example6_gradient.py 文件源码

def printnorm_backward(self, input_, output):
    global backward_grad
    # input is a tuple of packed inputs
    # output is a Variable. output.data is the Tensor we are interested
    print('Inside ' + self.__class__.__name__ + ' backward')
    print('')
    print('input: ', type(input_))
    print('input[0]: ', type(input_[0]))
    print('output: ', type(output))
    print('output[0]: ', type(output[0]))
    print('')
    print('input size:', input_[0].size())
    print('output size:', len(output))
    print('output[0] size:', output[0].size())
    print('output norm:', output[0].data.norm())

    backward_grad = input_[0].data.numpy()


# This could be useful for using the features produced by a pretrained network
# If all you care about is this feature vector, then use a Variable with volatile=True to speed up inference