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Minor touch up in release notes for 2.12.

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PiperOrigin-RevId: 506185475
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Francois Chollet 2023-01-31 19:41:21 -08:00 committed by TensorFlower Gardener
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@ -41,10 +41,6 @@
* `tf.keras`
* Added a `model.export(filepath)` API to create a lightweight SavedModel
artifact that can be used for inference (e.g. with TF-Serving).
* Added `keras.export.ExportArchive` class for low-level customization of
the process of exporting SavedModel artifacts for inference.
* Moved all saving-related utilities to a new namespace, `keras.saving`,
i.e. `keras.saving.load_model`, `keras.saving.save_model`,
`keras.saving.custom_object_scope`, `keras.saving.get_custom_objects`,
@ -94,7 +90,24 @@
* The new Keras model saving format (`.keras`) is available. You can start
using it via `model.save(f"{fname}.keras", save_format="keras_v3")`. In
the future it will become the default for all files with the `.keras`
extension. This file format targets the Python runtime only.
extension. This file format targets the Python runtime only and makes
it possible to reload Python objects identical to the saved originals.
The format supports non-numerical state such as vocabulary files and
lookup tables, and it is easy to customize in the case of custom layers
with exotic elements of state (e.g. a FIFOQueue). The format
does not rely on bytecode or pickling, and is safe by default. Note
that as a result, Python `lambdas` are disallowed at loading time. If
you want to use `lambdas`, you can pass `safe_mode=False` to the loading
method (only do this if you trust the source of the model).
* Added a `model.export(filepath)` API to create a lightweight SavedModel
artifact that can be used for inference (e.g. with TF-Serving).
* Added `keras.export.ExportArchive` class for low-level customization of
the process of exporting SavedModel artifacts for inference.
Both ways of exporting models are based on `tf.function` tracing
and produce a TF program composed of TF ops. They are meant primarily
for environments where the TF runtime is available,
but not the Python interpreter, as is typical
for production with TF Serving.
* Added utility `tf.keras.utils.FeatureSpace`, a one-stop shop for
structured data preprocessing and encoding.
* Added `tf.SparseTensor` input support to `tf.keras.layers.Embedding`