Experimental Neural Nets

This is not intended to be a release of the main KataGo program, but rather just an update to the neural nets. The latest release of the code and executables can be found here.

New Nets

Uploaded here are some new experimentally-trained neural nets! These neural nets have been trained using some amount of human and other external data (varyingly 5%-10%) as initial starting positions or "hint" positions for self-play games (in which an potential unexpected good move is guaranteed to be noised and searched more deeply). So still all data is generated by self-play and rather than taken from outside, but the positions (and rare "hints") in such games are may come from positions well outside the normal distribution of positions that the net would see if self-playing the entire game from the empty board.

Additionally, they have been trained to hopefully understand Mi Yuting's flying dagger joseki significantly better - although understanding may still of course be imperfect due to the immense complexity of the joseki.

These nets are not necessarily stronger than the nets bundled with the v1.4.0 release, which were the final and strongest non-external-data-biased nets.

As measured by pure self-play, the new nets may even be slightly weaker in some cases than the previous nets, perhaps due to having to "spend effort" to learn more kinds of shapes that didn't often come up in matches only against itself. However, one hopes that they on average may handle some new kinds of positions better and/or generalize against other opponents better. But that remains to be tested!

Many more than just the three new nets attached to GitHub here have been uploaded.
They can be found at the usual KataGo g170 download site. These are intermediate versions sampled from between the v1.4.0-bundled final nets and the ones attached and are described in the readme in case you are interested in testing across intermediate versions and seeing how the introduction of successive kinds of external data has progressively affected the policy and evaluation of specific positions.

Enjoy!