Fix bug with correlate for tensorflow

[danielenricocahall]

Addresses #21617 by ensuring the "full" and "same" case are properly handled. For simplicity, "same" just reuses "full" and truncates. To validate, a unit test has been added which uses the original data from the issue.

[gemini-code-assist]

Summary of Changes

Hello @danielenricocahall, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves a bug in the correlate function within the Keras NumPy API's TensorFlow backend. The changes ensure that the 'full' and 'same' correlation modes produce accurate results, aligning with standard NumPy behavior. The 'same' mode's implementation has been streamlined by leveraging the 'full' mode's output, and a new test case has been introduced to confirm the fix.

Highlights

• Bug Fix for correlate function: The correlate function in the TensorFlow backend has been updated to correctly handle the 'full' and 'same' modes, addressing a reported issue.
• Improved 'same' mode implementation: The 'same' correlation mode now reuses the output from the 'full' mode and truncates it, simplifying the logic and ensuring correctness.
• New Unit Test: A dedicated unit test, test_correlate_bug, has been added to validate the fix using the specific data that caused the original issue.

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[gemini-code-assist]

Code Review

This pull request fixes a bug in the correlate function for "full" and "same" modes. The logic for both modes has been corrected, and a new unit test has been added to verify the fix using data from the original issue. The changes look correct and the test is a good addition. I've added one suggestion to improve the code structure and robustness by using an if/elif/else block and adding explicit validation for the mode parameter, which aligns with the Keras API design guidelines on error handling.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 83.33333% with 5 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.63%. Comparing base (47fcb39) to head (718a92f).
⚠️ Report is 22 commits behind head on master.

Files with missing lines	Patch %	Lines
keras/src/backend/tensorflow/numpy.py	83.33%	3 Missing and 2 partials ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21778      +/-   ##
==========================================
- Coverage   82.69%   82.63%   -0.06%     
==========================================
  Files         573      577       +4     
  Lines       58888    59425     +537     
  Branches     9218     9315      +97     
==========================================
+ Hits        48696    49107     +411     
- Misses       7845     7913      +68     
- Partials     2347     2405      +58     

Flag	Coverage Δ
keras	82.46% <83.33%> (-0.04%)	⬇️
keras-jax	63.32% <6.66%> (+0.07%)	⬆️
keras-numpy	57.55% <6.66%> (-0.17%)	⬇️
keras-openvino	34.30% <6.66%> (-0.11%)	⬇️
keras-tensorflow	64.13% <83.33%> (+0.11%)	⬆️
keras-torch	63.62% <6.66%> (+0.05%)	⬆️

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[fchollet]

LGTM, thanks for the fix

[hertschuh]

@danielenricocahall

Thank you for fixing this! This was a tricky one.

[danielenricocahall]

Hey @hertschuh thank you for the feedback! I think I've addressed all comments - to confirm, for squeeze, we wanted to be explicit about only dropping the first and last (batch and channel) axes to ensure we don't squeeze the correlation result if it winds up being of size 1?

[hertschuh]

Hey @hertschuh thank you for the feedback! I think I've addressed all comments - to confirm, for squeeze, we wanted to be explicit about only dropping the first and last (batch and channel) axes to ensure we don't squeeze the correlation result if it winds up being of size 1?

Yes, exactly!