Highlights

• New file editor utility: keras.saving.KerasFileEditor. Use it to inspect, diff, modify and resave Keras weights files. See basic workflow here.
• New keras.utils.Config class for managing experiment config parameters.

BREAKING changes

• When using keras.utils.get_file, with extract=True or untar=True, the return value will be the path of the extracted directory, rather than the path of the archive.

Other changes and additions

• Logging is now asynchronous in fit(), evaluate(), predict(). This enables 100% compact stacking of train_step calls on accelerators (e.g. when running small models on TPU).
  • If you are using custom callbacks that rely on on_batch_end, this will disable async logging. You can force it back by adding self.async_safe = True to your callbacks. Note that the TensorBoard callback isn't considered async safe by default. Default callbacks like the progress bar are async safe.
• Added keras.saving.KerasFileEditor utility to inspect, diff, modify and resave Keras weights file.
• Added keras.utils.Config class. It behaves like a dictionary, with a few nice features:
  • All entries are accessible and settable as attributes, in addition to dict-style (e.g. config.foo = 2 or config["foo"] are both valid)
  • You can easily serialize it to JSON via config.to_json().
  • You can easily freeze it, preventing future changes, via config.freeze().
• Added bitwise numpy ops:
  • bitwise_and
  • bitwise_invert
  • bitwise_left_shift
  • bitwise_not
  • bitwise_or
  • bitwise_right_shift
  • bitwise_xor
• Added math op keras.ops.logdet.
• Added numpy op keras.ops.trunc.
• Added keras.ops.dot_product_attention.
• Added keras.ops.histogram.
• Allow infinite PyDataset instances to use multithreading.
• Added argument verbose in keras.saving.ExportArchive.write_out() method for exporting TF SavedModel.
• Added epsilon argument in keras.ops.normalize.
• Added Model.get_state_tree() method for retrieving a nested dict mapping variable paths to variable values (either as numpy arrays or backend tensors (default)). This is useful for rolling out custom JAX training loops.
• Added image augmentation/preprocessing layers keras.layers.AutoContrast, keras.layers.Solarization.
• Added keras.layers.Pipeline class, to apply a sequence of layers to an input. This class is useful to build a preprocessing pipeline. Compared to a Sequential model, Pipeline features a few important differences:
  • It's not a Model, just a plain layer.
  • When the layers in the pipeline are compatible with tf.data, the pipeline will also remain tf.data compatible, independently of the backend you use.

New Contributors

• @alexhartl made their first contribution in https://github.com/keras-team/keras/pull/20125
• @Doch88 made their first contribution in https://github.com/keras-team/keras/pull/20156
• @edbosne made their first contribution in https://github.com/keras-team/keras/pull/20151
• @ghsanti made their first contribution in https://github.com/keras-team/keras/pull/20185
• @joehiggi1758 made their first contribution in https://github.com/keras-team/keras/pull/20223
• @AryazE made their first contribution in https://github.com/keras-team/keras/pull/20228
• @sanskarmodi8 made their first contribution in https://github.com/keras-team/keras/pull/20237
• @himalayo made their first contribution in https://github.com/keras-team/keras/pull/20262
• @nate2s made their first contribution in https://github.com/keras-team/keras/pull/20305
• @DavidLandup0 made their first contribution in https://github.com/keras-team/keras/pull/20316

Full Changelog: https://github.com/keras-team/keras/compare/v3.5.0...v3.6.0