Hi Andrew!



 localh:

weights = [2.0, 1.0, 1.0]



Yes, this is a reasonable approach to more accurately classify
class-0 samples.
That is, adding these class weights to your loss function will train
your network to more often correctly label class-0 samples as
class-0 (fewer false negatives), but at the cost of more frequently
incorrectly labelling class-1 and class-2 samples as class-0 (more
false positives from the perspective of class-0).


more attention to getting the 0s right.
…
punish mistakes for the 0s more than 1 or 2 by:


Just to emphasize what I alluded to above, this depends on what
you mean by “getting the 0s right” and “punish mistakes for the 0s”.
If you consider mislabelling a class-1 as a class-0 (a false positive)
as not “getting the 0s right” and a “mistake for the 0s,” then this
likely won’t accomplish your goal.  But if you’re only counting
mislabelling an actual class-0 (a false negative) as a mistake, this
is the way to go.
Best.
K. Frank