Dear Community,
I found that some people use the Kullback leibler divergence loss for training their model when the output layer consists of a one-hot-vector using pytorch.
I actually tried it myself, and it works well. However I do not understand why that works. As given in the documentation, the loss over the output layer L (of length n) to my understanding is:
loss = \sum_{_i}^{n} y_i  log(y_i/x_i)

with i = 0…n, y being the label and x being the estimated prob of the model. But that assumes that y_i is always > 0. Otherwise the loss would be undefined due to log(0).  Therefore I do not understand how I get good results using the KLdiv loss.
Can someone explain whats going on inside the KLdiv function of pytorch? is the label smoothed such that no zeros occur anymore?
I am thankful for any advice.
Cheers,
Dennis
edit: i don’t know why the latex formatting is not working, maybe someone can fix it