I would be surprised if my model ever lost to GNU Go on 9x9. It's a lot stronger than Fuego, which already stomps GNU Go. It would be a waste of time to test it vs. GNU Go or even MCTS bots. I only plan on running tests vs. current best models to see how it does against the state of the art 9x9 nets
On Mon, Jan 27, 2020, 06:39 cody2007 via Computer-go < computer-go@computer-go.org> wrote: > Thanks again for your thoughts and experiences Rémi and Igor. > > I'm still puzzled by what is making training slower for me than Rémi > (although I wouldn't be surprised if Igor's results were faster when > matched for hardware, model size, strength etc-- see below). Certainly komi > sounds like it might help a lot. I'm going to have to check out the code > from David Wu. > > It takes me longer than a day for "training" to actually start with my > code -- because I first generate 128*2*32*35 = 285k training samples before > even running the first round of backprop. After the first day, therefore, > my model is always still entirely random. So, possibly: > > (1) either your and David Wu's implementations are faster in wall clock > time computationally > (2) backprop is being started before the initial training buffer is filled > (the Wu paper used 250k but it's not 100% clear to me if training did not > start until that initial buffer was filled) > (3) "training" time is being counted as the time when backprop starts > regardless of how long the initial training buffer took to create. > > Another thing is that I'm not using any of the techniques beyond AlphaGo > Zero that David Wu used. So, depending on if you guys are using some or all > of those additional features and/or loss functions, it'd be expected that > you're getting much faster training than me. I was actually starting to > test adding some of his ideas from that paper to my code a while back but > then coincidentally discovered the models I was training weren't as > horrible as I had first thought. > > Have either of you ever benchmarked your 7x7 (or 9x9) models against GNU > Go? > > By the way, all benchmarking against GNU Go that I've reported was in > single-pass mode only (i.e., I was not running the tree search on top of > the net outputs) > > Thanks, > Cody > > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ > On Sunday, January 26, 2020 11:22 AM, Igor Polyakov < > weiqiprogramm...@gmail.com> wrote: > > I trained using David Wu's code for a few months on 9x9 only and it's been > superhuman after a few months. > > I'm not sure if anyone's interested, but I can release my network to the > world. It's around the strength of KataGo, but only on 9x9. I could do a > final test before releasing it into the wild > > On Mon, Jan 27, 2020, 00:17 Rémi Coulom <remi.cou...@gmail.com> wrote: > >> Yes, using komi would help a lot. Still, I feel that something else must >> be wrong, because winning 100% of the games as Black without komi should be >> very easy on 7x7. >> >> I have not written anything about what I did with Crazy Stone. But my >> experiments and ideas were really very similar to what David Wu did: >> https://blog.janestreet.com/accelerating-self-play-learning-in-go/ >> >> To clarify what I wrote in my previous message: "strong from scratch in a >> single day" was for 7x7. I like testing new ideas with small networks on >> small boards, because training is very fast, and what works on small boards >> with small networks usually also works on large boards with big networks. >> >> Rémi >> >> On Sun, Jan 26, 2020 at 12:30 AM cody2007 <cody2...@protonmail.com> >> wrote: >> >>> Hi Rémi, >>> >>> Thanks for your comments! I am not using any komi and had not given much >>> thought to it. Although, I suppose by having black win most games, I'm >>> depriving the network of its only learning signal. I will have to try with >>> an appropriately set komi next... >>> >>> >When I started to develop the Zero version of Crazy Stone, I spend a >>> lot of time optimizing my method on a single (V100) GPU >>> Any chance you've written about it somewhere? I'd be interested to learn >>> more but wasn't able to find anything on the Crazy Stone website. >>> >>> Thanks, >>> Cody >>> >>> ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ >>> On Saturday, January 25, 2020 5:49 PM, Rémi Coulom < >>> remi.cou...@gmail.com> wrote: >>> >>> Hi, >>> >>> Thanks for sharing your experiments. >>> >>> Your match results are strange. Did you use a komi? You should use a >>> komi of 9: >>> https://senseis.xmp.net/?7x7 >>> >>> The final strength of your network looks surprisingly weak. When I >>> started to develop the Zero version of Crazy Stone, I spend a lot of time >>> optimizing my method on a single (V100) GPU. I could train a strong network >>> from scratch in a single day. Using a wrong komi might have hurt you. Also, >>> on such a small board, it is not so easy to make sure that the self-play >>> games have enough variety. You'd have to find many balanced random initial >>> positions in order to avoid replicating the same game again and again. >>> >>> Rémi >>> >>> >>> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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