- Python >= 3.6
- Tensorflow >= 2.0
- Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:
$ pip install cookiecutter
or
$ conda config --add channels conda-forge
$ conda install cookiecutter
cookiecutter https://github.com/thanhtcptit/tf_project_template
The directory structure of your new project looks like this:
├── configs <- Store experiment config files
├── data
│ ├── external <- Data from third party sources
│ ├── interim <- Intermediate data that has been transformed
│ ├── processed <- The final, canonical data sets for modeling
│ └── raw <- The original, immutable data dump
├── train_logs <- Trained and serialized models, model predictions, or model summaries
├── notebooks <- Jupyter notebooks
├── references <- Data dictionaries, manuals, and all other explanatory materials
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc
├── resources <- Other resources for the project
├── src <- Source code for use in this project.
│ ├── data <- Scripts to download or generate data
│ ├── features <- Scripts to turn raw data into features for modeling
│ ├── models <- Scripts to define models
│ ├── utils <- Scripts to define helper function
│ ├── visualization <- Scripts to create exploratory and results oriented visualizations
│ ├── __init__.py
│ ├── evaluate.py
│ └── train.py
├── README.md <- The top-level README for developers using this project
├── requirements.txt <- The requirements file for reproducing the analysis environment
├── run.py <- Script to run tasks
└── setup.py <- Makes project pip installable (pip install -e .) so src can be imported
pip install -r requirements.txt