[Solved] What is the correct way to implement custom loss function?

[Hengck (Heng Cher Keng)]

can i confirm that there are two ways to write customized loss function:

1. using nn.Moudule
   Build your own loss function in PyTorch - #18 by edgarriba
   Write Custom Loss Function - #2 by fmassa

Here you need to write functions for init() and forward().
backward is not requied. But how do I indicate that the target does not need to compute gradient?

2)using Functional (this post)

[Solved] What is the correct way to implement custom loss function?

I tried to implement my own custom loss based on the tutorial in extending autograd. Here is the implementation outline: class MyCustomLoss(Function): def forward(self, input, target): ... # implementation return loss # a single number (averaged loss over batch samples) def backward(self, grad_output): ... # implementation return grad_input The forward function take an input from the previous layer and target which contains array of labels (categoric…

Here you need to write functions for both forward() and backward()