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)