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,
