keras.utils.set_random_seed clear the global SeedGenerator.

[hertschuh]

This is needed to get reproducible results with keras.random ops.

Also introduced a constant for "global_seed_generator".

[gemini-code-assist]

Summary of Changes

Hello @hertschuh, 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 addresses an issue where keras.random operations might not yield reproducible results after keras.utils.set_random_seed was called. The core change involves ensuring that the global SeedGenerator is properly reset when a new random seed is set, thereby enforcing consistent behavior across subsequent random number generation calls. This enhancement is critical for maintaining determinism in machine learning experiments and debugging.

Highlights

• Reproducibility Fix: The keras.utils.set_random_seed function now explicitly clears the global SeedGenerator to ensure it is re-initialized with the new seed, guaranteeing reproducible results for keras.random operations.
• Test Enhancement: A new test case, test_set_random_seed_with_global_seed_generator, has been added to validate that keras.random operations produce identical outputs after set_random_seed is called multiple times with the same seed.
• Test Refactoring: The existing test test_set_random_seed was renamed to test_set_random_seed_with_seed_generator and its pytest.mark.skipif decorator was removed, indicating broader applicability.

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[gemini-code-assist]

Code Review

This pull request correctly addresses an issue with reproducibility in keras.random operations by ensuring the global SeedGenerator is cleared when keras.utils.set_random_seed is invoked. The implementation is sound, and the new test case provides good coverage for the fix. I have a couple of suggestions: one to improve maintainability by replacing a magic string with a constant, and another to address a potential test failure on the NumPy backend.

[gemini-code-assist]

This test was previously skipped for the NumPy backend. By removing the skipif decorator, this test will now run for the NumPy backend. However, the get_model_output function uses tf.data.Dataset, which is specific to TensorFlow and will likely cause the test to fail when run with the NumPy backend.

To ensure this test can run across all backends, you could modify get_model_output to not use tf.data.Dataset. For example:

def get_model_output():
    model = keras.Sequential(
        [
            keras.layers.Dense(10),
            keras.layers.Dropout(0.5),
            keras.layers.Dense(10),
        ]
    )
    x = np.random.random((32, 10)).astype("float32")
    return model.predict(x)

Alternatively, if the intention is to keep this test for TensorFlow-based backends only, the skipif decorator should be restored.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.56%. Comparing base (8287e48) to head (271999a).
⚠️ Report is 5 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21874      +/-   ##
==========================================
- Coverage   82.57%   82.56%   -0.02%     
==========================================
  Files         577      578       +1     
  Lines       59586    59789     +203     
  Branches     9347     9384      +37     
==========================================
+ Hits        49205    49366     +161     
- Misses       7975     8002      +27     
- Partials     2406     2421      +15     

Flag	Coverage Δ
keras	82.38% <100.00%> (-0.02%)	⬇️
keras-jax	62.89% <100.00%> (+0.01%)	⬆️
keras-numpy	57.46% <100.00%> (-0.07%)	⬇️
keras-openvino	34.34% <100.00%> (-0.01%)	⬇️
keras-tensorflow	64.42% <100.00%> (+0.01%)	⬆️
keras-torch	63.60% <100.00%> (+0.01%)	⬆️

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[divyashreepathihalli]

left two comments. LGTM.

[divyashreepathihalli]

maybe the docstring can be updated to mention that it now resets the Keras global random generator?

[hertschuh]

Done

[divyashreepathihalli]

maybe also add a test to check that different seed produces different results. To make sure the reset mechanism doesn't accidentally lock the generator to a single state.

[hertschuh]

Good idea, added