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.

New Contributors

• @alexhartl made their first contribution in #20125
• @Doch88 made their first contribution in #20156
• @edbosne made their first contribution in #20151
• @ghsanti made their first contribution in #20185
• @joehiggi1758 made their first contribution in #20223
• @AryazE made their first contribution in #20228
• @sanskarmodi8 made their first contribution in #20237
• @himalayo made their first contribution in #20262
• @nate2s made their first contribution in #20305
• @DavidLandup0 made their first contribution in #20316

Full Changelog: v3.5.0...v3.6.0

What's Changed

• Add integration with the Hugging Face Hub. You can now save models to Hugging Face Hub directly from keras.Model.save() and load .keras models directly from Hugging Face Hub with keras.saving.load_model().
• Ensure compatibility with NumPy 2.0.
• Add keras.optimizers.Lamb optimizer.
• Improve keras.distribution API support for very large models.
• Add keras.ops.associative_scan op.
• Add keras.ops.searchsorted op.
• Add keras.utils.PyDataset.on_epoch_begin() method.
• Add data_format argument to keras.layers.ZeroPadding1D layer.
• Bug fixes and performance improvements.

Full Changelog: v3.4.1...v3.5.0

This is a minor bugfix release.

Highlights

• Add support for arbitrary, deeply nested input/output structures in Functional models (e.g. dicts of dicts of lists of inputs or outputs...)
• Add support for optional Functional inputs.
• Introduce keras.dtype_policies.DTypePolicyMap for easy configuration of dtype policies of nested sublayers of a subclassed layer/model.
• New ops:
  • keras.ops.argpartition
  • keras.ops.scan
  • keras.ops.lstsq
  • keras.ops.switch
  • keras.ops.dtype
  • keras.ops.map
  • keras.ops.image.rgb_to_hsv
  • keras.ops.image.hsv_to_rgb

What's changed

• Add support for float8 inference for Dense and EinsumDense layers.
• Add custom name argument in all Keras Applications models.
• Add axis argument in keras.losses.Dice.
• Enable keras.utils.FeatureSpace to be used in a tf.data pipeline even when the backend isn't TensorFlow.
• StringLookup layer can now take tf.SparseTensor as input.
• Metric.variables is now recursive.
• Add training argument to Model.compute_loss().
• Add dtype argument to all losses.
• keras.utils.split_dataset now supports nested structures in dataset.
• Bugs fixes and performance improvements.

Full Changelog: v3.3.3...v3.4.0

This is a minor bugfix release.

This is a simple fix release that re-surfaces legacy Keras 2 APIs that aren't part of Keras package proper, but that are still featured in tf.keras. No other content has changed.

This is a simple fix release that moves the legacy _tf_keras API directory to the root of the Keras pip package. This is done in order to preserve import paths like from tensorflow.keras import layers without making any changes to the TensorFlow API files.

No other content has changed.

What's Changed

• Introduce float8 training.
• Add LoRA to ConvND layers.
• Add keras.ops.ctc_decode for JAX and TensorFlow.
• Add keras.ops.vectorize, keras.ops.select.
• Add keras.ops.image.rgb_to_grayscale.
• Add keras.losses.Tversky loss.
• Add full bincount and digitize sparse support.
• Models and layers now return owned metrics recursively.
• Add pickling support for Keras models. Note that pickling is not recommended, prefer using Keras saving APIs.
• Bug fixes and performance improvements.

In addition, the codebase structure has evolved:

• All source files are now in keras/src/.
• All API files are now in keras/api/.
• The codebase structure stays unchanged when building the Keras pip package. This means you can pip install Keras directly from the GitHub sources.

New Contributors

• @kapoor1992 made their first contribution in #19484
• @IMvision12 made their first contribution in #19393
• @alanwilter made their first contribution in #19438
• @chococigar made their first contribution in #19323
• @LukeWood made their first contribution in #19555
• @AlexanderLavelle made their first contribution in #19575

Full Changelog: v3.2.1...v3.3.0

What's Changed

This is a minor bugfix release.

Full Changelog: v3.2.0...v3.2.1

What changed

• Introduce QLoRA-like technique for LoRA fine-tuning of Dense and EinsumDense layers (thereby any LLM) in int8 precision.
• Extend keras.ops.custom_gradient support to PyTorch.
• Add keras.layers.JaxLayer and keras.layers.FlaxLayer to wrap JAX/Flax modules as Keras layers.
• Allow save_model & load_model to accept a file-like object.
• Add quantization support to the Embedding layer.
• Make it possible to update metrics inside a custom compute_loss method with all backends.
• Make it possible to access self.losses inside a custom compute_loss method with the JAX backend.
• Add keras.losses.Dice loss.
• Add keras.ops.correlate.
• Make it possible to use cuDNN LSTM & GRU with a mask with the TensorFlow backend.
• Better JAX support in model.export(): add support for aliases, finer control over jax2tf options, and dynamic batch shapes.
• Bug fixes and performance improvements.

New Contributors

• @abhaskumarsinha made their first contribution in #19302
• @qaqland made their first contribution in #19378
• @tvogel made their first contribution in #19310
• @lpizzinidev made their first contribution in #19409
• @Murhaf made their first contribution in #19444

Full Changelog: v3.1.1...v3.2.0