Hi,
in my customised Loss function, I need to change the value of net output in positions where a condition is met. And I must use operations that guarantee a correct  back propagation.
The code:
output[target != 1] = 1 - output[target != 1] 
throws an error: “RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation”
I am wandering if I can bypass this error by using bultin torch functions.
I tried to use:
output = torch.where(target != 1, 1 - output[target != 1] , output)

but I now get the error:
“RuntimeError: The size of tensor a (800) must match the size of tensor b (3686385) at non-singleton dimension 3”
I think this is due to the fact that in torch.where(condition, a, b)
a and b must be scalars. They cannot be matrices. Is this correct?
Any suggestion for solving my problem?