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android / platform / external / tensorflow / 71a74e6882e
commit71a74e6882e89221de9dba99a1a4378a47034ff0[log] [tgz]
authorGiorgio Arena <giorgio.arena@arm.com>Thu Jun 25 15:56:09 2020 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Thu Jun 25 16:02:10 2020 -0700
treed56c5567d186863dc8187f1fc29466d55df5b845
parent7e798a2afeab42ac28380072d7a7a422a53a27d6 [diff]
PR #35000: Add tests in TFLite micro for Float/Uint8/Int8 Tanh activation

Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/35000

Copybara import of the project:

--
de4b77729bb86450608f8536c56a53d2fe214e9f by Giorgio Arena <giorgio.arena@arm.com>:

Add tests in TFLite micro for Float/Uint8/Int8 Tanh activation

--
c7264109a6bd17a89bd28607dac22d8a9caa8880 by Giorgio Arena <giorgio.arena@arm.com>:

Remove dynamic allocations and use new tensor quantization functions

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59c5ff87faa9aea204774cd7050c42248aa50a74 by Giorgio Arena <giorgio.arena@arm.com>:

Move reference/optimized tanh activation functions to their own header

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7aa85f0da1c49a9183b1c10ec7aff6925535303d by Giorgio Arena <giorgio.arena@arm.com>:

Use const int[] for shapes in TanH test and remove elementwise op

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24653f7127bb3142002696142be66162c46c2c5c by Giorgio Arena <giorgio.arena@arm.com>:

Fix build failures for tanh op in activation_tests

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6407b349e691ea50c6c6e04b64551e73f16bf201 by Giorgio Arena <giorgio.arena@arm.com>:

Fix build failures and use TF_LITE_KERNEL_LOG

COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/35000 from giorgio-arenarm:tanh_s8_tests 6407b349e691ea50c6c6e04b64551e73f16bf201
PiperOrigin-RevId: 318365260
Change-Id: I6bed099dfab3112363f80bc4f3724b39366f4e3a
4 files changed
tree: d56c5567d186863dc8187f1fc29466d55df5b845
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .pylintrc ⇨ tensorflow/tools/ci_build/pylintrc
  6. .bazelrc
  7. .bazelversion
  8. .gitignore
  9. ACKNOWLEDGMENTS
  10. ADOPTERS.md
  11. arm_compiler.BUILD
  12. AUTHORS
  13. BUILD
  14. CODE_OF_CONDUCT.md
  15. CODEOWNERS
  16. configure
  17. configure.cmd
  18. configure.py
  19. CONTRIBUTING.md
  20. ISSUE_TEMPLATE.md
  21. ISSUES.md
  22. LICENSE
  23. models.BUILD
  24. README.md
  25. RELEASE.md
  26. SECURITY.md
  27. WORKSPACE
README.md

Python PyPI

Documentation
Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development:

Fuzzing Status CII Best Practices Contributor Covenant

Continuous build status

Official Builds

Build TypeStatusArtifacts
Linux CPUStatusPyPI
Linux GPUStatusPyPI
Linux XLAStatusTBA
macOSStatusPyPI
Windows CPUStatusPyPI
Windows GPUStatusPyPI
AndroidStatusDownload
Raspberry Pi 0 and 1StatusPy3
Raspberry Pi 2 and 3StatusPy3
Libtensorflow MacOS CPUStatusGCS
Libtensorflow Linux CPUStatusGCS
Libtensorflow Linux GPUStatusGCS
Libtensorflow Windows CPUStatusGCS
Libtensorflow Windows GPUStatusGCS

Community Supported Builds

Build TypeStatusArtifacts
Linux AMD ROCm GPU NightlyBuild StatusNightly
Linux AMD ROCm GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux s390x NightlyBuild StatusNightly
Linux s390x CPU Stable ReleaseBuild StatusRelease
Linux ppc64le CPU NightlyBuild StatusNightly
Linux ppc64le CPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux ppc64le GPU NightlyBuild StatusNightly
Linux ppc64le GPU Stable ReleaseBuild StatusRelease 1.15 / 2.x
Linux CPU with Intel® MKL-DNN NightlyBuild StatusNightly
Linux CPU with Intel® MKL-DNN Stable ReleaseBuild StatusRelease 1.15 / 2.x
Red Hat® Enterprise Linux® 7.6 CPU & GPU
Python 2.7, 3.6		Build Status1.13.1 PyPI

Resources

Learn more about the TensorFlow community and how to contribute.

License

Apache License 2.0