simple-generative-model-regressor.py 文件源码

def get_basic_generative_model(input_size):
    input = Input(shape=(1, input_size, 1))
    l1a, l1b = wavenetBlock(10, 5, 2, 1, 3)(input)
    l2a, l2b = wavenetBlock(1, 2, 4, 1, 3)(l1a)
    l3a, l3b = wavenetBlock(1, 2, 8, 1, 3)(l2a)
    l4a, l4b = wavenetBlock(1, 2, 16, 1, 3)(l3a)
    l5a, l5b = wavenetBlock(1, 2, 32, 1, 3)(l4a)
    l6 = merge([l1b, l2b, l3b, l4b, l5b], mode='sum')
    l7 = Lambda(relu)(l6)
    l8 = Convolution2D(1, 1, 1, activation='relu')(l7)
    l9 = Convolution2D(1, 1, 1)(l8)
    l10 = Flatten()(l9)
    l11 = Dense(1, activation='tanh')(l10)
    model = Model(input=input, output=l11)
    model.compile(loss='mse', optimizer='rmsprop', metrics=['accuracy'])
    model.summary()
    return model