Add a percentage correct metric for BC training #402
Merged
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
This patch adds a test for the metrics in the trainer class to ensure that they are actually set. This patch just directly inspects the state rather than trying to ensure that we log to tensorboard too, but this should be good enough, and is definitely better than what we had before (nothing).
This patch adds a percentage correct for BC training, which makes it a lot easier to interpret how a model is doing rather than just staring at loss values.
mtrofin
approved these changes
Dec 19, 2024
Base automatically changed from
users/boomanaiden154/trainer-metrics-tests
to
main
December 19, 2024 06:04
boomanaiden154
deleted the
users/boomanaiden154/bc-percentage-correct
branch
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Outdated suggestions cannot be applied.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 patch adds a percentage correct for BC training, which makes it a lot easier to interpret how a model is doing rather than just staring at loss values.