model = Sequential()
model.add(Cropping2D(cropping=((70, 25), (0, 0)), input_shape=(160, 320, 3)))
model.add(Lambda(lambda x: (x/127.5) - 0.5))
model.add(Convolution2D(24, 5, 5, subsample=(2, 2), activation="elu"))
model.add(Convolution2D(36, 5, 5, subsample=(2, 2), activation="elu"))
model.add(Convolution2D(48, 5, 5, subsample=(2, 2), activation="elu"))
model.add(Convolution2D(64, 3, 3, activation="elu"))
model.add(Convolution2D(64, 3, 3, activation="elu"))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(100, activation='elu'))
model.add(Dense(50, activation='elu'))
model.add(Dense(10, activation='elu'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer=Adam(lr=1.0e-4))
model.summary()