vgg_network.py 文件源码

def content_loss(self, layers):
        activations = [self.activations_for_layer(i) for i in layers]
        activation_diffs = [
            tf.sub(
                tf.tile(tf.slice(a, [self.num_style, 0, 0, 0], [self.num_content, -1, -1, -1]), [self.num_synthesized - self.num_content + 1, 1, 1, 1]),
                tf.slice(a, [self.num_style + self.num_content, 0, 0, 0], [self.num_content, -1, -1, -1]))
            for a in activations]
        # This normalizer is in JCJohnson's paper, but not Gatys' I think?
        Ns = [a.get_shape().as_list()[1] * a.get_shape().as_list()[2] * a.get_shape().as_list()[3] for a in activations]
        content_loss = tf.div(tf.add_n([tf.div(tf.reduce_sum(tf.square(a)), n) for a, n in zip(activation_diffs, Ns)]), 2.0)
        return content_loss