FIX: Unit testing for Jax/TensorFlow cost functions. #95
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
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Outdated suggestions cannot be applied.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.
In this commit:
* Unit testing added for Jax/TensorFlow cost functions and gradients
* Unit testing resulted in making several fixes to ensure stability of calculated gradients of Jax/TensorFlow by substituting automatic differentiation with evaluating the gradient with the explicit form like in SciPy engine.
* TensorFlow engine convergence criteria relaxed to max_iterations = 100 and < 0.1% change in cost function. Output results were shown to be insensitive to this change with much faster convergence.
The added unit testing and fixes addresses issue #94.