Hi Aja, Couple of questions:
1. connectivity, number of parameters Just to check, each filter connects to all the feature maps below it, is that right? I tried to check that by ball-park estimating number of parameters in that case, and comparing to the section paragraph in your section 4. And that seems to support that hypothesis. But actually my estimate is for some reason under-estimating the number of parameters, by about 20%: Estimated total number of parameters approx = 12 layers * 128 filters * 128 previous featuremaps * 3 * 3 filtersize = 1.8 million But you say 2.3 million. It's similar, so seems feature maps are fully connected to lower level feature maps, but I'm not sure where the extra 500,000 parameters should come from? 2. Symmetry Aja, you say in section 5.1 that adding symmetry does not modify the accuracy, neither higher or lower. Since adding symmetry presumably reduces the number of weights, and therefore increases learning speed, why did you thus decide not to implement symmetry? Hugh _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
