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The neural network used for the original 2018 computer shogi implementation consists of four weight layers: W1 (16-bit integers) and W2, W3 and W4 (8-bit). It has 4 fully-connected layers,
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tree. While being slower than handcrafted evaluation functions, NNUE does not suffer from the 'blindness beyond the current move' problem.
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671:"Efficiently Updatable Neural-Network-based Evaluation Function for computer Shogi (Unofficial English Translation)"
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W1 encoded the king's position and therefore this layer needed only to be re-evaluated once the king moved. It used
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471:, or variants thereof like the king-piece-square table. NNUE is used primarily for the leaf nodes of the
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653:"Efficiently Updatable Neural-Network-based Evaluation Function for computer Shogi"
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activation functions, and outputs a single number, being the score of the board.
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in 2018. On 6 August 2020, NNUE was for the first time ported to a chess engine,
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12. Since 2021, many of the top rated classical chess engines such as
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548:- The chapter about NNUE features a visualization of NNUE.
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have an NNUE implementation to remain competitive (with
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738:NNUE evaluation functions for computer shogi
709:"official-stockfish / Stockfish, NNUE merge"
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527:(SIMD) techniques along with appropriate
16:Neural network based evaluation function
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93:Efficiently updatable neural networks
22:This article is part of the series on
707:Joost VandeVondele (July 25, 2020).
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445:efficiently updatable neural network
641:
625:"Stockfish - Chessprogramming wiki"
13:
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571:Gary Linscott (April 30, 2021).
525:single instruction multiple data
98:Handcrafted evaluation functions
34:
734:on the Chess Programming Wiki.
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694:"Introducing NNUE Evaluation"
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505:without a requirement for a
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113:Stochastic gradient descent
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762:Artificial neural networks
669:Yu Nasu (April 28, 2018).
651:Yu Nasu (April 28, 2018).
163:Principal variation search
501:NNUE runs efficiently on
498:as a notable exception).
451:, a Japanese wordplay on
629:www.chessprogramming.org
507:graphics processing unit
503:central processing units
455:, sometimes stylised as
521:incremental computation
173:Monte Carlo tree search
552:List of chess software
546:Stockfish chess engine
529:intrinsic instructions
283:Dragon by Komodo Chess
108:Reinforcement learning
478:NNUE was invented by
128:Unsupervised learning
46:Board representations
78:Deep neural networks
71:Evaluation functions
767:Japanese inventions
541:elmo (shogi engine)
469:piece-square tables
465:evaluation function
118:Supervised learning
103:Piece-square tables
757:Evaluation methods
482:and introduced to
158:Alpha-beta pruning
467:whose inputs are
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168:Quiescence search
147:search algorithms
28:Chess programming
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696:. 6 August 2020.
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338:Leela Chess Zero
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186:Chess computers
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777:Computer chess
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772:Computer shogi
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726:External links
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658:(in Japanese).
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604:Stockfish Blog
600:"Stockfish 12"
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484:computer shogi
461:neural network
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492:Komodo Dragon
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632:. Retrieved
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607:. Retrieved
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584:December 12,
582:. Retrieved
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218:Deep Thought
198:ChessMachine
123:Texel tuning
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82:Transformers
273:CuckooChess
263:Chess Tiger
751:Categories
742:github.com
634:2020-08-18
609:19 October
558:References
473:alpha–beta
343:MChess Pro
278:Deep Fritz
208:Cray Blitz
488:Stockfish
398:Turochamp
388:Stockfish
383:SmarThink
328:KnightCap
303:GNU Chess
288:Fairy-Max
258:AlphaZero
213:Deep Blue
88:Attention
58:Bitboards
535:See also
509:(GPU).
373:Shredder
233:Mephisto
203:ChipTest
480:Yu Nasu
463:-based
459:) is a
348:Mittens
313:Houdini
153:Minimax
714:GitHub
679:GitHub
578:GitHub
573:"NNUE"
353:MuZero
333:Komodo
323:Junior
318:Ikarus
308:HIARCS
268:Crafty
238:Saitek
223:HiTech
674:(PDF)
656:(PDF)
403:Zappa
393:Torch
378:Sjeng
368:Rybka
363:REBEL
298:Fruit
293:Fritz
228:Hydra
193:Belle
141:Graph
732:NNUE
611:2020
586:2020
523:and
514:ReLU
457:ƎUИИ
449:NNUE
358:Naum
145:tree
143:and
53:0x88
740:on
453:Nue
443:An
753::
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643:^
627:.
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575:.
531:.
717:.
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637:.
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447:(
432:e
425:t
418:v
84:)
80:(
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
