Conclusion

In problems that are at first glance completely different (single-label classification, multi-label classification, and regression), we end up using the same model with just different numbers of outputs. The loss function is the one thing that changes, which is why it’s important to double-check that you are using the right loss function for your problem.

fastai will automatically try to pick the right one from the data you built, but if you are using pure PyTorch to build your DataLoaders, make sure you think hard when you have to decide on your choice of loss function, and remember that you most probably want:

• nn.CrossEntropyLoss for single-label classification
• nn.BCEWithLogitsLoss for multi-label classification
• nn.MSELoss for regression