fault_detection.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noXOZRjBZYQb",
        "outputId": "1970cfb9-4d7c-43b6-eec7-4d007a97ce7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount(\"/content/gdrive\", force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gloq8aWEuhU6",
        "outputId": "f5602e32-7f00-41f0-d291-4f1c4fc4bb41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.8/dist-packages (7.1.2)\n"
          ]
        }
      ],
      "source": [
        "# Download dependencies\n",
        "!pip install pillow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "WPAZvDyAZjkA"
      },
      "outputs": [],
      "source": [
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "960hXdOrmFcw"
      },
      "outputs": [],
      "source": [
        "os.chdir(\"/content/gdrive/MyDrive/Colab_Notebooks/Fault_detection\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "w_1o5i2XmbSf"
      },
      "outputs": [],
      "source": [
        "# import required packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from utils.elpv_reader import load_dataset\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gV3hAlqhcPt"
      },
      "source": [
        "# Dataset Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0l6GfUfc60uS"
      },
      "source": [
        "## Loading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1r66LjfpvSdU"
      },
      "outputs": [],
      "source": [
        "def load_data():\n",
        "  images, probas, labels = load_dataset()\n",
        "\n",
        "  # Convert the type of the solar module \n",
        "  # to numerical values\n",
        "  labels[labels == \"mono\"] = 0\n",
        "  labels[labels == \"poly\"] = 1\n",
        "\n",
        "  # Convert the probabilities to classes\n",
        "  probas[probas >= 0.5] = 1. # the cell is defective\n",
        "  probas[probas < 0.5] = 0. # the cell is not defective\n",
        "\n",
        "  # Convert grayscale to rgb\n",
        "  # This is needed to adapt the data\n",
        "  # to the model's input format\n",
        "  rgb_imgs = np.repeat(images[..., np.newaxis],3,-1)\n",
        "  \n",
        "  # The images and the type of the cell\n",
        "  # are going to be the inputs to our model\n",
        "  X1 = rgb_imgs\n",
        "  X2 = labels\n",
        "\n",
        "  # The probabilities of being defective\n",
        "  # are going to be the outputs of our model\n",
        "  Y = probas\n",
        "\n",
        "  return X1,X2,Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "SmccQBE67HMG"
      },
      "outputs": [],
      "source": [
        "X1,X2,Y = load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQUbEGI5hf8q"
      },
      "source": [
        "## Exploring the range of pixel intensities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQqZjYOodWhH",
        "outputId": "4cfa8f12-9d2a-4419-986a-ade454bc02fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The range of pixel intensities is between 0-255\n"
          ]
        }
      ],
      "source": [
        "min_intensity = np.min(X1)\n",
        "max_intensity = np.max(X1)\n",
        "\n",
        "print(f\"The range of pixel intensities is between {min_intensity}-{max_intensity}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wkjMi3ZhmSA"
      },
      "source": [
        "## Exploring the target class distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ThurkZL3eJBt",
        "outputId": "b0196939-f6f5-4dab-a3a2-3f09e623d863"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The dataset has 31.29 % defective cells and 68.71 % non-defective cells\n"
          ]
        }
      ],
      "source": [
        "pos_class = np.sum(Y)/len(Y)*100\n",
        "neg_class = (len(Y)-np.sum(Y))/len(Y)*100\n",
        "\n",
        "print(f'The dataset has {np.round(pos_class,2)} % defective cells and {np.round(neg_class,2)} % non-defective cells')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VT8hL9E2zD8w"
      },
      "source": [
        "## Splitting the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNQSM4BgB_fH",
        "outputId": "46de1991-258a-4c7f-8b8f-dba4a9c25297"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training set size: (2099, 300, 300, 3) \n",
            "Validation set size: (525, 300, 300, 3)\n"
          ]
        }
      ],
      "source": [
        "# splitting the dataset\n",
        "# to train and validation\n",
        "X1_train,X1_val,Y_train,Y_val = train_test_split(X1,Y,test_size=0.2,random_state=13,stratify=Y)\n",
        "X2_train,X2_val,Y_train,Y_val = train_test_split(X2,Y,test_size=0.2,random_state=13,stratify=Y)\n",
        "\n",
        "print(f\"Training set size: {X1_train.shape} \\nValidation set size: {X1_val.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lK-fAhFxy6X7"
      },
      "source": [
        "## Visualizing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "zBDvH5cUy9kz",
        "outputId": "1a73fc71-5439-4b61-82d7-af042b2023e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x432 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "defective_images = X1_train[Y_train == 1][:10]\n",
        "non_defective_images = X1_train[Y_train == 0][:5]\n",
        "\n",
        "cell_types_def = X2_train[Y_train == 1.][:5]\n",
        "cell_types_ndef = X2_train[Y_train ==0.][:5]\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "\n",
        "for i in range(5):\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(defective_images[i,...])\n",
        "    plt.title(\"Defective cell type : \"+ (\"Poly\" if cell_types_ndef[i-5] == 1 else \"Mono\"))\n",
        "\n",
        "for i in range(5,10):\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.imshow(non_defective_images[i-5,...])\n",
        "    plt.title(f\"Good cell type: \"+ (\"Poly\" if cell_types_ndef[i-5] == 1 else \"Mono\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQouLt4N0-Nw"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s7t6KzKlCiZA"
      },
      "outputs": [],
      "source": [
        "Y_train = to_categorical(Y_train,num_classes=2)\n",
        "Y_val = to_categorical(Y_val,num_classes=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8fkEYr-OyWf"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 32\n",
        "# this is the default size used by most of the pretrained Keras models\n",
        "image_size = (224, 224)\n",
        "\n",
        "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "        samplewise_center=False,\n",
        "        samplewise_std_normalization=False,\n",
        "        rescale=1./255,\n",
        "        shear_range=0.07, # do some data augmentation here\n",
        "        zoom_range=0.2,\n",
        "        rotation_range=30,\n",
        "        horizontal_flip=True,\n",
        "        vertical_flip=True)\n",
        "\n",
        "# create testing and validation data generator without augmentation\n",
        "val_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "        samplewise_center=False, \n",
        "        samplewise_std_normalization=False,\n",
        "        rescale=1./255\n",
        "        )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mx4nLtX1PGLB"
      },
      "outputs": [],
      "source": [
        "train_ds = train_datagen.flow(x=tf.image.resize(X1_train,size=(224,224)),\n",
        "                              y=Y_train, \n",
        "                              batch_size=BATCH_SIZE)\n",
        "val_ds = val_datagen.flow(x=tf.image.resize(X1_val,size=(224,224)),\n",
        "                          y=Y_val,\n",
        "                          batch_size=BATCH_SIZE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcVVHIImY1zF"
      },
      "source": [
        "# Building the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmDaOqCMb2Ah"
      },
      "source": [
        "# Transfer Learning EfficientNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdRazYjgiWB5",
        "outputId": "0b485dc1-2819-4cc2-b3dc-ed8f3aaeb236"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)             (None, 112, 112, 32  864         ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalization)   (None, 112, 112, 32  128         ['stem_conv[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " stem_activation (Activation)   (None, 112, 112, 32  0           ['stem_bn[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv2D)  (None, 112, 112, 16  4608        ['stem_activation[0][0]']        \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1a_project_conv[0][0]']   \n",
            " lization)                      )                                                                 \n",
            "                                                                                                  \n",
            " block1a_project_activation (Ac  (None, 112, 112, 16  0          ['block1a_project_bn[0][0]']     \n",
            " tivation)                      )                                                                 \n",
            "                                                                                                  \n",
            " block1b_project_conv (Conv2D)  (None, 112, 112, 16  2304        ['block1a_project_activation[0][0\n",
            "                                )                                ]']                              \n",
            "                                                                                                  \n",
            " block1b_project_bn (BatchNorma  (None, 112, 112, 16  64         ['block1b_project_conv[0][0]']   \n",
            " lization)                      )                                                                 \n",
            "                                                                                                  \n",
            " block1b_project_activation (Ac  (None, 112, 112, 16  0          ['block1b_project_bn[0][0]']     \n",
            " tivation)                      )                                                                 \n",
            "                                                                                                  \n",
            " block1b_drop (Dropout)         (None, 112, 112, 16  0           ['block1b_project_activation[0][0\n",
            "                                )                                ]']                              \n",
            "                                                                                                  \n",
            " block1b_add (Add)              (None, 112, 112, 16  0           ['block1b_drop[0][0]',           \n",
            "                                )                                 'block1a_project_activation[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2D)   (None, 56, 56, 64)   9216        ['block1b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNormal  (None, 56, 56, 64)  256         ['block2a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block2a_expand_activation (Act  (None, 56, 56, 64)  0           ['block2a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv2D)  (None, 56, 56, 32)   2048        ['block2a_expand_activation[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchNorma  (None, 56, 56, 32)  128         ['block2a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2D)   (None, 56, 56, 128)  36864       ['block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNormal  (None, 56, 56, 128)  512        ['block2b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_expand_activation (Act  (None, 56, 56, 128)  0          ['block2b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv2D)  (None, 56, 56, 32)   4096        ['block2b_expand_activation[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchNorma  (None, 56, 56, 32)  128         ['block2b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)         (None, 56, 56, 32)   0           ['block2b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2b_add (Add)              (None, 56, 56, 32)   0           ['block2b_drop[0][0]',           \n",
            "                                                                  'block2a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2c_expand_conv (Conv2D)   (None, 56, 56, 128)  36864       ['block2b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block2c_expand_bn (BatchNormal  (None, 56, 56, 128)  512        ['block2c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block2c_expand_activation (Act  (None, 56, 56, 128)  0          ['block2c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block2c_project_conv (Conv2D)  (None, 56, 56, 32)   4096        ['block2c_expand_activation[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " block2c_project_bn (BatchNorma  (None, 56, 56, 32)  128         ['block2c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block2c_drop (Dropout)         (None, 56, 56, 32)   0           ['block2c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block2c_add (Add)              (None, 56, 56, 32)   0           ['block2c_drop[0][0]',           \n",
            "                                                                  'block2b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2D)   (None, 28, 28, 128)  36864       ['block2c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNormal  (None, 28, 28, 128)  512        ['block3a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_expand_activation (Act  (None, 28, 28, 128)  0          ['block3a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv2D)  (None, 28, 28, 56)   7168        ['block3a_expand_activation[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchNorma  (None, 28, 28, 56)  224         ['block3a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2D)   (None, 28, 28, 224)  112896      ['block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNormal  (None, 28, 28, 224)  896        ['block3b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_expand_activation (Act  (None, 28, 28, 224)  0          ['block3b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv2D)  (None, 28, 28, 56)   12544       ['block3b_expand_activation[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchNorma  (None, 28, 28, 56)  224         ['block3b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)         (None, 28, 28, 56)   0           ['block3b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3b_add (Add)              (None, 28, 28, 56)   0           ['block3b_drop[0][0]',           \n",
            "                                                                  'block3a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3c_expand_conv (Conv2D)   (None, 28, 28, 224)  112896      ['block3b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block3c_expand_bn (BatchNormal  (None, 28, 28, 224)  896        ['block3c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block3c_expand_activation (Act  (None, 28, 28, 224)  0          ['block3c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block3c_project_conv (Conv2D)  (None, 28, 28, 56)   12544       ['block3c_expand_activation[0][0]\n",
            "                                                                 ']                               \n",
            "                                                                                                  \n",
            " block3c_project_bn (BatchNorma  (None, 28, 28, 56)  224         ['block3c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block3c_drop (Dropout)         (None, 28, 28, 56)   0           ['block3c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block3c_add (Add)              (None, 28, 28, 56)   0           ['block3c_drop[0][0]',           \n",
            "                                                                  'block3b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2D)   (None, 28, 28, 224)  12544       ['block3c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNormal  (None, 28, 28, 224)  896        ['block4a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_expand_activation (Act  (None, 28, 28, 224)  0          ['block4a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4a_dwconv2 (DepthwiseConv  (None, 14, 14, 224)  2016       ['block4a_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormalization  (None, 14, 14, 224)  896        ['block4a_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_activation (Activation  (None, 14, 14, 224)  0          ['block4a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (GlobalAver  (None, 224)         0           ['block4a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshape)   (None, 1, 1, 224)    0           ['block4a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)     (None, 1, 1, 14)     3150        ['block4a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)     (None, 1, 1, 224)    3360        ['block4a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multiply)   (None, 14, 14, 224)  0           ['block4a_activation[0][0]',     \n",
            "                                                                  'block4a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv2D)  (None, 14, 14, 104)  23296       ['block4a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchNorma  (None, 14, 14, 104)  416        ['block4a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2D)   (None, 14, 14, 416)  43264       ['block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNormal  (None, 14, 14, 416)  1664       ['block4b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_expand_activation (Act  (None, 14, 14, 416)  0          ['block4b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4b_dwconv2 (DepthwiseConv  (None, 14, 14, 416)  3744       ['block4b_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormalization  (None, 14, 14, 416)  1664       ['block4b_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_activation (Activation  (None, 14, 14, 416)  0          ['block4b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (GlobalAver  (None, 416)         0           ['block4b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshape)   (None, 1, 1, 416)    0           ['block4b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)     (None, 1, 1, 26)     10842       ['block4b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)     (None, 1, 1, 416)    11232       ['block4b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multiply)   (None, 14, 14, 416)  0           ['block4b_activation[0][0]',     \n",
            "                                                                  'block4b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv2D)  (None, 14, 14, 104)  43264       ['block4b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchNorma  (None, 14, 14, 104)  416        ['block4b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)         (None, 14, 14, 104)  0           ['block4b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4b_add (Add)              (None, 14, 14, 104)  0           ['block4b_drop[0][0]',           \n",
            "                                                                  'block4a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2D)   (None, 14, 14, 416)  43264       ['block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNormal  (None, 14, 14, 416)  1664       ['block4c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_expand_activation (Act  (None, 14, 14, 416)  0          ['block4c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4c_dwconv2 (DepthwiseConv  (None, 14, 14, 416)  3744       ['block4c_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormalization  (None, 14, 14, 416)  1664       ['block4c_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_activation (Activation  (None, 14, 14, 416)  0          ['block4c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (GlobalAver  (None, 416)         0           ['block4c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshape)   (None, 1, 1, 416)    0           ['block4c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)     (None, 1, 1, 26)     10842       ['block4c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)     (None, 1, 1, 416)    11232       ['block4c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multiply)   (None, 14, 14, 416)  0           ['block4c_activation[0][0]',     \n",
            "                                                                  'block4c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv2D)  (None, 14, 14, 104)  43264       ['block4c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchNorma  (None, 14, 14, 104)  416        ['block4c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)         (None, 14, 14, 104)  0           ['block4c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4c_add (Add)              (None, 14, 14, 104)  0           ['block4c_drop[0][0]',           \n",
            "                                                                  'block4b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4d_expand_conv (Conv2D)   (None, 14, 14, 416)  43264       ['block4c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block4d_expand_bn (BatchNormal  (None, 14, 14, 416)  1664       ['block4d_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block4d_expand_activation (Act  (None, 14, 14, 416)  0          ['block4d_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block4d_dwconv2 (DepthwiseConv  (None, 14, 14, 416)  3744       ['block4d_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block4d_bn (BatchNormalization  (None, 14, 14, 416)  1664       ['block4d_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4d_activation (Activation  (None, 14, 14, 416)  0          ['block4d_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block4d_se_squeeze (GlobalAver  (None, 416)         0           ['block4d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block4d_se_reshape (Reshape)   (None, 1, 1, 416)    0           ['block4d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block4d_se_reduce (Conv2D)     (None, 1, 1, 26)     10842       ['block4d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block4d_se_expand (Conv2D)     (None, 1, 1, 416)    11232       ['block4d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block4d_se_excite (Multiply)   (None, 14, 14, 416)  0           ['block4d_activation[0][0]',     \n",
            "                                                                  'block4d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block4d_project_conv (Conv2D)  (None, 14, 14, 104)  43264       ['block4d_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block4d_project_bn (BatchNorma  (None, 14, 14, 104)  416        ['block4d_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block4d_drop (Dropout)         (None, 14, 14, 104)  0           ['block4d_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block4d_add (Add)              (None, 14, 14, 104)  0           ['block4d_drop[0][0]',           \n",
            "                                                                  'block4c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2D)   (None, 14, 14, 624)  64896       ['block4d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNormal  (None, 14, 14, 624)  2496       ['block5a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_expand_activation (Act  (None, 14, 14, 624)  0          ['block5a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5a_dwconv2 (DepthwiseConv  (None, 14, 14, 624)  5616       ['block5a_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormalization  (None, 14, 14, 624)  2496       ['block5a_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_activation (Activation  (None, 14, 14, 624)  0          ['block5a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (GlobalAver  (None, 624)         0           ['block5a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshape)   (None, 1, 1, 624)    0           ['block5a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)     (None, 1, 1, 26)     16250       ['block5a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)     (None, 1, 1, 624)    16848       ['block5a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multiply)   (None, 14, 14, 624)  0           ['block5a_activation[0][0]',     \n",
            "                                                                  'block5a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv2D)  (None, 14, 14, 120)  74880       ['block5a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchNorma  (None, 14, 14, 120)  480        ['block5a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2D)   (None, 14, 14, 720)  86400       ['block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNormal  (None, 14, 14, 720)  2880       ['block5b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_expand_activation (Act  (None, 14, 14, 720)  0          ['block5b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5b_dwconv2 (DepthwiseConv  (None, 14, 14, 720)  6480       ['block5b_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormalization  (None, 14, 14, 720)  2880       ['block5b_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_activation (Activation  (None, 14, 14, 720)  0          ['block5b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (GlobalAver  (None, 720)         0           ['block5b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multiply)   (None, 14, 14, 720)  0           ['block5b_activation[0][0]',     \n",
            "                                                                  'block5b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv2D)  (None, 14, 14, 120)  86400       ['block5b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchNorma  (None, 14, 14, 120)  480        ['block5b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)         (None, 14, 14, 120)  0           ['block5b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5b_add (Add)              (None, 14, 14, 120)  0           ['block5b_drop[0][0]',           \n",
            "                                                                  'block5a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2D)   (None, 14, 14, 720)  86400       ['block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNormal  (None, 14, 14, 720)  2880       ['block5c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_expand_activation (Act  (None, 14, 14, 720)  0          ['block5c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5c_dwconv2 (DepthwiseConv  (None, 14, 14, 720)  6480       ['block5c_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormalization  (None, 14, 14, 720)  2880       ['block5c_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_activation (Activation  (None, 14, 14, 720)  0          ['block5c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (GlobalAver  (None, 720)         0           ['block5c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multiply)   (None, 14, 14, 720)  0           ['block5c_activation[0][0]',     \n",
            "                                                                  'block5c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv2D)  (None, 14, 14, 120)  86400       ['block5c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchNorma  (None, 14, 14, 120)  480        ['block5c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)         (None, 14, 14, 120)  0           ['block5c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5c_add (Add)              (None, 14, 14, 120)  0           ['block5c_drop[0][0]',           \n",
            "                                                                  'block5b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5d_expand_conv (Conv2D)   (None, 14, 14, 720)  86400       ['block5c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5d_expand_bn (BatchNormal  (None, 14, 14, 720)  2880       ['block5d_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5d_expand_activation (Act  (None, 14, 14, 720)  0          ['block5d_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5d_dwconv2 (DepthwiseConv  (None, 14, 14, 720)  6480       ['block5d_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block5d_bn (BatchNormalization  (None, 14, 14, 720)  2880       ['block5d_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5d_activation (Activation  (None, 14, 14, 720)  0          ['block5d_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5d_se_squeeze (GlobalAver  (None, 720)         0           ['block5d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5d_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5d_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5d_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5d_se_excite (Multiply)   (None, 14, 14, 720)  0           ['block5d_activation[0][0]',     \n",
            "                                                                  'block5d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5d_project_conv (Conv2D)  (None, 14, 14, 120)  86400       ['block5d_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5d_project_bn (BatchNorma  (None, 14, 14, 120)  480        ['block5d_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5d_drop (Dropout)         (None, 14, 14, 120)  0           ['block5d_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5d_add (Add)              (None, 14, 14, 120)  0           ['block5d_drop[0][0]',           \n",
            "                                                                  'block5c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5e_expand_conv (Conv2D)   (None, 14, 14, 720)  86400       ['block5d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5e_expand_bn (BatchNormal  (None, 14, 14, 720)  2880       ['block5e_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5e_expand_activation (Act  (None, 14, 14, 720)  0          ['block5e_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5e_dwconv2 (DepthwiseConv  (None, 14, 14, 720)  6480       ['block5e_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block5e_bn (BatchNormalization  (None, 14, 14, 720)  2880       ['block5e_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5e_activation (Activation  (None, 14, 14, 720)  0          ['block5e_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5e_se_squeeze (GlobalAver  (None, 720)         0           ['block5e_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5e_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5e_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5e_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5e_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5e_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5e_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5e_se_excite (Multiply)   (None, 14, 14, 720)  0           ['block5e_activation[0][0]',     \n",
            "                                                                  'block5e_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5e_project_conv (Conv2D)  (None, 14, 14, 120)  86400       ['block5e_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5e_project_bn (BatchNorma  (None, 14, 14, 120)  480        ['block5e_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5e_drop (Dropout)         (None, 14, 14, 120)  0           ['block5e_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5e_add (Add)              (None, 14, 14, 120)  0           ['block5e_drop[0][0]',           \n",
            "                                                                  'block5d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5f_expand_conv (Conv2D)   (None, 14, 14, 720)  86400       ['block5e_add[0][0]']            \n",
            "                                                                                                  \n",
            " block5f_expand_bn (BatchNormal  (None, 14, 14, 720)  2880       ['block5f_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block5f_expand_activation (Act  (None, 14, 14, 720)  0          ['block5f_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block5f_dwconv2 (DepthwiseConv  (None, 14, 14, 720)  6480       ['block5f_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block5f_bn (BatchNormalization  (None, 14, 14, 720)  2880       ['block5f_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5f_activation (Activation  (None, 14, 14, 720)  0          ['block5f_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block5f_se_squeeze (GlobalAver  (None, 720)         0           ['block5f_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block5f_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block5f_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block5f_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block5f_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block5f_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block5f_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block5f_se_excite (Multiply)   (None, 14, 14, 720)  0           ['block5f_activation[0][0]',     \n",
            "                                                                  'block5f_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block5f_project_conv (Conv2D)  (None, 14, 14, 120)  86400       ['block5f_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block5f_project_bn (BatchNorma  (None, 14, 14, 120)  480        ['block5f_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block5f_drop (Dropout)         (None, 14, 14, 120)  0           ['block5f_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block5f_add (Add)              (None, 14, 14, 120)  0           ['block5f_drop[0][0]',           \n",
            "                                                                  'block5e_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2D)   (None, 14, 14, 720)  86400       ['block5f_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNormal  (None, 14, 14, 720)  2880       ['block6a_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_expand_activation (Act  (None, 14, 14, 720)  0          ['block6a_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6a_dwconv2 (DepthwiseConv  (None, 7, 7, 720)   6480        ['block6a_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormalization  (None, 7, 7, 720)   2880        ['block6a_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_activation (Activation  (None, 7, 7, 720)   0           ['block6a_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (GlobalAver  (None, 720)         0           ['block6a_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshape)   (None, 1, 1, 720)    0           ['block6a_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)     (None, 1, 1, 30)     21630       ['block6a_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)     (None, 1, 1, 720)    22320       ['block6a_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multiply)   (None, 7, 7, 720)    0           ['block6a_activation[0][0]',     \n",
            "                                                                  'block6a_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv2D)  (None, 7, 7, 208)    149760      ['block6a_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6a_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6b_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6b_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6b_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6b_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6b_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_activation (Activation  (None, 7, 7, 1248)  0           ['block6b_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (GlobalAver  (None, 1248)        0           ['block6b_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6b_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6b_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6b_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6b_activation[0][0]',     \n",
            "                                                                  'block6b_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6b_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6b_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)         (None, 7, 7, 208)    0           ['block6b_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6b_add (Add)              (None, 7, 7, 208)    0           ['block6b_drop[0][0]',           \n",
            "                                                                  'block6a_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6c_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6c_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6c_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6c_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6c_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_activation (Activation  (None, 7, 7, 1248)  0           ['block6c_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (GlobalAver  (None, 1248)        0           ['block6c_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6c_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6c_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6c_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6c_activation[0][0]',     \n",
            "                                                                  'block6c_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6c_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6c_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)         (None, 7, 7, 208)    0           ['block6c_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6c_add (Add)              (None, 7, 7, 208)    0           ['block6c_drop[0][0]',           \n",
            "                                                                  'block6b_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6d_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6d_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6d_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6d_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6d_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_activation (Activation  (None, 7, 7, 1248)  0           ['block6d_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (GlobalAver  (None, 1248)        0           ['block6d_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6d_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6d_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6d_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6d_activation[0][0]',     \n",
            "                                                                  'block6d_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6d_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6d_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)         (None, 7, 7, 208)    0           ['block6d_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6d_add (Add)              (None, 7, 7, 208)    0           ['block6d_drop[0][0]',           \n",
            "                                                                  'block6c_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6e_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6e_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6e_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6e_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6e_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6e_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6e_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6e_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6e_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6e_activation (Activation  (None, 7, 7, 1248)  0           ['block6e_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6e_se_squeeze (GlobalAver  (None, 1248)        0           ['block6e_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6e_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6e_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6e_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6e_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6e_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6e_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6e_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6e_activation[0][0]',     \n",
            "                                                                  'block6e_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6e_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6e_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6e_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6e_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6e_drop (Dropout)         (None, 7, 7, 208)    0           ['block6e_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6e_add (Add)              (None, 7, 7, 208)    0           ['block6e_drop[0][0]',           \n",
            "                                                                  'block6d_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6f_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6e_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6f_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6f_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6f_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6f_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6f_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6f_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6f_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6f_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6f_activation (Activation  (None, 7, 7, 1248)  0           ['block6f_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6f_se_squeeze (GlobalAver  (None, 1248)        0           ['block6f_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6f_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6f_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6f_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6f_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6f_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6f_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6f_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6f_activation[0][0]',     \n",
            "                                                                  'block6f_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6f_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6f_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6f_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6f_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6f_drop (Dropout)         (None, 7, 7, 208)    0           ['block6f_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6f_add (Add)              (None, 7, 7, 208)    0           ['block6f_drop[0][0]',           \n",
            "                                                                  'block6e_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6g_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6f_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6g_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6g_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6g_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6g_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6g_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6g_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6g_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6g_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6g_activation (Activation  (None, 7, 7, 1248)  0           ['block6g_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6g_se_squeeze (GlobalAver  (None, 1248)        0           ['block6g_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6g_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6g_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6g_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6g_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6g_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6g_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6g_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6g_activation[0][0]',     \n",
            "                                                                  'block6g_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6g_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6g_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6g_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6g_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6g_drop (Dropout)         (None, 7, 7, 208)    0           ['block6g_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6g_add (Add)              (None, 7, 7, 208)    0           ['block6g_drop[0][0]',           \n",
            "                                                                  'block6f_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6h_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6g_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6h_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6h_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6h_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6h_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6h_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6h_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6h_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6h_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6h_activation (Activation  (None, 7, 7, 1248)  0           ['block6h_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6h_se_squeeze (GlobalAver  (None, 1248)        0           ['block6h_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6h_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6h_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6h_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6h_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6h_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6h_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6h_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6h_activation[0][0]',     \n",
            "                                                                  'block6h_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6h_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6h_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6h_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6h_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6h_drop (Dropout)         (None, 7, 7, 208)    0           ['block6h_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6h_add (Add)              (None, 7, 7, 208)    0           ['block6h_drop[0][0]',           \n",
            "                                                                  'block6g_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6i_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6h_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6i_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6i_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6i_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6i_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6i_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6i_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6i_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6i_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6i_activation (Activation  (None, 7, 7, 1248)  0           ['block6i_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6i_se_squeeze (GlobalAver  (None, 1248)        0           ['block6i_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6i_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6i_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6i_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6i_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6i_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6i_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6i_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6i_activation[0][0]',     \n",
            "                                                                  'block6i_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6i_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6i_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6i_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6i_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6i_drop (Dropout)         (None, 7, 7, 208)    0           ['block6i_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6i_add (Add)              (None, 7, 7, 208)    0           ['block6i_drop[0][0]',           \n",
            "                                                                  'block6h_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6j_expand_conv (Conv2D)   (None, 7, 7, 1248)   259584      ['block6i_add[0][0]']            \n",
            "                                                                                                  \n",
            " block6j_expand_bn (BatchNormal  (None, 7, 7, 1248)  4992        ['block6j_expand_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block6j_expand_activation (Act  (None, 7, 7, 1248)  0           ['block6j_expand_bn[0][0]']      \n",
            " ivation)                                                                                         \n",
            "                                                                                                  \n",
            " block6j_dwconv2 (DepthwiseConv  (None, 7, 7, 1248)  11232       ['block6j_expand_activation[0][0]\n",
            " 2D)                                                             ']                               \n",
            "                                                                                                  \n",
            " block6j_bn (BatchNormalization  (None, 7, 7, 1248)  4992        ['block6j_dwconv2[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6j_activation (Activation  (None, 7, 7, 1248)  0           ['block6j_bn[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " block6j_se_squeeze (GlobalAver  (None, 1248)        0           ['block6j_activation[0][0]']     \n",
            " agePooling2D)                                                                                    \n",
            "                                                                                                  \n",
            " block6j_se_reshape (Reshape)   (None, 1, 1, 1248)   0           ['block6j_se_squeeze[0][0]']     \n",
            "                                                                                                  \n",
            " block6j_se_reduce (Conv2D)     (None, 1, 1, 52)     64948       ['block6j_se_reshape[0][0]']     \n",
            "                                                                                                  \n",
            " block6j_se_expand (Conv2D)     (None, 1, 1, 1248)   66144       ['block6j_se_reduce[0][0]']      \n",
            "                                                                                                  \n",
            " block6j_se_excite (Multiply)   (None, 7, 7, 1248)   0           ['block6j_activation[0][0]',     \n",
            "                                                                  'block6j_se_expand[0][0]']      \n",
            "                                                                                                  \n",
            " block6j_project_conv (Conv2D)  (None, 7, 7, 208)    259584      ['block6j_se_excite[0][0]']      \n",
            "                                                                                                  \n",
            " block6j_project_bn (BatchNorma  (None, 7, 7, 208)   832         ['block6j_project_conv[0][0]']   \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block6j_drop (Dropout)         (None, 7, 7, 208)    0           ['block6j_project_bn[0][0]']     \n",
            "                                                                                                  \n",
            " block6j_add (Add)              (None, 7, 7, 208)    0           ['block6j_drop[0][0]',           \n",
            "                                                                  'block6i_add[0][0]']            \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)              (None, 7, 7, 1408)   292864      ['block6j_add[0][0]']            \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization)    (None, 7, 7, 1408)   5632        ['top_conv[0][0]']               \n",
            "                                                                                                  \n",
            " top_activation (Activation)    (None, 7, 7, 1408)   0           ['top_bn[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 7, 7, 1408)  5632        ['top_activation[0][0]']         \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " global_average_pooling2d (Glob  (None, 1408)        0           ['batch_normalization[0][0]']    \n",
            " alAveragePooling2D)                                                                              \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 1408)        5632        ['global_average_pooling2d[0][0]'\n",
            " rmalization)                                                    ]                                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 1408)         0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 2)            2818        ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 8,783,456\n",
            "Trainable params: 8,450\n",
            "Non-trainable params: 8,775,006\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def create_transfer_learning_model( \n",
        "                                   fine_tune=False,\n",
        "                                   fine_tune_at=None,\n",
        "                                   input_shape=(224,224,3),\n",
        "                                   base_learning_rate = 0.01\n",
        "                                   ):\n",
        "  \n",
        "\n",
        "    inputs = tf.keras.Input(shape=input_shape)\n",
        "    base_model = tf.keras.applications.EfficientNetV2B2(input_shape=input_shape, \n",
        "                                                     include_top=False,\n",
        "                                                     input_tensor=inputs,\n",
        "                                                     weights='imagenet',\n",
        "                                                     include_preprocessing=False,\n",
        "                                                   )\n",
        "    # Freeze the base model\n",
        "    base_model.trainable = False\n",
        "    \n",
        "    if fine_tune :\n",
        "        if not fine_tune_at:\n",
        "            raise Exception(\"You should specify from which\"+\n",
        "                            \" layer the model will be fine tuned\"\n",
        "                            )\n",
        "        else:\n",
        "            base_model.trainable = True\n",
        "            \n",
        "            # Freeze the lowest layers \n",
        "            # and fine tune the top layers\n",
        "            # starting from index \"fine_tune_at\"\n",
        "            for layer in base_model.layers[:fine_tune_at]:\n",
        "                layer.trainable = False\n",
        "  \n",
        "    x = base_model.output\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.GlobalAveragePooling2D()(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = tf.keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "    outputs = layers.Dense(2,activation=\"softmax\")(x)\n",
        "    \n",
        "    model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "    # Compile the model\n",
        "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
        "                  loss= tf.keras.losses.CategoricalCrossentropy(from_logits=False),\n",
        "                  metrics=[tf.keras.metrics.CategoricalAccuracy()])\n",
        "    return model, base_model\n",
        "  \n",
        "model, base_model = create_transfer_learning_model()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6mMQb3szf196"
      },
      "outputs": [],
      "source": [
        "# Define Callbacks to save the best model\n",
        "if not os.path.exists(\"./models/\"):\n",
        "  os.makedirs(\"./models/\")\n",
        "  \n",
        "chkp_filepath= \"./models/best_efficient_net_b2_final2_{epoch:02d}.h5\"\n",
        "checkpoint = tf.keras.callbacks.ModelCheckpoint(chkp_filepath, verbose=1,\n",
        "                             save_best_only=True,monitor=\"val_categorical_accuracy\",mode='max',save_freq=\"epoch\")\n",
        "\n",
        "filename='logs_effecient_net_b2_final.csv'\n",
        "history_logger= tf.keras.callbacks.CSVLogger(filename, separator=\",\", append=True)\n",
        "\n",
        "'Custom callback to decrease learning rate by a factor of 3 every 3rd epoch'\n",
        "class DecreaseLR(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        if epoch > 4 and epoch % 2 == 0:\n",
        "            self.model.optimizer.lr = self.model.optimizer.lr / 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUqq5jUos9hb",
        "outputId": "aeac4482-12a0-47ac-fde7-a20fc3ece454"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "17/17 [==============================] - 12s 101ms/step - loss: 0.6213 - categorical_accuracy: 0.6876\n"
          ]
        }
      ],
      "source": [
        "tf.random.set_seed(64)\n",
        "\n",
        "initial_epochs = 18\n",
        "\n",
        "loss0, accuracy0 = model.evaluate(val_ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TksFtSq2ddfn",
        "outputId": "62453d8f-4c0c-4b01-8d17-a56a75ca841d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.0448 - categorical_accuracy: 0.6465\n",
            "Epoch 1: val_categorical_accuracy improved from -inf to 0.79810, saving model to ./models/best_efficient_net_b2_final2_01.h5\n",
            "66/66 [==============================] - 37s 424ms/step - loss: 1.0448 - categorical_accuracy: 0.6465 - val_loss: 0.4521 - val_categorical_accuracy: 0.7981\n",
            "Epoch 2/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1372 - categorical_accuracy: 0.6698\n",
            "Epoch 2: val_categorical_accuracy did not improve from 0.79810\n",
            "66/66 [==============================] - 24s 359ms/step - loss: 1.1372 - categorical_accuracy: 0.6698 - val_loss: 0.5116 - val_categorical_accuracy: 0.7257\n",
            "Epoch 3/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.9360 - categorical_accuracy: 0.6960\n",
            "Epoch 3: val_categorical_accuracy did not improve from 0.79810\n",
            "66/66 [==============================] - 24s 361ms/step - loss: 0.9360 - categorical_accuracy: 0.6960 - val_loss: 1.1826 - val_categorical_accuracy: 0.6248\n",
            "Epoch 4/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.9189 - categorical_accuracy: 0.6803\n",
            "Epoch 4: val_categorical_accuracy did not improve from 0.79810\n",
            "66/66 [==============================] - 24s 360ms/step - loss: 0.9189 - categorical_accuracy: 0.6803 - val_loss: 0.5224 - val_categorical_accuracy: 0.7790\n",
            "Epoch 5/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.9548 - categorical_accuracy: 0.6684\n",
            "Epoch 5: val_categorical_accuracy did not improve from 0.79810\n",
            "66/66 [==============================] - 25s 384ms/step - loss: 0.9548 - categorical_accuracy: 0.6684 - val_loss: 0.7560 - val_categorical_accuracy: 0.7219\n",
            "Epoch 6/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.8936 - categorical_accuracy: 0.6760\n",
            "Epoch 6: val_categorical_accuracy did not improve from 0.79810\n",
            "66/66 [==============================] - 24s 363ms/step - loss: 0.8936 - categorical_accuracy: 0.6760 - val_loss: 0.8658 - val_categorical_accuracy: 0.7962\n",
            "Epoch 7/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7965 - categorical_accuracy: 0.6832\n",
            "Epoch 7: val_categorical_accuracy improved from 0.79810 to 0.82857, saving model to ./models/best_efficient_net_b2_final2_07.h5\n",
            "66/66 [==============================] - 27s 415ms/step - loss: 0.7965 - categorical_accuracy: 0.6832 - val_loss: 0.4835 - val_categorical_accuracy: 0.8286\n",
            "Epoch 8/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.6366 - categorical_accuracy: 0.6941\n",
            "Epoch 8: val_categorical_accuracy did not improve from 0.82857\n",
            "66/66 [==============================] - 26s 388ms/step - loss: 0.6366 - categorical_accuracy: 0.6941 - val_loss: 0.5275 - val_categorical_accuracy: 0.8076\n",
            "Epoch 9/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5211 - categorical_accuracy: 0.7175\n",
            "Epoch 9: val_categorical_accuracy did not improve from 0.82857\n",
            "66/66 [==============================] - 24s 363ms/step - loss: 0.5211 - categorical_accuracy: 0.7175 - val_loss: 0.4778 - val_categorical_accuracy: 0.7695\n",
            "Epoch 10/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5104 - categorical_accuracy: 0.7213\n",
            "Epoch 10: val_categorical_accuracy did not improve from 0.82857\n",
            "66/66 [==============================] - 24s 362ms/step - loss: 0.5104 - categorical_accuracy: 0.7213 - val_loss: 0.4261 - val_categorical_accuracy: 0.8095\n",
            "Epoch 11/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5001 - categorical_accuracy: 0.7222\n",
            "Epoch 11: val_categorical_accuracy did not improve from 0.82857\n",
            "66/66 [==============================] - 24s 363ms/step - loss: 0.5001 - categorical_accuracy: 0.7222 - val_loss: 0.4325 - val_categorical_accuracy: 0.8210\n",
            "Epoch 12/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4999 - categorical_accuracy: 0.7132\n",
            "Epoch 12: val_categorical_accuracy improved from 0.82857 to 0.83619, saving model to ./models/best_efficient_net_b2_final2_12.h5\n",
            "66/66 [==============================] - 25s 377ms/step - loss: 0.4999 - categorical_accuracy: 0.7132 - val_loss: 0.4074 - val_categorical_accuracy: 0.8362\n",
            "Epoch 13/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4792 - categorical_accuracy: 0.7075\n",
            "Epoch 13: val_categorical_accuracy did not improve from 0.83619\n",
            "66/66 [==============================] - 24s 366ms/step - loss: 0.4792 - categorical_accuracy: 0.7075 - val_loss: 0.4315 - val_categorical_accuracy: 0.8152\n",
            "Epoch 14/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4686 - categorical_accuracy: 0.7261\n",
            "Epoch 14: val_categorical_accuracy did not improve from 0.83619\n",
            "66/66 [==============================] - 25s 386ms/step - loss: 0.4686 - categorical_accuracy: 0.7261 - val_loss: 0.4311 - val_categorical_accuracy: 0.8133\n",
            "Epoch 15/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4518 - categorical_accuracy: 0.7237\n",
            "Epoch 15: val_categorical_accuracy did not improve from 0.83619\n",
            "66/66 [==============================] - 24s 360ms/step - loss: 0.4518 - categorical_accuracy: 0.7237 - val_loss: 0.4227 - val_categorical_accuracy: 0.8152\n",
            "Epoch 16/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4703 - categorical_accuracy: 0.7303\n",
            "Epoch 16: val_categorical_accuracy did not improve from 0.83619\n",
            "66/66 [==============================] - 24s 363ms/step - loss: 0.4703 - categorical_accuracy: 0.7303 - val_loss: 0.4135 - val_categorical_accuracy: 0.8229\n",
            "Epoch 17/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4630 - categorical_accuracy: 0.7208\n",
            "Epoch 17: val_categorical_accuracy did not improve from 0.83619\n",
            "66/66 [==============================] - 24s 364ms/step - loss: 0.4630 - categorical_accuracy: 0.7208 - val_loss: 0.4115 - val_categorical_accuracy: 0.8229\n",
            "Epoch 18/18\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4580 - categorical_accuracy: 0.7251\n",
            "Epoch 18: val_categorical_accuracy did not improve from 0.83619\n",
            "66/66 [==============================] - 26s 388ms/step - loss: 0.4580 - categorical_accuracy: 0.7251 - val_loss: 0.4117 - val_categorical_accuracy: 0.8210\n"
          ]
        }
      ],
      "source": [
        "from sklearn.utils import class_weight\n",
        "\n",
        "# Calculate the weights for each class so that we can balance the data\n",
        "# y = np.argmax(Y_train,axis=-1)\n",
        "# weights = class_weight.compute_class_weight(class_weight = \"balanced\",\n",
        "#                                        classes = np.unique(y),\n",
        "#                                        y = y)\n",
        "# weights = dict(zip(np.unique(Y_train), weights))\n",
        "weights = {0.0:0.5, 1.0:1.5}\n",
        "\n",
        "initial_epochs = 18\n",
        "history = model.fit(train_ds,\n",
        "                    epochs=initial_epochs,\n",
        "                    validation_data=val_ds,\n",
        "                    callbacks=[history_logger, checkpoint,DecreaseLR()],\n",
        "                    class_weight=weights\n",
        "                    )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ax27zjC_OwqO"
      },
      "source": [
        "## Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "OIWQOMuCapDF",
        "outputId": "e68979e1-bd9d-45ad-c6f3-e1294ab74ada"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "acc = history.history['categorical_accuracy']\n",
        "val_acc = history.history['val_categorical_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpVIp8x6O1jp"
      },
      "source": [
        "## Visualize Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "UIjF0CPtru9A"
      },
      "outputs": [],
      "source": [
        "# Load the best model trained in the previous phase\n",
        "# Notice that here I changed its name\n",
        "# So that you don't confuse it with your saved model\n",
        "# When reproducing the code\n",
        "best_model = tf.keras.models.load_model(\"./models/best_model.h5\")# Replace the path here with the path to your best model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "meNn53vb0Rt7",
        "outputId": "fe57fa21-709b-47cd-8064-248e8b73ba09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 202ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Let's load only 2 images \n",
        "# from the defective cells\n",
        "# Elsewhere we might run out of resources\n",
        "# When using GradCam\n",
        "image_btch = X1[Y == 1][1:3]\n",
        "labels = Y[Y== 1][1:3]\n",
        "\n",
        "# Preprocessing the images\n",
        "images = tf.image.resize(image_btch,size=(224,224))/255.\n",
        "\n",
        "# Running inference\n",
        "predictions = best_model.predict(images)\n",
        "predictions = tf.argmax(predictions,axis=-1)\n",
        "\n",
        "plt.figure(figsize=(20,8))\n",
        "\n",
        "for i in range(2):\n",
        "  plt.subplot(1,2,i+1)\n",
        "  plt.imshow(image_btch[i,...])\n",
        "  plt.title(f\"Defective cell? Pred : {predictions[i]}, Truth: {y_btch[i]}\")\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUyrGycsKcWB"
      },
      "source": [
        "# GRAD-CAM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emf7svH6fBKA",
        "outputId": "3e56f814-ac0f-4e3b-df33-92669721d051"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tf-keras-vis\n",
            "  Downloading tf_keras_vis-0.8.4-py3-none-any.whl (52 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.1/52.1 KB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: imageio in /usr/local/lib/python3.8/dist-packages (from tf-keras-vis) (2.9.0)\n",
            "Collecting deprecated\n",
            "  Downloading Deprecated-1.2.13-py2.py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.8/dist-packages (from tf-keras-vis) (7.1.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from tf-keras-vis) (1.7.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from tf-keras-vis) (21.3)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.8/dist-packages (from deprecated->tf-keras-vis) (1.14.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from imageio->tf-keras-vis) (1.21.6)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging->tf-keras-vis) (3.0.9)\n",
            "Installing collected packages: deprecated, tf-keras-vis\n",
            "Successfully installed deprecated-1.2.13 tf-keras-vis-0.8.4\n"
          ]
        }
      ],
      "source": [
        "!pip install tf-keras-vis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "-wLApsLeamwf"
      },
      "outputs": [],
      "source": [
        "from matplotlib import cm\n",
        "def plot_images(images=None, maps=None):\n",
        "    num_images = 0\n",
        "    if images is not None:\n",
        "        num_images = len(images)\n",
        "    else:\n",
        "        num_images = len(maps)\n",
        "    _, ax = plt.subplots(1, num_images, figsize=(8, 4))\n",
        "    for i in range(num_images):\n",
        "        alpha = 0.3\n",
        "        if images is not None:\n",
        "            alpha = 0.3\n",
        "            image = images[i]\n",
        "            ax[i].imshow((image+1)/2,alpha=0.7)\n",
        "        if maps is not None:\n",
        "            mp = maps[i]\n",
        "            heatmap = np.uint8(cm.jet(mp)[..., :3] * 255)\n",
        "            ax[i].imshow(heatmap, cmap='jet', alpha=0.3)\n",
        "        ax[i].axis('off')\n",
        "    plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "7aakmAoVPnNt"
      },
      "outputs": [],
      "source": [
        "#from tf_keras_vis.utils.scores import CategoricalScore\n",
        "#score_function = CategoricalScore(labels.astype('int8').tolist())\n",
        "# Instead of using CategoricalScore object,\n",
        "# you can also define the function from scratch as follows:\n",
        "def score_function(output):\n",
        "    # The `output` variable refers to the output of the model,\n",
        "    # so, in this case, `output` shape is  (samples, nb_classes).\n",
        "    return [output[i, labels[i].astype('int8')] for i in range(output.shape[0])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVQx50LGnVFE",
        "outputId": "caaacf5d-ad55-4eb0-87f1-03f9217aa025"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([None, 7, 7, 1408])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_model.layers[-6].output.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "wCW_CfSxsRRA"
      },
      "outputs": [],
      "source": [
        "from tensorflow.python.ops.numpy_ops import np_config\n",
        "np_config.enable_numpy_behavior()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saZa67SQYvoY",
        "outputId": "96d55577-c897-4081-aec3-5ba0f65e9632"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "88/88 [==============================] - 220s 2s/step\n"
          ]
        }
      ],
      "source": [
        "from tf_keras_vis.scorecam import Scorecam\n",
        "# Create ScoreCAM object\n",
        "scorecam = Scorecam(best_model)\n",
        "# Generate heatmap with Faster-ScoreCAM\n",
        "cam = scorecam(score_function, images[1:3], \n",
        "               penultimate_layer=-6,\n",
        "               seek_penultimate_conv_layer= False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "KQ_mVJE2Wxxn",
        "outputId": "df7e823a-d3ff-4c83-aeec-e4f6d5327c4d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Render\n",
        "plot_images(images=images[1:3,...], maps=cam)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.0 (tags/v3.10.0:b494f59, Oct  4 2021, 19:00:18) [MSC v.1929 64 bit (AMD64)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "fbc768028c3e6ead51d9a200ddcb2ec858ae62844dcd1994729a8279be9b48f2"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}