On 19-06-17 17:38, Vincent Richard wrote: > During my research, I’ve trained a lot of different networks, first on > 9x9 then on 19x19, and as far as I remember all the nets I’ve worked > with learned quickly (especially during the first batches), except the > value net which has always been problematic (diverge easily, doesn't > learn quickly,...) . I have been stuck on the 19x19 value network for a > couple months now. I’ve tried countless of inputs (feature planes) and > lots of different models, even using the exact same code as others. Yet, > whatever I try, the loss value doesn’t move an inch and accuracy stays > at 50% (even after days of training). I've tried to change the learning > rate (increase/decrease), it doesn't change. However, if I feed a stupid > value as target output (for example black always win) it has no trouble > learning. > It is even more frustrating that training any other kind of network > (predicting next move, territory,...) goes smoothly and fast. > > Has anyone experienced a similar problem with value networks or has an > idea of the cause?
1) What is the training data for the value network? How big is it, how is it presented/shuffled/prepared? 2) What is the *exact* structure of the network and training setup? My best guess would be an error in the construction of the final layers. -- GCP _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
