kerasy 0.0.2

A set of python modules for machine learning and data mining especially in the biological field.

Kerasy

I want to deepen my understanding of deep learning by imitating the sophisticated neural networks API, Keras.

Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

How to generate the articles.

.Kerasy
├── MkDocs
│   ├── MkDocs-important
|   |   |   ├── img
|   |   |   ├── theme
│   │   │   └── index.md
│   │   └── yml-templates.yml
│   ├── site
│   ├── MkDocs-src
│   └── mkdocs.yml
├── README.md
├── doc
├── kerasy
├── pelican
│   ├── Makefile
│   ├── backdrop
│   ├── pelican-src
│   ├── pelican-works
│   ├── pelicanconf.py
│   └── publishconf.py
└── pelican2mkdocs.py

1. Prepare articles (.md or .ipynb.) NOTE: article name (XXX.md) and Slug(YYY) must be the same.(XXX=YYY)

2. Generate the html article by ``pelican` <https://docs.getpelican.com/en/stable/>`_. .. code-block:: sh

   # @Kerasy/pelican $ make html # pelican-src(.md, .ipynb) → pelican-works (.html)

3. Move html files (made by pelican) to MkDocs-src as a .md style.

4. Make a mkdocs.yml file

   • Paset from yml-templates.yml

   • Get information from the Hierarchical structure of pelican-src. .. code-block:

     # @Kerasy
     $ python pelican2mkdocs

5. Generate the articles by mkdocs build. .. code-block:

   # @Kerasy/MkDocs
   $ mkdocs build # MkDocs-src(.md) → site (.html)

6. Copy some important static files (at MkDocs-important) to site dir

7. Move MkDocs/site to doc.

※ A program that performs these operations collectively is ```GithubKerasy.sh`` <https://github.com/iwasakishuto/iwasakishuto.github.io/blob/master/ShellScripts/GithubKerasy.sh>`_.

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# [Library packaging]
# Normal. (source distribution.)
# $ python setup.py sdist
# wheel version. (Recommended.)
$ python setup.py bdist_wheel

# [Upload to PyPI]
$ twine upload dist/*