This is my guess as to what the number of parameters actually is: First layer: 128 * (5*5*36 + 19*19) (128 filters of size 5x5 on 36 layers of input, position-dependent biases) 11 hidden layers: 11 * 128 * (3*3*128 + 19*19) (128 filters of size 3x3 on 128 layers of input, position-dependent biases) Final layer: 2 *(3*3*128 + 19*19) (2 filters of size 3x3 on 128 layers of input, position-dependent biases)
Total number of parameters: 2294738 Did I get that right? I have the same question about the use of symmetry as Hugh. Álvaro. On Thu, Dec 25, 2014 at 8:49 PM, Hugh Perkins <hughperk...@gmail.com> wrote: > 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 >
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