Detlef wrote: > P.S.: As we all might be trying to start incorporating NN into our engines, we might bundle our resources, at least for the first start?! Maybe exchanging open source software links for NN. I personally would have started trying NN some time ago, if iOS had OpenCL support, as my aim is to get a strong iPad go program....
I would think that if we just want the 'playing game' part, that we don't each need the whole training-bit, just the results of the training, and since this is the most expensive bit, one option could be to somehow 'crowd-source' generation of these weights? (Although, if that turns out anything like voxforge, that will takes years .... http://voxforge.org/home/downloads/metrics ... so it might be easier to just shell out the 2000usd or so to run training oneself.) On 12/20/14, [email protected] <[email protected]> wrote: > Send Computer-go mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > http://computer-go.org/mailman/listinfo/computer-go > or, via email, send a message with subject or body 'help' to > [email protected] > > You can reach the person managing the list at > [email protected] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Computer-go digest..." > > > Today's Topics: > > 1. Re: Teaching Deep Convolutional Neural Networks toPlay Go > (Hiroshi Yamashita) > 2. Teaching Deep Convolutional Neural Networks to Play Go > (Hugh Perkins) > 3. Move Evaluation in Go Using Deep Convolutional Neural > Networks (Hugh Perkins) > 4. Re: Move Evaluation in Go Using Deep Convolutional Neural > Networks (Stefan Kaitschick) > 5. Re: Move Evaluation in Go Using Deep Convolutional Neural > Networks (Robert Jasiek) > 6. Re: Move Evaluation in Go Using Deep Convolutional Neural > Networks (Detlef Schmicker) > 7. Re: Move Evaluation in Go Using Deep Convolutional > NeuralNetworks (Hiroshi Yamashita) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sat, 20 Dec 2014 14:16:18 +0900 > From: "Hiroshi Yamashita" <[email protected]> > To: <[email protected]> > Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks > toPlay Go > Message-ID: <40BCB936B9524E928ECA04073CBD800D@i3540> > Content-Type: text/plain; format=flowed; charset="iso-8859-1"; > reply-type=original > > Hi Martin, > >> I put two commented games on >> http://webdocs.cs.ualberta.ca/~mmueller/fuego/Convolutional-Neural-Network.html >> > > Thank you for the report. It was fun. > I'm also surprised CNN can play move 185 in Game 1. > CNN uses "1, 2, or 3 or more liberties" info. B libs changed > from 4 to 3. And W libs was 3. It looks CNN can not understand > this difference, but he could. > > Hiroshi Yamashita > > > > ------------------------------ > > Message: 2 > Date: Sat, 20 Dec 2014 15:54:02 +0800 > From: Hugh Perkins <[email protected]> > To: [email protected] > Subject: [Computer-go] Teaching Deep Convolutional Neural Networks to > Play Go > Message-ID: > <canvm6qauhlnom16bdrakdkcqzboeicv8ntsgjvjrifj9zwz...@mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > On Sun Dec 14 23:53:45 UTC 201, Hiroshi Yamashita wrote: >> Teaching Deep Convolutional Neural Networks to Play Go >> http://arxiv.org/pdf/1412.3409v1.pdf > > Wow, this resembles somewhat what I was hoping to do! But now I > should look for some other avenue :-) But > I'm surprised it's only published on arxiv, I was hoping that > something like this could > be conference-worthy? > > > ------------------------------ > > Message: 3 > Date: Sat, 20 Dec 2014 16:37:21 +0800 > From: Hugh Perkins <[email protected]> > To: [email protected] > Subject: [Computer-go] Move Evaluation in Go Using Deep Convolutional > Neural Networks > Message-ID: > <CANvm6qCGKcuafdaSY2bcgH16KF=vg15sytjv8byzkq8vipt...@mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > On Fri Dec 19 23:17:23 UTC 2014, Aja Huang wrote: >> We've just submitted our paper to ICLR. We made the draft available at >> http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf > > Cool... just out of curiosity, did a back-of-an-envelope estimation of the > cost of training your and Clark and Storkey's network, if renting time > on AWS GPU instances and came up with: > - Clark and Storkey: 125 usd (4 days * 2 instances * 0.65usd/hour) > - Yours: 2025usd(cost of Clark and Storkey * 25/7 epochs * > 29.4/14.7 action-pairs * 12/8 layers) > > Probably a bit high for me personally to just spend one weekend for fun, > but not outrageous at all in fact, if the same technique was being used by > an organization. > > > ------------------------------ > > Message: 4 > Date: Sat, 20 Dec 2014 09:43:49 +0100 > From: Stefan Kaitschick <[email protected]> > To: [email protected] > Subject: Re: [Computer-go] Move Evaluation in Go Using Deep > Convolutional Neural Networks > Message-ID: > <CAJRqE7xjy5r=rwxq9pjmoxsv3baca1j1qiq3a-cgfzajnre...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Great work. Looks like the age of nn is here. > How does this compare in computation time to a heavy MC move generator? > > One very minor quibble, I feel like a nag for even mentioning it: You write > "The most frequently cited reason for the difficulty of Go, compared to > games such as Chess, Scrabble > or Shogi, is the difficulty of constructing an evaluation function that can > differentiate good moves > from bad in a given position." > > If MC has shown anything, it's that computationally, it's much easier to > suggest a good move, than to evaluate the position. > This is still true with your paper, it's just that the move suggestion has > become even better. > > Stefan > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: > <http://computer-go.org/pipermail/computer-go/attachments/20141220/27c1afe7/attachment-0001.html> > > ------------------------------ > > Message: 5 > Date: Sat, 20 Dec 2014 10:24:03 +0100 > From: Robert Jasiek <[email protected]> > To: [email protected] > Subject: Re: [Computer-go] Move Evaluation in Go Using Deep > Convolutional Neural Networks > Message-ID: <[email protected]> > Content-Type: text/plain; charset=UTF-8; format=flowed > > On 20.12.2014 09:43, Stefan Kaitschick wrote: >> If MC has shown anything, it's that computationally, it's much easier to >> suggest a good move, than to evaluate the position. > > Such can only mean an improper understanding of positional judgement. > Positional judgement depends on reading (or MC simulation of reading) > but the reading has a much smaller computational complexity because > localisation and quiescience apply. > > The major aspects of positional judgement are territory and influence. > Evaluating influence is much easier than evaluating territory if one > uses a partial influence concept: influence stone difference. Its major > difficulty is the knowledge of which stones are alive or not, however, > MC simulations applied to outside stones should be able to assess such > with reasonable certainty fairly quickly. Hence, the major work of > positional judgement is assessment of territory. See my book Positional > Judgement 1 - Territory for that. By designing (heuristically or using a > low level expert system) MC for its methods, territorial positional > judgement by MC should be much faster than ordinary MC because much > fewer simulations should do. However, it is not as elegant as ordinary > MC because some expert knowledge is necessary or must be approximated > heuristically. Needless to say, keep the computational complexity of > this expert knowledge low. > > -- > robert jasiek > > > ------------------------------ > > Message: 6 > Date: Sat, 20 Dec 2014 11:21:16 +0100 > From: Detlef Schmicker <[email protected]> > To: [email protected] > Subject: Re: [Computer-go] Move Evaluation in Go Using Deep > Convolutional Neural Networks > Message-ID: <[email protected]> > Content-Type: text/plain; charset="UTF-8" > > Am Samstag, den 20.12.2014, 09:43 +0100 schrieb Stefan Kaitschick: >> Great work. Looks like the age of nn is here. >> >> How does this compare in computation time to a heavy MC move >> generator? >> >> >> One very minor quibble, I feel like a nag for even mentioning it: You >> write >> "The most frequently cited reason for the difficulty of Go, compared >> to games such as Chess, Scrabble >> or Shogi, is the difficulty of constructing an evaluation function >> that can differentiate good moves >> from bad in a given position." >> >> >> If MC has shown anything, it's that computationally, it's much easier >> to suggest a good move, than to evaluate the position. >> >> This is still true with your paper, it's just that the move suggestion >> has become even better. > > It is, but I do not think, that this is necessarily a feature of NN. > NNs might be a good evaluators, but it is much easier to train them for > a move predictor, as it is not easy to get training data sets for an > evaluation function?! > > Detlef > > P.S.: As we all might be trying to start incorporating NN into our > engines, we might bundle our resources, at least for the first start?! > Maybe exchanging open source software links for NN. I personally would > have started trying NN some time ago, if iOS had OpenCL support, as my > aim is to get a strong iPad go program.... > >> >> >> Stefan >> >> >> >> >> _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://computer-go.org/mailman/listinfo/computer-go > > > > > ------------------------------ > > Message: 7 > Date: Sat, 20 Dec 2014 20:33:30 +0900 > From: "Hiroshi Yamashita" <[email protected]> > To: <[email protected]> > Subject: Re: [Computer-go] Move Evaluation in Go Using Deep > Convolutional NeuralNetworks > Message-ID: <BC600A945BD9499E9D0B86865BA36BD5@i3540> > Content-Type: text/plain; format=flowed; charset="UTF-8"; > reply-type=original > > Hi Aja, > >> I hope you enjoy our work. Comments and questions are welcome. > > I have three questions. > > I don't understand minibatch. > Does CNN need 0.15sec for a positon, or 0.15sec for 128 positions? > > ABCDEFGHJ > 9......... White(O) to move. > 8...OO.... Previous Black move is H5(X) > 7..XXXOO.. > 6.....XXO. > 5.......X. > 4......... > 3....XO... > 2....OX... > 1......... > ABCDEFGHJ > > "Liberties after move" means > H7(O) is 5, F8(O) is 6. > "Liberties" means > H5(X) is 3, H6(O) is 2. > "Ladder move" means > G2(O), not E6(O). > > Is this correct? > > Is "KGS rank" set 9 dan when it plays against Fuego? > > Regards, > Hiroshi Yamashita > > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > Computer-go mailing list > [email protected] > http://computer-go.org/mailman/listinfo/computer-go > > ------------------------------ > > End of Computer-go Digest, Vol 59, Issue 25 > ******************************************* > _______________________________________________ Computer-go mailing list [email protected] http://computer-go.org/mailman/listinfo/computer-go
