What network architecture did you use? Can you give us some details?
On Sun, Feb 8, 2015 at 5:22 AM, Detlef Schmicker <d...@physik.de> wrote: > Hi, > > I am working on a CNN for winrate and territory: > > approach: > - input 2 layers for b and w stones > - 1. output: 1 layer territory (0.0 for owned by white, 1.0 for owned by > black (because I missed TANH in the first place I used SIGMOID)) > - 2. output: label for -60 to +60 territory leading by black > the loss of both outputs is trained > > the idea is, that this way I do not have to put komi into input and make > the winrate from the statistics of the trained label: > > e.g. komi 6.5: I sum the probabilites from +7 to +60 and get something > like a winrate > > I trained with 800000 positions with territory information through 500 > playouts from oakfoam, which I symmetrized by the 8 transformation leading > to >6000000 positions. (It is expensive to produce the positions due to the > playouts....) > > The layers are the same as the large network from Christopher Clark > <http://arxiv.org/find/cs/1/au:+Clark_C/0/1/0/all/0/1>, Amos Storkey > <http://arxiv.org/find/cs/1/au:+Storkey_A/0/1/0/all/0/1> : > http://arxiv.org/abs/1412.3409 > > > I get reasonable territory predictions from this network (compared to 500 > playouts of oakfoam), the winrates seems to be overestimated. But anyway, > it looks as it is worth to do some more work on it. > > The idea is, I can do the equivalent of lets say 1000 playouts with a call > to the CNN for the cost of 2 playouts some time... > > > Now I try to do a soft turnover from conventional playouts to CNN > predicted winrates within the framework of MC. > > I do have some ideas, but I am not happy with them. > > Maybe you have better ones :) > > > Thanks a lot > > Detlef > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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