Keras 3.6 Changes

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.

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