wgan_cnn.py 文件源码

def build_critic(c=0.01):
    # build a relatively standard conv net with LeakyReLUs
    cnn = Sequential()

    cnn.add(Conv2D(32, 3, padding='same', strides=(2, 2),
                   input_shape=(1, 28, 28)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Conv2D(64, 3, padding='same', strides=(1, 1)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Conv2D(128, 3, padding='same', strides=(2, 2)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Conv2D(256, 3, padding='same', strides=(1, 1)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Flatten())

    image = Input(shape=(1, 28, 28))

    features = cnn(image)
    fake = Dense(1, activation='linear', name='critic')(features)

    return Model(inputs=image, outputs=fake)