covid-multiclass-ct-scan-efficientnetb2.ipynb

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    "# **Classification of COVID Image Data Multiclass CT Scan**\n",
    "\n",
    "## Hands On Data Science Trainee - Jakarta Smart City\n",
    "\n",
    "**Description:**\n",
    "* Development of COVID Multiclass CT scan image classification using a Machine Learning model, especially the Deep Learning approach with the Transfer Learning architecture of the EfficientNetB2 Convolutional Neural Network.\n",
    "* The dataset contains 4173 CT scan image data of the lungs (Chest CT Scan) with 3 different classes including Covid, Healthy, Others.\n",
    "\n",
    "**Dataset details:**\n",
    "\n",
    "The dataset comes from https://www.kaggle.com/datasets/plameneduardo/a-covid-multiclass-dataset-of-ct-scans which contains 4173 COVID CT scan images in png format\n",
    "* 758 CT scans for healthy patients (Healthy)\n",
    "* 2168 CT scans for patients infected with SASR-CoV-2 or COVID-19 (Covid)\n",
    "* 1247 CT scans for patients with other lung disorders (Others)\n",
    "* TOTAL: 4173 CT scans for 210 Sao Paulo - Brazil patients (average 20 CT scans per patient).\n",
    "\n"
   ]
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   "source": [
    "# **Preparation**\n",
    "### (Check Python Version, GPU, Install, Import Library, Check Dataset)"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Check python and GPU versions"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "Python 3.7.10\r\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
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     "text": [
      "Sun Jun 18 11:19:12 2023       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 470.161.03   Driver Version: 470.161.03   CUDA Version: 11.4     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\r\n",
      "| N/A   39C    P8     8W /  70W |      0MiB / 15109MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   1  Tesla T4            Off  | 00000000:00:05.0 Off |                    0 |\r\n",
      "| N/A   40C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|  No running processes found                                                 |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi #show the allocated GPU"
   ]
  },
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     "start_time": "2023-06-18T11:19:12.756114",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "### Install the EfficientNet Pre-trained Model"
   ]
  },
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     "text": [
      "Collecting efficientnet\r\n",
      "  Downloading efficientnet-1.1.1-py3-none-any.whl (18 kB)\r\n",
      "Collecting keras-applications<=1.0.8,>=1.0.7\r\n",
      "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\r\n",
      "\u001b[K     |████████████████████████████████| 50 kB 2.8 MB/s \r\n",
      "\u001b[?25hRequirement already satisfied: scikit-image in /opt/conda/lib/python3.7/site-packages (from efficientnet) (0.18.3)\r\n",
      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.19.5)\r\n",
      "Requirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.10.0)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.15.0)\r\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (1.1.1)\r\n",
      "Requirement already satisfied: networkx>=2.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (2.5)\r\n",
      "Requirement already satisfied: imageio>=2.3.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (2.9.0)\r\n",
      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (3.4.3)\r\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (8.2.0)\r\n",
      "Requirement already satisfied: scipy>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (1.7.1)\r\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.7/site-packages (from scikit-image->efficientnet) (2021.8.30)\r\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.4.7)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.8.0)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.3.1)\r\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.7/site-packages (from networkx>=2.0->scikit-image->efficientnet) (5.0.9)\r\n",
      "Installing collected packages: keras-applications, efficientnet\r\n",
      "Successfully installed efficientnet-1.1.1 keras-applications-1.0.8\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install efficientnet"
   ]
  },
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   "cell_type": "markdown",
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     "start_time": "2023-06-18T11:19:23.110645",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Import Library \n",
    "Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1b0335f",
   "metadata": {
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "from sklearn.metrics import roc_curve, auc, classification_report, confusion_matrix, accuracy_score, recall_score, f1_score, precision_score\n",
    "from sklearn.model_selection import KFold\n",
    "import random\n",
    "import sys\n",
    "import pandas\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns \n",
    "import os\n",
    "import gc\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import models\n",
    "from tensorflow.keras.models import Model,load_model, Sequential\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout, Input, GlobalAveragePooling2D, BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from efficientnet.tfkeras import EfficientNetB2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706cda50",
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     "start_time": "2023-06-18T11:19:29.544564",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Check Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a714a692",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Healthy', 'Covid', 'Others']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check dataset\n",
    "base_dir = '/kaggle/input/covid-19-ct/New_Data_CoV2'\n",
    "ct_scans = os.listdir(base_dir)\n",
    "ct_scans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa7362af",
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    {
     "name": "stdout",
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     "text": [
      "Healthy: 758 data\n",
      "Covid: 2168 data\n",
      "Others: 1247 data\n",
      "__________________________________\n",
      "\n",
      "Total: 4173 data\n"
     ]
    }
   ],
   "source": [
    "# Total dataset\n",
    "total = 0\n",
    "\n",
    "for i in ct_scans:\n",
    "    dir = os.path.join(base_dir, i)\n",
    "    num = len(os.listdir(dir))\n",
    "    print(i+':', num, 'data')\n",
    "    total = total + num\n",
    "\n",
    "print('__________________________________')\n",
    "print('\\nTotal:', total, 'data')"
   ]
  },
  {
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   },
   "source": [
    "# **Pre-Processing**\n",
    "\n",
    "### Dataset Splitting\n",
    "\n",
    "The dataset contains 4173 CT scan image data of the lungs with 3 classes:\n",
    "* Covid (2168 data)\n",
    "* Healthy (758 data)\n",
    "* Others (1247 data)\n",
    "\n",
    "The data is divided by a ratio of 0.80 for training data, 0.1 for validation data, and 0.1 for testing data. So that:\n",
    "* Training (3335 data)\n",
    "* Validation (415 data)\n",
    "* Testing (215 data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86f8b429",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting split-folders\r\n",
      "  Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB)\r\n",
      "Installing collected packages: split-folders\r\n",
      "Successfully installed split-folders-0.5.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install split-folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62bac559",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Copying files: 4173 files [00:29, 139.16 files/s]\n"
     ]
    }
   ],
   "source": [
    "# Split folder to divide the dataset into training, validation, and testing data\n",
    "import splitfolders\n",
    "splitfolders.ratio(base_dir, output=\"./train-test\", seed=42, ratio=(.80, 0.1, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ecaebd5",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "# dataset directory\n",
    "train_dir = '/kaggle/working/train-test/train'\n",
    "val_dir = '/kaggle/working/train-test/val'\n",
    "test_dir = '/kaggle/working/train-test/test'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98d8651",
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     "start_time": "2023-06-18T11:20:09.241645",
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    },
    "tags": []
   },
   "source": [
    "# **Data Augmentation (Pre-processing)**\n",
    "\n",
    "* Data augmentation is a method used to avoid overfitting problems and to increase the number of available datasets\n",
    "* Initialize the required hyperparameters such as learning rate, batch, epoch, and others.\n",
    "* Pre-processing: transformation of raw data before it is entered or used into the model (rescale, etc.).\n",
    "* Cropping or resizing, by changing the data size to 224 x 224 x 3 pixels so that all data has the same size.\n",
    "* Data augmentation to overcome overfitting and improve the accuracy of the models used (rotation, shift, shear, zoom, flip, fill mode)\n",
    "* Training data is then randomized to generalize the model and reduce overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "574f09cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T11:20:09.600392Z",
     "iopub.status.busy": "2023-06-18T11:20:09.599665Z",
     "iopub.status.idle": "2023-06-18T11:20:09.934445Z",
     "shell.execute_reply": "2023-06-18T11:20:09.933953Z",
     "shell.execute_reply.started": "2023-05-31T14:33:15.067494Z"
    },
    "papermill": {
     "duration": 0.446263,
     "end_time": "2023-06-18T11:20:09.934620",
     "exception": false,
     "start_time": "2023-06-18T11:20:09.488357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3336 images belonging to 3 classes.\n",
      "Found 415 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter Initiation\n",
    "input_tensor  = Input(shape=(224,224,3))\n",
    "learning_rate = 0.0001\n",
    "epochs        = 60\n",
    "image_size    = (224, 224)\n",
    "batch_size    = 16\n",
    "\n",
    "# Data augmentation on training data\n",
    "train_datagen = ImageDataGenerator(\n",
    "                    rescale=1./255,\n",
    "                    rotation_range=15, \n",
    "                    width_shift_range=0.1,\n",
    "                    height_shift_range=0.1,\n",
    "                    shear_range=0.2,\n",
    "                    zoom_range=0.2,\n",
    "                    horizontal_flip = True,\n",
    "                    fill_mode='nearest')\n",
    "\n",
    "# Rescale on validation data\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "#flow_from_directory() method takes a path of a directory and generates \n",
    "# batches of augmented data.\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_dir,\n",
    "                    target_size=image_size,  \n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    class_mode='categorical',\n",
    "                    #subset='training'\n",
    ")\n",
    "\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "                    val_dir,\n",
    "                    target_size=image_size,  \n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=False,\n",
    "                    class_mode='categorical', \n",
    "                    #subset='validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1a8960",
   "metadata": {
    "papermill": {
     "duration": 0.09976,
     "end_time": "2023-06-18T11:20:10.132977",
     "exception": false,
     "start_time": "2023-06-18T11:20:10.033217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Explore some training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0c97ab1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T11:20:10.345668Z",
     "iopub.status.busy": "2023-06-18T11:20:10.344988Z",
     "iopub.status.idle": "2023-06-18T11:20:12.157883Z",
     "shell.execute_reply": "2023-06-18T11:20:12.158423Z",
     "shell.execute_reply.started": "2023-05-31T14:33:15.408591Z"
    },
    "papermill": {
     "duration": 1.924766,
     "end_time": "2023-06-18T11:20:12.158599",
     "exception": false,
     "start_time": "2023-06-18T11:20:10.233833",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [k for k in train_generator.class_indices]\n",
    "sample_generate = train_generator.__next__()\n",
    "images = sample_generate[0]\n",
    "titles = sample_generate[1]\n",
    "plt.figure(figsize = (20 , 20))\n",
    "\n",
    "for i in range(15):\n",
    "    plt.subplot(5 , 5, i+1)\n",
    "    plt.subplots_adjust(hspace = 0.3 , wspace = 0.3)\n",
    "    plt.imshow(images[i])\n",
    "    plt.title(f'Class: {labels[np.argmax(titles[i],axis=0)]}')\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8858e84d",
   "metadata": {
    "papermill": {
     "duration": 0.11845,
     "end_time": "2023-06-18T11:20:12.395684",
     "exception": false,
     "start_time": "2023-06-18T11:20:12.277234",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Building Models with CNN (Convolutional Neural Network)**\n",
    "\n",
    "* Model designed using CNN transfer learning technique (Xception architecture). Xception architecture is used as the base model which is then added with GlobalAveragePooling2D layer, Dense layer with ReLU activation function, dropout layer, and BatchNormalization at the end.\n",
    "* Dropout and BatchNormalization are used to prevent the model from overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a539917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T11:20:12.652081Z",
     "iopub.status.busy": "2023-06-18T11:20:12.651314Z",
     "iopub.status.idle": "2023-06-18T11:20:23.169126Z",
     "shell.execute_reply": "2023-06-18T11:20:23.168115Z",
     "shell.execute_reply.started": "2023-05-31T14:33:17.110228Z"
    },
    "papermill": {
     "duration": 10.654309,
     "end_time": "2023-06-18T11:20:23.169277",
     "exception": false,
     "start_time": "2023-06-18T11:20:12.514968",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/qubvel/efficientnet/releases/download/v0.0.1/efficientnet-b2_noisy-student_notop.h5\n",
      "31784960/31782304 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "def getModel():\n",
    "        base_model = EfficientNetB2(weights='noisy-student',include_top=False, input_tensor=input_tensor)\n",
    "        base_model = base_model.output\n",
    "        model = GlobalAveragePooling2D()(base_model)\n",
    "        model = Dense(256, activation='relu')(model)\n",
    "        model = Dropout(0.2)(model)\n",
    "        model = BatchNormalization()(model)\n",
    "        model = Dense(3, activation='softmax')(model)\n",
    "        model = Model(inputs=input_tensor, outputs=model)\n",
    " \n",
    "        for layer in model.layers:\n",
    "            layer.trainable = True\n",
    "      \n",
    "            model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=optimizers.Adam(learning_rate=learning_rate),\n",
    "              metrics=['accuracy'])\n",
    "  \n",
    "        return model\n",
    "\n",
    "model=getModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d4cad41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T11:20:23.425075Z",
     "iopub.status.busy": "2023-06-18T11:20:23.422742Z",
     "iopub.status.idle": "2023-06-18T11:20:23.594827Z",
     "shell.execute_reply": "2023-06-18T11:20:23.595620Z",
     "shell.execute_reply.started": "2023-05-31T14:33:28.904088Z"
    },
    "papermill": {
     "duration": 0.299614,
     "end_time": "2023-06-18T11:20:23.595854",
     "exception": false,
     "start_time": "2023-06-18T11:20:23.296240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv (Conv2D)              (None, 112, 112, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "stem_bn (BatchNormalization)    (None, 112, 112, 32) 128         stem_conv[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_activation (Activation)    (None, 112, 112, 32) 0           stem_bn[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1a_dwconv (DepthwiseConv2D (None, 112, 112, 32) 288         stem_activation[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1a_bn (BatchNormalization) (None, 112, 112, 32) 128         block1a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1a_activation (Activation) (None, 112, 112, 32) 0           block1a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_excite (Multiply)    (None, 112, 112, 32) 0           block1a_activation[0][0]         \n",
      "                                                                 block1a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_conv (Conv2D)   (None, 112, 112, 16) 512         block1a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_bn (BatchNormal (None, 112, 112, 16) 64          block1a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1b_dwconv (DepthwiseConv2D (None, 112, 112, 16) 144         block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_bn (BatchNormalization) (None, 112, 112, 16) 64          block1b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1b_activation (Activation) (None, 112, 112, 16) 0           block1b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_squeeze (GlobalAvera (None, 16)           0           block1b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_reshape (Reshape)    (None, 1, 1, 16)     0           block1b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_reduce (Conv2D)      (None, 1, 1, 4)      68          block1b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_expand (Conv2D)      (None, 1, 1, 16)     80          block1b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_excite (Multiply)    (None, 112, 112, 16) 0           block1b_activation[0][0]         \n",
      "                                                                 block1b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_project_conv (Conv2D)   (None, 112, 112, 16) 256         block1b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_project_bn (BatchNormal (None, 112, 112, 16) 64          block1b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1b_drop (FixedDropout)     (None, 112, 112, 16) 0           block1b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_add (Add)               (None, 112, 112, 16) 0           block1b_drop[0][0]               \n",
      "                                                                 block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_conv (Conv2D)    (None, 112, 112, 96) 1536        block1b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_bn (BatchNormali (None, 112, 112, 96) 384         block2a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_activation (Acti (None, 112, 112, 96) 0           block2a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv (DepthwiseConv2D (None, 56, 56, 96)   864         block2a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2a_bn (BatchNormalization) (None, 56, 56, 96)   384         block2a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2a_activation (Activation) (None, 56, 56, 96)   0           block2a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_excite (Multiply)    (None, 56, 56, 96)   0           block2a_activation[0][0]         \n",
      "                                                                 block2a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_conv (Conv2D)   (None, 56, 56, 24)   2304        block2a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_bn (BatchNormal (None, 56, 56, 24)   96          block2a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_bn (BatchNormali (None, 56, 56, 144)  576         block2b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_activation (Acti (None, 56, 56, 144)  0           block2b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_dwconv (DepthwiseConv2D (None, 56, 56, 144)  1296        block2b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2b_bn (BatchNormalization) (None, 56, 56, 144)  576         block2b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2b_activation (Activation) (None, 56, 56, 144)  0           block2b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_excite (Multiply)    (None, 56, 56, 144)  0           block2b_activation[0][0]         \n",
      "                                                                 block2b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_conv (Conv2D)   (None, 56, 56, 24)   3456        block2b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_bn (BatchNormal (None, 56, 56, 24)   96          block2b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_drop (FixedDropout)     (None, 56, 56, 24)   0           block2b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_add (Add)               (None, 56, 56, 24)   0           block2b_drop[0][0]               \n",
      "                                                                 block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_bn (BatchNormali (None, 56, 56, 144)  576         block2c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_activation (Acti (None, 56, 56, 144)  0           block2c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_dwconv (DepthwiseConv2D (None, 56, 56, 144)  1296        block2c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2c_bn (BatchNormalization) (None, 56, 56, 144)  576         block2c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2c_activation (Activation) (None, 56, 56, 144)  0           block2c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_squeeze (GlobalAvera (None, 144)          0           block2c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_excite (Multiply)    (None, 56, 56, 144)  0           block2c_activation[0][0]         \n",
      "                                                                 block2c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_project_conv (Conv2D)   (None, 56, 56, 24)   3456        block2c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_project_bn (BatchNormal (None, 56, 56, 24)   96          block2c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2c_drop (FixedDropout)     (None, 56, 56, 24)   0           block2c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_add (Add)               (None, 56, 56, 24)   0           block2c_drop[0][0]               \n",
      "                                                                 block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_bn (BatchNormali (None, 56, 56, 144)  576         block3a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_activation (Acti (None, 56, 56, 144)  0           block3a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv (DepthwiseConv2D (None, 28, 28, 144)  3600        block3a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3a_bn (BatchNormalization) (None, 28, 28, 144)  576         block3a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3a_activation (Activation) (None, 28, 28, 144)  0           block3a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_excite (Multiply)    (None, 28, 28, 144)  0           block3a_activation[0][0]         \n",
      "                                                                 block3a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_conv (Conv2D)   (None, 28, 28, 48)   6912        block3a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_bn (BatchNormal (None, 28, 28, 48)   192         block3a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_conv (Conv2D)    (None, 28, 28, 288)  13824       block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_bn (BatchNormali (None, 28, 28, 288)  1152        block3b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_activation (Acti (None, 28, 28, 288)  0           block3b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_dwconv (DepthwiseConv2D (None, 28, 28, 288)  7200        block3b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3b_bn (BatchNormalization) (None, 28, 28, 288)  1152        block3b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3b_activation (Activation) (None, 28, 28, 288)  0           block3b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_squeeze (GlobalAvera (None, 288)          0           block3b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reshape (Reshape)    (None, 1, 1, 288)    0           block3b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block3b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block3b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_excite (Multiply)    (None, 28, 28, 288)  0           block3b_activation[0][0]         \n",
      "                                                                 block3b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_conv (Conv2D)   (None, 28, 28, 48)   13824       block3b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_bn (BatchNormal (None, 28, 28, 48)   192         block3b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_drop (FixedDropout)     (None, 28, 28, 48)   0           block3b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_add (Add)               (None, 28, 28, 48)   0           block3b_drop[0][0]               \n",
      "                                                                 block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_conv (Conv2D)    (None, 28, 28, 288)  13824       block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_bn (BatchNormali (None, 28, 28, 288)  1152        block3c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_activation (Acti (None, 28, 28, 288)  0           block3c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_dwconv (DepthwiseConv2D (None, 28, 28, 288)  7200        block3c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3c_bn (BatchNormalization) (None, 28, 28, 288)  1152        block3c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3c_activation (Activation) (None, 28, 28, 288)  0           block3c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_squeeze (GlobalAvera (None, 288)          0           block3c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_reshape (Reshape)    (None, 1, 1, 288)    0           block3c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block3c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block3c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_excite (Multiply)    (None, 28, 28, 288)  0           block3c_activation[0][0]         \n",
      "                                                                 block3c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_project_conv (Conv2D)   (None, 28, 28, 48)   13824       block3c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_project_bn (BatchNormal (None, 28, 28, 48)   192         block3c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3c_drop (FixedDropout)     (None, 28, 28, 48)   0           block3c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_add (Add)               (None, 28, 28, 48)   0           block3c_drop[0][0]               \n",
      "                                                                 block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_conv (Conv2D)    (None, 28, 28, 288)  13824       block3c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_bn (BatchNormali (None, 28, 28, 288)  1152        block4a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_activation (Acti (None, 28, 28, 288)  0           block4a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv (DepthwiseConv2D (None, 14, 14, 288)  2592        block4a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4a_bn (BatchNormalization) (None, 14, 14, 288)  1152        block4a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4a_activation (Activation) (None, 14, 14, 288)  0           block4a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_squeeze (GlobalAvera (None, 288)          0           block4a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reshape (Reshape)    (None, 1, 1, 288)    0           block4a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reduce (Conv2D)      (None, 1, 1, 12)     3468        block4a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_expand (Conv2D)      (None, 1, 1, 288)    3744        block4a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_excite (Multiply)    (None, 14, 14, 288)  0           block4a_activation[0][0]         \n",
      "                                                                 block4a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_conv (Conv2D)   (None, 14, 14, 88)   25344       block4a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_bn (BatchNormal (None, 14, 14, 88)   352         block4a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_conv (Conv2D)    (None, 14, 14, 528)  46464       block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_bn (BatchNormali (None, 14, 14, 528)  2112        block4b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_activation (Acti (None, 14, 14, 528)  0           block4b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_dwconv (DepthwiseConv2D (None, 14, 14, 528)  4752        block4b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4b_bn (BatchNormalization) (None, 14, 14, 528)  2112        block4b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4b_activation (Activation) (None, 14, 14, 528)  0           block4b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_squeeze (GlobalAvera (None, 528)          0           block4b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reshape (Reshape)    (None, 1, 1, 528)    0           block4b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reduce (Conv2D)      (None, 1, 1, 22)     11638       block4b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_expand (Conv2D)      (None, 1, 1, 528)    12144       block4b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_excite (Multiply)    (None, 14, 14, 528)  0           block4b_activation[0][0]         \n",
      "                                                                 block4b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_conv (Conv2D)   (None, 14, 14, 88)   46464       block4b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_bn (BatchNormal (None, 14, 14, 88)   352         block4b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_drop (FixedDropout)     (None, 14, 14, 88)   0           block4b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_add (Add)               (None, 14, 14, 88)   0           block4b_drop[0][0]               \n",
      "                                                                 block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_conv (Conv2D)    (None, 14, 14, 528)  46464       block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_bn (BatchNormali (None, 14, 14, 528)  2112        block4c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_activation (Acti (None, 14, 14, 528)  0           block4c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_dwconv (DepthwiseConv2D (None, 14, 14, 528)  4752        block4c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4c_bn (BatchNormalization) (None, 14, 14, 528)  2112        block4c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4c_activation (Activation) (None, 14, 14, 528)  0           block4c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_squeeze (GlobalAvera (None, 528)          0           block4c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reshape (Reshape)    (None, 1, 1, 528)    0           block4c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reduce (Conv2D)      (None, 1, 1, 22)     11638       block4c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_expand (Conv2D)      (None, 1, 1, 528)    12144       block4c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_excite (Multiply)    (None, 14, 14, 528)  0           block4c_activation[0][0]         \n",
      "                                                                 block4c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_conv (Conv2D)   (None, 14, 14, 88)   46464       block4c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_bn (BatchNormal (None, 14, 14, 88)   352         block4c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4c_drop (FixedDropout)     (None, 14, 14, 88)   0           block4c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_add (Add)               (None, 14, 14, 88)   0           block4c_drop[0][0]               \n",
      "                                                                 block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_conv (Conv2D)    (None, 14, 14, 528)  46464       block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_bn (BatchNormali (None, 14, 14, 528)  2112        block4d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_activation (Acti (None, 14, 14, 528)  0           block4d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_dwconv (DepthwiseConv2D (None, 14, 14, 528)  4752        block4d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4d_bn (BatchNormalization) (None, 14, 14, 528)  2112        block4d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4d_activation (Activation) (None, 14, 14, 528)  0           block4d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_squeeze (GlobalAvera (None, 528)          0           block4d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_reshape (Reshape)    (None, 1, 1, 528)    0           block4d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_reduce (Conv2D)      (None, 1, 1, 22)     11638       block4d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_expand (Conv2D)      (None, 1, 1, 528)    12144       block4d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_excite (Multiply)    (None, 14, 14, 528)  0           block4d_activation[0][0]         \n",
      "                                                                 block4d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_project_conv (Conv2D)   (None, 14, 14, 88)   46464       block4d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_project_bn (BatchNormal (None, 14, 14, 88)   352         block4d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4d_drop (FixedDropout)     (None, 14, 14, 88)   0           block4d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_add (Add)               (None, 14, 14, 88)   0           block4d_drop[0][0]               \n",
      "                                                                 block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_conv (Conv2D)    (None, 14, 14, 528)  46464       block4d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_bn (BatchNormali (None, 14, 14, 528)  2112        block5a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_activation (Acti (None, 14, 14, 528)  0           block5a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_dwconv (DepthwiseConv2D (None, 14, 14, 528)  13200       block5a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5a_bn (BatchNormalization) (None, 14, 14, 528)  2112        block5a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5a_activation (Activation) (None, 14, 14, 528)  0           block5a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_squeeze (GlobalAvera (None, 528)          0           block5a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reshape (Reshape)    (None, 1, 1, 528)    0           block5a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reduce (Conv2D)      (None, 1, 1, 22)     11638       block5a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_expand (Conv2D)      (None, 1, 1, 528)    12144       block5a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_excite (Multiply)    (None, 14, 14, 528)  0           block5a_activation[0][0]         \n",
      "                                                                 block5a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_conv (Conv2D)   (None, 14, 14, 120)  63360       block5a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_bn (BatchNormal (None, 14, 14, 120)  480         block5a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_conv (Conv2D)    (None, 14, 14, 720)  86400       block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_bn (BatchNormali (None, 14, 14, 720)  2880        block5b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_activation (Acti (None, 14, 14, 720)  0           block5b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_dwconv (DepthwiseConv2D (None, 14, 14, 720)  18000       block5b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5b_bn (BatchNormalization) (None, 14, 14, 720)  2880        block5b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5b_activation (Activation) (None, 14, 14, 720)  0           block5b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_squeeze (GlobalAvera (None, 720)          0           block5b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reshape (Reshape)    (None, 1, 1, 720)    0           block5b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reduce (Conv2D)      (None, 1, 1, 30)     21630       block5b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_expand (Conv2D)      (None, 1, 1, 720)    22320       block5b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_excite (Multiply)    (None, 14, 14, 720)  0           block5b_activation[0][0]         \n",
      "                                                                 block5b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_conv (Conv2D)   (None, 14, 14, 120)  86400       block5b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_bn (BatchNormal (None, 14, 14, 120)  480         block5b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_drop (FixedDropout)     (None, 14, 14, 120)  0           block5b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_add (Add)               (None, 14, 14, 120)  0           block5b_drop[0][0]               \n",
      "                                                                 block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_conv (Conv2D)    (None, 14, 14, 720)  86400       block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_bn (BatchNormali (None, 14, 14, 720)  2880        block5c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_activation (Acti (None, 14, 14, 720)  0           block5c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_dwconv (DepthwiseConv2D (None, 14, 14, 720)  18000       block5c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5c_bn (BatchNormalization) (None, 14, 14, 720)  2880        block5c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5c_activation (Activation) (None, 14, 14, 720)  0           block5c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_squeeze (GlobalAvera (None, 720)          0           block5c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reshape (Reshape)    (None, 1, 1, 720)    0           block5c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reduce (Conv2D)      (None, 1, 1, 30)     21630       block5c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_expand (Conv2D)      (None, 1, 1, 720)    22320       block5c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_excite (Multiply)    (None, 14, 14, 720)  0           block5c_activation[0][0]         \n",
      "                                                                 block5c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_conv (Conv2D)   (None, 14, 14, 120)  86400       block5c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_bn (BatchNormal (None, 14, 14, 120)  480         block5c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5c_drop (FixedDropout)     (None, 14, 14, 120)  0           block5c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_add (Add)               (None, 14, 14, 120)  0           block5c_drop[0][0]               \n",
      "                                                                 block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_conv (Conv2D)    (None, 14, 14, 720)  86400       block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_bn (BatchNormali (None, 14, 14, 720)  2880        block5d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_activation (Acti (None, 14, 14, 720)  0           block5d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_dwconv (DepthwiseConv2D (None, 14, 14, 720)  18000       block5d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5d_bn (BatchNormalization) (None, 14, 14, 720)  2880        block5d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5d_activation (Activation) (None, 14, 14, 720)  0           block5d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_squeeze (GlobalAvera (None, 720)          0           block5d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_reshape (Reshape)    (None, 1, 1, 720)    0           block5d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_reduce (Conv2D)      (None, 1, 1, 30)     21630       block5d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_expand (Conv2D)      (None, 1, 1, 720)    22320       block5d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_excite (Multiply)    (None, 14, 14, 720)  0           block5d_activation[0][0]         \n",
      "                                                                 block5d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_project_conv (Conv2D)   (None, 14, 14, 120)  86400       block5d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_project_bn (BatchNormal (None, 14, 14, 120)  480         block5d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5d_drop (FixedDropout)     (None, 14, 14, 120)  0           block5d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_add (Add)               (None, 14, 14, 120)  0           block5d_drop[0][0]               \n",
      "                                                                 block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_conv (Conv2D)    (None, 14, 14, 720)  86400       block5d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_bn (BatchNormali (None, 14, 14, 720)  2880        block6a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_activation (Acti (None, 14, 14, 720)  0           block6a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_dwconv (DepthwiseConv2D (None, 7, 7, 720)    18000       block6a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6a_bn (BatchNormalization) (None, 7, 7, 720)    2880        block6a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6a_activation (Activation) (None, 7, 7, 720)    0           block6a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_squeeze (GlobalAvera (None, 720)          0           block6a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reshape (Reshape)    (None, 1, 1, 720)    0           block6a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_reduce (Conv2D)      (None, 1, 1, 30)     21630       block6a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_expand (Conv2D)      (None, 1, 1, 720)    22320       block6a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_se_excite (Multiply)    (None, 7, 7, 720)    0           block6a_activation[0][0]         \n",
      "                                                                 block6a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_conv (Conv2D)   (None, 7, 7, 208)    149760      block6a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6a_project_bn (BatchNormal (None, 7, 7, 208)    832         block6a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_conv (Conv2D)    (None, 7, 7, 1248)   259584      block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_bn (BatchNormali (None, 7, 7, 1248)   4992        block6b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6b_expand_activation (Acti (None, 7, 7, 1248)   0           block6b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_dwconv (DepthwiseConv2D (None, 7, 7, 1248)   31200       block6b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6b_bn (BatchNormalization) (None, 7, 7, 1248)   4992        block6b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6b_activation (Activation) (None, 7, 7, 1248)   0           block6b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_squeeze (GlobalAvera (None, 1248)         0           block6b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reshape (Reshape)    (None, 1, 1, 1248)   0           block6b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_reduce (Conv2D)      (None, 1, 1, 52)     64948       block6b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_expand (Conv2D)      (None, 1, 1, 1248)   66144       block6b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_se_excite (Multiply)    (None, 7, 7, 1248)   0           block6b_activation[0][0]         \n",
      "                                                                 block6b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_conv (Conv2D)   (None, 7, 7, 208)    259584      block6b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6b_project_bn (BatchNormal (None, 7, 7, 208)    832         block6b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6b_drop (FixedDropout)     (None, 7, 7, 208)    0           block6b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6b_add (Add)               (None, 7, 7, 208)    0           block6b_drop[0][0]               \n",
      "                                                                 block6a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_conv (Conv2D)    (None, 7, 7, 1248)   259584      block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_bn (BatchNormali (None, 7, 7, 1248)   4992        block6c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6c_expand_activation (Acti (None, 7, 7, 1248)   0           block6c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_dwconv (DepthwiseConv2D (None, 7, 7, 1248)   31200       block6c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6c_bn (BatchNormalization) (None, 7, 7, 1248)   4992        block6c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6c_activation (Activation) (None, 7, 7, 1248)   0           block6c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_squeeze (GlobalAvera (None, 1248)         0           block6c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reshape (Reshape)    (None, 1, 1, 1248)   0           block6c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_reduce (Conv2D)      (None, 1, 1, 52)     64948       block6c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_expand (Conv2D)      (None, 1, 1, 1248)   66144       block6c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_se_excite (Multiply)    (None, 7, 7, 1248)   0           block6c_activation[0][0]         \n",
      "                                                                 block6c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_conv (Conv2D)   (None, 7, 7, 208)    259584      block6c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6c_project_bn (BatchNormal (None, 7, 7, 208)    832         block6c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6c_drop (FixedDropout)     (None, 7, 7, 208)    0           block6c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6c_add (Add)               (None, 7, 7, 208)    0           block6c_drop[0][0]               \n",
      "                                                                 block6b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_conv (Conv2D)    (None, 7, 7, 1248)   259584      block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_bn (BatchNormali (None, 7, 7, 1248)   4992        block6d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6d_expand_activation (Acti (None, 7, 7, 1248)   0           block6d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_dwconv (DepthwiseConv2D (None, 7, 7, 1248)   31200       block6d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6d_bn (BatchNormalization) (None, 7, 7, 1248)   4992        block6d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6d_activation (Activation) (None, 7, 7, 1248)   0           block6d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_squeeze (GlobalAvera (None, 1248)         0           block6d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reshape (Reshape)    (None, 1, 1, 1248)   0           block6d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_reduce (Conv2D)      (None, 1, 1, 52)     64948       block6d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_expand (Conv2D)      (None, 1, 1, 1248)   66144       block6d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_se_excite (Multiply)    (None, 7, 7, 1248)   0           block6d_activation[0][0]         \n",
      "                                                                 block6d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_conv (Conv2D)   (None, 7, 7, 208)    259584      block6d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6d_project_bn (BatchNormal (None, 7, 7, 208)    832         block6d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6d_drop (FixedDropout)     (None, 7, 7, 208)    0           block6d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6d_add (Add)               (None, 7, 7, 208)    0           block6d_drop[0][0]               \n",
      "                                                                 block6c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6e_expand_conv (Conv2D)    (None, 7, 7, 1248)   259584      block6d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6e_expand_bn (BatchNormali (None, 7, 7, 1248)   4992        block6e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6e_expand_activation (Acti (None, 7, 7, 1248)   0           block6e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_dwconv (DepthwiseConv2D (None, 7, 7, 1248)   31200       block6e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block6e_bn (BatchNormalization) (None, 7, 7, 1248)   4992        block6e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block6e_activation (Activation) (None, 7, 7, 1248)   0           block6e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_squeeze (GlobalAvera (None, 1248)         0           block6e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_reshape (Reshape)    (None, 1, 1, 1248)   0           block6e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_reduce (Conv2D)      (None, 1, 1, 52)     64948       block6e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_expand (Conv2D)      (None, 1, 1, 1248)   66144       block6e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_se_excite (Multiply)    (None, 7, 7, 1248)   0           block6e_activation[0][0]         \n",
      "                                                                 block6e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_project_conv (Conv2D)   (None, 7, 7, 208)    259584      block6e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block6e_project_bn (BatchNormal (None, 7, 7, 208)    832         block6e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block6e_drop (FixedDropout)     (None, 7, 7, 208)    0           block6e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6e_add (Add)               (None, 7, 7, 208)    0           block6e_drop[0][0]               \n",
      "                                                                 block6d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_conv (Conv2D)    (None, 7, 7, 1248)   259584      block6e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_bn (BatchNormali (None, 7, 7, 1248)   4992        block7a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7a_expand_activation (Acti (None, 7, 7, 1248)   0           block7a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_dwconv (DepthwiseConv2D (None, 7, 7, 1248)   11232       block7a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7a_bn (BatchNormalization) (None, 7, 7, 1248)   4992        block7a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7a_activation (Activation) (None, 7, 7, 1248)   0           block7a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_squeeze (GlobalAvera (None, 1248)         0           block7a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reshape (Reshape)    (None, 1, 1, 1248)   0           block7a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_reduce (Conv2D)      (None, 1, 1, 52)     64948       block7a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_expand (Conv2D)      (None, 1, 1, 1248)   66144       block7a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_se_excite (Multiply)    (None, 7, 7, 1248)   0           block7a_activation[0][0]         \n",
      "                                                                 block7a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_conv (Conv2D)   (None, 7, 7, 352)    439296      block7a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7a_project_bn (BatchNormal (None, 7, 7, 352)    1408        block7a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block7b_expand_conv (Conv2D)    (None, 7, 7, 2112)   743424      block7a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_expand_bn (BatchNormali (None, 7, 7, 2112)   8448        block7b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7b_expand_activation (Acti (None, 7, 7, 2112)   0           block7b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_dwconv (DepthwiseConv2D (None, 7, 7, 2112)   19008       block7b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block7b_bn (BatchNormalization) (None, 7, 7, 2112)   8448        block7b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block7b_activation (Activation) (None, 7, 7, 2112)   0           block7b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_squeeze (GlobalAvera (None, 2112)         0           block7b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_reshape (Reshape)    (None, 1, 1, 2112)   0           block7b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_reduce (Conv2D)      (None, 1, 1, 88)     185944      block7b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_expand (Conv2D)      (None, 1, 1, 2112)   187968      block7b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_se_excite (Multiply)    (None, 7, 7, 2112)   0           block7b_activation[0][0]         \n",
      "                                                                 block7b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_project_conv (Conv2D)   (None, 7, 7, 352)    743424      block7b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block7b_project_bn (BatchNormal (None, 7, 7, 352)    1408        block7b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block7b_drop (FixedDropout)     (None, 7, 7, 352)    0           block7b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7b_add (Add)               (None, 7, 7, 352)    0           block7b_drop[0][0]               \n",
      "                                                                 block7a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "top_conv (Conv2D)               (None, 7, 7, 1408)   495616      block7b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "top_bn (BatchNormalization)     (None, 7, 7, 1408)   5632        top_conv[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "top_activation (Activation)     (None, 7, 7, 1408)   0           top_bn[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 1408)         0           top_activation[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          360704      global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 256)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 256)          1024        dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 3)            771         batch_normalization[0][0]        \n",
      "==================================================================================================\n",
      "Total params: 8,131,061\n",
      "Trainable params: 8,062,981\n",
      "Non-trainable params: 68,080\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995fbb5e",
   "metadata": {
    "papermill": {
     "duration": 0.11964,
     "end_time": "2023-06-18T11:20:23.839683",
     "exception": false,
     "start_time": "2023-06-18T11:20:23.720043",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Callbacks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3397a52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T11:20:24.088077Z",
     "iopub.status.busy": "2023-06-18T11:20:24.087300Z",
     "iopub.status.idle": "2023-06-18T11:20:24.090971Z",
     "shell.execute_reply": "2023-06-18T11:20:24.090458Z",
     "shell.execute_reply.started": "2023-05-31T14:33:29.083111Z"
    },
    "papermill": {
     "duration": 0.131604,
     "end_time": "2023-06-18T11:20:24.091101",
     "exception": false,
     "start_time": "2023-06-18T11:20:23.959497",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save output model\n",
    "model_file_path='./efficientnet_model.hdf5'\n",
    "\n",
    "# This callback will stop the training when there is no improvement in\n",
    "# the val_loss for 20 consecutive epochs\n",
    "early_stop = EarlyStopping(patience=20, \n",
    "                          verbose=1, \n",
    "                          mode='auto')\n",
    "\n",
    "# Reduce learning rate when a metric has stopped improving.\n",
    "learn_control = ReduceLROnPlateau(monitor='val_accuracy', patience=10,\n",
    "                                  verbose=1,factor=0.2, min_lr=0.000001)\n",
    "\n",
    "# save the best model (best Validation Accuracy)\n",
    "checkpoints = ModelCheckpoint(model_file_path,monitor='val_accuracy',\n",
    "                                  mode='max',save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e87772",
   "metadata": {
    "papermill": {
     "duration": 0.124018,
     "end_time": "2023-06-18T11:20:24.341232",
     "exception": false,
     "start_time": "2023-06-18T11:20:24.217214",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "61b46520",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T11:20:24.596228Z",
     "iopub.status.busy": "2023-06-18T11:20:24.595275Z",
     "iopub.status.idle": "2023-06-18T12:16:31.186550Z",
     "shell.execute_reply": "2023-06-18T12:16:31.185985Z"
    },
    "papermill": {
     "duration": 3366.724869,
     "end_time": "2023-06-18T12:16:31.186696",
     "exception": false,
     "start_time": "2023-06-18T11:20:24.461827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "208/208 [==============================] - 97s 355ms/step - loss: 1.1453 - accuracy: 0.5035 - val_loss: 0.8767 - val_accuracy: 0.5904\n",
      "Epoch 2/60\n",
      "208/208 [==============================] - 71s 339ms/step - loss: 0.7717 - accuracy: 0.6875 - val_loss: 0.8717 - val_accuracy: 0.6145\n",
      "Epoch 3/60\n",
      "208/208 [==============================] - 71s 341ms/step - loss: 0.6130 - accuracy: 0.7471 - val_loss: 0.7462 - val_accuracy: 0.6867\n",
      "Epoch 4/60\n",
      "208/208 [==============================] - 70s 337ms/step - loss: 0.5437 - accuracy: 0.7981 - val_loss: 0.3034 - val_accuracy: 0.8916\n",
      "Epoch 5/60\n",
      "208/208 [==============================] - 71s 341ms/step - loss: 0.4164 - accuracy: 0.8390 - val_loss: 0.6178 - val_accuracy: 0.8217\n",
      "Epoch 6/60\n",
      "208/208 [==============================] - 74s 352ms/step - loss: 0.3958 - accuracy: 0.8607 - val_loss: 0.2897 - val_accuracy: 0.9036\n",
      "Epoch 7/60\n",
      "208/208 [==============================] - 73s 351ms/step - loss: 0.3595 - accuracy: 0.8574 - val_loss: 0.4463 - val_accuracy: 0.8723\n",
      "Epoch 8/60\n",
      "208/208 [==============================] - 72s 347ms/step - loss: 0.3378 - accuracy: 0.8708 - val_loss: 0.4253 - val_accuracy: 0.8988\n",
      "Epoch 9/60\n",
      "208/208 [==============================] - 71s 342ms/step - loss: 0.2930 - accuracy: 0.8883 - val_loss: 0.4872 - val_accuracy: 0.8675\n",
      "Epoch 10/60\n",
      "208/208 [==============================] - 71s 339ms/step - loss: 0.2435 - accuracy: 0.9101 - val_loss: 0.4752 - val_accuracy: 0.8843\n",
      "Epoch 11/60\n",
      "208/208 [==============================] - 71s 341ms/step - loss: 0.2300 - accuracy: 0.9193 - val_loss: 0.3396 - val_accuracy: 0.9012\n",
      "Epoch 12/60\n",
      "208/208 [==============================] - 71s 338ms/step - loss: 0.2108 - accuracy: 0.9189 - val_loss: 0.2632 - val_accuracy: 0.9301\n",
      "Epoch 13/60\n",
      "208/208 [==============================] - 71s 338ms/step - loss: 0.2058 - accuracy: 0.9188 - val_loss: 0.2988 - val_accuracy: 0.9229\n",
      "Epoch 14/60\n",
      "208/208 [==============================] - 71s 340ms/step - loss: 0.1796 - accuracy: 0.9311 - val_loss: 0.2654 - val_accuracy: 0.9181\n",
      "Epoch 15/60\n",
      "208/208 [==============================] - 71s 342ms/step - loss: 0.1708 - accuracy: 0.9388 - val_loss: 0.3093 - val_accuracy: 0.9108\n",
      "Epoch 16/60\n",
      "208/208 [==============================] - 71s 339ms/step - loss: 0.1605 - accuracy: 0.9395 - val_loss: 0.2694 - val_accuracy: 0.9229\n",
      "Epoch 17/60\n",
      "208/208 [==============================] - 71s 339ms/step - loss: 0.1370 - accuracy: 0.9579 - val_loss: 0.2682 - val_accuracy: 0.9277\n",
      "Epoch 18/60\n",
      "208/208 [==============================] - 71s 340ms/step - loss: 0.1161 - accuracy: 0.9596 - val_loss: 0.5183 - val_accuracy: 0.9108\n",
      "Epoch 19/60\n",
      "208/208 [==============================] - 73s 347ms/step - loss: 0.1447 - accuracy: 0.9504 - val_loss: 0.3389 - val_accuracy: 0.9277\n",
      "Epoch 20/60\n",
      "208/208 [==============================] - 71s 340ms/step - loss: 0.1389 - accuracy: 0.9483 - val_loss: 0.3511 - val_accuracy: 0.9205\n",
      "Epoch 21/60\n",
      "208/208 [==============================] - 71s 342ms/step - loss: 0.1182 - accuracy: 0.9552 - val_loss: 0.4648 - val_accuracy: 0.9084\n",
      "Epoch 22/60\n",
      "208/208 [==============================] - 72s 344ms/step - loss: 0.1415 - accuracy: 0.9487 - val_loss: 0.2335 - val_accuracy: 0.9470\n",
      "Epoch 23/60\n",
      "208/208 [==============================] - 73s 346ms/step - loss: 0.1254 - accuracy: 0.9496 - val_loss: 0.2465 - val_accuracy: 0.9181\n",
      "Epoch 24/60\n",
      "208/208 [==============================] - 74s 352ms/step - loss: 0.0986 - accuracy: 0.9602 - val_loss: 0.4012 - val_accuracy: 0.9012\n",
      "Epoch 25/60\n",
      "208/208 [==============================] - 72s 344ms/step - loss: 0.0990 - accuracy: 0.9664 - val_loss: 0.2981 - val_accuracy: 0.9398\n",
      "Epoch 26/60\n",
      "208/208 [==============================] - 73s 349ms/step - loss: 0.0933 - accuracy: 0.9656 - val_loss: 0.1686 - val_accuracy: 0.9590\n",
      "Epoch 27/60\n",
      "208/208 [==============================] - 72s 343ms/step - loss: 0.0847 - accuracy: 0.9699 - val_loss: 0.3285 - val_accuracy: 0.9398\n",
      "Epoch 28/60\n",
      "208/208 [==============================] - 72s 344ms/step - loss: 0.0783 - accuracy: 0.9738 - val_loss: 0.2315 - val_accuracy: 0.9325\n",
      "Epoch 29/60\n",
      "208/208 [==============================] - 72s 345ms/step - loss: 0.0862 - accuracy: 0.9743 - val_loss: 0.3678 - val_accuracy: 0.9373\n",
      "Epoch 30/60\n",
      "208/208 [==============================] - 72s 344ms/step - loss: 0.0688 - accuracy: 0.9774 - val_loss: 0.1783 - val_accuracy: 0.9494\n",
      "Epoch 31/60\n",
      "208/208 [==============================] - 72s 344ms/step - loss: 0.0662 - accuracy: 0.9772 - val_loss: 0.1850 - val_accuracy: 0.9470\n",
      "Epoch 32/60\n",
      "208/208 [==============================] - 73s 350ms/step - loss: 0.1002 - accuracy: 0.9733 - val_loss: 0.3249 - val_accuracy: 0.9036\n",
      "Epoch 33/60\n",
      "208/208 [==============================] - 73s 350ms/step - loss: 0.0483 - accuracy: 0.9882 - val_loss: 0.2906 - val_accuracy: 0.9446\n",
      "Epoch 34/60\n",
      "208/208 [==============================] - 73s 349ms/step - loss: 0.0656 - accuracy: 0.9727 - val_loss: 0.2498 - val_accuracy: 0.9518\n",
      "Epoch 35/60\n",
      "208/208 [==============================] - 72s 346ms/step - loss: 0.0746 - accuracy: 0.9751 - val_loss: 0.3991 - val_accuracy: 0.9133\n",
      "Epoch 36/60\n",
      "208/208 [==============================] - 73s 349ms/step - loss: 0.0567 - accuracy: 0.9816 - val_loss: 0.2390 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00036: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
      "Epoch 37/60\n",
      "208/208 [==============================] - 72s 346ms/step - loss: 0.0533 - accuracy: 0.9828 - val_loss: 0.2347 - val_accuracy: 0.9590\n",
      "Epoch 38/60\n",
      "208/208 [==============================] - 73s 347ms/step - loss: 0.0496 - accuracy: 0.9850 - val_loss: 0.2457 - val_accuracy: 0.9639\n",
      "Epoch 39/60\n",
      "208/208 [==============================] - 72s 347ms/step - loss: 0.0394 - accuracy: 0.9869 - val_loss: 0.2598 - val_accuracy: 0.9494\n",
      "Epoch 40/60\n",
      "208/208 [==============================] - 73s 351ms/step - loss: 0.0497 - accuracy: 0.9838 - val_loss: 0.2217 - val_accuracy: 0.9590\n",
      "Epoch 41/60\n",
      "208/208 [==============================] - 73s 349ms/step - loss: 0.0336 - accuracy: 0.9890 - val_loss: 0.2013 - val_accuracy: 0.9566\n",
      "Epoch 42/60\n",
      "208/208 [==============================] - 73s 351ms/step - loss: 0.0336 - accuracy: 0.9905 - val_loss: 0.1779 - val_accuracy: 0.9687\n",
      "Epoch 43/60\n",
      "208/208 [==============================] - 74s 354ms/step - loss: 0.0317 - accuracy: 0.9921 - val_loss: 0.1798 - val_accuracy: 0.9590\n",
      "Epoch 44/60\n",
      "208/208 [==============================] - 74s 352ms/step - loss: 0.0307 - accuracy: 0.9929 - val_loss: 0.1854 - val_accuracy: 0.9663\n",
      "Epoch 45/60\n",
      "208/208 [==============================] - 74s 353ms/step - loss: 0.0243 - accuracy: 0.9931 - val_loss: 0.1733 - val_accuracy: 0.9663\n",
      "Epoch 46/60\n",
      "208/208 [==============================] - 75s 357ms/step - loss: 0.0255 - accuracy: 0.9928 - val_loss: 0.1950 - val_accuracy: 0.9614\n",
      "Epoch 00046: early stopping\n"
     ]
    }
   ],
   "source": [
    "model=getModel()\n",
    "\n",
    "# fit model\n",
    "steps_per_epoch=train_generator.n/batch_size\n",
    "validation_steps=validation_generator.n/batch_size\n",
    "\n",
    "history = model.fit(\n",
    "              train_generator, \n",
    "              steps_per_epoch=steps_per_epoch,  # how many batches will be executed in each epoch\n",
    "              epochs=epochs,\n",
    "              validation_data=validation_generator, # displays the accuracy of validation data testing\n",
    "              validation_steps=validation_steps,  # how many batches will be executed in each epoch\n",
    "              callbacks=[early_stop,learn_control,checkpoints],\n",
    "              verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab83057d",
   "metadata": {
    "papermill": {
     "duration": 3.3574,
     "end_time": "2023-06-18T12:16:37.937889",
     "exception": false,
     "start_time": "2023-06-18T12:16:34.580489",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Evaluation\n",
    "\n",
    "### Plot Model Accuracy and Model Loss\n",
    "To evaluate Underfitting or Overfitting. Visualize training accuracy vs. validation accuracy and training loss vs. validation loss over several epochs to determine whether the model has been sufficiently trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "28b12e3d",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-06-18T12:16:44.821800Z",
     "iopub.status.idle": "2023-06-18T12:16:45.312092Z",
     "shell.execute_reply": "2023-06-18T12:16:45.311455Z"
    },
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     "duration": 3.919046,
     "end_time": "2023-06-18T12:16:45.312236",
     "exception": false,
     "start_time": "2023-06-18T12:16:41.393190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Model Accuracy\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, label='Training Accuracy')\n",
    "plt.plot(epochs, val_acc, label='Validation Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot Model Loss\n",
    "loss_train = history.history['loss']\n",
    "loss_val = history.history['val_loss']\n",
    "plt.plot(epochs, loss_train,  label='Training Loss')\n",
    "plt.plot(epochs, loss_val, label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e04283e",
   "metadata": {
    "papermill": {
     "duration": 3.497195,
     "end_time": "2023-06-18T12:16:52.420219",
     "exception": false,
     "start_time": "2023-06-18T12:16:48.923024",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Confusion Matrix and Classification Report\n",
    "* Confusion Matrix is a table that is often used to describe the performance of a classification model on a set of test data (here is validation data) whose true value is known.\n",
    "* Classification Report is used to measure the prediction quality of the classification algorithm. How many predictions were Right and how many were Wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01870209",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T12:16:59.801537Z",
     "iopub.status.busy": "2023-06-18T12:16:59.796500Z",
     "iopub.status.idle": "2023-06-18T12:18:01.102710Z",
     "shell.execute_reply": "2023-06-18T12:18:01.102045Z"
    },
    "papermill": {
     "duration": 64.835467,
     "end_time": "2023-06-18T12:18:01.102894",
     "exception": false,
     "start_time": "2023-06-18T12:16:56.267427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "209/209 [==============================] - 53s 256ms/step - loss: 0.0035 - accuracy: 0.9991\n",
      "26/26 [==============================] - 3s 99ms/step - loss: 0.1779 - accuracy: 0.9687\n",
      "Train Accuracy: 99.91%\n",
      "Validation Accuracy: 96.87%\n",
      "25/25 [==============================] - 4s 90ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              ---Classification Report---\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Covid     0.9953    0.9722    0.9836       216\n",
      "     Healthy     0.8621    1.0000    0.9259        75\n",
      "      Others     1.0000    0.9435    0.9710       124\n",
      "\n",
      "    accuracy                         0.9687       415\n",
      "   macro avg     0.9524    0.9719    0.9602       415\n",
      "weighted avg     0.9726    0.9687    0.9694       415\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Load model\n",
    "model.load_weights(model_file_path)\n",
    "validation_generator.reset()\n",
    "\n",
    "# Model evaluation\n",
    "scores_train = model.evaluate(train_generator,verbose=1)\n",
    "scores_validation = model.evaluate(validation_generator, verbose=1)\n",
    "print(\"Train Accuracy: %.2f%%\" % (scores_train[1]*100))\n",
    "print(\"Validation Accuracy: %.2f%%\" % (scores_validation[1]*100))\n",
    "\n",
    "# Model Prediction\n",
    "pred=model.predict(validation_generator, steps=validation_steps, verbose=1)\n",
    "predicted_class_indices=np.argmax(pred,axis=1)\n",
    "labels = (validation_generator.class_indices)\n",
    "labels = dict((v,k) for k,v in labels.items())\n",
    "predictions = [labels[k] for k in predicted_class_indices]\n",
    "\n",
    "actual_class = validation_generator.classes\n",
    "actual = [labels[k] for k in actual_class]\n",
    "val_labels = [fn.split('/')[0] for fn in actual]\n",
    "\n",
    "target_names=list(validation_generator.class_indices.keys())\n",
    "\n",
    "# Confusion Matrix\n",
    "cm=confusion_matrix(val_labels, predictions)\n",
    "    \n",
    "# Confusion Matrix Figure\n",
    "fig = sns.heatmap(cm, annot=True, cmap = \"Blues\")\n",
    "fig.set(title = 'Confusion Matrix', xlabel='Predicted Labels', ylabel='Ground Truth Labels')\n",
    "fig.set_xticklabels(target_names,rotation=20)\n",
    "fig.set_yticklabels(target_names,rotation=20)\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.show(fig)\n",
    "\n",
    "# Classification Report\n",
    "cr=classification_report(val_labels, predictions, digits=4)\n",
    "print(\"\\n              ---Classification Report---\\n\")\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c009566b",
   "metadata": {
    "papermill": {
     "duration": 3.463217,
     "end_time": "2023-06-18T12:18:08.041000",
     "exception": false,
     "start_time": "2023-06-18T12:18:04.577783",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Testing Model\n",
    "Testing the model using test data (testing data) to determine the accuracy of the model on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40ccdc35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-18T12:18:15.022572Z",
     "iopub.status.busy": "2023-06-18T12:18:15.021892Z",
     "iopub.status.idle": "2023-06-18T12:18:23.772100Z",
     "shell.execute_reply": "2023-06-18T12:18:23.771579Z"
    },
    "papermill": {
     "duration": 12.233075,
     "end_time": "2023-06-18T12:18:23.772245",
     "exception": false,
     "start_time": "2023-06-18T12:18:11.539170",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 420 images belonging to 3 classes.\n",
      "26/26 [==============================] - 5s 110ms/step\n",
      "[[207   3   7]\n",
      " [  0  76   1]\n",
      " [  1   3 122]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "              ---Classification Report---\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Covid     0.9952    0.9539    0.9741       217\n",
      "     Healthy     0.9268    0.9870    0.9560        77\n",
      "      Others     0.9385    0.9683    0.9531       126\n",
      "\n",
      "    accuracy                         0.9643       420\n",
      "   macro avg     0.9535    0.9697    0.9611       420\n",
      "weighted avg     0.9656    0.9643    0.9645       420\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Rescale\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "                 test_dir,\n",
    "                 target_size=image_size, \n",
    "                 batch_size=batch_size,\n",
    "                 class_mode=None,\n",
    "                 shuffle=False)\n",
    "test_steps=test_generator.n/batch_size\n",
    "test_generator.reset()\n",
    "\n",
    "#Calling the saved model for making predictions\n",
    "model_file='./efficientnet_model.hdf5'\n",
    "tl_img_aug_cnn = load_model(model_file)\n",
    "pred=tl_img_aug_cnn.predict(test_generator,\n",
    "                            verbose=1,\n",
    "                            steps=test_steps)\n",
    "predicted_class_indices=np.argmax(pred,axis=1)\n",
    "labels = (test_generator.class_indices)\n",
    "labels = dict((v,k) for k,v in labels.items())\n",
    "predictions = [labels[k] for k in predicted_class_indices]\n",
    "filenames=test_generator.filenames\n",
    "results=pd.DataFrame({\"Filename\":filenames,\n",
    "                      \"Predictions\":predictions})\n",
    "results.to_csv(\"results_test.csv\",index=False)\n",
    "target_names=list(validation_generator.class_indices.keys())\n",
    "test_labels = [fn.split('/')[0] for fn in filenames]\n",
    "cm=confusion_matrix(test_labels,predictions)\n",
    "print(cm)\n",
    "\n",
    "    \n",
    "#Confussion Matrix Figure\n",
    "fig = sns.heatmap(cm, annot=True, cmap = \"Blues\")\n",
    "fig.set(title = 'Confussion Matrix', xlabel='Predicted Labels', ylabel='Ground Truth Labels')\n",
    "fig.set_xticklabels(target_names,rotation=20)\n",
    "fig.set_yticklabels(target_names,rotation=20)\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.show(fig)\n",
    "\n",
    "#Classification Report\n",
    "cr=classification_report(test_labels, predictions, digits=4)\n",
    "print(\"\\n              ---Classification Report---\\n\")\n",
    "print(cr)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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