model.py 文件源码

def steering_net():
    model = Sequential()
    model.add(Convolution2D(24, 5, 5, init = normal_init, subsample= (2, 2), name='conv1_1', input_shape=(66, 200, 3)))
    model.add(Activation('relu'))
    model.add(Convolution2D(36, 5, 5, init = normal_init, subsample= (2, 2), name='conv2_1'))
    model.add(Activation('relu'))
    model.add(Convolution2D(48, 5, 5, init = normal_init, subsample= (2, 2), name='conv3_1'))
    model.add(Activation('relu'))
    model.add(Convolution2D(64, 3, 3, init = normal_init, subsample= (1, 1), name='conv4_1'))
    model.add(Activation('relu'))
    model.add(Convolution2D(64, 3, 3, init = normal_init, subsample= (1, 1), name='conv4_2'))
    model.add(Activation('relu'))
    model.add(Flatten())
    model.add(Dense(1164, init = normal_init, name = "dense_0"))
    model.add(Activation('relu'))
    #model.add(Dropout(p))
    model.add(Dense(100, init = normal_init,  name = "dense_1"))
    model.add(Activation('relu'))
    #model.add(Dropout(p))
    model.add(Dense(50, init = normal_init, name = "dense_2"))
    model.add(Activation('relu'))
    #model.add(Dropout(p))
    model.add(Dense(10, init = normal_init, name = "dense_3"))
    model.add(Activation('relu'))
    model.add(Dense(1, init = normal_init, name = "dense_4"))
    model.add(Lambda(atan_layer, output_shape = atan_layer_shape, name = "atan_0"))

    return model