['conv2d_92[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)          (None, 5, 5, 192)            393216    ['average_pooling2d_8[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_85 (Ba  (None, 5, 5, 320)            960       ['conv2d_85[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_87 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_87[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_88 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_88[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_91 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_91[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_92 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_92[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_93 (Ba  (None, 5, 5, 192)            576       ['conv2d_93[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_85 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_85[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)      (None, 5, 5, 768)            0         ['activation_87[0][0]',       \n",
            "                                                                     'activation_88[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 5, 5, 768)            0         ['activation_91[0][0]',       \n",
            " )                                                                   'activation_92[0][0]']       \n",
            "                                                                                                  \n",
            " activation_93 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_93[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)       (None, 5, 5, 2048)           0         ['activation_85[0][0]',       \n",
            "                                                                     'mixed9_1[0][0]',            \n",
            "                                                                     'concatenate_1[0][0]',       \n",
            "                                                                     'activation_93[0][0]']       \n",
            "                                                                                                  \n",
            " global_average_pooling2d (  (None, 2048)                 0         ['mixed10[0][0]']             \n",
            " GlobalAveragePooling2D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 1024)                 2098176   ['global_average_pooling2d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 1)                    1025      ['dense[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23901985 (91.18 MB)\n",
            "Trainable params: 2099201 (8.01 MB)\n",
            "Non-trainable params: 21802784 (83.17 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#base model 1 InceptionV3\n",
        "base_model_1 = InceptionV3(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "x = base_model_1.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "predictions = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "model_1 = Model(inputs=base_model_1.input, outputs=predictions)\n",
        "for layer in base_model_1.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model_1.summary()\n",
        "\n",
        "model_1.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy', tf.keras.metrics.Precision(name='precision'),\n",
        "                       tf.keras.metrics.Recall(name='recall'),\n",
        "                       tf.keras.metrics.AUC(name='auc')])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6CB5wWiI7Nv1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f1b0b6c-5f7a-4082-82bb-318e9cba42db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " global_average_pooling2d_2  (None, 512)               0         \n",
            "  (GlobalAveragePooling2D)                                       \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20024897 (76.39 MB)\n",
            "Trainable params: 513 (2.00 KB)\n",
            "Non-trainable params: 20024384 (76.39 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# base model 2 VGG19\n",
        "base_model_2 = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "x = base_model_2.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "model_2 = Model(inputs=base_model_2.input, outputs=x)\n",
        "for layer in base_model_2.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model_2.summary()\n",
        "\n",
        "model_2.compile(optimizer='adam',\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy', tf.keras.metrics.Precision(name='precision'),\n",
        "                         tf.keras.metrics.Recall(name='recall'),\n",
        "                         tf.keras.metrics.AUC(name='auc')])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wNBbMj2h_Qri",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52f3f8c1-4b48-42d3-f6bc-30c9bd933eae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "219055592/219055592 [==============================] - 7s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 111, 111, 32)         864       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 111, 111, 32)         96        ['conv2d[0][0]']              \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 111, 111, 32)         0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 32)         9216      ['activation[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_1 (Bat  (None, 109, 109, 32)         96        ['conv2d_1[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)   (None, 109, 109, 32)         0         ['batch_normalization_1[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 109, 109, 64)         18432     ['activation_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_2 (Bat  (None, 109, 109, 64)         192       ['conv2d_2[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_2 (Activation)   (None, 109, 109, 64)         0         ['batch_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2  (None, 54, 54, 64)           0         ['activation_2[0][0]']        \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)           (None, 54, 54, 80)           5120      ['max_pooling2d[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_3 (Bat  (None, 54, 54, 80)           240       ['conv2d_3[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_3 (Activation)   (None, 54, 54, 80)           0         ['batch_normalization_3[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)           (None, 52, 52, 192)          138240    ['activation_3[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_4 (Bat  (None, 52, 52, 192)          576       ['conv2d_4[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_4 (Activation)   (None, 52, 52, 192)          0         ['batch_normalization_4[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPoolin  (None, 25, 25, 192)          0         ['activation_4[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 25, 25, 64)           192       ['conv2d_8[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_8 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 25, 25, 48)           9216      ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 25, 25, 96)           55296     ['activation_8[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (Bat  (None, 25, 25, 48)           144       ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 25, 25, 96)           288       ['conv2d_9[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_6 (Activation)   (None, 25, 25, 48)           0         ['batch_normalization_6[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_9 (Activation)   (None, 25, 25, 96)           0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 25, 25, 192)          0         ['max_pooling2d_1[0][0]']     \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 96)           18432     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 25, 25, 64)           76800     ['activation_6[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_9[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 25, 25, 64)           12288     ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_5 (Bat  (None, 25, 25, 96)           288       ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 25, 25, 64)           192       ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_10 (Ba  (None, 25, 25, 96)           288       ['conv2d_10[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_11 (Ba  (None, 25, 25, 64)           192       ['conv2d_11[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_5 (Activation)   (None, 25, 25, 96)           0         ['batch_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_7 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_10 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_10[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_11 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_11[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed_5b (Concatenate)      (None, 25, 25, 320)          0         ['activation_5[0][0]',        \n",
            "                                                                     'activation_7[0][0]',        \n",
            "                                                                     'activation_10[0][0]',       \n",
            "                                                                     'activation_11[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 25, 25, 32)           10240     ['mixed_5b[0][0]']            \n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 25, 25, 32)           96        ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_15 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 25, 25, 32)           10240     ['mixed_5b[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_15[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 25, 25, 32)           96        ['conv2d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 25, 25, 48)           144       ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_13 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_16 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 25, 25, 32)           10240     ['mixed_5b[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_13[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_16[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 25, 25, 32)           96        ['conv2d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 25, 25, 32)           96        ['conv2d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 25, 25, 64)           192       ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_12 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_14 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_17 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_1_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_12[0][0]',       \n",
            " te)                                                                 'activation_14[0][0]',       \n",
            "                                                                     'activation_17[0][0]']       \n",
            "                                                                                                  \n",
            " block35_1_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_1_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer (Custom  (None, 25, 25, 320)          0         ['mixed_5b[0][0]',            \n",
            " ScaleLayer)                                                         'block35_1_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_1_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_1_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 25, 25, 32)           96        ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_21 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_1_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_21[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 25, 25, 32)           96        ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 25, 25, 48)           144       ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_19 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_22 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_1_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_19[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_22[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 25, 25, 32)           96        ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 25, 25, 32)           96        ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 25, 25, 64)           192       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_18 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_20 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_23 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_2_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_18[0][0]',       \n",
            " te)                                                                 'activation_20[0][0]',       \n",
            "                                                                     'activation_23[0][0]']       \n",
            "                                                                                                  \n",
            " block35_2_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_2_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_1 (Cust  (None, 25, 25, 320)          0         ['block35_1_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_2_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_2_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_1[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_2_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 25, 25, 32)           96        ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_27 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_2_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_27[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 25, 25, 32)           96        ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 25, 25, 48)           144       ['conv2d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_25 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_28 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_2_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_25[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_28[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 25, 25, 32)           96        ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 25, 25, 32)           96        ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 25, 25, 64)           192       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_24 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_26 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_29 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_3_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_24[0][0]',       \n",
            " te)                                                                 'activation_26[0][0]',       \n",
            "                                                                     'activation_29[0][0]']       \n",
            "                                                                                                  \n",
            " block35_3_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_3_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_2 (Cust  (None, 25, 25, 320)          0         ['block35_2_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_3_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_3_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_2[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_3_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_33 (Ba  (None, 25, 25, 32)           96        ['conv2d_33[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_33 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_33[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_3_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_33[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 25, 25, 32)           96        ['conv2d_31[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_34 (Ba  (None, 25, 25, 48)           144       ['conv2d_34[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_31 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_34 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_34[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_3_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_31[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_34[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 25, 25, 32)           96        ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_32 (Ba  (None, 25, 25, 32)           96        ['conv2d_32[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_35 (Ba  (None, 25, 25, 64)           192       ['conv2d_35[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_30 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_32 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_32[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_35 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_35[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_4_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_30[0][0]',       \n",
            " te)                                                                 'activation_32[0][0]',       \n",
            "                                                                     'activation_35[0][0]']       \n",
            "                                                                                                  \n",
            " block35_4_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_4_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_3 (Cust  (None, 25, 25, 320)          0         ['block35_3_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_4_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_4_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_3[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_4_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_39 (Ba  (None, 25, 25, 32)           96        ['conv2d_39[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_39 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_39[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_4_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_39[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_37 (Ba  (None, 25, 25, 32)           96        ['conv2d_37[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_40 (Ba  (None, 25, 25, 48)           144       ['conv2d_40[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_37 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_37[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_40 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_40[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_4_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_37[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_40[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_36 (Ba  (None, 25, 25, 32)           96        ['conv2d_36[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_38 (Ba  (None, 25, 25, 32)           96        ['conv2d_38[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_41 (Ba  (None, 25, 25, 64)           192       ['conv2d_41[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_36 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_36[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_38 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_38[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_41 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_41[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_5_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_36[0][0]',       \n",
            " te)                                                                 'activation_38[0][0]',       \n",
            "                                                                     'activation_41[0][0]']       \n",
            "                                                                                                  \n",
            " block35_5_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_5_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_4 (Cust  (None, 25, 25, 320)          0         ['block35_4_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_5_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_5_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_4[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_5_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_45 (Ba  (None, 25, 25, 32)           96        ['conv2d_45[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_45 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_45[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_5_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_45[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_43 (Ba  (None, 25, 25, 32)           96        ['conv2d_43[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_46 (Ba  (None, 25, 25, 48)           144       ['conv2d_46[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_43 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_43[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_46 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_46[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_5_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_43[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_46[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_42 (Ba  (None, 25, 25, 32)           96        ['conv2d_42[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_44 (Ba  (None, 25, 25, 32)           96        ['conv2d_44[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_47 (Ba  (None, 25, 25, 64)           192       ['conv2d_47[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_42 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_42[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_44 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_44[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_47 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_47[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_6_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_42[0][0]',       \n",
            " te)                                                                 'activation_44[0][0]',       \n",
            "                                                                     'activation_47[0][0]']       \n",
            "                                                                                                  \n",
            " block35_6_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_6_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_5 (Cust  (None, 25, 25, 320)          0         ['block35_5_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_6_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_6_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_5[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_6_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_51 (Ba  (None, 25, 25, 32)           96        ['conv2d_51[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_51 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_51[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_6_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_51[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_49 (Ba  (None, 25, 25, 32)           96        ['conv2d_49[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_52 (Ba  (None, 25, 25, 48)           144       ['conv2d_52[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_49 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_49[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_52 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_52[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_6_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_49[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_52[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_48 (Ba  (None, 25, 25, 32)           96        ['conv2d_48[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_50 (Ba  (None, 25, 25, 32)           96        ['conv2d_50[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_53 (Ba  (None, 25, 25, 64)           192       ['conv2d_53[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_48 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_48[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_50 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_50[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_53 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_53[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_7_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_48[0][0]',       \n",
            " te)                                                                 'activation_50[0][0]',       \n",
            "                                                                     'activation_53[0][0]']       \n",
            "                                                                                                  \n",
            " block35_7_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_7_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_6 (Cust  (None, 25, 25, 320)          0         ['block35_6_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_7_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_7_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_6[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_7_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_57 (Ba  (None, 25, 25, 32)           96        ['conv2d_57[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_57 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_57[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_7_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_57[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_55 (Ba  (None, 25, 25, 32)           96        ['conv2d_55[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_58 (Ba  (None, 25, 25, 48)           144       ['conv2d_58[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_55 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_55[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_58 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_58[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_7_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_55[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_58[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_54 (Ba  (None, 25, 25, 32)           96        ['conv2d_54[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_56 (Ba  (None, 25, 25, 32)           96        ['conv2d_56[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_59 (Ba  (None, 25, 25, 64)           192       ['conv2d_59[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_54 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_54[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_56 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_56[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_59 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_59[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_8_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_54[0][0]',       \n",
            " te)                                                                 'activation_56[0][0]',       \n",
            "                                                                     'activation_59[0][0]']       \n",
            "                                                                                                  \n",
            " block35_8_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_8_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_7 (Cust  (None, 25, 25, 320)          0         ['block35_7_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_8_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_8_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_7[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_8_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_63 (Ba  (None, 25, 25, 32)           96        ['conv2d_63[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_63 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_63[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_8_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_63[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_61 (Ba  (None, 25, 25, 32)           96        ['conv2d_61[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_64 (Ba  (None, 25, 25, 48)           144       ['conv2d_64[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_61 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_61[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_64 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_64[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_8_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_61[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_64[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_60 (Ba  (None, 25, 25, 32)           96        ['conv2d_60[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_62 (Ba  (None, 25, 25, 32)           96        ['conv2d_62[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_65 (Ba  (None, 25, 25, 64)           192       ['conv2d_65[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_60 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_60[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_62 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_62[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_65 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_65[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_9_mixed (Concatena  (None, 25, 25, 128)          0         ['activation_60[0][0]',       \n",
            " te)                                                                 'activation_62[0][0]',       \n",
            "                                                                     'activation_65[0][0]']       \n",
            "                                                                                                  \n",
            " block35_9_conv (Conv2D)     (None, 25, 25, 320)          41280     ['block35_9_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_8 (Cust  (None, 25, 25, 320)          0         ['block35_8_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_9_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block35_9_ac (Activation)   (None, 25, 25, 320)          0         ['custom_scale_layer_8[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_9_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_69 (Ba  (None, 25, 25, 32)           96        ['conv2d_69[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_69 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_69[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_9_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)          (None, 25, 25, 48)           13824     ['activation_69[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_67 (Ba  (None, 25, 25, 32)           96        ['conv2d_67[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_70 (Ba  (None, 25, 25, 48)           144       ['conv2d_70[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_67 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_67[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_70 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_70[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)          (None, 25, 25, 32)           10240     ['block35_9_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)          (None, 25, 25, 32)           9216      ['activation_67[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)          (None, 25, 25, 64)           27648     ['activation_70[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_66 (Ba  (None, 25, 25, 32)           96        ['conv2d_66[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_68 (Ba  (None, 25, 25, 32)           96        ['conv2d_68[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_71 (Ba  (None, 25, 25, 64)           192       ['conv2d_71[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_66 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_66[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_68 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_68[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_71 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_71[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block35_10_mixed (Concaten  (None, 25, 25, 128)          0         ['activation_66[0][0]',       \n",
            " ate)                                                                'activation_68[0][0]',       \n",
            "                                                                     'activation_71[0][0]']       \n",
            "                                                                                                  \n",
            " block35_10_conv (Conv2D)    (None, 25, 25, 320)          41280     ['block35_10_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_9 (Cust  (None, 25, 25, 320)          0         ['block35_9_ac[0][0]',        \n",
            " omScaleLayer)                                                       'block35_10_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block35_10_ac (Activation)  (None, 25, 25, 320)          0         ['custom_scale_layer_9[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)          (None, 25, 25, 256)          81920     ['block35_10_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_73 (Ba  (None, 25, 25, 256)          768       ['conv2d_73[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_73 (Activation)  (None, 25, 25, 256)          0         ['batch_normalization_73[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)          (None, 25, 25, 256)          589824    ['activation_73[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_74 (Ba  (None, 25, 25, 256)          768       ['conv2d_74[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_74 (Activation)  (None, 25, 25, 256)          0         ['batch_normalization_74[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)          (None, 12, 12, 384)          1105920   ['block35_10_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)          (None, 12, 12, 384)          884736    ['activation_74[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_72 (Ba  (None, 12, 12, 384)          1152      ['conv2d_72[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_75 (Ba  (None, 12, 12, 384)          1152      ['conv2d_75[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_72 (Activation)  (None, 12, 12, 384)          0         ['batch_normalization_72[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_75 (Activation)  (None, 12, 12, 384)          0         ['batch_normalization_75[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPoolin  (None, 12, 12, 320)          0         ['block35_10_ac[0][0]']       \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed_6a (Concatenate)      (None, 12, 12, 1088)         0         ['activation_72[0][0]',       \n",
            "                                                                     'activation_75[0][0]',       \n",
            "                                                                     'max_pooling2d_2[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)          (None, 12, 12, 128)          139264    ['mixed_6a[0][0]']            \n",
            "                                                                                                  \n",
            " batch_normalization_77 (Ba  (None, 12, 12, 128)          384       ['conv2d_77[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_77 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_77[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)          (None, 12, 12, 160)          143360    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_78 (Ba  (None, 12, 12, 160)          480       ['conv2d_78[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_78 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_78[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)          (None, 12, 12, 192)          208896    ['mixed_6a[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_78[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_76 (Ba  (None, 12, 12, 192)          576       ['conv2d_76[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_79 (Ba  (None, 12, 12, 192)          576       ['conv2d_79[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_76 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_76[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_79 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_79[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block17_1_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_76[0][0]',       \n",
            " te)                                                                 'activation_79[0][0]']       \n",
            "                                                                                                  \n",
            " block17_1_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_1_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_10 (Cus  (None, 12, 12, 1088)         0         ['mixed_6a[0][0]',            \n",
            " tomScaleLayer)                                                      'block17_1_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_1_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_10[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)          (None, 12, 12, 128)          139264    ['block17_1_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_81 (Ba  (None, 12, 12, 128)          384       ['conv2d_81[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_81 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_81[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)          (None, 12, 12, 160)          143360    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_82 (Ba  (None, 12, 12, 160)          480       ['conv2d_82[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_82 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_82[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)          (None, 12, 12, 192)          208896    ['block17_1_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_82[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_80 (Ba  (None, 12, 12, 192)          576       ['conv2d_80[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_83 (Ba  (None, 12, 12, 192)          576       ['conv2d_83[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_80 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_80[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_83 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_83[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block17_2_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_80[0][0]',       \n",
            " te)                                                                 'activation_83[0][0]']       \n",
            "                                                                                                  \n",
            " block17_2_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_2_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_11 (Cus  (None, 12, 12, 1088)         0         ['block17_1_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_2_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_2_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_11[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)          (None, 12, 12, 128)          139264    ['block17_2_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_85 (Ba  (None, 12, 12, 128)          384       ['conv2d_85[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_85 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_85[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)          (None, 12, 12, 160)          143360    ['activation_85[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_86 (Ba  (None, 12, 12, 160)          480       ['conv2d_86[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_86 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_86[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)          (None, 12, 12, 192)          208896    ['block17_2_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_84 (Ba  (None, 12, 12, 192)          576       ['conv2d_84[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_87 (Ba  (None, 12, 12, 192)          576       ['conv2d_87[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_84 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_84[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_87 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_87[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block17_3_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_84[0][0]',       \n",
            " te)                                                                 'activation_87[0][0]']       \n",
            "                                                                                                  \n",
            " block17_3_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_3_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_12 (Cus  (None, 12, 12, 1088)         0         ['block17_2_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_3_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_3_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_12[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)          (None, 12, 12, 128)          139264    ['block17_3_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_89 (Ba  (None, 12, 12, 128)          384       ['conv2d_89[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_89 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_89[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)          (None, 12, 12, 160)          143360    ['activation_89[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_90 (Ba  (None, 12, 12, 160)          480       ['conv2d_90[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_90 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_90[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)          (None, 12, 12, 192)          208896    ['block17_3_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_88 (Ba  (None, 12, 12, 192)          576       ['conv2d_88[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_91 (Ba  (None, 12, 12, 192)          576       ['conv2d_91[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_88 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_88[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_91 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_91[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block17_4_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_88[0][0]',       \n",
            " te)                                                                 'activation_91[0][0]']       \n",
            "                                                                                                  \n",
            " block17_4_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_4_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_13 (Cus  (None, 12, 12, 1088)         0         ['block17_3_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_4_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_4_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_13[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)          (None, 12, 12, 128)          139264    ['block17_4_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_93 (Ba  (None, 12, 12, 128)          384       ['conv2d_93[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_93 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_93[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_94 (Conv2D)          (None, 12, 12, 160)          143360    ['activation_93[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_94 (Ba  (None, 12, 12, 160)          480       ['conv2d_94[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_94 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_94[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)          (None, 12, 12, 192)          208896    ['block17_4_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_95 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_94[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_92 (Ba  (None, 12, 12, 192)          576       ['conv2d_92[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_95 (Ba  (None, 12, 12, 192)          576       ['conv2d_95[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_92 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_92[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_95 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_95[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block17_5_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_92[0][0]',       \n",
            " te)                                                                 'activation_95[0][0]']       \n",
            "                                                                                                  \n",
            " block17_5_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_5_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_14 (Cus  (None, 12, 12, 1088)         0         ['block17_4_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_5_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_5_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_14[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_97 (Conv2D)          (None, 12, 12, 128)          139264    ['block17_5_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_97 (Ba  (None, 12, 12, 128)          384       ['conv2d_97[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_97 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_97[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_98 (Conv2D)          (None, 12, 12, 160)          143360    ['activation_97[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_98 (Ba  (None, 12, 12, 160)          480       ['conv2d_98[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_98 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_98[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_96 (Conv2D)          (None, 12, 12, 192)          208896    ['block17_5_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_99 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_98[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_96 (Ba  (None, 12, 12, 192)          576       ['conv2d_96[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_99 (Ba  (None, 12, 12, 192)          576       ['conv2d_99[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_96 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_96[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_99 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_99[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " block17_6_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_96[0][0]',       \n",
            " te)                                                                 'activation_99[0][0]']       \n",
            "                                                                                                  \n",
            " block17_6_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_6_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_15 (Cus  (None, 12, 12, 1088)         0         ['block17_5_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_6_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_6_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_15[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_101 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_6_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_101 (B  (None, 12, 12, 128)          384       ['conv2d_101[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_101 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_101[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_102 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_101[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_102 (B  (None, 12, 12, 160)          480       ['conv2d_102[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_102 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_102[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_100 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_6_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_103 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_102[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_100 (B  (None, 12, 12, 192)          576       ['conv2d_100[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_103 (B  (None, 12, 12, 192)          576       ['conv2d_103[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_100 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_100[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_103 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_103[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_7_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_100[0][0]',      \n",
            " te)                                                                 'activation_103[0][0]']      \n",
            "                                                                                                  \n",
            " block17_7_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_7_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_16 (Cus  (None, 12, 12, 1088)         0         ['block17_6_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_7_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_7_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_16[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_105 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_7_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_105 (B  (None, 12, 12, 128)          384       ['conv2d_105[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_105 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_105[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_106 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_105[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_106 (B  (None, 12, 12, 160)          480       ['conv2d_106[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_106 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_106[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_104 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_7_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_107 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_106[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_104 (B  (None, 12, 12, 192)          576       ['conv2d_104[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_107 (B  (None, 12, 12, 192)          576       ['conv2d_107[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_104 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_104[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_107 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_107[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_8_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_104[0][0]',      \n",
            " te)                                                                 'activation_107[0][0]']      \n",
            "                                                                                                  \n",
            " block17_8_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_8_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_17 (Cus  (None, 12, 12, 1088)         0         ['block17_7_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_8_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_8_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_17[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_109 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_8_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_109 (B  (None, 12, 12, 128)          384       ['conv2d_109[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_109 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_109[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_110 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_109[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_110 (B  (None, 12, 12, 160)          480       ['conv2d_110[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_110 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_110[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_108 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_8_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_111 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_110[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_108 (B  (None, 12, 12, 192)          576       ['conv2d_108[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_111 (B  (None, 12, 12, 192)          576       ['conv2d_111[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_108 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_108[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_111 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_111[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_9_mixed (Concatena  (None, 12, 12, 384)          0         ['activation_108[0][0]',      \n",
            " te)                                                                 'activation_111[0][0]']      \n",
            "                                                                                                  \n",
            " block17_9_conv (Conv2D)     (None, 12, 12, 1088)         418880    ['block17_9_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_18 (Cus  (None, 12, 12, 1088)         0         ['block17_8_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_9_conv[0][0]']      \n",
            "                                                                                                  \n",
            " block17_9_ac (Activation)   (None, 12, 12, 1088)         0         ['custom_scale_layer_18[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_113 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_9_ac[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_113 (B  (None, 12, 12, 128)          384       ['conv2d_113[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_113 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_113[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_114 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_113[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_114 (B  (None, 12, 12, 160)          480       ['conv2d_114[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_114 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_114[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_112 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_9_ac[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_115 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_114[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_112 (B  (None, 12, 12, 192)          576       ['conv2d_112[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_115 (B  (None, 12, 12, 192)          576       ['conv2d_115[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_112 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_112[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_115 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_115[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_10_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_112[0][0]',      \n",
            " ate)                                                                'activation_115[0][0]']      \n",
            "                                                                                                  \n",
            " block17_10_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_10_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_19 (Cus  (None, 12, 12, 1088)         0         ['block17_9_ac[0][0]',        \n",
            " tomScaleLayer)                                                      'block17_10_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_10_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_19[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_117 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_10_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_117 (B  (None, 12, 12, 128)          384       ['conv2d_117[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_117 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_117[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_118 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_117[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_118 (B  (None, 12, 12, 160)          480       ['conv2d_118[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_118 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_118[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_116 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_10_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_119 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_118[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_116 (B  (None, 12, 12, 192)          576       ['conv2d_116[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_119 (B  (None, 12, 12, 192)          576       ['conv2d_119[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_116 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_116[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_119 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_119[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_11_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_116[0][0]',      \n",
            " ate)                                                                'activation_119[0][0]']      \n",
            "                                                                                                  \n",
            " block17_11_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_11_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_20 (Cus  (None, 12, 12, 1088)         0         ['block17_10_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_11_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_11_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_20[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_121 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_11_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_121 (B  (None, 12, 12, 128)          384       ['conv2d_121[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_121 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_121[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_122 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_121[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_122 (B  (None, 12, 12, 160)          480       ['conv2d_122[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_122 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_122[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_120 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_11_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_123 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_122[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_120 (B  (None, 12, 12, 192)          576       ['conv2d_120[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_123 (B  (None, 12, 12, 192)          576       ['conv2d_123[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_120 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_120[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_123 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_123[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_12_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_120[0][0]',      \n",
            " ate)                                                                'activation_123[0][0]']      \n",
            "                                                                                                  \n",
            " block17_12_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_12_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_21 (Cus  (None, 12, 12, 1088)         0         ['block17_11_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_12_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_12_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_21[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_125 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_12_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_125 (B  (None, 12, 12, 128)          384       ['conv2d_125[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_125 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_125[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_126 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_125[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_126 (B  (None, 12, 12, 160)          480       ['conv2d_126[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_126 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_126[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_124 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_12_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_127 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_126[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_124 (B  (None, 12, 12, 192)          576       ['conv2d_124[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_127 (B  (None, 12, 12, 192)          576       ['conv2d_127[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_124 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_124[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_127 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_127[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_13_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_124[0][0]',      \n",
            " ate)                                                                'activation_127[0][0]']      \n",
            "                                                                                                  \n",
            " block17_13_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_13_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_22 (Cus  (None, 12, 12, 1088)         0         ['block17_12_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_13_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_13_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_22[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_129 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_13_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_129 (B  (None, 12, 12, 128)          384       ['conv2d_129[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_129 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_129[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_130 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_129[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_130 (B  (None, 12, 12, 160)          480       ['conv2d_130[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_130 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_130[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_128 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_13_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_131 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_130[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_128 (B  (None, 12, 12, 192)          576       ['conv2d_128[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_131 (B  (None, 12, 12, 192)          576       ['conv2d_131[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_128 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_128[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_131 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_131[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_14_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_128[0][0]',      \n",
            " ate)                                                                'activation_131[0][0]']      \n",
            "                                                                                                  \n",
            " block17_14_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_14_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_23 (Cus  (None, 12, 12, 1088)         0         ['block17_13_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_14_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_14_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_23[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_133 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_14_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_133 (B  (None, 12, 12, 128)          384       ['conv2d_133[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_133 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_133[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_134 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_133[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_134 (B  (None, 12, 12, 160)          480       ['conv2d_134[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_134 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_134[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_132 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_14_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_135 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_134[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_132 (B  (None, 12, 12, 192)          576       ['conv2d_132[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_135 (B  (None, 12, 12, 192)          576       ['conv2d_135[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_132 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_132[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_135 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_135[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_15_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_132[0][0]',      \n",
            " ate)                                                                'activation_135[0][0]']      \n",
            "                                                                                                  \n",
            " block17_15_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_15_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_24 (Cus  (None, 12, 12, 1088)         0         ['block17_14_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_15_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_15_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_24[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_137 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_15_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_137 (B  (None, 12, 12, 128)          384       ['conv2d_137[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_137 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_137[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_138 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_137[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_138 (B  (None, 12, 12, 160)          480       ['conv2d_138[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_138 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_138[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_136 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_15_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_139 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_138[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_136 (B  (None, 12, 12, 192)          576       ['conv2d_136[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_139 (B  (None, 12, 12, 192)          576       ['conv2d_139[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_136 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_136[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_139 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_139[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_16_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_136[0][0]',      \n",
            " ate)                                                                'activation_139[0][0]']      \n",
            "                                                                                                  \n",
            " block17_16_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_16_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_25 (Cus  (None, 12, 12, 1088)         0         ['block17_15_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_16_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_16_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_25[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_141 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_16_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_141 (B  (None, 12, 12, 128)          384       ['conv2d_141[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_141 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_141[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_142 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_141[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_142 (B  (None, 12, 12, 160)          480       ['conv2d_142[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_142 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_142[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_140 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_16_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_143 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_142[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_140 (B  (None, 12, 12, 192)          576       ['conv2d_140[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_143 (B  (None, 12, 12, 192)          576       ['conv2d_143[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_140 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_140[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_143 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_143[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_17_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_140[0][0]',      \n",
            " ate)                                                                'activation_143[0][0]']      \n",
            "                                                                                                  \n",
            " block17_17_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_17_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_26 (Cus  (None, 12, 12, 1088)         0         ['block17_16_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_17_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_17_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_26[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_145 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_17_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_145 (B  (None, 12, 12, 128)          384       ['conv2d_145[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_145 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_145[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_146 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_145[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_146 (B  (None, 12, 12, 160)          480       ['conv2d_146[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_146 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_146[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_144 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_17_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_147 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_146[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_144 (B  (None, 12, 12, 192)          576       ['conv2d_144[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_147 (B  (None, 12, 12, 192)          576       ['conv2d_147[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_144 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_144[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_147 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_147[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_18_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_144[0][0]',      \n",
            " ate)                                                                'activation_147[0][0]']      \n",
            "                                                                                                  \n",
            " block17_18_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_18_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_27 (Cus  (None, 12, 12, 1088)         0         ['block17_17_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_18_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_18_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_27[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_149 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_18_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_149 (B  (None, 12, 12, 128)          384       ['conv2d_149[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_149 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_149[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_150 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_149[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_150 (B  (None, 12, 12, 160)          480       ['conv2d_150[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_150 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_150[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_148 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_18_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_151 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_150[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_148 (B  (None, 12, 12, 192)          576       ['conv2d_148[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_151 (B  (None, 12, 12, 192)          576       ['conv2d_151[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_148 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_148[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_151 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_151[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_19_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_148[0][0]',      \n",
            " ate)                                                                'activation_151[0][0]']      \n",
            "                                                                                                  \n",
            " block17_19_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_19_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_28 (Cus  (None, 12, 12, 1088)         0         ['block17_18_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_19_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_19_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_28[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_153 (Conv2D)         (None, 12, 12, 128)          139264    ['block17_19_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_153 (B  (None, 12, 12, 128)          384       ['conv2d_153[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_153 (Activation  (None, 12, 12, 128)          0         ['batch_normalization_153[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_154 (Conv2D)         (None, 12, 12, 160)          143360    ['activation_153[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_154 (B  (None, 12, 12, 160)          480       ['conv2d_154[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_154 (Activation  (None, 12, 12, 160)          0         ['batch_normalization_154[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_152 (Conv2D)         (None, 12, 12, 192)          208896    ['block17_19_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_155 (Conv2D)         (None, 12, 12, 192)          215040    ['activation_154[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_152 (B  (None, 12, 12, 192)          576       ['conv2d_152[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_155 (B  (None, 12, 12, 192)          576       ['conv2d_155[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_152 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_152[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_155 (Activation  (None, 12, 12, 192)          0         ['batch_normalization_155[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block17_20_mixed (Concaten  (None, 12, 12, 384)          0         ['activation_152[0][0]',      \n",
            " ate)                                                                'activation_155[0][0]']      \n",
            "                                                                                                  \n",
            " block17_20_conv (Conv2D)    (None, 12, 12, 1088)         418880    ['block17_20_mixed[0][0]']    \n",
            "                                                                                                  \n",
            " custom_scale_layer_29 (Cus  (None, 12, 12, 1088)         0         ['block17_19_ac[0][0]',       \n",
            " tomScaleLayer)                                                      'block17_20_conv[0][0]']     \n",
            "                                                                                                  \n",
            " block17_20_ac (Activation)  (None, 12, 12, 1088)         0         ['custom_scale_layer_29[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_160 (Conv2D)         (None, 12, 12, 256)          278528    ['block17_20_ac[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_160 (B  (None, 12, 12, 256)          768       ['conv2d_160[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_160 (Activation  (None, 12, 12, 256)          0         ['batch_normalization_160[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_156 (Conv2D)         (None, 12, 12, 256)          278528    ['block17_20_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_158 (Conv2D)         (None, 12, 12, 256)          278528    ['block17_20_ac[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_161 (Conv2D)         (None, 12, 12, 288)          663552    ['activation_160[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_156 (B  (None, 12, 12, 256)          768       ['conv2d_156[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_158 (B  (None, 12, 12, 256)          768       ['conv2d_158[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_161 (B  (None, 12, 12, 288)          864       ['conv2d_161[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_156 (Activation  (None, 12, 12, 256)          0         ['batch_normalization_156[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_158 (Activation  (None, 12, 12, 256)          0         ['batch_normalization_158[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_161 (Activation  (None, 12, 12, 288)          0         ['batch_normalization_161[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_157 (Conv2D)         (None, 5, 5, 384)            884736    ['activation_156[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_159 (Conv2D)         (None, 5, 5, 288)            663552    ['activation_158[0][0]']      \n",
            "                                                                                                  \n",
            " conv2d_162 (Conv2D)         (None, 5, 5, 320)            829440    ['activation_161[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_157 (B  (None, 5, 5, 384)            1152      ['conv2d_157[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_159 (B  (None, 5, 5, 288)            864       ['conv2d_159[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_162 (B  (None, 5, 5, 320)            960       ['conv2d_162[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_157 (Activation  (None, 5, 5, 384)            0         ['batch_normalization_157[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_159 (Activation  (None, 5, 5, 288)            0         ['batch_normalization_159[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_162 (Activation  (None, 5, 5, 320)            0         ['batch_normalization_162[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPoolin  (None, 5, 5, 1088)           0         ['block17_20_ac[0][0]']       \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed_7a (Concatenate)      (None, 5, 5, 2080)           0         ['activation_157[0][0]',      \n",
            "                                                                     'activation_159[0][0]',      \n",
            "                                                                     'activation_162[0][0]',      \n",
            "                                                                     'max_pooling2d_3[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_164 (Conv2D)         (None, 5, 5, 192)            399360    ['mixed_7a[0][0]']            \n",
            "                                                                                                  \n",
            " batch_normalization_164 (B  (None, 5, 5, 192)            576       ['conv2d_164[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_164 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_164[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_165 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_164[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_165 (B  (None, 5, 5, 224)            672       ['conv2d_165[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_165 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_165[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_163 (Conv2D)         (None, 5, 5, 192)            399360    ['mixed_7a[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_166 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_165[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_163 (B  (None, 5, 5, 192)            576       ['conv2d_163[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_166 (B  (None, 5, 5, 256)            768       ['conv2d_166[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_163 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_163[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_166 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_166[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_1_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_163[0][0]',      \n",
            " e)                                                                  'activation_166[0][0]']      \n",
            "                                                                                                  \n",
            " block8_1_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_1_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_30 (Cus  (None, 5, 5, 2080)           0         ['mixed_7a[0][0]',            \n",
            " tomScaleLayer)                                                      'block8_1_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_1_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_30[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_168 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_1_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_168 (B  (None, 5, 5, 192)            576       ['conv2d_168[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_168 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_168[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_169 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_168[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_169 (B  (None, 5, 5, 224)            672       ['conv2d_169[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_169 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_169[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_167 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_1_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_170 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_169[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_167 (B  (None, 5, 5, 192)            576       ['conv2d_167[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_170 (B  (None, 5, 5, 256)            768       ['conv2d_170[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_167 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_167[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_170 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_170[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_2_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_167[0][0]',      \n",
            " e)                                                                  'activation_170[0][0]']      \n",
            "                                                                                                  \n",
            " block8_2_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_2_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_31 (Cus  (None, 5, 5, 2080)           0         ['block8_1_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_2_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_2_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_31[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_172 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_2_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_172 (B  (None, 5, 5, 192)            576       ['conv2d_172[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_172 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_172[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_173 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_172[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_173 (B  (None, 5, 5, 224)            672       ['conv2d_173[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_173 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_173[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_171 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_2_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_174 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_173[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_171 (B  (None, 5, 5, 192)            576       ['conv2d_171[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_174 (B  (None, 5, 5, 256)            768       ['conv2d_174[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_171 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_171[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_174 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_174[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_3_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_171[0][0]',      \n",
            " e)                                                                  'activation_174[0][0]']      \n",
            "                                                                                                  \n",
            " block8_3_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_3_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_32 (Cus  (None, 5, 5, 2080)           0         ['block8_2_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_3_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_3_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_32[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_176 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_3_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_176 (B  (None, 5, 5, 192)            576       ['conv2d_176[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_176 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_176[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_177 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_176[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_177 (B  (None, 5, 5, 224)            672       ['conv2d_177[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_177 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_177[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_175 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_3_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_178 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_177[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_175 (B  (None, 5, 5, 192)            576       ['conv2d_175[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_178 (B  (None, 5, 5, 256)            768       ['conv2d_178[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_175 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_175[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_178 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_178[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_4_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_175[0][0]',      \n",
            " e)                                                                  'activation_178[0][0]']      \n",
            "                                                                                                  \n",
            " block8_4_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_4_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_33 (Cus  (None, 5, 5, 2080)           0         ['block8_3_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_4_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_4_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_33[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_180 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_4_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_180 (B  (None, 5, 5, 192)            576       ['conv2d_180[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_180 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_180[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_181 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_180[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_181 (B  (None, 5, 5, 224)            672       ['conv2d_181[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_181 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_181[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_179 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_4_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_182 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_181[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_179 (B  (None, 5, 5, 192)            576       ['conv2d_179[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_182 (B  (None, 5, 5, 256)            768       ['conv2d_182[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_179 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_179[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_182 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_182[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_5_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_179[0][0]',      \n",
            " e)                                                                  'activation_182[0][0]']      \n",
            "                                                                                                  \n",
            " block8_5_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_5_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_34 (Cus  (None, 5, 5, 2080)           0         ['block8_4_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_5_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_5_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_34[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_184 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_5_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_184 (B  (None, 5, 5, 192)            576       ['conv2d_184[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_184 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_184[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_185 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_184[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_185 (B  (None, 5, 5, 224)            672       ['conv2d_185[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_185 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_185[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_183 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_5_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_186 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_185[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_183 (B  (None, 5, 5, 192)            576       ['conv2d_183[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_186 (B  (None, 5, 5, 256)            768       ['conv2d_186[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_183 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_183[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_186 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_186[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_6_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_183[0][0]',      \n",
            " e)                                                                  'activation_186[0][0]']      \n",
            "                                                                                                  \n",
            " block8_6_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_6_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_35 (Cus  (None, 5, 5, 2080)           0         ['block8_5_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_6_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_6_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_35[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_188 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_6_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_188 (B  (None, 5, 5, 192)            576       ['conv2d_188[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_188 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_188[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_189 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_188[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_189 (B  (None, 5, 5, 224)            672       ['conv2d_189[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_189 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_189[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_187 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_6_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_190 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_189[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_187 (B  (None, 5, 5, 192)            576       ['conv2d_187[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_190 (B  (None, 5, 5, 256)            768       ['conv2d_190[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_187 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_187[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_190 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_190[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_7_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_187[0][0]',      \n",
            " e)                                                                  'activation_190[0][0]']      \n",
            "                                                                                                  \n",
            " block8_7_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_7_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_36 (Cus  (None, 5, 5, 2080)           0         ['block8_6_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_7_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_7_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_36[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_192 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_7_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_192 (B  (None, 5, 5, 192)            576       ['conv2d_192[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_192 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_192[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_193 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_192[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_193 (B  (None, 5, 5, 224)            672       ['conv2d_193[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_193 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_193[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_191 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_7_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_194 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_193[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_191 (B  (None, 5, 5, 192)            576       ['conv2d_191[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_194 (B  (None, 5, 5, 256)            768       ['conv2d_194[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_191 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_191[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_194 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_194[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_8_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_191[0][0]',      \n",
            " e)                                                                  'activation_194[0][0]']      \n",
            "                                                                                                  \n",
            " block8_8_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_8_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_37 (Cus  (None, 5, 5, 2080)           0         ['block8_7_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_8_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_8_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_37[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_196 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_8_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_196 (B  (None, 5, 5, 192)            576       ['conv2d_196[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_196 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_196[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_197 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_196[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_197 (B  (None, 5, 5, 224)            672       ['conv2d_197[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_197 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_197[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_195 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_8_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_198 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_197[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_195 (B  (None, 5, 5, 192)            576       ['conv2d_195[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_198 (B  (None, 5, 5, 256)            768       ['conv2d_198[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_195 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_195[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_198 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_198[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_9_mixed (Concatenat  (None, 5, 5, 448)            0         ['activation_195[0][0]',      \n",
            " e)                                                                  'activation_198[0][0]']      \n",
            "                                                                                                  \n",
            " block8_9_conv (Conv2D)      (None, 5, 5, 2080)           933920    ['block8_9_mixed[0][0]']      \n",
            "                                                                                                  \n",
            " custom_scale_layer_38 (Cus  (None, 5, 5, 2080)           0         ['block8_8_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_9_conv[0][0]']       \n",
            "                                                                                                  \n",
            " block8_9_ac (Activation)    (None, 5, 5, 2080)           0         ['custom_scale_layer_38[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_200 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_9_ac[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_200 (B  (None, 5, 5, 192)            576       ['conv2d_200[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_200 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_200[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_201 (Conv2D)         (None, 5, 5, 224)            129024    ['activation_200[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_201 (B  (None, 5, 5, 224)            672       ['conv2d_201[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_201 (Activation  (None, 5, 5, 224)            0         ['batch_normalization_201[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " conv2d_199 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_9_ac[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_202 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_201[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_199 (B  (None, 5, 5, 192)            576       ['conv2d_199[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " batch_normalization_202 (B  (None, 5, 5, 256)            768       ['conv2d_202[0][0]']          \n",
            " atchNormalization)                                                                               \n",
            "                                                                                                  \n",
            " activation_199 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_199[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " activation_202 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_202[0][0\n",
            " )                                                                  ]']                           \n",
            "                                                                                                  \n",
            " block8_10_mixed (Concatena  (None, 5, 5, 448)            0         ['activation_199[0][0]',      \n",
            " te)                                                                 'activation_202[0][0]']      \n",
            "                                                                                                  \n",
            " block8_10_conv (Conv2D)     (None, 5, 5, 2080)           933920    ['block8_10_mixed[0][0]']     \n",
            "                                                                                                  \n",
            " custom_scale_layer_39 (Cus  (None, 5, 5, 2080)           0         ['block8_9_ac[0][0]',         \n",
            " tomScaleLayer)                                                      'block8_10_conv[0][0]']      \n",
            "                                                                                                  \n",
            " conv_7b (Conv2D)            (None, 5, 5, 1536)           3194880   ['custom_scale_layer_39[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv_7b_bn (BatchNormaliza  (None, 5, 5, 1536)           4608      ['conv_7b[0][0]']             \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " conv_7b_ac (Activation)     (None, 5, 5, 1536)           0         ['conv_7b_bn[0][0]']          \n",
            "                                                                                                  \n",
            " global_average_pooling2d (  (None, 1536)                 0         ['conv_7b_ac[0][0]']          \n",
            " GlobalAveragePooling2D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 1)                    1537      ['global_average_pooling2d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 54338273 (207.28 MB)\n",
            "Trainable params: 1537 (6.00 KB)\n",
            "Non-trainable params: 54336736 (207.28 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# base model 3 InceptionResNetV2\n",
        "base_model_3 = InceptionResNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "x = base_model_3.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "model_3 = Model(inputs=base_model_3.input, outputs=x)\n",
        "for layer in base_model_3.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model_3.summary()\n",
        "\n",
        "model_3.compile(optimizer='adam',\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy', tf.keras.metrics.Precision(name='precision'),\n",
        "                         tf.keras.metrics.Recall(name='recall'),\n",
        "                         tf.keras.metrics.AUC(name='auc')])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oNbBrpwkBwoB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56359d70-4898-4056-ddec-5e7c7227dbf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "19/19 [==============================] - 29s 674ms/step - loss: 0.4411 - accuracy: 0.8591 - precision: 0.8453 - recall: 0.8421 - auc: 0.9455 - val_loss: 0.3787 - val_accuracy: 0.8912 - val_precision: 0.9776 - val_recall: 0.8188 - val_auc: 0.9758\n",
            "Epoch 2/25\n",
            "19/19 [==============================] - 10s 533ms/step - loss: 0.1701 - accuracy: 0.9457 - precision: 0.9432 - recall: 0.9361 - auc: 0.9818 - val_loss: 0.1930 - val_accuracy: 0.9286 - val_precision: 0.9728 - val_recall: 0.8938 - val_auc: 0.9844\n",
            "Epoch 4/25\n",
            "19/19 [==============================] - 9s 482ms/step - loss: 0.2447 - accuracy: 0.9134 - precision: 0.8909 - recall: 0.9211 - auc: 0.9702 - val_loss: 0.1828 - val_accuracy: 0.9320 - val_precision: 0.9730 - val_recall: 0.9000 - val_auc: 0.9854\n",
            "Epoch 5/25\n",
            "19/19 [==============================] - 8s 425ms/step - loss: 0.1459 - accuracy: 0.9559 - precision: 0.9615 - recall: 0.9398 - auc: 0.9863 - val_loss: 0.1512 - val_accuracy: 0.9388 - val_precision: 0.9277 - val_recall: 0.9625 - val_auc: 0.9881\n",
            "Epoch 6/25\n",
            "19/19 [==============================] - 10s 536ms/step - loss: 0.1253 - accuracy: 0.9525 - precision: 0.9407 - recall: 0.9549 - auc: 0.9903 - val_loss: 0.1502 - val_accuracy: 0.9490 - val_precision: 0.9618 - val_recall: 0.9438 - val_auc: 0.9871\n",
            "Epoch 7/25\n",
            "19/19 [==============================] - 9s 469ms/step - loss: 0.0983 - accuracy: 0.9559 - precision: 0.9511 - recall: 0.9511 - auc: 0.9941 - val_loss: 0.1533 - val_accuracy: 0.9388 - val_precision: 0.9551 - val_recall: 0.9312 - val_auc: 0.9861\n",
            "Epoch 8/25\n",
            "19/19 [==============================] - 8s 418ms/step - loss: 0.1197 - accuracy: 0.9576 - precision: 0.9513 - recall: 0.9549 - auc: 0.9908 - val_loss: 0.1753 - val_accuracy: 0.9286 - val_precision: 0.9664 - val_recall: 0.9000 - val_auc: 0.9834\n",
            "Epoch 9/25\n",
            "19/19 [==============================] - 9s 497ms/step - loss: 0.1357 - accuracy: 0.9508 - precision: 0.9472 - recall: 0.9436 - auc: 0.9874 - val_loss: 0.1587 - val_accuracy: 0.9422 - val_precision: 0.9613 - val_recall: 0.9312 - val_auc: 0.9863\n",
            "Epoch 10/25\n",
            "19/19 [==============================] - 7s 388ms/step - loss: 0.1112 - accuracy: 0.9626 - precision: 0.9519 - recall: 0.9662 - auc: 0.9922 - val_loss: 0.1472 - val_accuracy: 0.9422 - val_precision: 0.9613 - val_recall: 0.9312 - val_auc: 0.9870\n",
            "Epoch 11/25\n",
            "19/19 [==============================] - 8s 423ms/step - loss: 0.1153 - accuracy: 0.9508 - precision: 0.9405 - recall: 0.9511 - auc: 0.9923 - val_loss: 0.1389 - val_accuracy: 0.9490 - val_precision: 0.9448 - val_recall: 0.9625 - val_auc: 0.9881\n",
            "Epoch 12/25\n",
            "19/19 [==============================] - 10s 509ms/step - loss: 0.1610 - accuracy: 0.9440 - precision: 0.9464 - recall: 0.9286 - auc: 0.9840 - val_loss: 0.1405 - val_accuracy: 0.9490 - val_precision: 0.9618 - val_recall: 0.9438 - val_auc: 0.9894\n",
            "Epoch 13/25\n",
            "19/19 [==============================] - 8s 449ms/step - loss: 0.1263 - accuracy: 0.9542 - precision: 0.9377 - recall: 0.9624 - auc: 0.9895 - val_loss: 0.1477 - val_accuracy: 0.9524 - val_precision: 0.9679 - val_recall: 0.9438 - val_auc: 0.9880\n",
            "Epoch 14/25\n",
            "19/19 [==============================] - 8s 425ms/step - loss: 0.1446 - accuracy: 0.9372 - precision: 0.9490 - recall: 0.9098 - auc: 0.9872 - val_loss: 0.1418 - val_accuracy: 0.9456 - val_precision: 0.9500 - val_recall: 0.9500 - val_auc: 0.9879\n",
            "Epoch 15/25\n",
            "19/19 [==============================] - 10s 545ms/step - loss: 0.0989 - accuracy: 0.9643 - precision: 0.9623 - recall: 0.9586 - auc: 0.9939 - val_loss: 0.1538 - val_accuracy: 0.9388 - val_precision: 0.9610 - val_recall: 0.9250 - val_auc: 0.9882\n",
            "Epoch 16/25\n",
            "19/19 [==============================] - 9s 490ms/step - loss: 0.1027 - accuracy: 0.9694 - precision: 0.9697 - recall: 0.9624 - auc: 0.9930 - val_loss: 0.1985 - val_accuracy: 0.9354 - val_precision: 0.9732 - val_recall: 0.9062 - val_auc: 0.9826\n",
            "Epoch 17/25\n",
            "19/19 [==============================] - 8s 420ms/step - loss: 0.0926 - accuracy: 0.9694 - precision: 0.9662 - recall: 0.9662 - auc: 0.9943 - val_loss: 0.1556 - val_accuracy: 0.9490 - val_precision: 0.9618 - val_recall: 0.9438 - val_auc: 0.9849\n",
            "Epoch 18/25\n",
            "19/19 [==============================] - 9s 497ms/step - loss: 0.0943 - accuracy: 0.9728 - precision: 0.9735 - recall: 0.9662 - auc: 0.9933 - val_loss: 0.1905 - val_accuracy: 0.9354 - val_precision: 0.9732 - val_recall: 0.9062 - val_auc: 0.9823\n",
            "Epoch 19/25\n",
            "19/19 [==============================] - 8s 438ms/step - loss: 0.0826 - accuracy: 0.9626 - precision: 0.9552 - recall: 0.9624 - auc: 0.9960 - val_loss: 0.1805 - val_accuracy: 0.9456 - val_precision: 0.9737 - val_recall: 0.9250 - val_auc: 0.9851\n",
            "Epoch 20/25\n",
            "19/19 [==============================] - 9s 446ms/step - loss: 0.0852 - accuracy: 0.9694 - precision: 0.9697 - recall: 0.9624 - auc: 0.9954 - val_loss: 0.1882 - val_accuracy: 0.9252 - val_precision: 0.9012 - val_recall: 0.9688 - val_auc: 0.9800\n",
            "Epoch 21/25\n",
            "19/19 [==============================] - 10s 535ms/step - loss: 0.0880 - accuracy: 0.9660 - precision: 0.9556 - recall: 0.9699 - auc: 0.9948 - val_loss: 0.1967 - val_accuracy: 0.9320 - val_precision: 0.9667 - val_recall: 0.9062 - val_auc: 0.9832\n",
            "Epoch 22/25\n",
            "19/19 [==============================] - 8s 413ms/step - loss: 0.1233 - accuracy: 0.9559 - precision: 0.9545 - recall: 0.9474 - auc: 0.9882 - val_loss: 0.1941 - val_accuracy: 0.9320 - val_precision: 0.9730 - val_recall: 0.9000 - val_auc: 0.9823\n",
            "Epoch 23/25\n",
            "19/19 [==============================] - 8s 429ms/step - loss: 0.1008 - accuracy: 0.9610 - precision: 0.9551 - recall: 0.9586 - auc: 0.9937 - val_loss: 0.1840 - val_accuracy: 0.9388 - val_precision: 0.9551 - val_recall: 0.9312 - val_auc: 0.9820\n",
            "Epoch 24/25\n",
            "19/19 [==============================] - 10s 506ms/step - loss: 0.0870 - accuracy: 0.9660 - precision: 0.9695 - recall: 0.9549 - auc: 0.9951 - val_loss: 0.1742 - val_accuracy: 0.9422 - val_precision: 0.9613 - val_recall: 0.9312 - val_auc: 0.9817\n",
            "Epoch 25/25\n",
            "19/19 [==============================] - 8s 431ms/step - loss: 0.0851 - accuracy: 0.9762 - precision: 0.9737 - recall: 0.9737 - auc: 0.9956 - val_loss: 0.2281 - val_accuracy: 0.9354 - val_precision: 0.9732 - val_recall: 0.9062 - val_auc: 0.9824\n",
            "\n",
            "Nombre total d'images générées: 14400\n"
          ]
        }
      ],
      "source": [
        "#Entrainement de bases models\n",
        "\n",
        "# Augmentation de données pendant l'entraînement\n",
        "train_generator = datagen.flow(x_train, y_train, batch_size=batch_size)\n",
        "\n",
        "model_1.fit(train_generator, epochs=epochs, batch_size=batch_size, validation_data=(x_val, y_val), verbose=1)\n",
        "\n",
        "# Calculer le nombre total d'itérations pendant toutes les époques\n",
        "total_iterations = steps_per_epoch * epochs\n",
        "\n",
        "# Afficher le nombre total d'images générées\n",
        "total_generated_images = total_iterations * batch_size\n",
        "\n",
        "# Afficher le nombre total d'images générées\n",
        "print(f\"\\nNombre total d'images générées: {total_generated_images}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.save(\"base_model_1.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8ZgbwqA_BtQ",
        "outputId": "828d5414-a98e-4ce7-bcfb-c19c8d767d35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RyZD1QcOfxY2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a326580-d5f1-4ac5-dc50-984271d23206"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "19/19 [==============================] - 15s 688ms/step - loss: 0.6918 - accuracy: 0.5161 - precision: 0.5093 - recall: 0.3772 - auc: 0.5386 - val_loss: 0.6681 - val_accuracy: 0.6497 - val_precision: 0.9273 - val_recall: 0.3400 - val_auc: 0.9173\n",
            "Epoch 2/25\n",
            "19/19 [==============================] - 11s 613ms/step - loss: 0.6499 - accuracy: 0.7708 - precision: 0.9053 - recall: 0.5952 - auc: 0.9022 - val_loss: 0.6241 - val_accuracy: 0.9014 - val_precision: 0.8758 - val_recall: 0.9400 - val_auc: 0.9731\n",
            "Epoch 3/25\n",
            "19/19 [==============================] - 11s 597ms/step - loss: 0.6160 - accuracy: 0.8693 - precision: 0.8313 - recall: 0.9204 - auc: 0.9411 - val_loss: 0.5868 - val_accuracy: 0.9218 - val_precision: 0.9379 - val_recall: 0.9067 - val_auc: 0.9805\n",
            "Epoch 4/25\n",
            "19/19 [==============================] - 13s 689ms/step - loss: 0.5846 - accuracy: 0.9117 - precision: 0.9044 - recall: 0.9170 - auc: 0.9595 - val_loss: 0.5497 - val_accuracy: 0.9082 - val_precision: 0.8773 - val_recall: 0.9533 - val_auc: 0.9825\n",
            "Epoch 5/25\n",
            "19/19 [==============================] - 9s 480ms/step - loss: 0.5526 - accuracy: 0.9117 - precision: 0.8963 - recall: 0.9273 - auc: 0.9689 - val_loss: 0.5182 - val_accuracy: 0.9184 - val_precision: 0.9091 - val_recall: 0.9333 - val_auc: 0.9835\n",
            "Epoch 6/25\n",
            "19/19 [==============================] - 12s 594ms/step - loss: 0.5271 - accuracy: 0.9151 - precision: 0.9410 - recall: 0.8824 - auc: 0.9677 - val_loss: 0.4897 - val_accuracy: 0.9184 - val_precision: 0.8889 - val_recall: 0.9600 - val_auc: 0.9837\n",
            "Epoch 7/25\n",
            "19/19 [==============================] - 9s 449ms/step - loss: 0.5067 - accuracy: 0.9083 - precision: 0.8803 - recall: 0.9412 - auc: 0.9677 - val_loss: 0.4642 - val_accuracy: 0.9150 - val_precision: 0.8882 - val_recall: 0.9533 - val_auc: 0.9839\n",
            "Epoch 8/25\n",
            "19/19 [==============================] - 11s 568ms/step - loss: 0.4795 - accuracy: 0.9134 - precision: 0.8993 - recall: 0.9273 - auc: 0.9722 - val_loss: 0.4413 - val_accuracy: 0.9116 - val_precision: 0.8735 - val_recall: 0.9667 - val_auc: 0.9843\n",
            "Epoch 9/25\n",
            "19/19 [==============================] - 10s 530ms/step - loss: 0.4612 - accuracy: 0.8947 - precision: 0.8492 - recall: 0.9550 - auc: 0.9706 - val_loss: 0.4208 - val_accuracy: 0.9150 - val_precision: 0.8882 - val_recall: 0.9533 - val_auc: 0.9846\n",
            "Epoch 10/25\n",
            "19/19 [==============================] - 11s 543ms/step - loss: 0.4410 - accuracy: 0.9304 - precision: 0.9189 - recall: 0.9412 - auc: 0.9716 - val_loss: 0.4023 - val_accuracy: 0.9184 - val_precision: 0.8987 - val_recall: 0.9467 - val_auc: 0.9846\n",
            "Epoch 11/25\n",
            "19/19 [==============================] - 12s 638ms/step - loss: 0.4308 - accuracy: 0.9219 - precision: 0.9147 - recall: 0.9273 - auc: 0.9711 - val_loss: 0.3846 - val_accuracy: 0.9218 - val_precision: 0.8896 - val_recall: 0.9667 - val_auc: 0.9848\n",
            "Epoch 12/25\n",
            "19/19 [==============================] - 13s 694ms/step - loss: 0.4177 - accuracy: 0.9168 - precision: 0.8922 - recall: 0.9446 - auc: 0.9688 - val_loss: 0.3694 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9848\n",
            "Epoch 13/25\n",
            "19/19 [==============================] - 11s 609ms/step - loss: 0.4018 - accuracy: 0.9168 - precision: 0.9054 - recall: 0.9273 - auc: 0.9693 - val_loss: 0.3568 - val_accuracy: 0.9252 - val_precision: 0.9103 - val_recall: 0.9467 - val_auc: 0.9849\n",
            "Epoch 14/25\n",
            "19/19 [==============================] - 13s 688ms/step - loss: 0.3889 - accuracy: 0.9134 - precision: 0.8967 - recall: 0.9308 - auc: 0.9692 - val_loss: 0.3431 - val_accuracy: 0.9218 - val_precision: 0.8896 - val_recall: 0.9667 - val_auc: 0.9850\n",
            "Epoch 15/25\n",
            "19/19 [==============================] - 11s 593ms/step - loss: 0.3721 - accuracy: 0.9219 - precision: 0.9037 - recall: 0.9412 - auc: 0.9733 - val_loss: 0.3320 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9851\n",
            "Epoch 16/25\n",
            "19/19 [==============================] - 12s 620ms/step - loss: 0.3661 - accuracy: 0.9168 - precision: 0.8922 - recall: 0.9446 - auc: 0.9711 - val_loss: 0.3214 - val_accuracy: 0.9116 - val_precision: 0.8875 - val_recall: 0.9467 - val_auc: 0.9851\n",
            "Epoch 17/25\n",
            "19/19 [==============================] - 11s 565ms/step - loss: 0.3531 - accuracy: 0.9287 - precision: 0.9158 - recall: 0.9412 - auc: 0.9732 - val_loss: 0.3121 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9851\n",
            "Epoch 18/25\n",
            "19/19 [==============================] - 9s 463ms/step - loss: 0.3476 - accuracy: 0.9219 - precision: 0.9037 - recall: 0.9412 - auc: 0.9720 - val_loss: 0.3030 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9852\n",
            "Epoch 19/25\n",
            "19/19 [==============================] - 12s 613ms/step - loss: 0.3439 - accuracy: 0.9270 - precision: 0.9155 - recall: 0.9377 - auc: 0.9679 - val_loss: 0.2949 - val_accuracy: 0.9116 - val_precision: 0.8875 - val_recall: 0.9467 - val_auc: 0.9852\n",
            "Epoch 20/25\n",
            "19/19 [==============================] - 9s 451ms/step - loss: 0.3268 - accuracy: 0.9287 - precision: 0.9076 - recall: 0.9516 - auc: 0.9747 - val_loss: 0.2875 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9852\n",
            "Epoch 21/25\n",
            "19/19 [==============================] - 10s 511ms/step - loss: 0.3292 - accuracy: 0.9185 - precision: 0.9085 - recall: 0.9273 - auc: 0.9684 - val_loss: 0.2814 - val_accuracy: 0.9218 - val_precision: 0.9045 - val_recall: 0.9467 - val_auc: 0.9853\n",
            "Epoch 22/25\n",
            "19/19 [==============================] - 12s 644ms/step - loss: 0.3212 - accuracy: 0.9066 - precision: 0.8774 - recall: 0.9412 - auc: 0.9703 - val_loss: 0.2745 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9852\n",
            "Epoch 23/25\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.3146 - accuracy: 0.9151 - precision: 0.9024 - recall: 0.9273 - auc: 0.9703 - val_loss: 0.2688 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9853\n",
            "Epoch 24/25\n",
            "19/19 [==============================] - 10s 512ms/step - loss: 0.3061 - accuracy: 0.9202 - precision: 0.8929 - recall: 0.9516 - auc: 0.9742 - val_loss: 0.2633 - val_accuracy: 0.9150 - val_precision: 0.8931 - val_recall: 0.9467 - val_auc: 0.9853\n",
            "Epoch 25/25\n",
            "19/19 [==============================] - 9s 469ms/step - loss: 0.3042 - accuracy: 0.9270 - precision: 0.9155 - recall: 0.9377 - auc: 0.9715 - val_loss: 0.2594 - val_accuracy: 0.9252 - val_precision: 0.9103 - val_recall: 0.9467 - val_auc: 0.9854\n",
            "\n",
            "Nombre total d'images générées: 14400\n"
          ]
        }
      ],
      "source": [
        "#Entrainement de bases models\n",
        "\n",
        "# Augmentation de données pendant l'entraînement\n",
        "train_generator = datagen.flow(x_train, y_train, batch_size=batch_size)\n",
        "\n",
        "model_2.fit(train_generator, epochs=epochs, batch_size=batch_size, validation_data=(x_val, y_val), verbose=1)\n",
        "\n",
        "# Calculer le nombre total d'itérations pendant toutes les époques\n",
        "total_iterations = steps_per_epoch * epochs\n",
        "\n",
        "# Afficher le nombre total d'images générées\n",
        "total_generated_images = total_iterations * batch_size\n",
        "\n",
        "# Afficher le nombre total d'images générées\n",
        "print(f\"\\nNombre total d'images générées: {total_generated_images}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2.save(\"base_model_2.h5\")"
      ],
      "metadata": {
        "id": "v_RSaEk7_YYq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fm0LSxYyf44_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5763ccf2-1dea-44fc-c82b-4746b2efca31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "19/19 [==============================] - 41s 899ms/step - loss: 0.5798 - accuracy: 0.6944 - precision: 0.7422 - recall: 0.5779 - auc: 0.7604 - val_loss: 0.3191 - val_accuracy: 0.9150 - val_precision: 0.9699 - val_recall: 0.8600 - val_auc: 0.9683\n",
            "Epoch 2/25\n",
            "19/19 [==============================] - 11s 590ms/step - loss: 0.2618 - accuracy: 0.9168 - precision: 0.9317 - recall: 0.8962 - auc: 0.9658 - val_loss: 0.2078 - val_accuracy: 0.9286 - val_precision: 0.9510 - val_recall: 0.9067 - val_auc: 0.9766\n",
            "Epoch 3/25\n",
            "19/19 [==============================] - 12s 640ms/step - loss: 0.2094 - accuracy: 0.9270 - precision: 0.9457 - recall: 0.9031 - auc: 0.9740 - val_loss: 0.1989 - val_accuracy: 0.9320 - val_precision: 0.9577 - val_recall: 0.9067 - val_auc: 0.9787\n",
            "Epoch 4/25\n",
            "19/19 [==============================] - 11s 585ms/step - loss: 0.1896 - accuracy: 0.9338 - precision: 0.9464 - recall: 0.9170 - auc: 0.9780 - val_loss: 0.1820 - val_accuracy: 0.9286 - val_precision: 0.9388 - val_recall: 0.9200 - val_auc: 0.9809\n",
            "Epoch 5/25\n",
            "19/19 [==============================] - 10s 532ms/step - loss: 0.2009 - accuracy: 0.9304 - precision: 0.9460 - recall: 0.9100 - auc: 0.9738 - val_loss: 0.1779 - val_accuracy: 0.9388 - val_precision: 0.9583 - val_recall: 0.9200 - val_auc: 0.9821\n",
            "Epoch 6/25\n",
            "19/19 [==============================] - 11s 611ms/step - loss: 0.1878 - accuracy: 0.9304 - precision: 0.9189 - recall: 0.9412 - auc: 0.9786 - val_loss: 0.1993 - val_accuracy: 0.9320 - val_precision: 0.9643 - val_recall: 0.9000 - val_auc: 0.9818\n",
            "Epoch 7/25\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.1796 - accuracy: 0.9338 - precision: 0.9401 - recall: 0.9239 - auc: 0.9778 - val_loss: 0.1706 - val_accuracy: 0.9388 - val_precision: 0.9583 - val_recall: 0.9200 - val_auc: 0.9832\n",
            "Epoch 8/25\n",
            "19/19 [==============================] - 11s 588ms/step - loss: 0.1741 - accuracy: 0.9389 - precision: 0.9534 - recall: 0.9204 - auc: 0.9802 - val_loss: 0.1663 - val_accuracy: 0.9388 - val_precision: 0.9521 - val_recall: 0.9267 - val_auc: 0.9836\n",
            "Epoch 9/25\n",
            "19/19 [==============================] - 9s 481ms/step - loss: 0.1708 - accuracy: 0.9355 - precision: 0.9466 - recall: 0.9204 - auc: 0.9827 - val_loss: 0.1651 - val_accuracy: 0.9422 - val_precision: 0.9650 - val_recall: 0.9200 - val_auc: 0.9840\n",
            "Epoch 10/25\n",
            "19/19 [==============================] - 10s 518ms/step - loss: 0.1645 - accuracy: 0.9440 - precision: 0.9571 - recall: 0.9273 - auc: 0.9835 - val_loss: 0.1615 - val_accuracy: 0.9388 - val_precision: 0.9521 - val_recall: 0.9267 - val_auc: 0.9843\n",
            "Epoch 11/25\n",
            "19/19 [==============================] - 11s 577ms/step - loss: 0.1670 - accuracy: 0.9491 - precision: 0.9544 - recall: 0.9412 - auc: 0.9809 - val_loss: 0.1588 - val_accuracy: 0.9422 - val_precision: 0.9586 - val_recall: 0.9267 - val_auc: 0.9848\n",
            "Epoch 12/25\n",
            "19/19 [==============================] - 10s 524ms/step - loss: 0.1541 - accuracy: 0.9457 - precision: 0.9541 - recall: 0.9343 - auc: 0.9853 - val_loss: 0.1743 - val_accuracy: 0.9388 - val_precision: 0.9648 - val_recall: 0.9133 - val_auc: 0.9852\n",
            "Epoch 13/25\n",
            "19/19 [==============================] - 9s 479ms/step - loss: 0.1606 - accuracy: 0.9406 - precision: 0.9504 - recall: 0.9273 - auc: 0.9836 - val_loss: 0.1622 - val_accuracy: 0.9422 - val_precision: 0.9650 - val_recall: 0.9200 - val_auc: 0.9854\n",
            "Epoch 14/25\n",
            "19/19 [==============================] - 11s 577ms/step - loss: 0.1671 - accuracy: 0.9423 - precision: 0.9603 - recall: 0.9204 - auc: 0.9832 - val_loss: 0.1552 - val_accuracy: 0.9388 - val_precision: 0.9459 - val_recall: 0.9333 - val_auc: 0.9858\n",
            "Epoch 15/25\n",
            "19/19 [==============================] - 10s 548ms/step - loss: 0.1615 - accuracy: 0.9406 - precision: 0.9472 - recall: 0.9308 - auc: 0.9841 - val_loss: 0.1834 - val_accuracy: 0.9286 - val_precision: 0.9640 - val_recall: 0.8933 - val_auc: 0.9859\n",
            "Epoch 16/25\n",
            "19/19 [==============================] - 11s 530ms/step - loss: 0.1540 - accuracy: 0.9440 - precision: 0.9604 - recall: 0.9239 - auc: 0.9852 - val_loss: 0.1569 - val_accuracy: 0.9422 - val_precision: 0.9404 - val_recall: 0.9467 - val_auc: 0.9866\n",
            "Epoch 17/25\n",
            "19/19 [==============================] - 11s 572ms/step - loss: 0.1510 - accuracy: 0.9508 - precision: 0.9610 - recall: 0.9377 - auc: 0.9848 - val_loss: 0.1577 - val_accuracy: 0.9422 - val_precision: 0.9650 - val_recall: 0.9200 - val_auc: 0.9870\n",
            "Epoch 18/25\n",
            "19/19 [==============================] - 12s 638ms/step - loss: 0.1524 - accuracy: 0.9457 - precision: 0.9509 - recall: 0.9377 - auc: 0.9848 - val_loss: 0.1486 - val_accuracy: 0.9490 - val_precision: 0.9655 - val_recall: 0.9333 - val_auc: 0.9877\n",
            "Epoch 19/25\n",
            "19/19 [==============================] - 10s 543ms/step - loss: 0.1398 - accuracy: 0.9559 - precision: 0.9747 - recall: 0.9343 - auc: 0.9875 - val_loss: 0.1489 - val_accuracy: 0.9490 - val_precision: 0.9655 - val_recall: 0.9333 - val_auc: 0.9876\n",
            "Epoch 20/25\n",
            "19/19 [==============================] - 10s 537ms/step - loss: 0.1517 - accuracy: 0.9542 - precision: 0.9645 - recall: 0.9412 - auc: 0.9856 - val_loss: 0.1524 - val_accuracy: 0.9456 - val_precision: 0.9653 - val_recall: 0.9267 - val_auc: 0.9876\n",
            "Epoch 21/25\n",
            "19/19 [==============================] - 11s 541ms/step - loss: 0.1473 - accuracy: 0.9457 - precision: 0.9477 - recall: 0.9412 - auc: 0.9874 - val_loss: 0.1460 - val_accuracy: 0.9490 - val_precision: 0.9592 - val_recall: 0.9400 - val_auc: 0.9881\n",
            "Epoch 22/25\n",
            "19/19 [==============================] - 11s 582ms/step - loss: 0.1392 - accuracy: 0.9525 - precision: 0.9711 - recall: 0.9308 - auc: 0.9889 - val_loss: 0.1459 - val_accuracy: 0.9490 - val_precision: 0.9530 - val_recall: 0.9467 - val_auc: 0.9883\n",
            "Epoch 23/25\n",
            "19/19 [==============================] - 10s 527ms/step - loss: 0.1433 - accuracy: 0.9491 - precision: 0.9450 - recall: 0.9516 - auc: 0.9890 - val_loss: 0.1536 - val_accuracy: 0.9320 - val_precision: 0.9643 - val_recall: 0.9000 - val_auc: 0.9884\n",
            "Epoch 24/25\n",
            "19/19 [==============================] - 11s 579ms/step - loss: 0.1342 - accuracy: 0.9474 - precision: 0.9607 - recall: 0.9308 - auc: 0.9900 - val_loss: 0.1431 - val_accuracy: 0.9524 - val_precision: 0.9658 - val_recall: 0.9400 - val_auc: 0.9886\n",
            "Epoch 25/25\n",
            "19/19 [==============================] - 11s 585ms/step - loss: 0.1340 - accuracy: 0.9542 - precision: 0.9679 - recall: 0.9377 - auc: 0.9890 - val_loss: 0.1483 - val_accuracy: 0.9388 - val_precision: 0.9648 - val_recall: 0.9133 - val_auc: 0.9888\n",
            "\n",
            "Nombre total d'images générées: 14400\n"
          ]
        }
      ],
      "source": [
        "#Entrainement de bases models\n",
        "\n",
        "# Augmentation de données pendant l'entraînement\n",
        "train_generator = datagen.flow(x_train, y_train, batch_size=batch_size)\n",
        "\n",
        "model_3.fit(train_generator, epochs=epochs, batch_size=batch_size, validation_data=(x_val, y_val), verbose=1)\n",
        "\n",
        "# Calculer le nombre total d'itérations pendant toutes les époques\n",
        "total_iterations = steps_per_epoch * epochs\n",
        "\n",
        "# Afficher le nombre total d'images générées\n",
        "total_generated_images = total_iterations * batch_size\n",
        "\n",
        "# Afficher le nombre total d'images générées\n",
        "print(f\"\\nNombre total d'images générées: {total_generated_images}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_3.save(\"base_model_3.h5\")"
      ],
      "metadata": {
        "id": "kmyFMWkG_ee-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a4f8f9d-70d9-4461-9805-f8e3a0c4bad8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7_yJeySYD6oL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07f9680b-13f4-4cde-c513-879b13587a5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 4s 152ms/step\n",
            "10/10 [==============================] - 6s 257ms/step\n",
            "10/10 [==============================] - 6s 272ms/step\n"
          ]
        }
      ],
      "source": [
        "#Obtention des predictions\n",
        "from tensorflow import keras\n",
        "\n",
        "pred_model_1 = keras.models.load_model('drive/MyDrive/base_model_1.h5')\n",
        "pred_model_2 = keras.models.load_model('drive/MyDrive/base_model_2.h5')\n",
        "pred_model_3 = keras.models.load_model('drive/MyDrive/base_model_3.h5')\n",
        "\n",
        "predictions_base_model_1 = pred_model_1.predict(x_test)\n",
        "predictions_base_model_2 = pred_model_2.predict(x_test)\n",
        "predictions_base_model_3 = pred_model_3.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrnfOyKRsE0m",
        "outputId": "69fd8492-c801-4cf7-a8a0-e448de53e2c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "295"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X0LuLpLtEdVd"
      },
      "outputs": [],
      "source": [
        "#Creation de l'ensemble de données pour le meta model\n",
        "stacked_x_test = np.hstack([predictions_base_model_1, predictions_base_model_2, predictions_base_model_3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2LWDVrPed2jI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c49a38c-9ab2-4858-d90d-b696d2adbe61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "295"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "len(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#meta model\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten,Dense\n",
        "\n",
        "\n",
        "adam_optimizer = Adam(learning_rate=0.01)"
      ],
      "metadata": {
        "id": "KJq9YXS-l4s2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "meta_model = Sequential()\n",
        "meta_model.add(Dense(256, input_dim = 3, activation='relu'))\n",
        "meta_model.add(Dense(128, activation='relu'))\n",
        "meta_model.add(Dense(64, activation='relu'))\n",
        "meta_model.add(Dense(32, activation='relu'))\n",
        "meta_model.add(layers.Dropout(0.5))\n",
        "meta_model.add(Dense(1, activation='sigmoid'))\n",
        "meta_model.compile(optimizer=adam_optimizer,\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy', tf.keras.metrics.Precision(name='precision'),\n",
        "                       tf.keras.metrics.Recall(name='recall'),\n",
        "                       tf.keras.metrics.AUC(name='auc')])\n"
      ],
      "metadata": {
        "id": "R34Xk_C1vxw2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "meta_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTFS5Pp85XeP",
        "outputId": "6bd33474-b727-46eb-bccc-25178da003af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_1 (Dense)             (None, 256)               1024      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 44289 (173.00 KB)\n",
            "Trainable params: 44289 (173.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "meta_model.fit(stacked_x_test, y_test, epochs=100, batch_size=128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNDwVDFZmKOv",
        "outputId": "10620b84-c4aa-41e0-ead2-4299cb371520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "3/3 [==============================] - 3s 12ms/step - loss: 0.5852 - accuracy: 0.6847 - precision: 0.6228 - recall: 0.9530 - auc: 0.9159\n",
            "Epoch 2/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.3696 - accuracy: 0.9356 - precision: 0.8963 - recall: 0.9866 - auc: 0.9803\n",
            "Epoch 3/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.2279 - accuracy: 0.9559 - precision: 0.9789 - recall: 0.9329 - auc: 0.9586\n",
            "Epoch 4/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1497 - accuracy: 0.9661 - precision: 0.9728 - recall: 0.9597 - auc: 0.9799\n",
            "Epoch 5/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1931 - accuracy: 0.9559 - precision: 0.9416 - recall: 0.9732 - auc: 0.9826\n",
            "Epoch 6/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.1278 - accuracy: 0.9661 - precision: 0.9664 - recall: 0.9664 - auc: 0.9868\n",
            "Epoch 7/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.1617 - accuracy: 0.9559 - precision: 0.9789 - recall: 0.9329 - auc: 0.9878\n",
            "Epoch 8/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1280 - accuracy: 0.9661 - precision: 0.9793 - recall: 0.9530 - auc: 0.9871\n",
            "Epoch 9/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1565 - accuracy: 0.9695 - precision: 0.9730 - recall: 0.9664 - auc: 0.9846\n",
            "Epoch 10/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.1198 - accuracy: 0.9627 - precision: 0.9662 - recall: 0.9597 - auc: 0.9875\n",
            "Epoch 11/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1325 - accuracy: 0.9695 - precision: 0.9730 - recall: 0.9664 - auc: 0.9863\n",
            "Epoch 12/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1163 - accuracy: 0.9661 - precision: 0.9728 - recall: 0.9597 - auc: 0.9897\n",
            "Epoch 13/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1186 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9886\n",
            "Epoch 14/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.1147 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9877\n",
            "Epoch 15/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1109 - accuracy: 0.9627 - precision: 0.9726 - recall: 0.9530 - auc: 0.9906\n",
            "Epoch 16/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0930 - accuracy: 0.9661 - precision: 0.9728 - recall: 0.9597 - auc: 0.9950\n",
            "Epoch 17/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1060 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9883\n",
            "Epoch 18/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1278 - accuracy: 0.9661 - precision: 0.9793 - recall: 0.9530 - auc: 0.9877\n",
            "Epoch 19/100\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.1012 - accuracy: 0.9627 - precision: 0.9792 - recall: 0.9463 - auc: 0.9946\n",
            "Epoch 20/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1054 - accuracy: 0.9627 - precision: 0.9792 - recall: 0.9463 - auc: 0.9943\n",
            "Epoch 21/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0986 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9930\n",
            "Epoch 22/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.1010 - accuracy: 0.9627 - precision: 0.9600 - recall: 0.9664 - auc: 0.9943\n",
            "Epoch 23/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1003 - accuracy: 0.9661 - precision: 0.9603 - recall: 0.9732 - auc: 0.9925\n",
            "Epoch 24/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0981 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9891\n",
            "Epoch 25/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.1139 - accuracy: 0.9661 - precision: 0.9793 - recall: 0.9530 - auc: 0.9906\n",
            "Epoch 26/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1022 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9936\n",
            "Epoch 27/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0983 - accuracy: 0.9695 - precision: 0.9730 - recall: 0.9664 - auc: 0.9936\n",
            "Epoch 28/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0855 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9956\n",
            "Epoch 29/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1048 - accuracy: 0.9695 - precision: 0.9730 - recall: 0.9664 - auc: 0.9914\n",
            "Epoch 30/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0964 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9935\n",
            "Epoch 31/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1044 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9926\n",
            "Epoch 32/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0979 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9913\n",
            "Epoch 33/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1221 - accuracy: 0.9627 - precision: 0.9792 - recall: 0.9463 - auc: 0.9925\n",
            "Epoch 34/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0992 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9900\n",
            "Epoch 35/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.1281 - accuracy: 0.9695 - precision: 0.9605 - recall: 0.9799 - auc: 0.9882\n",
            "Epoch 36/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1042 - accuracy: 0.9695 - precision: 0.9730 - recall: 0.9664 - auc: 0.9912\n",
            "Epoch 37/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.1109 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9909\n",
            "Epoch 38/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1010 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9921\n",
            "Epoch 39/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0908 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9948\n",
            "Epoch 40/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.0883 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9943\n",
            "Epoch 41/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.1087 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9915\n",
            "Epoch 42/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0932 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9916\n",
            "Epoch 43/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1028 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9905\n",
            "Epoch 44/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.1089 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9911\n",
            "Epoch 45/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1058 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9917\n",
            "Epoch 46/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0949 - accuracy: 0.9763 - precision: 0.9733 - recall: 0.9799 - auc: 0.9907\n",
            "Epoch 47/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0908 - accuracy: 0.9729 - precision: 0.9732 - recall: 0.9732 - auc: 0.9899\n",
            "Epoch 48/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0925 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9937\n",
            "Epoch 49/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.1059 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9906\n",
            "Epoch 50/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0935 - accuracy: 0.9729 - precision: 0.9669 - recall: 0.9799 - auc: 0.9927\n",
            "Epoch 51/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0903 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9916\n",
            "Epoch 52/100\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.0930 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9926\n",
            "Epoch 53/100\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.0848 - accuracy: 0.9695 - precision: 0.9795 - recall: 0.9597 - auc: 0.9944\n",
            "Epoch 54/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0935 - accuracy: 0.9729 - precision: 0.9862 - recall: 0.9597 - auc: 0.9948\n",
            "Epoch 55/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0916 - accuracy: 0.9729 - precision: 0.9862 - recall: 0.9597 - auc: 0.9942\n",
            "Epoch 56/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0860 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9923\n",
            "Epoch 57/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0883 - accuracy: 0.9729 - precision: 0.9732 - recall: 0.9732 - auc: 0.9933\n",
            "Epoch 58/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0957 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9930\n",
            "Epoch 59/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0842 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9915\n",
            "Epoch 60/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0829 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9942\n",
            "Epoch 61/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0856 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9930\n",
            "Epoch 62/100\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.0775 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9960\n",
            "Epoch 63/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.1039 - accuracy: 0.9729 - precision: 0.9732 - recall: 0.9732 - auc: 0.9893\n",
            "Epoch 64/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0800 - accuracy: 0.9661 - precision: 0.9603 - recall: 0.9732 - auc: 0.9953\n",
            "Epoch 65/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0945 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9917\n",
            "Epoch 66/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0754 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9976\n",
            "Epoch 67/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0883 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9895\n",
            "Epoch 68/100\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.0777 - accuracy: 0.9729 - precision: 0.9732 - recall: 0.9732 - auc: 0.9935\n",
            "Epoch 69/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.1045 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9913\n",
            "Epoch 70/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0826 - accuracy: 0.9831 - precision: 0.9865 - recall: 0.9799 - auc: 0.9930\n",
            "Epoch 71/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0961 - accuracy: 0.9695 - precision: 0.9545 - recall: 0.9866 - auc: 0.9924\n",
            "Epoch 72/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0922 - accuracy: 0.9797 - precision: 0.9735 - recall: 0.9866 - auc: 0.9929\n",
            "Epoch 73/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0931 - accuracy: 0.9661 - precision: 0.9728 - recall: 0.9597 - auc: 0.9953\n",
            "Epoch 74/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0982 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9919\n",
            "Epoch 75/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0958 - accuracy: 0.9729 - precision: 0.9732 - recall: 0.9732 - auc: 0.9897\n",
            "Epoch 76/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0856 - accuracy: 0.9729 - precision: 0.9732 - recall: 0.9732 - auc: 0.9956\n",
            "Epoch 77/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0908 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9925\n",
            "Epoch 78/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0947 - accuracy: 0.9729 - precision: 0.9796 - recall: 0.9664 - auc: 0.9908\n",
            "Epoch 79/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0892 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9920\n",
            "Epoch 80/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0795 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9952\n",
            "Epoch 81/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0809 - accuracy: 0.9797 - precision: 0.9864 - recall: 0.9732 - auc: 0.9929\n",
            "Epoch 82/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0856 - accuracy: 0.9831 - precision: 0.9800 - recall: 0.9866 - auc: 0.9929\n",
            "Epoch 83/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0655 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9971\n",
            "Epoch 84/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0825 - accuracy: 0.9797 - precision: 0.9799 - recall: 0.9799 - auc: 0.9936\n",
            "Epoch 85/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0873 - accuracy: 0.9797 - precision: 0.9799 - recall: 0.9799 - auc: 0.9898\n",
            "Epoch 86/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0797 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9962\n",
            "Epoch 87/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0638 - accuracy: 0.9831 - precision: 0.9865 - recall: 0.9799 - auc: 0.9973\n",
            "Epoch 88/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0693 - accuracy: 0.9831 - precision: 0.9865 - recall: 0.9799 - auc: 0.9965\n",
            "Epoch 89/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0760 - accuracy: 0.9831 - precision: 0.9865 - recall: 0.9799 - auc: 0.9954\n",
            "Epoch 90/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0788 - accuracy: 0.9864 - precision: 0.9866 - recall: 0.9866 - auc: 0.9941\n",
            "Epoch 91/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0908 - accuracy: 0.9797 - precision: 0.9864 - recall: 0.9732 - auc: 0.9927\n",
            "Epoch 92/100\n",
            "3/3 [==============================] - 0s 9ms/step - loss: 0.0875 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9953\n",
            "Epoch 93/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0835 - accuracy: 0.9695 - precision: 0.9545 - recall: 0.9866 - auc: 0.9937\n",
            "Epoch 94/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0740 - accuracy: 0.9797 - precision: 0.9735 - recall: 0.9866 - auc: 0.9935\n",
            "Epoch 95/100\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.1165 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9919\n",
            "Epoch 96/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0860 - accuracy: 0.9763 - precision: 0.9863 - recall: 0.9664 - auc: 0.9932\n",
            "Epoch 97/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0845 - accuracy: 0.9763 - precision: 0.9797 - recall: 0.9732 - auc: 0.9933\n",
            "Epoch 98/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0669 - accuracy: 0.9831 - precision: 0.9865 - recall: 0.9799 - auc: 0.9959\n",
            "Epoch 99/100\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 0.0727 - accuracy: 0.9797 - precision: 0.9799 - recall: 0.9799 - auc: 0.9931\n",
            "Epoch 100/100\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.0859 - accuracy: 0.9831 - precision: 0.9865 - recall: 0.9799 - auc: 0.9904\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x797634ff4ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "meta_model.save(\"meta_model.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zo_8bT5D_0XU",
        "outputId": "d56a17e8-199a-4305-87b7-807bda1eaef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "\n",
        "stacked_predictions_test = meta_model.predict(stacked_x_test)\n",
        "\n",
        "# Évaluer les performances du modèle méta sur l'ensemble de test\n",
        "predictions_binary = np.round(stacked_predictions_test)\n",
        "accuracy_test = accuracy_score(y_test, predictions_binary)\n",
        "precision_test = precision_score(y_test, predictions_binary)\n",
        "recall_test = recall_score(y_test, predictions_binary)\n",
        "\n",
        "\n",
        "print(f\"Accuracy of Stacked Model on Test Set: {accuracy_test}\")\n",
        "print(f\"Precision of Stacked Model on Test Set: {precision_test}\")\n",
        "print(f\"Recall of Stacked Model on Test Set: {recall_test}\")"
      ],
      "metadata": {
        "id": "PamA7LwOmToP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "874ea20b-e450-44aa-e278-599d5a347dff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 2ms/step\n",
            "Accuracy of Stacked Model on Test Set: 0.9864406779661017\n",
            "Precision of Stacked Model on Test Set: 0.9865771812080537\n",
            "Recall of Stacked Model on Test Set: 0.9865771812080537\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make prediction\n",
        "from tensorflow import keras\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "pred_model_1 = keras.models.load_model('base_model_1.h5')\n",
        "pred_model_2 = keras.models.load_model('base_model_2.h5')\n",
        "pred_model_3 = keras.models.load_model('base_model_3.tf')\n",
        "\n",
        "\n",
        "\n",
        "pred_meta_model = keras.models.load_model('meta_model.h5')"
      ],
      "metadata": {
        "id": "QquGn7TAN3YR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOLWyG8MHpvD",
        "outputId": "1e82c04d-4b6d-4e97-e7d6-0c94606288f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load image\n",
        "\n",
        "image_path = \"drive/MyDrive/mild.png\"\n",
        "\n",
        "#preprocessing\n",
        "original_image = cv2.imread(image_path)\n",
        "gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)\n",
        "zero_brightness_image = np.zeros_like(original_image)\n",
        "mask = np.zeros_like(gray_image)\n",
        "cv2.circle(mask, (gray_image.shape[1] // 2, gray_image.shape[0] // 2), int(min(gray_image.shape) // 2 * 0.8), 255, -1)\n",
        "vignette_image = cv2.merge([gray_image * (mask / 255.0)] * 3)\n",
        "clarity_image = cv2.detailEnhance(vignette_image, sigma_s=10, sigma_r=0.15)\n",
        "resized_image = cv2.resize(clarity_image, (image_size, image_size))\n",
        "normalized_image = resized_image / 255.0\n",
        "\n",
        "data = normalized_image\n",
        "\n"
      ],
      "metadata": {
        "id": "bJkEhEhcVZry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "swBxHgiXQeGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(data)\n",
        "plt.xlabel(\"Unknow\")\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "eWYwx0oUrPnC",
        "outputId": "3ad042cc-a318-45c6-f419-464c9d5a85d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WfM2B-CbiVM",
        "outputId": "5096eeab-2444-477c-c224-33c21c41402a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_normalized = np.expand_dims(data, axis=0)\n",
        "data_normalized.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JzSChzYddtn",
        "outputId": "6c05952b-c675-4014-9c7d-10656b07fa82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predictions base models\n",
        "\n",
        "prediction_1 = pred_model_1.predict(data_normalized)\n",
        "prediction_2 = pred_model_2.predict(data_normalized)\n",
        "prediction_3 = pred_model_3.predict(data_normalized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5jOOdquY-H2",
        "outputId": "a8e78231-5cce-4858-a472-5164f9457448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "1/1 [==============================] - 1s 692ms/step\n",
            "1/1 [==============================] - 5s 5s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_meta_model = np.hstack([prediction_1, prediction_2, prediction_3])\n"
      ],
      "metadata": {
        "id": "MZQNlGvQozC_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_prediction = meta_model.predict(input_meta_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8L04Ow5pKxy",
        "outputId": "e4971455-5c36-40e6-de70-ac1c360f603a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntriJ_AopREL",
        "outputId": "ac723ccc-0108-4b51-e39f-8028084b3ded"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00012968]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 0.4\n",
        "\n",
        "# Convertir la prédiction continue en classe binaire\n",
        "final_class = 'cataract' if final_prediction[0, 0] > threshold else 'normal'\n",
        "\n",
        "print(\"La prédiction finale est:\", final_class)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Q-njFgIi09r",
        "outputId": "92148be8-1e16-4823-e9d0-d37c013903b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "La prédiction finale est: normal\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}