The method is not likely to work, since the goal of NN training s to reduce the residual error to a random function of the NN inputs. If the NN succeeds at this, then there will be no signal to train against. If the NN fails, then it could be because the NN is not large enough, or because there were aspects of training that were set up incorrectly.
What this comes down to: my experience is that you would be better off making a single network that is twice as large. This feedback applies strictly to the exact method being proposed. It is very likely that there are other ways to use multiple networks that would be significant improvements over using a single network for the whole space. From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Huazuo Gao Sent: Thursday, February 4, 2016 7:51 AM To: computer-go@computer-go.org Subject: Re: [Computer-go] Neural Net move prediction Sounds like some kind of boosting, I suppose? On Thu, Feb 4, 2016 at 7:52 PM Marc Landgraf <mahrgel...@gmail.com <mailto:mahrgel...@gmail.com> > wrote: Hi, lately a friend and me wondered about the following idea. Let's assume you have a reasonably strong move prediction DCNN. What happens if you now train a second net on the same database. When training the first net, you tried to maximize the judgement value of the expert move. But for the second net you now try to maximize the maximum of the judgement of both nets. This means, that the second net does not profit from finding moves the first net can easily find, but instead will try to fill in the weaknesses of the first net. In practical application the easy static usage would be to first expand the top2 candidates of the first net, then mix in the top candidate of the second net, then again the next 2 candidates from the first net, etc. What do you guys think about that? Cheers, Marc _______________________________________________ Computer-go mailing list Computer-go@computer-go.org <mailto:Computer-go@computer-go.org> http://computer-go.org/mailman/listinfo/computer-go
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