Bump tensorflow from 1.15.0 to 2.0.0

Bumps tensorflow from 1.15.0 to 2.0.0.

Release notes

*Sourced from [tensorflow's releases](https://github.com/tensorflow/tensorflow/releases).* > ## TensorFlow 2.0.0 > # Release 2.0.0 > > ## Major Features and Improvements > > TensorFlow 2.0 focuses on **simplicity** and **ease of use**, featuring updates like: > > * 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/guide/migrate) 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/edit#gid=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`. > ... (truncated)

Changelog

*Sourced from [tensorflow's changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md).* > # Release 1.15.0 > This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year. > > ## Major Features and Improvements > * As [announced](https://groups.google.com/a/tensorflow.org/forum/#!topic/developers/iRCt5m4qUz0), `tensorflow` pip package will by default include GPU support (same as `tensorflow-gpu` now) for the platforms we currently have GPU support (Linux and Windows). It will work on machines with and without Nvidia GPUs. `tensorflow-gpu` will still be available, and CPU-only packages can be downloaded at `tensorflow-cpu` for users who are concerned about package size. > * TensorFlow 1.15 contains a complete implementation of the 2.0 API in its `compat.v2` module. It contains a copy of the 1.15 main module (without `contrib`) in the `compat.v1` module. TensorFlow 1.15 is able to emulate 2.0 behavior using the `enable_v2_behavior()` function. > This enables writing forward compatible code: by explicitly importing either `tensorflow.compat.v1` or `tensorflow.compat.v2`, you can ensure that your code works without modifications against an installation of 1.15 or 2.0. > * EagerTensor now supports numpy buffer interface for tensors. > * 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`. > * 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. > * Adds `enable_tensor_equality()`, which switches the behavior such that: > * Tensors are no longer hashable. > * Tensors can be compared with `==` and `!=`, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0. > > ## Breaking Changes > * 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. > * TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow. > * Deprecated the use of `constraint=` and `.constraint` with ResourceVariable. > * `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. > * `keras.backend.resize_images` (and consequently, `keras.layers.Upsampling2D`) behavior has changed, a bug in the resizing implementation was fixed. > * 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 TensorFlow2, 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. > * Some `tf.assert_*` methods now raise assertions at operation creation time (i.e. when this Python line executes) if the input tensors' values are known at that time, not during the session.run(). 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). > > ## Bug Fixes and Other Changes > * `tf.estimator`: > * `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`. > * Fix tests in canned estimators. > * Expose Head as public API. > * Fixes critical bugs that help with `DenseFeatures` usability in TF2 > * `tf.data`: > * 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.keras`: > * `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`. > * Saving a Keras Model using `tf.saved_model.save` now saves the list of variables, trainable variables, regularization losses, and the call function. > * 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. > * 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. > * Enable the Keras compile API `experimental_run_tf_function` flag by default. This flag enables single training/eval/predict execution path. With this 1. All input types are converted to `Dataset`. 2. When distribution strategy is not specified this goes through the no-op distribution strategy path. 3. Execution is wrapped in tf.function unless `run_eagerly=True` is set in compile. > * Raise error if `batch_size` argument is used when input is dataset/generator/keras sequence. > * `tf.lite` > * Add `GATHER` support to NN API delegate. > * tflite object detection script has a debug mode. > * Add delegate support for `QUANTIZE`. > * Added evaluation script for COCO minival. > * Add delegate support for `QUANTIZED_16BIT_LSTM`. > * Converts hardswish subgraphs into atomic ops. > * Add support for defaulting the value of `cycle_length` argument of `tf.data.Dataset.interleave` to the number of schedulable CPU cores. > ... (truncated)

Commits

- [`64c3d38`](https://github.com/tensorflow/tensorflow/commit/64c3d382cadf7bbe8e7e99884bede8284ff67f56) Update RELEASE.md - [`2845767`](https://github.com/tensorflow/tensorflow/commit/2845767d913eb2e970c3039749f7333ab2fdebc0) Update RELEASE.md - [`3d230aa`](https://github.com/tensorflow/tensorflow/commit/3d230aaa1f5021c83143b8c6be8f49678c8a77db) Update release notes for tensorrt and mixed precision - [`b1c5361`](https://github.com/tensorflow/tensorflow/commit/b1c53619cf1709249df17bf0faf70a584a940885) Update RELEASE.md - [`5105437`](https://github.com/tensorflow/tensorflow/commit/51054374eaa2f478ddf17a2ed901daf4c65b1178) Update RELEASE.md - [`cf6180b`](https://github.com/tensorflow/tensorflow/commit/cf6180b8415870924cb278502785cc19d26ee7f4) Update RELEASE.md - [`ec8d660`](https://github.com/tensorflow/tensorflow/commit/ec8d660892eedba0f8cd5eb414769aab5dd95c77) Release Notes for 2.0.0-rc0 - [`ac24e9e`](https://github.com/tensorflow/tensorflow/commit/ac24e9eb3a369b9f09a10415ca06ecb1ac97d9fe) Merge pull request [#32861](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/32861) from guptapriya/cherrypicks_5NZHH - [`23a9413`](https://github.com/tensorflow/tensorflow/commit/23a94133f5033ee156c5e1cc58a6cb54ad1e8a6e) Mark tf.keras.utils.multi_gpu_model as deprecated. - [`1f372a0`](https://github.com/tensorflow/tensorflow/commit/1f372a0968f9f75d3ca54d0c3d2392c7c1eb316b) Merge pull request [#32742](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/32742) from rmlarsen/cherrypicks_BX1WK - Additional commits viewable in [compare view](https://github.com/tensorflow/tensorflow/compare/v1.15.0...v2.0.0)

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot ignore this [patch|minor|major] version` will close this PR and stop Dependabot creating any more for this minor/major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) - `@dependabot use these labels` will set the current labels as the default for future PRs for this repo and language - `@dependabot use these reviewers` will set the current reviewers as the default for future PRs for this repo and language - `@dependabot use these assignees` will set the current assignees as the default for future PRs for this repo and language - `@dependabot use this milestone` will set the current milestone as the default for future PRs for this repo and language You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/sidhomj/DeepTCR/network/alerts).

sidhomj / DeepTCR

Bump tensorflow from 1.15.0 to 2.0.0 #24