def_model.py

import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization
from tensorflow.keras.optimizers import SGD, Adam
# define the cnn model
def define_model():
  model = Sequential()
  model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))
  model.add(BatchNormalization())
  model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  model.add(BatchNormalization())
  model.add(MaxPooling2D((2, 2)))
  model.add(Dropout(0.2))
  model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  model.add(BatchNormalization())
  model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  model.add(BatchNormalization())
  model.add(MaxPooling2D((2, 2)))
  model.add(Dropout(0.3))
  model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  model.add(BatchNormalization())
  model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))
  model.add(BatchNormalization())
  model.add(MaxPooling2D((2, 2)))
  model.add(Dropout(0.4))
  model.add(Flatten())
  model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
  model.add(BatchNormalization())
  model.add(Dropout(0.5))
  model.add(BatchNormalization())
  model.add(Dense(10, activation='softmax'))
  # compile model
  opt = SGD(learning_rate=0.005, momentum=0.9)
  model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
  # print(model.summary())
  return model