Keras

The code under `tensorflow/python/keras` is considered legacy and will be removed in future releases (tf 2.7 or later). For any user who import `tensorflow.python.keras`, please update your code to public `tf.keras` instead.

The API endpoints for tf.keras stay unchanged, but are now backed by the keras PIP package. All Keras-related PRs and issues should now be directed to the GitHub repository `keras-team/keras`.

For the detailed release notes about `tf.keras` behavior changes, please take a look for tensorflow release notes.

Keras Release 2.6.0 RC3 fix a security issue for loading keras models via yaml, which could allow arbitrary code execution.

Keras 2.6.0 RC2 is a minor bug-fix release.

1. Fix TextVectorization layer with output_sequence_length on unknown input shapes.
2. Output int64 by default from Discretization layer.
3. Fix serialization of Hashing layer.
4. Add more explicit error message for instance type checking of optimizer.

As [previously announced](https://github.com/keras-team/keras/releases/tag/2.3.0), we have discontinued multi-backend Keras to refocus exclusively on the TensorFlow implementation of Keras.

In the future, we will develop the TensorFlow implementation of Keras in the present repo, at `keras-team/keras`. For the time being, it is being developed in `tensorflow/tensorflow` and distributed as `tensorflow.keras`. In this future, the `keras` package on PyPI will be the same as `tf.keras`.

This release (2.4.0) simply redirects all APIs in the standalone `keras` package to point to `tf.keras`. This helps address user confusion regarding differences and incompatibilities between `tf.keras` and the standalone `keras` package. There is now only one Keras: `tf.keras`.

- Note that this release may be breaking for some workflows when going from Keras 2.3.1 to 2.4.0. Test before upgrading.
- Note that we still recommend that you import Keras as `from tensorflow import keras`, rather than `import keras`, for the time being.