Update tensorflow to 2.3.0

[pyup-bot]

This PR updates tensorflow from 1.13.1 to 2.3.0.

Changelog

2.3.0

Breaking Changes

*   `tf.image.extract_glimpse` has been updated to correctly process the case
 where `centered=False` and `normalized=False`. This is a breaking change as
 the output is different from (incorrect) previous versions. Note this
 breaking change only impacts `tf.image.extract_glimpse` and
 `tf.compat.v2.image.extract_glimpse` API endpoints. The behavior of
 `tf.compat.v1.image.extract_glimpse` does not change. The behavior of
 exsiting C++ kernel `ExtractGlimpse` does not change as well, so saved
 models will not be impacted.

Bug Fixes and Other Changes
* Mutable tables now restore checkpointed values when loaded from SavedModel.

2.2.0

TensorFlow 2.2 discontinues support for Python 2, [previously announced](https://groups.google.com/a/tensorflow.org/d/msg/announce/gVwS5RC8mds/dCt1ka2XAAAJ) as following [Python 2's EOL on January 1, 2020](https://www.python.org/dev/peps/pep-0373/update).

Coinciding with this change, new releases of [TensorFlow's Docker images](https://hub.docker.com/r/tensorflow/tensorflow/) provide Python 3 exclusively. Because all images now use Python 3, Docker tags containing `-py3` will no longer be provided and existing `-py3` tags like `latest-py3` will not be updated.

Major Features and Improvements

* Replaced the scalar type for string tensors from `std::string` to `tensorflow::tstring` which is now ABI stable.
* A new Profiler for TF 2 for CPU/GPU/TPU. It offers both device and host performance analysis, including input pipeline and TF Ops. Optimization advisory is provided whenever possible. Please see [this tutorial](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras) and [guide](https://www.tensorflow.org/guide/profiler) for usage guidelines.
* Export C++ functions to Python using `pybind11` as opposed to `SWIG` as a part of our [deprecation of swig efforts](https://github.com/tensorflow/community/blob/master/rfcs/20190208-pybind11.md).
* `tf.distribute`:
* Support added for global sync `BatchNormalization` by using the newly added `tf.keras.layers.experimental.SyncBatchNormalization` layer. This layer will sync `BatchNormalization` statistics every step across all replicas taking part in sync training.
* Performance improvements for GPU multi-worker distributed training using `tf.distribute.experimental.MultiWorkerMirroredStrategy`
 * Update NVIDIA `NCCL` to `2.5.7-1` for better performance and performance tuning. Please see [nccl developer guide](https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/env.html) for more information on this.
 * Support gradient `allreduce` in `float16`. See this [example](https://github.com/tensorflow/models/blob/master/official/staging/training/grad_utils.py) usage.
 * Experimental support of [all reduce gradient packing](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/CollectiveHints) to allow overlapping gradient aggregation with backward path computation.
 * Deprecated `experimental_run_v2` method for distribution strategies and renamed the method `run` as it is no longer experimental.
 * Add CompositeTensor support for DistributedIterators. This should help prevent unnecessary function retracing and memory leaks.
* `tf.keras`:
* `Model.fit` major improvements:
  * You can now use custom training logic with `Model.fit` by overriding `Model.train_step`.
  * Easily write state-of-the-art training loops without worrying about all of the features `Model.fit` handles for you (distribution strategies, callbacks, data formats, looping logic, etc)
  * See the default [`Model.train_step`](https://github.com/tensorflow/tensorflow/blob/1381fc8e15e22402417b98e3881dfd409998daea/tensorflow/python/keras/engine/training.pyL540) for an example of what this function should look like. Same applies for validation and inference via `Model.test_step` and `Model.predict_step`.
  * SavedModel uses its own `Model._saved_model_inputs_spec` attr now instead of
    relying on `Model.inputs` and `Model.input_names`, which are no longer set for subclass Models.
    This attr is set in eager, `tf.function`, and graph modes. This gets rid of the need for users to
    manually call `Model._set_inputs` when using Custom Training Loops(CTLs).
  * Dynamic shapes are supported for generators by calling the Model on the first batch we "peek" from the generator.
    This used to happen implicitly in `Model._standardize_user_data`. Long-term, a solution where the
    `DataAdapter` doesn't need to call the Model is probably preferable.
* The SavedModel format now supports all Keras built-in layers (including metrics, preprocessing layers, and stateful RNN layers)
* Update Keras batch normalization layer to use the running mean and average computation in the `fused_batch_norm`. You should see significant performance improvements when using `fused_batch_norm` in Eager mode.

* `tf.lite`:
* Enable TFLite experimental new converter by default.
* XLA
* XLA now builds and works on windows. All prebuilt packages come with XLA available.
* XLA can be [enabled for a `tf.function`](https://www.tensorflow.org/xlaexplicit_compilation_with_tffunction
) with “compile or throw exception” semantics on CPU and GPU.

Breaking Changes
* `tf.keras`:
* In `tf.keras.applications` the name of the "top" layer has been standardized to "predictions". This is only a problem if your code relies on the exact name of the layer.
* Huber loss function has been updated to be consistent with other Keras losses. It now computes mean over the last axis of per-sample losses before applying the reduction function.
* AutoGraph no longer converts functions passed to `tf.py_function`, `tf.py_func` and `tf.numpy_function`.
* Deprecating `XLA_CPU` and `XLA_GPU` devices with this release.
* Increasing the minimum bazel version to build TF to 2.0.0 to use Bazel's `cc_experimental_shared_library`.
* Keras compile/fit behavior for functional and subclassed models have been unified. Model properties such as `metrics`, `metrics_names` will now be available only after **training/evaluating the model on actual data** for functional models. `metrics` will **now include** model `loss` and output losses.`loss_functions` property has been removed from the model. This was an undocumented property that was accidentally public and has now been removed.

Known Caveats
* The current TensorFlow release now **requires** [gast](https://pypi.org/project/gast/) version 0.3.3.

Bug Fixes and Other Changes

*   `tf.data`:
 *   Removed `autotune_algorithm` from experimental optimization options.
*   TF Core:
 *   `tf.constant` always creates CPU tensors irrespective of the current
     device context.
 *   Eager `TensorHandles` maintain a list of mirrors for any copies to local
     or remote devices. This avoids any redundant copies due to op execution.
 *   For `tf.Tensor` & `tf.Variable`, `.experimental_ref()` is no longer
     experimental and is available as simply `.ref()`.
 *   `pfor/vectorized_map`: Added support for vectorizing 56 more ops.
     Vectorizing `tf.cond` is also supported now.
 *   Set as much partial shape as we can infer statically within the gradient
     impl of the gather op.
 *   Gradient of `tf.while_loop` emits `StatelessWhile` op if `cond` and body
     functions are stateless. This allows multiple gradients while ops to run
     in parallel under distribution strategy.
 *   Speed up `GradientTape` in eager mode by auto-generating list of op
     inputs/outputs which are unused and hence not cached for gradient
     functions.
 *   Support `back_prop=False` in `while_v2` but mark it as deprecated.
 *   Improve error message when attempting to use `None` in data-dependent
     control flow.
 *   Add `RaggedTensor.numpy()`.
 *   Update `RaggedTensor.__getitem__` to preserve uniform dimensions & allow
     indexing into uniform dimensions.
 *   Update `tf.expand_dims` to always insert the new dimension as a
     non-ragged dimension.
 *   Update `tf.embedding_lookup` to use `partition_strategy` and `max_norm`
     when `ids` is ragged.
 *   Allow `batch_dims==rank(indices)` in `tf.gather`.
 *   Add support for bfloat16 in `tf.print`.
*   `tf.distribute`:
 *   Support `embedding_column` with variable-length input features for
     `MultiWorkerMirroredStrategy`.
*   `tf.keras`:
 *   Added `experimental_aggregate_gradients` argument to
     `tf.keras.optimizer.Optimizer.apply_gradients`. This allows custom
     gradient aggregation and processing aggregated gradients in custom
     training loop.
 *   Allow `pathlib.Path` paths for loading models via Keras API.
*   `tf.function`/AutoGraph:
 *   AutoGraph is now available in `ReplicaContext.merge_call`,
     `Strategy.extended.update` and `Strategy.extended.update_non_slot`.
 *   Experimental support for shape invariants has been enabled in
     `tf.function`. See the API docs for
     `tf.autograph.experimental.set_loop_options` for additional info.
 *   AutoGraph error messages now exclude frames corresponding to APIs
     internal to AutoGraph.
 *   Improve shape inference for `tf.function` input arguments to unlock more
     Grappler optimizations in TensorFlow 2.x.
 *   Improve automatic control dependency management of resources by allowing
     resource reads to occur in parallel and synchronizing only on writes.
 *   Fix execution order of multiple stateful calls to `experimental_run_v2`
     in `tf.function`.
 *   You can now iterate over `RaggedTensors` using a for loop inside
     `tf.function`.
*   `tf.lite`:
 *   Migrated the `tf.lite` C inference API out of experimental into lite/c.
 *   Add an option to disallow `NNAPI` CPU / partial acceleration on Android
     10
 *   TFLite Android AARs now include the C headers and APIs are required to
     use TFLite from native code.
 *   Refactors the delegate and delegate kernel sources to allow usage in the
     linter.
 *   Limit delegated ops to actually supported ones if a device name is
     specified or `NNAPI` CPU Fallback is disabled.
 *   TFLite now supports `tf.math.reciprocal1` op by lowering to `tf.div op`.
 *   TFLite's unpack op now supports boolean tensor inputs.
 *   Microcontroller and embedded code moved from experimental to main
     TensorFlow Lite folder
 *   Check for large TFLite tensors.
 *   Fix GPU delegate crash with C++17.
 *   Add 5D support to TFLite `strided_slice`.
 *   Fix error in delegation of `DEPTH_TO_SPACE` to `NNAPI` causing op not to
     be accelerated.
 *   Fix segmentation fault when running a model with LSTM nodes using
     `NNAPI` Delegate
 *   Fix `NNAPI` delegate failure when an operand for Maximum/Minimum
     operation is a scalar.
 *   Fix `NNAPI` delegate failure when Axis input for reduce operation is a
     scalar.
 *   Expose option to limit the number of partitions that will be delegated
     to `NNAPI`.
 *   If a target accelerator is specified, use its feature level to determine
     operations to delegate instead of SDK version.
*   `tf.random`:
 *   Various random number generation improvements:
 *   Add a fast path for default `random_uniform`
 *   `random_seed` documentation improvement.
 *   `RandomBinomial` broadcasts and appends the sample shape to the left
     rather than the right.
 *   Added `tf.random.stateless_binomial`, `tf.random.stateless_gamma`,
     `tf.random.stateless_poisson`
 *   `tf.random.stateless_uniform` now supports unbounded sampling of `int`
     types.
*   Math and Linear Algebra:
 *   Add `tf.linalg.LinearOperatorTridiag`.
 *   Add `LinearOperatorBlockLowerTriangular`
 *   Add broadcasting support to
     tf.linalg.triangular_solve[26204](https://github.com/tensorflow/tensorflow/issues/26204),
     tf.math.invert_permutation.
 *   Add `tf.math.sobol_sample` op.
 *   Add `tf.math.xlog1py`.
 *   Add `tf.math.special.{dawsn,expi,fresnel_cos,fresnel_sin,spence}`.
 *   Add a Modified Discrete Cosine Transform (MDCT) and its inverse to
     `tf.signal`.
*   TPU Enhancements:
 *   Refactor `TpuClusterResolver` to move shared logic to a separate pip
     package.
 *   Support configuring TPU software version from cloud tpu client.
 *   Allowed TPU embedding weight decay factor to be multiplied by learning
     rate.
*   XLA Support:
 *   Add standalone XLA AOT runtime target + relevant .cc sources to pip
     package.
 *   Add check for memory alignment to MemoryAllocation::MemoryAllocation()
     on 32-bit ARM. This ensures a deterministic early exit instead of a hard
     to debug bus error later.
 *   `saved_model_cli aot_compile_cpu` allows you to compile saved models to
     XLA header+object files and include them in your C++ programs.
 *   Enable `Igamma`, `Igammac` for XLA.
*   Deterministic Op Functionality:
 *   XLA reduction emitter is deterministic when the environment variable
     `TF_DETERMINISTIC_OPS` is set to "true" or "1". This extends
     deterministic `tf.nn.bias_add` back-prop functionality (and therefore
     also deterministic back-prop of bias-addition in Keras layers) to
     include when XLA JIT compilation is enabled.
 *   Fix problem, when running on a CUDA GPU and when either environment
     variable `TF_DETERMINISTIC_OPS` or environment variable
     `TF_CUDNN_DETERMINISTIC` is set to "true" or "1", in which some layer
     configurations led to an exception with the message "No algorithm
     worked!"
*   Tracing and Debugging:
 *   Add source, destination name to `_send` traceme to allow easier
     debugging.
 *   Add traceme event to `fastpathexecute`.
*   Other:
 *   Fix an issue with AUC.reset_states for multi-label AUC
     [35852](https://github.com/tensorflow/tensorflow/issues/35852)
 *   Fix the TF upgrade script to not delete files when there is a parsing
     error and the output mode is `in-place`.
 *   Move `tensorflow/core:framework/*_pyclif` rules to
     `tensorflow/core/framework:*_pyclif`.

Thanks to our Contributors

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

372046933, 8bitmp3, aaronhma, Abin Shahab, Aditya Patwardhan, Agoniii, Ahti Kitsik, Alan Yee, Albin Joy, Alex Hoffman, Alexander Grund, Alexandre E. Eichenberger, Amit Kumar Jaiswal, amoitra, Andrew Anderson, Angus-Luo, Anthony Barbier, Anton Kachatkou, Anuj Rawat, archis, Arpan-Dhatt, Arvind Sundararajan, Ashutosh Hathidara, autoih, Bairen Yi, Balint Cristian, Bas Aarts, BashirSbaiti, Basit Ayantunde, Ben Barsdell, Benjamin Gaillard, boron, Brett Koonce, Bryan Cutler, Christian Goll, Christian Sachs, Clayne Robison, comet, Daniel Falbel, Daria Zhuravleva, darsh8200, David Truby, Dayananda-V, deepakm, Denis Khalikov, Devansh Singh, Dheeraj R Reddy, Diederik Van Liere, Diego Caballero, Dominic Jack, dothinking, Douman, Drake Gens, Duncan Riach, Ehsan Toosi, ekuznetsov139, Elena Zhelezina, elzino, Ending2015a, Eric Schweitz, Erik Zettel, Ethan Saadia, Eugene Kuznetsov, Evgeniy Zheltonozhskiy, Ewout Ter Hoeven, exfalso, FAIJUL, Fangjun Kuang, Fei Hu, Frank Laub, Frederic Bastien, Fredrik Knutsson, frreiss, Frédéric Rechtenstein, fsx950223, Gaurav Singh, gbaned, George Grzegorz Pawelczak, George Sterpu, Gian Marco Iodice, Giorgio Arena, Hans Gaiser, Hans Pabst, Haoyu Wu, Harry Slatyer, hsahovic, Hugo, Hugo Sjöberg, IrinaM21, jacco, Jake Tae, Jean-Denis Lesage, Jean-Michel Gorius, Jeff Daily, Jens Elofsson, Jerry Shih, jerryyin, Jin Mingjian, Jinjing Zhou, JKIsaacLee, jojimonv, Jonathan Dekhtiar, Jose Ignacio Gomez, Joseph-Rance, Judd, Julian Gross, Kaixi Hou, Kaustubh Maske Patil, Keunwoo Choi, Kevin Hanselman, Khor Chean Wei, Kilaru Yasaswi Sri Chandra Gandhi, Koan-Sin Tan, Koki Ibukuro, Kristian Holsheimer, kurileo, Lakshay Tokas, Lee Netherton, leike666666, Leslie-Fang-Intel, Li, Guizi, LIUJIAN435, Lukas Geiger, Lyo Nguyen, madisetti, Maher Jendoubi, Mahmoud Abuzaina, Manuel Freiberger, Marcel Koester, Marco Jacopo Ferrarotti, Markus Franke, marload, Mbah-Javis, mbhuiyan, Meng Zhang, Michael Liao, MichaelKonobeev, Michal Tarnowski, Milan Straka, minoring, Mohamed Nour Abouelseoud, MoussaMM, Mrinal Jain, mrTsjolder, Måns Nilsson, Namrata Bhave, Nicholas Gao, Niels Ole Salscheider, nikochiko, Niranjan Hasabnis, Nishidha Panpaliya, nmostafa, Noah Trenaman, nuka137, Officium, Owen L - Sfe, Pallavi G, Paul Andrey, Peng Sun, Peng Wu, Phil Pearl, PhilipMay, pingsutw, Pooya Davoodi, PragmaTwice, pshiko, Qwerty71, R Gomathi, Rahul Huilgol, Richard Xiao, Rick Wierenga, Roberto Rosmaninho, ruchit2801, Rushabh Vasani, Sami, Sana Damani, Sarvesh Dubey, Sasan Jafarnejad, Sergii Khomenko, Shane Smiskol, Shaochen Shi, sharkdtu, Shawn Presser, ShengYang1, Shreyash Patodia, Shyam Sundar Dhanabalan, Siju Samuel, Somyajit Chakraborty Sam, Srihari Humbarwadi, srinivasan.narayanamoorthy, Srishti Yadav, Steph-En-M, Stephan Uphoff, Stephen Mugisha, SumanSudhir, Taehun Kim, Tamas Bela Feher, TengLu, Tetragramm, Thierry Herrmann, Tian Jin, tigertang, Tom Carchrae, Tom Forbes, Trent Lo, Victor Peng, vijayphoenix, Vincent Abriou, Vishal Bhola, Vishnuvardhan Janapati, vladbataev, VoVAllen, Wallyss Lima, Wen-Heng (Jack) Chung, wenxizhu, William D. Irons, William Zhang, Xiaoming (Jason) Cui, Xiaoquan Kong, Xinan Jiang, Yasir Modak, Yasuhiro Matsumoto, Yaxun (Sam) Liu, Yong Tang, Ytyt-Yt, yuan, Yuan Mingshuai, Yuan Tang, Yuki Ueda, Yusup, zhangshijin, zhuwenxi

2.1.1

Bug Fixes and Other Changes
* Updates `sqlite3` to `3.31.01` to handle [CVE-2019-19880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19880), [CVE-2019-19244](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19244) and [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645)
* Updates `curl` to `7.69.1` to handle [CVE-2019-15601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-15601)
* Updates `libjpeg-turbo` to `2.0.4` to handle [CVE-2018-19664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-19664), [CVE-2018-20330](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-20330) and [CVE-2019-13960](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-13960)
* Updates Apache Spark to `2.4.5` to handle [CVE-2019-10099](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-10099), [CVE-2018-17190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17190) and [CVE-2018-11770](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-11770)
* Fixes a versioning bug which causes Keras layers from TF 1.x to be used instead of those from TF 2.x

2.1.0

TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support [officially ends an January 1, 2020](https://www.python.org/dev/peps/pep-0373/update). [As announced earlier](https://groups.google.com/a/tensorflow.org/d/msg/announce/gVwS5RC8mds/dCt1ka2XAAAJ), TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.

Major Features and Improvements

*   The `tensorflow` pip package now includes GPU support by default (same as
 `tensorflow-gpu`) for both Linux and Windows. This runs on machines with and
 without NVIDIA GPUs. `tensorflow-gpu` is still available, and CPU-only
 packages can be downloaded at `tensorflow-cpu` for users who are concerned
 about package size.
*   **Windows users:** Officially-released `tensorflow` Pip packages are now
 built with Visual Studio 2019 version 16.4 in order to take advantage of the
 new `/d2ReducedOptimizeHugeFunctions` compiler flag. To use these new
 packages, you must install "Microsoft Visual C++ Redistributable for Visual
 Studio 2015, 2017 and 2019", available from Microsoft's website
 [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).
 *   This does not change the minimum required version for building
     TensorFlow from source on Windows, but builds enabling
     `EIGEN_STRONG_INLINE` can take over 48 hours to compile without this
     flag. Refer to `configure.py` for more information about
     `EIGEN_STRONG_INLINE` and `/d2ReducedOptimizeHugeFunctions`.
 *   If either of the required DLLs, `msvcp140.dll` (old) or `msvcp140_1.dll`
     (new), are missing on your machine, `import tensorflow` will print a
     warning message.
*   The `tensorflow` pip package is built with CUDA 10.1 and cuDNN 7.6.
*   `tf.keras`
 *   Experimental support for mixed precision is available on GPUs and Cloud
     TPUs. See
     [usage guide](https://www.tensorflow.org/guide/keras/mixed_precision).
 *   Introduced the `TextVectorization` layer, which takes as input raw
     strings and takes care of text standardization, tokenization, n-gram
     generation, and vocabulary indexing. See this
     [end-to-end text classification example](https://colab.research.google.com/drive/1RvCnR7h0_l4Ekn5vINWToI9TNJdpUZB3).
 *   Keras `.compile` `.fit` `.evaluate` and `.predict` are allowed to be
     outside of the DistributionStrategy scope, as long as the model was
     constructed inside of a scope.
 *   Experimental support for Keras `.compile`, `.fit`, `.evaluate`, and
     `.predict` is available for Cloud TPUs, Cloud TPU, for all types of
     Keras models (sequential, functional and subclassing models).
 *   Automatic outside compilation is now enabled for Cloud TPUs. This allows
     `tf.summary` to be used more conveniently with Cloud TPUs.
 *   Dynamic batch sizes with DistributionStrategy and Keras are supported on
     Cloud TPUs.
 *   Support for `.fit`, `.evaluate`, `.predict` on TPU using numpy data, in
     addition to `tf.data.Dataset`.
 *   Keras reference implementations for many popular models are available in
     the TensorFlow
     [Model Garden](https://github.com/tensorflow/models/tree/master/official).
*   `tf.data`
 *   Changes rebatching for `tf.data datasets` + DistributionStrategy for
     better performance. Note that the dataset also behaves slightly
     differently, in that the rebatched dataset cardinality will always be a
     multiple of the number of replicas.
 *   `tf.data.Dataset` now supports automatic data distribution and sharding
     in distributed environments, including on TPU pods.
 *   Distribution policies for `tf.data.Dataset` can now be tuned with 1.
     `tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA)` 2.
     `tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)`
*   `tf.debugging`
 *   Add `tf.debugging.enable_check_numerics()` and
     `tf.debugging.disable_check_numerics()` to help debugging the root
     causes of issues involving infinities and `NaN`s.
*   `tf.distribute`
 *   Custom training loop support on TPUs and TPU pods is available through
     `strategy.experimental_distribute_dataset`,
     `strategy.experimental_distribute_datasets_from_function`,
     `strategy.experimental_run_v2`, `strategy.reduce`.
 *   Support for a global distribution strategy through
     `tf.distribute.experimental_set_strategy(),` in addition to
     `strategy.scope()`.
*   `TensorRT`
 *   [TensorRT 6.0](https://developer.nvidia.com/tensorrttensorrt-whats-new)
     is now supported and enabled by default. This adds support for more
     TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D,
     MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the
     TensorFlow-TensorRT python conversion API is exported as
     `tf.experimental.tensorrt.Converter`.
*   Environment variable `TF_DETERMINISTIC_OPS` has been added. When set to
 "true" or "1", this environment variable makes `tf.nn.bias_add` operate
 deterministically (i.e. reproducibly), but currently only when XLA JIT
 compilation is *not* enabled. Setting `TF_DETERMINISTIC_OPS` to "true" or
 "1" also makes cuDNN convolution and max-pooling operate deterministically.
 This makes Keras Conv\*D and MaxPool\*D layers operate deterministically in
 both the forward and backward directions when running on a CUDA-enabled GPU.

Breaking Changes
* Deletes `Operation.traceback_with_start_lines` for which we know of no usages.
* Removed `id` from `tf.Tensor.__repr__()` as `id` is not useful other than internal debugging.
* Some `tf.assert_*` methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the `session.run()`. This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in `feed_dict` argument to `session.run()`, an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).
* The following APIs are not longer experimental: `tf.config.list_logical_devices`, `tf.config.list_physical_devices`, `tf.config.get_visible_devices`, `tf.config.set_visible_devices`, `tf.config.get_logical_device_configuration`, `tf.config.set_logical_device_configuration`.
* `tf.config.experimentalVirtualDeviceConfiguration` has been renamed to `tf.config.LogicalDeviceConfiguration`.
* `tf.config.experimental_list_devices` has been removed, please use
`tf.config.list_logical_devices`.

Bug Fixes and Other Changes
* `tf.data`
* Fixes concurrency issue with `tf.data.experimental.parallel_interleave` with `sloppy=True`.
* Add `tf.data.experimental.dense_to_ragged_batch()`.
* Extend `tf.data` parsing ops to support `RaggedTensors`.
* `tf.distribute`
* Fix issue where GRU would crash or give incorrect output when a `tf.distribute.Strategy` was used.
* `tf.estimator`
* Added option in `tf.estimator.CheckpointSaverHook` to not save the `GraphDef`.
* Moving the checkpoint reader from swig to pybind11.
* `tf.keras`
* Export `depthwise_conv2d` in `tf.keras.backend`.
* In Keras Layers and Models, Variables in `trainable_weights`, `non_trainable_weights`, and `weights` are explicitly deduplicated.
* Keras `model.load_weights` now accepts `skip_mismatch` as an argument. This was available in external Keras, and has now been copied over to `tf.keras`.
* Fix the input shape caching behavior of Keras convolutional layers.
* `Model.fit_generator`, `Model.evaluate_generator`, `Model.predict_generator`, `Model.train_on_batch`, `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect the `run_eagerly` property, and will correctly run using `tf.function` by default. Note that `Model.fit_generator`, `Model.evaluate_generator`, and `Model.predict_generator` are deprecated endpoints. They are subsumed by `Model.fit`, `Model.evaluate`, and `Model.predict` which now support generators and Sequences.
* `tf.lite`
* Legalization for `NMS` ops in TFLite.
* add `narrow_range` and `axis` to `quantize_v2` and `dequantize` ops.
* Added support for `FusedBatchNormV3` in converter.
* Add an `errno`-like field to `NNAPI` delegate for detecting `NNAPI` errors for fallback behaviour.
* Refactors `NNAPI` Delegate to support detailed reason why an operation is not accelerated.
* Converts hardswish subgraphs into atomic ops.
* Other
* Critical stability updates for TPUs, especially in cases where the XLA compiler produces compilation errors.
* TPUs can now be re-initialized multiple times, using `tf.tpu.experimental.initialize_tpu_system`.
* Add `RaggedTensor.merge_dims()`.
* Added new `uniform_row_length` row-partitioning tensor to `RaggedTensor`.
* Add `shape` arg to `RaggedTensor.to_tensor`; Improve speed of `RaggedTensor.to_tensor`.
* `tf.io.parse_sequence_example` and `tf.io.parse_single_sequence_example` now support ragged features.
* Fix `while_v2` with variables in custom gradient.
* Support taking gradients of V2 `tf.cond` and `tf.while_loop` using `LookupTable`.
* Fix bug where `vectorized_map` failed on inputs with unknown static shape.
* Add preliminary support for sparse CSR matrices.
* Tensor equality with `None` now behaves as expected.
* Make calls to `tf.function(f)()`, `tf.function(f).get_concrete_function` and `tf.function(f).get_initialization_function` thread-safe.
* Extend `tf.identity` to work with CompositeTensors (such as SparseTensor)
* Added more `dtypes` and zero-sized inputs to `Einsum` Op and improved its performance
* Enable multi-worker `NCCL` `all-reduce` inside functions executing eagerly.
* Added complex128 support to `RFFT`, `RFFT2D`, `RFFT3D`, `IRFFT`, `IRFFT2D`, and `IRFFT3D`.
* Add `pfor` converter for `SelfAdjointEigV2`.
* Add `tf.math.ndtri` and `tf.math.erfinv`.
* Add `tf.config.experimental.enable_mlir_bridge` to allow using MLIR compiler bridge in eager model.
* Added support for MatrixSolve on Cloud TPU / XLA.
* Added `tf.autodiff.ForwardAccumulator` for forward-mode autodiff
* Add `LinearOperatorPermutation`.
* A few performance optimizations on `tf.reduce_logsumexp`.
* Added multilabel handling to `AUC` metric
* Optimization on `zeros_like`.
* Dimension constructor now requires `None` or types with an `__index__` method.
* Add `tf.random.uniform` microbenchmark.
* Use `_protogen` suffix for proto library targets instead of `_cc_protogen` suffix.
* Moving the checkpoint reader from `swig` to `pybind11`.
* `tf.device` & `MirroredStrategy` now supports passing in a `tf.config.LogicalDevice`
* If you're building Tensorflow from source, consider using [bazelisk](https://github.com/bazelbuild/bazelisk) to automatically download and use the correct Bazel version. Bazelisk reads the `.bazelversion` file at the root of the project directory.

Thanks to our Contributors

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

8bitmp3, Aaron Ma, AbdüLhamit Yilmaz, Abhai Kollara, aflc, Ag Ramesh, Albert Z. Guo, Alex Torres, amoitra, Andrii Prymostka, angeliand, Anshuman Tripathy, Anthony Barbier, Anton Kachatkou, Anubh-V, Anuja Jakhade, Artem Ryabov, autoih, Bairen Yi, Bas Aarts, Basit Ayantunde, Ben Barsdell, Bhavani Subramanian, Brett Koonce, candy.dc, Captain-Pool, caster, cathy, Chong Yan, Choong Yin Thong, Clayne Robison, Colle, Dan Ganea, David Norman, David Refaeli, dengziming, Diego Caballero, Divyanshu, djshen, Douman, Duncan Riach, EFanZh, Elena Zhelezina, Eric Schweitz, Evgenii Zheltonozhskii, Fei Hu, fo40225, Fred Reiss, Frederic Bastien, Fredrik Knutsson, fsx950223, fwcore, George Grzegorz Pawelczak, George Sterpu, Gian Marco Iodice, Giorgio Arena, giuros01, Gomathi Ramamurthy, Guozhong Zhuang, Haifeng Jin, Haoyu Wu, HarikrishnanBalagopal, HJYOO, Huang Chen-Yi, Ilham Firdausi Putra, Imran Salam, Jared Nielsen, Jason Zaman, Jasper Vicenti, Jeff Daily, Jeff Poznanovic, Jens Elofsson, Jerry Shih, jerryyin, Jesper Dramsch, jim.meyer, Jongwon Lee, Jun Wan, Junyuan Xie, Kaixi Hou, kamalkraj, Kan Chen, Karthik Muthuraman, Keiji Ariyama, Kevin Rose, Kevin Wang, Koan-Sin Tan, kstuedem, Kwabena W. Agyeman, Lakshay Tokas, latyas, Leslie-Fang-Intel, Li, Guizi, Luciano Resende, Lukas Folle, Lukas Geiger, Mahmoud Abuzaina, Manuel Freiberger, Mark Ryan, Martin Mlostek, Masaki Kozuki, Matthew Bentham, Matthew Denton, mbhuiyan, mdfaijul, Muhwan Kim, Nagy Mostafa, nammbash, Nathan Luehr, Nathan Wells, Niranjan Hasabnis, Oleksii Volkovskyi, Olivier Moindrot, olramde, Ouyang Jin, OverLordGoldDragon, Pallavi G, Paul Andrey, Paul Wais, pkanwar23, Pooya Davoodi, Prabindh Sundareson, Rajeshwar Reddy T, Ralovich, Kristof, Refraction-Ray, Richard Barnes, richardbrks, Robert Herbig, Romeo Kienzler, Ryan Mccormick, saishruthi, Saket Khandelwal, Sami Kama, Sana Damani, Satoshi Tanaka, Sergey Mironov, Sergii Khomenko, Shahid, Shawn Presser, ShengYang1, Siddhartha Bagaria, Simon Plovyt, skeydan, srinivasan.narayanamoorthy, Stephen Mugisha, sunway513, Takeshi Watanabe, Taylor Jakobson, TengLu, TheMindVirus, ThisIsIsaac, Tim Gates, Timothy Liu, Tomer Gafner, Trent Lo, Trevor Hickey, Trevor Morris, vcarpani, Wei Wang, Wen-Heng (Jack) Chung, wenshuai, Wenshuai-Xiaomi, wenxizhu, william, William D. Irons, Xinan Jiang, Yannic, Yasir Modak, Yasuhiro Matsumoto, Yong Tang, Yongfeng Gu, Youwei Song, Zaccharie Ramzi, Zhang, Zhenyu Guo, 王振华 (Zhenhua Wang), 韩董, 이중건 Isaac Lee

2.0.2

Bug Fixes and Other Changes
* Updates `sqlite3` to `3.31.01` to handle [CVE-2019-19880](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19880), [CVE-2019-19244](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19244) and [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645)
* Updates `curl` to `7.69.1` to handle [CVE-2019-15601](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-15601)
* Updates `libjpeg-turbo` to `2.0.4` to handle [CVE-2018-19664](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-19664), [CVE-2018-20330](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-20330) and [CVE-2019-13960](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-13960)
* Updates Apache Spark to `2.4.5` to handle [CVE-2019-10099](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-10099), [CVE-2018-17190](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17190) and [CVE-2018-11770](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-11770)

2.0.1

Bug Fixes and Other Changes
* Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215))
* Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481)
* Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168)

2.0

* Easy model building with Keras and eager execution.
* Robust model deployment in production on any platform.
* Powerful experimentation for research.
* API simplification by reducing duplication and removing deprecated endpoints.

For details on best practices with 2.0, see [the Effective 2.0 guide](https://www.tensorflow.org/beta/guide/effective_tf2)


For information on upgrading your existing TensorFlow 1.x models, please refer to our [Upgrade](https://medium.com/tensorflow/upgrading-your-code-to-tensorflow-2-0-f72c3a4d83b5) and [Migration](https://www.tensorflow.org/beta/guide/migration_guide) guides. We have also released a collection of [tutorials and getting started guides](https://www.tensorflow.org/beta).

Highlights

*   TF 2.0 delivers Keras as the central high level API used to build and train
 models. Keras provides several model-building APIs such as Sequential,
 Functional, and Subclassing along with eager execution, for immediate
 iteration and intuitive debugging, and `tf.data`, for building scalable
 input pipelines. Checkout
 [guide](https://www.tensorflow.org/beta/guide/keras/overview) for additional
 details.
*   Distribution Strategy: TF 2.0 users will be able to use the
 [`tf.distribute.Strategy`](https://www.tensorflow.org/beta/guide/distribute_strategy)
 API to distribute training with minimal code changes, yielding great
 out-of-the-box performance. It supports distributed training with Keras
 model.fit, as well as with custom training loops. Multi-GPU support is
 available, along with experimental support for multi worker and Cloud TPUs.
 Check out the
 [guide](https://www.tensorflow.org/beta/guide/distribute_strategy) for more
 details.
*   Functions, not Sessions. The traditional declarative programming model of
 building a graph and executing it via a `tf.Session` is discouraged, and
 replaced with by writing regular Python functions. Using the `tf.function`
 decorator, such functions can be turned into graphs which can be executed
 remotely, serialized, and optimized for performance.
*   Unification of `tf.train.Optimizers` and `tf.keras.Optimizers`. Use
 `tf.keras.Optimizers` for TF2.0. `compute_gradients` is removed as public
 API, use `GradientTape` to compute gradients.
*   AutoGraph translates Python control flow into TensorFlow expressions,
 allowing users to write regular Python inside `tf.function`-decorated
 functions. AutoGraph is also applied in functions used with tf.data,
 tf.distribute and tf.keras APIs.
*   Unification of exchange formats to SavedModel. All TensorFlow ecosystem
 projects (TensorFlow Lite, TensorFlow JS, TensorFlow Serving, TensorFlow
 Hub) accept SavedModels. Model state should be saved to and restored from
 SavedModels.
*   API Changes: Many API symbols have been renamed or removed, and argument
 names have changed. Many of these changes are motivated by consistency and
 clarity. The 1.x API remains available in the compat.v1 module. A list of
 all symbol changes can be found
 [here](https://docs.google.com/spreadsheets/d/1FLFJLzg7WNP6JHODX5q8BDgptKafq_slHpnHVbJIteQ/editgid=0).
 *   API clean-up, included removing `tf.app`, `tf.flags`, and `tf.logging`
     in favor of [absl-py](https://github.com/abseil/abseil-py).
*   No more global variables with helper methods like
 `tf.global_variables_initializer` and `tf.get_global_step`.
*   Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`
 for enabling/disabling v2 control flow.
*   Enable v2 control flow as part of `tf.enable_v2_behavior()` and
 `TF2_BEHAVIOR=1`.
*   Fixes autocomplete for most TensorFlow API references by switching to use
 relative imports in API `__init__.py` files.
*   Auto Mixed-Precision graph optimizer simplifies converting models to
 `float16` for acceleration on Volta and Turing Tensor Cores. This feature
 can be enabled by wrapping an optimizer class with
 `tf.train.experimental.enable_mixed_precision_graph_rewrite()`.
*   Add environment variable `TF_CUDNN_DETERMINISTIC`. Setting to "true" or "1"
 forces the selection of deterministic cuDNN convolution and max-pooling
 algorithms. When this is enabled, the algorithm selection procedure itself
 is also deterministic.

Breaking Changes
* Many backwards incompatible API changes have been made to clean up the APIs and make them more consistent.
* Toolchains:
* TensorFlow 2.0.0 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow.
* Tensorflow code now produces 2 different pip packages: tensorflow_core containing all the code (in the future it will contain only the private implementation) and tensorflow which is a virtual pip package doing forwarding to tensorflow_core (and in the future will contain only the public API of tensorflow). We don't expect this to be breaking, unless you were importing directly from the implementation.
Removed the `freeze_graph` command line tool; `SavedModel` should be used in place of frozen graphs.

* `tf.contrib`:
* `tf.contrib` has been deprecated, and functionality has been either migrated to the core TensorFlow API, to an ecosystem project such as [tensorflow/addons](https://www.github.com/tensorflow/addons) or [tensorflow/io](https://www.github.com/tensorflow/io), or removed entirely.
* Remove `tf.contrib.timeseries` dependency on TF distributions.
* Replace contrib references with `tf.estimator.experimental.*` for apis in `early_stopping.py`.

* `tf.estimator`:
* Premade estimators in the tf.estimator.DNN/Linear/DNNLinearCombined family have been updated to use `tf.keras.optimizers` instead of the `tf.compat.v1.train.Optimizer`s. If you do not pass in an `optimizer=` arg or if you use a string, the premade estimator will use the Keras optimizer. This is checkpoint breaking, as the optimizers have separate variables. A checkpoint converter tool for converting optimizers is included with the release,  but if you want to avoid any change, switch to the v1 version of the estimator:  `tf.compat.v1.estimator.DNN/Linear/DNNLinearCombined*`.
* Default aggregation for canned Estimators is now `SUM_OVER_BATCH_SIZE`. To maintain previous default behavior, please pass `SUM` as the loss aggregation method.
* Canned Estimators don’t support `input_layer_partitioner` arg in the API. If you have this arg, you will have to switch to `tf.compat.v1 canned Estimators`.
* `Estimator.export_savedmodel` has been renamed to `export_saved_model`.
* When saving to SavedModel, Estimators will strip default op attributes. This is almost always the correct behavior, as it is more forwards compatible, but if you require that default attributes to be saved with the model, please use `tf.compat.v1.Estimator`.
* Feature Columns have been upgraded to be more Eager-friendly and to work with Keras. As a result, `tf.feature_column.input_layer` has been deprecated in favor of `tf.keras.layers.DenseFeatures`. v1 feature columns have direct analogues in v2 except for `shared_embedding_columns`, which are not cross-compatible with v1 and v2. Use `tf.feature_column.shared_embeddings` instead.

* `tf.keras`:
* `OMP_NUM_THREADS` is no longer used by the default Keras config.  To configure the number of threads, use `tf.config.threading` APIs.
* `tf.keras.model.save_model` and `model.save` now defaults to saving a TensorFlow SavedModel. HDF5 files are still supported.
* Deprecated `tf.keras.experimental.export_saved_model` and `tf.keras.experimental.function`. Please use `tf.keras.models.save_model(..., save_format='tf')` and `tf.keras.models.load_model` instead.
* Layers now default to float32, and automatically cast their inputs to the layer's dtype. If you had a model that used float64, it will probably silently use float32 in TensorFlow 2, and a warning will be issued that starts with `Layer <layer-name>` is casting an input tensor from dtype float64 to the layer's dtype of float32. To fix, either set the default dtype to float64 with `tf.keras.backend.set_floatx('float64')`, or pass `dtype='float64'` to each of the Layer constructors. See `tf.keras.layers.Layer` for more information.

* `tf.lite`:
* Removed `lite.OpHint`, `lite.experimental`, and `lite.constant` from 2.0 API.
* Tensors are no longer hashable, but instead compare element-wise with `==` and `!=`. Use `tf.compat.v1.disable_tensor_equality()` to return to the previous behavior.
* Performing equality operations on Tensors or Variables with incompatible shapes an exception is no longer thrown. Instead `__eq__` returns False and `__ne__` returns True.
* Removed `tf.string_split` from v2 API.
* Deprecated the use of `constraint=` and `.constraint` with ResourceVariable.
* Add `UnifiedGRU` as the new GRU implementation for tf2.0. Change the default recurrent activation function for GRU from `hard_sigmoid` to `sigmoid`, and `reset_after` to True in 2.0. Historically recurrent activation is `hard_sigmoid` since it is fast than 'sigmoid'. With new unified backend between CPU and GPU mode, since the CuDNN kernel is using sigmoid, we change the default for CPU mode to sigmoid as well. With that, the default GRU will be compatible with both CPU and GPU kernel. This will enable user with GPU to use CuDNN kernel by default and get a 10x performance boost in training. Note that this is checkpoint breaking change. If user want to use their 1.x pre-trained checkpoint, please construct the layer with GRU(recurrent_activation='hard_sigmoid', reset_after=False) to fallback to 1.x behavior.
* `CUDNN_INSTALL_PATH`, `TENSORRT_INSTALL_PATH`, `NCCL_INSTALL_PATH`, `NCCL_HDR_PATH` are deprecated. Use `TF_CUDA_PATHS` instead which supports a comma-separated list of base paths that are searched to find CUDA libraries and headers.

Refer to our [public project status tracker](https://github.com/orgs/tensorflow/projects/4) and [issues tagged with `2.0`](https://github.com/tensorflow/tensorflow/issues?q=is%3Aopen+is%3Aissue+label%3A2.0) on GitHub for insight into recent issues and development progress.

If you experience any snags when using TF 2.0, please let us know at the [TF 2.0 Testing User Group](https://groups.google.com/a/tensorflow.org/forum/?utm_medium=email&utm_source=footer!forum/testing). We have a support mailing list as well as weekly testing meetings, and would love to hear your migration feedback and questions.


Bug Fixes and Other Changes

*   `tf.contrib`:

 *   Expose `tf.contrib.proto.*` ops in `tf.io` (they will exist in TF2)

*   `tf.data`:

 *   Add support for TensorArrays to `tf.data Dataset`.
 *   Integrate Ragged Tensors with `tf.data`.
 *   All core and experimental tf.data transformations that input
     user-defined functions can span multiple devices now.
 *   Extending the TF 2.0 support for `shuffle(...,
     reshuffle_each_iteration=True)` and `cache()` to work across different
     Python iterators for the same dataset.
 *   Removing the `experimental_numa_aware` option from `tf.data.Options`.
 *   Add `num_parallel_reads` and passing in a Dataset containing filenames
     into `TextLineDataset` and `FixedLengthRecordDataset`.
 *   Add support for defaulting the value of `cycle_length` argument of
     `tf.data.Dataset.interleave` to the number of schedulable CPU cores.
 *   Promoting `tf.data.experimental.enumerate_dataset` to core as
     `tf.data.Dataset.enumerate`.
 *   Promoting `tf.data.experimental.unbatch` to core as
     `tf.data.Dataset.unbatch`.
 *   Adds option for introducing slack in the pipeline to reduce CPU
     contention, via `tf.data.Options().experimental_slack = True`
 *   Added experimental support for parallel batching to `batch()` and
     `padded_batch()`. This functionality can be enabled through
     `tf.data.Options()`.
 *   Support cancellation of long-running `reduce`.
 *   Now we use `dataset` node name as prefix instead of the op name, to
     identify the component correctly in metrics, for pipelines with repeated
     components.
 *   Improve the performance of datasets using `from_tensors()`.
 *   Promoting `unbatch` from experimental to core API.
 *   Adding support for datasets as inputs to `from_tensors` and
     `from_tensor_slices` and batching and unbatching of nested datasets.

*   `tf.distribute`:

 *   Enable `tf.distribute.experimental.MultiWorkerMirroredStrategy` working
     in eager mode.
 *   Callbacks are supported in `MultiWorkerMirroredStrategy`.
 *   Disable `run_eagerly` and distribution strategy if there are symbolic
     tensors added to the model using `add_metric` or `add_loss`.
 *   Loss and gradients should now more reliably be correctly scaled w.r.t.
     the global batch size when using a `tf.distribute.Strategy`.
 *   Set default loss reduction as `AUTO` for improving reliability of loss
     scaling with distribution strategy and custom training loops. `AUTO`
     indicates that the reduction option will be determined by the usage
     context. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`.
     When used in distribution strategy scope, outside of built-in training
     loops such as `tf.keras` `compile` and `fit`, we expect reduction value
     to be 'None' or 'SUM'. Using other values will raise an error.
 *   Support for multi-host `ncclAllReduce` in Distribution Strategy.

*   `tf.estimator`:

 *   Replace `tf.contrib.estimator.add_metrics` with
     `tf.estimator.add_metrics`
 *   Use `tf.compat.v1.estimator.inputs` instead of `tf.estimator.inputs`
 *   Replace contrib references with `tf.estimator.experimental.*` for apis
     in early_s in Estimator
 *   Canned Estimators will now use keras optimizers by default. An error
     will be raised if tf.train.Optimizers are used, and you will have to
     switch to tf.keras.optimizers or tf.compat.v1 canned Estimators.
 *   A checkpoint converter for canned Estimators has been provided to
     transition canned Estimators that are warm started from
     `tf.train.Optimizers` to `tf.keras.optimizers`.
 *   Losses are scaled in canned estimator v2 and not in the optimizers
     anymore. If you are using Estimator + distribution strategy + optimikzer
     v1 then the behavior does not change. This implies that if you are using
     custom estimator with optimizer v2, you have to scale losses. We have
     new utilities to help scale losses `tf.nn.compute_average_loss`,
     `tf.nn.scale_regularization_loss`.

*   `tf.keras`:

 *   Premade models (including Linear and WideDeep) have been introduced for
     the purpose of replacing Premade estimators.
 *   Model saving changes
 *   `model.save` and `tf.saved_model.save` may now save to the TensorFlow
     SavedModel format. The model can be restored using
     `tf.keras.models.load_model`. HDF5 files are still supported, and may be
     used by specifying `save_format="h5"` when saving.
 *   Raw TensorFlow functions can now be used in conjunction with the Keras
     Functional API during model creation. This obviates the need for users
     to create Lambda layers in most cases when using the Functional API.
     Like Lambda layers, TensorFlow functions that result in Variable
     creation or assign ops are not supported.
 *   Add support for passing list of lists to the `metrics` argument in Keras
     `compile`.
 *   Add `tf.keras.layers.AbstractRNNCell` as the preferred implementation
     for RNN cells in TF v2. User can use it to implement RNN cells with
     custom behavior.
 *   Keras training and validation curves are shown on the same plot when
     using the TensorBoard callback.
 *   Switched Keras `fit/evaluate/predict` execution to use only a single
     unified path by default unless eager execution has been explicitly
     disabled, regardless of input type. This unified path places an
     eager-friendly training step inside of a `tf.function`. With this
 *   All input types are converted to `Dataset`.
 *   The path assumes there is always a distribution strategy. when
     distribution strategy is not specified the path uses a no-op
     distribution strategy.
 *   The training step is wrapped in `tf.function` unless `run_eagerly=True`
     is set in compile. The single path execution code does not yet support
     all use cases. We fallback to the existing v1 execution paths if your
     model contains the following:
     1.  `sample_weight_mode` in compile
     2.  `weighted_metrics` in compile
     3.  v1 optimizer
     4.  target tensors in compile If you are experiencing any issues because
         of this change, please inform us (file an issue) about your use case
         and you can unblock yourself by setting
         `experimental_run_tf_function=False` in compile meanwhile. We have
         seen couple of use cases where the model usage pattern is not as
         expected and would not work with this change.
 *   output tensors of one layer is used in the constructor of another.
 *   symbolic tensors outside the scope of the model are used in custom loss
     functions. The flag can be disabled for these cases and ideally the
     usage pattern will need to be fixed.
 *   Mark Keras `set_session` as `compat.v1` only.
 *   `tf.keras.estimator.model_to_estimator` now supports exporting to
     `tf.train.Checkpoint format`, which allows the saved checkpoints to be
     compatible with `model.load_weights`.
 *   `keras.backend.resize_images` (and consequently,
     `keras.layers.Upsampling2D`) behavior has changed, a bug in the resizing
     implementation was fixed.
 *   Add an `implementation=3` mode for `tf.keras.layers.LocallyConnected2D`
     and `tf.keras.layers.LocallyConnected1D` layers using `tf.SparseTensor`
     to store weights, allowing a dramatic speedup for large sparse models.
 *   Raise error if `batch_size` argument is used when input is
     dataset/generator/keras sequence.
 *   Update TF 2.0 `keras.backend.name_scope` to use TF 2.0 `name_scope`.
 *   Add v2 module aliases for losses, metrics, initializers and optimizers:
     `tf.losses = tf.keras.losses` & `tf.metrics = tf.keras.metrics` &
     `tf.initializers = tf.keras.initializers` & `tf.optimizers =
     tf.keras.optimizers`.
 *   Updates binary cross entropy logic in Keras when input is probabilities.
     Instead of converting probabilities to logits, we are using the cross
     entropy formula for probabilities.
 *   Added public APIs for `cumsum` and `cumprod` keras backend functions.
 *   Add support for temporal sample weight mode in subclassed models.
 *   Raise `ValueError` if an integer is passed to the training APIs.
 *   Added fault-tolerance support for training Keras model via `model.fit()`
     with `MultiWorkerMirroredStrategy`, tutorial available.
 *   Custom Callback tutorial is now available.
 *   To train with `tf.distribute`, Keras API is recommended over estimator.
 *   `steps_per_epoch` and `steps` arguments are supported with numpy arrays.
 *   New error message when unexpected keys are used in
     sample_weight/class_weight dictionaries
 *   Losses are scaled in Keras compile/fit and not in the optimizers
     anymore. If you are using custom training loop, we have new utilities to
     help scale losses `tf.nn.compute_average_loss`,
     `tf.nn.scale_regularization_loss`.
 *   `Layer` apply and add_variable APIs are deprecated.
 *   Added support for channels first data format in cross entropy losses
     with logits and support for tensors with unknown ranks.
 *   Error messages will be raised if `add_update`, `add_metric`, `add_loss`,
     activity regularizers are used inside of a control flow branch.
 *   New loss reduction types:
 *   `AUTO`: Indicates that the reduction option will be determined by the
     usage context. For almost all cases this defaults to
     `SUM_OVER_BATCH_SIZE`. When used with `tf.distribute.Strategy`, outside
     of built-in training loops such as `tf.keras` `compile` and `fit`, we
     expect reduction value to be `SUM` or `NONE`. Using `AUTO` in that case
     will raise an error.
 *   `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
     specified by loss function). When this reduction type used with built-in
     Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
     passed to the optimizer but the reported loss will be a scalar value.
 *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
     `SUM` divided by number of elements in losses. This reduction type is
     not supported when used with `tf.distribute.Strategy` outside of
     built-in training loops like `tf.keras` `compile`/`fit`.
 *   Wraps losses passed to the `compile` API (strings and v1 losses) which
     are not instances of v2 `Loss` class in `LossWrapper` class. => All
     losses will now use `SUM_OVER_BATCH_SIZE` reduction as default.
 *   `model.add_loss(symbolic_tensor)` should work in ambient eager.
 *   Update metric name to always reflect what the user has given in compile.
     Affects following cases
 *   When name is given as 'accuracy'/'crossentropy'
 *   When an aliased function name is used eg. 'mse'
 *   Removing the `weighted` prefix from weighted metric names.
 *   Allow non-Tensors through v2 losses.
 *   Add v2 sparse categorical crossentropy metric.
 *   Add v2 APIs for `AUCCurve` and `AUCSummationMethod` enums.
 *   `add_update` can now be passed a zero-arg callable in order to support
     turning off the update when setting `trainable=False` on a Layer of a
     Model compiled with `run_eagerly=True`.
 *   Standardize the LayerNormalization API by replacing the args `norm_axis`
     and `params_axis` with `axis`.
 *   Fixed critical bugs that help with DenseFeatures usability in TF2

*   `tf.lite`:

 *   Added evaluation script for `COCO` minival
 *   Add delegate support for `QUANTIZE`.
 *   Add `GATHER` support to NN API delegate.
 *   Added support for TFLiteConverter Python API in 2.0. Contains functions
     from_saved_model, from_keras_file, and from_concrete_functions.
 *   Add `EXPAND_DIMS` support to NN API delegate TEST.
 *   Add `narrow_range` attribute to QuantizeAndDequantizeV2 and V3.
 *   Added support for `tflite_convert` command line tool in 2.0.
 *   Post-training quantization tool supports quantizing weights shared by
     multiple operations. The models made with versions of this tool will use
     INT8 types for weights and will only be executable interpreters from
     this version onwards.
 *   Post-training quantization tool supports fp16 weights and GPU delegate
     acceleration for fp16.
 *   Add delegate support for `QUANTIZED_16BIT_LSTM`.
 *   Extracts `NNAPIDelegateKernel` from nnapi_delegate.cc

*   TensorRT

 *   Add TensorFlow 2.0-compatible `TrtGraphConverterV2` API for TensorRT
     conversion. TensorRT initialization arguments are now passed wrapped in
     a named-tuple, `TrtConversionParams`, rather than as separate arguments
     as in `TrtGraphConverter`.
 *   Changed API to optimize TensorRT enginges during graph optimization.
     This is now done by calling `converter.build()` where previously
     `is_dynamic_op=False` would be set.
 *   `converter.convert()` no longer returns a `tf.function`. Now the
     function must be accessed from the saved model.
 *   The `converter.calibrate()` method has been removed. To trigger
     calibration, a `calibration_input_fn` should be provided to
     `converter.convert()`.

*   Other:

 *   Fix accidental quadratic graph construction cost in graph-mode
     `tf.gradients()`.
 *   ResourceVariable's gather op supports batch dimensions.
 *   ResourceVariable support for `gather_nd`.
 *   `ResourceVariable` and `Variable` no longer accepts `constraint` in the
     constructor, nor expose it as a property.
 *   Added gradient for `SparseToDense` op.
 *   Expose a flag that allows the number of threads to vary across Python
     benchmarks.
 *   `image.resize` in 2.0 now supports gradients for the new resize kernels.
 *   `image.resize` now considers proper pixel centers and has new kernels
     (incl. anti-aliasing).
 *   Renamed `tf.image` functions to remove duplicate "image" where it is
     redundant.
 *   Variadic reduce is supported on CPU Variadic reduce is supported on CPU
 *   Remove unused `StringViewVariantWrapper`.
 *   Delete unused `Fingerprint64Map` op registration
 *   Add broadcasting support to `tf.matmul`.
 *   Add C++ Gradient for `BatchMatMulV2`.
 *   Add `tf.math.cumulative_logsumexp` operation.
 *   Add ellipsis (...) support for `tf.einsum()`.
 *   Add expand_composites argument to all `nest.*` methods.
 *   Added `strings.byte_split`.
 *   Add a new "result_type" parameter to `tf.strings.split`.
 *   Add name argument to `tf.string_split` and `tf.strings_split`.
 *   Extend `tf.strings.split` to support inputs with any rank.
 *   Added `tf.random.binomial`.
 *   Added `key` and `skip` methods to `random.experimental.Generator`.
 *   Extend `tf.function` with basic support for CompositeTensors arguments
     (such as `SparseTensor` and `RaggedTensor`).
 *   `parallel_for.pfor`: add converters for Softmax, LogSoftmax, IsNaN, All,
     Any, and MatrixSetDiag.
 *   `parallel_for`: add converters for LowerTriangularSolve and Cholesky.
 *   `parallel_for`: add converters for `LogMatrixDeterminant` and
     `MatrixBandPart`.
 *   `parallel_for`: Add converter for `MatrixDiag`.
 *   `parallel_for`: Add converters for `OneHot`, `LowerBound`, `UpperBound`.
 *   `parallel_for`: add converter for `BroadcastTo`.
 *   Add `pfor` converter for `Squeeze`.
 *   Add `RaggedTensor.placeholder()`.
 *   Add ragged tensor support to `tf.squeeze`.
 *   Update RaggedTensors to support int32 row_splits.
 *   Allow `LinearOperator.solve` to take a `LinearOperator`.
 *   Allow all dtypes for `LinearOperatorCirculant`.
 *   Introduce MaxParallelism method
 *   Add `LinearOperatorHouseholder`.
 *   Adds Philox support to new stateful RNG's XLA path.
 *   Added `TensorSpec` support for CompositeTensors.
 *   Added `tf.linalg.tridiagonal_solve` op.
 *   Added partial_pivoting input parameter to `tf.linalg.tridiagonal_solve`.
 *   Added gradient to `tf.linalg.tridiagonal_solve`.
 *   Added `tf.linalg.tridiagonal_mul op`.
 *   Added GPU implementation of `tf.linalg.tridiagonal_matmul`.
 *   Added `LinearOperatorToeplitz`.
 *   Upgraded LIBXSMM to version 1.11.
 *   Uniform processing of quantized embeddings by Gather and EmbeddingLookup
     Ops.
 *   Correct a misstatement in the documentation of the sparse softmax cross
     entropy logit parameter.
 *   Add `tf.ragged.boolean_mask`.
 *   `tf.switch_case` added, which selects a branch_fn based on a
     branch_index.
 *   The C++ kernel of gather op supports batch dimensions.
 *   Fixed default value and documentation for `trainable` arg of
     tf.Variable.
 *   `EagerTensor` now supports numpy buffer interface for tensors.
 *   This change bumps the version number of the `FullyConnected` Op to 5.
 *   Added new op: `tf.strings.unsorted_segment_join`.
 *   Added HW acceleration support for `topK_v2`.
 *   CloudBigtable version updated to v0.10.0 BEGIN_PUBLIC CloudBigtable
     version updated to v0.10.0.
 *   Expose `Head` as public API.
 *   Added `tf.sparse.from_dense` utility function.
 *   Improved ragged tensor support in `TensorFlowTestCase`.
 *   Added a function `nested_value_rowids` for ragged tensors.
 *   Added `tf.ragged.stack`.
 *   Makes the a-normal form transformation in Pyct configurable as to which
     nodes are converted to variables and which are not.
 *   `ResizeInputTensor` now works for all delegates.
 *   `tf.cond` emits a StatelessIf op if the branch functions are stateless
     and do not touch any resources.
 *   Add support of local soft device placement for eager op.
 *   Pass partial_pivoting to the `_TridiagonalSolveGrad`.
 *   Add HW acceleration support for `LogSoftMax`.
 *   Add guard to avoid acceleration of L2 Normalization with input rank != 4
 *   Fix memory allocation problem when calling `AddNewInputConstantTensor`.
 *   Delegate application failure leaves interpreter in valid state
 *   `tf.while_loop` emits a StatelessWhile op if the cond and body functions
     are stateless and do not touch any resources.
 *   `tf.cond`, `tf.while` and if and while in AutoGraph now accept a
     nonscalar predicate if has a single element. This does not affect non-V2
     control flow.
 *   Fix potential security vulnerability where decoding variant tensors from
     proto could result in heap out of bounds memory access.
 *   Only create a GCS directory object if the object does not already exist.
 *   Introduce `dynamic` constructor argument in Layer and Model, which
     should be set to `True` when using imperative control flow in the `call`
     method.
 *   Begin adding Go wrapper for C Eager API.
 *   XLA HLO graphs can be inspected with interactive_graphviz tool now.
 *   Add dataset ops to the graph (or create kernels in Eager execution)
     during the python Dataset object creation instead doing it during
     Iterator creation time.
 *   Add `batch_dims` argument to `tf.gather`.
 *   The behavior of `tf.gather` is now correct when `axis=None` and
     `batch_dims<0`.
 *   Update docstring for gather to properly describe the non-empty
     `batch_dims` ca

[coveralls]

Coverage remained the same at 95.533% when pulling 9d33c1c on pyup-update-tensorflow-1.13.1-to-2.3.0 into 247410c on master.