-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 > One possibility is that 0=loss, 1=win, and the number they are quoting is > sqrt(average((prediction-outcome)^2)).
this makes perfectly sense for figure 2. even playouts seem reasonable. But figure 2 is not consistent with the numbers in section 3 would be 0.234 (test set of the self-play data base. The figure looks more like 0.3 - 0.35 or even higher... Am 04.02.2016 um 21:43 schrieb Álvaro Begué: > I just want to see how to get 0.5 for the initial position on the > board with some definition. > > One possibility is that 0=loss, 1=win, and the number they are > quoting is sqrt(average((prediction-outcome)^2)). > > > On Thu, Feb 4, 2016 at 3:40 PM, Hideki Kato > <hideki_ka...@ybb.ne.jp> wrote: > >> I think the error is defined as the difference between the output >> of the value network and the average output of the simulations >> done by the policy network (RL) at each position. >> >> Hideki >> >> Michael Markefka: >> <CAJg7PAN9G2_htRs0mfKuFi82yef7gNFCsouE4ez4f37_pK= >> k...@mail.gmail.com>: >>> That sounds like it'd be the MSE as classification error of the >>> eventual >> result. >> >>> >> >>> I'm currently not able to look at the paper, but couldn't you >>> use a >> >>> softmax output layer with two nodes and take the probability >> >>> distribution as winrate? >> >>> >> >>> On Thu, Feb 4, 2016 at 8:34 PM, Álvaro Begué >>> <alvaro.be...@gmail.com> >> wrote: >> >>>> I am not sure how exactly they define MSE. If you look at the >>>> plot in >> figure >> >>>> 2b, the MSE at the very beginning of the game (where you >>>> can't possibly >> know >> >>>> anything about the result) is 0.50. That suggests it's >>>> something else >> than >> >>>> your [very sensible] interpretation. >> >>>> >> >>>> Álvaro. >> >>>> >> >>>> >> >>>> >> >>>> On Thu, Feb 4, 2016 at 2:24 PM, Detlef Schmicker >>>> <d...@physik.de> wrote: >> >>>>> >> >>> >>>>>>>> Since all positions of all games in the dataset are >>>>>>>> used, winrate >>> >>>>>>>> should distributes from 0% to 100%, or -1 to 1, not >>>>>>>> 1. Then, the >>> >>>>>>>> number 70% could be wrong. MSE is 0.37 just means >>>>>>>> the average >>> >>>>>>>> error is about 0.6, I think. >>> > >>> > 0.6 in the range of -1 to 1, >>> > >>> > which means -1 (eg lost by b) games -> typical value -0.4 >>> > and +1 games -> typical value +0.4 of the value network >>> > >>> > if I rescale -1 to +1 to 0 - 100% (eg winrate for b) than I get > about >>> > 30% for games lost by b and 70% for games won by B? >>> > >>> > Detlef >>> > >>> > >>> > Am 04.02.2016 um 20:10 schrieb Hideki Kato: >>> >>>>>>> Detlef Schmicker: <56b385ce.4080...@physik.de>: Hi, >>> >>>>>>> >>> >>>>>>> I try to reproduce numbers from section 3: training the >>>>>>> value >>> >>>>>>> network >>> >>>>>>> >>> >>>>>>> On the test set of kgs games the MSE is 0.37. Is it >>>>>>> correct, that >>> >>>>>>> the results are represented as +1 and -1? >>> >>>>>>> >>> >>>>>>>> Looks correct. >>> >>>>>>> >>> >>>>>>> This means, that in a typical board position you get a >>>>>>> value of >>> >>>>>>> 1-sqrt(0.37) = 0.4 --> this would correspond to a win >>>>>>> rate of 70% >>> >>>>>>> ?! >>> >>>>>>> >>> >>>>>>>> Since all positions of all games in the dataset are >>>>>>>> used, winrate >>> >>>>>>>> should distributes from 0% to 100%, or -1 to 1, not >>>>>>>> 1. Then, the >>> >>>>>>>> number 70% could be wrong. MSE is 0.37 just means >>>>>>>> the average >>> >>>>>>>> error is about 0.6, I think. >>> >>>>>>> >>> >>>>>>>> Hideki >>> >>>>>>> >>> >>>>>>> Is it really true, that a typical kgs 6d+ position is >>>>>>> judeged with >>> >>>>>>> such a high win rate (even though it it is overfitted, >>>>>>> so the test >>> >>>>>>> set number is to bad!), or do I misinterpret the MSE >>>>>>> calculation?! >>> >>>>>>> >>> >>>>>>> Any help would be great, >>> >>>>>>> >>> >>>>>>> Detlef >>> >>>>>>> >>> >>>>>>> Am 27.01.2016 um 19:46 schrieb Aja Huang: >>> >>>>>>>>>> Hi all, >>> >>>>>>>>>> >>> >>>>>>>>>> We are very excited to announce that our Go >>>>>>>>>> program, AlphaGo, >>> >>>>>>>>>> has beaten a professional player for the first >>>>>>>>>> time. AlphaGo >>> >>>>>>>>>> beat the European champion Fan Hui by 5 games to >>>>>>>>>> 0. We hope >>> >>>>>>>>>> you enjoy our paper, published in Nature today. >>>>>>>>>> The paper and >>> >>>>>>>>>> all the games can be found here: >>> >>>>>>>>>> >>> >>>>>>>>>> http://www.deepmind.com/alpha-go.html >>> >>>>>>>>>> >>> >>>>>>>>>> AlphaGo will be competing in a match against Lee >>>>>>>>>> Sedol in >>> >>>>>>>>>> Seoul, this March, to see whether we finally have >>>>>>>>>> a Go >>> >>>>>>>>>> program that is stronger than any human! >>> >>>>>>>>>> >>> >>>>>>>>>> Aja >>> >>>>>>>>>> >>> >>>>>>>>>> PS I am very busy preparing AlphaGo for the >>>>>>>>>> match, so >>> >>>>>>>>>> apologies in advance if I cannot respond to all >>>>>>>>>> questions >>> >>>>>>>>>> about AlphaGo. >>> >>>>>>>>>> >>> >>>>>>>>>> >>> >>>>>>>>>> >>> >>>>>>>>>> _______________________________________________ >>>>>>>>>> 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 >>> >> >>>>> _______________________________________________ >> >>>>> 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 >> >>> _______________________________________________ >> >>> Computer-go mailing list >> >>> Computer-go@computer-go.org >> >>> http://computer-go.org/mailman/listinfo/computer-go >> -- Hideki Kato <mailto:hideki_ka...@ybb.ne.jp> >> _______________________________________________ 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 > -----BEGIN PGP SIGNATURE----- Version: GnuPG v2.0.22 (GNU/Linux) iQIcBAEBAgAGBQJWs8ZGAAoJEInWdHg+Znf4D4sP/Rr7HPtRpE0rgzIjzSvI4NtM EZMldUdsEyJ8u6C4o8cWUfHX7TChfgjUpDpJL/uvmAgiunvB3RXccT3DKLWAbo8G t9QUsMgd791g4RkFsJ5ZZWJ/bGrchov9bXIcPO9QzJ1FJRrVuwMfH43SBnPItee7 Z9QH7FF6jgyBjFxeNChhF8FMOD55+uuu8/o3htMCAHBZ6Y4aMEdFQYQHmHdGUYHF Vtgy++yRIP9V0BiiBqNCKxT41cK5kaEzbUYgIoLs0kHpxTzJd/WAiLxSHAPyYnLY WL9/NU1/dW6/Ef7wbi8I68lDz+COfIaZ8KMH75Q4O90OIta+O7eBznNBEC3Ei5iH 3BvlfzPZ+fHZb6Yw7MrbVJFfPJzXRM0C/C9uHjDcdi6wZTpoEhWiYFKeGogRRSg3 2Y+xJrFh/p+akLjo70BcD48TwJIYdDdgFUfgj5vvyru3H9oZ/fJKLX6WPx1brCnj RXtmH+k+G6Gi+WRACKEgtw59Rm5h7F/sQv3apqXFii8QnHcChNnsXcn/mCYBqlnM W4e2fk6+HJbth0bLobAG4DaM+j9C/gde0ruUhTtYIap4iC5hkf8zrZTZzzVdsCcc tBv8CFXif8cjAAQwYIhMt/VDMbIoPwwczCsJS6XXr7j7vzoKiiMCrSLZ8DF+IXEi 0nKF0PbVS4JPpajYpGkL =tLXF -----END PGP SIGNATURE----- _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
