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
This is a general guideline to make pull request (PR).
Go to https://github.com/henrysky/astroNN, click the
Download your own astroNN fork to your computer by
$git clone https://github.com/your_username/astroNN
Create a new branch with a short simple name that represents the change you want to make
Make commits locally in that new branch, and push to your own astroNN fork repository
Create a pull request by clicking the
New pull requestbutton.
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__.pyand add the model class name to
Add a documentation page for the new model and add link it appropriately in
Add the new model to the tree diagram and API under appropriate class in
Add the new model to the release history in
If your new model is proposed along with a paper, add your model to the test suite in
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