nn_bin_insertion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Flatten,Dense,Dropout, Input\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.applications import InceptionV3\n",
    "from tensorflow.keras.applications import DenseNet121\n",
    "\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout\n",
    "\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "\n",
    "from tensorflow.keras.optimizers.schedules import ExponentialDecay\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.1\n",
      "TensorFlow version: 2.10.1\n",
      "Num GPUs Available:  1\n",
      "GPU details: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "# Print TensorFlow version\n",
    "print(\"TensorFlow version:\", tf.__version__)\n",
    "\n",
    "# Check for GPU\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    print(\"Num GPUs Available: \", len(gpus))\n",
    "    print(\"GPU details:\", gpus)\n",
    "else:\n",
    "    print(\"No GPU detected. Ensure CUDA and cuDNN are properly installed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11835 images belonging to 2 classes.\n",
      "Found 2958 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#load and preprocess data\n",
    "train_dir = '../big_data_v2/'\n",
    "\n",
    "img_height, img_width = 224,224\n",
    "batch_size = 50\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    "    validation_split = 0.2,\n",
    "    rotation_range = 20,\n",
    "    width_shift_range = 0.2,\n",
    "    height_shift_range = 0.2,\n",
    "    shear_range = 0.2,\n",
    "    horizontal_flip = True,\n",
    "    fill_mode = 'nearest'\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size = (img_height, img_width),\n",
    "    batch_size = batch_size,\n",
    "    class_mode = 'binary',\n",
    "    subset = 'training'\n",
    ")\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size = (img_height,img_width),\n",
    "    batch_size = batch_size,\n",
    "    class_mode = 'binary',\n",
    "    subset = 'validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#build Model\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "input_shape = (img_height, img_width, 3) \n",
    "\n",
    "model = Sequential([\n",
    "\n",
    "    Input(shape=input_shape), \n",
    "\n",
    "    Conv2D(32,(3,3), activation = 'relu'), \n",
    "    MaxPooling2D(pool_size = (2,2)),\n",
    "\n",
    "    Conv2D(64, (3,3), activation = 'relu'),\n",
    "    MaxPooling2D(pool_size = (2,2)),\n",
    "\n",
    "    Conv2D(128, (3,3), activation = 'relu'),\n",
    "    MaxPooling2D(pool_size = (2,2)),\n",
    "\n",
    "    Flatten(),\n",
    "    Dense(512,activation = 'relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(1,activation = 'sigmoid')\n",
    "\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Summary of the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(1024, 1024, 3))\n",
    "x = base_model.output\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.Dense(1024, activation='relu')(x)\n",
    "predictions = tf.keras.layers.Dense(1, activation='softmax')(x)\n",
    "\n",
    "model_densenet = tf.keras.Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "model_densenet.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "# Summary of the model\n",
    "model_densenet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_4 (InputLayer)           [(None, 1024, 1024,  0           []                               \n",
      "                                 3)]                                                              \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 511, 511, 32  864         ['input_4[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 511, 511, 32  96         ['conv2d[0][0]']                 \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 511, 511, 32  0           ['batch_normalization[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 509, 509, 32  9216        ['activation[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 509, 509, 32  96         ['conv2d_1[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 509, 509, 32  0           ['batch_normalization_1[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 509, 509, 64  18432       ['activation_1[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 509, 509, 64  192        ['conv2d_2[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 509, 509, 64  0           ['batch_normalization_2[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 254, 254, 64  0           ['activation_2[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 254, 254, 80  5120        ['max_pooling2d[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 254, 254, 80  240        ['conv2d_3[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 254, 254, 80  0           ['batch_normalization_3[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 252, 252, 19  138240      ['activation_3[0][0]']           \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 252, 252, 19  576        ['conv2d_4[0][0]']               \n",
      " rmalization)                   2)                                                                \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 252, 252, 19  0           ['batch_normalization_4[0][0]']  \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 125, 125, 19  0          ['activation_4[0][0]']           \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 125, 125, 64  12288       ['max_pooling2d_1[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 125, 125, 64  192        ['conv2d_8[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 125, 125, 64  0           ['batch_normalization_8[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 125, 125, 48  9216        ['max_pooling2d_1[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 125, 125, 96  55296       ['activation_8[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 125, 125, 48  144        ['conv2d_6[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 125, 125, 96  288        ['conv2d_9[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 125, 125, 48  0           ['batch_normalization_6[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 125, 125, 96  0           ['batch_normalization_9[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 125, 125, 19  0          ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                         2)                                                                \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 125, 125, 64  12288       ['max_pooling2d_1[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 125, 125, 64  76800       ['activation_6[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 125, 125, 96  82944       ['activation_9[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 125, 125, 32  6144        ['average_pooling2d[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 125, 125, 64  192        ['conv2d_5[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 125, 125, 64  192        ['conv2d_7[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 125, 125, 96  288        ['conv2d_10[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 125, 125, 32  96         ['conv2d_11[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 125, 125, 64  0           ['batch_normalization_5[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 125, 125, 64  0           ['batch_normalization_7[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 125, 125, 96  0           ['batch_normalization_10[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 125, 125, 32  0           ['batch_normalization_11[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 125, 125, 25  0           ['activation_5[0][0]',           \n",
      "                                6)                                'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 125, 125, 64  16384       ['mixed0[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 125, 125, 64  192        ['conv2d_15[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_15[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 125, 125, 48  12288       ['mixed0[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 125, 125, 96  55296       ['activation_15[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 125, 125, 48  144        ['conv2d_13[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 125, 125, 96  288        ['conv2d_16[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 125, 125, 48  0           ['batch_normalization_13[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 125, 125, 96  0           ['batch_normalization_16[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " average_pooling2d_1 (AveragePo  (None, 125, 125, 25  0          ['mixed0[0][0]']                 \n",
      " oling2D)                       6)                                                                \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 125, 125, 64  16384       ['mixed0[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 125, 125, 64  76800       ['activation_13[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 125, 125, 96  82944       ['activation_16[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 125, 125, 64  16384       ['average_pooling2d_1[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 125, 125, 64  192        ['conv2d_12[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 125, 125, 64  192        ['conv2d_14[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 125, 125, 96  288        ['conv2d_17[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 125, 125, 64  192        ['conv2d_18[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_12[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_14[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 125, 125, 96  0           ['batch_normalization_17[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_18[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 125, 125, 28  0           ['activation_12[0][0]',          \n",
      "                                8)                                'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 125, 125, 64  18432       ['mixed1[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 125, 125, 64  192        ['conv2d_22[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_22[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 125, 125, 48  13824       ['mixed1[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 125, 125, 96  55296       ['activation_22[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 125, 125, 48  144        ['conv2d_20[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 125, 125, 96  288        ['conv2d_23[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 125, 125, 48  0           ['batch_normalization_20[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 125, 125, 96  0           ['batch_normalization_23[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " average_pooling2d_2 (AveragePo  (None, 125, 125, 28  0          ['mixed1[0][0]']                 \n",
      " oling2D)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 125, 125, 64  18432       ['mixed1[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 125, 125, 64  76800       ['activation_20[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 125, 125, 96  82944       ['activation_23[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 125, 125, 64  18432       ['average_pooling2d_2[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 125, 125, 64  192        ['conv2d_19[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 125, 125, 64  192        ['conv2d_21[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 125, 125, 96  288        ['conv2d_24[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 125, 125, 64  192        ['conv2d_25[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_19[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_21[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 125, 125, 96  0           ['batch_normalization_24[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_25[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 125, 125, 28  0           ['activation_19[0][0]',          \n",
      "                                8)                                'activation_21[0][0]',          \n",
      "                                                                  'activation_24[0][0]',          \n",
      "                                                                  'activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 125, 125, 64  18432       ['mixed2[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 125, 125, 64  192        ['conv2d_27[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 125, 125, 64  0           ['batch_normalization_27[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 125, 125, 96  55296       ['activation_27[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 125, 125, 96  288        ['conv2d_28[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 125, 125, 96  0           ['batch_normalization_28[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 62, 62, 384)  995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 62, 62, 96)   82944       ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 62, 62, 384)  1152       ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 62, 62, 96)  288         ['conv2d_29[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 62, 62, 384)  0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 62, 62, 96)   0           ['batch_normalization_29[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 62, 62, 288)  0          ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 62, 62, 768)  0           ['activation_26[0][0]',          \n",
      "                                                                  'activation_29[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 62, 62, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 62, 62, 128)  384        ['conv2d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 62, 62, 128)  0           ['batch_normalization_34[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 62, 62, 128)  114688      ['activation_34[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 62, 62, 128)  384        ['conv2d_35[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 62, 62, 128)  0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 62, 62, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 62, 62, 128)  114688      ['activation_35[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 62, 62, 128)  384        ['conv2d_31[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 62, 62, 128)  384        ['conv2d_36[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 62, 62, 128)  0           ['batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 62, 62, 128)  0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 62, 62, 128)  114688      ['activation_31[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 62, 62, 128)  114688      ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 62, 62, 128)  384        ['conv2d_32[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 62, 62, 128)  384        ['conv2d_37[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 62, 62, 128)  0           ['batch_normalization_32[0][0]'] \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 62, 62, 128)  0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_3 (AveragePo  (None, 62, 62, 768)  0          ['mixed3[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 62, 62, 192)  172032      ['activation_32[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 62, 62, 192)  172032      ['activation_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 62, 62, 192)  147456      ['average_pooling2d_3[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 62, 62, 192)  576        ['conv2d_30[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 62, 62, 192)  576        ['conv2d_33[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 62, 62, 192)  576        ['conv2d_38[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 62, 62, 192)  576        ['conv2d_39[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_30[0][0]'] \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_33[0][0]'] \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 62, 62, 768)  0           ['activation_30[0][0]',          \n",
      "                                                                  'activation_33[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n",
      "                                                                  'activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 62, 62, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, 62, 62, 160)  480        ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_44[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_44[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, 62, 62, 160)  480        ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_45[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 62, 62, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 62, 62, 160)  480        ['conv2d_41[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, 62, 62, 160)  480        ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_41[0][0]'] \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_46[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_46[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, 62, 62, 160)  480        ['conv2d_42[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, 62, 62, 160)  480        ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_42[0][0]'] \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_47[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_4 (AveragePo  (None, 62, 62, 768)  0          ['mixed4[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 62, 62, 192)  215040      ['activation_42[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 62, 62, 192)  215040      ['activation_47[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 62, 62, 192)  147456      ['average_pooling2d_4[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 62, 62, 192)  576        ['conv2d_40[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, 62, 62, 192)  576        ['conv2d_43[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, 62, 62, 192)  576        ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, 62, 62, 192)  576        ['conv2d_49[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_40[0][0]'] \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_43[0][0]'] \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_48[0][0]'] \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_49[0][0]'] \n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 62, 62, 768)  0           ['activation_40[0][0]',          \n",
      "                                                                  'activation_43[0][0]',          \n",
      "                                                                  'activation_48[0][0]',          \n",
      "                                                                  'activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, 62, 62, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, 62, 62, 160)  480        ['conv2d_54[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_54[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_54[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_55 (BatchN  (None, 62, 62, 160)  480        ['conv2d_55[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_55[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 62, 62, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_55[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, 62, 62, 160)  480        ['conv2d_51[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, 62, 62, 160)  480        ['conv2d_56[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_51[0][0]'] \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_56[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_51[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, 62, 62, 160)  179200      ['activation_56[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, 62, 62, 160)  480        ['conv2d_52[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, 62, 62, 160)  480        ['conv2d_57[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_52[0][0]'] \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, 62, 62, 160)  0           ['batch_normalization_57[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_5 (AveragePo  (None, 62, 62, 768)  0          ['mixed5[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, 62, 62, 192)  215040      ['activation_52[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, 62, 62, 192)  215040      ['activation_57[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, 62, 62, 192)  147456      ['average_pooling2d_5[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, 62, 62, 192)  576        ['conv2d_50[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, 62, 62, 192)  576        ['conv2d_53[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, 62, 62, 192)  576        ['conv2d_58[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, 62, 62, 192)  576        ['conv2d_59[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_50[0][0]'] \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_53[0][0]'] \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_58[0][0]'] \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_59[0][0]'] \n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 62, 62, 768)  0           ['activation_50[0][0]',          \n",
      "                                                                  'activation_53[0][0]',          \n",
      "                                                                  'activation_58[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, 62, 62, 192)  576        ['conv2d_64[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_64[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_64[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, 62, 62, 192)  576        ['conv2d_65[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_65[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_61 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_65[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, 62, 62, 192)  576        ['conv2d_61[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, 62, 62, 192)  576        ['conv2d_66[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_61[0][0]'] \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_66[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_61[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_66[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, 62, 62, 192)  576        ['conv2d_62[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, 62, 62, 192)  576        ['conv2d_67[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_62[0][0]'] \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_67[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_6 (AveragePo  (None, 62, 62, 768)  0          ['mixed6[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_62[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_67[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, 62, 62, 192)  147456      ['average_pooling2d_6[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, 62, 62, 192)  576        ['conv2d_60[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, 62, 62, 192)  576        ['conv2d_63[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, 62, 62, 192)  576        ['conv2d_68[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, 62, 62, 192)  576        ['conv2d_69[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_60[0][0]'] \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_63[0][0]'] \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_68[0][0]'] \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_69[0][0]'] \n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 62, 62, 768)  0           ['activation_60[0][0]',          \n",
      "                                                                  'activation_63[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_72 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_72 (BatchN  (None, 62, 62, 192)  576        ['conv2d_72[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_72 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_72[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_73 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_72[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_73 (BatchN  (None, 62, 62, 192)  576        ['conv2d_73[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_73 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_73[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_70 (Conv2D)             (None, 62, 62, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_74 (Conv2D)             (None, 62, 62, 192)  258048      ['activation_73[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_70 (BatchN  (None, 62, 62, 192)  576        ['conv2d_70[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_74 (BatchN  (None, 62, 62, 192)  576        ['conv2d_74[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_70 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_70[0][0]'] \n",
      "                                                                                                  \n",
      " activation_74 (Activation)     (None, 62, 62, 192)  0           ['batch_normalization_74[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_71 (Conv2D)             (None, 30, 30, 320)  552960      ['activation_70[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_75 (Conv2D)             (None, 30, 30, 192)  331776      ['activation_74[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_71 (BatchN  (None, 30, 30, 320)  960        ['conv2d_71[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_75 (BatchN  (None, 30, 30, 192)  576        ['conv2d_75[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_71 (Activation)     (None, 30, 30, 320)  0           ['batch_normalization_71[0][0]'] \n",
      "                                                                                                  \n",
      " activation_75 (Activation)     (None, 30, 30, 192)  0           ['batch_normalization_75[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 30, 30, 768)  0          ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, 30, 30, 1280  0           ['activation_71[0][0]',          \n",
      "                                )                                 'activation_75[0][0]',          \n",
      "                                                                  'max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_80 (Conv2D)             (None, 30, 30, 448)  573440      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_80 (BatchN  (None, 30, 30, 448)  1344       ['conv2d_80[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_80 (Activation)     (None, 30, 30, 448)  0           ['batch_normalization_80[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_77 (Conv2D)             (None, 30, 30, 384)  491520      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_81 (Conv2D)             (None, 30, 30, 384)  1548288     ['activation_80[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_77 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_77[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_81 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_81[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_77 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_77[0][0]'] \n",
      "                                                                                                  \n",
      " activation_81 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_81[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_78 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_79 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_82 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_83 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_7 (AveragePo  (None, 30, 30, 1280  0          ['mixed8[0][0]']                 \n",
      " oling2D)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_76 (Conv2D)             (None, 30, 30, 320)  409600      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_78 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_78[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_79 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_79[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_82 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_82[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_83 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_83[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_84 (Conv2D)             (None, 30, 30, 192)  245760      ['average_pooling2d_7[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_76 (BatchN  (None, 30, 30, 320)  960        ['conv2d_76[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_78 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_78[0][0]'] \n",
      "                                                                                                  \n",
      " activation_79 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_79[0][0]'] \n",
      "                                                                                                  \n",
      " activation_82 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_82[0][0]'] \n",
      "                                                                                                  \n",
      " activation_83 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_83[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_84 (BatchN  (None, 30, 30, 192)  576        ['conv2d_84[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_76 (Activation)     (None, 30, 30, 320)  0           ['batch_normalization_76[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, 30, 30, 768)  0           ['activation_78[0][0]',          \n",
      "                                                                  'activation_79[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 30, 30, 768)  0           ['activation_82[0][0]',          \n",
      "                                                                  'activation_83[0][0]']          \n",
      "                                                                                                  \n",
      " activation_84 (Activation)     (None, 30, 30, 192)  0           ['batch_normalization_84[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, 30, 30, 2048  0           ['activation_76[0][0]',          \n",
      "                                )                                 'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate[0][0]',            \n",
      "                                                                  'activation_84[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_89 (Conv2D)             (None, 30, 30, 448)  917504      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_89 (BatchN  (None, 30, 30, 448)  1344       ['conv2d_89[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_89 (Activation)     (None, 30, 30, 448)  0           ['batch_normalization_89[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_86 (Conv2D)             (None, 30, 30, 384)  786432      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_90 (Conv2D)             (None, 30, 30, 384)  1548288     ['activation_89[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_86 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_86[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_90 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_90[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_86 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_86[0][0]'] \n",
      "                                                                                                  \n",
      " activation_90 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_90[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_87 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_88 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_91 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_92 (Conv2D)             (None, 30, 30, 384)  442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_8 (AveragePo  (None, 30, 30, 2048  0          ['mixed9[0][0]']                 \n",
      " oling2D)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_85 (Conv2D)             (None, 30, 30, 320)  655360      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_87 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_87[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_88 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_88[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_91 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_91[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_92 (BatchN  (None, 30, 30, 384)  1152       ['conv2d_92[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_93 (Conv2D)             (None, 30, 30, 192)  393216      ['average_pooling2d_8[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_85 (BatchN  (None, 30, 30, 320)  960        ['conv2d_85[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_87 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_87[0][0]'] \n",
      "                                                                                                  \n",
      " activation_88 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_88[0][0]'] \n",
      "                                                                                                  \n",
      " activation_91 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_91[0][0]'] \n",
      "                                                                                                  \n",
      " activation_92 (Activation)     (None, 30, 30, 384)  0           ['batch_normalization_92[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_93 (BatchN  (None, 30, 30, 192)  576        ['conv2d_93[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_85 (Activation)     (None, 30, 30, 320)  0           ['batch_normalization_85[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, 30, 30, 768)  0           ['activation_87[0][0]',          \n",
      "                                                                  'activation_88[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 30, 30, 768)  0           ['activation_91[0][0]',          \n",
      "                                                                  'activation_92[0][0]']          \n",
      "                                                                                                  \n",
      " activation_93 (Activation)     (None, 30, 30, 192)  0           ['batch_normalization_93[0][0]'] \n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, 30, 30, 2048  0           ['activation_85[0][0]',          \n",
      "                                )                                 'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_1[0][0]',          \n",
      "                                                                  'activation_93[0][0]']          \n",
      "                                                                                                  \n",
      " global_average_pooling2d_1 (Gl  (None, 2048)        0           ['mixed10[0][0]']                \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense_6 (Dense)                (None, 1024)         2098176     ['global_average_pooling2d_1[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_7 (Dense)                (None, 1)            1025        ['dense_6[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,901,985\n",
      "Trainable params: 23,867,553\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(1024, 1024, 3))\n",
    "x = base_model.output\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.Dense(1024, activation='relu')(x)\n",
    "predictions = tf.keras.layers.Dense(1, activation='softmax')(x)\n",
    "\n",
    "model_inception = tf.keras.Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "model_inception.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "# Summary of the model\n",
    "model_inception.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_5 (InputLayer)           [(None, 1024, 1024,  0           []                               \n",
      "                                 3)]                                                              \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 1030, 1030,   0           ['input_5[0][0]']                \n",
      "                                3)                                                                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 512, 512, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 512, 512, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 512, 512, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 514, 514, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 256, 256, 64  0           ['pool1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 256, 256, 64  4160        ['pool1_pool[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 256, 256, 64  256        ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 256, 256, 64  0          ['conv2_block1_1_bn[0][0]']      \n",
      " n)                             )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 256, 256, 64  36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 256, 256, 64  256        ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 256, 256, 64  0          ['conv2_block1_2_bn[0][0]']      \n",
      " n)                             )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 256, 256, 25  16640       ['pool1_pool[0][0]']             \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 256, 256, 25  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 256, 256, 25  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                       6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 256, 256, 25  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                       6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 256, 256, 25  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                6)                                'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 256, 256, 25  0           ['conv2_block1_add[0][0]']       \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 256, 256, 64  16448       ['conv2_block1_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 256, 256, 64  256        ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 256, 256, 64  0          ['conv2_block2_1_bn[0][0]']      \n",
      " n)                             )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 256, 256, 64  36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 256, 256, 64  256        ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 256, 256, 64  0          ['conv2_block2_2_bn[0][0]']      \n",
      " n)                             )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 256, 256, 25  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 256, 256, 25  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                       6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 256, 256, 25  0           ['conv2_block1_out[0][0]',       \n",
      "                                6)                                'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 256, 256, 25  0           ['conv2_block2_add[0][0]']       \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 256, 256, 64  16448       ['conv2_block2_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 256, 256, 64  256        ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 256, 256, 64  0          ['conv2_block3_1_bn[0][0]']      \n",
      " n)                             )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 256, 256, 64  36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 256, 256, 64  256        ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 256, 256, 64  0          ['conv2_block3_2_bn[0][0]']      \n",
      " n)                             )                                                                 \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 256, 256, 25  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 256, 256, 25  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                       6)                                                                \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 256, 256, 25  0           ['conv2_block2_out[0][0]',       \n",
      "                                6)                                'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 256, 256, 25  0           ['conv2_block3_add[0][0]']       \n",
      "                                6)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 128, 128, 12  32896       ['conv2_block3_out[0][0]']       \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 128, 128, 12  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 128, 128, 51  131584      ['conv2_block3_out[0][0]']       \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 128, 128, 51  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 128, 128, 51  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                       2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 128, 128, 51  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                       2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 128, 128, 51  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                2)                                'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 128, 128, 51  0           ['conv3_block1_add[0][0]']       \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 128, 128, 12  65664       ['conv3_block1_out[0][0]']       \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 128, 128, 12  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 128, 128, 51  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 128, 128, 51  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                       2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 128, 128, 51  0           ['conv3_block1_out[0][0]',       \n",
      "                                2)                                'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 128, 128, 51  0           ['conv3_block2_add[0][0]']       \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 128, 128, 12  65664       ['conv3_block2_out[0][0]']       \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 128, 128, 12  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 128, 128, 51  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 128, 128, 51  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                       2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 128, 128, 51  0           ['conv3_block2_out[0][0]',       \n",
      "                                2)                                'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 128, 128, 51  0           ['conv3_block3_add[0][0]']       \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 128, 128, 12  65664       ['conv3_block3_out[0][0]']       \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 128, 128, 12  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 128, 128, 12  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 128, 128, 12  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                             8)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 128, 128, 51  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 128, 128, 51  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                       2)                                                                \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 128, 128, 51  0           ['conv3_block3_out[0][0]',       \n",
      "                                2)                                'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 128, 128, 51  0           ['conv3_block4_add[0][0]']       \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 64, 64, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 64, 64, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 64, 64, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 64, 64, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 64, 64, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 64, 64, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 64, 64, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 64, 64, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 64, 64, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 64, 64, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 64, 64, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 64, 64, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 64, 64, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 64, 64, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 64, 64, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 64, 64, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 64, 64, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 64, 64, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 64, 64, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 64, 64, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 64, 64, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 64, 64, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 64, 64, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 64, 64, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 64, 64, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 64, 64, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 64, 64, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 64, 64, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 64, 64, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 64, 64, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 64, 64, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 64, 64, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 64, 64, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 64, 64, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 32, 32, 512)  524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 32, 32, 512)  2048       ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 32, 32, 512)  0          ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 32, 32, 512)  2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 32, 32, 512)  2048       ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 32, 32, 512)  0          ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 32, 32, 2048  2099200     ['conv4_block6_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 32, 32, 2048  1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 32, 32, 2048  8192       ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 32, 32, 2048  8192       ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 32, 32, 2048  0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 32, 32, 2048  0           ['conv5_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 32, 32, 512)  1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 32, 32, 512)  2048       ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 32, 32, 512)  0          ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 32, 32, 512)  2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 32, 32, 512)  2048       ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 32, 32, 512)  0          ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 32, 32, 2048  1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 32, 32, 2048  8192       ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 32, 32, 2048  0           ['conv5_block1_out[0][0]',       \n",
      "                                )                                 'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 32, 32, 2048  0           ['conv5_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 32, 32, 512)  1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 32, 32, 512)  2048       ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 32, 32, 512)  0          ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 32, 32, 512)  2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 32, 32, 512)  2048       ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 32, 32, 512)  0          ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 32, 32, 2048  1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 32, 32, 2048  8192       ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 32, 32, 2048  0           ['conv5_block2_out[0][0]',       \n",
      "                                )                                 'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 32, 32, 2048  0           ['conv5_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " global_average_pooling2d_2 (Gl  (None, 2048)        0           ['conv5_block3_out[0][0]']       \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense_8 (Dense)                (None, 1024)         2098176     ['global_average_pooling2d_2[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_9 (Dense)                (None, 1)            1025        ['dense_8[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 25,686,913\n",
      "Trainable params: 25,633,793\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(1024, 1024, 3))\n",
    "x = base_model.output\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.Dense(1024, activation='relu')(x)\n",
    "predictions = tf.keras.layers.Dense(1, activation='softmax')(x)\n",
    "\n",
    "model_resnet = tf.keras.Model(inputs=base_model.input, outputs=predictions)\n",
    "model_resnet.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "# Summary of the model\n",
    "model_resnet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 25088)             0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 512)               12845568  \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 513       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 27,560,769\n",
      "Trainable params: 12,846,081\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n",
    "x = base_model.output\n",
    "x = Flatten()(x)\n",
    "x = Dense(512, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False  # Freeze base model layers\n",
    "\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Summary of the model\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_7 (InputLayer)           [(None, 512, 512, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " rescaling (Rescaling)          (None, 512, 512, 3)  0           ['input_7[0][0]']                \n",
      "                                                                                                  \n",
      " normalization (Normalization)  (None, 512, 512, 3)  7           ['rescaling[0][0]']              \n",
      "                                                                                                  \n",
      " rescaling_1 (Rescaling)        (None, 512, 512, 3)  0           ['normalization[0][0]']          \n",
      "                                                                                                  \n",
      " stem_conv_pad (ZeroPadding2D)  (None, 513, 513, 3)  0           ['rescaling_1[0][0]']            \n",
      "                                                                                                  \n",
      " stem_conv (Conv2D)             (None, 256, 256, 64  1728        ['stem_conv_pad[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " stem_bn (BatchNormalization)   (None, 256, 256, 64  256         ['stem_conv[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " stem_activation (Activation)   (None, 256, 256, 64  0           ['stem_bn[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1a_dwconv (DepthwiseConv2  (None, 256, 256, 64  576        ['stem_activation[0][0]']        \n",
      " D)                             )                                                                 \n",
      "                                                                                                  \n",
      " block1a_bn (BatchNormalization  (None, 256, 256, 64  256        ['block1a_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1a_activation (Activation  (None, 256, 256, 64  0          ['block1a_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1a_se_squeeze (GlobalAver  (None, 64)          0           ['block1a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1a_se_reshape (Reshape)   (None, 1, 1, 64)     0           ['block1a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1a_se_reduce (Conv2D)     (None, 1, 1, 16)     1040        ['block1a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1a_se_expand (Conv2D)     (None, 1, 1, 64)     1088        ['block1a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_se_excite (Multiply)   (None, 256, 256, 64  0           ['block1a_activation[0][0]',     \n",
      "                                )                                 'block1a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_project_conv (Conv2D)  (None, 256, 256, 32  2048        ['block1a_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1a_project_bn (BatchNorma  (None, 256, 256, 32  128        ['block1a_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block1b_dwconv (DepthwiseConv2  (None, 256, 256, 32  288        ['block1a_project_bn[0][0]']     \n",
      " D)                             )                                                                 \n",
      "                                                                                                  \n",
      " block1b_bn (BatchNormalization  (None, 256, 256, 32  128        ['block1b_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1b_activation (Activation  (None, 256, 256, 32  0          ['block1b_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1b_se_squeeze (GlobalAver  (None, 32)          0           ['block1b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1b_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1b_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1b_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1b_se_excite (Multiply)   (None, 256, 256, 32  0           ['block1b_activation[0][0]',     \n",
      "                                )                                 'block1b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1b_project_conv (Conv2D)  (None, 256, 256, 32  1024        ['block1b_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1b_project_bn (BatchNorma  (None, 256, 256, 32  128        ['block1b_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block1b_drop (Dropout)         (None, 256, 256, 32  0           ['block1b_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1b_add (Add)              (None, 256, 256, 32  0           ['block1b_drop[0][0]',           \n",
      "                                )                                 'block1a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_dwconv (DepthwiseConv2  (None, 256, 256, 32  288        ['block1b_add[0][0]']            \n",
      " D)                             )                                                                 \n",
      "                                                                                                  \n",
      " block1c_bn (BatchNormalization  (None, 256, 256, 32  128        ['block1c_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1c_activation (Activation  (None, 256, 256, 32  0          ['block1c_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1c_se_squeeze (GlobalAver  (None, 32)          0           ['block1c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1c_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1c_se_excite (Multiply)   (None, 256, 256, 32  0           ['block1c_activation[0][0]',     \n",
      "                                )                                 'block1c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1c_project_conv (Conv2D)  (None, 256, 256, 32  1024        ['block1c_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1c_project_bn (BatchNorma  (None, 256, 256, 32  128        ['block1c_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block1c_drop (Dropout)         (None, 256, 256, 32  0           ['block1c_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1c_add (Add)              (None, 256, 256, 32  0           ['block1c_drop[0][0]',           \n",
      "                                )                                 'block1b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block1d_dwconv (DepthwiseConv2  (None, 256, 256, 32  288        ['block1c_add[0][0]']            \n",
      " D)                             )                                                                 \n",
      "                                                                                                  \n",
      " block1d_bn (BatchNormalization  (None, 256, 256, 32  128        ['block1d_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1d_activation (Activation  (None, 256, 256, 32  0          ['block1d_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block1d_se_squeeze (GlobalAver  (None, 32)          0           ['block1d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1d_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1d_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1d_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1d_se_excite (Multiply)   (None, 256, 256, 32  0           ['block1d_activation[0][0]',     \n",
      "                                )                                 'block1d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1d_project_conv (Conv2D)  (None, 256, 256, 32  1024        ['block1d_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1d_project_bn (BatchNorma  (None, 256, 256, 32  128        ['block1d_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block1d_drop (Dropout)         (None, 256, 256, 32  0           ['block1d_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block1d_add (Add)              (None, 256, 256, 32  0           ['block1d_drop[0][0]',           \n",
      "                                )                                 'block1c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2a_expand_conv (Conv2D)   (None, 256, 256, 19  6144        ['block1d_add[0][0]']            \n",
      "                                2)                                                                \n",
      "                                                                                                  \n",
      " block2a_expand_bn (BatchNormal  (None, 256, 256, 19  768        ['block2a_expand_conv[0][0]']    \n",
      " ization)                       2)                                                                \n",
      "                                                                                                  \n",
      " block2a_expand_activation (Act  (None, 256, 256, 19  0          ['block2a_expand_bn[0][0]']      \n",
      " ivation)                       2)                                                                \n",
      "                                                                                                  \n",
      " block2a_dwconv_pad (ZeroPaddin  (None, 257, 257, 19  0          ['block2a_expand_activation[0][0]\n",
      " g2D)                           2)                               ']                               \n",
      "                                                                                                  \n",
      " block2a_dwconv (DepthwiseConv2  (None, 128, 128, 19  1728       ['block2a_dwconv_pad[0][0]']     \n",
      " D)                             2)                                                                \n",
      "                                                                                                  \n",
      " block2a_bn (BatchNormalization  (None, 128, 128, 19  768        ['block2a_dwconv[0][0]']         \n",
      " )                              2)                                                                \n",
      "                                                                                                  \n",
      " block2a_activation (Activation  (None, 128, 128, 19  0          ['block2a_bn[0][0]']             \n",
      " )                              2)                                                                \n",
      "                                                                                                  \n",
      " block2a_se_squeeze (GlobalAver  (None, 192)         0           ['block2a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_se_excite (Multiply)   (None, 128, 128, 19  0           ['block2a_activation[0][0]',     \n",
      "                                2)                                'block2a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_project_conv (Conv2D)  (None, 128, 128, 48  9216        ['block2a_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2a_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2a_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2b_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2a_project_bn[0][0]']     \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block2b_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block2b_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2b_expand_activation (Act  (None, 128, 128, 28  0          ['block2b_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2b_dwconv (DepthwiseConv2  (None, 128, 128, 28  2592       ['block2b_expand_activation[0][0]\n",
      " D)                             8)                               ']                               \n",
      "                                                                                                  \n",
      " block2b_bn (BatchNormalization  (None, 128, 128, 28  1152       ['block2b_dwconv[0][0]']         \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2b_activation (Activation  (None, 128, 128, 28  0          ['block2b_bn[0][0]']             \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2b_se_squeeze (GlobalAver  (None, 288)         0           ['block2b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2b_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_se_excite (Multiply)   (None, 128, 128, 28  0           ['block2b_activation[0][0]',     \n",
      "                                8)                                'block2b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_project_conv (Conv2D)  (None, 128, 128, 48  13824       ['block2b_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2b_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2b_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2b_drop (Dropout)         (None, 128, 128, 48  0           ['block2b_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2b_add (Add)              (None, 128, 128, 48  0           ['block2b_drop[0][0]',           \n",
      "                                )                                 'block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2b_add[0][0]']            \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block2c_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block2c_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2c_expand_activation (Act  (None, 128, 128, 28  0          ['block2c_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2c_dwconv (DepthwiseConv2  (None, 128, 128, 28  2592       ['block2c_expand_activation[0][0]\n",
      " D)                             8)                               ']                               \n",
      "                                                                                                  \n",
      " block2c_bn (BatchNormalization  (None, 128, 128, 28  1152       ['block2c_dwconv[0][0]']         \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2c_activation (Activation  (None, 128, 128, 28  0          ['block2c_bn[0][0]']             \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2c_se_squeeze (GlobalAver  (None, 288)         0           ['block2c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2c_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2c_se_excite (Multiply)   (None, 128, 128, 28  0           ['block2c_activation[0][0]',     \n",
      "                                8)                                'block2c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2c_project_conv (Conv2D)  (None, 128, 128, 48  13824       ['block2c_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2c_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2c_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2c_drop (Dropout)         (None, 128, 128, 48  0           ['block2c_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2c_add (Add)              (None, 128, 128, 48  0           ['block2c_drop[0][0]',           \n",
      "                                )                                 'block2b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2d_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2c_add[0][0]']            \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block2d_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block2d_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2d_expand_activation (Act  (None, 128, 128, 28  0          ['block2d_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2d_dwconv (DepthwiseConv2  (None, 128, 128, 28  2592       ['block2d_expand_activation[0][0]\n",
      " D)                             8)                               ']                               \n",
      "                                                                                                  \n",
      " block2d_bn (BatchNormalization  (None, 128, 128, 28  1152       ['block2d_dwconv[0][0]']         \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2d_activation (Activation  (None, 128, 128, 28  0          ['block2d_bn[0][0]']             \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2d_se_squeeze (GlobalAver  (None, 288)         0           ['block2d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2d_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2d_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2d_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2d_se_excite (Multiply)   (None, 128, 128, 28  0           ['block2d_activation[0][0]',     \n",
      "                                8)                                'block2d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2d_project_conv (Conv2D)  (None, 128, 128, 48  13824       ['block2d_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2d_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2d_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2d_drop (Dropout)         (None, 128, 128, 48  0           ['block2d_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2d_add (Add)              (None, 128, 128, 48  0           ['block2d_drop[0][0]',           \n",
      "                                )                                 'block2c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2e_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2d_add[0][0]']            \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block2e_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block2e_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2e_expand_activation (Act  (None, 128, 128, 28  0          ['block2e_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2e_dwconv (DepthwiseConv2  (None, 128, 128, 28  2592       ['block2e_expand_activation[0][0]\n",
      " D)                             8)                               ']                               \n",
      "                                                                                                  \n",
      " block2e_bn (BatchNormalization  (None, 128, 128, 28  1152       ['block2e_dwconv[0][0]']         \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2e_activation (Activation  (None, 128, 128, 28  0          ['block2e_bn[0][0]']             \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2e_se_squeeze (GlobalAver  (None, 288)         0           ['block2e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2e_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2e_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2e_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2e_se_excite (Multiply)   (None, 128, 128, 28  0           ['block2e_activation[0][0]',     \n",
      "                                8)                                'block2e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2e_project_conv (Conv2D)  (None, 128, 128, 48  13824       ['block2e_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2e_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2e_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2e_drop (Dropout)         (None, 128, 128, 48  0           ['block2e_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2e_add (Add)              (None, 128, 128, 48  0           ['block2e_drop[0][0]',           \n",
      "                                )                                 'block2d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2f_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2e_add[0][0]']            \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block2f_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block2f_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2f_expand_activation (Act  (None, 128, 128, 28  0          ['block2f_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2f_dwconv (DepthwiseConv2  (None, 128, 128, 28  2592       ['block2f_expand_activation[0][0]\n",
      " D)                             8)                               ']                               \n",
      "                                                                                                  \n",
      " block2f_bn (BatchNormalization  (None, 128, 128, 28  1152       ['block2f_dwconv[0][0]']         \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2f_activation (Activation  (None, 128, 128, 28  0          ['block2f_bn[0][0]']             \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2f_se_squeeze (GlobalAver  (None, 288)         0           ['block2f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2f_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2f_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2f_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2f_se_excite (Multiply)   (None, 128, 128, 28  0           ['block2f_activation[0][0]',     \n",
      "                                8)                                'block2f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2f_project_conv (Conv2D)  (None, 128, 128, 48  13824       ['block2f_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2f_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2f_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2f_drop (Dropout)         (None, 128, 128, 48  0           ['block2f_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2f_add (Add)              (None, 128, 128, 48  0           ['block2f_drop[0][0]',           \n",
      "                                )                                 'block2e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2g_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2f_add[0][0]']            \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block2g_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block2g_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2g_expand_activation (Act  (None, 128, 128, 28  0          ['block2g_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block2g_dwconv (DepthwiseConv2  (None, 128, 128, 28  2592       ['block2g_expand_activation[0][0]\n",
      " D)                             8)                               ']                               \n",
      "                                                                                                  \n",
      " block2g_bn (BatchNormalization  (None, 128, 128, 28  1152       ['block2g_dwconv[0][0]']         \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2g_activation (Activation  (None, 128, 128, 28  0          ['block2g_bn[0][0]']             \n",
      " )                              8)                                                                \n",
      "                                                                                                  \n",
      " block2g_se_squeeze (GlobalAver  (None, 288)         0           ['block2g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2g_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2g_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2g_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2g_se_excite (Multiply)   (None, 128, 128, 28  0           ['block2g_activation[0][0]',     \n",
      "                                8)                                'block2g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2g_project_conv (Conv2D)  (None, 128, 128, 48  13824       ['block2g_se_excite[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2g_project_bn (BatchNorma  (None, 128, 128, 48  192        ['block2g_project_conv[0][0]']   \n",
      " lization)                      )                                                                 \n",
      "                                                                                                  \n",
      " block2g_drop (Dropout)         (None, 128, 128, 48  0           ['block2g_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block2g_add (Add)              (None, 128, 128, 48  0           ['block2g_drop[0][0]',           \n",
      "                                )                                 'block2f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3a_expand_conv (Conv2D)   (None, 128, 128, 28  13824       ['block2g_add[0][0]']            \n",
      "                                8)                                                                \n",
      "                                                                                                  \n",
      " block3a_expand_bn (BatchNormal  (None, 128, 128, 28  1152       ['block3a_expand_conv[0][0]']    \n",
      " ization)                       8)                                                                \n",
      "                                                                                                  \n",
      " block3a_expand_activation (Act  (None, 128, 128, 28  0          ['block3a_expand_bn[0][0]']      \n",
      " ivation)                       8)                                                                \n",
      "                                                                                                  \n",
      " block3a_dwconv_pad (ZeroPaddin  (None, 131, 131, 28  0          ['block3a_expand_activation[0][0]\n",
      " g2D)                           8)                               ']                               \n",
      "                                                                                                  \n",
      " block3a_dwconv (DepthwiseConv2  (None, 64, 64, 288)  7200       ['block3a_dwconv_pad[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block3a_bn (BatchNormalization  (None, 64, 64, 288)  1152       ['block3a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3a_activation (Activation  (None, 64, 64, 288)  0          ['block3a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3a_se_squeeze (GlobalAver  (None, 288)         0           ['block3a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3a_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block3a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block3a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block3a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_se_excite (Multiply)   (None, 64, 64, 288)  0           ['block3a_activation[0][0]',     \n",
      "                                                                  'block3a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_project_conv (Conv2D)  (None, 64, 64, 80)   23040       ['block3a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block3b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_expand_activation (Act  (None, 64, 64, 480)  0          ['block3b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_dwconv (DepthwiseConv2  (None, 64, 64, 480)  12000      ['block3b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3b_bn (BatchNormalization  (None, 64, 64, 480)  1920       ['block3b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3b_activation (Activation  (None, 64, 64, 480)  0          ['block3b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3b_se_squeeze (GlobalAver  (None, 480)         0           ['block3b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_se_excite (Multiply)   (None, 64, 64, 480)  0           ['block3b_activation[0][0]',     \n",
      "                                                                  'block3b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_project_conv (Conv2D)  (None, 64, 64, 80)   38400       ['block3b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_drop (Dropout)         (None, 64, 64, 80)   0           ['block3b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_add (Add)              (None, 64, 64, 80)   0           ['block3b_drop[0][0]',           \n",
      "                                                                  'block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3c_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block3c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3c_expand_activation (Act  (None, 64, 64, 480)  0          ['block3c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3c_dwconv (DepthwiseConv2  (None, 64, 64, 480)  12000      ['block3c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3c_bn (BatchNormalization  (None, 64, 64, 480)  1920       ['block3c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3c_activation (Activation  (None, 64, 64, 480)  0          ['block3c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3c_se_squeeze (GlobalAver  (None, 480)         0           ['block3c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3c_se_excite (Multiply)   (None, 64, 64, 480)  0           ['block3c_activation[0][0]',     \n",
      "                                                                  'block3c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3c_project_conv (Conv2D)  (None, 64, 64, 80)   38400       ['block3c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3c_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3c_drop (Dropout)         (None, 64, 64, 80)   0           ['block3c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_add (Add)              (None, 64, 64, 80)   0           ['block3c_drop[0][0]',           \n",
      "                                                                  'block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3d_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3d_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block3d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3d_expand_activation (Act  (None, 64, 64, 480)  0          ['block3d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3d_dwconv (DepthwiseConv2  (None, 64, 64, 480)  12000      ['block3d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3d_bn (BatchNormalization  (None, 64, 64, 480)  1920       ['block3d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3d_activation (Activation  (None, 64, 64, 480)  0          ['block3d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3d_se_squeeze (GlobalAver  (None, 480)         0           ['block3d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3d_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3d_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3d_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3d_se_excite (Multiply)   (None, 64, 64, 480)  0           ['block3d_activation[0][0]',     \n",
      "                                                                  'block3d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3d_project_conv (Conv2D)  (None, 64, 64, 80)   38400       ['block3d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3d_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3d_drop (Dropout)         (None, 64, 64, 80)   0           ['block3d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3d_add (Add)              (None, 64, 64, 80)   0           ['block3d_drop[0][0]',           \n",
      "                                                                  'block3c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3e_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3e_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block3e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3e_expand_activation (Act  (None, 64, 64, 480)  0          ['block3e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3e_dwconv (DepthwiseConv2  (None, 64, 64, 480)  12000      ['block3e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3e_bn (BatchNormalization  (None, 64, 64, 480)  1920       ['block3e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3e_activation (Activation  (None, 64, 64, 480)  0          ['block3e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3e_se_squeeze (GlobalAver  (None, 480)         0           ['block3e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3e_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3e_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3e_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3e_se_excite (Multiply)   (None, 64, 64, 480)  0           ['block3e_activation[0][0]',     \n",
      "                                                                  'block3e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3e_project_conv (Conv2D)  (None, 64, 64, 80)   38400       ['block3e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3e_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3e_drop (Dropout)         (None, 64, 64, 80)   0           ['block3e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3e_add (Add)              (None, 64, 64, 80)   0           ['block3e_drop[0][0]',           \n",
      "                                                                  'block3d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3f_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3f_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block3f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3f_expand_activation (Act  (None, 64, 64, 480)  0          ['block3f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3f_dwconv (DepthwiseConv2  (None, 64, 64, 480)  12000      ['block3f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3f_bn (BatchNormalization  (None, 64, 64, 480)  1920       ['block3f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3f_activation (Activation  (None, 64, 64, 480)  0          ['block3f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3f_se_squeeze (GlobalAver  (None, 480)         0           ['block3f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3f_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3f_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3f_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3f_se_excite (Multiply)   (None, 64, 64, 480)  0           ['block3f_activation[0][0]',     \n",
      "                                                                  'block3f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3f_project_conv (Conv2D)  (None, 64, 64, 80)   38400       ['block3f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3f_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3f_drop (Dropout)         (None, 64, 64, 80)   0           ['block3f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3f_add (Add)              (None, 64, 64, 80)   0           ['block3f_drop[0][0]',           \n",
      "                                                                  'block3e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3g_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3g_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block3g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3g_expand_activation (Act  (None, 64, 64, 480)  0          ['block3g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3g_dwconv (DepthwiseConv2  (None, 64, 64, 480)  12000      ['block3g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3g_bn (BatchNormalization  (None, 64, 64, 480)  1920       ['block3g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3g_activation (Activation  (None, 64, 64, 480)  0          ['block3g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3g_se_squeeze (GlobalAver  (None, 480)         0           ['block3g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3g_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3g_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3g_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3g_se_excite (Multiply)   (None, 64, 64, 480)  0           ['block3g_activation[0][0]',     \n",
      "                                                                  'block3g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3g_project_conv (Conv2D)  (None, 64, 64, 80)   38400       ['block3g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3g_project_bn (BatchNorma  (None, 64, 64, 80)  320         ['block3g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3g_drop (Dropout)         (None, 64, 64, 80)   0           ['block3g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3g_add (Add)              (None, 64, 64, 80)   0           ['block3g_drop[0][0]',           \n",
      "                                                                  'block3f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4a_expand_conv (Conv2D)   (None, 64, 64, 480)  38400       ['block3g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4a_expand_bn (BatchNormal  (None, 64, 64, 480)  1920       ['block4a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_expand_activation (Act  (None, 64, 64, 480)  0          ['block4a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_dwconv_pad (ZeroPaddin  (None, 65, 65, 480)  0          ['block4a_expand_activation[0][0]\n",
      " g2D)                                                            ']                               \n",
      "                                                                                                  \n",
      " block4a_dwconv (DepthwiseConv2  (None, 32, 32, 480)  4320       ['block4a_dwconv_pad[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4a_bn (BatchNormalization  (None, 32, 32, 480)  1920       ['block4a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_activation (Activation  (None, 32, 32, 480)  0          ['block4a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_se_squeeze (GlobalAver  (None, 480)         0           ['block4a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_se_excite (Multiply)   (None, 32, 32, 480)  0           ['block4a_activation[0][0]',     \n",
      "                                                                  'block4a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_conv (Conv2D)  (None, 32, 32, 160)  76800       ['block4a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_expand_activation (Act  (None, 32, 32, 960)  0          ['block4b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4b_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_activation (Activation  (None, 32, 32, 960)  0          ['block4b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_se_squeeze (GlobalAver  (None, 960)         0           ['block4b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4b_activation[0][0]',     \n",
      "                                                                  'block4b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_drop (Dropout)         (None, 32, 32, 160)  0           ['block4b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_add (Add)              (None, 32, 32, 160)  0           ['block4b_drop[0][0]',           \n",
      "                                                                  'block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4c_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_expand_activation (Act  (None, 32, 32, 960)  0          ['block4c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4c_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_activation (Activation  (None, 32, 32, 960)  0          ['block4c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_se_squeeze (GlobalAver  (None, 960)         0           ['block4c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4c_activation[0][0]',     \n",
      "                                                                  'block4c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4c_drop (Dropout)         (None, 32, 32, 160)  0           ['block4c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_add (Add)              (None, 32, 32, 160)  0           ['block4c_drop[0][0]',           \n",
      "                                                                  'block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4d_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4d_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4d_expand_activation (Act  (None, 32, 32, 960)  0          ['block4d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4d_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4d_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4d_activation (Activation  (None, 32, 32, 960)  0          ['block4d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4d_se_squeeze (GlobalAver  (None, 960)         0           ['block4d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4d_activation[0][0]',     \n",
      "                                                                  'block4d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4d_drop (Dropout)         (None, 32, 32, 160)  0           ['block4d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_add (Add)              (None, 32, 32, 160)  0           ['block4d_drop[0][0]',           \n",
      "                                                                  'block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4e_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4e_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4e_expand_activation (Act  (None, 32, 32, 960)  0          ['block4e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4e_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4e_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4e_activation (Activation  (None, 32, 32, 960)  0          ['block4e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4e_se_squeeze (GlobalAver  (None, 960)         0           ['block4e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4e_activation[0][0]',     \n",
      "                                                                  'block4e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4e_drop (Dropout)         (None, 32, 32, 160)  0           ['block4e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_add (Add)              (None, 32, 32, 160)  0           ['block4e_drop[0][0]',           \n",
      "                                                                  'block4d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4f_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4f_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4f_expand_activation (Act  (None, 32, 32, 960)  0          ['block4f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4f_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4f_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4f_activation (Activation  (None, 32, 32, 960)  0          ['block4f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4f_se_squeeze (GlobalAver  (None, 960)         0           ['block4f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4f_activation[0][0]',     \n",
      "                                                                  'block4f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4f_drop (Dropout)         (None, 32, 32, 160)  0           ['block4f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_add (Add)              (None, 32, 32, 160)  0           ['block4f_drop[0][0]',           \n",
      "                                                                  'block4e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4g_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4g_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4g_expand_activation (Act  (None, 32, 32, 960)  0          ['block4g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4g_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4g_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4g_activation (Activation  (None, 32, 32, 960)  0          ['block4g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4g_se_squeeze (GlobalAver  (None, 960)         0           ['block4g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4g_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4g_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4g_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4g_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4g_activation[0][0]',     \n",
      "                                                                  'block4g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4g_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4g_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4g_drop (Dropout)         (None, 32, 32, 160)  0           ['block4g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4g_add (Add)              (None, 32, 32, 160)  0           ['block4g_drop[0][0]',           \n",
      "                                                                  'block4f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4h_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4h_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4h_expand_activation (Act  (None, 32, 32, 960)  0          ['block4h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4h_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4h_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4h_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4h_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4h_activation (Activation  (None, 32, 32, 960)  0          ['block4h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4h_se_squeeze (GlobalAver  (None, 960)         0           ['block4h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4h_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4h_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4h_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4h_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4h_activation[0][0]',     \n",
      "                                                                  'block4h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4h_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4h_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4h_drop (Dropout)         (None, 32, 32, 160)  0           ['block4h_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4h_add (Add)              (None, 32, 32, 160)  0           ['block4h_drop[0][0]',           \n",
      "                                                                  'block4g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4i_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4i_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4i_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4i_expand_activation (Act  (None, 32, 32, 960)  0          ['block4i_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4i_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4i_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4i_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4i_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4i_activation (Activation  (None, 32, 32, 960)  0          ['block4i_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4i_se_squeeze (GlobalAver  (None, 960)         0           ['block4i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4i_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4i_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4i_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4i_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4i_activation[0][0]',     \n",
      "                                                                  'block4i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4i_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4i_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4i_drop (Dropout)         (None, 32, 32, 160)  0           ['block4i_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4i_add (Add)              (None, 32, 32, 160)  0           ['block4i_drop[0][0]',           \n",
      "                                                                  'block4h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4j_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4j_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block4j_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4j_expand_activation (Act  (None, 32, 32, 960)  0          ['block4j_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4j_dwconv (DepthwiseConv2  (None, 32, 32, 960)  8640       ['block4j_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4j_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block4j_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4j_activation (Activation  (None, 32, 32, 960)  0          ['block4j_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4j_se_squeeze (GlobalAver  (None, 960)         0           ['block4j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4j_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4j_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4j_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4j_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block4j_activation[0][0]',     \n",
      "                                                                  'block4j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4j_project_conv (Conv2D)  (None, 32, 32, 160)  153600      ['block4j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4j_project_bn (BatchNorma  (None, 32, 32, 160)  640        ['block4j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4j_drop (Dropout)         (None, 32, 32, 160)  0           ['block4j_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4j_add (Add)              (None, 32, 32, 160)  0           ['block4j_drop[0][0]',           \n",
      "                                                                  'block4i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_conv (Conv2D)   (None, 32, 32, 960)  153600      ['block4j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_bn (BatchNormal  (None, 32, 32, 960)  3840       ['block5a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_expand_activation (Act  (None, 32, 32, 960)  0          ['block5a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_dwconv (DepthwiseConv2  (None, 32, 32, 960)  24000      ['block5a_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5a_bn (BatchNormalization  (None, 32, 32, 960)  3840       ['block5a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_activation (Activation  (None, 32, 32, 960)  0          ['block5a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_se_squeeze (GlobalAver  (None, 960)         0           ['block5a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_se_excite (Multiply)   (None, 32, 32, 960)  0           ['block5a_activation[0][0]',     \n",
      "                                                                  'block5a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_conv (Conv2D)  (None, 32, 32, 224)  215040      ['block5a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5a_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5b_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5b_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5b_expand_activation (Act  (None, 32, 32, 1344  0          ['block5b_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5b_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5b_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5b_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5b_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5b_activation (Activation  (None, 32, 32, 1344  0          ['block5b_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5b_se_squeeze (GlobalAver  (None, 1344)        0           ['block5b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5b_activation[0][0]',     \n",
      "                                )                                 'block5b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_drop (Dropout)         (None, 32, 32, 224)  0           ['block5b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_add (Add)              (None, 32, 32, 224)  0           ['block5b_drop[0][0]',           \n",
      "                                                                  'block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5b_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5c_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5c_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5c_expand_activation (Act  (None, 32, 32, 1344  0          ['block5c_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5c_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5c_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5c_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5c_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5c_activation (Activation  (None, 32, 32, 1344  0          ['block5c_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5c_se_squeeze (GlobalAver  (None, 1344)        0           ['block5c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5c_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5c_activation[0][0]',     \n",
      "                                )                                 'block5c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5c_drop (Dropout)         (None, 32, 32, 224)  0           ['block5c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_add (Add)              (None, 32, 32, 224)  0           ['block5c_drop[0][0]',           \n",
      "                                                                  'block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5c_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5d_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5d_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5d_expand_activation (Act  (None, 32, 32, 1344  0          ['block5d_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5d_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5d_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5d_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5d_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5d_activation (Activation  (None, 32, 32, 1344  0          ['block5d_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5d_se_squeeze (GlobalAver  (None, 1344)        0           ['block5d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5d_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5d_activation[0][0]',     \n",
      "                                )                                 'block5d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5d_drop (Dropout)         (None, 32, 32, 224)  0           ['block5d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_add (Add)              (None, 32, 32, 224)  0           ['block5d_drop[0][0]',           \n",
      "                                                                  'block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5d_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5e_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5e_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5e_expand_activation (Act  (None, 32, 32, 1344  0          ['block5e_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5e_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5e_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5e_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5e_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5e_activation (Activation  (None, 32, 32, 1344  0          ['block5e_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5e_se_squeeze (GlobalAver  (None, 1344)        0           ['block5e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5e_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5e_activation[0][0]',     \n",
      "                                )                                 'block5e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5e_drop (Dropout)         (None, 32, 32, 224)  0           ['block5e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_add (Add)              (None, 32, 32, 224)  0           ['block5e_drop[0][0]',           \n",
      "                                                                  'block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5f_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5e_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5f_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5f_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5f_expand_activation (Act  (None, 32, 32, 1344  0          ['block5f_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5f_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5f_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5f_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5f_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5f_activation (Activation  (None, 32, 32, 1344  0          ['block5f_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5f_se_squeeze (GlobalAver  (None, 1344)        0           ['block5f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5f_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5f_activation[0][0]',     \n",
      "                                )                                 'block5f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5f_drop (Dropout)         (None, 32, 32, 224)  0           ['block5f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_add (Add)              (None, 32, 32, 224)  0           ['block5f_drop[0][0]',           \n",
      "                                                                  'block5e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5g_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5f_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5g_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5g_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5g_expand_activation (Act  (None, 32, 32, 1344  0          ['block5g_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5g_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5g_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5g_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5g_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5g_activation (Activation  (None, 32, 32, 1344  0          ['block5g_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5g_se_squeeze (GlobalAver  (None, 1344)        0           ['block5g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5g_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5g_activation[0][0]',     \n",
      "                                )                                 'block5g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5g_drop (Dropout)         (None, 32, 32, 224)  0           ['block5g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_add (Add)              (None, 32, 32, 224)  0           ['block5g_drop[0][0]',           \n",
      "                                                                  'block5f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5h_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5g_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5h_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5h_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5h_expand_activation (Act  (None, 32, 32, 1344  0          ['block5h_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5h_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5h_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5h_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5h_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5h_activation (Activation  (None, 32, 32, 1344  0          ['block5h_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5h_se_squeeze (GlobalAver  (None, 1344)        0           ['block5h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5h_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5h_activation[0][0]',     \n",
      "                                )                                 'block5h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5h_drop (Dropout)         (None, 32, 32, 224)  0           ['block5h_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_add (Add)              (None, 32, 32, 224)  0           ['block5h_drop[0][0]',           \n",
      "                                                                  'block5g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5i_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5h_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5i_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5i_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5i_expand_activation (Act  (None, 32, 32, 1344  0          ['block5i_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5i_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5i_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5i_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5i_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5i_activation (Activation  (None, 32, 32, 1344  0          ['block5i_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5i_se_squeeze (GlobalAver  (None, 1344)        0           ['block5i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5i_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5i_activation[0][0]',     \n",
      "                                )                                 'block5i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5i_drop (Dropout)         (None, 32, 32, 224)  0           ['block5i_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_add (Add)              (None, 32, 32, 224)  0           ['block5i_drop[0][0]',           \n",
      "                                                                  'block5h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5j_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5i_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block5j_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block5j_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5j_expand_activation (Act  (None, 32, 32, 1344  0          ['block5j_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block5j_dwconv (DepthwiseConv2  (None, 32, 32, 1344  33600      ['block5j_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block5j_bn (BatchNormalization  (None, 32, 32, 1344  5376       ['block5j_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5j_activation (Activation  (None, 32, 32, 1344  0          ['block5j_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block5j_se_squeeze (GlobalAver  (None, 1344)        0           ['block5j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5j_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5j_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5j_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5j_se_excite (Multiply)   (None, 32, 32, 1344  0           ['block5j_activation[0][0]',     \n",
      "                                )                                 'block5j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5j_project_conv (Conv2D)  (None, 32, 32, 224)  301056      ['block5j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5j_project_bn (BatchNorma  (None, 32, 32, 224)  896        ['block5j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5j_drop (Dropout)         (None, 32, 32, 224)  0           ['block5j_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5j_add (Add)              (None, 32, 32, 224)  0           ['block5j_drop[0][0]',           \n",
      "                                                                  'block5i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_conv (Conv2D)   (None, 32, 32, 1344  301056      ['block5j_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6a_expand_bn (BatchNormal  (None, 32, 32, 1344  5376       ['block6a_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6a_expand_activation (Act  (None, 32, 32, 1344  0          ['block6a_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6a_dwconv_pad (ZeroPaddin  (None, 35, 35, 1344  0          ['block6a_expand_activation[0][0]\n",
      " g2D)                           )                                ']                               \n",
      "                                                                                                  \n",
      " block6a_dwconv (DepthwiseConv2  (None, 16, 16, 1344  33600      ['block6a_dwconv_pad[0][0]']     \n",
      " D)                             )                                                                 \n",
      "                                                                                                  \n",
      " block6a_bn (BatchNormalization  (None, 16, 16, 1344  5376       ['block6a_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6a_activation (Activation  (None, 16, 16, 1344  0          ['block6a_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6a_se_squeeze (GlobalAver  (None, 1344)        0           ['block6a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6a_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block6a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block6a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block6a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_se_excite (Multiply)   (None, 16, 16, 1344  0           ['block6a_activation[0][0]',     \n",
      "                                )                                 'block6a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_conv (Conv2D)  (None, 16, 16, 384)  516096      ['block6a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6a_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6b_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6b_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6b_expand_activation (Act  (None, 16, 16, 2304  0          ['block6b_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6b_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6b_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6b_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6b_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6b_activation (Activation  (None, 16, 16, 2304  0          ['block6b_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6b_se_squeeze (GlobalAver  (None, 2304)        0           ['block6b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6b_activation[0][0]',     \n",
      "                                )                                 'block6b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_drop (Dropout)         (None, 16, 16, 384)  0           ['block6b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_add (Add)              (None, 16, 16, 384)  0           ['block6b_drop[0][0]',           \n",
      "                                                                  'block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6b_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6c_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6c_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6c_expand_activation (Act  (None, 16, 16, 2304  0          ['block6c_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6c_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6c_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6c_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6c_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6c_activation (Activation  (None, 16, 16, 2304  0          ['block6c_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6c_se_squeeze (GlobalAver  (None, 2304)        0           ['block6c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6c_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6c_activation[0][0]',     \n",
      "                                )                                 'block6c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6c_drop (Dropout)         (None, 16, 16, 384)  0           ['block6c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_add (Add)              (None, 16, 16, 384)  0           ['block6c_drop[0][0]',           \n",
      "                                                                  'block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6c_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6d_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6d_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6d_expand_activation (Act  (None, 16, 16, 2304  0          ['block6d_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6d_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6d_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6d_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6d_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6d_activation (Activation  (None, 16, 16, 2304  0          ['block6d_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6d_se_squeeze (GlobalAver  (None, 2304)        0           ['block6d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6d_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6d_activation[0][0]',     \n",
      "                                )                                 'block6d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6d_drop (Dropout)         (None, 16, 16, 384)  0           ['block6d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_add (Add)              (None, 16, 16, 384)  0           ['block6d_drop[0][0]',           \n",
      "                                                                  'block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6d_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6e_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6e_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6e_expand_activation (Act  (None, 16, 16, 2304  0          ['block6e_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6e_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6e_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6e_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6e_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6e_activation (Activation  (None, 16, 16, 2304  0          ['block6e_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6e_se_squeeze (GlobalAver  (None, 2304)        0           ['block6e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6e_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6e_activation[0][0]',     \n",
      "                                )                                 'block6e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6e_drop (Dropout)         (None, 16, 16, 384)  0           ['block6e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_add (Add)              (None, 16, 16, 384)  0           ['block6e_drop[0][0]',           \n",
      "                                                                  'block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6e_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6f_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6f_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6f_expand_activation (Act  (None, 16, 16, 2304  0          ['block6f_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6f_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6f_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6f_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6f_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6f_activation (Activation  (None, 16, 16, 2304  0          ['block6f_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6f_se_squeeze (GlobalAver  (None, 2304)        0           ['block6f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6f_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6f_activation[0][0]',     \n",
      "                                )                                 'block6f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6f_drop (Dropout)         (None, 16, 16, 384)  0           ['block6f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_add (Add)              (None, 16, 16, 384)  0           ['block6f_drop[0][0]',           \n",
      "                                                                  'block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6f_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6g_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6g_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6g_expand_activation (Act  (None, 16, 16, 2304  0          ['block6g_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6g_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6g_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6g_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6g_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6g_activation (Activation  (None, 16, 16, 2304  0          ['block6g_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6g_se_squeeze (GlobalAver  (None, 2304)        0           ['block6g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6g_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6g_activation[0][0]',     \n",
      "                                )                                 'block6g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6g_drop (Dropout)         (None, 16, 16, 384)  0           ['block6g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_add (Add)              (None, 16, 16, 384)  0           ['block6g_drop[0][0]',           \n",
      "                                                                  'block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6g_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6h_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6h_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6h_expand_activation (Act  (None, 16, 16, 2304  0          ['block6h_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6h_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6h_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6h_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6h_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6h_activation (Activation  (None, 16, 16, 2304  0          ['block6h_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6h_se_squeeze (GlobalAver  (None, 2304)        0           ['block6h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6h_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6h_activation[0][0]',     \n",
      "                                )                                 'block6h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6h_drop (Dropout)         (None, 16, 16, 384)  0           ['block6h_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_add (Add)              (None, 16, 16, 384)  0           ['block6h_drop[0][0]',           \n",
      "                                                                  'block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6i_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6h_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6i_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6i_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6i_expand_activation (Act  (None, 16, 16, 2304  0          ['block6i_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6i_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6i_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6i_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6i_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6i_activation (Activation  (None, 16, 16, 2304  0          ['block6i_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6i_se_squeeze (GlobalAver  (None, 2304)        0           ['block6i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6i_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6i_activation[0][0]',     \n",
      "                                )                                 'block6i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6i_drop (Dropout)         (None, 16, 16, 384)  0           ['block6i_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_add (Add)              (None, 16, 16, 384)  0           ['block6i_drop[0][0]',           \n",
      "                                                                  'block6h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6j_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6i_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6j_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6j_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6j_expand_activation (Act  (None, 16, 16, 2304  0          ['block6j_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6j_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6j_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6j_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6j_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6j_activation (Activation  (None, 16, 16, 2304  0          ['block6j_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6j_se_squeeze (GlobalAver  (None, 2304)        0           ['block6j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6j_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6j_activation[0][0]',     \n",
      "                                )                                 'block6j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6j_drop (Dropout)         (None, 16, 16, 384)  0           ['block6j_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_add (Add)              (None, 16, 16, 384)  0           ['block6j_drop[0][0]',           \n",
      "                                                                  'block6i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6k_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6j_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6k_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6k_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6k_expand_activation (Act  (None, 16, 16, 2304  0          ['block6k_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6k_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6k_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6k_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6k_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6k_activation (Activation  (None, 16, 16, 2304  0          ['block6k_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6k_se_squeeze (GlobalAver  (None, 2304)        0           ['block6k_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6k_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6k_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6k_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6k_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6k_activation[0][0]',     \n",
      "                                )                                 'block6k_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6k_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6k_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6k_drop (Dropout)         (None, 16, 16, 384)  0           ['block6k_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_add (Add)              (None, 16, 16, 384)  0           ['block6k_drop[0][0]',           \n",
      "                                                                  'block6j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6l_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6k_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6l_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6l_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6l_expand_activation (Act  (None, 16, 16, 2304  0          ['block6l_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6l_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6l_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6l_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6l_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6l_activation (Activation  (None, 16, 16, 2304  0          ['block6l_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6l_se_squeeze (GlobalAver  (None, 2304)        0           ['block6l_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6l_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6l_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6l_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6l_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6l_activation[0][0]',     \n",
      "                                )                                 'block6l_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6l_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6l_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6l_drop (Dropout)         (None, 16, 16, 384)  0           ['block6l_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_add (Add)              (None, 16, 16, 384)  0           ['block6l_drop[0][0]',           \n",
      "                                                                  'block6k_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6m_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6l_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block6m_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block6m_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6m_expand_activation (Act  (None, 16, 16, 2304  0          ['block6m_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block6m_dwconv (DepthwiseConv2  (None, 16, 16, 2304  57600      ['block6m_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block6m_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block6m_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6m_activation (Activation  (None, 16, 16, 2304  0          ['block6m_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block6m_se_squeeze (GlobalAver  (None, 2304)        0           ['block6m_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6m_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6m_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6m_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6m_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block6m_activation[0][0]',     \n",
      "                                )                                 'block6m_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_project_conv (Conv2D)  (None, 16, 16, 384)  884736      ['block6m_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_project_bn (BatchNorma  (None, 16, 16, 384)  1536       ['block6m_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6m_drop (Dropout)         (None, 16, 16, 384)  0           ['block6m_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_add (Add)              (None, 16, 16, 384)  0           ['block6m_drop[0][0]',           \n",
      "                                                                  'block6l_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7a_expand_conv (Conv2D)   (None, 16, 16, 2304  884736      ['block6m_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block7a_expand_bn (BatchNormal  (None, 16, 16, 2304  9216       ['block7a_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7a_expand_activation (Act  (None, 16, 16, 2304  0          ['block7a_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7a_dwconv (DepthwiseConv2  (None, 16, 16, 2304  20736      ['block7a_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block7a_bn (BatchNormalization  (None, 16, 16, 2304  9216       ['block7a_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7a_activation (Activation  (None, 16, 16, 2304  0          ['block7a_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7a_se_squeeze (GlobalAver  (None, 2304)        0           ['block7a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7a_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block7a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7a_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block7a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7a_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block7a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_se_excite (Multiply)   (None, 16, 16, 2304  0           ['block7a_activation[0][0]',     \n",
      "                                )                                 'block7a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_project_conv (Conv2D)  (None, 16, 16, 640)  1474560     ['block7a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_project_bn (BatchNorma  (None, 16, 16, 640)  2560       ['block7a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7b_expand_conv (Conv2D)   (None, 16, 16, 3840  2457600     ['block7a_project_bn[0][0]']     \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block7b_expand_bn (BatchNormal  (None, 16, 16, 3840  15360      ['block7b_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7b_expand_activation (Act  (None, 16, 16, 3840  0          ['block7b_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7b_dwconv (DepthwiseConv2  (None, 16, 16, 3840  34560      ['block7b_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block7b_bn (BatchNormalization  (None, 16, 16, 3840  15360      ['block7b_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7b_activation (Activation  (None, 16, 16, 3840  0          ['block7b_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7b_se_squeeze (GlobalAver  (None, 3840)        0           ['block7b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7b_se_reshape (Reshape)   (None, 1, 1, 3840)   0           ['block7b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_se_reduce (Conv2D)     (None, 1, 1, 160)    614560      ['block7b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_se_expand (Conv2D)     (None, 1, 1, 3840)   618240      ['block7b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7b_se_excite (Multiply)   (None, 16, 16, 3840  0           ['block7b_activation[0][0]',     \n",
      "                                )                                 'block7b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7b_project_conv (Conv2D)  (None, 16, 16, 640)  2457600     ['block7b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7b_project_bn (BatchNorma  (None, 16, 16, 640)  2560       ['block7b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7b_drop (Dropout)         (None, 16, 16, 640)  0           ['block7b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_add (Add)              (None, 16, 16, 640)  0           ['block7b_drop[0][0]',           \n",
      "                                                                  'block7a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_expand_conv (Conv2D)   (None, 16, 16, 3840  2457600     ['block7b_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block7c_expand_bn (BatchNormal  (None, 16, 16, 3840  15360      ['block7c_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7c_expand_activation (Act  (None, 16, 16, 3840  0          ['block7c_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7c_dwconv (DepthwiseConv2  (None, 16, 16, 3840  34560      ['block7c_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block7c_bn (BatchNormalization  (None, 16, 16, 3840  15360      ['block7c_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7c_activation (Activation  (None, 16, 16, 3840  0          ['block7c_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7c_se_squeeze (GlobalAver  (None, 3840)        0           ['block7c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7c_se_reshape (Reshape)   (None, 1, 1, 3840)   0           ['block7c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_se_reduce (Conv2D)     (None, 1, 1, 160)    614560      ['block7c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_se_expand (Conv2D)     (None, 1, 1, 3840)   618240      ['block7c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7c_se_excite (Multiply)   (None, 16, 16, 3840  0           ['block7c_activation[0][0]',     \n",
      "                                )                                 'block7c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7c_project_conv (Conv2D)  (None, 16, 16, 640)  2457600     ['block7c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7c_project_bn (BatchNorma  (None, 16, 16, 640)  2560       ['block7c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7c_drop (Dropout)         (None, 16, 16, 640)  0           ['block7c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_add (Add)              (None, 16, 16, 640)  0           ['block7c_drop[0][0]',           \n",
      "                                                                  'block7b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7d_expand_conv (Conv2D)   (None, 16, 16, 3840  2457600     ['block7c_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block7d_expand_bn (BatchNormal  (None, 16, 16, 3840  15360      ['block7d_expand_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7d_expand_activation (Act  (None, 16, 16, 3840  0          ['block7d_expand_bn[0][0]']      \n",
      " ivation)                       )                                                                 \n",
      "                                                                                                  \n",
      " block7d_dwconv (DepthwiseConv2  (None, 16, 16, 3840  34560      ['block7d_expand_activation[0][0]\n",
      " D)                             )                                ']                               \n",
      "                                                                                                  \n",
      " block7d_bn (BatchNormalization  (None, 16, 16, 3840  15360      ['block7d_dwconv[0][0]']         \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7d_activation (Activation  (None, 16, 16, 3840  0          ['block7d_bn[0][0]']             \n",
      " )                              )                                                                 \n",
      "                                                                                                  \n",
      " block7d_se_squeeze (GlobalAver  (None, 3840)        0           ['block7d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7d_se_reshape (Reshape)   (None, 1, 1, 3840)   0           ['block7d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7d_se_reduce (Conv2D)     (None, 1, 1, 160)    614560      ['block7d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7d_se_expand (Conv2D)     (None, 1, 1, 3840)   618240      ['block7d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7d_se_excite (Multiply)   (None, 16, 16, 3840  0           ['block7d_activation[0][0]',     \n",
      "                                )                                 'block7d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7d_project_conv (Conv2D)  (None, 16, 16, 640)  2457600     ['block7d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7d_project_bn (BatchNorma  (None, 16, 16, 640)  2560       ['block7d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7d_drop (Dropout)         (None, 16, 16, 640)  0           ['block7d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7d_add (Add)              (None, 16, 16, 640)  0           ['block7d_drop[0][0]',           \n",
      "                                                                  'block7c_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_conv (Conv2D)              (None, 16, 16, 2560  1638400     ['block7d_add[0][0]']            \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " top_bn (BatchNormalization)    (None, 16, 16, 2560  10240       ['top_conv[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " top_activation (Activation)    (None, 16, 16, 2560  0           ['top_bn[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " global_average_pooling2d_3 (Gl  (None, 2560)        0           ['top_activation[0][0]']         \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense_12 (Dense)               (None, 1024)         2622464     ['global_average_pooling2d_3[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_13 (Dense)               (None, 1)            1025        ['dense_12[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 66,721,176\n",
      "Trainable params: 66,410,449\n",
      "Non-trainable params: 310,727\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import EfficientNetB7\n",
    "\n",
    "base_model = EfficientNetB7(weights='imagenet', include_top=False, input_shape=(512, 512, 3))\n",
    "x = base_model.output\n",
    "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = tf.keras.layers.Dense(1024, activation='relu')(x)\n",
    "predictions = tf.keras.layers.Dense(1, activation='softmax')(x)\n",
    "\n",
    "model_efficient_net = tf.keras.Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "\n",
    "model_efficient_net.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Summary of the model\n",
    "model_efficient_net.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_learning_rate = 0.001\n",
    "#K.set_value(model.optimizer.learning_rate, new_learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "237/237 [==============================] - 109s 459ms/step - loss: 0.0263 - accuracy: 0.9894 - val_loss: 0.0501 - val_accuracy: 0.9824 - lr: 2.5000e-04\n",
      "Epoch 2/5\n",
      "237/237 [==============================] - 109s 459ms/step - loss: 0.0221 - accuracy: 0.9915 - val_loss: 0.0363 - val_accuracy: 0.9875 - lr: 2.5000e-04\n",
      "Epoch 3/5\n",
      "237/237 [==============================] - 109s 462ms/step - loss: 0.0196 - accuracy: 0.9928 - val_loss: 0.0640 - val_accuracy: 0.9817 - lr: 2.5000e-04\n",
      "Epoch 4/5\n",
      "237/237 [==============================] - 109s 459ms/step - loss: 0.0217 - accuracy: 0.9926 - val_loss: 0.0388 - val_accuracy: 0.9861 - lr: 2.5000e-04\n",
      "Epoch 5/5\n",
      "237/237 [==============================] - 109s 458ms/step - loss: 0.0197 - accuracy: 0.9929 - val_loss: 0.0662 - val_accuracy: 0.9797 - lr: 2.5000e-04\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "\n",
    "epochs = 5\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5,\n",
    "                              patience=3, min_lr=0.00001)\n",
    "\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    epochs=epochs,\n",
    "    callbacks = [reduce_lr],\n",
    "    validation_data=validation_generator\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('VGG16_bincheck_hole_224x224.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60/60 [==============================] - 22s 368ms/step - loss: 0.0602 - accuracy: 0.9828\n",
      "Validation Loss: 0.060163188725709915\n",
      "Validation Accuracy: 0.982758641242981\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation data\n",
    "#model = load_model('VGG16_bincheck_walls_224x224.h5')\n",
    "val_loss, val_accuracy = model.evaluate(validation_generator)\n",
    "print(f\"Validation Loss: {val_loss}\")\n",
    "print(f\"Validation Accuracy: {val_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 23ms/step\n",
      "The image is classified as: Bincheck passed, expected result: Bincheck passed\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 21ms/step\n",
      "The image is classified as: Bincheck failed, expected result: Bincheck failed\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def predict_image(img_path, model):\n",
    "    img = image.load_img(img_path, target_size=(img_height, img_width))\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    img_array = image.img_to_array(img)\n",
    "    img_array = np.expand_dims(img_array, axis=0)  # Create batch axis\n",
    "    img_array /= 255.0  # Normalize\n",
    "    prediction = model.predict(img_array)\n",
    "   \n",
    "    return 'Bincheck passed' if prediction[0] > 0.5 else 'Bincheck failed'\n",
    "\n",
    "# Example prediction\n",
    "img_path = f'../big_data_v2/positive/positive_{np.random.randint(0,5000)}.jpg'\n",
    "result = predict_image(img_path, model)\n",
    "print(f\"The image is classified as: {result}, expected result: Bincheck passed\")\n",
    "\n",
    "img_path = f'../big_data_v2/negative/negative_{np.random.randint(0,5000)}.jpg'\n",
    "result = predict_image(img_path, model)\n",
    "print(f\"The image is classified as: {result}, expected result: Bincheck failed\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}