Keras

This is a minor bugfix release over 3.1.0.

What's Changed
* Unwrap variable values in all stateless calls. by hertschuh in https://github.com/keras-team/keras/pull/19287
* Fix `draw_seed` causing device discrepancy issue during `torch`'s symbolic execution by KhawajaAbaid in https://github.com/keras-team/keras/pull/19289
* Fix TestCase.run_layer_test for multi-output layers by shkarupa-alex in https://github.com/keras-team/keras/pull/19293
* Sine docstring by grasskin in https://github.com/keras-team/keras/pull/19295
* Fix `keras.ops.softmax` for the tensorflow backend by tirthasheshpatel in https://github.com/keras-team/keras/pull/19300
* Fix mixed precision check in TestCase.run_layer_test: compare with output_spec dtype instead of hardcoded float16 by shkarupa-alex in https://github.com/keras-team/keras/pull/19297
* ArrayDataAdapter no longer converts to NumPy and supports sparse tens… by hertschuh in https://github.com/keras-team/keras/pull/19298
* add token to codecov by haifeng-jin in https://github.com/keras-team/keras/pull/19312
* Add Tensorflow support for variable `scatter_update` in optimizers. by hertschuh in https://github.com/keras-team/keras/pull/19313
* Replace `dm-tree` with `optree` by james77777778 in https://github.com/keras-team/keras/pull/19306
* downgrade codecov to v3 by haifeng-jin in https://github.com/keras-team/keras/pull/19319
* Allow tensors in `tf.Dataset`s to have different dimensions. by hertschuh in https://github.com/keras-team/keras/pull/19318
* update codecov setting by haifeng-jin in https://github.com/keras-team/keras/pull/19320
* Set dtype policy for uint8 by sampathweb in https://github.com/keras-team/keras/pull/19327
* Use Value dim shape for Attention compute_output_shape by sampathweb in https://github.com/keras-team/keras/pull/19284

New Contributors
* tirthasheshpatel made their first contribution in https://github.com/keras-team/keras/pull/19300

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.1.0...v3.1.1

New features
* Add support for `int8` inference. Just call `model.quantize("int8")` to do an in-place conversion of a bfloat16 or float32 model to an int8 model. Note that only `Dense` and `EinsumDense` layers will be converted (this covers LLMs and all Transformers in general). We may add more supported layers over time.
* Add `keras.config.set_backend(backend)` utility to reload a different backend.
* Add `keras.layers.MelSpectrogram` layer for turning raw audio data into Mel spectrogram representation.
* Add `keras.ops.custom_gradient` decorator (only for JAX and TensorFlow).
* Add `keras.ops.image.crop_images`.
* Add `pad_to_aspect_ratio` argument to `image_dataset_from_directory`.
* Add `keras.random.binomial` and `keras.random.beta` functions.
* Enable `keras.ops.einsum` to run with int8 x int8 inputs and int32 output.
* Add `verbose` argument in all dataset-creation utilities.

Notable fixes
* Fix Functional model slicing
* Fix for TF XLA compilation error for `SpectralNormalization`
* Refactor `axis` logic across all backends and add support for multiple axes in `expand_dims` and `squeeze`

New Contributors
* mykolaskrynnyk made their first contribution in https://github.com/keras-team/keras/pull/19190
* chicham made their first contribution in https://github.com/keras-team/keras/pull/19201
* joycebrum made their first contribution in https://github.com/keras-team/keras/pull/19214
* EtiNL made their first contribution in https://github.com/keras-team/keras/pull/19228

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.0.5...v3.1.0

This release brings many bug fixes and performance improvements, new linear algebra ops, and sparse tensor support for the JAX backend.

Highlights

* Add support for sparse tensors with the JAX backend.
* Add support for saving/loading in bfloat16.
* Add linear algebra ops in `keras.ops.linalg`.
* Support nested structures in `while_loop` op.
* Add `erfinv` op.
* Add `normalize` op.
* Add support for `IterableDataset` to `TorchDataLoaderAdapter`.

New Contributors
* frazane made their first contribution in https://github.com/keras-team/keras/pull/19107
* SamanehSaadat made their first contribution in https://github.com/keras-team/keras/pull/19111
* sitamgithub-MSIT made their first contribution in https://github.com/keras-team/keras/pull/19142
* timotheeMM made their first contribution in https://github.com/keras-team/keras/pull/19169

**Full Changelog**: https://github.com/keras-team/keras/compare/v3.0.4...v3.0.5

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