EfficientNetV2B0_PlantVillage.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OmidGhadami95/EfficientNet-PlantVillage/blob/main/EfficientNetV2B0_PlantVillage.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import utils,  optimizers, losses\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Dropout\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "import matplotlib.image as mpimg\n",
        "import seaborn as sns\n",
        "import itertools\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from keras import Sequential\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from keras.layers import Flatten,Dense,BatchNormalization,Activation,Dropout\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import osqp\n",
        "import tensorflow\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.applications import EfficientNetV2B0\n",
        "from keras import Input\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arDHHm_bEwQK",
        "outputId": "4ae504a8-d66e-40a8-9590-aaa55e858f4c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3miyP5fP6983",
        "outputId": "dca7b2fb-19e6-4796-e265-f9e4473a0519"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'PlantVillage-Dataset'...\n",
            "remote: Enumerating objects: 163229, done.\u001b[K\n",
            "remote: Total 163229 (delta 0), reused 0 (delta 0), pack-reused 163229\u001b[K\n",
            "Receiving objects: 100% (163229/163229), 2.00 GiB | 31.15 MiB/s, done.\n",
            "Resolving deltas: 100% (99/99), done.\n",
            "Updating files: 100% (182401/182401), done.\n",
            "/content/PlantVillage-Dataset\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/spMohanty/PlantVillage-Dataset\n",
        "%cd PlantVillage-Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "BCOZs0217LQF"
      },
      "outputs": [],
      "source": [
        "fpath = \"/content/PlantVillage-Dataset/raw/color\"\n",
        "random_seed = 111\n",
        "\n",
        "categories = os.listdir(fpath)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "s2W-6wfj7qkF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46d5488b-8ad8-47cb-8742-916aaf47985d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Tomato___Bacterial_spot',\n",
              " 'Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " 'Cherry_(including_sour)___healthy',\n",
              " 'Apple___Black_rot',\n",
              " 'Grape___Black_rot',\n",
              " 'Tomato___healthy',\n",
              " 'Pepper,_bell___Bacterial_spot',\n",
              " 'Potato___Early_blight',\n",
              " 'Soybean___healthy',\n",
              " 'Raspberry___healthy',\n",
              " 'Pepper,_bell___healthy',\n",
              " 'Corn_(maize)___Northern_Leaf_Blight',\n",
              " 'Strawberry___Leaf_scorch',\n",
              " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " 'Apple___Apple_scab',\n",
              " 'Peach___Bacterial_spot',\n",
              " 'Grape___healthy',\n",
              " 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
              " 'Potato___Late_blight',\n",
              " 'Tomato___Target_Spot',\n",
              " 'Tomato___Late_blight',\n",
              " 'Squash___Powdery_mildew',\n",
              " 'Tomato___Septoria_leaf_spot',\n",
              " 'Peach___healthy',\n",
              " 'Tomato___Tomato_mosaic_virus',\n",
              " 'Corn_(maize)___healthy',\n",
              " 'Cherry_(including_sour)___Powdery_mildew',\n",
              " 'Orange___Haunglongbing_(Citrus_greening)',\n",
              " 'Strawberry___healthy',\n",
              " 'Grape___Esca_(Black_Measles)',\n",
              " 'Apple___healthy',\n",
              " 'Blueberry___healthy',\n",
              " 'Tomato___Early_blight',\n",
              " 'Apple___Cedar_apple_rust',\n",
              " 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
              " 'Tomato___Leaf_Mold',\n",
              " 'Potato___healthy',\n",
              " 'Corn_(maize)___Common_rust_']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "os.listdir(fpath)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5fVP2D_Nirx",
        "outputId": "e0fddb6b-e809-4941-c5eb-be885257e671"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All the class labels:-  ['Cherry_(including_sour)___healthy', 'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', 'Squash___Powdery_mildew', 'Corn_(maize)___healthy', 'Apple___healthy', 'Corn_(maize)___Common_rust_'] \n",
            "\n",
            "No. of categories =  7\n"
          ]
        }
      ],
      "source": [
        "categories.remove(\"Tomato___Tomato_Yellow_Leaf_Curl_Virus\")\n",
        "categories.remove(\"Tomato___Target_Spot\")\n",
        "categories.remove(\"Tomato___Tomato_mosaic_virus\")\n",
        "categories.remove(\"Tomato___Bacterial_spot\")\n",
        "categories.remove(\"Apple___Cedar_apple_rust\")\n",
        "# categories.remove(\"Cherry_(including_sour)___healthy\")\n",
        "# categories.remove(\"Apple___healthy\")\n",
        "categories.remove(\"Pepper,_bell___healthy\")\n",
        "categories.remove(\"Tomato___Septoria_leaf_spot\")\n",
        "# categories.remove(\"Squash___Powdery_mildew\")\n",
        "categories.remove(\"Peach___healthy\")\n",
        "categories.remove(\"Tomato___Early_blight\")\n",
        "categories.remove(\"Cherry_(including_sour)___Powdery_mildew\")\n",
        "categories.remove(\"Tomato___Late_blight\")\n",
        "categories.remove(\"Grape___healthy\")\n",
        "categories.remove(\"Raspberry___healthy\")\n",
        "categories.remove(\"Pepper,_bell___Bacterial_spot\")\n",
        "categories.remove(\"Tomato___healthy\")\n",
        "categories.remove(\"Apple___Black_rot\")\n",
        "categories.remove(\"Tomato___Spider_mites Two-spotted_spider_mite\")\n",
        "categories.remove(\"Potato___healthy\")\n",
        "categories.remove(\"Peach___Bacterial_spot\")\n",
        "categories.remove(\"Strawberry___healthy\")\n",
        "categories.remove(\"Grape___Black_rot\")\n",
        "categories.remove(\"Blueberry___healthy\")\n",
        "categories.remove(\"Apple___Apple_scab\")\n",
        "categories.remove(\"Soybean___healthy\")\n",
        "categories.remove(\"Orange___Haunglongbing_(Citrus_greening)\")\n",
        "categories.remove(\"Grape___Esca_(Black_Measles)\")\n",
        "categories.remove(\"Strawberry___Leaf_scorch\")\n",
        "categories.remove(\"Grape___Leaf_blight_(Isariopsis_Leaf_Spot)\")\n",
        "categories.remove(\"Potato___Late_blight\")\n",
        "categories.remove(\"Potato___Early_blight\")\n",
        "categories.remove(\"Tomato___Leaf_Mold\")\n",
        "print(\"All the class labels:- \",categories,\"\\n\\nNo. of categories = \", len(categories))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxZ66kyfVjU3",
        "outputId": "9fb12f47-5714-4792-9344-e208463b636d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of images loaded =  6352 \n",
            "No. of labels loaded =  6352\n",
            "<class 'list'> <class 'list'>\n"
          ]
        }
      ],
      "source": [
        "def load_images_and_labels(categories):\n",
        "    img_lst=[]\n",
        "    labels=[]\n",
        "    for index, category in enumerate(categories):\n",
        "        for image_name in os.listdir(fpath+\"/\"+category)[:1000]:\n",
        "            file_ext = image_name.split(\".\")[-1]\n",
        "            if (file_ext.lower() == \"jpg\") or (file_ext.lower() == \"jpeg\"):\n",
        "                img = cv2.imread(fpath+\"/\"+category+\"/\"+image_name)\n",
        "                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "                img_array = Image.fromarray(img, 'RGB')\n",
        "                resized_img = img_array.resize((128, 128))\n",
        "                img_lst.append(np.array(resized_img))\n",
        "                labels.append(index)\n",
        "    return img_lst, labels\n",
        "\n",
        "images, labels = load_images_and_labels(categories)\n",
        "print(\"No. of images loaded = \",len(images),\"\\nNo. of labels loaded = \",len(labels))\n",
        "print(type(images),type(labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTsxNJTk6lgA",
        "outputId": "02ed86aa-dec4-47cb-8c62-1c9e4a3f35bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images shape =  (6352, 128, 128, 3) \n",
            "Labels shape =  (6352,)\n",
            "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "images = np.array(images)\n",
        "labels = np.array(labels)\n",
        "\n",
        "print(\"Images shape = \",images.shape,\"\\nLabels shape = \",labels.shape)\n",
        "print(type(images),type(labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpr0An3CDsCA",
        "outputId": "4a83b262-7eb5-47ca-b789-729936ce1af4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape =  (5399, 128, 128, 3)\n",
            "y_train shape =  (5399,)\n",
            "\n",
            "x_test shape =  (953, 128, 128, 3)\n",
            "y_test shape =  (953,)\n"
          ]
        }
      ],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size = 0.15)\n",
        "\n",
        "print(\"x_train shape = \",x_train.shape)\n",
        "print(\"y_train shape = \",y_train.shape)\n",
        "print(\"\\nx_test shape = \",x_test.shape)\n",
        "print(\"y_test shape = \",y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xp2FauRMDNqL",
        "outputId": "538027a9-a43a-411d-dc10-a16b147edde4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape =  (3779, 128, 128, 3)\n",
            "y_train shape =  (3779,)\n",
            "\n",
            "x_val shape =  (1620, 128, 128, 3)\n",
            "y_val shape =  (1620,)\n"
          ]
        }
      ],
      "source": [
        "x_train,x_val,y_train,y_val=train_test_split(x_train,y_train,test_size=0.3)\n",
        "\n",
        "print(\"x_train shape = \",x_train.shape)\n",
        "print(\"y_train shape = \",y_train.shape)\n",
        "print(\"\\nx_val shape = \",x_val.shape)\n",
        "print(\"y_val shape = \",y_val.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZ-wuaRFRblI",
        "outputId": "0d29622a-71b2-4388-8525-b3b335a50524"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "((3779, 128, 128, 3), (3779, 7))\n",
            "((1620, 128, 128, 3), (1620, 7))\n",
            "((953, 128, 128, 3), (953, 7))\n"
          ]
        }
      ],
      "source": [
        "y_train=to_categorical(y_train)\n",
        "y_val=to_categorical(y_val)\n",
        "y_test=to_categorical(y_test)\n",
        "\n",
        "print((x_train.shape,y_train.shape))\n",
        "print((x_val.shape,y_val.shape))\n",
        "print((x_test.shape,y_test.shape))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "1t5IdpELSH4b"
      },
      "outputs": [],
      "source": [
        "# Augmentation\n",
        "\n",
        "train_generatorENB0 = ImageDataGenerator(preprocessing_function=lambda x: (x / 127.5) - 1.0,\n",
        "                                         rotation_range=40,\n",
        "                                         width_shift_range=0.25,\n",
        "                                         height_shift_range=0.25,\n",
        "                                         shear_range=0.25,\n",
        "                                         zoom_range=0.25,\n",
        "                                         horizontal_flip=True,\n",
        "                                         fill_mode='nearest',)\n",
        "\n",
        "val_generatorENB0 = ImageDataGenerator(preprocessing_function=lambda x: (x / 127.5) - 1.0)\n",
        "test_generatorENB0 = ImageDataGenerator(preprocessing_function=lambda x: (x / 127.5) - 1.0)\n",
        "\n",
        "train_generatorENB0.fit(x_train)\n",
        "val_generatorENB0.fit(x_val)\n",
        "test_generatorENB0.fit(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "3vrHkOnDyfIG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0588097-9d88-427c-ffc9-fa2875f0510e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "last layer output shape:  (None, 1280)\n",
            "KerasTensor(type_spec=TensorSpec(shape=(None, 1280), dtype=tf.float32, name=None), name='top_dropout/Identity:0', description=\"created by layer 'top_dropout'\")\n",
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)        [(None, 128, 128, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)          (None, 64, 64, 32)           864       ['input_3[0][0]']             \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalizatio  (None, 64, 64, 32)           128       ['stem_conv[0][0]']           \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " stem_activation (Activatio  (None, 64, 64, 32)           0         ['stem_bn[0][0]']             \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv  (None, 64, 64, 16)           4608      ['stem_activation[0][0]']     \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchN  (None, 64, 64, 16)           64        ['block1a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block1a_project_activation  (None, 64, 64, 16)           0         ['block1a_project_bn[0][0]']  \n",
            "  (Activation)                                                                                    \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2  (None, 32, 32, 64)           9216      ['block1a_project_activation[0\n",
            " D)                                                                 ][0]']                        \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNo  (None, 32, 32, 64)           256       ['block2a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2a_expand_activation   (None, 32, 32, 64)           0         ['block2a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv  (None, 32, 32, 32)           2048      ['block2a_expand_activation[0]\n",
            " 2D)                                                                [0]']                         \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchN  (None, 32, 32, 32)           128       ['block2a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2  (None, 32, 32, 128)          36864     ['block2a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNo  (None, 32, 32, 128)          512       ['block2b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2b_expand_activation   (None, 32, 32, 128)          0         ['block2b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv  (None, 32, 32, 32)           4096      ['block2b_expand_activation[0]\n",
            " 2D)                                                                [0]']                         \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchN  (None, 32, 32, 32)           128       ['block2b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)      (None, 32, 32, 32)           0         ['block2b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2b_add (Add)           (None, 32, 32, 32)           0         ['block2b_drop[0][0]',        \n",
            "                                                                     'block2a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2  (None, 16, 16, 128)          36864     ['block2b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNo  (None, 16, 16, 128)          512       ['block3a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3a_expand_activation   (None, 16, 16, 128)          0         ['block3a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv  (None, 16, 16, 48)           6144      ['block3a_expand_activation[0]\n",
            " 2D)                                                                [0]']                         \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchN  (None, 16, 16, 48)           192       ['block3a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2  (None, 16, 16, 192)          82944     ['block3a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNo  (None, 16, 16, 192)          768       ['block3b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3b_expand_activation   (None, 16, 16, 192)          0         ['block3b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv  (None, 16, 16, 48)           9216      ['block3b_expand_activation[0]\n",
            " 2D)                                                                [0]']                         \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchN  (None, 16, 16, 48)           192       ['block3b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)      (None, 16, 16, 48)           0         ['block3b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3b_add (Add)           (None, 16, 16, 48)           0         ['block3b_drop[0][0]',        \n",
            "                                                                     'block3a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2  (None, 16, 16, 192)          9216      ['block3b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNo  (None, 16, 16, 192)          768       ['block4a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4a_expand_activation   (None, 16, 16, 192)          0         ['block4a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4a_dwconv2 (Depthwise  (None, 8, 8, 192)            1728      ['block4a_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormaliza  (None, 8, 8, 192)            768       ['block4a_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4a_activation (Activa  (None, 8, 8, 192)            0         ['block4a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (Global  (None, 192)                  0         ['block4a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshap  (None, 1, 1, 192)            0         ['block4a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)  (None, 1, 1, 12)             2316      ['block4a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)  (None, 1, 1, 192)            2496      ['block4a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multipl  (None, 8, 8, 192)            0         ['block4a_activation[0][0]',  \n",
            " y)                                                                  'block4a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv  (None, 8, 8, 96)             18432     ['block4a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchN  (None, 8, 8, 96)             384       ['block4a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2  (None, 8, 8, 384)            36864     ['block4a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNo  (None, 8, 8, 384)            1536      ['block4b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4b_expand_activation   (None, 8, 8, 384)            0         ['block4b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4b_dwconv2 (Depthwise  (None, 8, 8, 384)            3456      ['block4b_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormaliza  (None, 8, 8, 384)            1536      ['block4b_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4b_activation (Activa  (None, 8, 8, 384)            0         ['block4b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (Global  (None, 384)                  0         ['block4b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshap  (None, 1, 1, 384)            0         ['block4b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)  (None, 1, 1, 24)             9240      ['block4b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)  (None, 1, 1, 384)            9600      ['block4b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multipl  (None, 8, 8, 384)            0         ['block4b_activation[0][0]',  \n",
            " y)                                                                  'block4b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv  (None, 8, 8, 96)             36864     ['block4b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchN  (None, 8, 8, 96)             384       ['block4b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)      (None, 8, 8, 96)             0         ['block4b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4b_add (Add)           (None, 8, 8, 96)             0         ['block4b_drop[0][0]',        \n",
            "                                                                     'block4a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2  (None, 8, 8, 384)            36864     ['block4b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNo  (None, 8, 8, 384)            1536      ['block4c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4c_expand_activation   (None, 8, 8, 384)            0         ['block4c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4c_dwconv2 (Depthwise  (None, 8, 8, 384)            3456      ['block4c_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormaliza  (None, 8, 8, 384)            1536      ['block4c_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4c_activation (Activa  (None, 8, 8, 384)            0         ['block4c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (Global  (None, 384)                  0         ['block4c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshap  (None, 1, 1, 384)            0         ['block4c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)  (None, 1, 1, 24)             9240      ['block4c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)  (None, 1, 1, 384)            9600      ['block4c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multipl  (None, 8, 8, 384)            0         ['block4c_activation[0][0]',  \n",
            " y)                                                                  'block4c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv  (None, 8, 8, 96)             36864     ['block4c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchN  (None, 8, 8, 96)             384       ['block4c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)      (None, 8, 8, 96)             0         ['block4c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_add (Add)           (None, 8, 8, 96)             0         ['block4c_drop[0][0]',        \n",
            "                                                                     'block4b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2  (None, 8, 8, 576)            55296     ['block4c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNo  (None, 8, 8, 576)            2304      ['block5a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5a_expand_activation   (None, 8, 8, 576)            0         ['block5a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5a_dwconv2 (Depthwise  (None, 8, 8, 576)            5184      ['block5a_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormaliza  (None, 8, 8, 576)            2304      ['block5a_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5a_activation (Activa  (None, 8, 8, 576)            0         ['block5a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (Global  (None, 576)                  0         ['block5a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshap  (None, 1, 1, 576)            0         ['block5a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)  (None, 1, 1, 24)             13848     ['block5a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)  (None, 1, 1, 576)            14400     ['block5a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multipl  (None, 8, 8, 576)            0         ['block5a_activation[0][0]',  \n",
            " y)                                                                  'block5a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv  (None, 8, 8, 112)            64512     ['block5a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchN  (None, 8, 8, 112)            448       ['block5a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2  (None, 8, 8, 672)            75264     ['block5a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNo  (None, 8, 8, 672)            2688      ['block5b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5b_expand_activation   (None, 8, 8, 672)            0         ['block5b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5b_dwconv2 (Depthwise  (None, 8, 8, 672)            6048      ['block5b_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormaliza  (None, 8, 8, 672)            2688      ['block5b_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5b_activation (Activa  (None, 8, 8, 672)            0         ['block5b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (Global  (None, 672)                  0         ['block5b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block5b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block5b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block5b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multipl  (None, 8, 8, 672)            0         ['block5b_activation[0][0]',  \n",
            " y)                                                                  'block5b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv  (None, 8, 8, 112)            75264     ['block5b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchN  (None, 8, 8, 112)            448       ['block5b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)      (None, 8, 8, 112)            0         ['block5b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5b_add (Add)           (None, 8, 8, 112)            0         ['block5b_drop[0][0]',        \n",
            "                                                                     'block5a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2  (None, 8, 8, 672)            75264     ['block5b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNo  (None, 8, 8, 672)            2688      ['block5c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5c_expand_activation   (None, 8, 8, 672)            0         ['block5c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5c_dwconv2 (Depthwise  (None, 8, 8, 672)            6048      ['block5c_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormaliza  (None, 8, 8, 672)            2688      ['block5c_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5c_activation (Activa  (None, 8, 8, 672)            0         ['block5c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (Global  (None, 672)                  0         ['block5c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block5c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block5c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block5c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multipl  (None, 8, 8, 672)            0         ['block5c_activation[0][0]',  \n",
            " y)                                                                  'block5c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv  (None, 8, 8, 112)            75264     ['block5c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchN  (None, 8, 8, 112)            448       ['block5c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)      (None, 8, 8, 112)            0         ['block5c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_add (Add)           (None, 8, 8, 112)            0         ['block5c_drop[0][0]',        \n",
            "                                                                     'block5b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5d_expand_conv (Conv2  (None, 8, 8, 672)            75264     ['block5c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5d_expand_bn (BatchNo  (None, 8, 8, 672)            2688      ['block5d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5d_expand_activation   (None, 8, 8, 672)            0         ['block5d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5d_dwconv2 (Depthwise  (None, 8, 8, 672)            6048      ['block5d_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block5d_bn (BatchNormaliza  (None, 8, 8, 672)            2688      ['block5d_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5d_activation (Activa  (None, 8, 8, 672)            0         ['block5d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5d_se_squeeze (Global  (None, 672)                  0         ['block5d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5d_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block5d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5d_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block5d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5d_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block5d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5d_se_excite (Multipl  (None, 8, 8, 672)            0         ['block5d_activation[0][0]',  \n",
            " y)                                                                  'block5d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5d_project_conv (Conv  (None, 8, 8, 112)            75264     ['block5d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5d_project_bn (BatchN  (None, 8, 8, 112)            448       ['block5d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5d_drop (Dropout)      (None, 8, 8, 112)            0         ['block5d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5d_add (Add)           (None, 8, 8, 112)            0         ['block5d_drop[0][0]',        \n",
            "                                                                     'block5c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5e_expand_conv (Conv2  (None, 8, 8, 672)            75264     ['block5d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5e_expand_bn (BatchNo  (None, 8, 8, 672)            2688      ['block5e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5e_expand_activation   (None, 8, 8, 672)            0         ['block5e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5e_dwconv2 (Depthwise  (None, 8, 8, 672)            6048      ['block5e_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block5e_bn (BatchNormaliza  (None, 8, 8, 672)            2688      ['block5e_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5e_activation (Activa  (None, 8, 8, 672)            0         ['block5e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5e_se_squeeze (Global  (None, 672)                  0         ['block5e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5e_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block5e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5e_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block5e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5e_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block5e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5e_se_excite (Multipl  (None, 8, 8, 672)            0         ['block5e_activation[0][0]',  \n",
            " y)                                                                  'block5e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5e_project_conv (Conv  (None, 8, 8, 112)            75264     ['block5e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5e_project_bn (BatchN  (None, 8, 8, 112)            448       ['block5e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5e_drop (Dropout)      (None, 8, 8, 112)            0         ['block5e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5e_add (Add)           (None, 8, 8, 112)            0         ['block5e_drop[0][0]',        \n",
            "                                                                     'block5d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2  (None, 8, 8, 672)            75264     ['block5e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNo  (None, 8, 8, 672)            2688      ['block6a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6a_expand_activation   (None, 8, 8, 672)            0         ['block6a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6a_dwconv2 (Depthwise  (None, 4, 4, 672)            6048      ['block6a_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormaliza  (None, 4, 4, 672)            2688      ['block6a_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6a_activation (Activa  (None, 4, 4, 672)            0         ['block6a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (Global  (None, 672)                  0         ['block6a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block6a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block6a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block6a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multipl  (None, 4, 4, 672)            0         ['block6a_activation[0][0]',  \n",
            " y)                                                                  'block6a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv  (None, 4, 4, 192)            129024    ['block6a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6b_expand_activation   (None, 4, 4, 1152)           0         ['block6b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6b_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6b_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6b_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6b_activation (Activa  (None, 4, 4, 1152)           0         ['block6b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (Global  (None, 1152)                 0         ['block6b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6b_activation[0][0]',  \n",
            " y)                                                                  'block6b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)      (None, 4, 4, 192)            0         ['block6b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6b_add (Add)           (None, 4, 4, 192)            0         ['block6b_drop[0][0]',        \n",
            "                                                                     'block6a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6c_expand_activation   (None, 4, 4, 1152)           0         ['block6c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6c_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6c_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6c_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6c_activation (Activa  (None, 4, 4, 1152)           0         ['block6c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (Global  (None, 1152)                 0         ['block6c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6c_activation[0][0]',  \n",
            " y)                                                                  'block6c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)      (None, 4, 4, 192)            0         ['block6c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_add (Add)           (None, 4, 4, 192)            0         ['block6c_drop[0][0]',        \n",
            "                                                                     'block6b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6d_expand_activation   (None, 4, 4, 1152)           0         ['block6d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6d_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6d_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6d_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6d_activation (Activa  (None, 4, 4, 1152)           0         ['block6d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (Global  (None, 1152)                 0         ['block6d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6d_activation[0][0]',  \n",
            " y)                                                                  'block6d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)      (None, 4, 4, 192)            0         ['block6d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6d_add (Add)           (None, 4, 4, 192)            0         ['block6d_drop[0][0]',        \n",
            "                                                                     'block6c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6e_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6e_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6e_expand_activation   (None, 4, 4, 1152)           0         ['block6e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6e_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6e_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6e_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6e_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6e_activation (Activa  (None, 4, 4, 1152)           0         ['block6e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6e_se_squeeze (Global  (None, 1152)                 0         ['block6e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6e_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6e_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6e_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6e_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6e_activation[0][0]',  \n",
            " y)                                                                  'block6e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6e_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6e_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6e_drop (Dropout)      (None, 4, 4, 192)            0         ['block6e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6e_add (Add)           (None, 4, 4, 192)            0         ['block6e_drop[0][0]',        \n",
            "                                                                     'block6d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6f_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6f_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6f_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6f_expand_activation   (None, 4, 4, 1152)           0         ['block6f_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6f_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6f_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6f_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6f_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6f_activation (Activa  (None, 4, 4, 1152)           0         ['block6f_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6f_se_squeeze (Global  (None, 1152)                 0         ['block6f_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6f_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6f_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6f_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6f_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6f_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6f_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6f_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6f_activation[0][0]',  \n",
            " y)                                                                  'block6f_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6f_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6f_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6f_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6f_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6f_drop (Dropout)      (None, 4, 4, 192)            0         ['block6f_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6f_add (Add)           (None, 4, 4, 192)            0         ['block6f_drop[0][0]',        \n",
            "                                                                     'block6e_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6g_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6f_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6g_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6g_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6g_expand_activation   (None, 4, 4, 1152)           0         ['block6g_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6g_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6g_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6g_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6g_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6g_activation (Activa  (None, 4, 4, 1152)           0         ['block6g_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6g_se_squeeze (Global  (None, 1152)                 0         ['block6g_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6g_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6g_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6g_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6g_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6g_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6g_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6g_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6g_activation[0][0]',  \n",
            " y)                                                                  'block6g_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6g_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6g_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6g_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6g_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6g_drop (Dropout)      (None, 4, 4, 192)            0         ['block6g_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6g_add (Add)           (None, 4, 4, 192)            0         ['block6g_drop[0][0]',        \n",
            "                                                                     'block6f_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6h_expand_conv (Conv2  (None, 4, 4, 1152)           221184    ['block6g_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6h_expand_bn (BatchNo  (None, 4, 4, 1152)           4608      ['block6h_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6h_expand_activation   (None, 4, 4, 1152)           0         ['block6h_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6h_dwconv2 (Depthwise  (None, 4, 4, 1152)           10368     ['block6h_expand_activation[0]\n",
            " Conv2D)                                                            [0]']                         \n",
            "                                                                                                  \n",
            " block6h_bn (BatchNormaliza  (None, 4, 4, 1152)           4608      ['block6h_dwconv2[0][0]']     \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6h_activation (Activa  (None, 4, 4, 1152)           0         ['block6h_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6h_se_squeeze (Global  (None, 1152)                 0         ['block6h_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6h_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6h_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6h_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6h_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6h_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6h_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6h_se_excite (Multipl  (None, 4, 4, 1152)           0         ['block6h_activation[0][0]',  \n",
            " y)                                                                  'block6h_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6h_project_conv (Conv  (None, 4, 4, 192)            221184    ['block6h_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6h_project_bn (BatchN  (None, 4, 4, 192)            768       ['block6h_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6h_drop (Dropout)      (None, 4, 4, 192)            0         ['block6h_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6h_add (Add)           (None, 4, 4, 192)            0         ['block6h_drop[0][0]',        \n",
            "                                                                     'block6g_add[0][0]']         \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)           (None, 4, 4, 1280)           245760    ['block6h_add[0][0]']         \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization  (None, 4, 4, 1280)           5120      ['top_conv[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " top_activation (Activation  (None, 4, 4, 1280)           0         ['top_bn[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePoo  (None, 1280)                 0         ['top_activation[0][0]']      \n",
            " ling2D)                                                                                          \n",
            "                                                                                                  \n",
            " top_dropout (Dropout)       (None, 1280)                 0         ['avg_pool[0][0]']            \n",
            "                                                                                                  \n",
            " dense_2 (Dense)             (None, 7)                    8967      ['top_dropout[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 5928279 (22.61 MB)\n",
            "Trainable params: 5867671 (22.38 MB)\n",
            "Non-trainable params: 60608 (236.75 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "pre_trained_model = EfficientNetV2B0(include_top = True,\n",
        "                         include_preprocessing=False,\n",
        "                         classifier_activation='softmax',\n",
        "                         pooling=None,\n",
        "                         weights = 'imagenet',\n",
        "                         input_tensor=Input(shape=(128, 128, 3)))\n",
        "\n",
        "last_layer = pre_trained_model.get_layer('top_dropout')\n",
        "print('last layer output shape: ', last_layer.output_shape)\n",
        "last_output = last_layer.output\n",
        "print(last_output)\n",
        "x = layers.Dense(7, activation='softmax')(last_output)\n",
        "model = Model(pre_trained_model.input, x)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "rTOyQJmYCAqj"
      },
      "outputs": [],
      "source": [
        "#Defining the parameters\n",
        "batch_size= 16\n",
        "epochs=10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "lQeukGZzCKsm"
      },
      "outputs": [],
      "source": [
        "#Compiling the model\n",
        "model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8_leaYBCQZN",
        "outputId": "e7b17382-5963-44e7-c4a8-13b026999111"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-d4f2e33d4158>:2: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  model.fit_generator(train_generatorENB0.flow(x_train, y_train, batch_size = batch_size), epochs = 10, steps_per_epoch = 236, validation_data = val_generatorENB0.flow(x_val, y_val, batch_size = batch_size), validation_steps = 101, verbose = 1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "236/236 [==============================] - 74s 122ms/step - loss: 0.3127 - accuracy: 0.8926 - val_loss: 0.1061 - val_accuracy: 0.9666\n",
            "Epoch 2/10\n",
            "236/236 [==============================] - 27s 113ms/step - loss: 0.1470 - accuracy: 0.9487 - val_loss: 0.0889 - val_accuracy: 0.9759\n",
            "Epoch 3/10\n",
            "236/236 [==============================] - 26s 111ms/step - loss: 0.1131 - accuracy: 0.9591 - val_loss: 0.0739 - val_accuracy: 0.9821\n",
            "Epoch 4/10\n",
            "236/236 [==============================] - 27s 114ms/step - loss: 0.1326 - accuracy: 0.9543 - val_loss: 0.1005 - val_accuracy: 0.9629\n",
            "Epoch 5/10\n",
            "236/236 [==============================] - 24s 103ms/step - loss: 0.1233 - accuracy: 0.9554 - val_loss: 0.0959 - val_accuracy: 0.9691\n",
            "Epoch 6/10\n",
            "236/236 [==============================] - 25s 107ms/step - loss: 0.0913 - accuracy: 0.9668 - val_loss: 0.0600 - val_accuracy: 0.9821\n",
            "Epoch 7/10\n",
            "236/236 [==============================] - 26s 111ms/step - loss: 0.0945 - accuracy: 0.9644 - val_loss: 0.0762 - val_accuracy: 0.9827\n",
            "Epoch 8/10\n",
            "236/236 [==============================] - 27s 112ms/step - loss: 0.1040 - accuracy: 0.9663 - val_loss: 0.0719 - val_accuracy: 0.9802\n",
            "Epoch 9/10\n",
            "236/236 [==============================] - 26s 110ms/step - loss: 0.0836 - accuracy: 0.9713 - val_loss: 0.0850 - val_accuracy: 0.9728\n",
            "Epoch 10/10\n",
            "236/236 [==============================] - 25s 107ms/step - loss: 0.0860 - accuracy: 0.9742 - val_loss: 0.0955 - val_accuracy: 0.9740\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7fe59a688a60>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "#Training the model\n",
        "model.fit_generator(train_generatorENB0.flow(x_train, y_train, batch_size = batch_size), epochs = 10, steps_per_epoch = 236, validation_data = val_generatorENB0.flow(x_val, y_val, batch_size = batch_size), validation_steps = 101, verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/drive/MyDrive/ENV2B0-PlantVillage.h5')"
      ],
      "metadata": {
        "id": "5HaB1Quog3Z9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn"
      ],
      "metadata": {
        "id": "wxuqZrMa08Ul"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!install scipy"
      ],
      "metadata": {
        "id": "bybAQWBIBH97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade scikit-learn\n",
        "!pip install --upgrade numpy\n"
      ],
      "metadata": {
        "id": "_f7AzgkIA6EM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall sklearn\n",
        "!pip uninstall scikit-learn\n",
        "!pip install sklearn"
      ],
      "metadata": {
        "id": "3oivHG06BQiV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tensorflow.keras.models.load_model('/drive/MyDrive/ENV2B0-PlantVillage.h5')"
      ],
      "metadata": {
        "id": "2rTpFqNFgykl"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test, verbose=1)\n",
        "predicted_labels = np.argmax(predictions, axis=1)\n",
        "true_labels = np.argmax(y_test, axis=1)\n",
        "cm = confusion_matrix(true_labels, predicted_labels)\n",
        "class_counts = np.unique(true_labels, return_counts=True)[1]\n",
        "class_percents = np.round(100 * class_counts / sum(class_counts), decimals=1)\n",
        "class_names = sorted(np.unique(true_labels))\n",
        "plt.figure(figsize=(6, 6))\n",
        "sns.set(font_scale=1.4)\n",
        "sns.heatmap(np.round(100 * cm / cm.sum(axis=1)[:, np.newaxis], decimals=1), annot=True, fmt='',\n",
        "            cmap=\"Blues\", cbar=False, square=True,\n",
        "            xticklabels=class_names,\n",
        "            yticklabels=class_names)\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "D1lPuCP67o6F",
        "outputId": "5cff1b81-7a7f-42b4-83f5-8416d1509992"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30/30 [==============================] - 2s 18ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes, normalize=False, cmap=plt.cm.Blues):\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in np.ndindex(cm.shape):\n",
        "        plt.text(j, i, format(cm[i, j], '.0f'),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n",
        "cm = confusion_matrix(true_labels, predicted_labels)\n",
        "class_names = sorted(np.unique(true_labels))\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plot_confusion_matrix(cm, classes=class_names, normalize=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "id": "RBXaxxoS-xit",
        "outputId": "af800ca1-5b27-4351-8f66-6f174501a03f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the maximum number of images to evaluate\n",
        "max_images = 3589\n",
        "\n",
        "# Calculate precision, recall, and f1-score\n",
        "precision = np.diag(cm) / np.sum(cm, axis=0)\n",
        "recall = np.diag(cm) / np.sum(cm, axis=1)\n",
        "f1 = 2 * (precision * recall) / (precision + recall)\n",
        "\n",
        "# Calculate macro/micro-averaged precision, recall, and f1-score\n",
        "macro_precision = np.mean(precision[:max_images])\n",
        "macro_recall = np.mean(recall[:max_images])\n",
        "macro_f1_score = np.mean(f1[:max_images])\n",
        "\n",
        "micro_precision = np.sum(np.diag(cm)[:max_images]) / np.sum(cm[:max_images])\n",
        "micro_recall = np.sum(np.diag(cm)[:max_images]) / np.sum(cm[:max_images], axis=None)\n",
        "micro_f1_score = 2 * (micro_precision * micro_recall) / (micro_precision + micro_recall)\n",
        "\n",
        "\n",
        "# Calculate weighted precision, recall, and f1-score\n",
        "weighted_precision = precision_score(true_labels[:max_images], predicted_labels[:max_images], average='weighted')\n",
        "weighted_recall = recall_score(true_labels[:max_images], predicted_labels[:max_images], average='weighted')\n",
        "weighted_f1_score = f1_score(true_labels[:max_images], predicted_labels[:max_images], average='weighted')\n",
        "\n",
        "# Create a dictionary of evaluation metrics\n",
        "metrics = {'Precision': [macro_precision, micro_precision, weighted_precision],\n",
        "           'Recall': [macro_recall, micro_recall, weighted_recall],\n",
        "           'F1-Score': [macro_f1_score, micro_f1_score, weighted_f1_score]}\n",
        "\n",
        "# Create a Pandas DataFrame from the dictionary\n",
        "df = pd.DataFrame(metrics, index=['Macro-Averaged', 'Micro-Averaged', 'Weighted'])\n",
        "\n",
        "# Print the table\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaq4Fphmm9hX",
        "outputId": "93063519-9b07-4304-d4a0-992157b6cccc"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                Precision    Recall  F1-Score\n",
            "Macro-Averaged   0.979555  0.976927  0.978025\n",
            "Micro-Averaged   0.981112  0.981112  0.981112\n",
            "Weighted         0.981253  0.981112  0.981002\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}