cifar10_ae.py 文件源码

def decode(y, relu_max):
    print 'decoder input shape:', y._keras_shape
    assert len(y._keras_shape) == 2
    if relu_max:
        x = GaussianNoise(0.2)(y)
        # x = Activation(utils.relu_n(1))(x)
    else:
        x = y

    x = Reshape((1, 1, LATENT_DIM))(x)
    # 1, 1, LATENT_DIM
    if relu_max:
        print 'in decode: relu_max:', relu_max
        x = Activation(utils.scale_up(relu_max))(x)
    # x = BN(mode=2, axis=3)(x) # this bn seems not good? NOT VERIFIED

    # why use 512 instead of 256 here?
    batch_size = keras.backend.shape(x)[0]
    x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512],
                 activation='relu', border_mode='same', subsample=(4,4))(x)
    x = BN(mode=2, axis=3)(x)
    # 4, 4, 512
    x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256],
                 activation='relu', border_mode='same', subsample=(2,2))(x)
    x = BN(mode=2, axis=3)(x)
    # 8, 8, 256
    x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128),
                 activation='relu', border_mode='same', subsample=(2,2))(x)
    x = BN(mode=2, axis=3)(x)
    # 16, 16, 256
    x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64),
                 activation='relu', border_mode='same', subsample=(2,2))(x)
    x = BN(mode=2, axis=3)(x)
    # 32, 32, 64
    x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3),
                 activation='linear', border_mode='same', subsample=(1,1))(x)
    # 32, 32, 3
    x = BN(mode=2, axis=3)(x)
    return x