ctak/src/cnn_train.py, branch lazy_queue An implementation of Tak and a computer opponent in C https://git.l-3.space/ctak/atom?h=lazy_queue 2026-08-28T18:37:41Z Corrected generation of training data for 6s 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-03-18T03:10:53Z urn:sha1:c9823c76dd43aa40bef3f67fa0940a78cb40bf05 Small typo in generated output for weights 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-05T21:37:32Z urn:sha1:5bc1e056412cb7d96831ab7ae32842c166b38436 Tried some naive iterative deepening. Work on TEI interface next 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-02T02:46:24Z urn:sha1:11956b2e940f5e1839efab187d898092e819a766 If TEI is implemented, then i could make use of Morten's racetrack (https://github.com/MortenLohne/racetrack) and develop a quantitative measure of the bot's performance. This is the current priority. Just some #weightgoals ;) 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-01T04:33:39Z urn:sha1:328c8d1e3094a942d6a2edd933c9cc4ab09daab1 It turns out that while i was training on a 0/1 classification problem, i was using 2*eval - 1. Training using this function instead, and on bot-dominated game choices (chosen_player in extract.sh) seems to have given a better evaluation function. At the least, Morten's swindle doesn't work anymore. Syntax errors, small tweak to training data generation 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-01-29T19:05:06Z urn:sha1:fcface1079b1c44c7ccca3d117c698347c978f20