Totally, I’m in agreement there. My mentioning of “number goes down” is in a critical sense as it omits the rest. So folding a second number in as a list means it’s easier to lose the significance of the second number and it’s importance. Even though it’s clunkier, my original suggestion of having a second, explicitly different, name sent out would mean that they are decoupled. So you can conceptually (and visually/patching) separate fit (i.e. trainingloss) and validationloss and can then plot them like @doett does in the other thread:
You can obviously still unpack a list after route fit, but you would need to know that you have to do this in a first place and why.
Having spent a week steeped in some of this stuff from a more datascience-y perspective, and seeing the manifest difference in what a properly trained NN can do vs what I was doing with my iteration loop, I know that it’s not a simple problem when it comes to UX, and short of some kind of hyper-parameter tuning thing, the results will be limited to whatever architecture you pop in when making the object in the first place.
Being able to plot both sets of losses (in the first place), and perhaps having this baked into the patches/examples with some context, would go a long way to getting some better trained NNs within Max without having to just set a huge fit message and pinwheel until it (presumably) returns back that it’s bailed at some point.