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Viren6
0716b845fd
Part 1: PyTorch Training, linrock Trained with a 10-stage sequence from scratch, starting in May 2023: https://github.com/linrock/nnue-tools/blob/master/exp-sequences/3072-10stage-SFNNv9.yml While the training methods were similar to the L1-2560 training sequence, the last two stages introduced min-v2 binpacks, where bestmove capture and in-check position scores were not zeroed during minimization, for compatibility with skipping SEE >= 0 positions and future research. Training data can be found at: https://robotmoon.com/nnue-training-data This net was tested at epoch 679 of the 10th training stage: https://tests.stockfishchess.org/tests/view/65f32e460ec64f0526c48dbc Part 2: SPSA Training, Viren6 The net was then SPSA tuned. This consisted of the output weights (32 * 8) and biases (8) as well as the L3 biases (32 * 8) and L2 biases (16 * 8), totalling 648 params in total. The SPSA tune can be found here: https://tests.stockfishchess.org/tests/view/65fc33ba0ec64f0526c512e3 With the help of Disservin , the initial weights were extracted with: https://github.com/Viren6/Stockfish/tree/new228 The net was saved with the tuned weights using: https://github.com/Viren6/Stockfish/tree/new241 Earlier nets of the SPSA failed STC compared to the base 3072 net of part 1: https://tests.stockfishchess.org/tests/view/65ff356e0ec64f0526c53c98 Therefore it is suspected that the SPSA at VVLTC has added extra scaling on top of the scaling of increasing the L1 size. Passed VVLTC 1: https://tests.stockfishchess.org/tests/view/6604a9020ec64f0526c583da LLR: 2.94 (-2.94,2.94) <0.00,2.00> Total: 53042 W: 13554 L: 13256 D: 26232 Ptnml(0-2): 12, 5147, 15903, 5449, 10 Passed VVLTC 2: https://tests.stockfishchess.org/tests/view/660ad1b60ec64f0526c5dd23 LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 17506 W: 4574 L: 4315 D: 8617 Ptnml(0-2): 1, 1567, 5362, 1818, 5 STC Elo estimate: https://tests.stockfishchess.org/tests/view/660b834d01aaec5069f87cb0 Elo: -7.66 ± 3.8 (95%) LOS: 0.0% Total: 9618 W: 2440 L: 2652 D: 4526 Ptnml(0-2): 80, 1281, 2261, 1145, 42 nElo: -13.94 ± 6.9 (95%) PairsRatio: 0.87 closes https://tests.stockfishchess.org/tests/view/660b834d01aaec5069f87cb0 bench 1823302 Co-Authored-By: Linmiao Xu <lin@robotmoon.com>
56 lines
1.7 KiB
C++
56 lines
1.7 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2024 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 "types.h"
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namespace Stockfish {
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class Position;
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namespace Eval {
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constexpr inline int SmallNetThreshold = 1165, PsqtOnlyThreshold = 2500;
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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 or the location where this macro is defined, as it is used
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// in the Makefile/Fishtest.
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#define EvalFileDefaultNameBig "nn-ae6a388e4a1a.nnue"
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#define EvalFileDefaultNameSmall "nn-baff1ede1f90.nnue"
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namespace NNUE {
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struct Networks;
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}
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std::string trace(Position& pos, const Eval::NNUE::Networks& networks);
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int simple_eval(const Position& pos, Color c);
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Value evaluate(const NNUE::Networks& networks, const Position& pos, int optimism);
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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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