'checkpoints/{}/{}_training_history_{}.pkl'.format(
expt, model, time_stamp)
sep()
logging.info('Saving: {}'.format(checkpoint_location))
pkl.dump(h, open(checkpoint_location, 'wb'))
if __name__ == "__main__":
expt = 'mimic'
model = 'ind_gan'
marker = 'H'
pr_time, fl_time = time_stp()
logger(expt, model, fl_time, marker)
log_time('Start', pr_time)
args = comparison_argparse()
main(model=model,
time_stamp=fl_time,
device=args['device'],
ally_classes=args['n_ally'],
advr_1_classes=args['n_advr_1'],
advr_2_classes=args['n_advr_2'],
encoding_dim=args['dim'],
hidden_dim=args['hidden_dim'],
leaky=args['leaky'],
test_size=args['test_size'],
batch_size=args['batch_size'],
n_epochs=args['n_epochs'],
shuffle=args['shuffle'] == 1,
lr_ally=args['lr_ally'],
for idx, advr in enumerate(advrs):
model_ckpt = 'ckpts/{}/models/{}_advr_{}.stop'.format(
expt, template, idx)
logging.info('Saving: {}'.format(model_ckpt))
torch.save(advr.state_dict(), model_ckpt)
if __name__ == '__main__':
expt = 'mnist'
model = 'encd_pretrain'
marker = 'A'
pr_time, fl_time = time_stp()
logger(expt, model, fl_time, marker)
log_time('Start', pr_time)
args = eigan_argparse()
main(
model=model,
device=args['device'],
ally_classes=args['n_ally'],
advr_classes=args['n_advr'],
batch_size=args['batch_size'],
n_epochs=args['n_epochs'],
lr_encd=args['lr_encd'],
lr_ally=args['lr_ally'],
lr_advr=args['lr_advr'],
expt=args['expt'],
marker=marker,
)
