hi,
in performance(classification) operator, cross entropy is defined as sum of logarithms of confidence of true label classes divided by nb. of examples, however, I get only the correct results, if I do this but divide by number of examples +1

I know its not a big thing, but I spent a lot of time wondering why I get wrong results according to that definition, but then divided by nb. of ex. +1 and get right results:

cross entropy:
-(log2(1)+log2(0.385)+log2(0.615))/3 = 0.692803777838436
but :

I get the same if I divide by 4 instead of 3.