[Computer-go] CNN with 54% prediction on KGS 6d+ data
Álvaro Begué
alvaro.begue at gmail.com
Tue Dec 8 10:52:24 PST 2015
Of course whether these "neuro-playouts" are any better than the heavy
playouts currently being used by strong programs is an empirical question.
But I would love to see it answered...
On Tue, Dec 8, 2015 at 1:31 PM, David Ongaro <david.ongaro at hamburg.de>
wrote:
> Did everyone forget the fact that stronger playouts don't necessarily lead
> to an better evaluation function? (Yes, that what playouts essential are, a
> dynamic evaluation function.) This is even under the assumption that we can
> reach the same number of playouts per move.
>
>
> On 08 Dec 2015, at 10:21, Álvaro Begué <alvaro.begue at gmail.com> wrote:
>
> I don't think the CPU-GPU communication is what's going to kill this idea.
> The latency in actually computing the feed-forward pass of the CNN is going
> to be in the order of 0.1 seconds (I am guessing here), which means
> finishing the first playout will take many seconds.
>
> So perhaps it would be interesting to do something like this for
> correspondence games, but not for regular games.
>
>
> Álvaro.
>
>
>
> On Tue, Dec 8, 2015 at 12:03 PM, Petr Baudis <pasky at ucw.cz> wrote:
>
>> Hi!
>>
>> Well, for this to be practical the entire playout would have to be
>> executed on the GPU, with no round-trips to the CPU. That's what my
>> email was aimed at.
>>
>> On Tue, Dec 08, 2015 at 04:37:05PM +0000, Josef Moudrik 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.
>> >
>> > 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
>> > > Computer-go at computer-go.org
>> > > http://computer-go.org/mailman/listinfo/computer-go
>>
>> > _______________________________________________
>> > Computer-go mailing list
>> > Computer-go at computer-go.org
>> > http://computer-go.org/mailman/listinfo/computer-go
>>
>>
>> --
>> Petr Baudis
>> If you have good ideas, good data and fast computers,
>> you can do almost anything. -- Geoffrey Hinton
>> _______________________________________________
>> Computer-go mailing list
>> Computer-go at computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
>>
>
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