How to define a “don't care” class in Pytorch?
I have a time series classification task in which I should output a classification of 3 classes for every time stamp t.
All data is labeled per frame.
In the data set are more than 3 classes [which are also imbalanced].
My net should see all samples sequentially, because it uses that for historical information.
Thus, I can't just eliminate all irrelevant class samples at preprocessing time.
In case of a prediction on a frame which is labeled differently than those 3 classes, I don't care about the result.

How to do this correctly in Pytorch?

Following from this discussion, which was not google searchable, there are two options, both are options of the CrossEntropyLoss:
Option 1
If there is only one class to ignore, use ignore_index=class_index when instantiating the loss.
Option 2
If there are more classes, use weight=weights, with weights.shape==n_classes and torch.sum(weights[ignored_classes]) == 0
