Questions on loss calculation

[data-science-lover]

I have several questions about this. First of all, I noticed that the calculation of Loss for a two-class model was treated differently. The model I am using being of two classes, errors were appearing (including memory limits). So I removed this condition to calculate only with the cross entropy F.cross_entropy(output, target) because a binary classification can be treated as multi-class classification:

The same thing was done at the different locations where the Loss was calculated.

I have some doubts about the loss values calculated in the train() function of train_distilled_images ... Indeed, the loss increases at each step instead of decreasing...

[data-science-lover]

I don't understand because the loss calculated in the test_runner of the main script, gives VERY different values from those obtained in train_distilled_images ...

So to compare the loss obtained with the distilled images and those obtained with the real dataset should we rather rely on the values of the main.py script or train_distilled_images?

Thank you in advance.