ptrblck:

I’m not sure I understand this approach. Could you explain it a bit more?


Of course. The idea here would be to concatenate one hot encoding of each 5 classes so that I would have 25 outcome/target from the network, 5 for each classes as I want, then split this 25 array in 5 accordingly supposing there will be one hot in each of the 5 sub arrays and use the argmax to get the predicted class.
Does that make sense? in this case I don’t need 5 different heads, but not sure what loss would be appropriate, as from the documentation is not super clear whether cross_entropy can still work or I should use something like MultiLabelMarginLoss as others requires the target to be between 0 and 1.