I want to deepen my understanding of deep learning by imitating the sophisticated neural networks API, 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
Prepare articles (
.ipynb.) NOTE: article name (
XXX.md) and Slug(
YYY) must be the same.(XXX=YYY)
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)
Move html files (made by pelican) to
Get information from the Hierarchical structure of
pelican-src. .. code-block:
# @Kerasy $ python pelican2mkdocs
Generate the articles by
mkdocs build. .. code-block:
# @Kerasy/MkDocs $ mkdocs build # MkDocs-src(.md) → site (.html)
Copy some important static files (at
MkDocs-important) to site dir
※ A program that performs these operations collectively is ```GithubKerasy.sh`` <https://github.com/iwasakishuto/iwasakishuto.github.io/blob/master/ShellScripts/GithubKerasy.sh>`_.
Upload to PyPI
Create your account : https://pypi.org/
# [Library packaging] # Normal. (source distribution.) # $ python setup.py sdist # wheel version. (Recommended.) $ python setup.py bdist_wheel # [Upload to PyPI] $ twine upload dist/*