These padding masks will be combined with any attention_mask passed in directly when calling the layer. Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer.This optimizer is similar as the existing, and works in the DTensor training use case. The training state can now be restored at the exact epoch and step at which it was previously saved before failing. Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts.Added tf._dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g.Added subset="both" support in tf._dataset_from_directory, tf._dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.Added tf._dataset_from_directory utility to easily generate audio classification datasets from directories of.EinsumDense layer moved from experimental to core.Upgrade Flatbuffers v2.0.5 from v1.12.0.tfl.scatter_nd now supports I1 for update arg.tf.unsortedsegmentprod op is supported.
tf.einsum is supported with multiple unknown shapes. This is because it would produce the same values each time, which may not be intended. An unseeded initializer will raise a warning if it is reused (called) multiple times. For unseeded initializers ( seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds). Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). Keras initializers will now use stateless random ops to generate random numbers. RNG behavior change for tf.keras.initializers. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. The current tf. will continue to be supported as tf., e.g., tf. tf. will graduate in Release 2.11, which means tf. will be an alias of tf. Please update your imports accordingly, the old files will be removed in Release 2.11. Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Causal attention in and is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers. 
,, ,, , Release 2.10.0 Breaking Changes This release contains contributions from many people at Google, as well as:
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