a20180502 / deeplearningzerotoall

TensorFlow Basic Tutorial Labs

Lab code (WIP), but call for comments

This is code for labs covered in TensorFlow basic tutorials (in Korean) at https://www.youtube.com/watch?v=BS6O0zOGX4E&list=PLlMkM4tgfjnLSOjrEJN31gZATbcj_MpUm. (We also have a plan to record videos in English.)

This is work in progress, and may have bugs. However, we call for your comments and pull requests. Check out our style guide line:

• More TF (1.0) style: use more recent and decent TF APIs.
• More Pythonic: fully leverage the power of python
• Readability (over efficiency): Since it's for instruction purposes, we prefer readability over others.
• Understandability (over everything): Understanding TF key concepts is the main goal of this code.
• KISS: Keep It Simple Stupid! https://www.techopedia.com/definition/20262/keep-it-simple-stupid-principle-kiss-principle

Lab slides:

• https://goo.gl/jPtWNt

We welcome your comments on slides.

File naming rule:

• klab-XX-X-[name].py: Keras labs code
• lab-XX-X-[name].py: TensorFlow lab code
• mxlab-XX-X-[name].py: MXNet lab code

Install requirements

pip install -r requirements.txt

Run test and autopep8

TODO: Need to add more test cases

python -m unittest discover -s tests;

# http://stackoverflow.com/questions/14328406/
pip install autopep8 # if you haven't install
autopep8 . --recursive --in-place --pep8-passes 2000 --verbose

Automatically create requirements.txt

pip install pipreqs

pipreqs /path/to/project

http://stackoverflow.com/questions/31684375

Contributions/Comments

We always welcome your comments and pull requests.

Reference Implementations

• https://github.com/nlintz/TensorFlow-Tutorials/
• https://github.com/golbin/TensorFlow-ML-Exercises
• https://github.com/FuZer/Study_TensorFlow
• https://github.com/fchollet/keras/tree/master/examples