def __init__(self, layer):
'''
# Arguments
layer: an instance of Convolution2D layer, whose configuration
will be used to initiate DConvolution2D(input_shape,
output_shape, weights)
'''
self.layer = layer
weights = layer.get_weights()
W, b = weights
config = layer.get_config()
# Set up_func for DConvolution2D
input = Input(shape=layer.input_shape[1:])
output = Convolution2D.from_config(config)(input)
up_func = Model(input, output)
up_func.layers[1].set_weights(weights)
self.up_func = up_func
# Flip W horizontally and vertically,
# and set down_func for DConvolution2D
W = np.transpose(W, (0, 1, 3, 2))
W = W[::-1, ::-1, :, :]
config['filters'] = W.shape[3]
config['kernel_size'] = (W.shape[0], W.shape[1])
b = np.zeros(config['filters'])
input = Input(shape=layer.output_shape[1:])
output = Convolution2D.from_config(config)(input)
down_func = Model(input, output)
down_func.layers[1].set_weights((W, b))
self.down_func = down_func
