This repository contains the code used for training and testing a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars and accompanies the article Martínez-Palomera et al. 2020.
Light curves taken from OGLE 3, which contains the following variability classes:
- Eclipsing Binaries
- Anomalous Cepheids
- Cepheids
- Type II Cepheids
- RR Lyrae
- Long Period Variables
- Ellipsoidal Variables
- Delta Scuti
Training data is avalaible here.
Use vae_main.py to train a cVAE model with the following parameters:
--dry-run Only load data and initialize model [False]
--machine were to is running ([Jorges-MBP], colab, exalearn)
--data data used for training (OGLE3)
--use-err use magnitude errors ([T],F)
--cls drop or select ony one class
([all],drop_"vartype",only_"vartype")
--lr learning rate [1e-4]
--lr-sch learning rate shceduler ([None], step, exp,cosine,
plateau)
--beta beta factor for latent KL div ([1],step)
--batch-size batch size [128]
--num-epochs total number of training epochs [150]
--cond label conditional VAE (F,[T])
--phy physical parameters to use for conditioning ([],[tm])
--latent-dim dimension of latent space [6]
--latent-mode wheather to sample from a 3d or 2d tensor
([repeat],linear,convt)
--arch architecture for Enc & Dec ([tcn],lstm,gru)
--transpose use tranpose convolution in Dec ([F],T)
--units number of hidden units [32]
--layers number of layers/levels for lstm/tcn [5]
--dropout dropout for lstm/tcn layers [0.2]
--kernel-size kernel size for tcn conv, use odd ints [5]
--comment extra comments
Architecture available are [TCN, LSTM, GRU]. The encoder-decoder contain a sequential artchitecture followed by a set of dense layers.
This train the models and generate a tensorboard event log (located in ./logs) of the training progress.
