I’m rather new to pytorch (and NN architecture in general).  While experimenting with my model I see that the various Loss classes for pytorch will accept a reduction parameter (none | sum | mean) for example.  The differences are rather obvious regarding what will be returned, but I’m curious when it would be useful to use sum as opposed to mean?  Does it have an effect on the backprop during training?  Or am I really only choosing between a large loss value or smaller (average) loss value for aesthetic reasons for human readability?