image_converter.py 文件源码

def _to_texture_feature(self, layers):
        """
        :param layers: predicted value of each layer
        :type layers: List[chainer.Variable]
        """
        subvectors = []
        for layer_index in range(len(layers)):
            layer = layers[layer_index]
            beta = numpy.sqrt(numpy.float32(self.model.beta[layer_index]) / len(layers))
            texture_matrix = float(beta) * neural_art.utility.get_matrix(layer)
            texture_matrix /= numpy.sqrt(numpy.prod(texture_matrix.data.shape))  # normalize
            subvector = chainer.functions.reshape(texture_matrix, (numpy.prod(texture_matrix.data.shape),))
            subvectors.append(subvector)
        return chainer.functions.concat(subvectors, axis=0)