style_transfer.py 文件源码

def st_conv_inception_4_superresolution(input_shape, weights_path=None, mode=0, nb_res_layer=5):
    if K.image_dim_ordering() == 'tf':
        channel_axis = 3
    else:
        channel_axis = 1

    input = Input(shape=input_shape, name='input_node', dtype=K.floatx())
    out = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(input)
    last_out = BatchNormalization(mode=mode, axis=channel_axis, momentum=0.9, gamma_init='he_normal')(out)
    last_out = Activation('relu')(last_out)

    for i in range(nb_res_layer):
        out = inception_layer(last_out, K.image_dim_ordering(), channel_axis, mode)
        last_out = merge([last_out, out], mode='sum')

    out = Convolution2D(128, 3, 3, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(1, 1), border_mode='same', activation='linear')(last_out)
    out = ConvolutionTranspose2D(3, 5, 5, dim_ordering=K.image_dim_ordering(), 
        init='he_normal', subsample=(4, 4), border_mode='same', activation='linear')(out)
    out = ScaledSigmoid(scaling=255., name="output_node")(out)

    model = Model(input=[input], output=[out])

    if weights_path:
        model.load_weights(weights_path)

    return model