settings = conf.get_settings()
# setting random seeds
np.random.seed(settings.seed)
import torch
torch.manual_seed(settings.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(settings.seed)
################
# DATA
################
if settings.data:
# Build an HDF5 database
make_dataset.make_dataset(settings)
lu.print_blue("Finished constructing dataset")
################
# TRAINING
################
if settings.train_rnn:
# Train
if settings.cyclic:
train_rnn.train_cyclic(settings)
else:
train_rnn.train(settings)
# Obtain predictions
validate_rnn.get_predictions(settings)
"""Example for running SuperNNova as a module if installed by "pip install supernnova" you can run this code in the parent folder (where run.py is) """ # get config args args = conf.get_args() # create database args.data = True # conf: making new dataset args.dump_dir = "tests/dump" # conf: where the dataset will be saved args.raw_dir = "tests/raw" # conf: where raw photometry files are saved args.fits_dir = "tests/fits" # conf: where salt2fits are saved settings = conf.get_settings(args) # conf: set settings make_dataset.make_dataset(settings) # make dataset # train model args.data = False # conf: no database creation args.train_rnn = True # conf: train rnn args.dump_dir = "tests/dump" # conf: where the dataset is saved args.nb_epoch = 2 # conf: training epochs settings = conf.get_settings(args) # conf: set settings train_rnn.train(settings) # train rnn # validate (test set classificatio) args.data = False # conf: no database creation args.train_rnn = False # conf: no train rnn args.validate_rnn = False # conf: validate rnn args.dump_dir = "tests/dump" # conf: where the dataset is saved settings = conf.get_settings(args) # conf: set settings