Add a MicroPrintf function that is independant of the ErrorReporter.

Additionally,
  * remove the global error reporter from micro_test.h
  * change all the kernel tests to make use of MicroPrintf
  * add a GetMicroErrorReporter() function that returns a pointer to a
    singleton MicroErrorReporter object.
      - This enables the current change to not spread beyond the tests.
      - Even if we move large parts of the TFLM code to make use
        MicroPrintf (in favor of error_reporter), there is still going to
        be shared TfLite/TFLM code that will need an error_reporter.

Next steps, if we want to continue down this path
  * remove the error_reporter from the TFLM functions and class
    implementations and instead use either MicroPrintf or
    GetMicroErrorReporter()
  * Add new APIs that do not have error_reporter to the TFLM classes and
    functions.
  * Ask users to switch to the new error_reporter-free APIs and
    depreacte the APIs that do make use of the error_reporter.
  * Remove the error_reporter APIs completely.

Prior to this change, we would have to use the ErrorReporter interface
for all the logging.

This was problematic on a few fronts:
 * The name ErrorReporter was often misleading since sometimes we just
   want to log, even when there isn't an error.
 * For even the simplest logging, we need to have access to an
   ErrorReporter object which means that pointers to an ErrorReporter
   are part of most classes in TFLM.

With this change, we can simply call MicroPrintf(), and it can be a no-op
if binary size is important.

If we find this approach useful, we can consider incrementally reducing
the usage of ErrorReporter from TFLM.

Progress towards http://b/158205789

starting to address review comments.

re-do micro_test.h
66 files changed
tree: 3729abf695c5319eff5126b0a375412de33fb37b
  1. .github/
  2. tensorflow/
  3. third_party/
  4. tools/
  5. .bazelrc
  6. .bazelversion
  7. .gitignore
  8. ACKNOWLEDGMENTS
  9. arm_compiler.BUILD
  10. AUTHORS
  11. BUILD
  12. CODE_OF_CONDUCT.md
  13. CODEOWNERS
  14. configure
  15. configure.cmd
  16. configure.py
  17. CONTRIBUTING.md
  18. ISSUE_TEMPLATE.md
  19. LICENSE
  20. models.BUILD
  21. README.md
  22. RELEASE.md
  23. SECURITY.md
  24. 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:

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