Changelog

v1.2 series

v1.2.0 (xx XXXX 2024)

This release mainly for the transition to Keras v3

New features:

• Compatible with Keras v3 with Tensorflow and PyTorch backend

Improvement:

• Documentation and test coverage improvement

Breaking Changes:

• Python 3.10 or above only

• Keras 3.5 or above only

• Tensorflow 2.17 or above only

• PyTorch 2.4 or above only

• Removal of all deprecated functions

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: ApogeeKeplerEchelle 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

Improvement:

• 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. A new version has been added with improved quality and named as 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

Improvement:

• 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!!!