Make net nn-50144f835024.nnue the default
trained with the Python command
c:\nnue>python train.py i:/bin/all.binpack i:/bin/all.binpack --gpus 1 --threads 4 --num-workers 30 --batch-size 16384 --progress_bar_refresh_rate 300 --smart-fen-skipping --random-fen-skipping 3 --features=HalfKAv2^ --lambda=1.0 --max_epochs=440 --seed %random%%random% --default_root_dir exp/run_8 --resume-from-model ./pt/nn-6ad41a9207d0.pt
`
all.binpack equaled 4 parts Wrong_NNUE_2.binpack https://drive.google.com/file/d/1seGNOqcVdvK_vPNq98j-zV3XPE5zWAeq/view?usp=sharing plus two parts of Training_Data.binpack https://drive.google.com/file/d/1RFkQES3DpsiJqsOtUshENtzPfFgUmEff/view?usp=sharing
Each set was concatenated together - make one large Wrong_NNUE 2 binpack and one large Training_Data of approximate size. They were then interleaved together. The idea was to give Wrong_NNUE.binpack closer to equal weighting with the Training _Data binpack .
nn-6ad41a9207d0.pt was derived from a net vondele ran which passed STC quickly,
but faltered in LTC. https://tests.stockfishchess.org/tests/view/60cba666457376eb8bcab443
STC:
LLR: 2.95 (-2.94,2.94) <-0.50,2.50>
Total: 18792 W: 2068 L: 1889 D: 14835
Ptnml(0-2): 82, 1480, 6117, 1611, 106
https://tests.stockfishchess.org/tests/view/60ccda8b457376eb8bcab568
LTC:
LLR: 2.94 (-2.94,2.94) <0.50,3.50>
Total: 11376 W: 574 L: 454 D: 10348
Ptnml(0-2): 4, 412, 4747, 510, 15
https://tests.stockfishchess.org/tests/view/60ccf952457376eb8bcab58d
closes https://github.com/official-stockfish/Stockfish/pull/3568
Bench: 4900906