import waitGPU waitGPU.wait(utilization=20, available_memory=10000, interval=10) import problems as pblm from trainer import * import setproctitle if __name__ == "__main__": args = pblm.argparser(prefix='mnist', method='task_spec_robust', opt='adam', starting_epsilon=0.05, epsilon=0.2, thres=0.035) kwargs = pblm.args2kwargs(args) setproctitle.setproctitle('python') print("threshold for classification error: {:.1%}".format(args.thres)) print('Matrix type: {0}\t\t' 'Category: {1}\t\t' 'Epoch number: {2}\t\t' 'Targeted epsilon: {3}\t\t' 'Starting epsilon: {4}\t\t' 'Sechduled length: {5}'.format(args.type, args.category, args.epochs, args.epsilon, args.starting_epsilon, args.schedule_length), end='\n') if args.l1_proj is not None: print('Projection vectors: {0}\t\t' 'Train estimation: {1}\t\t' 'Test estimation: {2}'.format(args.l1_proj, args.l1_train,
if __name__ == "__main__": args = pblm.argparser(opt='adam', verbose=200, starting_epsilon=0.01) print("saving file to {}".format(args.prefix)) setproctitle.setproctitle(args.prefix) train_log = open(args.prefix + "_train.log", "w") test_log = open(args.prefix + "_test.log", "w") train_loader, _ = pblm.mnist_loaders(args.batch_size) _, test_loader = pblm.mnist_loaders(args.test_batch_size) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) for X, y in train_loader: break kwargs = pblm.args2kwargs(args, X=Variable(X.cuda())) best_err = 1 sampler_indices = [] model = [select_model(args.model)] for _ in range(0, args.cascade): if _ > 0: # reduce dataset to just uncertified examples print("Reducing dataset...") train_loader = sampler_robust_cascade(train_loader, model, args.epsilon, args.test_batch_size, norm_type=args.norm_test, bounded_input=True,
args = pblm.argparser(opt='adam', verbose=200, starting_epsilon=0.01) print("saving file to {}".format(args.prefix)) setproctitle.setproctitle(args.prefix) train_log = open(args.prefix + "_train.log", "w") test_log = open(args.prefix + "_test.log", "w") train_loader, _ = pblm.mnist_loaders(args.batch_size) _, test_loader = pblm.mnist_loaders(args.test_batch_size) torch.manual_seed(args.seed) torch.manual_seed(args.seed) for X, y in train_loader: break kwargs = pblm.args2kwargs(args, X=Variable(X.to(device))) best_err = 1 sampler_indices = [] model = [select_model(args.model)] for _ in range(0, args.cascade): if _ > 0: # reduce dataset to just uncertified examples print("Reducing dataset...") train_loader = sampler_robust_cascade(train_loader, model, args.epsilon, args.test_batch_size, norm_type=args.norm_test, bounded_input=True,