Bump the pip group across 1 directory with 2 updates

[dependabot]

Bumps the pip group with 2 updates in the / directory: django and keras.

Updates django from 5.0.7 to 5.0.10

Commits

• 3b3a5f4 [5.0.x] Bumped version for 5.0.10 release.
• ff08bb6 [5.0.x] Fixed CVE-2024-53908 -- Prevented SQL injections in direct HasKeyLook...
• a5a89ea [5.0.x] Fixed CVE-2024-53907 -- Mitigated potential DoS in strip_tags().
• baf63eb [5.0.x] Added stub release notes and release date for 5.0.10, and 4.2.17.
• c8ce36b [5.0.x] Fixed docs build on Sphinx 8.1+.
• 5064ddb [5.0.x] Refs #35844 -- Expanded compatibility for expected error messages in ...
• 901ec7a [5.0.x] Added CVE-2024-45230 and CVE-2024-45231 to security archive.
• cc13485 [5.0.x] Post-release version bump.
• 8e68f93 [5.0.x] Bumped version for 5.0.9 release.
• 96d8404 [5.0.x] Fixed CVE-2024-45231 -- Avoided server error on password reset when e...
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Updates keras from 2.13.1 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

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