Highlights
* New file editor utility: `keras.saving.KerasFileEditor`. Use it to inspect, diff, modify and resave Keras weights files. [See basic workflow here](https://colab.research.google.com/drive/1b1Rxf8xbOkMyvjpdJDrGzSnisyXatJsW?usp=sharing).
* 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