mnist_acgan.py 文件源码

def build_discriminator():
    # build a relatively standard conv net, with LeakyReLUs as suggested in
    # the reference paper
    cnn = Sequential()

    cnn.add(Convolution2D(32, 3, 3, border_mode='same', subsample=(2, 2),
                          input_shape=(1, 28, 28)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Convolution2D(64, 3, 3, border_mode='same', subsample=(1, 1)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Convolution2D(128, 3, 3, border_mode='same', subsample=(2, 2)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Convolution2D(256, 3, 3, border_mode='same', subsample=(1, 1)))
    cnn.add(LeakyReLU())
    cnn.add(Dropout(0.3))

    cnn.add(Flatten())

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

    features = cnn(image)

    # first output (name=generation) is whether or not the discriminator
    # thinks the image that is being shown is fake, and the second output
    # (name=auxiliary) is the class that the discriminator thinks the image
    # belongs to.
    fake = Dense(1, activation='sigmoid', name='generation')(features)
    aux = Dense(10, activation='softmax', name='auxiliary')(features)

    return Model(input=image, output=[fake, aux])