disp_str += ' | {}: {:.4f}'.format(k,
v / config.eval_period)
disp_str += ' | [Eval] unl acc: {:.4f}, gen acc: {:.4f}, max unl acc: {:.4f}, max gen acc: {:.4f}'.format(
unl_acc, gen_acc, max_unl_acc, max_gen_acc)
disp_str += ' | lr: {:.5f}'.format(
self.dis_optimizer.param_groups[0]['lr'])
disp_str += '\n'
monitor = OrderedDict()
self.logger.write(disp_str)
sys.stdout.write(disp_str)
sys.stdout.flush()
iter += 1
self.iter_cnt += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='svhn_trainer.py')
parser.add_argument('-suffix',
default='run0',
type=str,
help="Suffix added to the save images.")
args = parser.parse_args()
trainer = Trainer(config.svhn_config(), args)
trainer.train()
default=100,
type=int,
help="Max Epochs")
parser.add_argument('--suffix',
default='run0',
type=str,
help="Suffix added to the save directory.")
args = parser.parse_args()
if args.dataset == 'mnist':
conf = config.mnist_config()
num_examples = 60000
Trainer = mnist_trainer.Trainer
elif args.dataset == 'svhn':
conf = config.svhn_config()
num_examples = 73257
Trainer = svhn_trainer.Trainer
elif args.dataset == 'cifar':
conf = config.cifar_config()
num_examples = 50000
Trainer = cifar_trainer.Trainer
else:
raise NotImplementedError
conf.log_root = check_folder(
conf.log_root +
'/{}_{}_{}'.format(args.dataset, args.budget, args.suffix))
conf.max_epochs = args.max_epochs
mask = np.zeros(num_examples, dtype=np.bool)