Ok, thanks for the clarification. I agree if you are overfitting lowest loss is not always best, and I did not think about that aspect when I wrote my reply. I

Ok, thanks for the clarification. I agree if you are overfitting lowest loss is not always best, and I did not think about that aspect when I wrote my reply. I think with everything you describe is one of the main "gripes" I as a more traditional engineer/scientist have with ML. It often seems like a giant black box that I know is generally vastly overfitted and it is often extremely difficult/impossible why we get the solutions that we get, i.e. we don't really get scientific insight. Nevertheless I realise it is an extremely powerful tool. Hope you can catch up with some sleep today, sleep deprivation effects can only be ignored for so long (especially as you get older as I have noticed).