Scheduled biweekly dependency update for week 07

[pyup-bot]

Update tensorflow from 2.9.1 to 2.15.0.post1.

Changelog

2.15.0

TensorFlow

Breaking Changes

* `tf.types.experimental.GenericFunction` has been renamed to `tf.types.experimental.PolymorphicFunction`.

Major Features and Improvements

*   [oneDNN CPU performance optimizations](https://github.com/tensorflow/community/blob/master/rfcs/20210930-enable-onednn-ops.md) Windows x64 & x86.

 *   **Windows x64 & x86 packages:**
     *   oneDNN optimizations are *enabled by default* on X86 CPUs
 *   To explicitly enable or disable oneDNN optimizations, set the environment variable `TF_ENABLE_ONEDNN_OPTS` to `1` (enable) or `0` (disable) before running TensorFlow. To fall back to default settings, unset the environment variable.
 *   oneDNN optimizations can yield slightly different numerical results compared to when oneDNN optimizations are disabled due to floating-point round-off errors from
different computation approaches and orders.
 *   To verify if oneDNN optimizations are on, look for a message with *"oneDNN custom operations are on"* in the log. If the exact phrase is not there, it means they are off.

* Making the `tf.function` type system fully available:

 * `tf.types.experimental.TraceType` now allows custom tf.function inputs to declare Tensor decomposition and type casting support.
 * Introducing `tf.types.experimental.FunctionType` as the comprehensive representation of the signature of `tf.function` callables. It can be accessed through the `function_type` property of `tf.function`s and `ConcreteFunction`s. See the `tf.types.experimental.FunctionType` documentation for more details.

* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python.

 * Can be accessed through `inference_fn` property of `ConcreteFunction`s
 * Does not support gradients.
 * See `tf.types.experimental.AtomicFunction` documentation for how to call and use it.


*   `tf.data`:

 *   Moved option `warm_start` from `tf.data.experimental.OptimizationOptions` to `tf.data.Options`.

*   `tf.lite`:

 *   `sub_op` and `mul_op` support broadcasting up to 6 dimensions.

 *  The `tflite::SignatureRunner` class, which provides support for named parameters and for multiple named computations within a single TF Lite model, is no longer considered experimental. Likewise for the following signature-related methods of `tflite::Interpreter`:

    *   `tflite::Interpreter::GetSignatureRunner`
    *   `tflite::Interpreter::signature_keys`
    *   `tflite::Interpreter::signature_inputs`
    *   `tflite::Interpreter::signature_outputs`
    *   `tflite::Interpreter::input_tensor_by_signature`
    *   `tflite::Interpreter::output_tensor_by_signature`

 *  Similarly, the following signature runner functions in the TF Lite C API are no longer considered experimental:

    *    `TfLiteInterpreterGetSignatureCount`
    *    `TfLiteInterpreterGetSignatureKey`
    *    `TfLiteInterpreterGetSignatureRunner`
    *    `TfLiteSignatureRunnerAllocateTensors`
    *    `TfLiteSignatureRunnerGetInputCount`
    *    `TfLiteSignatureRunnerGetInputName`
    *    `TfLiteSignatureRunnerGetInputTensor`
    *    `TfLiteSignatureRunnerGetOutputCount`
    *    `TfLiteSignatureRunnerGetOutputName`
    *    `TfLiteSignatureRunnerGetOutputTensor`
    *    `TfLiteSignatureRunnerInvoke`
    *    `TfLiteSignatureRunnerResizeInputTensor`

 * New C API function `TfLiteExtensionApisVersion` added to `tensorflow/lite/c/c_api.h`.

 * Add int8 and int16x8 support for RSQRT operator

* Android NDK r25 is supported.

Bug Fixes and Other Changes

*   Add TensorFlow Quantizer to TensorFlow pip package.

*   `tf.sparse.segment_sum` `tf.sparse.segment_mean` `tf.sparse.segment_sqrt_n` `SparseSegmentSum/Mean/SqrtN[WithNumSegments]`

 *   Added `sparse_gradient` option (default=false) that makes the gradient of these functions/ops sparse (`IndexedSlices`) instead of dense (`Tensor`), using new `SparseSegmentSum/Mean/SqrtNGradV2` ops.

*   `tf.nn.embedding_lookup_sparse`

 *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`

 *   Provided a new `experimental_skip_saver` argument which, if specified, will suppress the addition of `SavedModel`-native save and restore ops to the `SavedModel`, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.

Keras

Bug Fixes and Other Changes

* Add ops to `tensorflow.raw_ops` that were missing.
* `tf.CheckpointOptions`
 * It now takes in a new argument called `experimental_write_callbacks`. These are callbacks that will be executed after a saving event finishes writing the checkpoint file.
* Add an option `disable_eager_executer_streaming_enqueue` to `tensorflow.ConfigProto.Experimental` to control the eager runtime's behavior around parallel remote function invocations; when set to `True`, the eager runtime will be allowed to execute multiple function invocations in parallel.
* `tf.constant_initializer`
 * It now takes a new argument called `support_partition`. If True, constant_initializers can create sharded variables. This is disabled by default, similar to existing behavior.

* `tf.lite`
 * Added support for `stablehlo.scatter`.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aiden Grossman, Akash Patel, Akhil Goel, Alexander Pivovarov, Andrew Goodbody, Ayan Moitra, Ben Barsdell, Ben Olson, Bhavani Subramanian, Boian Petkantchin, Bruce Lai, Chao Chen, Christian Steinmeyer, cjflan, David Korczynski, Donghak Park, Dragan Mladjenovic, Eli Kobrin, Fadi Arafeh, Feiyue Chen, Frédéric Bastien, guozhong.zhuang, halseycamilla, Harshavardhan Bellamkonda, James Ward, jameshollyer, Jane Liu, johnnkp, jswag180, justkw, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, Kun-Lu, kushanam, Lu Teng, mdfaijul, Mehdi Drissi, mgokulkrish, mraunak, Mustafa Uzun, Namrata Bhave, Pavel Emeliyanenko, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, Rahul Batra, Raunak, redwrasse, Saoirse Stewart, SaoirseARM, seanshpark, Shanbin Ke, Spenser Bauman, Surya, sushreebarsa, Tai Ly, Thibaut Goetghebuer-Planchon, tilakrayal, Tirumalesh, Tj Xu, Vladislav, weihanmines, Wen Chen, wenchenvincent, wenscarl, William Muir, Zhoulong, Jiang

2.14.1

Security

*   Updates `curl` to `8.4.0` to handle [CVE-2023-38545](https://curl.se/docs/CVE-2023-38545.html) and [CVE-2023-38546](https://curl.se/docs/CVE-2023-38546.html).

2.14.0

Tensorflow

Breaking Changes

*  `tf.Tensor`
 * The class hierarchy for `tf.Tensor` has changed, and there are now explicit `EagerTensor` and `SymbolicTensor` classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. `type(t) == tf.Tensor`) will need to update their code to use `isinstance(t, tf.Tensor)`. The `tf.is_symbolic_tensor` helper added in 2.13 may be used when it is necessary to determine if a value is specifically a symbolic tensor.

*   `tf.compat.v1.Session`
 * `tf.compat.v1.Session.partial_run` and `tf.compat.v1.Session.partial_run_setup` will be deprecated in the next release.

Known Caveats

* `tf.lite`
 * when converter flag "_experimenal_use_buffer_offset" is enabled, additional metadata is automatically excluded from the generated model. The behaviour is the same as "exclude_conversion_metadata" is set
 * If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set

Major Features and Improvements

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more than 2**32 elements.
 *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
 *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

* `tf.lite`
 * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

Bug Fixes and Other Changes

* `tf.py_function` and `tf.numpy_function` can now be used as function decorators for clearer code:

tf.py_function(Tout=tf.float32)
def my_fun(x):
  print("This always executes eagerly.")
  return x+1


* `tf.lite`
 * Strided_Slice now supports `UINT32`.

* `tf.config.experimental.enable_tensor_float_32_execution`
 * Disabling TensorFloat-32 execution now causes TPUs to use float32 precision for float32 matmuls and other ops. TPUs have always used bfloat16 precision for certain ops, like matmul, when such ops had float32 inputs. Now, disabling TensorFloat-32 by calling `tf.config.experimental.enable_tensor_float_32_execution(False)` will cause TPUs to use float32 precision for such ops instead of bfloat16.

*  `tf.experimental.dtensor`
 * API changes for Relayout. Added a new API, `dtensor.relayout_like`, for relayouting a tensor according to the layout of another tensor.
 * Added `dtensor.get_default_mesh`, for retrieving the current default mesh under the dtensor context.
 * \*fft\* ops now support dtensors with any layout. Fixed bug in 'fft2d/ fft3d', 'ifft2d/ifft3d', 'rfft2d/rfft3d', and 'irfft2d/irfft3d' for sharded input.

*  `tf.experimental.strict_mode`
 * Added a new API, `strict_mode`, which converts all deprecation warnings into runtime errors with instructions on switching to a recommended substitute.

*   TensorFlow Debugger (tfdbg) CLI: ncurses-based CLI for tfdbg v1 was removed.

*   TensorFlow now supports C++ RTTI on mobile and Android. To enable this feature, pass the flag `--define=tf_force_rtti=true` to Bazel when building TensorFlow. This may be needed when linking TensorFlow into RTTI-enabled programs since mixing RTTI and non-RTTI code can cause ABI issues.

* `tf.ones`, `tf.zeros`, `tf.fill`, `tf.ones_like`, `tf.zeros_like` now take an additional Layout argument that controls the output layout of their results.

* `tf.nest` and `tf.data` now support user defined classes implementing `__tf_flatten__` and `__tf_unflatten__` methods. See [nest_util code examples](https://github.com/tensorflow/tensorflow/blob/04869b4e63bfc03cb13627b3e1b879fdd0f69e34/tensorflow/python/util/nest_util.py#L97) for an example.

Keras

Keras is a framework built on top of the TensorFlow. See more details on the Keras [website](https://keras.io/).

Major Features and Improvements

* `tf.keras`
 * `Model.compile` now support `steps_per_execution='auto'` as a parameter, allowing automatic tuning of steps per execution during `Model.fit`, `Model.predict`, and `Model.evaluate` for a significant performance boost.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aakar Dwivedi, Adrian Popescu, ag.ramesh, Akhil Goel, Albert Zeyer, Alex Rosen, Alexey Vishnyakov, Andrew Goodbody, angerson, Ashiq Imran, Ayan Moitra, Ben Barsdell, Bhavani Subramanian, Boian Petkantchin, BrianWieder, Chris Mc, cloudhan, Connor Flanagan, Daniel Lang, Daniel Yudelevich, Darya Parygina, David Korczynski, David Svantesson, dingyuqing05, Dragan Mladjenovic, dskkato, Eli Kobrin, Erick Ochoa, Erik Schultheis, Frédéric Bastien, gaikwadrahul8, Gauri1 Deshpande, georgiie, guozhong.zhuang, H. Vetinari, Isaac Cilia Attard, Jake Hall, Jason Furmanek, Jerry Ge, Jinzhe Zeng, JJ, johnnkp, Jonathan Albrecht, jongkweh, justkw, Kanvi Khanna, kikoxia, Koan-Sin Tan, Kun-Lu, Learning-To-Play, ltsai1, Lu Teng, luliyucoordinate, Mahmoud Abuzaina, mdfaijul, Milos Puzovic, Nathan Luehr, Om Thakkar, pateldeev, Peng Sun, Philipp Hack, pjpratik, Poliorcetics, rahulbatra85, rangjiaheng, Renato Arantes, Robert Kalmar, roho, Rylan Justice, Sachin Muradi, samypr100, Saoirse Stewart, Shanbin Ke, Shivam Mishra, shuw, Song Ziming, Stephan Hartmann, Sulav, sushreebarsa, T Coxon, Tai Ly, talyz, Tensorflow Jenkins, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tirumalesh, Tj Xu, Tom Allsop, Trevor Morris, Varghese, Jojimon, Wen Chen, Yaohui Liu, Yimei Sun, Zhoulong Jiang, Zhoulong, Jiang

2.13.1

Bug Fixes and Other Changes

*  Refactor CpuExecutable to propagate LLVM errors.

2.13.0

TensorFlow

Breaking Changes

* The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure.

Major Features and Improvements

*   `tf.lite`

 *   Add 16-bit and 64-bit float type support for built-in op `cast`.
 *   The Python TF Lite Interpreter bindings now have an option `experimental_disable_delegate_clustering` to turn-off delegate clustering.
 *   Add int16x8 support for the built-in op `exp`
 *   Add int16x8 support for the built-in op `mirror_pad`
 *   Add int16x8 support for the built-in ops `space_to_batch_nd` and `batch_to_space_nd`
 *   Add 16-bit int type support for built-in op `less`, `greater_than`, `equal`
 *   Add 8-bit and 16-bit support for `floor_div` and `floor_mod`.
 *   Add 16-bit and 32-bit int support for the built-in op `bitcast`.
 *   Add 8-bit/16-bit/32-bit int/uint support for the built-in op `bitwise_xor`
 *   Add int16 indices support for built-in op `gather` and `gather_nd`.
 *   Add 8-bit/16-bit/32-bit int/uint support for the built-in op `right_shift`
 *   Add reference implementation for 16-bit int unquantized `add`.
 *   Add reference implementation for 16-bit int and 32-bit unsigned int unquantized `mul`.
 *   `add_op` supports broadcasting up to 6 dimensions.
 *   Add 16-bit support for `top_k`.
 
*   `tf.function`

 *   ConcreteFunction (`tf.types.experimental.ConcreteFunction`) as generated through `get_concrete_function` now performs holistic input validation similar to calling `tf.function` directly. This can cause breakages where existing calls pass Tensors with the wrong shape or omit certain non-Tensor arguments (including default values).

*   `tf.nn`

 *   `tf.nn.embedding_lookup_sparse` and `tf.nn.safe_embedding_lookup_sparse` now support ids and weights described by `tf.RaggedTensor`s.
 *   Added a new boolean argument `allow_fast_lookup` to `tf.nn.embedding_lookup_sparse` and `tf.nn.safe_embedding_lookup_sparse`, which enables a simplified and typically faster lookup procedure.

*   `tf.data`

 *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
 *   `tf.data.Dataset.shuffle` now supports full shuffling. To specify that data should be fully shuffled, use `dataset = dataset.shuffle(dataset.cardinality())`. This will load the full dataset into memory so that it can be shuffled, so make sure to only use this with datasets of filenames or other small datasets.

*   `tf.math`

 * `tf.nn.top_k` now supports specifying the output index type via parameter `index_type`.  Supported types are `tf.int16`, `tf.int32` (default), and `tf.int64`.

*   `tf.SavedModel`

 *   Introduce class method `tf.saved_model.experimental.Fingerprint.from_proto(proto)`, which can be used to construct a `Fingerprint` object directly from a protobuf.
 *   Introduce member method `tf.saved_model.experimental.Fingerprint.singleprint()`, which provides a convenient way to uniquely identify a SavedModel.

Bug Fixes and Other Changes

*   `tf.Variable`

 *   Changed resource variables to inherit from `tf.compat.v2.Variable` instead of `tf.compat.v1.Variable`. Some checks for `isinstance(v, tf compat.v1.Variable)` that previously returned True may now return False.

*   `tf.distribute`

 *   Opened an experimental API, `tf.distribute.experimental.coordinator.get_current_worker_index`, for retrieving the worker index from within a worker, when using parameter server training with a custom training loop.

*   `tf.experimental.dtensor`

 *   Deprecated `dtensor.run_on` in favor of `dtensor.default_mesh` to correctly indicate that the context does not override the mesh that the ops and functions will run on, it only sets a fallback default mesh.
 *   List of members of dtensor.Layout and dtensor.Mesh have slightly changed as part of efforts to consolidate the C++ and Python source code with pybind11. Most notably, Layout.serialized_string is removed.
 *   Minor API changes to represent Single Device Layout for non-distributed Tensors inside DTensor functions. Runtime support will be added soon.

*   `tf.experimental.ExtensionType`

 *   `tf.experimental.ExtensionType` now supports Python `tuple` as the type annotation of its fields.

*   `tf.nest`

 *   Deprecated API `tf.nest.is_sequence` has now been deleted. Please use `tf.nest.is_nested` instead.


Keras

Keras is a framework built on top of the TensorFlow. See more details on the [Keras website](https://keras.io/).

Breaking Changes

*  `tf.keras`

 *  Removed the Keras scikit-learn API wrappers (`KerasClassifier` and `KerasRegressor`), which had been deprecated in August 2021. We recommend using [SciKeras](https://github.com/adriangb/scikeras) instead.
 *  The default Keras model saving format is now the Keras v3 format: calling `model.save("xyz.keras")` will no longer create a H5 file, it will create a native Keras model file. This will only be breaking for you if you were manually inspecting or modifying H5 files saved by Keras under a `.keras` extension. If this breaks you, simply add `save_format="h5"` to your `.save()` call to revert back to the prior behavior.
 *  Added `keras.utils.TimedThread` utility to run a timed thread every x seconds. It can be used to run a threaded function alongside model training or any other snippet of code.
 *  In the `keras` PyPI package, accessible symbols are now restricted to symbols that are intended to be public. This may affect your code if you were using `import keras` and you used `keras` functions that were not public APIs, but were accessible in earlier versions with direct imports. In those cases, please use the following guideline:
     -  The API may be available in the public Keras API under a different name, so make sure to look for it on keras.io or TensorFlow docs and switch to the public version.
     -  It could also be a simple python or TF utility that you could easily copy over to your own codebase. In those case, just make it your own!
     -  If you believe it should definitely be a public Keras API, please open a feature request in keras GitHub repo.
     -  As a workaround, you could import the same private symbol keras `keras.src`, but keep in mind the `src` namespace is not stable and those APIs may change or be removed in the future.

Major Features and Improvements

*   `tf.keras`

 *   Added F-Score metrics `tf.keras.metrics.FBetaScore`, `tf.keras.metrics.F1Score`, and `tf.keras.metrics.R2Score`.
 *   Added activation function `tf.keras.activations.mish`.
 *   Added experimental `keras.metrics.experimental.PyMetric` API for metrics that run Python code on the host CPU (compiled outside of the TensorFlow graph). This can be used for integrating metrics from external Python libraries (like sklearn or pycocotools) into Keras as first-class Keras metrics.
 *   Added `tf.keras.optimizers.Lion` optimizer.
 *   Added `tf.keras.layers.SpectralNormalization` layer wrapper to perform spectral normalization on the weights of a target layer.
 *   The `SidecarEvaluatorModelExport` callback has been added to Keras as `keras.callbacks.SidecarEvaluatorModelExport`. This callback allows for exporting the model the best-scoring model as evaluated by a `SidecarEvaluator` evaluator. The evaluator regularly evaluates the model and exports it if the user-defined comparison function determines that it is an improvement.
 *   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase.
 *   Added experimental support for an exactly-once visitation guarantee for evaluating Keras models trained with `tf.distribute ParameterServerStrategy`, via the `exact_evaluation_shards` argument in `Model.fit` and `Model.evaluate`.
 *   Added `tf.keras.__internal__.KerasTensor`,`tf.keras.__internal__.SparseKerasTensor`, and `tf.keras.__internal__.RaggedKerasTensor` classes. You can use these classes to do instance type checking and type annotations for layer/model inputs and outputs.
 *   All the `tf.keras.dtensor.experimental.optimizers` classes have been merged with `tf.keras.optimizers`. You can migrate your code to use `tf.keras.optimizers` directly. The API namespace for `tf.keras.dtensor.experimental.optimizers` will be removed in future releases.
 *   Added support for `class_weight` for 3+ dimensional targets (e.g. image segmentation masks) in `Model.fit`.
 *   Added a new loss, `keras.losses.CategoricalFocalCrossentropy`.
 *   Remove the `tf.keras.dtensor.experimental.layout_map_scope()`. You can user the `tf.keras.dtensor.experimental.LayoutMap.scope()` instead.

Security

*   N/A

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar, Aakar Dwivedi, Abinash Satapathy, Aditya Kane, ag.ramesh, Alexander Grund, Andrei Pikas, andreii, Andrew Goodbody, angerson, Anthony_256, Ashay Rane, Ashiq Imran, Awsaf, Balint Cristian, Banikumar Maiti (Intel Aipg), Ben Barsdell, bhack, cfRod, Chao Chen, chenchongsong, Chris Mc, Daniil Kutz, David Rubinstein, dianjiaogit, dixr, Dongfeng Yu, dongfengy, drah, Eric Kunze, Feiyue Chen, Frederic Bastien, Gauri1 Deshpande, guozhong.zhuang, hDn248, HYChou, ingkarat, James Hilliard, Jason Furmanek, Jaya, Jens Glaser, Jerry Ge, Jiao Dian'S Power Plant, Jie Fu, Jinzhe Zeng, Jukyy, Kaixi Hou, Kanvi Khanna, Karel Ha, karllessard, Koan-Sin Tan, Konstantin Beluchenko, Kulin Seth, Kun Lu, Kyle Gerard Felker, Leopold Cambier, Lianmin Zheng, linlifan, liuyuanqiang, Lukas Geiger, Luke Hutton, Mahmoud Abuzaina, Manas Mohanty, Mateo Fidabel, Maxiwell S. Garcia, Mayank Raunak, mdfaijul, meatybobby, Meenakshi Venkataraman, Michael Holman, Nathan John Sircombe, Nathan Luehr, nitins17, Om Thakkar, Patrice Vignola, Pavani Majety, per1234, Philipp Hack, pollfly, Prianka Liz Kariat, Rahul Batra, rahulbatra85, ratnam.parikh, Rickard Hallerbäck, Roger Iyengar, Rohit Santhanam, Roman Baranchuk, Sachin Muradi, sanadani, Saoirse Stewart, seanshpark, Shawn Wang, shuw, Srinivasan Narayanamoorthy, Stewart Miles, Sunita Nadampalli, SuryanarayanaY, Takahashi Shuuji, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tirumalesh, TJ, Tony Sung, Trevor Morris, unda, Vertexwahn, venkat2469, William Muir, Xavier Bonaventura, xiang.zhang, Xiao-Yong Jin, yleeeee, Yong Tang, Yuriy Chernyshov, Zhang, Xiangze, zhaozheng09

2.12.1

Bug Fixes and Other Changes

*  The use of the ambe config to build and test aarch64 is not needed. The ambe config will be removed in the future. Making cpu_arm64_pip.sh and cpu_arm64_nonpip.sh more similar for easier future maintenance.

2.12.0

Breaking Changes

*   Build, Compilation and Packaging

 *   Removal of redundant packages: the `tensorflow-gpu` and `tf-nightly-gpu` packages have been effectively removed and replaced with packages that direct users to switch to `tensorflow` or `tf-nightly` respectively. The naming difference was the only difference between the two sets of packages ever since TensorFlow 2.1, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

*   `tf.function`:

 *   `tf.function` now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on.
 *   This can break certain cases that were previously ignored where the signature is malformed, such as:
     *   Using `functools.wraps` on a function with different signature
     *   Using `functools.partial` with an invalid `tf.function` input
 *   `tf.function` now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
 *   Parameterless `tf.function`s are assumed to have an empty `input_signature` instead of an undefined one even if the `input_signature` is unspecified.
 *   `tf.types.experimental.TraceType` now requires an additional `placeholder_value` method to be defined.
 *   `tf.function` now traces with placeholder values generated by TraceType instead of the value itself.

*   Experimental APIs `tf.config.experimental.enable_mlir_graph_optimization` and `tf.config.experimental.disable_mlir_graph_optimization` were removed.

*   `tf.keras`:

 * Moved all saving-related utilities to a new namespace, `keras.saving`, i.e. `keras.saving.load_model`, `keras.saving.save_model`, `keras.saving.custom_object_scope`, `keras.saving.get_custom_objects`, `keras.saving.register_keras_serializable`,`keras.saving.get_registered_name` and `keras.saving.get_registered_object`. The previous API locations (in `keras.utils` and `keras.models`) will stay available indefinitely, but we recommend that you update your code to point to the new API locations.
 * Improvements and fixes in Keras loss masking:
     * Whether you represent a ragged tensor as a `tf.RaggedTensor` or using [keras masking](https://www.tensorflow.org/guide/keras/masking_and_padding), the returned loss values should be the identical to each other. In previous versions Keras may have silently ignored the mask.
     * If you use masked losses with Keras the loss values may be different in TensorFlow `2.12` compared to previous versions.
     * In cases where the mask was previously ignored, you will now get an error if you pass a mask with an incompatible shape.

*   `tf.SavedModel`:

 * Introduced new class `tf.saved_model.experimental.Fingerprint` that contains the fingerprint of the SavedModel. See the [SavedModel Fingerprinting RFC](https://github.com/tensorflow/community/pull/415) for details.
 * Introduced API `tf.saved_model.experimental.read_fingerprint(export_dir)` for reading the fingerprint of a SavedModel.

Major Features and Improvements

*   `tf.lite`:

 *   Add 16-bit float type support for built-in op `fill`.
 *   Transpose now supports 6D tensors.
 *   Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

*   `tf.keras`:

 *   The new Keras model saving format (`.keras`) is available. You can start using it via `model.save(f"{fname}.keras", save_format="keras_v3")`. In the future it will become the default for all files with the `.keras` extension. This file format targets the Python runtime only and makes it possible to reload Python objects identical to the saved originals. The format supports non-numerical state such as vocabulary files and lookup tables, and it is easy to customize in the case of custom layers with exotic elements of state (e.g. a FIFOQueue). The format does not rely on bytecode or pickling, and is safe by default. Note that as a result, Python `lambdas` are disallowed at loading time. If you want to use `lambdas`, you can pass `safe_mode=False` to the loading method (only do this if you trust the source of the model).
 *   Added a `model.export(filepath)` API to create a lightweight SavedModel artifact that can be used for inference (e.g. with TF-Serving).
 *   Added `keras.export.ExportArchive` class for low-level customization of the process of exporting SavedModel artifacts for inference. Both ways of exporting models are based on `tf.function` tracing and produce a TF program composed of TF ops. They are meant primarily for environments where the TF runtime is available, but not the Python interpreter, as is typical for production with TF Serving.
 *   Added utility `tf.keras.utils.FeatureSpace`, a one-stop shop for structured data preprocessing and encoding.
 *   Added `tf.SparseTensor` input support to `tf.keras.layers.Embedding` layer. The layer now accepts a new boolean argument `sparse`. If `sparse` is set to True, the layer returns a SparseTensor instead of a dense Tensor. Defaults to False.
 *   Added `jit_compile` as a settable property to `tf.keras.Model`.
 *   Added `synchronized` optional parameter to `layers.BatchNormalization`.
 *   Added deprecation warning to `layers.experimental.SyncBatchNormalization` and suggested to use `layers.BatchNormalization` with `synchronized=True` instead.
 *   Updated `tf.keras.layers.BatchNormalization` to support masking of the inputs (`mask` argument) when computing the mean and variance.
 *   Add `tf.keras.layers.Identity`, a placeholder pass-through layer.
 *   Add `show_trainable` option to `tf.keras.utils.model_to_dot` to display layer trainable status in model plots.
 *   Add ability to save a `tf.keras.utils.FeatureSpace` object, via `feature_space.save("myfeaturespace.keras")`, and reload it via `feature_space = tf.keras.models.load_model("myfeaturespace.keras")`.
 *   Added utility `tf.keras.utils.to_ordinal` to convert class vector to ordinal regression / classification matrix.

*   `tf.experimental.dtensor`:

 *   Coordination service now works with `dtensor.initialize_accelerator_system`, and enabled by default.
 *   Add `tf.experimental.dtensor.is_dtensor` to check if a tensor is a DTensor instance.

*   `tf.data`:

 *   Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the `experimental_symbolic_checkpoint` option of `tf.data.Options()`.
 *   Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.random()` operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If `seed` is set and `rerandomize_each_iteration=True`, the `random()` operation will produce a different (deterministic) sequence of numbers every epoch.
 *   Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.sample_from_datasets()` operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If `seed` is set and `rerandomize_each_iteration=True`, the `sample_from_datasets()` operation will use a different (deterministic) sequence of numbers every epoch.

*   `tf.test`:

 *   Added `tf.test.experimental.sync_devices`, which is useful for accurately measuring performance in benchmarks.

*   `tf.experimental.dtensor`:

 *   Added experimental support to ReduceScatter fuse on GPU (NCCL).

Bug Fixes and Other Changes

* `tf.random`
* Added non-experimental aliases for `tf.random.split` and `tf.random.fold_in`, the experimental endpoints are still available so no code changes are necessary.
* `tf.experimental.ExtensionType`
* Added function `experimental.extension_type.as_dict()`, which converts an instance of `tf.experimental.ExtensionType` to a `dict` representation.
* `stream_executor`
* Top level `stream_executor` directory has been deleted, users should use equivalent headers and targets under `compiler/xla/stream_executor`.
* `tf.nn`
* Added `tf.nn.experimental.general_dropout`, which is similar to `tf.random.experimental.stateless_dropout` but accepts a custom sampler function.
* `tf.types.experimental.GenericFunction`
* The `experimental_get_compiler_ir` method supports tf.TensorSpec compilation arguments.
*  `tf.config.experimental.mlir_bridge_rollout`
 *   Removed enums `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED` and `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED` which are no longer used by the tf2xla bridge

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar, Aakar Dwivedi, Abinash Satapathy, Aditya Kane, ag.ramesh, Alexander Grund, Andrei Pikas, andreii, Andrew Goodbody, angerson, Anthony_256, Ashay Rane, Ashiq Imran, Awsaf, Balint Cristian, Banikumar Maiti (Intel Aipg), Ben Barsdell, bhack, cfRod, Chao Chen, chenchongsong, Chris Mc, Daniil Kutz, David Rubinstein, dianjiaogit, dixr, Dongfeng Yu, dongfengy, drah, Eric Kunze, Feiyue Chen, Frederic Bastien, Gauri1 Deshpande, guozhong.zhuang, hDn248, HYChou, ingkarat, James Hilliard, Jason Furmanek, Jaya, Jens Glaser, Jerry Ge, Jiao Dian'S Power Plant, Jie Fu, Jinzhe Zeng, Jukyy, Kaixi Hou, Kanvi Khanna, Karel Ha, karllessard, Koan-Sin Tan, Konstantin Beluchenko, Kulin Seth, Kun Lu, Kyle Gerard Felker, Leopold Cambier, Lianmin Zheng, linlifan, liuyuanqiang, Lukas Geiger, Luke Hutton, Mahmoud Abuzaina, Manas Mohanty, Mateo Fidabel, Maxiwell S. Garcia, Mayank Raunak, mdfaijul, meatybobby, Meenakshi Venkataraman, Michael Holman, Nathan John Sircombe, Nathan Luehr, nitins17, Om Thakkar, Patrice Vignola, Pavani Majety, per1234, Philipp Hack, pollfly, Prianka Liz Kariat, Rahul Batra, rahulbatra85, ratnam.parikh, Rickard Hallerbäck, Roger Iyengar, Rohit Santhanam, Roman Baranchuk, Sachin Muradi, sanadani, Saoirse Stewart, seanshpark, Shawn Wang, shuw, Srinivasan Narayanamoorthy, Stewart Miles, Sunita Nadampalli, SuryanarayanaY, Takahashi Shuuji, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tirumalesh, TJ, Tony Sung, Trevor Morris, unda, Vertexwahn, Vinila S, William Muir, Xavier Bonaventura, xiang.zhang, Xiao-Yong Jin, yleeeee, Yong Tang, Yuriy Chernyshov, Zhang, Xiangze, zhaozheng09

2.11.1

**Note**: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.
*   Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourself [steps](https://github.com/tensorflow/tensorflow#patching-guidelines). You can refer to the [release notes](https://github.com/tensorflow/tensorflow/releases) of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.

This release also introduces several vulnerability fixes:

*   Fixes an FPE in TFLite in conv kernel [CVE-2023-27579](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-27579)
*   Fixes a double free in Fractional(Max/Avg)Pool [CVE-2023-25801](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25801)
*   Fixes a null dereference on ParallelConcat with XLA [CVE-2023-25676](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25676)
*   Fixes a segfault in Bincount with XLA [CVE-2023-25675](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25675)
*   Fixes an NPE in RandomShuffle with XLA enable [CVE-2023-25674](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25674)
*   Fixes an FPE in TensorListSplit with XLA [CVE-2023-25673](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25673)
*   Fixes segmentation fault in tfg-translate [CVE-2023-25671](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25671)
*   Fixes an NPE in QuantizedMatMulWithBiasAndDequantize [CVE-2023-25670](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25670)
*   Fixes an FPE in AvgPoolGrad with XLA [CVE-2023-25669](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25669)
*   Fixes a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation [CVE-2023-25668](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25668)
*   Fixes a segfault when opening multiframe gif [CVE-2023-25667](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25667)
*   Fixes an NPE in SparseSparseMaximum [CVE-2023-25665](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25665)
*   Fixes an FPE in AudioSpectrogram [CVE-2023-25666](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25666)
*   Fixes a heap-buffer-overflow in AvgPoolGrad  [CVE-2023-25664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25664)
*   Fixes a NPE in TensorArrayConcatV2  [CVE-2023-25663](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25663)
*   Fixes a Integer overflow in EditDistance  [CVE-2023-25662](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25662)
*   Fixes a Seg fault in `tf.raw_ops.Print` [CVE-2023-25660](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25660)
*   Fixes a OOB read in DynamicStitch [CVE-2023-25659](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25659)
*   Fixes a OOB Read in GRUBlockCellGrad [CVE-2023-25658](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2023-25658)

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](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental)
 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.

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 moved from experimental to core. Its import path
     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.
 *   Added [`tf.keras.dtensor.experimental.optimizers.AdamW`](https://www.tensorflow.org/api_docs/python/tf/keras/dtensor/experimental/optimizers/AdamW).
     This optimizer is similar as the existing
     [`keras.optimizers.experimental.AdamW`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/experimental/AdamW), and
     works in the DTensor training use case.
 *   Improved masking support for [tf.keras.layers.MultiHeadAttention](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MultiHeadAttention).
     *   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](https://www.tensorflow.org/api_docs/python/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.
 *   Added `ignore_class` argument in the loss
     `SparseCategoricalCrossentropy` and metrics `IoU` and `MeanIoU`,
     to specify a class index to be ignored
     during loss/metric computation (e.g. a background/void class).
 *   Added [`tf.keras.models.experimental.SharpnessAwareMinimization`](https://www.tensorflow.org/api_docs/python/tf/keras/models/experimental/SharpnessAwareMinimization).
     This class implements the sharpness-aware minimization technique, which
     boosts model performance on various tasks, e.g., ResNet on image
     classification.

*   `tf.data`:

 *   Added support for cross-trainer data caching in tf.data service. This
     saves computation resources when concurrent training jobs train from the
     same dataset. See
     https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers
     for more details.
 *   Added `dataset_id` to `tf.data.experimental.service.register_dataset`.
     If provided, tf.data service will use the provided ID for the dataset.
     If the dataset ID already exists, no new dataset will be registered.
     This is useful if multiple training jobs need to use the same dataset
     for training. In this case, users should call `register_dataset` with
     the same `dataset_id`.
 *   Added a new field, `inject_prefetch`, to
     `tf.data.experimental.OptimizationOptions`. If it is set to `True`,
     tf.data will now automatically add a `prefetch` transformation to
     datasets that end in synchronous transformations. This enables data
     generation to be overlapped with  data consumption. This may cause a
     small increase in memory usage due to buffering. To enable this
     behavior, set `inject_prefetch=True` in
     `tf.data.experimental.OptimizationOptions`.
 *   Added a new value to `tf.data.Options.autotune.autotune_algorithm`:
     STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it
     runs a new algorithm that can get the same performance with lower
     CPU/memory usage.
 *   Added [`tf.data.experimental.from_list`](https://www.tensorflow.org/api_docs/python/tf/data/experimental/from_list), a new API for creating
     `Dataset`s from lists of elements.

*   `tf.distribute`:

 *   Added [`tf.distribute.experimental.PreemptionCheckpointHandler`](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/PreemptionCheckpointHandler)
     to handle worker preemption/maintenance and cluster-wise consistent
     error reporting for `tf.distribute.MultiWorkerMirroredStrategy`.
     Specifically, for the type of interruption with advance notice, it
     automatically saves a checkpoint, exits the program without raising an
     unrecoverable error, and restores the progress when training restarts.

*   `tf.math`:

 *   Added `tf.math.approx_max_k` and `tf.math.approx_min_k` which are the
     optimized alternatives to `tf.math.top_k` on TPU. The performance
     difference range from 8 to 100 times depending on the size of k. When
     running on CPU and GPU, a non-optimized XLA kernel is used.

*   `tf.train`:

 *  Added `tf.train.TrackableView` which allows users to inspect the
    TensorFlow Trackable object (e.g. `tf.Module`, Keras Layers and models).

*   `tf.vectorized_map`:

 *   Added an optional parameter: `warn`. This parameter controls whether or
     not warnings will be printed when operations in the provided `fn` fall
     back to a while loop.

*   XLA:
 *   MWMS is now compilable with XLA.

Bug Fixes and Other Changes

*  New argument `experimental_device_ordinal` in `LogicalDeviceConfiguration`
to control the order of logical devices. (GPU only)

*   `tf.keras`:

 *   Changed the TensorBoard tag names produced by the
     `tf.keras.callbacks.TensorBoard` callback, so that summaries logged
     automatically for model weights now include either a `/histogram` or
     `/image` suffix in their tag names, in order to prevent tag name
     collisions across summary types.

*   When running on GPU (with cuDNN version 7.6.3 or later),
 `tf.nn.depthwise_conv2d` backprop to `filter` (and therefore also
 `tf.keras.layers.DepthwiseConv2D`) now operate deterministically (and
 `tf.errors.UnimplementedError` is no longer thrown) when op-determinism has
 been enabled via `tf.config.experimental.enable_op_determinism`. This closes
 issue [47174](https://github.com/tensorflow/tensorflow/issues/47174).

* `tf.random`
 * Added `tf.random.experimental.stateless_shuffle`, a stateless version of
   `tf.random.shuffle`.

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`.



Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abolfazl Shahbazi, Adam Lanicek, Amin Benarieb, andreii, Andrew Fitzgibbon, Andrew Goodbody, angerson, Ashiq Imran, Aurélien Geron, Banikumar Maiti (Intel Aipg), Ben Barsdell, Ben Mares, bhack, Bhavani Subramanian, Bill Schnurr, Byungsoo Oh, Chandra Sr Potula, Chengji Yao, Chris Carpita, Christopher Bate, chunduriv, Cliff Woolley, Cliffs Dover, Cloud Han, Code-Review-Doctor, DEKHTIARJonathan, Deven Desai, Djacon, Duncan Riach, fedotoff, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, guozhong.zhuang, Hui Peng, James Gerity, Jason Furmanek, Jonathan Dekhtiar, Jueon Park, Kaixi Hou, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, kushanam, Learning-To-Play, Li-Wen Chang, lipracer, liuyuanqiang, Louis Sugy, Lucas David, Lukas Geiger, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, Meenakshi Venkataraman, Michal Szutenberg, Michele Di Giorgio, Mickaël Salamin, Nathan John Sircombe, Nathan Luehr, Neil Girdhar, Nils Reichardt, Nishidha Panpaliya, Nobuo Tsukamoto, Om Thakkar, Patrice Vignola, Philipp Hack, Pooya Jannaty, Prianka Liz Kariat, pshiko, Rajeshwar Reddy T, rdl4199, Rohit Santhanam, Rsanthanam-Amd, Sachin Muradi, Saoirse Stewart, Serge Panev, Shu Wang, Srinivasan Narayanamoorthy, Stella Stamenova, Stephan Hartmann, Sunita Nadampalli, synandi, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Trevor Morris, Xiaoming (Jason) Cui, Yimei Sun, Yong Tang, Yuanqiang Liu, Yulv-Git, Zhoulong Jiang, ZihengJiang

2.11.0

Breaking Changes
*   `tf.keras.optimizers.Optimizer` now points to the new Keras optimizer, and old optimizers have moved to the `tf.keras.optimizers.legacy` namespace.
 If you find your workflow failing due to this change, you may be facing one of the following issues:
 *   **Checkpoint loading failure.** The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of
     checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old
     checkpoint, please change your optimizer to `tf.keras.optimizer.legacy.XXX` (e.g. `tf.keras.optimizer.legacy.Adam`).
 *   **TF1 compatibility.** The new optimizer, `tf.keras.optimizers.Optimizer`, does not support TF1 any more, so please use the legacy optimizer
     `tf.keras.optimizer.legacy.XXX`.
     We highly recommend to migrate your workflow to TF2 for stable support and new features.
 *   **Old optimizer API not found.** The new optimizer, `tf.keras.optimizers.Optimizer`, has a different set of public APIs from the old optimizer.
     These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives
     to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
 *   **Learning rate schedule access.** When using a `LearningRateSchedule`, The new optimizer's `learning_rate` property returns the
     current learning rate value instead of a `LearningRateSchedule` object as before. If you need to access the `LearningRateSchedule` object,
     please use `optimizer._learning_rate`.
 *   **If you implemented a custom optimizer based on the old optimizer.** Please set your optimizer to subclass
     `tf.keras.optimizer.legacy.XXX`. If you want to migrate to the new optimizer and find it does not support your optimizer, please file
     an issue in the Keras GitHub repo.
 *   **Errors, such as `Cannot recognize variable...`.** The new optimizer requires all optimizer variables to be created at the first
     `apply_gradients()` or `minimize()` call. If your workflow calls optimizer to update different parts of model in multiple stages,
     please call `optimizer.build(model.trainable_variables)` before the training loop.
 *   **Timeout or performance loss.** We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file
     an issue in the Keras GitHub repo.

 The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example,
 `tf.keras.optimizers.Adafactor`) will only be implemented based on `tf.keras.optimizers.Optimizer`, the new base class.

Major Features and Improvements

*   `tf.lite`:

 *   New operations supported: `tf.unsortedsegmentmin`, `tf.atan2` and `tf.sign`.
 *   Updates to existing operations:
       * `tfl.mul` now supports complex32 inputs.

*   `tf.experimental.StructuredTensor`

 *   Introduced `tf.experimental.StructuredTensor`, which provides a flexible and TensorFlow-native way to encode structured data such as protocol
     buffers or pandas dataframes.

*   `tf.keras`:

 *   Added a new `get_metrics_result()` method to `tf.keras.models.Model`.
     *   Returns the current metrics values of the model as a dict.
 *   Added a new group normalization layer - `tf.keras.layers.GroupNormalization`.
 *   Added weight decay support for all Keras optimizers.
 *   Added Adafactor optimizer `tf.keras.optimizers.Adafactor`.
 *   Added `warmstart_embedding_matrix` to `tf.keras.utils`.
     *   This utility can be used to warmstart an embeddings matrix, so you reuse previously-learned word embeddings when working with a new set of
     words which may include previously unseen words (the embedding vectors for unseen words will be randomly initialized).

*   `tf.Variable`:

 *   Added `CompositeTensor` as a baseclass to `ResourceVariable`.
     *   This allows `tf.Variable`s to be nested in `tf.experimental.ExtensionType`s.
 *   Added a new constructor argument `experimental_enable_variable_lifting` to `tf.Variable`, defaulting to True.
     *   When it's `False`, the variable won't be lifted out of `tf.function`, thus it can be used as a `tf.function`-local variable: during each
     execution of the `tf.function`, the variable will be created and then disposed, similar to a local (that is, stack-allocated) variable in C/C++. 
     Currently, `experimental_enable_variable_lifting=False` only works on non-XLA devices (for example, under `tf.function(jit_compile=False)`).

*   TF SavedModel:
 *   Added `fingerprint.pb` to the SavedModel directory. The `fingerprint.pb` file is a protobuf containing the "fingerprint" of the SavedModel. See
     the [RFC](https://github.com/tensorflow/community/pull/415) for more details regarding its design and properties.
     
*   TF pip:
 *   Windows CPU-builds for x86/x64 processors are now built, maintained, tested and released by a third party: Intel. Installing the windows-native
     pip packages for `tensorflow` or `tensorflow-cpu` would install Intel's tensorflow-intel package. These packages are provided as-is. Tensorflow
     will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to
     release the pip package. For using TensorFlow GPU on Windows, you will need to install TensorFlow in WSL2.

Bug Fixes and Other Changes

*   `tf.image`
 *   Added an optional parameter `return_index_map` to `tf.image.ssim` which causes the returned value to be the local SSIM map instead of the global
     mean.

*   TF Core:

 *   `tf.custom_gradient` can now be applied to functions that accept "composite" tensors, such as `tf.RaggedTensor`, as inputs.
 *   Fix device placement issues related to datasets with ragged tensors of strings (i.e. variant encoded data with types not supported on GPU).
 *   `experimental_follow_type_hints` for tf.function has been deprecated. Please `use input_signature` or `reduce_retracing` to minimize retracing.

*   `tf.SparseTensor`:
 *   Introduced `set_shape`, which sets the static dense shape of the sparse tensor and has the same semantics as `tf.Tensor.set_shape`.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

103yiran, 8bitmp3, Aakar Dwivedi, Alexander Grund, alif_elham, Aman Agarwal, amoitra, Andrei Ivanov, andreii, Andrew Goodbody, angerson, Ashay Rane, Azeem Shaikh, Ben Barsdell, bhack, Bhavani Subramanian, Cedric Nugteren, Chandra Kumar Ramasamy, Christopher Bate, CohenAriel, Cotarou, cramasam, Enrico Minack, Francisco Unda, Frederic Bastien, gadagashwini, Gauri1 Deshpande, george, Jake, Jeff, Jerry Ge, Jingxuan He, Jojimon Varghese, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, kcoul, Keith Smiley, Kevin Hu, Kun Lu, kushanam, Lianmin Zheng, liuyuanqiang, Louis Sugy, Mahmoud Abuzaina, Marius Brehler, mdfaijul, Meenakshi Venkataraman, Milos Puzovic, mohantym, Namrata-Ibm, Nathan John Sircombe, Nathan Luehr, Olaf Lipinski, Om Thakkar, Osman F Bayram, Patrice Vignola, Pavani Majety, Philipp Hack, Prianka Liz Kariat, Rahul Batra, RajeshT, Renato Golin, riestere, Roger Iyengar, Rohit Santhanam, Rsanthanam-Amd, Sadeed Pv, Samuel Marks, Shimokawa, Naoaki, Siddhesh Kothadi, Simengliu-Nv, Sindre Seppola, snadampal, Srinivasan Narayanamoorthy, sushreebarsa, syedshahbaaz, Tamas Bela Feher, Tatwai Chong, Thibaut Goetghebuer-Planchon, tilakrayal, Tom Anderson, Tomohiro Endo, Trevor Morris, vibhutisawant, Victor Zhang, Vremold, Xavier Bonaventura, Yanming Wang, Yasir Modak, Yimei Sun, Yong Tang, Yulv-Git, zhuoran.liu, zotanika

2.10.1

This release introduces several vulnerability fixes:
*   Fixes an OOB seg fault in `DynamicStitch` due to missing validation ([CVE-2022-41883](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41883))
*   Fixes an overflow in `tf.keras.losses.poisson` ([CVE-2022-41887](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41887))
*   Fixes a heap OOB failure in `ThreadUnsafeUnigramCandidateSampler` caused by missing validation ([CVE-2022-41880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41880))
*   Fixes a segfault in `ndarray_tensor_bridge` ([CVE-2022-41884](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41884))
*   Fixes an overflow in `FusedResizeAndPadConv2D` ([CVE-2022-41885](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41885))
*   Fixes a overflow in `ImageProjectiveTransformV2` ([CVE-2022-41886](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41886))
*   Fixes an FPE in `tf.image.generate_bounding_box_proposals` on GPU ([CVE-2022-41888](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41888))
*   Fixes a segfault in `pywrap_tfe_src` caused by invalid attributes ([CVE-2022-41889](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41889))
*   Fixes a `CHECK` fail in `BCast` ([CVE-2022-41890](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41890))
*   Fixes a segfault in `TensorListConcat` ([CVE-2022-41891](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41891))
*   Fixes a `CHECK_EQ` fail in `TensorListResize` ([CVE-2022-41893](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41893))
*   Fixes an overflow in `CONV_3D_TRANSPOSE` on TFLite ([CVE-2022-41894](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41894))
*   Fixes a heap OOB in `MirrorPadGrad` ([CVE-2022-41895](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41895))
*   Fixes a crash in `Mfcc` ([CVE-2022-41896](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41896))
*   Fixes a heap OOB in `FractionalMaxPoolGrad` ([CVE-2022-41897](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41897))
*   Fixes a `CHECK` fail in `SparseFillEmptyRowsGrad` ([CVE-2022-41898](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41898))
*   Fixes a `CHECK` fail in `SdcaOptimizer` ([CVE-2022-41899](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41899))
*   Fixes a heap OOB in `FractionalAvgPool` and `FractionalMaxPool`([CVE-2022-41900](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41900))
*   Fixes a `CHECK_EQ` in `SparseMatrixNNZ` ([CVE-2022-41901](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41901))
*   Fixes an OOB write in grappler ([CVE-2022-41902](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41902))
*   Fixes a overflow in `ResizeNearestNeighborGrad` ([CVE-2022-41907](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41907))
*   Fixes a `CHECK` fail in `PyFunc` ([CVE-2022-41908](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41908))
*   Fixes a segfault in `CompositeTensorVariantToComponents` ([CVE-2022-41909](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41909))
*   Fixes a invalid char to bool conversion in printing a tensor ([CVE-2022-41911](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41911))
*   Fixes a heap overflow in `QuantizeAndDequantizeV2` ([CVE-2022-41910](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41910))
*   Fixes a `CHECK` failure in `SobolSample` via missing validation ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935))
*   Fixes a `CHECK` fail in `TensorListScatter` and `TensorListScatterV2` in eager mode ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935))

2.10.0

Breaking Changes

*   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.

2.9.3

This release introduces several vulnerability fixes:

*   Fixes an overflow in `tf.keras.losses.poisson` ([CVE-2022-41887](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41887))
*   Fixes a heap OOB failure in `ThreadUnsafeUnigramCandidateSampler` caused by missing validation ([CVE-2022-41880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41880))
*   Fixes a segfault in `ndarray_tensor_bridge` ([CVE-2022-41884](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41884))
*   Fixes an overflow in `FusedResizeAndPadConv2D` ([CVE-2022-41885](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41885))
*   Fixes a overflow in `ImageProjectiveTransformV2` ([CVE-2022-41886](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41886))
*   Fixes an FPE in `tf.image.generate_bounding_box_proposals` on GPU ([CVE-2022-41888](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41888))
*   Fixes a segfault in `pywrap_tfe_src` caused by invalid attributes ([CVE-2022-41889](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41889))
*   Fixes a `CHECK` fail in `BCast` ([CVE-2022-41890](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41890))
*   Fixes a segfault in `TensorListConcat` ([CVE-2022-41891](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41891))
*   Fixes a `CHECK_EQ` fail in `TensorListResize` ([CVE-2022-41893](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41893))
*   Fixes an overflow in `CONV_3D_TRANSPOSE` on TFLite ([CVE-2022-41894](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41894))
*   Fixes a heap OOB in `MirrorPadGrad` ([CVE-2022-41895](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41895))
*   Fixes a crash in `Mfcc` ([CVE-2022-41896](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41896))
*   Fixes a heap OOB in `FractionalMaxPoolGrad` ([CVE-2022-41897](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41897))
*   Fixes a `CHECK` fail in `SparseFillEmptyRowsGrad` ([CVE-2022-41898](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41898))
*   Fixes a `CHECK` fail in `SdcaOptimizer` ([CVE-2022-41899](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41899))
*   Fixes a heap OOB in `FractionalAvgPool` and `FractionalMaxPool`([CVE-2022-41900](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41900))
*   Fixes a `CHECK_EQ` in `SparseMatrixNNZ` ([CVE-2022-41901](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41901))
*   Fixes an OOB write in grappler ([CVE-2022-41902](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41902))
*   Fixes a overflow in `ResizeNearestNeighborGrad` ([CVE-2022-41907](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41907))
*   Fixes a `CHECK` fail in `PyFunc` ([CVE-2022-41908](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41908))
*   Fixes a segfault in `CompositeTensorVariantToComponents` ([CVE-2022-41909](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41909))
*   Fixes a invalid char to bool conversion in printing a tensor ([CVE-2022-41911](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41911))
*   Fixes a heap overflow in `QuantizeAndDequantizeV2` ([CVE-2022-41910](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-41910))
*   Fixes a `CHECK` failure in `SobolSample` via missing validation ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935))
*   Fixes a `CHECK` fail in `TensorListScatter` and `TensorListScatterV2` in eager mode ([CVE-2022-35935](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-35935))

2.9.2

This releases introduces several

[pyup-bot]

Closing this in favor of #1288