keras_auto_encoder.py 文件源码

def deep_decoder2(input_shape):
    encoded = Input(shape=input_shape)
    print 'encoded shape:', encoded.get_shape().as_list()
    x = encoded
    # x = BatchNormalization(mode=2, axis=3)(encoded)

    # batch_size, h, w, _ = tf.shape(x)
    batch_size = tf.shape(x)[0]
    # dim: (1, 1, 512)
    x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512],
                 activation='relu', border_mode='same', subsample=(4, 4))(encoded)
    x = BatchNormalization(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 = BatchNormalization(mode=2, axis=3)(x)
    # dim: (8, 8, 236)
    # h *= 2; w *= 2
    x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128),
                 activation='relu', border_mode='same', subsample=(2, 2))(x)
    x = BatchNormalization(mode=2, axis=3)(x)
    # dim: (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 = BatchNormalization(mode=2, axis=3)(x)
    # dim: (32, 32, 64)
    x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3),
                 activation='linear', border_mode='same', subsample=(1, 1))(x)
    decoded = BatchNormalization(mode=2, axis=3)(x)
    return Model(encoded, decoded)