[Computer-go] CNN for winrate and territory
Hi, I am working on a CNN for winrate and territory: approach: - input 2 layers for b and w stones - 1. output: 1 layer territory (0.0 for owned by white, 1.0 for owned by black (because I missed TANH in the first place I used SIGMOID)) - 2. output: label for -60 to +60 territory leading by black the loss of both outputs is trained the idea is, that this way I do not have to put komi into input and make the winrate from the statistics of the trained label: e.g. komi 6.5: I sum the probabilites from +7 to +60 and get something like a winrate I trained with 80 positions with territory information through 500 playouts from oakfoam, which I symmetrized by the 8 transformation leading to 600 positions. (It is expensive to produce the positions due to the playouts) The layers are the same as the large network from Christopher Clark http://arxiv.org/find/cs/1/au:+Clark_C/0/1/0/all/0/1, Amos Storkey http://arxiv.org/find/cs/1/au:+Storkey_A/0/1/0/all/0/1 : http://arxiv.org/abs/1412.3409 I get reasonable territory predictions from this network (compared to 500 playouts of oakfoam), the winrates seems to be overestimated. But anyway, it looks as it is worth to do some more work on it. The idea is, I can do the equivalent of lets say 1000 playouts with a call to the CNN for the cost of 2 playouts some time... Now I try to do a soft turnover from conventional playouts to CNN predicted winrates within the framework of MC. I do have some ideas, but I am not happy with them. Maybe you have better ones :) Thanks a lot Detlef ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
Re: [Computer-go] CNN for winrate and territory
What network architecture did you use? Can you give us some details? On Sun, Feb 8, 2015 at 5:22 AM, Detlef Schmicker d...@physik.de wrote: Hi, I am working on a CNN for winrate and territory: approach: - input 2 layers for b and w stones - 1. output: 1 layer territory (0.0 for owned by white, 1.0 for owned by black (because I missed TANH in the first place I used SIGMOID)) - 2. output: label for -60 to +60 territory leading by black the loss of both outputs is trained the idea is, that this way I do not have to put komi into input and make the winrate from the statistics of the trained label: e.g. komi 6.5: I sum the probabilites from +7 to +60 and get something like a winrate I trained with 80 positions with territory information through 500 playouts from oakfoam, which I symmetrized by the 8 transformation leading to 600 positions. (It is expensive to produce the positions due to the playouts) The layers are the same as the large network from Christopher Clark http://arxiv.org/find/cs/1/au:+Clark_C/0/1/0/all/0/1, Amos Storkey http://arxiv.org/find/cs/1/au:+Storkey_A/0/1/0/all/0/1 : http://arxiv.org/abs/1412.3409 I get reasonable territory predictions from this network (compared to 500 playouts of oakfoam), the winrates seems to be overestimated. But anyway, it looks as it is worth to do some more work on it. The idea is, I can do the equivalent of lets say 1000 playouts with a call to the CNN for the cost of 2 playouts some time... Now I try to do a soft turnover from conventional playouts to CNN predicted winrates within the framework of MC. I do have some ideas, but I am not happy with them. Maybe you have better ones :) Thanks a lot Detlef ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
Re: [Computer-go] CNN for winrate and territory
Detleft wrote: The idea is, I can do the equivalent of lets say 1000 playouts with a call to the CNN for the cost of 2 playouts some time... That sounds like a good plan :-) ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
Re: [Computer-go] CNN for winrate and territory
Exactly the one from the cited paper:
The best network had one convolutional layer with 64 7x7
filters, two convolutional layers with 64 5x5 filters, two lay-
ers with 48 5x5 filters, two layers with 32 5x5 filters, and
one fully connected layer.
I use caffe and the definition of the training network is:
name: LogReg
layers {
name: mnist
type: DATA
top: data_orig
top: label
data_param {
source: train_result_leveldb/
batch_size: 256
}
include: { phase: TRAIN }
}
layers {
name: mnist
type: DATA
top: data_orig
top: label
data_param {
source: test_result_leveldb/
batch_size: 256
}
include: { phase: TEST }
}
layers {
name: slice
type: SLICE
bottom: data_orig
top: data
top: data_territory
slice_param {
slice_dim: 1
slice_point : 2
}
}
#this part should be the same in learning and prediction network
layers {
name: conv1_7x7_64
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: data
top: conv2
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu2
type: TANH
bottom: conv2
top: conv2u
}
layers {
name: conv2_5x5_64
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: conv2u
top: conv3
convolution_param {
num_output: 64
kernel_size: 5
pad: 2
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu3
type: TANH
bottom: conv3
top: conv3u
}
layers {
name: conv3_5x5_64
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: conv3u
top: conv4
convolution_param {
num_output: 64
kernel_size: 5
pad: 2
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu4
type: TANH
bottom: conv4
top: conv4u
}
layers {
name: conv4_5x5_48
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: conv4u
top: conv5
convolution_param {
num_output: 48
kernel_size: 5
pad: 2
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu5
type: TANH
bottom: conv5
top: conv5u
}
layers {
name: conv5_5x5_48
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: conv5u
top: conv6
convolution_param {
num_output: 48
kernel_size: 5
pad: 2
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu6
type: TANH
bottom: conv6
top: conv6u
}
layers {
name: conv6_5x5_32
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: conv6u
top: conv7
convolution_param {
num_output: 32
kernel_size: 5
pad: 2
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu7
type: TANH
bottom: conv7
top: conv7u
}
layers {
name: conv7_5x5_32
type: CONVOLUTION
blobs_lr: 1.
blobs_lr: 2.
bottom: conv7u
top: conv8
convolution_param {
num_output: 32
kernel_size: 5
pad: 2
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: relu8
type: TANH
bottom: conv8
top: conv8u
}
layers {
name: flat
type: FLATTEN
bottom: conv8u
top: conv8_flat
}
layers {
name: split
type: SPLIT
bottom: conv8_flat
top: conv8_flata
top: conv8_flatb
}
layers {
name: ip
type: INNER_PRODUCT
bottom: conv8_flata
top: ip_zw
inner_product_param {
num_output: 361
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
layers {
name: sigmoid
type: SIGMOID
bottom: ip_zw
top: ip_zws
}
layers {
name: ip2
type: INNER_PRODUCT
bottom: conv8_flatb
top: ip_label
inner_product_param {
num_output: 121
weight_filler {
type: xavier
}
bias_filler {
type: constant
}
}
}
#only learning framework
layers {
name: flat
type: FLATTEN
bottom: data_territory
top: flat
}
layers {
name: loss
type: EUCLIDEAN_LOSS
bottom: ip_zws
bottom: flat
top: lossa
}
layers {
name: accuracy
type: ACCURACY
bottom: ip_label
bottom: label
top: accuracy
}
layers {
name: loss
type: SOFTMAX_LOSS
bottom: ip_label
bottom: label
top: lossb
}
Am 08.02.2015 um 11:43 schrieb Álvaro Begué:
What network architecture did you use? Can you give us some details?
On Sun, Feb 8, 2015 at 5:22 AM, Detlef Schmicker d...@physik.de
mailto:d...@physik.de wrote:
Hi,
I am working on a CNN for winrate and territory:
approach:
- input 2 layers for b and w stones
- 1. output: 1 layer territory (0.0 for owned by white, 1.0 for
owned by black (because I missed TANH in the first place I used
SIGMOID))
- 2. output: label for -60 to +60 territory leading by black
the
