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Add a [224, 32, 1] NN to augment the existing Pawn King evaluation.
This network has 224 inputs, mapped to white King bb, the white Pawn bb (minus the promotion ranks), the black King bb, and the black Pawn bb (minus the promotion ranks). We incrementally update the 1st layer of Neurons. Due to Pawn Hashing, Eval Caching, and the TT, we very rarely actually have to perform the computation to move from the 1st layer to the output Neuron. The training code for these networks is a private implementation using the PyTorch framework. The goal of the trainer is to train to output a centipawn value, which is then put through a sigmoid after being offset by a static evaluation of the position. This work could not have been done without the work of @KierenP , the author of https://github.com/KierenP/Halogen . Halogen offers some generic NN structure code which made trying out new nets quick and easy. While developing the networks, I used a c++ fork of Ethereal which contained Halogen's NN code. The final code does not contain anything from Halogen, nor does it contain anything from Stockfish or the Leela projects. The code is entirely new, and is not based in full or in part upon the work on the Stockfish project's NNUE. This network _augments_ the existing evaluation. This network does _not_ replace the existing evaluation in any way, shape, or form. ELO | 25.81 +- 10.38 (95%) SPRT | 10.0+0.1s Threads=1 Hash=8MB LLR | 2.95 (-2.94, 2.94) [0.00, 5.00] Games | N: 1888 W: 484 L: 344 D: 1060 http://chess.grantnet.us/test/7422/ ELO | 23.39 +- 8.71 (95%) SPRT | 60.0+0.6s Threads=1 Hash=64MB LLR | 2.95 (-2.94, 2.94) [0.00, 5.00] Games | N: 1904 W: 362 L: 234 D: 1308 http://chess.grantnet.us/test/7423/ BENCH : 4,679,412
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