BCEWithLogitsLoss Here are some additional explanations on using Binary Cross-Entropy Loss with Pytorch in python.

## Introduction

I have been asked recently on How to find the code BCEWithLogitsLoss (https://discuss.pytorch.org/t/implementation-of-binary-cross-entropy/98715/2) for a function called in PYTORCH with the function handle_torch_function it takes me some time to understand it, and i will share it with you if it helps:

The question was about BCEWithLogitsLoss = BCELoss + sigmoid() ? My answer can be apply if you want to analyse the code of all the functions that figures in the ret dictionnary from https://github.com/pytorch/pytorch/blob/master/torch/overrides.py

BCEWithLogitsLoss is a combination of BCELOSS + a Sigmoid layer i. This is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, it takes advantage of the log-sum-exp trick for numerical stability see : https://en.wikipedia.org/wiki/LogSumExp

## BCEWithLogitsLoss in details

- The code of the BCEWithLogitsLoss Class can be found in https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/loss.py

def forward(self, input: Tensor, target: Tensor) -> Tensor: return F.binary_cross_entropy_with_logits(input, target, self.weight, pos_weight=self.pos_weight, reduction=self.reduction)

The F oject is imported from functionnal.py here : https://github.com/pytorch/pytorch/blob/master/torch/nn/functional.py

You will find the function called

def binary_cross_entropy_with_logits(input, target, weight=None, size_average=None, reduce=None, reduction='mean', pos_weight=None):

It calls the handle_torch_function in https://github.com/pytorch/pytorch/blob/master/torch/overrides.py

You will find an entry of the function binary_cross_entropy_with_logits in the ret dictionnary wich contain every function that can be overriden in pytorch.

This is the Python implementation of **torch_function**

More info in https://github.com/pytorch/pytorch/issues/24015

Then the code called is in the C++ File

https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Loss.cpp

Tensor binary_cross_entropy_with_logits(const Tensor& input, const Tensor& target, const Tensor& weight, const Tensor& pos_weight, int64_t ...

## LIENS DE RESSOURCES :

https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html

https://discuss.pytorch.org/t/bceloss-vs-bcewithlogitsloss/33586

https://www.semicolonworld.com/question/61244/implementing-bcewithlogitsloss-from-pytorch-in-keras

https://128mots.com/index.php/en/category/non-classe-en/