mnist_acgan.py 文件源码

def build_generator(latent_size):
    # we will map a pair of (z, L), where z is a latent vector and L is a
    # label drawn from P_c, to image space (..., 1, 28, 28)
    cnn = Sequential()

    cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))
    cnn.add(Dense(128 * 7 * 7, activation='relu'))
    cnn.add(Reshape((128, 7, 7)))

    # upsample to (..., 14, 14)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Convolution2D(256, 5, 5, border_mode='same',
                          activation='relu', init='glorot_normal'))

    # upsample to (..., 28, 28)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Convolution2D(128, 5, 5, border_mode='same',
                          activation='relu', init='glorot_normal'))

    # take a channel axis reduction
    cnn.add(Convolution2D(1, 2, 2, border_mode='same',
                          activation='tanh', init='glorot_normal'))

    # this is the z space commonly refered to in GAN papers
    latent = Input(shape=(latent_size, ))

    # this will be our label
    image_class = Input(shape=(1,), dtype='int32')

    # 10 classes in MNIST
    cls = Flatten()(Embedding(10, latent_size,
                              init='glorot_normal')(image_class))

    # hadamard product between z-space and a class conditional embedding
    h = merge([latent, cls], mode='mul')

    fake_image = cnn(h)

    return Model(input=[latent, image_class], output=fake_image)