Keras

This is a minor release with improvements to the LoRA API required by the next release of KerasNLP.

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.0.3...v3.0.4

This is a minor Keras release.

What's Changed

* Add built-in LoRA (low-rank adaptation) API to all relevant layers (`Dense`, `EinsumDense`, `Embedding`).
* Add `SwapEMAWeights` callback to make it easier to evaluate model metrics using EMA weights during training.
* All `DataAdapters` now create a native iterator for each backend, improving performance.
* Add built-in prefetching for JAX, improving performance.
* The `bfloat16` dtype is now allowed in the global `set_dtype` configuration utility.
* Bug fixes and performance improvements.

New Contributors

* kiraksi made their first contribution in https://github.com/keras-team/keras/pull/18977
* dugujiujian1999 made their first contribution in https://github.com/keras-team/keras/pull/19010
* neo-alex made their first contribution in https://github.com/keras-team/keras/pull/18997
* anas-rz made their first contribution in https://github.com/keras-team/keras/pull/19057

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.0.2...v3.0.3

Breaking changes

There are no known breaking changes in this release compared to 3.0.1.

API changes

- Add `keras.random.binomial` and `keras.random.beta` RNG functions.
- Add masking support to `BatchNormalization`.
- Add `keras.losses.CTC` (loss function for sequence-to-sequence tasks) as well as the lower-level operation `keras.ops.ctc_loss`.
- Add `ops.random.alpha_dropout` and `layers.AlphaDropout`.
- Add gradient accumulation support for all backends, and enable optimizer EMA for JAX and torch

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.0.1...v3.0.2

This is a minor release focused on bug fixes and performance improvements.

What's Changed

* Bug fixes and performance improvements.
* Add `stop_evaluating` and `stop_predicting` model attributes for callbacks, similar to `stop_training`.
* Add `keras.device()` scope for managing device placement in a multi-backend way.
* Support dict items in `PyDataset`.
* Add `hard_swish` activation and op.
* Fix cuDNN LSTM performance on TensorFlow backend.
* Add a `force_download` arg to `get_file` to force cache invalidation.

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.0.0...v3.0.1

Major updates

See the [release announcement](https://keras.io/keras_3/) for a detailed list of major changes. Main highlights compared to Keras 2 are:

- Keras can now be run on top of JAX, PyTorch, TensorFlow, and even NumPy (note that the NumPy backend is inference-only).
- New low-level `keras.ops` API for building cross-framework components.
- New large-scale model distribution `keras.distribution` based on JAX.
- New stateless API for layers, models, optimizers, and metrics.

Breaking changes

See [this thread](https://github.com/keras-team/keras/issues/18467) for a complete list of breaking changes, as well as the [Keras 3 migration guide](https://keras.io/guides/migrating_to_keras_3/).

What's Changed
* Typofixes for `StringLookup` documentation by cw118 in https://github.com/keras-team/keras/pull/18333
* Fix ModelCheckpoint trained-on batch counting when using steps_per_execution>1 by jasnyj in https://github.com/keras-team/keras/pull/17632
* Fix legacy optimizer handling in `compile_from_config()`. by nkovela1 in https://github.com/keras-team/keras/pull/18492
* Remove options arg from ModelCheckpoint callback for Keras V3 saving, streamline ModelCheckpoint saving flow. Parameterize associated tests. by nkovela1 in https://github.com/keras-team/keras/pull/18545
* Use TENSORFLOW_VERSION when available during pip_build script by sampathweb in https://github.com/keras-team/keras/pull/18739

New Contributors
* cw118 made their first contribution in https://github.com/keras-team/keras/pull/18333
* jasnyj made their first contribution in https://github.com/keras-team/keras/pull/17632

**Full Changelog**: https://github.com/keras-team/keras/compare/v2.14.0...v2.15.0