Hi KFrank,
Thank you very much for your quick response! I was focused on both output and labels having the same dimension that I couldn’t see beyond that. Your answer was on point.
However, as I understood better how NLLLoss works I realized my problem works better as a Multilabel classification problem instead of a Multiclass one, since each time instant can have multiple classes and NLLLoss is strictly for multiclass problems where only one class can be assigned.
After searching a bit I found that BCEWithLogitsLoss 2 can be a good option to adapt my network to a multilabel problem.
I hope this gives you or anyone that visits this post a bit more insight. I’ll update the post if it works for me. Thank you for your help!
Best,
Patxi.