Face2Gene_VGG_face_10_W&B_hyperparameter.ipynb

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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Aayushktyagi/DeepLearning_Resources/blob/master/Face2Gene_VGG_face_10_W%26B_hyperparameter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "f4e6d3af-d863-44b0-88f8-f12d93a1da88"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "Wt5rgXrKnxb1",
        "outputId": "177bda47-85d6-4ee4-88d4-ddd43b5285a2"
      },
      "source": [
        "# Class and samples\n",
        "'''\n",
        "\n",
        "Angelman:     205\n",
        "Williams:     231\n",
        "FragileX:     163\n",
        "CDL:  250\n",
        "TreacherCollins:      103\n",
        "Apert:        200\n",
        "Down: 197\n",
        "Progeria:     150\n",
        "Charge: 178\n",
        "Noonan: 154\n",
        "BWS: 211\n",
        "\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n\\nAngelman:     205\\nWilliams:     231\\nFragileX:     163\\nCDL:  250\\nTreacherCollins:      103\\nApert:        200\\nDown: 197\\nProgeria:     150\\nCharge: 178\\nNoonan: 154\\nBWS: 211\\n\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PVmXWHKO99g",
        "outputId": "48db5559-e025-44d6-eb1e-027ca3238d1f"
      },
      "source": [
        "!pip install keras_applications\n",
        "!pip install keras_vggface\n",
        "!pip install tensorflow-addons"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: keras_applications in /usr/local/lib/python3.7/dist-packages (1.0.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras_applications) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras_applications) (1.19.5)\n",
            "Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from h5py->keras_applications) (1.5.2)\n",
            "Requirement already satisfied: keras_vggface in /usr/local/lib/python3.7/dist-packages (0.6)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (7.1.2)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (2.4.3)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (1.19.5)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (3.1.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (3.13)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (1.15.0)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from keras_vggface) (1.4.1)\n",
            "Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from h5py->keras_vggface) (1.5.2)\n",
            "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.7/dist-packages (0.13.0)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kB32DAktuTgv",
        "outputId": "ef2f3847-ca0f-4e53-e5c8-f922dca2002b"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fri Jun 11 12:46:01 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.27       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   34C    P8    29W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gU6HHP2MuwRj",
        "outputId": "def6b3eb-7357-4df6-fb43-955879a90f74"
      },
      "source": [
        "!pip install wandb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wandb in /usr/local/lib/python3.7/dist-packages (0.10.32)\n",
            "Requirement already satisfied: six>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.15.0)\n",
            "Requirement already satisfied: GitPython>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.1.17)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.7/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.8.1)\n",
            "Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.12.4)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (7.1.2)\n",
            "Requirement already satisfied: subprocess32>=3.5.3 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.5.4)\n",
            "Requirement already satisfied: configparser>=3.8.1 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.0.2)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.23.0)\n",
            "Requirement already satisfied: sentry-sdk>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.1.0)\n",
            "Requirement already satisfied: shortuuid>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (1.0.1)\n",
            "Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (3.13)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (4.0.7)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.0; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from GitPython>=1.0.0->wandb) (3.7.4.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.12.0->wandb) (57.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.0.0->wandb) (1.24.3)\n",
            "Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb) (4.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "td7ugDq1u1qZ",
        "outputId": "e9a0b304-502c-43b3-b7d4-f95d06e5f982"
      },
      "source": [
        "import wandb\n",
        "from wandb.keras import WandbCallback\n",
        "\n",
        "wandb.login()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33maayushktyagi\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNH6kONHn9Gj"
      },
      "source": [
        "import numpy as np \n",
        "import sys \n",
        "import os \n",
        "import time \n",
        "import cv2 \n",
        "import random\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "from sklearn.metrics import roc_auc_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOtefTJFn_5x"
      },
      "source": [
        "# #Tensorflow utilities\n",
        "# import tensorflow as tf\n",
        "# import tensorflow_addons as tfa\n",
        "# from tensorflow.keras.callbacks import Callback\n",
        "# from tensorflow.keras.backend import clear_session\n",
        "# from tensorflow.keras.models import Model , load_model, Sequential\n",
        "# from tensorflow.keras.layers import Dense , Input , Flatten , Dropout ,BatchNormalization\n",
        "# from tensorflow.keras.applications import ResNet50\n",
        "# from tensorflow.keras.applications import DenseNet121\n",
        "# from tensorflow.keras.applications import NASNetLarge\n",
        "# from tensorflow.keras.applications import Xception\n",
        "# from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
        "# from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "# from tensorflow.keras.optimizers import Adam\n",
        "# from keras_vggface.vggface import VGGFace\n",
        "# from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLp9mao1mfdY"
      },
      "source": [
        "#keras\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import tensorflow_addons as tfa\n",
        "from keras.callbacks import Callback\n",
        "from keras.backend import clear_session\n",
        "from keras.models import Model , load_model, Sequential\n",
        "from keras.layers import Dense , Input , Flatten , Dropout ,BatchNormalization\n",
        "# from keras.applications import ResNet50\n",
        "# from keras.applications import DenseNet121\n",
        "# from keras.applications import NASNetLarge\n",
        "# from keras.applications import Xception\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.optimizers import Adam\n",
        "from keras_vggface.vggface import VGGFace\n",
        "from keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "\n",
        "# Set the random seeds\n",
        "os.environ['TF_CUDNN_DETERMINISTIC'] = '1' \n",
        "random.seed(hash(\"setting random seeds\") % 2**32 - 1)\n",
        "np.random.seed(hash(\"improves reproducibility\") % 2**32 - 1)\n",
        "tf.random.set_seed(hash(\"by removing stochasticity\") % 2**32 - 1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Klpm7EI7oEOZ"
      },
      "source": [
        "# base data path \n",
        "train_path = '/content/drive/MyDrive/ColabNotebooks/Cancer/Face2Gene/Dataset/Face2Gene_10_split/train'\n",
        "val_path = '/content/drive/MyDrive/ColabNotebooks/Cancer/Face2Gene/Dataset/Face2Gene_10_split/val'\n",
        "test_path = '/content/drive/MyDrive/ColabNotebooks/Cancer/Face2Gene/Dataset/Face2Gene_10_split/test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hs0S7AXg17jt"
      },
      "source": [
        "# parameters\n",
        "image_size = 224\n",
        "train_batch_size = 32\n",
        "val_batch_size = 32\n",
        "model_name = 'VGG_Face'  # ResNet50/DenseNet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13yQaJLyF-jQ",
        "outputId": "6fd4bace-6678-46b7-94a5-b0f2d522fddc"
      },
      "source": [
        "# loading data\n",
        "\n",
        "def load_dataset():\n",
        "\n",
        "    train_datagen = ImageDataGenerator(rescale = 1/255.,\n",
        "                                       rotation_range = 10,\n",
        "                                      #  width_shift_range = 0.10,\n",
        "                                      #  height_shift_range = 0.1,\n",
        "                                      #  zoom_range=0.10, \n",
        "                                       horizontal_flip = True\n",
        "                                      )\n",
        "\n",
        "    valid_datagen = ImageDataGenerator(rescale = 1/255.)\n",
        "\n",
        "    test_datagen = ImageDataGenerator(rescale = 1/255.)\n",
        "\n",
        "\n",
        "    train_generator = train_datagen.flow_from_directory(\n",
        "            train_path,\n",
        "            target_size=(image_size, image_size),\n",
        "            batch_size= train_batch_size,\n",
        "            class_mode='categorical',\n",
        "            shuffle=True)\n",
        "\n",
        "    validation_generator = valid_datagen.flow_from_directory(\n",
        "        val_path,\n",
        "        target_size=(image_size, image_size),\n",
        "        batch_size= val_batch_size,\n",
        "        class_mode='categorical',\n",
        "        shuffle=True)\n",
        "\n",
        "    test_generator = test_datagen.flow_from_directory(\n",
        "                test_path,\n",
        "                target_size = (image_size, image_size),\n",
        "                batch_size = 1,\n",
        "                class_mode = 'categorical',\n",
        "                shuffle = False)\n",
        "    \n",
        "    return train_generator, validation_generator, test_generator\n",
        "\n",
        "train_generator, validation_generator, test_generator = load_dataset()\n",
        "\n",
        "\n",
        "train_num_classes = len(train_generator.class_indices)\n",
        "val_num_classes = len(validation_generator.class_indices)\n",
        "test_num_classes = len(test_generator.class_indices)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1499 images belonging to 10 classes.\n",
            "Found 185 images belonging to 10 classes.\n",
            "Found 194 images belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "MByyGGVNoZCn",
        "outputId": "7356a413-b887-44fe-fc02-8452540d4c6d"
      },
      "source": [
        "# Visualize images\n",
        "\n",
        "def display_images_sample(images, labels, class_list ,columns=4,rows=3):\n",
        "    fig = plt.figure(figsize=(2*columns,2*rows))\n",
        "\n",
        "    for i in range(columns*rows):\n",
        "        fig.add_subplot(rows,columns,i+1)\n",
        "        img = images[i]\n",
        "        image_id = np.argmax(labels[i])\n",
        "        class_label = class_list[image_id]\n",
        "        plt.title(class_label)\n",
        "        plt.imshow(img)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "class_list = list(train_generator.class_indices.keys())\n",
        "images , labels = next(validation_generator)\n",
        "display_images_sample(images, labels, class_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjXcXUKSoYJF"
      },
      "source": [
        "# loading data\n",
        "\n",
        "def load_dataset(config):\n",
        "\n",
        "    train_datagen = ImageDataGenerator(rescale = 1/255.,\n",
        "                                       rotation_range = 10,\n",
        "                                      #  width_shift_range = 0.10,\n",
        "                                      #  height_shift_range = 0.1,\n",
        "                                      #  zoom_range=0.10, \n",
        "                                       horizontal_flip = True\n",
        "                                      )\n",
        "\n",
        "    valid_datagen = ImageDataGenerator(rescale = 1/255.)\n",
        "\n",
        "    test_datagen = ImageDataGenerator(rescale = 1/255.)\n",
        "\n",
        "\n",
        "    train_generator = train_datagen.flow_from_directory(\n",
        "            train_path,\n",
        "            target_size=(image_size, image_size),\n",
        "            batch_size= config.batch_size,\n",
        "            class_mode='categorical',\n",
        "            shuffle=True)\n",
        "\n",
        "    validation_generator = valid_datagen.flow_from_directory(\n",
        "        val_path,\n",
        "        target_size=(image_size, image_size),\n",
        "        batch_size= config.val_batch_size,\n",
        "        class_mode='categorical',\n",
        "        shuffle=True)\n",
        "\n",
        "    test_generator = test_datagen.flow_from_directory(\n",
        "                test_path,\n",
        "                target_size = (image_size, image_size),\n",
        "                batch_size = 1,\n",
        "                class_mode = 'categorical',\n",
        "                shuffle = False)\n",
        "    \n",
        "    return train_generator, validation_generator, test_generator\n",
        "\n",
        "# train_generator, validation_generator, test_generator = load_dataset()\n",
        "\n",
        "\n",
        "# train_num_classes = len(train_generator.class_indices)\n",
        "# val_num_classes = len(validation_generator.class_indices)\n",
        "# test_num_classes = len(test_generator.class_indices)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fmz70b-Uobjr"
      },
      "source": [
        "# Load model \n",
        "def load_model(config, model_name = 'VGG_Face'):\n",
        "    if model_name == 'ResNet50':\n",
        "        img = Input(shape=(image_size,image_size,3))\n",
        "        base_model = ResNet50(include_top = False,\n",
        "                              weights= 'imagenet',\n",
        "                              input_tensor=img,\n",
        "                              pooling='avg',\n",
        "                              input_shape = None)\n",
        "\n",
        "        final_layer = base_model.layers[-1].output\n",
        "        final_layer = Dropout(0.2)(final_layer)\n",
        "        dense_layer1 = Dense(512, activation='relu')(final_layer)\n",
        "        dense_layer1 = Dropout(0.2)(dense_layer1)\n",
        "        # dense_layer1 = BatchNormalization()(dense_layer1)\n",
        "        dense_layer2 = Dense(256,activation= 'relu')(dense_layer1)\n",
        "        dense_layer2 = Dropout(0.2)(dense_layer2)\n",
        "        dense_layer3 = Dense(128,activation= 'relu')(dense_layer2)\n",
        "        output_layer = Dense(train_num_classes,activation='softmax')(dense_layer3)\n",
        "\n",
        "        model = Model(img,output_layer)\n",
        "        for layer in base_model.layers:\n",
        "            layer.trainable = False\n",
        "\n",
        "        model.summary()\n",
        "        \n",
        "    if model_name == 'VGG_Face':\n",
        "        img = Input(shape=(image_size,image_size,3))\n",
        "        base_model = VGGFace(model='resnet50', include_top=False, input_shape=None, input_tensor= img)\n",
        "\n",
        "        final_layer = base_model.get_layer('avg_pool').output\n",
        "        final_layer = Flatten()(final_layer)\n",
        "        # final_layer = Dropout(0.2)(final_layer)\n",
        "        dense_layer1 = Dense(512, activation='relu')(final_layer)\n",
        "        dense_layer1 = BatchNormalization()(dense_layer1)\n",
        "        dense_layer1 = Dropout(config.dropout)(dense_layer1)\n",
        "        dense_layer2 = Dense(256,activation= 'relu')(dense_layer1)\n",
        "        dense_layer1 = BatchNormalization()(dense_layer1)\n",
        "        dense_layer2 = Dropout(config.dropout)(dense_layer2)\n",
        "        dense_layer3 = Dense(128,activation= 'relu')(dense_layer2)\n",
        "        dense_layer1 = BatchNormalization()(dense_layer1)\n",
        "        output_layer = Dense(train_num_classes,activation='softmax')(dense_layer3)\n",
        "\n",
        "        model = Model(img,output_layer)\n",
        "        for layer in base_model.layers:\n",
        "            layer.trainable = True\n",
        "\n",
        "        model.summary()\n",
        "        \n",
        "    if model_name == 'DenseNet':\n",
        "        img = Input(shape=(image_size,image_size,3))\n",
        "        base_model = DenseNet121(include_top = False,\n",
        "                              weights= 'imagenet',\n",
        "                              input_tensor=img,\n",
        "                              pooling='avg',\n",
        "                              input_shape = None)\n",
        "\n",
        "        final_layer = base_model.layers[-1].output\n",
        "        final_layer = Dropout(0.2)(final_layer)\n",
        "        dense_layer1 = Dense(512, activation='relu')(final_layer)\n",
        "        dense_layer1 = Dropout(0.2)(dense_layer1)\n",
        "        dense_layer1 = BatchNormalization()(dense_layer1)\n",
        "        dense_layer2 = Dense(256,activation= 'relu')(dense_layer1)\n",
        "        dense_layer2 = Dropout(0.2)(dense_layer2)\n",
        "        dense_layer3 = Dense(128,activation= 'relu')(dense_layer2)\n",
        "        output_layer = Dense(train_num_classes,activation='softmax')(dense_layer3)\n",
        "\n",
        "        model = Model(img,output_layer)\n",
        "        for layer in base_model.layers:\n",
        "            layer.trainable = False\n",
        "\n",
        "        model.summary()\n",
        "        \n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isAMBWYFoiZK"
      },
      "source": [
        "def train():\n",
        "    wandb.init(project='Face2Gene',\n",
        "               config={\n",
        "                \"learning_rate\": 0.01,\n",
        "                \"Tags\":\"10_Class\",\n",
        "                \"epochs\": 200,\n",
        "                \"dropout\": random.uniform(0.01, 0.80),\n",
        "                \"batch_size\": 64,\n",
        "                \"loss_function\": \"categorical_crossentropy\",\n",
        "                \"architecture\": \"VGG_Face\",\n",
        "                \"dataset\": \"Face2Gene\",\n",
        "                \"val_batch_size\":8\n",
        "               })\n",
        "    config = wandb.config\n",
        "    \n",
        "    # Load dataset\n",
        "    train_generator, validation_generator, test_generator = load_dataset(config)\n",
        "    \n",
        "    #load model\n",
        "    model = load_model(config,model_name = 'VGG_Face')\n",
        "    \n",
        "    # compile model like you usually do.\n",
        "    # notice use of config.\n",
        "    optimizer = tf.keras.optimizers.SGD(learning_rate = config.learning_rate)\n",
        "    model.compile(optimizer, config.loss_function, metrics=['categorical_accuracy','Precision','Recall',tfa.metrics.F1Score(num_classes=train_num_classes, average='macro')])\n",
        "    \n",
        "    # Callback \n",
        "    callbacks = [\n",
        "        EarlyStopping(patience=25, verbose=1,monitor='val_loss'),\n",
        "        ReduceLROnPlateau(factor=0.05, patience=5, min_lr=0.000000001, verbose=1,monitor='val_loss'),\n",
        "        # ModelCheckpoint('/content/drive/MyDrive/ColabNotebooks/Cancer/Face2Gene/Models/VGG_face_ResNet_11_basemodel_sgd_lr01_hf_r10.h5', verbose=1, save_best_only=True, save_weights_only=True)\n",
        "    ]\n",
        "    \n",
        "    # Train model\n",
        "    \n",
        "    fit_history = model.fit_generator(\n",
        "        train_generator,\n",
        "        steps_per_epoch= np.ceil(len(train_generator) // config.batch_size),\n",
        "        epochs = config.epochs,\n",
        "        validation_data = validation_generator,\n",
        "        validation_steps= np.ceil(len(validation_generator) // config.val_batch_size),\n",
        "        callbacks= [callbacks,WandbCallback(save_model = False)]\n",
        "        )\n",
        "    \n",
        "    scores = model.evaluate(test_generator,steps= len(test_generator),verbose = 1)\n",
        "    print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
        "    \n",
        "    # Add test results to W&B\n",
        "    wandb.log({'Test Accuracy': scores[1]*100})\n",
        "       "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Hp7Hr8iCLzV",
        "outputId": "3a29cad4-8272-433c-f438-2696ae0d3a0f"
      },
      "source": [
        "len(train_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTsmVh4none3"
      },
      "source": [
        "sweep_config = {\n",
        "  'method': 'bayes', \n",
        "  'metric': {\n",
        "      'name': 'val_loss',\n",
        "      'goal': 'minimize'\n",
        "  },\n",
        "  'early_terminate':{\n",
        "      'type': 'hyperband',\n",
        "      'min_iter': 25\n",
        "  },\n",
        "  'parameters': {\n",
        "      'batch_size': {\n",
        "          'values': [8, 16, 32, 64]\n",
        "      },\n",
        "      'learning_rate':{\n",
        "          'values': [0.01, 0.005, 0.001, 0.0005, 0.0001]\n",
        "      }\n",
        "  }\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCHniS7-ooDk",
        "outputId": "705c5381-a172-4840-a8f9-0f440c2fa482"
      },
      "source": [
        "sweep_id = wandb.sweep(sweep_config, project=\"Face2Gene\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Create sweep with ID: 0cusra47\n",
            "Sweep URL: https://wandb.ai/aayushktyagi/Face2Gene/sweeps/0cusra47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "232c9b7367514870b50a197c30087e9a",
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            "5df2c5b2c1d84384a1e40d8cde80090d",
            "a05cb34bb6884753b8be78f21aacdece",
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        "id": "TebBpVsf2T6d",
        "outputId": "4ff84c18-eddb-4361-d69d-e2923a2c41f8"
      },
      "source": [
        "wandb.agent(sweep_id, function=train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 2s 2s/step - loss: 3.2170 - categorical_accuracy: 0.0938 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0659 - val_loss: 2.2410 - val_categorical_accuracy: 0.2083 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0658\n",
            "Epoch 29/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 2.8432 - categorical_accuracy: 0.1250 - precision: 0.4000 - recall: 0.0625 - f1_score: 0.0675 - val_loss: 2.3965 - val_categorical_accuracy: 0.0833 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0673\n",
            "Epoch 30/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 2.8462 - categorical_accuracy: 0.1250 - precision: 0.2500 - recall: 0.0312 - f1_score: 0.0672 - val_loss: 2.3066 - val_categorical_accuracy: 0.1250 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0668\n",
            "\n",
            "Epoch 00030: ReduceLROnPlateau reducing learning rate to 1e-09.\n",
            "Epoch 31/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 3.0418 - categorical_accuracy: 0.0625 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0670 - val_loss: 2.3256 - val_categorical_accuracy: 0.1250 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0668\n",
            "Epoch 32/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 3.0694 - categorical_accuracy: 0.0938 - precision: 0.1429 - recall: 0.0312 - f1_score: 0.0668 - val_loss: 2.2874 - val_categorical_accuracy: 0.1667 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0667\n",
            "Epoch 33/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 2.8601 - categorical_accuracy: 0.0625 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0667 - val_loss: 2.2697 - val_categorical_accuracy: 0.2917 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0668\n",
            "Epoch 34/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 2.6706 - categorical_accuracy: 0.1875 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0690 - val_loss: 2.3014 - val_categorical_accuracy: 0.2083 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0690\n",
            "Epoch 35/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 3.1190 - categorical_accuracy: 0.1250 - precision: 0.1667 - recall: 0.0312 - f1_score: 0.0697 - val_loss: 2.3651 - val_categorical_accuracy: 0.0000e+00 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0692\n",
            "Epoch 00035: early stopping\n",
            "194/194 [==============================] - 7s 36ms/step - loss: 2.3052 - categorical_accuracy: 0.1289 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0679\n",
            "\n",
            "categorical_accuracy: 12.89%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 7577<br/>Program ended successfully."
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210611_134317-ejz238dm/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find internal logs for this run at: <code>/content/wandb/run-20210611_134317-ejz238dm/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
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        {
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          "data": {
            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epoch</td><td>34</td></tr><tr><td>loss</td><td>3.11905</td></tr><tr><td>categorical_accuracy</td><td>0.125</td></tr><tr><td>precision</td><td>0.16667</td></tr><tr><td>recall</td><td>0.03125</td></tr><tr><td>f1_score</td><td>0.06969</td></tr><tr><td>val_loss</td><td>2.36506</td></tr><tr><td>val_categorical_accuracy</td><td>0.0</td></tr><tr><td>val_precision</td><td>0.0</td></tr><tr><td>val_recall</td><td>0.0</td></tr><tr><td>val_f1_score</td><td>0.06916</td></tr><tr><td>lr</td><td>0.0</td></tr><tr><td>_runtime</td><td>118</td></tr><tr><td>_timestamp</td><td>1623419115</td></tr><tr><td>_step</td><td>35</td></tr><tr><td>best_val_loss</td><td>2.21682</td></tr><tr><td>best_epoch</td><td>9</td></tr><tr><td>Test Accuracy</td><td>12.8866</td></tr></table>"
            ],
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              "<IPython.core.display.HTML object>"
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          "data": {
            "text/html": [
              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇███</td></tr><tr><td>loss</td><td>▃▆▅▆▆▅▇▃█▃▂▅▅▅▂▇▆▇▆▆▅▅▆▇▁▆▄█▅▅▆▆▅▃▇</td></tr><tr><td>categorical_accuracy</td><td>▆▅▄▂▃▄▃▆▆▄▅▄▃▃▅▂▃▃▁▆▅▃▃▃█▅▆▃▄▄▃▃▃▆▄</td></tr><tr><td>precision</td><td>▁▁▄▃▃▃▁▃▆▆▃▃▁▄▇▁▁▁▁▆▁█▁▁▇▅▆▁▇▅▁▃▁▁▃</td></tr><tr><td>recall</td><td>▁▁▅▅▅▅▁▅██▅▅▁▅█▁▁▁▁▅▁▅▁▁█▅▅▁█▅▁▅▁▁▅</td></tr><tr><td>f1_score</td><td>█▄▂▁▁▁▁▂▂▂▂▂▂▂▂▂▂▁▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂</td></tr><tr><td>val_loss</td><td>█▂▄▅▅▃▅▂▁▁▆▃▆▇▃▄▃▃▂▅▄▂▆▆▄▇▂▂▇▄▅▃▃▄▆</td></tr><tr><td>val_categorical_accuracy</td><td>▃▄▃▄▇▃▄▅▆▇▃▄▅▂▆▄▅▆▇▃▃▇▃▃▅▄▇▆▃▄▄▅█▆▁</td></tr><tr><td>val_precision</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val_recall</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val_f1_score</td><td>█▅▃▁▁▁▁▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▃▃▃</td></tr><tr><td>lr</td><td>███████▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>_runtime</td><td>▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇█</td></tr><tr><td>_timestamp</td><td>▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇█</td></tr><tr><td>_step</td><td>▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇███</td></tr><tr><td>Test Accuracy</td><td>▁</td></tr></table><br/>"
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            "text/html": [
              "Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">light-sweep-22</strong>: <a href=\"https://wandb.ai/aayushktyagi/Face2Gene/runs/ejz238dm\" target=\"_blank\">https://wandb.ai/aayushktyagi/Face2Gene/runs/ejz238dm</a><br/>\n",
              "                "
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: tehf8nv1 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tbatch_size: 32\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 0.0001\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep\n"
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              "\n",
              "                Tracking run with wandb version 0.10.32<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">unique-sweep-23</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/aayushktyagi/Face2Gene\" target=\"_blank\">https://wandb.ai/aayushktyagi/Face2Gene</a><br/>\n",
              "                Sweep page: <a href=\"https://wandb.ai/aayushktyagi/Face2Gene/sweeps/0cusra47\" target=\"_blank\">https://wandb.ai/aayushktyagi/Face2Gene/sweeps/0cusra47</a><br/>\n",
              "Run page: <a href=\"https://wandb.ai/aayushktyagi/Face2Gene/runs/tehf8nv1\" target=\"_blank\">https://wandb.ai/aayushktyagi/Face2Gene/runs/tehf8nv1</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210611_134524-tehf8nv1</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Found 1499 images belonging to 10 classes.\n",
            "Found 185 images belonging to 10 classes.\n",
            "Found 194 images belonging to 10 classes.\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2 (Conv2D)           (None, 112, 112, 64) 9408        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1/7x7_s2/bn (BatchNormaliza (None, 112, 112, 64) 256         conv1/7x7_s2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 112, 112, 64) 0           conv1/7x7_s2/bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 55, 55, 64)   0           activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce (Conv2D)     (None, 55, 55, 64)   4096        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 55, 55, 64)   0           conv2_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 55, 55, 64)   0           conv2_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj (Conv2D)       (None, 55, 55, 256)  16384       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj/bn (BatchNorma (None, 55, 55, 256)  1024        conv2_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 55, 55, 256)  0           conv2_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv2_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 55, 55, 256)  0           add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 55, 55, 64)   0           conv2_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 55, 55, 64)   0           conv2_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 55, 55, 256)  0           conv2_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 55, 55, 64)   0           conv2_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 55, 55, 64)   0           conv2_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 55, 55, 256)  0           conv2_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce (Conv2D)     (None, 28, 28, 128)  32768       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 28, 28, 128)  0           conv3_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 28, 28, 128)  0           conv3_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj (Conv2D)       (None, 28, 28, 512)  131072      activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj/bn (BatchNorma (None, 28, 28, 512)  2048        conv3_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 28, 28, 512)  0           conv3_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv3_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 28, 28, 512)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 28, 28, 128)  0           conv3_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 28, 28, 128)  0           conv3_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 28, 28, 512)  0           conv3_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 28, 28, 128)  0           conv3_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 28, 28, 128)  0           conv3_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 28, 28, 512)  0           conv3_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 28, 28, 128)  0           conv3_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 28, 28, 128)  0           conv3_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 28, 28, 512)  0           conv3_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce (Conv2D)     (None, 14, 14, 256)  131072      activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 14, 14, 256)  0           conv4_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 14, 14, 256)  0           conv4_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj (Conv2D)       (None, 14, 14, 1024) 524288      activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj/bn (BatchNorma (None, 14, 14, 1024) 4096        conv4_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 14, 14, 1024) 0           conv4_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv4_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 14, 14, 1024) 0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 14, 14, 256)  0           conv4_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 14, 14, 256)  0           conv4_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, 14, 14, 1024) 0           conv4_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 14, 14, 256)  0           conv4_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 14, 14, 256)  0           conv4_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 14, 14, 1024) 0           conv4_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 14, 14, 256)  0           conv4_4_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 14, 14, 256)  0           conv4_4_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 14, 14, 1024) 0           conv4_4_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_5_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 14, 14, 256)  0           conv4_5_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_5_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 14, 14, 256)  0           conv4_5_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_5_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 14, 14, 1024) 0           conv4_5_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_6_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 14, 14, 256)  0           conv4_6_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_6_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 14, 14, 256)  0           conv4_6_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_6_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 14, 14, 1024) 0           conv4_6_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce (Conv2D)     (None, 7, 7, 512)    524288      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 7, 7, 512)    0           conv5_1_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 512)    0           conv5_1_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj (Conv2D)       (None, 7, 7, 2048)   2097152     activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj/bn (BatchNorma (None, 7, 7, 2048)   8192        conv5_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, 7, 7, 2048)   0           conv5_1_1x1_increase/bn[0][0]    \n",
            "                                                                 conv5_1_1x1_proj/bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 2048)   0           add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 512)    0           conv5_2_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 512)    0           conv5_2_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, 7, 7, 2048)   0           conv5_2_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 512)    0           conv5_3_1x1_reduce/bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 512)    0           conv5_3_3x3/bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, 7, 7, 2048)   0           conv5_3_1x1_increase/bn[0][0]    \n",
            "                                                                 activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2048)         0           avg_pool[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 512)          1049088     flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 512)          2048        dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 512)          0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 256)          131328      dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 256)          0           dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 128)          32896       dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 10)           1290        dense_2[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 24,777,802\n",
            "Trainable params: 24,723,658\n",
            "Non-trainable params: 54,144\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/200\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Ctrl + C detected. Stopping sweep.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584,
          "referenced_widgets": [
            "4a300f2a15474780a2c04759df93d204",
            "e0fda0d2086147f190b7e8553dcd97e3",
            "0d8d6927266f4f48835fbab952d13cab",
            "f73a01b2ca404780b8e1fe532e2e1387",
            "0403b851b3f94457bc4ba8f2355d63ff",
            "57802185cf184f0e9c7bdc7ae95bd080",
            "cdab954b1276442ba9d6467cca352ab8",
            "bb5f9ffbc648486588253b1adc1dd98c"
          ]
        },
        "id": "DpGsRK_voqYV",
        "outputId": "7e7b162b-994a-4bde-8769-05ad8c557951"
      },
      "source": [
        "plt.figure(1, figsize = (15,8)) \n",
        "    \n",
        "plt.subplot(221)  \n",
        "plt.plot(fit_history.history['categorical_accuracy'])  \n",
        "plt.plot(fit_history.history['val_categorical_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'valid']) \n",
        "    \n",
        "plt.subplot(222)  \n",
        "plt.plot(fit_history.history['loss'])  \n",
        "plt.plot(fit_history.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'valid']) \n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-445b392814fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m221\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'categorical_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_categorical_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model accuracy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'fit_history' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 7786<br/>Program failed with code 1.  Press ctrl-c to abort syncing."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a300f2a15474780a2c04759df93d204",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhAQYYMFvdxP"
      },
      "source": [
        "# Load model \n",
        "# model.load_weights('/content/drive/MyDrive/ColabNotebooks/Cancer/Face2Gene/Models/VGG_face_ResNet_8_basemodel_sgd_lr01_hf_r5_s10.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4X9gsHwqot5X"
      },
      "source": [
        "scores = model.evaluate(test_generator,steps= len(test_generator),verbose = 1)\n",
        "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0SWSw6pBUKd"
      },
      "source": [
        "# Add test results to W&B\n",
        "wandb.log({'Test Accuracy': scores[1]*100})\n",
        "\n",
        "run.join()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8syqnMWNq0ME"
      },
      "source": [
        "pred = model.predict(test_generator, steps = len(test_generator), verbose = 1)\n",
        "predicted_class_indices = np.argmax(pred, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1zQbiUVq1_S"
      },
      "source": [
        "# class wise \n",
        "def predict(test_generator, predicted_class_index):\n",
        "\n",
        "    confusion_mat_score = confusion_matrix(test_generator,predicted_class_index)\n",
        "    sns.heatmap(confusion_mat_score, annot=True)\n",
        "    # sns.heatmap(results/np.sum(results), annot=True, fmt='.2%', cmap='Blues')\n",
        "    # print(results)\n",
        "    #Classification accuracy\n",
        "    accuracy = accuracy_score(test_generator,predicted_class_index)\n",
        "    print(\"Classification accuracy:{}\".format(accuracy))\n",
        "    print()\n",
        "    print(accuracy)\n",
        "    #Classification report \n",
        "    classify_report = classification_report(test_generator,predicted_class_index)\n",
        "    print(\"Classification report\")\n",
        "    print(classify_report)\n",
        "\n",
        "    return confusion_mat_score, accuracy, classify_report\n",
        "    \n",
        "confusion_mat_score, accuracy, classification_report = predict(test_generator.classes, predicted_class_indices)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1IqNKvJv6fo"
      },
      "source": [
        "# Confusion matrix\n",
        "df_cm = pd.DataFrame(confusion_mat_score, columns=class_list, index = class_list)\n",
        "sns.set(font_scale=1.4)#for label size\n",
        "plt.figure(figsize = (9,7))\n",
        "sns.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaIOTgu0o29t"
      },
      "source": [
        "# Confusion matrix\n",
        "cm_sum = np.sum(confusion_mat_score,axis=1, keepdims=True)\n",
        "cm_perc = confusion_mat_score / cm_sum.astype(float) * 100\n",
        "df_cm = pd.DataFrame(cm_perc, columns=class_list, index = class_list)\n",
        "sns.set(font_scale=1.4)#for label size\n",
        "plt.figure(figsize = (10,7))\n",
        "sns.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 16})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DSt7Pebq4Na"
      },
      "source": [
        "# set plot figure size\n",
        "fig, c_ax = plt.subplots(1,1, figsize = (12, 8))\n",
        "\n",
        "def multiclass_roc_auc_score(y_test, y_pred, average=\"macro\"):\n",
        "    lb = LabelBinarizer()\n",
        "    lb.fit(y_test)\n",
        "    y_test = lb.transform(y_test)\n",
        "    y_pred = lb.transform(y_pred)\n",
        "\n",
        "    for (idx, c_label) in enumerate(class_list): # all_labels: no of the labels, for ex. ['cat', 'dog', 'rat']\n",
        "        fpr, tpr, thresholds = roc_curve(y_test[:,idx].astype(int), y_pred[:,idx])\n",
        "        c_ax.plot(fpr, tpr, label = '%s (AUC:%0.2f)'  % (c_label, auc(fpr, tpr)))\n",
        "    c_ax.plot(fpr, fpr, 'b-', label = 'Random Guessing')\n",
        "    return roc_auc_score(y_test, y_pred, average=average)\n",
        "\n",
        "# calling\n",
        "test_generator.reset() # resetting generator\n",
        "y_pred = model.predict(test_generator, verbose = True)\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "multiclass_roc_auc_score(test_generator.classes, y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4vrW5xw4VG7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}