On 20/10/2017 22:48, fotl...@smart-games.com wrote:
> The paper describes 20 and 40 block networks, but the section on
> comparison says AlphaGo Zero uses 20 blocks. I think your protobuf
> describes a 40 block network. That's a factor of two
They compared with both, the final 5180 Elo number
On 20/10/2017 22:41, Sorin Gherman wrote:
> Training of AlphaGo Zero has been done on thousands of TPUs,
> according to this source:
> https://www.reddit.com/r/baduk/comments/777ym4/alphago_zero_learning_from_scratch_deepmind/dokj1uz/?context=3
>
> Maybe that should explain the difference in
I agree. Even on 19x19 you can use smaller searches. 400 iterations MCTS is
probably already a lot stronger than the raw network, especially if you are
expanding every node (very different from a normal program at 400
playouts!). Some tuning of these mini searches is important. Surely you
don't
> You can also start with 9x9 go. That way games are shorter, and you probably
> don't need 1600 network evaluations per move to do well.
Bonus points if you can have it play on goquest where many
of us can enjoy watching its progress, or even challenge it...
regards,
-John
AM
To: computer-go@computer-go.org
Subject: [Computer-go] Zero performance
I reconstructed the full AlphaGo Zero network in Caffe:
https://sjeng.org/dl/zero.prototxt
I did some performance measurements, with what should be state-of-the-art on
consumer hardware:
GTX 1080 Ti
NVIDIA-Caffe + CUDA 9
Training of AlphaGo Zero has been done on thousands of TPUs, according to
this source:
https://www.reddit.com/r/baduk/comments/777ym4/alphago_zero_learning_from_scratch_deepmind/dokj1uz/?context=3
Maybe that should explain the difference in orders of magnitude that you
noticed?
On Fri, Oct 20,
I suggest scaling down the problem until some experience is gained.
You don't need the full-fledge 40-block network to get started. You can
probably get away with using only 20 blocks and maybe 128 features (from
256). That should save you about a factor of 8, plus you can use larger
I reconstructed the full AlphaGo Zero network in Caffe:
https://sjeng.org/dl/zero.prototxt
I did some performance measurements, with what should be
state-of-the-art on consumer hardware:
GTX 1080 Ti
NVIDIA-Caffe + CUDA 9 + cuDNN 7
batch size = 8
Memory use is about ~2G. (It's much more for
