model_2d.py

from keras import Sequential
from keras.layers import Conv2D, BatchNormalization, MaxPool2D, Flatten, Dense, Dropout, ELU


def define_model():
    model = Sequential()
    model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(128, 128, 3), kernel_initializer='glorot_uniform', data_format="channels_last"))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(Conv2D(64, (3, 3), strides=(1, 1), kernel_initializer='glorot_uniform'))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(128, (3, 3), strides=(1, 1), kernel_initializer='glorot_uniform'))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(Conv2D(128, (3, 3), strides=(1, 1), kernel_initializer='glorot_uniform'))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(256, (3, 3), strides=(1, 1), kernel_initializer='glorot_uniform'))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(Conv2D(256, (3, 3), strides=(1, 1), kernel_initializer='glorot_uniform'))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Flatten())
    model.add(Dense(2048))
    model.add(ELU())
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Dense(8, activation='softmax'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model