On Wed, Dec 31, 2014 at 9:29 PM, Hugh Perkins <hughperk...@gmail.com> wrote: > - finally, started to get a signal, on the kgsgo data :-) Not a very strong > signal, but a signal :-) : > > test accuracy: 364/10000 3.64%
Up to 35.1% test accuracy for next-move-prediction task now, still 9% lower than Clarke and Storkey, but gradually moving forward... - https://github.com/hughperkins/kgsgo-dataset-preprocessor probably works ok now - three datasets available: - test: moves from 100 randomly selected (but fixed) games, totalling 18860 moves - train10k: moves from 10,000 randomly selected (but fixed) games, non-overlapping with test games, about 1.8 million moves - trainall: moves from all games up to end of 2014, non-overlapping with test games, about 32 million moves (I think; I've only used 16 millon of them so far) - Using 16 million moves from trainall, and 6 layers of 32 5x5 filters, results after 3 epochs: after epoch 3 6.05158e+07 ms annealed learning rate: 0.0001 training loss: 4.11055e+07 train accuracy: 5752089/16000000 35.9506% test accuracy: 6623/18860 35.1166% 6.05 * 10^7 milliseconds is about 14 hours. Note that no obvious overtraining here, so can probably add more layers (eg the 12 clarke and storkey use), to get more test accuracy. commandline used for this: ./clconvolve1 dataset=kgsgoall netdef=32c5{z}-32c5{z}-32c5{z}-32c5{z}-32c5{z}-32c5{z}-500n-361n numepochs=15 learningrate=0.0001 numtrain=16000000 _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
