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Hi,
thanks a lot for sharing! I try a slightly different approach at the
moment:
I use a combined policy / value network (adding 3-5 layers with about
16 filters at the end of the policy network for the value network to
avoid overfitting) and I use the results of the games as value. My
main problem is still overfitting!
As your results seem good I will try your bigger database to get more
games into training I think.
I will keep you posted
Detlef
Am 04.03.2016 um 16:23 schrieb Hiroshi Yamashita:
> Hi,
>
> I tried to make Value network.
>
> "Policy network + Value network" vs "Policy network" Winrate
> Wins/Games 70.7%322 / 455,1000 playouts/move 76.6%141 /
> 184, 1 playouts/move
>
> It seems more playouts, more Value network is effetctive. Games is
> not enough though. Search is similar to AlphaGo. Mixing parameter
> lambda is 0.5. Search is synchronous. Using one GTX 980. In 1
> playouts/move, Policy network is called 175 times, Value network is
> called 786 times. Node Expansion threshold is 33.
>
>
> Value network is 13 layers, 128 filters. (5x5_128, 3x3_128 x10,
> 1x1_1, fully connect, tanh) Policy network is 12 layers, 256
> filters. (5x5_256, 3x3_256 x10, 3x3_1), Accuracy is 50.1%
>
> For Value network, I collected 15804400 positions from 987775
> games. Games are from GoGoD, tygem 9d, 22477 games
> http://baduk.sourceforge.net/TygemAmateur.7z KGS 4d over, 1450946
> games http://www.u-go.net/gamerecords-4d/ (except handicaps
> games). And select 16 positions randomly from one game. One game is
> divided 16 game stage, and select one of each. 1st and 9th position
> are rotated in same symmetry. Then Aya searches with 500 playouts,
> with Policy network. And store winrate (-1 to +1). Komi is 7.5.
> This 500 playouts is around 2730 BayesElo on CGOS.
>
> I did some of this on Amazon EC2 g2.2xlarge, 11 instances. It took
> 2 days, and costed $54. Spot instance is reasonable. However
> g2.2xlarge(GRID K520), is 3x slower than GTX 980. My Pocicy
> network(12L 256F) takes 5.37ms(GTX 980), and 15.0ms(g2.2xlarge).
> Test and Traing loss are 0.00923 and 0.00778. I think there is no
> big overfitting.
>
> Value network is effective, but Aya has still fatal semeai
> weakness.
>
> Regards, Hiroshi Yamashita
>
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