I am running a Transfer Learning scenario with a ResNet model. The original work was a classifier with hundreds of classes, and it used the CrossEntropyLoss function nn.CrossEntropyLoss().
A thread 7  here suggest BCELoss, but there is BCEWithLogitsLoss that also seems fit.
In a confusion matrix, I want to optimize for the least amount of False Positives, even if it hurts my True Positive score.
What (of all) loss functions should I use ? And why ?