Subject: Latest abrok Development Version Jujy 4th Update default net to nn-3c0054ea9860.nnu (SF The gift that keeps on giving Mon Jul 04, 2022 2:33 pm
this PR is being made from court. Today, Tord and Stéphane, with broad support of the developer community are defending their complaint, filed in Munich, against ChessBase. With their products Houdini 6 and Fat Fritz 2, both Stockfish derivatives, ChessBase violated repeatedly the Stockfish GPLv3 license. Tord and Stéphane have terminated their license with ChessBase permanently. Today we have the opportunity to present our evidence to the judge and enforce that termination. To read up, have a look at our blog post https://stockfishchess.org/blog/2022/public-court-hearing-soon/ and https://stockfishchess.org/blog/2021/our-lawsuit-against-chessbase/
This PR introduces a net trained with an enhanced data set and a modified loss function in the trainer. A slight adjustment for the scaling was needed to get a pass on standard chess.
Local testing at a fixed 25k nodes resulted in Test run1026/easy_train_data/experiments/experiment_2/training/run_0/nn-epoch799.nnue localElo: 4.2 +- 1.6
The real strength of the net is in FRC and DFRC chess where it gains significantly.
Tested at STC with slightly different scaling: FRC: https://tests.stockfishchess.org/tests/view/62c13a4002ba5d0a774d20d4 Elo: 29.78 +-3.4 (95%) LOS: 100.0% Total: 10000 W: 2007 L: 1152 D: 6841 Elo +29.78 Ptnml(0-2): 31, 686, 2804, 1355, 124 nElo: 59.24 +-6.9 (95%) PairsRatio: 2.06
This is due to the mixing in a significant fraction of DFRC training data in the final training round. The net is trained using the easy_train.py script in the following way:
The training branch used is https://github.com/vondele/nnue-pytorch/commits/lossScan4 A PR to the main trainer repo will be made later. This contains a revised loss function, now computing the loss from the score based on the win rate model, which is a more accurate representation than what we had before. Scaling constants are tweaked there as well.
