model.py

from keras.models import Sequential
from keras.layers import Flatten, Dense, Lambda, Conv2D, Cropping2D, Dropout, \
    Reshape, BatchNormalization, ELU, MaxPooling2D
from keras.optimizers import Adam


def cnn_model():
    model = Sequential()
    model.add(Conv2D(12, (5, 5), activation='elu', input_shape=(240, 320, 3), kernel_initializer='he_normal'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(24, (5, 5), activation='elu', padding='SAME',  kernel_initializer='he_normal'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(36, (3, 3), activation='elu', kernel_initializer='he_normal'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(48, (3, 3), activation='elu', kernel_initializer='he_normal'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(64, (3, 3), strides=(1, 1), kernel_initializer='he_normal'))
    model.add(ELU())
    model.add(Dropout(0.5))
    model.add(Conv2D(60, (3, 3), strides=(1, 1), padding='valid', activation='elu', kernel_initializer='he_normal'))
    model.add(Flatten())
    model.add(Dense(1164, activation='elu'))
    model.add(Dense(100, kernel_initializer='he_normal', activation='elu'))
    model.add(Dense(50, kernel_initializer='he_normal', activation='elu'))
    model.add(Dense(10, kernel_initializer='he_normal', activation='elu'))
    model.add(Dense(1, kernel_initializer='he_normal', activation='elu'))
    adam = Adam(lr=1e-3)
    model.compile(optimizer=adam, loss='mse')

    return model