[Computer-go] CNN with 54% prediction on KGS 6d+ data
Josef Moudrik
j.moudrik at gmail.com
Tue Dec 8 08:37:05 PST 2015
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.
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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> --
> 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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