torch.nn.functional vs torch.nn - Pytorch
While adding loss in Pytorch, I have the same function in torch.nn.Functional as well as in torch.nn. what is the difference ?
 torch.nn.CrossEntropyLoss() and torch.nn.functional.cross_entropy 

Putting same text from PyTorch discussion forum @Alban D has given answer to similar question.
F.cross entropy vs torch.nn.Cross_Entropy_Loss

There isn’t much difference for losses.
The main difference between the nn.functional.xxx and the nn.Xxx is that one has a state and one does not.
This means that for a linear layer for example, if you use the functional version, you will need to handle the weights yourself (including passing them to the optimizer or moving them to the gpu) while the nn.Xxx version will do all of that for you with .parameters() or .to(device).
For loss functions, as no parameters are needed (in general), you won’t find much difference. Except for example, if you use cross entropy with some weighting between your classes, using the nn.CrossEntropyLoss() module, you will give your weights only once while creating the module and then use it. If you were using the functional version, you will need to pass the weights every single time you will use it.

