how to backward if my loss is something about the derivative of the net output?
for example:
my input is ‘u’, then my net output can be seemed as f(u).
and my target is target = a*( f ’ (u)|u ) + b, " f ’ (u)|u" means the derivative of ’ f ’ w.r.t ‘u’
so my loss may will be ‘| ground truth - target |’.
Is this kind of loss can be backwarded in pytorch?
I tried to backward something about the derivative, but it can’t work.
here is my code :

import torch
from torch.autograd import Variable
x = Variable(torch.ones(2, 2), requires_grad=True)
y = x + 2
out = y
out.backward(torch.ones(2, 2))
k = 2*x.grad
k.backward()

the error is :
k.backward()
RuntimeError: element 0 of variables tuple is volatile

it seems like pytorch will not save the parameters when it calculates the derivative.
Has there any method to solve this kind of questions?
Thank you.

For this to work you need to pass the create_graph=True option to the first .backward() to let it know that you need to be able to call .backward() on the grad itself.
Also if you need the gradient wrt a specific variable, you can use the autograd.grad(outputs, inputs) function to get the derivative of the output(s) wrt the input(s). For example out.backward() is equivalent to autograd.grad(out, model.parameters()).

Thank you, I have been looking for an example on how to use autograd api. IMO, a small example of using autograd.grad api should be added to pytorch doc. Furthermore, examples of higher-order derivatives would help as well. Say, for example Double Backpropagation, especially since loss.backward(create_graph=True) no longer works, causing memory leak due to some changes in C++ backend(weak to strong pointers), issue page.