Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. 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. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.
Deprecations
The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
Stop constructing Status objects from tensorflow::error::Code.
One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.
Major Features and Improvements
tf.lite:
New operations supported:
tflite SelectV2 now supports 5D.
tf.einsum is supported with multiple unknown shapes.
tf.unsortedsegmentprod op is supported.
tf.unsortedsegmentmax op is supported.
tf.unsortedsegmentsum op is supported.
Updates to existing operations:
tfl.scatter_nd now supports I1 for update arg.
Upgrade Flatbuffers v2.0.5 from v1.12.0
tf.keras:
EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_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.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. 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. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.
API changes under tf.experimental.dtensor:
New API for initialization of CPU/GPU/TPU in dtensor.
dtensor.initialize_accelerator_system and
dtensor.shutdown_accelerator_system.
The following existing API will be removed:
dtensor.initialize_multi_client, dtensor.initialize_tpu_system, and
dtensor.shutdown_tpu_system.
Deprecations
The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
Stop constructing Status objects from tensorflow::error::Code.
One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.
Major Features and Improvements
tf.lite:
New operations supported:
tflite SelectV2 now supports 5D.
tf.einsum is supported with multiple unknown shapes.
tf.unsortedsegmentprod op is supported.
tf.unsortedsegmentmax op is supported.
tf.unsortedsegmentsum op is supported.
Updates to existing operations:
tfl.scatter_nd now supports I1 for update arg.
Upgrade Flatbuffers v2.0.5 from v1.12.0
tf.keras:
EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_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.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
... (truncated)
Commits
359c3cd Merge pull request #57609 from tensorflow/vinila21-patch-6
724308f Update estimator and keras version in TF 2.10 branch for 2.10.0.
203b333 Merge pull request #57608 from tensorflow-jenkins/version-numbers-2.10.0-28960
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dependabot[bot]
commented
1 year ago
Bumps tensorflow-macos from 2.7.0 to 2.10.0.
Release notes
Sourced from tensorflow-macos's releases.
... (truncated)
Changelog
Sourced from tensorflow-macos's changelog.
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Commits
359c3cdMerge pull request #57609 from tensorflow/vinila21-patch-6724308fUpdate estimator and keras version in TF 2.10 branch for 2.10.0.203b333Merge pull request #57608 from tensorflow-jenkins/version-numbers-2.10.0-28960cd950ffUpdate version numbers to 2.10.09b13e9eMerge pull request #57510 from tensorflow/vinila21-patch-1ba47bc7Update release notes with security updatesf082fa9Merge pull request #57464 from tensorflow/r2.10-b5f6fbfba7660ed7ceRe-enable testTensorListReserveWithNonScalarNumElements to work with mlir as ...23cb0d3Merge pull request #57460 from tensorflow/revert-57075-r2.10-e9863e9a9cbf419a41Revert "r2.10 cherry-pick: e9863e9a9cb "Fix tf.raw_ops.EmptyTensorList vulner...Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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