v1.1 series

v1.1.0 (26 April 2023)

This release mainly targeted to the paper A variational encoder-decoder approach to precise spectroscopic age estimation for large Galactic surveys available at [arXiv:2302.05479] [ADS]

New features:
  • Added models: ApogeeKplerEchelle and ApokascEncoderDecoder

  • Input data can now be a dict, such as nn.train({'input': input_data, 'input': aux_input_data}, {'output': labels, 'output_aux': aux_labels})

  • Added numerical integrator for NeuralODE

  • tqdm progress bar for model prediction

  • Added a new improved version Galaxy10

  • Added multiple metrics based on median

  • Added functions transfer_weights forr transfer learning

  • Fully compatible with Tensorflow 2

  • Model training/inference should be much faster by using Tensorflow v2 eager execution (see: https://github.com/tensorflow/tensorflow/issues/33024#issuecomment-551184305)

  • Improved continuous integration testing with Github Actions, now actually test models learn properly with real world data instead of checking no syntax error with random data

  • Support sample_weight in all losss functions and training

  • Improved catalog coordinates matching

  • New documentation webpages

  • ~15% faster in Bayesian neural network inference by using parallelized loop

  • Loss/metrics functions and normalizer now check for NaN too

  • Updated many of notebooks to be compable with the latest Tensorflow

Breaking Changes:
  • Deprecated support for all Tensorflow 1.x

  • Tested with Tensorflow 2.11 and 2.12

  • Python 3.8 or above only

  • Incompatible to Tensorflow 1.x and <=2.3 due to necessary changes for Tensorflow eager execution API

  • Renamed neural network models train(), test(), train_on_batch() method to fit(), predict(), fit_on_batch()

  • Old Galaxy10 has been renamed to Galaxy10 SDSS and the new version will replace and call Galaxy10

v1.0 series

v1.0.1 (5 March 2019)

This release mainly targeted to the paper Simultaneous calibration of spectro-photometric distances and the Gaia DR2 parallax zero-point offset with deep learning available at [arXiv:1902.08634] [ADS]

Documentation for this version is available at https://astronn.readthedocs.io/en/v1.0.1/

New features:
  • Better and faster with IPython tab auto-completion

  • Added models : ApogeeDR14GaiaDR2BCNN

  • Improved data pipeline to generate data for NNs

Breaking Changes:
  • Tested with Tensorflow 1.11.0/1.12.0/1.13.1 and Keras 2.2.0/2.2.4

v1.0.0 (16 August 2018)

This is the first release of astroNN. This release mainly targeted to the paper Deep learning of multi-element abundances from high-resolution spectroscopic data available at [arXiv:1804.08622] [ADS]

Documentation for this version is available at https://astronn.readthedocs.io/en/v1.0.0/

New features:
  • Initial Release!!

Breaking Changes:
  • Tested with Tensorflow 1.8.0/1.9.0 and Keras 2.2.0/2.2.2

  • Python 3.6 or above only

v0.0 series

v0.0.0 (13 October 2017)

First commit of astroNN on Github!!!