Hi,

I came across this formula of how pytorch calculates cross-entropy loss-

```
loss(x, class) = -log(exp(x[class]) / (\sum_j exp(x[j])))
= -x[class] + log(\sum_j exp(x[j]))
```

Could anyone explain in more mathematical way which expression pytorch is using to calculate it as I have encountered only the following ways-

where p is the target and q is the predicted

while another representation,I have seen is

`Loss(Y_hat,Y) = -Ylog(Y_hat)`

I mean, I am not able to understand the code which pytorch uses to calculate it.