Well, if you have no real idea about why it works the best you can do is trying to plot the loss surface. There are some papers in which you can see that, for example, DenseNet loss has a single local minima and it’s a convex structure. Papers in the same line shows that residual connections makes loss surfaces smother and more convex. You can also try to plot the manifold of the classification in order to probe that by using your loss classes are better clusterized than using others.
Anyway I guess when you try a new loss you usually have a reason why. I don’t  think you could create a new loss just applaying random operators like hey im gonna take the log l1^2/norm(output). You probably have had some insight about why it should work better right?