doodle.py 文件源码

def content_loss(self):
        """Return a list of Theano expressions for the error function, measuring how different the current image is
        from the reference content that was loaded.
        """

        content_loss = []
        if args.content_weight == 0.0:
            return content_loss

        # First extract all the features we need from the model, these results after convolution.
        extractor = theano.function([self.model.tensor_img], self.model.get_outputs('conv', self.content_layers))
        result = extractor(self.content_img)

        # Build a list of loss components that compute the mean squared error by comparing current result to desired.
        for l, ref in zip(self.content_layers, result):
            layer = self.model.tensor_outputs['conv'+l]
            loss = T.mean((layer - ref) ** 2.0)
            content_loss.append(('content', l, args.content_weight * loss))
            print('  - Content layer conv{}: {} features in {:,}kb.'.format(l, ref.shape[1], ref.size//1000))
        return content_loss