Highlights

• Add support for arbitrary, deeply nested input/output structures in Functional models (e.g. dicts of dicts of lists of inputs or outputs...)
• Add support for optional Functional inputs.
• Introduce keras.dtype_policies.DTypePolicyMap for easy configuration of dtype policies of nested sublayers of a subclassed layer/model.
• New ops:
  • keras.ops.argpartition
  • keras.ops.scan
  • keras.ops.lstsq
  • keras.ops.switch
  • keras.ops.dtype
  • keras.ops.map
  • keras.ops.image.rgb_to_hsv
  • keras.ops.image.hsv_to_rgb

What's changed

• Add support for float8 inference for Dense and EinsumDense layers.
• Add custom name argument in all Keras Applications models.
• Add axis argument in keras.losses.Dice.
• Enable keras.utils.FeatureSpace to be used in a tf.data pipeline even when the backend isn't TensorFlow.
• StringLookup layer can now take tf.SparseTensor as input.
• Metric.variables is now recursive.
• Add training argument to Model.compute_loss().
• Add dtype argument to all losses.
• keras.utils.split_dataset now supports nested structures in dataset.
• Bugs fixes and performance improvements.

Full Changelog: https://github.com/keras-team/keras/compare/v3.3.3...v3.4.0