Keras 3.8.0

Keras 3.8.0

on Python PyPI

New: OpenVINO backend

OpenVINO is now available as an infererence-only Keras backend. You can start using it by setting the backend field to "open_vino" 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
  • Add keras.layers.RandomHue
  • Add keras.layers.RandomGrayscale
  • Add keras.layers.RandomSaturation
  • Add keras.layers.RandomColorJitter
  • Add keras.layers.RandomColorDegeneration
  • Add keras.layers.RandomSharpness
  • Add keras.layers.RandomShear
• Add argument axis to tversky loss

JAX specific changes

• Add support for JAX named scope

TensorFlow specific changes

• Make keras.random.shuffle XLA compilable

PyTorch specific changes

• Add support for model.export() and keras.export.ExportArchive with the PyTorch backend, supporting both the TF SavedModel format and the ONNX format.

New Contributors

• @LavanyaKV1234 made their first contribution in #20553
• @jakubxy08 made their first contribution in #20563
• @dhantule made their first contribution in #20565
• @roebel made their first contribution in #20575
• @Surya2k1 made their first contribution in #20613
• @edge7 made their first contribution in #20584
• @adrinjalali made their first contribution in #20599
• @mmicu made their first contribution in #20655
• @rkazants made their first contribution in #19727
• @lkk7 made their first contribution in #20682
• @Furkan-rgb made their first contribution in #20684
• @punkeel made their first contribution in #20694
• @kas2020-commits made their first contribution in #20709

Full Changelog: v3.7.0...v3.8.0