[tmva][sofie] Improve Keras test robustness and diagnostics#21749
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Test Results 21 files 21 suites 3d 8h 9m 11s ⏱️ For more details on these failures, see this check. Results for commit 4e6eeb8. ♻️ This comment has been updated with latest results. |
* Skip training for models without trainable weights to avoid spurious
Keras warnings about "no trainable weights"
* Suppress NumPy 2.0 __array__ DeprecationWarning during model.save()
(upstream TF/Keras compatibility issue) using a scoped warnings context
* Fix Keras inference call for single-input models by unwrapping
singleton input lists (Keras expects a tensor, not [tensor])
* Remove redundant load_weights() after load_model()
* Replace custom is_accurate() with np.testing.assert_allclose()
for clearer diagnostics and standardized comparison
* use atol=1e-2 and rtol=0 to preserve previous absolute tolerance behavior
* Update LeakyReLU argument (alpha -> negative_slope) to avoid
deprecation warnings
These changes make the tests more robust, reduce noise from external
dependencies, and significantly improve failure diagnostics.
lmoneta
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Mar 31, 2026
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LGTM!
Thanks Jonas for the test improvements!
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This is a spinoff from my Python dev mode PR:
Who knows, maybe the errors on
alma8with NumPy 2.4.4 go away with this, or at least we get some more informative errors! And even if not, this is a good improvement that allows us to run the test withpython -W error, which is what I plan generally to do in the CI.What is already clear from the NumPy warnings is that somehow TensorFlow/Keras doesn't implement the
__array__interface correctly as NumPy 2 expects.