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Adds keras.ops.hypot, which computes the element-wise hypotenuse of right triangles given two tensor inputs (x1 and x2).
Equivalent to computing sqrt(x12 + x22) with broadcasting support.

Supported across NumPy, TensorFlow, PyTorch, and JAX backends.
Not supported on OpenVINO.

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Summary of Changes

Hello @shashaka, 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 introduces a new hypot function to keras.ops, enabling element-wise calculation of the hypotenuse of right triangles. This operation is mathematically equivalent to sqrt(x12 + x22) and supports broadcasting. The implementation covers NumPy, TensorFlow, PyTorch, and JAX backends, with an explicit exclusion for OpenVINO.

Highlights

  • Introduced keras.ops.hypot: A new function for computing the element-wise hypotenuse of two tensors, x1 and x2, which is equivalent to sqrt(x12 + x22).
  • Multi-backend support: The hypot function has been implemented and integrated across the JAX, NumPy, TensorFlow, and PyTorch backends, ensuring consistent behavior.
  • OpenVINO exclusion: The hypot operation is explicitly marked as not supported for the OpenVINO backend, raising a NotImplementedError if attempted.
  • API exposure and documentation: The new hypot function is exposed under keras.ops and keras.ops.numpy, complete with comprehensive docstrings and usage examples.
  • Comprehensive testing: New tests have been added to keras/src/ops/numpy_test.py to verify the hypot function's symbolic shape inference, static shape behavior, correctness across various inputs, and dtype handling.
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Code Review

This pull request introduces keras.ops.hypot for element-wise hypotenuse calculation, with support for NumPy, JAX, PyTorch, and TensorFlow backends. The implementation is well-structured and includes necessary API exports and tests. My main feedback concerns the numerical stability of the TensorFlow backend implementation, which could fail with large or small input values. I've suggested a more robust implementation. I also recommended enhancing the tests to cover these numerical stability edge cases. Overall, this is a valuable addition to the Keras API.

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codecov-commenter commented Aug 21, 2025

Codecov Report

❌ Patch coverage is 96.66667% with 2 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (master@89a8676). Learn more about missing BASE report.
⚠️ Report is 12 commits behind head on master.

Files with missing lines Patch % Lines
keras/api/_tf_keras/keras/ops/__init__.py 0.00% 1 Missing ⚠️
keras/api/_tf_keras/keras/ops/numpy/__init__.py 0.00% 1 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff            @@
##             master   #21606   +/-   ##
=========================================
  Coverage          ?   82.75%           
=========================================
  Files             ?      572           
  Lines             ?    57321           
  Branches          ?     8970           
=========================================
  Hits              ?    47435           
  Misses            ?     7674           
  Partials          ?     2212           
Flag Coverage Δ
keras 82.55% <96.66%> (?)
keras-jax 63.75% <40.00%> (?)
keras-numpy 57.99% <48.33%> (?)
keras-openvino 34.40% <16.66%> (?)
keras-tensorflow 64.35% <60.00%> (?)
keras-torch 63.91% <51.66%> (?)

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LGTM, thank you for the PR!

@google-ml-butler google-ml-butler bot added kokoro:force-run ready to pull Ready to be merged into the codebase labels Aug 21, 2025
@google-ml-butler google-ml-butler bot removed the ready to pull Ready to be merged into the codebase label Aug 22, 2025
@shashaka shashaka requested a review from fchollet August 22, 2025 11:00
@fchollet fchollet merged commit 6dcf719 into keras-team:master Aug 22, 2025
8 checks passed
@shashaka shashaka deleted the hypot branch August 22, 2025 23:45
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5 participants