Bump keras from 2.14.0 to 3.8.0

[dependabot]

Bumps keras from 2.14.0 to 3.8.0.

Release notes

Sourced from keras's releases.

Keras 3.8.0

New: OpenVINO backend

OpenVINO is now available as an infererence-only Keras backend. You can start using it by setting the backend field to "openvino" in your keras.json config file.

OpenVINO is a deep learning inference-only framework tailored for CPU (x86, ARM), certain GPUs (OpenCL capable, integrated and discrete) and certain AI accelerators (Intel NPU).

Because OpenVINO does not support gradients, you cannot use it for training (e.g. model.fit()) -- only inference. You can train your models with the JAX/TensorFlow/PyTorch backends, and when trained, reload them with the OpenVINO backend for inference on a target device supported by OpenVINO.

New: ONNX model export

You can now export your Keras models to the ONNX format from the JAX, TensorFlow, and PyTorch backends.

Just pass format="onnx" in your model.export() call:

# Export the model as a ONNX artifact
model.export("path/to/location", format="onnx")
Load the artifact in a different process/environment
ort_session = onnxruntime.InferenceSession("path/to/location")
Run inference
ort_inputs = {
k.name: v for k, v in zip(ort_session.get_inputs(), input_data)
}
predictions = ort_session.run(None, ort_inputs)

New: Scikit-Learn API compatibility interface

It's now possible to easily integrate Keras models into Sciki-Learn pipelines! The following wrapper classes are available:

• keras.wrappers.SKLearnClassifier: implements the sklearn Classifier API
• keras.wrappers.SKLearnRegressor: implements the sklearn Regressor API
• keras.wrappers.SKLearnTransformer: implements the sklearn Transformer API

Other feature additions

• Add new ops:
  • Add keras.ops.diagflat
  • Add keras.ops.unravel_index
• Add new activations:
  • Add sparse_plus activation
  • Add sparsemax activation
• Add new image augmentation and preprocessing layers:
  • Add keras.layers.RandAugment
  • Add keras.layers.Equalization
  • Add keras.layers.MixUp

... (truncated)

Commits

• fbf0af7 Fixing batch_dim_name attribute (#20674)
• ab3c8f5 Add rand_augment processing layer (#20716)
• 8f04616 Specify window_length dtype requirement in tf.keras.ops.istft in math.py (#20...
• 1adaaec Add build method to supress warning (#20729)
• 881d8da Patch tf2onnx to ensure compatibility with numpy>=2.0.0 (#20725)
• 94977dd Refactor keras/src/export/export_lib and add export_onnx (#20710)
• 41c429e Update version number
• a91d006 Update BackupAndRestore class example (#20714)
• 2c95bfe Fix torch GPU CI
• 5b29974 implement transform_bounding_boxes for random_shear (#20704)
• Additional commits viewable in compare view

Most Recent Ignore Conditions Applied to This Pull Request

Dependency Name	Ignore Conditions
keras	[>= 2.11.a, < 2.12]

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 51.44%. Comparing base (fc535ef) to head (4cc341d).
Report is 5 commits behind head on main.

❌ Your project status has failed because the head coverage (51.44%) is below the target coverage (83.00%). You can increase the head coverage or adjust the target coverage.

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HEAD has 68 uploads less than BASE

Flag	BASE (fc535ef)	HEAD (4cc341d)
	81	13

Additional details and impacted files

@@             Coverage Diff             @@
##             main    #2563       +/-   ##
===========================================
- Coverage   85.41%   51.44%   -33.98%     
===========================================
  Files         334      334               
  Lines       31002    31002               
  Branches     5300     5300               
===========================================
- Hits        26481    15948    -10533     
- Misses       3053    14017    +10964     
+ Partials     1468     1037      -431     

see 167 files with indirect coverage changes

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[dependabot]

Superseded by #2588.