cnn_model_batch_normalization.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization
from keras.layers import Activation, Dropout, Flatten, Dense

# model istantiation
def istantiate_model(input_shape, print_bool = True):
    model = Sequential()
    #1st
    model.add(Conv2D(64, kernel_size=(3, 3),padding='same',input_shape=input_shape))
    model.add(Conv2D(64, kernel_size=(3, 3),padding='same'))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    #2nd
    model.add(Conv2D(64, kernel_size=(3, 3),padding='same',input_shape=input_shape))
    model.add(Conv2D(64, kernel_size=(3, 3),padding='same'))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    #3d
    model.add(Conv2D(64, kernel_size=(3, 3),padding='same',input_shape=input_shape))
    model.add(Conv2D(64, kernel_size=(3, 3),padding='same'))
    model.add(Activation('relu'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    #4th
    model.add(Flatten())
    model.add(Dense(128))
    model.add(Dropout(0.5))

    #5th
    model.add(Dense(128))
    model.add(Dropout(0.5))

    #6th
    model.add(Dense(22))
    model.add(Activation('softmax'))

    if (print_bool != False):
        model.summary()

    return model