Releases · keras-team/keras
Keras 3.11.2 Keras 3.11.1 Keras 3.11.0 What's Changedfit()/evaluate()/predict().keras.ops.kaiser function.keras.ops.hanning function.keras.ops.cbrt function.keras.ops.deg2rad function.keras.ops.layer_normalization function to leverage backend-specific performance optimizations.Flatten layer.Full Changelog: v3.10.0...v3.11.0
Keras 3.10.0 New featuresmodel.save(). It is controlled via the max_shard_size argument. Specifying this argument will split your Keras model weight file into chunks of this size at most. Use load_model() to reload the sharded files.keras.optimizers.Muonkeras.layers.RandomElasticTransformkeras.losses.CategoricalGeneralizedCrossEntropy (with functional version keras.losses.categorical_generalized_cross_entropy)axis argument to SparseCategoricalCrossentropylora_alpha to all LoRA-enabled layers. If set, this parameter scales the low-rank adaptation delta during the forward pass.keras.activations.sparse_sigmoidkeras.ops.image.elastic_transformkeras.ops.anglekeras.ops.bartlettkeras.ops.blackmankeras.ops.hammingkeras.ops.view_as_complex, keras.ops.view_as_realtf.RaggedTensor support to Embedding layersynchronization argumentFull Changelog: v3.9.0...v3.10.0
Keras 3.9.2 What's ChangedFull Changelog: v3.9.1...v3.9.2
Keras 3.9.1 What's ChangedFull Changelog: v3.9.0...v3.9.1
Keras 3.9.0 New featureskeras.RematScope and keras.remat. It can be used to turn on rematerizaliation for certain layers in fine-grained manner, e.g. only for layers larger than a certain size, or for a specific set of layers, or only for activations.keras.ops.rot90keras.ops.rearrange (Einops-style)keras.ops.signbitkeras.ops.polarkeras.ops.image.perspective_transformkeras.ops.image.gaussian_blurkeras.layers.RMSNormalizationkeras.layers.AugMixkeras.layers.CutMixkeras.layers.RandomInvertkeras.layers.RandomErasingkeras.layers.RandomGaussianBlurkeras.layers.RandomPerspectivedtype argument to JaxLayer and FlaxLayer layersBinaryAccuracy metricantialias argument to keras.layers.Resizing layer.npz model files (numpy format). Thanks to Peng Zhou for reporting the vulnerability.Full Changelog: v3.8.0...v3.9.0
Keras 3.8.0 New: OpenVINO backendOpenVINO is now available as an infererence-only Keras backend. You can start using it by setting the backend field to "openvino" in your keras.json config file.
OpenVINO is a deep learning inference-only framework tailored for CPU (x86, ARM), certain GPUs (OpenCL capable, integrated and discrete) and certain AI accelerators (Intel NPU).
Because OpenVINO does not support gradients, you cannot use it for training (e.g. model.fit()) -- only inference. You can train your models with the JAX/TensorFlow/PyTorch backends, and when trained, reload them with the OpenVINO backend for inference on a target device supported by OpenVINO.
You can now export your Keras models to the ONNX format from the JAX, TensorFlow, and PyTorch backends.
Just pass format="onnx" in your model.export() call:
# Export the model as a ONNX artifact
model.export("path/to/location", format="onnx")
# Load the artifact in a different process/environment
ort_session = onnxruntime.InferenceSession("path/to/location")
# Run inference
ort_inputs = {
k.name: v for k, v in zip(ort_session.get_inputs(), input_data)
}
predictions = ort_session.run(None, ort_inputs) New: Scikit-Learn API compatibility interface
It's now possible to easily integrate Keras models into Sciki-Learn pipelines! The following wrapper classes are available:
keras.wrappers.SKLearnClassifier: implements the sklearn Classifier APIkeras.wrappers.SKLearnRegressor: implements the sklearn Regressor APIkeras.wrappers.SKLearnTransformer: implements the sklearn Transformer APIkeras.ops.diagflatkeras.ops.unravel_indexsparse_plus activationsparsemax activationkeras.layers.RandAugmentkeras.layers.Equalizationkeras.layers.MixUpkeras.layers.RandomHuekeras.layers.RandomGrayscalekeras.layers.RandomSaturationkeras.layers.RandomColorJitterkeras.layers.RandomColorDegenerationkeras.layers.RandomSharpnesskeras.layers.RandomShearaxis to tversky losskeras.random.shuffle XLA compilablemodel.export() and keras.export.ExportArchive with the PyTorch backend, supporting both the TF SavedModel format and the ONNX format.Full Changelog: v3.7.0...v3.8.0
Keras 3.7.0 API changesflash_attention argument to keras.ops.dot_product_attention and to keras.layers.MultiHeadAttention.keras.layers.STFTSpectrogram layer (to extract STFT spectrograms from inputs as a preprocessing step) as well as its initializer keras.initializers.STFTInitializer.celu, glu, log_sigmoid, hard_tanh, hard_shrink, squareplus activations.keras.losses.Circle loss.keras.visualization.draw_bounding_boxes, keras.visualization.draw_segmentation_masks, keras.visualization.plot_image_gallery, keras.visualization.plot_segmentation_mask_gallery.double_checkpoint argument to BackupAndRestore to save a fallback checkpoint in case the first checkpoint gets corrupted.CenterCrop, RandomFlip, RandomZoom, RandomTranslation, RandomCrop.keras.ops.exp2, keras.ops.inner operations.bias_add.Full Changelog: v3.6.0...v3.7.0
Keras 3.6.0 Highlightskeras.saving.KerasFileEditor. Use it to inspect, diff, modify and resave Keras weights files. See basic workflow here.keras.utils.Config class for managing experiment config parameters.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.fit(), evaluate(), predict(). This enables 100% compact stacking of train_step calls on accelerators (e.g. when running small models on TPU).
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.keras.saving.KerasFileEditor utility to inspect, diff, modify and resave Keras weights file.keras.utils.Config class. It behaves like a dictionary, with a few nice features:
config.foo = 2 or config["foo"] are both valid)config.to_json().config.freeze().bitwise_andbitwise_invertbitwise_left_shiftbitwise_notbitwise_orbitwise_right_shiftbitwise_xorkeras.ops.logdet.keras.ops.trunc.keras.ops.dot_product_attention.keras.ops.histogram.PyDataset instances to use multithreading.verbose in keras.saving.ExportArchive.write_out() method for exporting TF SavedModel.epsilon argument in keras.ops.normalize.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.keras.layers.AutoContrast, keras.layers.Solarization.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:
Model, just a plain layer.tf.data, the pipeline will also remain tf.data compatible, independently of the backend you use.Full Changelog: v3.5.0...v3.6.0
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