TamaGo has a function to execute supervised learning with neural networks.

Prerequisites for training data files

TamaGo can load SGF (Smart Game Format) files. For more information about SGF, please check here
The following prerequisites are required.

• There are no branches for a move sequence.
• There are B-tags and W-tags for each player's moves (For policy target).
• There is RE-tag with the game result (For value target).
• Character code is UTF-8 or ASCII.

Hyperparameters for supervised learning

Hyperparameters for supervised learning are defined in learning_param.py.

Hyperparameters	Description	Example of value	Note
SL_LEARNING_RATE	Initial learning rate.	0.01
BATCH_SIZE	Mini-batch size for training.	256
MOMENTUM	Momentum parameter for an optimizer.	0.9
WEIGHT_DECAY	Weight of L2-regularization.	1e-4 (0.0001)
EPOCHS	The number of training epochs.	15
LEARNING_SCHEDULE	Learning rate decaying schedule.	see learning_param.py
DATA_SET_SIZE	Number of data to be stored in a npz file.	BATCH_SIZE * 4000
SL_VALUE_WEIGHT	Weight of value loss against policy loss.	0.02	This must be more than 0.0.

I have already confirmed that with data from 50000 games, supervised learning works well with the default hyperparameters. If you try to adjust hyperparameters, I recommend you to adjust LEARNING_RATE, LEARNING_SCHEDULE, EPOCHS, or BATCH_SIZE at first.

Definition of neural network structure.

Neural network is defined using the following four files.

File	Definition
nn/network/dual_net.py	Neural network definition.
nn/network/res_block.py	Residual block definition.
nn/network/head/policy_head.py	Policy head definition.
nn/network/head/value_head.py	Value head definition.

If you try to change structure, I recommend you to change value of filters or blocks at first.

TamaGo's supervised learning process

Supervised learning process runs in the following order,

1. Reading SGF files from the specified directory and outputting sl_data_*.npz files as training data under the "data" directory.
2. Executing supervised learning using sl_data_*.npz files under tha "data" directory.

Training data generation step

Running the output of training data files is optional, but

• When you want to change training data source
• When you change input features for neural network

you will need to rerun the data file generation process.

Supervised learning execution step

When executing supervised learning, sl_data_*.npz files under the "data" directory is split at a ratio of 8:2 into training data tobe used for training and validation data to evaluate the training progress. If you want to change this ratio, please change the following code in learn.py.

train_data_set, test_data_set = split_train_test_set(data_set, 0.8)

How to execute supervised learning

If you store SGF files to be used as supervised learining data under /home/user/sgf_files directory, execute following command in TamaGo home directory.

python train.py --kifu-dir /home/user/sgf_files

if you already have sl_data_*.npz files in the "data" directory and want to run supervised learning using those files, execute the following command in TamaGo home directory.

python train.py

When the supervised learning process is completed, a trained model file is output under the "model" directory with the name "sl-model.bin". How to use the trained model file is here.

Command line options

Option	Description	Example of value	Default value	Note
--kifu-dir	Path to the directory contains SGF files.	/home/user/sgf_files	None
--size	Go board size.	5	9
--use-gpu	Flag to use a GPU.	true	true	Value is true or false.
--rl	Flag to execute reinforcement learning.	false	false
--window-size	Window size for reinforcement learning.	500000	300000	This is an ignored option for supervised learning.