Release 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

  • Added 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.
  • Added int16x8 support for the built-in op exp
  • Added int16x8 support for the built-in op mirror_pad
  • Added int16x8 support for the built-in ops space_to_batch_nd and batch_to_space_nd
  • Added 16-bit int type support for built-in op less, greater_than, equal
  • Added 8-bit and 16-bit support for floor_div and floor_mod.
  • Added 16-bit and 32-bit int support for the built-in op bitcast.
  • Added 8-bit/16-bit/32-bit int/uint support for the built-in op bitwise_xor
  • Added int16 indices support for built-in op gather and gather_nd.
  • Added 8-bit/16-bit/32-bit int/uint support for the built-in op right_shift
  • Added reference implementation for 16-bit int unquantized add.
  • Added reference implementation for 16-bit int and 32-bit unsigned int unquantized mul.
  • add_op supports broadcasting up to 6 dimensions.
  • Added 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.RaggedTensors.
  • 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 tf.data.UNKNOWN_CARDINALITY When doing a "full shuffle" using dataset = dataset.shuffle(dataset.cardinality()). But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

• 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

  • Introduced class method tf.saved_model.experimental.Fingerprint.from_proto(proto), which can be used to construct a Fingerprint object directly from a protobuf.
  • Introduced 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, dtensor.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.

Breaking Changes

• Removed the Keras scikit-learn API wrappers (KerasClassifier and KerasRegressor), which had been deprecated in August 2021. We recommend using 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

• 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