Good stuff, Hiroshi. Looks like I don't need to answer the questions
regarding value network. :)
Aja
On Sat, Mar 19, 2016 at 9:23 PM, Hiroshi Yamashita <y...@bd.mbn.or.jp> wrote:
> What are you using for loss?
>>
>
> I use this,
>
> layers {
> name: "loss"
> type: EUCLIDEAN_LOSS
> bottom: "fc14"
> bottom: "label"
> top: "loss"
> }
>
> --------------------------------------------------------
> name: "AyaNet"
> layers {
> name: "mnist"
> type: DATA
> top: "data"
> data_param {
> source: "train_i50_v_2k_leveldb"
> # backend: LMDB
> batch_size: 256
> }
> include: { phase: TRAIN }
> }
> layers {
> name: "mnist"
> type: HDF5_DATA
> top: "label"
> hdf5_data_param {
> source: "/home/yss/test/train_v_2k_i50_11_only_hdf5/aya_data_list.txt"
> batch_size: 256
> }
> include: { phase: TRAIN }
> }
> layers {
> name: "mnist"
> type: DATA
> top: "data"
> data_param {
> source: "test_i50_v_2k_leveldb"
> # backend: LMDB
> batch_size: 256
> }
> include: { phase: TEST }
> }
> layers {
> name: "mnist"
> type: HDF5_DATA
> top: "label"
> hdf5_data_param {
> source: "/home/yss/test/test_v_2k_i50_11_only_hdf5/aya_data_list.txt"
> batch_size: 256
> }
> include: { phase: TEST }
> }
>
>
> #this part should be the same in learning and prediction network
> layers {
> name: "conv1_5x5_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "data"
> top: "conv1"
> convolution_param {
> num_output: 128
> kernel_size: 5
> pad: 2
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu1"
> type: RELU
> bottom: "conv1"
> top: "conv1"
> }
>
> layers {
> name: "conv2_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv1"
> top: "conv2"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu2"
> type: RELU
> bottom: "conv2"
> top: "conv2"
> }
>
> layers {
> name: "conv3_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv2"
> top: "conv3"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu3"
> type: RELU
> bottom: "conv3"
> top: "conv3"
> }
>
> layers {
> name: "conv4_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv3"
> top: "conv4"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu4"
> type: RELU
> bottom: "conv4"
> top: "conv4"
> }
>
>
> layers {
> name: "conv5_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv4"
> top: "conv5"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu5"
> type: RELU
> bottom: "conv5"
> top: "conv5"
> }
>
> layers {
> name: "conv6_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv5"
> top: "conv6"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu6"
> type: RELU
> bottom: "conv6"
> top: "conv6"
> }
>
> layers {
> name: "conv7_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv6"
> top: "conv7"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu7"
> type: RELU
> bottom: "conv7"
> top: "conv7"
> }
>
> layers {
> name: "conv8_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv7"
> top: "conv8"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu8"
> type: RELU
> bottom: "conv8"
> top: "conv8"
> }
>
> layers {
> name: "conv9_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv8"
> top: "conv9"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu9"
> type: RELU
> bottom: "conv9"
> top: "conv9"
> }
>
> layers {
> name: "conv10_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv9"
> top: "conv10"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu10"
> type: RELU
> bottom: "conv10"
> top: "conv10"
> }
>
> layers {
> name: "conv11_3x3_128"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv10"
> top: "conv11"
> convolution_param {
> num_output: 128
> kernel_size: 3
> pad: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu11"
> type: RELU
> bottom: "conv11"
> top: "conv11"
> }
>
> layers {
> name: "conv12_1x1_1"
> type: CONVOLUTION
> blobs_lr: 1.
> blobs_lr: 2.
> bottom: "conv11"
> top: "conv12"
> convolution_param {
> num_output: 1
> kernel_size: 1
> pad: 0
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu12"
> type: RELU
> bottom: "conv12"
> top: "conv12"
> }
>
> layers {
> name: "fc13"
> type: INNER_PRODUCT
> bottom: "conv12"
> top: "fc13"
> inner_product_param {
> num_output: 256
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "relu13"
> type: RELU
> bottom: "fc13"
> top: "fc13"
> }
>
> layers {
> name: "fc14"
> type: INNER_PRODUCT
> bottom: "fc13"
> top: "fc14"
> inner_product_param {
> num_output: 1
> weight_filler {
> type: "xavier"
> }
> bias_filler {
> type: "constant"
> }
> }
> }
> layers {
> name: "tanh14"
> type: TANH
> bottom: "fc14"
> top: "fc14"
> }
>
> layers {
> name: "loss"
> type: EUCLIDEAN_LOSS
> bottom: "fc14"
> bottom: "label"
> top: "loss"
> }
> --------------------------------------------------------
>
> Thanks,
> Hiroshi Yamashita
>
> ----- Original Message ----- From: "Detlef Schmicker" <d...@physik.de>
> To: <computer-go@computer-go.org>
> Sent: Saturday, March 19, 2016 7:41 PM
> Subject: Re: [Computer-go] Value Network
>
>
>
> What are you using for loss?
>>
>> this:
>>
>> layers {
>> name: "loss4"
>> type: EUCLIDEAN_LOSS
>> loss_weight: 2.0
>> bottom: "vvv"
>> bottom: "pool2"
>> top: "accloss4"
>> }
>>
>>
>> ?
>>
>> Am 04.03.2016 um 16:23 schrieb Hiroshi Yamashita:
>>
>>> Hi,
>>>
>>> I tried to make Value network.
>>>
>>> "Policy network + Value network" vs "Policy network" Winrate
>>> Wins/Games 70.7% 322 / 455, 1000 playouts/move 76.6% 141 /
>>> 184, 10000 playouts/move
>>>
>>> It seems more playouts, more Value network is effetctive. Games is
>>> not enough though. Search is similar to AlphaGo. Mixing parameter
>>> lambda is 0.5. Search is synchronous. Using one GTX 980. In 10000
>>> playouts/move, Policy network is called 175 times, Value network is
>>> called 786 times. Node Expansion threshold is 33.
>>>
>>>
>>> Value network is 13 layers, 128 filters. (5x5_128, 3x3_128 x10,
>>> 1x1_1, fully connect, tanh) Policy network is 12 layers, 256
>>> filters. (5x5_256, 3x3_256 x10, 3x3_1), Accuracy is 50.1%
>>>
>>> For Value network, I collected 15804400 positions from 987775
>>> games. Games are from GoGoD, tygem 9d, 22477 games
>>> http://baduk.sourceforge.net/TygemAmateur.7z KGS 4d over, 1450946
>>> games http://www.u-go.net/gamerecords-4d/ (except handicaps
>>> games). And select 16 positions randomly from one game. One game is
>>> divided 16 game stage, and select one of each. 1st and 9th position
>>> are rotated in same symmetry. Then Aya searches with 500 playouts, with
>>> Policy network. And store winrate (-1 to +1). Komi is 7.5. This 500
>>> playouts is around 2730 BayesElo on CGOS.
>>>
>>> I did some of this on Amazon EC2 g2.2xlarge, 11 instances. It took 2
>>> days, and costed $54. Spot instance is reasonable. However g2.2xlarge(GRID
>>> K520), is 3x slower than GTX 980. My Pocicy network(12L 256F) takes
>>> 5.37ms(GTX 980), and 15.0ms(g2.2xlarge). Test and Traing loss are 0.00923
>>> and 0.00778. I think there is no
>>> big overfitting.
>>>
>>> Value network is effective, but Aya has still fatal semeai
>>> weakness.
>>>
>>> Regards, Hiroshi Yamashita
>>>
>>
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