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
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
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
We always welcome your comments and pull requests.