History
v1.1 series
v1.1.0 (xx xxx 20xx)
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: https://github.com/tensorflow/tensorflow/issues/33024#issuecomment-551184305)
Improved continuous integration testing with Github Actions, now actually test model 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
Deprecated support for all Tensorflow 1.x
Dropped optional Keras support, now depends on Tensorflow only
Tested with Tensorflow 2.7 and 2.8
Incompatible to Tensorflow 1.x and <=2.3 due to necessary changes for Tensorflow eager execution API
Change 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 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/
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 series
v0.0.0 (13 October 2017)
First commit of astroNN on Github!!!