[kbob]

I am not an ML guy, so I'm asking from a position of ignorance. (-:

But what's going on when some of the implementations of a standard algorithm don't converge, and different hardware has different accuracy rates on the same algorithm? Are DNNs really that flaky? And does it really make sense to be doing performance comparisons when the accuracy performance doesn't match?

Is the root problem that ResNet-50 works best with a smaller batch size?

And how do you do meaningful research into new DNNs if there's always an "Maybe if I ran it again over there I'd get better results" factor?

Thank you.