Bump tensorflow from 1.15.0 to 2.0.0 in /docs

[dependabot]

Bumps tensorflow from 1.15.0 to 2.0.0.

Release notes

Sourced from tensorflow's 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

For information on upgrading your existing TensorFlow 1.x models, please refer to our Upgrade and Migration guides. We have also released a collection of tutorials and getting started guides.

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 for additional details.
• Distribution Strategy: TF 2.0 users will be able to use the tf.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 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.
• API clean-up, included removing tf.app, tf.flags, and tf.logging in favor of absl-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 or 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.Optimizers. 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.

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, 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 Update RELEASE.md
• 2845767 Update RELEASE.md
• 3d230aa Update release notes for tensorrt and mixed precision
• b1c5361 Update RELEASE.md
• 5105437 Update RELEASE.md
• cf6180b Update RELEASE.md
• ec8d660 Release Notes for 2.0.0-rc0
• ac24e9e Merge pull request #32861 from guptapriya/cherrypicks_5NZHH
• 23a9413 Mark tf.keras.utils.multi_gpu_model as deprecated.
• 1f372a0 Merge pull request #32742 from rmlarsen/cherrypicks_BX1WK
• Additional commits viewable in compare view

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.

[dependabot]

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

[dependabot]

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

[dependabot]

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

[dependabot]

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.

[dependabot]

Dependabot tried to update this pull request, but something went wrong. We're looking into it, but in the meantime you can retry the update by commenting @dependabot rebase.