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Contributor and Issue Reporting guide

When contributing to this repository, please first discuss the big changes you wish to make via opening issue, email, or any other method with the maintainers of this repository.

Submitting bug reports and feature requests

Bug reports and feature requests should be submitted by creating an issue on https://github.com/henrysky/astroNN

Pull Request

This is a general guideline to make pull request (PR).

  1. Go to https://github.com/henrysky/astroNN, click the Fork button.
  2. Download your own astroNN fork to your computer by $git clone https://github.com/your_username/astroNN
  3. Create a new branch with a short simple name that represents the change you want to make
  4. Make commits locally in that new branch, and push to your own astroNN fork repository
  5. Create a pull request by clicking the New pull request button.

New Model Proposal guide

astroNN acts as a platform to share astronomy-oriented neural networks, so you are welcome to do so.

To add new models:

  • Import your model in astroNN\models\__init__.py and add the model class name to __all__
  • Add a documentation page for the new model and add link it appropriately in docs\source\index.rst
  • Add the new model to the tree diagram and API under appropriate class in docs\souce\neuralnets\basic_usage.rst
  • Add the new model to the release history in docs\source\history.rst

If your new model is proposed along with a paper, add your model to the test suite in tests\test_paper_models.rst just to make sure your model works fine against future changes in astroNN.

Possible New Features and Improvement in the future

GPU/performance related issues

  • Data reduction pipeline on GPU?
  • Multiple GPU support!
  • Training on large dataset that cant fit into memory?

Neural Network related issues

  • Currently the Bayesian NN models only use Dropout VI, maybe introduce more methods especially from TF-Probability
  • Have some nice VAE or GAN thing, maybe on spectroscopic data first