On 23-08-16 08:57, Detlef Schmicker wrote: > So, if somebody is sure, it is measured against GoGod, I think a > number of other go programmers have to think again. I heard them > reaching 51% (e. g. posts by Hiroshi in this list)
I trained a 128 x 14 network for Leela 0.7.0 and this gets 51.1% on GoGoD. Something I noticed from the papers is that the prediction percentage keeps going upwards with more epochs, even if slowly, but still clearly up. In my experience my networks converge rather quickly (like >0.5% per epoch after the first), get stuck, get one more 0.5% gain if I lower the learning rate (by a factor 5 or 10) and don't gain any more regardless of what I do thereafter. I do use momentum. IIRC I tested without momentum once and it was worse, and much slower. I did not find any improvement in playing strength from doing Facebook's 3 move prediction. Perhaps it needs much bigger networks than 128 x 12. Adding ladder features also isn't good enough to (consistently) keep the network from playing into them. (And once it's played the first move, you're totally SOL because the resulting positions aren't in the training set and you'll get 99% confidence for continuing the losing ladder moves) I'm currently doing a more systematic comparison of all methods (and GoGoD vs KGS+GoGoD) on 128 x 12, and testing the resulting strength (rather than looking at prediction %). I'll post the results here, if anything definite comes out of it. -- GCP _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
