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retinopathy_test
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README.md

Retinopathy

Build Status

Retinopathy classification using Tensorflow

This repository contains a partial copy of the following repository of Niklas Köhler for use in the DEEP-Hybrid-DataCloud project: https://gitlab.com/niklaskoehler/retinopathy_model

The directories and files were created using cookiecutter given at https://github.com/indigo-dc/cookiecutter-data-science

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── docker             <- Directory for Dockerfile(s)
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials (if many user development),
│                         and a short `_` delimited description, e.g.
│                         `1.0-jqp-initial_data_exploration.ipynb`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so retinopathy_test can be imported
├── retinopathy_test    <- Source code for use in this project.
│   ├── __init__.py    <- Makes retinopathy_test a Python module
│   │
│   ├── dataset        <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   └── model.py
│   │
│   └── tests          <- Scripts to perfrom code testing + pylint script
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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