[Computer-go] CNN with 54% prediction on KGS 6d+ data
Aja Huang
ajahuang at google.com
Sat Dec 12 11:09:32 PST 2015
On Tue, Dec 8, 2015 at 4:37 PM, Josef Moudrik <j.moudrik at gmail.com> wrote:
> Regarding full CNN playouts, I think that problem is that a playout is a
> long serial process, given 200-300 moves a game. You need to construct
> planes and transfer them to GPU for each move and read result back (at
> least with current CNN implementations afaik), so my guess would be that
> such playout would take time in order of seconds. So there seems to be a
> tradeoff, CNN playouts are (probably much) better (at "playing better
> games") than e.g. distribution playouts, but whether this is worth the
> implied (probably much) lower height of the MC tree is a question.
>
You may want to take a look at this paper:
Convolutional Monte Carlo Rollouts in Go
http://arxiv.org/pdf/1512.03375v1.pdf
Aja
> Maybe if you had really a lot of GPUs and very high thinking time, this
> could be the way.
>
> Josef
>
> On Tue, Dec 8, 2015 at 5:17 PM Petr Baudis <pasky at ucw.cz> wrote:
>
>> Hi!
>>
>> In case someone is looking for a starting point to actually implement
>> Go rules etc. on GPU, you may find useful:
>>
>>
>> https://www.mail-archive.com/computer-go@computer-go.org/msg12485.html
>>
>> I wonder if you can easily integrate caffe GPU kernels in another GPU
>> kernel like this? But without training, reimplementing the NN could be
>> pretty straightforward.
>>
>> On Tue, Dec 08, 2015 at 04:53:14PM +0100, Michael Markefka wrote:
>> > Hello Detlef,
>> >
>> > I've got a question regarding CNN-based Go engines I couldn't find
>> > anything about on this list. As I've been following your posts here, I
>> > thought you might be the right person to ask.
>> >
>> > Have you ever tried using the CNN for complete playouts? I know that
>> > CNNs have been tried for move prediction, immediate scoring and move
>> > generation to be used in an MC evaluator, but couldn't find anything
>> > about CNN-based playouts.
>> >
>> > It might only be feasible to play out the CNN's first choice move for
>> > evaluation purposes, but considering how well the performance of batch
>> > sizes scales, especially on GPU-based CNN applications, it might be
>> > possible to setup something like 10 candidate moves, 10 reply
>> > candidate moves and then have the CNN play out the first choice move
>> > for those 100 board positions until the end and then sum up scores
>> > again for move evaluation (and/or possibly apply some other tried and
>> > tested methods like minimax). Given that the number of 10 moves is
>> > supposed to be illustrative rather than representative, other
>> > configurations of depth and width in position generation and
>> > evaluation would be possible.
>> >
>> > It feels like CNN can provide a very focused, high-quality width in
>> > move generation, but it might also be possible to apply that quality
>> > to depth of evaluation.
>> >
>> > Any thoughts to share?
>> >
>> >
>> > All the best
>> >
>> > Michael
>> >
>> > On Tue, Dec 8, 2015 at 4:13 PM, Detlef Schmicker <ds2 at physik.de> wrote:
>> > > -----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
>> > >
>> > >
>> > >
>> > >
>> > > code:
>> > > if (col==Go::BLACK) {
>> > > for (int j=0;j<size;j++)
>> > > for (int k=0;k<size;k++)
>> > > {
>> > > for (int l=0;l<caffe_test_net_input_dim;l++)
>> > > data[l*size*size+size*j+k]=0;
>> > > //fprintf(stderr,"%d %d %d\n",i,j,k);
>> > > int pos=Go::Position::xy2pos(j,k,size);
>> > > int libs=0;
>> > > if (board->inGroup(pos))
>> > > libs=board->getGroup(pos)->numRealLibs()-1;
>> > > if (libs>3) libs=3;
>> > > if (board->getColor(pos)==Go::BLACK)
>> > > {
>> > > data[(0+libs)*size*size + size*j + k]=1.0;
>> > > //data[size*size+size*j+k]=0.0;
>> > > }
>> > > else if (board->getColor(pos)==Go::WHITE)
>> > > {
>> > > //data[j*size+k]=0.0;
>> > > data[(4+libs)*size*size + size*j + k]=1.0;
>> > > }
>> > > else if
>> > > (board->getColor(Go::Position::xy2pos(j,k,size))==Go::EMPTY)
>> > > {
>> > > data[8*size*size + size*j + k]=1.0;
>> > > }
>> > > }
>> > > }
>> > > if (col==Go::WHITE) {
>> > > for (int j=0;j<size;j++)
>> > > for (int k=0;k<size;k++)
>> > > {//fprintf(stderr,"%d %d %d\n",i,j,k);
>> > > for (int l=0;l<caffe_test_net_input_dim;l++)
>> > > data[l*size*size+size*j+k]=0;
>> > > //fprintf(stderr,"%d %d %d\n",i,j,k);
>> > > int pos=Go::Position::xy2pos(j,k,size);
>> > > int libs=0;
>> > > if (board->inGroup(pos))
>> > > libs=board->getGroup(pos)->numRealLibs()-1;
>> > > if (libs>3) libs=3;
>> > > if (board->getColor(pos)==Go::BLACK)
>> > > {
>> > > data[(4+libs)*size*size + size*j + k]=1.0;
>> > > //data[size*size+size*j+k]=0.0;
>> > > }
>> > > else if (board->getColor(pos)==Go::WHITE)
>> > > {
>> > > //data[j*size+k]=0.0;
>> > > data[(0+libs)*size*size + size*j + k]=1.0;
>> > > }
>> > > else if (board->getColor(pos)==Go::EMPTY)
>> > > {
>> > > data[8*size*size + size*j + k]=1.0;
>> > > }
>> > > }
>> > > }
>> > > if (caffe_test_net_input_dim > 9) {
>> > > if (board->getLastMove().isNormal()) {
>> > > int
>> j=Go::Position::pos2x(board->getLastMove().getPosition(),size);
>> > > int
>> k=Go::Position::pos2y(board->getLastMove().getPosition(),size);
>> > > data[9*size*size+size*j+k]=1.0;
>> > > }
>> > > if (board->getSecondLastMove().isNormal()) {
>> > > int
>> > > j=Go::Position::pos2x(board->getSecondLastMove().getPosition(),size);
>> > > int
>> > > k=Go::Position::pos2y(board->getSecondLastMove().getPosition(),size);
>> > > data[10*size*size+size*j+k]=1.0;
>> > > }
>> > > if (board->getThirdLastMove().isNormal()) {
>> > > int
>> > > j=Go::Position::pos2x(board->getThirdLastMove().getPosition(),size);
>> > > int
>> > > k=Go::Position::pos2y(board->getThirdLastMove().getPosition(),size);
>> > > data[11*size*size+size*j+k]=1.0;
>> > > }
>> > > if (board->getForthLastMove().isNormal()) {
>> > > int
>> > > j=Go::Position::pos2x(board->getForthLastMove().getPosition(),size);
>> > > int
>> > > k=Go::Position::pos2y(board->getForthLastMove().getPosition(),size);
>> > > data[12*size*size+size*j+k]=1.0;
>> > > }
>> > > }
>> > >
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>> > > -----END PGP SIGNATURE-----
>> > > _______________________________________________
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>> --
>> Petr Baudis
>> If you have good ideas, good data and fast computers,
>> you can do almost anything. -- Geoffrey Hinton
>> _______________________________________________
>> Computer-go mailing list
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>
>
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