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Created by retraining the master net on the previous best dataset with additional filtering. No new data was added. More of the Leela-dfrc_n5000.binpack part of the dataset was pre-filtered with depth6 multipv2 search to remove bestmove captures. About 93% of the previous Leela/SF data and 99% of the SF dfrc data was filtered. Unfiltered parts of the dataset were left out. The new Leela T80 oct+nov data is the same as before. All early game positions with ply count <= 28 were skipped during training by modifying the training data loader in nnue-pytorch. Trained in a similar way as recent master nets, with a different nnue-pytorch branch for early ply skipping: python3 easy_train.py \ --experiment-name=leela93-dfrc99-filt-only-T80-oct-nov-skip28 \ --training-dataset=/data/leela93-dfrc99-filt-only-T80-oct-nov.binpack \ --start-from-engine-test-net True \ --nnue-pytorch-branch=linrock/nnue-pytorch/misc-fixes-skip-ply-lteq-28 \ --gpus="0," \ --start-lambda=1.0 \ --end-lambda=0.75 \ --gamma=0.995 \ --lr=4.375e-4 \ --tui=False \ --seed=$RANDOM \ --max_epoch=800 \ --network-testing-threads 20 \ --num-workers 6 For the exact training data used: https://robotmoon.com/nnue-training-data/ Details about the previous best dataset: #4295 Local testing at a fixed 25k nodes: experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28 Local Elo: run_0/nn-epoch779.nnue : 5.1 +/- 1.5 Passed STC https://tests.stockfishchess.org/tests/view/63adb3acae97a464904fd4e8 LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 36504 W: 9847 L: 9538 D: 17119 Ptnml(0-2): 108, 3981, 9784, 4252, 127 Passed LTC https://tests.stockfishchess.org/tests/view/63ae0ae25bd1e5f27f13d884 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 36592 W: 10017 L: 9717 D: 16858 Ptnml(0-2): 17, 3461, 11037, 3767, 14 closes https://github.com/official-stockfish/Stockfish/pull/4314 bench 4015511
63 lines
1.8 KiB
C++
63 lines
1.8 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2022 The Stockfish developers (see AUTHORS file)
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Stockfish is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Stockfish is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#ifndef EVALUATE_H_INCLUDED
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#define EVALUATE_H_INCLUDED
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#include <string>
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#include <optional>
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#include "types.h"
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namespace Stockfish {
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class Position;
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namespace Eval {
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std::string trace(Position& pos);
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Value evaluate(const Position& pos, int* complexity = nullptr);
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extern bool useNNUE;
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extern std::string currentEvalFileName;
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// The default net name MUST follow the format nn-[SHA256 first 12 digits].nnue
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// for the build process (profile-build and fishtest) to work. Do not change the
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// name of the macro, as it is used in the Makefile.
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#define EvalFileDefaultName "nn-60fa44e376d9.nnue"
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namespace NNUE {
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std::string trace(Position& pos);
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Value evaluate(const Position& pos, bool adjusted = false, int* complexity = nullptr);
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void init();
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void verify();
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bool load_eval(std::string name, std::istream& stream);
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bool save_eval(std::ostream& stream);
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bool save_eval(const std::optional<std::string>& filename);
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} // namespace NNUE
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} // namespace Eval
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} // namespace Stockfish
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#endif // #ifndef EVALUATE_H_INCLUDED
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