Fix compute_gradient to include frozen parameters#156
Open
Fix compute_gradient to include frozen parameters#156
compute_gradient to include frozen parameters#156Conversation
New test cases illustrates an error when calling compute_gradient on a Model instance that has frozen parameters and does not have a user-defined gradient. The error occurs because get/set parameter vector is called without including frozen parameters, whereas the indexing in get_gradient expects the result to include all parameters.
Fixes newly added test case.
This file contains hidden or 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
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Update
compute_gradientto include frozen parameters. Otherwise, the finite difference gradient does not include frozen parameters, which creates a shape mismatch for the array expected byget_gradient. This occurs when using a model that has frozen parameters but no user-defined gradient function. I have added such a regression test case.