deepgo

deepgo is a toolkit to use Convolutional Neural Network (CNN) for board game Go. Feature handling is implemented in scala and CNN is done with chainer. I have also implemented "long term prediction" (suggested by paper from Facebook AI research)

Contains:

• CNN implementation with Chainer
• training data (.sgf files) parsing
• Feature extractor
• Game Rules
• GTP commands subset

Requirement:

• scala environment
• chainer
• sqlite3

Usage

create features

Create sqlite database holds feature-maps(less than 100GB if 2000 sgf files).

sbt "run -mode db -color w -d path/to/sgffiles -pred-step 3"

Then deepgo.db will be created in ./depgo dir. This is time-consuming process (It takes few hours)

Training and save neural network

python scripts/train_net.py

Then foo.plk will be created. You can use GPU if you set use_gpu flag in train_net.py.