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]
- Added models:
ApogeeKplerEchelle
andApokascEncoderDecoder
- 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: tensorflow/tensorflow#33024 (comment))
- 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
- 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 tofit()
,predict()
,fit_on_batch()
- Old
Galaxy10
has been renamed toGalaxy10 SDSS
and the new version will replace and callGalaxy10
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/
- Better and faster with IPython tab auto-completion
- Added models :
ApogeeDR14GaiaDR2BCNN
- Improved data pipeline to generate data for NNs
- 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/
- Initial Release!!
- Tested with Tensorflow 1.8.0/1.9.0 and Keras 2.2.0/2.2.2
- Python 3.6 or above only
v0.0.0 (13 October 2017)
First commit of astroNN on Github!!!