Hi Jon.
jdart wrote: ↑Fri Apr 23, 2021 6:54 pm
I am looking at:
https://github.com/Matthies/NN
Re this:
nn-f21733c196-20201028.nnue: Learned from Rubi depth 10 training positions (TB disabled) using the SF learner binary; +95 Elo +/-25 vs. HCE
Doesn't that binary use the Stockfish eval, not the Rubichess eval?
Rubis evaluation is in the training data.
The SF learner binary is used to "create" (train, optimize) a network (starting from scratch or from an earlier Rubi network) that minimizes the difference of 1. an NNUE evaluation with this net and 2. the evaluation/result in the training data. The value of 1. is a standard NNUE evaluation (independent from the engine but given by the network topology) with the exception that a depth 1 search (SF qsearch) is used to get a quiet position. Neither the handcrafted evaluation of Stockfish nor the evaluation of a Stockfish net is used. At least this is what I read from the code and from my debugging.
jdart wrote: ↑Fri Apr 23, 2021 6:54 pm
And this:
nn-375bdd2d7f-20210112.nnue: Learned from Rubi depth 8 training using eval of last net, 6-men-TB and disabled pruning using trainer of SV mod branch
What is the "SV mod branch" (was it meant to say: SF mod branch?)
No. SV stands for Sergio Vieri. Beginning with the nn-375bdd2d7f-20210112.nnue I used his modification of the original Nodchip NNUE learner branch:
https://github.com/sergiovieri/Stockfish/tree/mod
Let me add some words about the (for me quite shocking) results of my last version 2.1:
My explanation for the nearly +100 Elo compared to version 2.0 are
1. I fixed some bugs in the training data generation code.
2. I created more and better data with deaper search.
3. I tried parameters for the learner that Sergio used in his Workflow (
https://github.com/sergiovieri/Stockfish/tree/mod/nnue)
When I realized how strong the nn-cf8c56d366-20210326.nnue is I asked Ed Schroeder for a similarity test with his NNUE research tools from
http://rebel13.nl/home/nnue.html and he kindly tested it immediately and sent me these results which seem to show less SF-similarity than many others:
Rebel wrote:Sat Mar 27, 2021 12:21 pm
Score comparison between
'sf12.epd' and 'rubi-nnue.epd'
Code: Select all
dev0 dev1 dev2 dev3 dev4 dev5 RMS SIM
2202 1767 1408 973 638 1236 74.83 54.95
Score comparison between
'sf13.epd' and 'rubi-nnue.epd'
Code: Select all
dev0 dev1 dev2 dev3 dev4 dev5 RMS SIM
1919 1652 1388 1028 797 1442 80.31 49.23
Excellent results.
I still have the goal to port a learner into Rubi to be completely independant from SF learner binary but this is not ready for now.
Regards, Andreas