Implement ELO progression, better progress tracking and archive flow #15
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…regularization to loss
The methods in dir.rs depende exclusively on the user data directories and their combinations with other path's; any test would effectively just be a test of the 'dirs' crate.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR adds scripts for tracking the progression of the model strength. It adds skeleton code for tracking several other long-term changes in model performance as well. It would be good to add tracking for average loss by generation as well as delta loss for each training run.
ELO ratings are currently a tournament-like performance estimate and need to be changed to a true ELO calculation as the model should clearly not be rated 700 as a worst possible performance.
This PR also refactors the archiving system into
dir.rs, removing the need for moving files and folders after every training session and eliminating the dependency onfs_extra. This will definitely require additional testing before merging.Incidentally this PR also contains modifications to the PUCT calucation as well as changing the model optimizer to RMSprop for dynamic learning rates with an added L2 regularization.
Closes #3
Closes #4
Closes #12
Remaining tasks before merging: