Hi Detlef,
Thank you for publishing your data and latest oakform code!
It was very helpful for me.
I tried your 54% data with Aya.
Aya with Detlef54% vs Aya with Detlef44%, 10000 playout/move
Aya with Detlef54%'s winrate is 0.569 (124wins / 218games).
CGOS BayseElo rating
Aya with Detlef44% (aya786n_Detlef_10k) 3040
Aya with Detlef54% (Aya786m_Det54_10k ) 3036
http://www.yss-aya.com/cgos/19x19/bayes.html
Detlef54% is a bit stronger in selfplay, but they are similar on CGOS.
Maybe Detlef54%'s prediction is strong, and Aya's playout strength
is not enough.
Speed for a position on GTS 450.
Detlef54% 21ms
Detlef44% 17ms
Cumulative accuracy from 1000 pro games.
move rank Aya Detlef54% Mixture
1 40.8 47.6 48.0
2 53.5 62.4 62.7
3 60.2 70.7 71.0
4 64.8 75.8 76.1
5 68.1 79.5 79.9
6 71.0 82.3 82.6
7 73.2 84.5 84.8
8 75.2 86.3 86.6
9 76.9 87.8 88.1
10 78.3 89.0 89.3
11 79.6 90.2 90.6
12 80.8 91.2 91.4
13 81.9 92.0 92.2
14 82.9 92.7 92.9
15 83.8 93.3 93.5
16 84.6 93.9 94.1
17 85.4 94.3 94.5
18 86.1 94.8 95.0
19 86.8 95.2 95.4
20 87.4 95.5 95.7
Mixture is pretty same as Detlef54%.
I changed learning method from MM to LFR.
Aya's own accuracy is from LFR rank, not MM gamma.
So comparison is difficult.
Cumulative accuracy Detlef44%
http://computer-go.org/pipermail/computer-go/2015-October/008031.html
Regards,
Hiroshi Yamashita
----- Original Message -----
From: "Detlef Schmicker" <d...@physik.de>
To: <computer-go@computer-go.org>
Sent: Wednesday, December 09, 2015 12:13 AM
Subject: [Computer-go] CNN with 54% prediction on KGS 6d+ data
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1
Hi,
as somebody ask I will offer my actual CNN for testing.
It has 54% prediction on KGS 6d+ data (which I thought would be state
of the art when I started training, but it is not anymore:).
it has:
1
2
3
4 libs playing color
1
2
3
4 libs opponent color
Empty points
last move
second last move
third last move
forth last move
input layers, and it is fully convolutional, so with just editing the
golast19.prototxt file you can use it for 13x13 as well, as I did on
last sunday. It was used in November tournament as well.
You can find it
http://physik.de/CNNlast.tar.gz
If you try here some points I like to get discussion:
- - it seems to me, that the playouts get much more important with such
a strong move prediction. Often the move prediction seems better the
playouts (I use 8000 at the moment against pachi 32000 with about 70%
winrate on 19x19, but with an extremely focused progressive widening
(a=400, a=20 was usual).
- - live and death becomes worse. My interpretation is, that the strong
CNN does not play moves, which obviously do not help to get a group
life, but would help the playouts to recognize the group is dead.
(http://physik.de/example.sgf top black group was with weaker move
prediction read very dead, with good CNN it was 30% alive or so :(
OK, hope you try it, as you know our engine oakfoam is open source :)
We just merged all the CNN stuff into the main branch!
https://bitbucket.org/francoisvn/oakfoam/wiki/Home
http://oakfoam.com
Do the very best with the CNN
Detlef
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