Adding a tutorial on CyclicalLearningRate

[sayakpaul]

Description

Brief Description of the PR:

This PR adds a tutorial on CyclicalLearningRate.

Fixes # (issue)

Type of change

• Bug fix
• New Tutorial
• Updated or additional documentation
• Additional Testing
• New Activation and the changes conform to the activation contribution guidelines
• New Callback and the changes conform to the callback contribution guidelines
• New Image addition and the changes conform to the image op contribution guidelines
• New Layer and the changes conform to the layer contribution guidelines
• New Loss and the changes conform to the loss contribution guidelines
• New Metric and the changes conform to the metric contribution guidelines
• New Optimizer and the changes conform to the optimizer contribution guidelines
• New RNN Cell and the changes conform to the rnn contribution guidelines
• New Seq2seq addition and the changes conform to the seq2seq contribution guidelines
• New Text addition and the changes conform to the text op contribution guidelines

Checklist:

• I've properly formatted my code according to the guidelines
  • By running Black + Flake8
  • By running pre-commit hooks
• This PR addresses an already submitted issue for TensorFlow Addons
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works
• This PR contains modifications to C++ custom-ops

How Has This Been Tested?

If you're adding a bugfix or new feature please describe the tests that you ran to verify your changes:
*

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[sayakpaul]

Regarding code-styling, I have tried adhering to what's instructed here.

[sayakpaul]

@seanpmorgan do you mind taking a look when you get a moment? Many thanks.

[sayakpaul]

I think I have added the right links but the logs do not suggest so:

Anything I might have missed?

[seanpmorgan]

@sayakpaul hmmm I don't really see the issues with the links either. Can you try running this to pass the second test and perhaps something with spacing will fix the regex:

Please install `nbfmt` and format:
$ python3 -m pip install -U --user git+https://github.com/tensorflow/docs
$ python3 -m tensorflow_docs.tools.nbfmt notebook.ipynb

[sayakpaul]

@seanpmorgan I actually ran it before creating the PR. That makes it even more surprising I guess.

[seanpmorgan]

@sayakpaul I was able to run the commands above and fix the format check, but I'm still unclear why those URLs are failing lint.

@MarkDaoust when time allows do you mind taking a quick look if you're familiar with that test we're running? I think it's likely something trivial.

[sayakpaul]

Thank you, @seanpmorgan. Appreciate the help.

[sayakpaul]

@MarkDaoust a gentle reminder on this. If you could help us point in the right direction, that would be great.

[seanpmorgan]

cc @lamberta. If time allows do you mind taking a quick look or letting us know if you've seen this before

[sayakpaul]

@seanpmorgan on a related note, apart from the build failure, is there anything I can do to improve/modify the tutorial notebook?

[lamberta]

Looks new to me. At least I haven't seen this on tf.org and don't find anything comparable on keras.io

[MarkDaoust]

Overall LGTM.

Attached are the lint fix and a couple of minor suggestions.

[MarkDaoust]

This is the problem. This needs to be tfo-notebook-buttons (tfo == tensorflow.org). The checks and the site-css won't work otherwise.

ref: https://github.com/tensorflow/docs/blob/master/tools/tensorflow_docs/tools/nblint/style/tensorflow.py#L85

[sayakpaul]

Done.

[sayakpaul]

Is there anything I can do to for progressing the PR?

[bhack]

We have a linting failure.

[sayakpaul]

@bhack I ran the Docs formatter and pushed the new notebook.

[sayakpaul]

Thanks, @bhack. Approximately by when the tutorial will be live? Asking this because it's my first time.

[sayakpaul]

@bhack is there anything we could do to fix the build failure?

[MarkDaoust]

This isn't automated. I'll run the update.

[sayakpaul]

I am sorry. First timer :(

[MarkDaoust]

No worries Sayak!

Thanks for submitting the tutorial. It's published:

https://tensorflow.org/addons/tutorials/optimizers_cyclicallearningrate