def train(model, data, device, maxepoch, data_path='./', seed=100):
torch.manual_seed(seed)
train_loader, test_loader = feed_dataset(data, data_path)
if (model == 'CNN'):
import deeprobust.image.netmodels.CNN as MODEL
#from deeprobust.image.netmodels.CNN import Net
train_net = MODEL.Net().to(device)
elif (model == 'ResNet18'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet18().to(device)
elif (model == 'ResNet34'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet34().to(device)
elif (model == 'ResNet50'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet50().to(device)
optimizer = optim.SGD(train_net.parameters(), lr=0.01, momentum=0.5)
save_model = True
for epoch in range(1, maxepoch + 1): ## 5 batches
print(epoch)
MODEL.train(train_net, device, train_loader, optimizer, epoch)
MODEL.test(train_net, device, test_loader)
if (save_model and (epoch % 10 == 0 or epoch == maxepoch)):
if os.path.isdir('./trained_models/'):
print('Save model.')
torch.save(
train_net.state_dict(), './trained_models/' + data + "_" +
model + "_epoch_" + str(epoch) + ".pt")
else:
os.mkdir('./trained_models/')
print('Make directory and save model.')
torch.save(
train_net.state_dict(), './trained_models/' + data + "_" +
model + "_epoch_" + str(epoch) + ".pt")
def train(model,
data,
device,
maxepoch,
data_path='./',
save_per_epoch=10,
seed=100):
"""train.
Parameters
----------
model :
model(option:'CNN', 'ResNet18', 'ResNet34', 'ResNet50', 'densenet', 'vgg11', 'vgg13', 'vgg16', 'vgg19')
data :
data(option:'MNIST','CIFAR10')
device :
device(option:'cpu', 'cuda')
maxepoch :
training epoch
data_path :
data path(default = './')
save_per_epoch :
save_per_epoch(default = 10)
seed :
seed
Examples
--------
>>>import deeprobust.image.netmodels.train_model as trainmodel
>>>trainmodel.train('CNN', 'MNIST', 'cuda', 20)
"""
torch.manual_seed(seed)
train_loader, test_loader = feed_dataset(data, data_path)
if (model == 'CNN'):
import deeprobust.image.netmodels.CNN as MODEL
# from deeprobust.image.netmodels.CNN import Net
train_net = MODEL.Net().to(device)
elif (model == 'ResNet18'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet18().to(device)
elif (model == 'ResNet34'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet34().to(device)
elif (model == 'ResNet50'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet50().to(device)
elif (model == 'densenet161'):
import deeprobust.image.netmodels.densenet as MODEL
train_net = MODEL.DenseNet161().to(device)
elif (model == 'densenet121'):
import deeprobust.image.netmodels.densenet as MODEL
train_net = MODEL.DenseNet121().to(device)
elif (model == 'densenet'):
import deeprobust.image.netmodels.densenet as MODEL
train_net = MODEL.densenet_cifar().to(device)
elif (model == 'vgg11'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG11').to(device)
elif (model == 'vgg13'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG13').to(device)
elif (model == 'vgg16'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG16').to(device)
elif (model == 'vgg19'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG19').to(device)
optimizer = optim.SGD(train_net.parameters(), lr=0.1, momentum=0.5)
scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)
save_model = True
for epoch in range(1, maxepoch + 1): ## 5 batches
print(epoch)
MODEL.train(train_net, device, train_loader, optimizer, epoch)
MODEL.test(train_net, device, test_loader)
if (save_model
and (epoch % (save_per_epoch) == 0 or epoch == maxepoch)):
if os.path.isdir('./trained_models/'):
print('Save model.')
torch.save(
train_net.state_dict(), './trained_models/' + data + "_" +
model + "_epoch_" + str(epoch) + ".pt")
else:
os.mkdir('./trained_models/')
print('Make directory and save model.')
torch.save(
train_net.state_dict(), './trained_models/' + data + "_" +
model + "_epoch_" + str(epoch) + ".pt")
scheduler.step()
def train(model,
data,
device,
maxepoch,
ensemble_size,
t,
data_path='./',
save_per_epoch=10,
seed=199,
seedin=100,
shuffle=False):
"""train.
Parameters
----------
model :
model(option:'CNN', 'ResNet18', 'ResNet34', 'ResNet50', 'densenet', 'vgg11', 'vgg13', 'vgg16', 'vgg19')
data :
data(option:'MNIST','CIFAR10')
device :
device(option:'cpu', 'cuda')
maxepoch :
training epoch
data_path :
data path(default = './')
save_per_epoch :
save_per_epoch(default = 10)
seed :
seed
random_test :
set train_set to be random drawn
Examples
--------
>>>import deeprobust.image.netmodels.train_model as trainmodel
>>>trainmodel.train('CNN', 'MNIST', 'cuda', 20)
"""
torch.manual_seed(seed)
import deeprobust.image.netmodels.feed_dataset as fd
train_loader, test_loader = fd.feed_dataset(data,
data_path,
seedin,
random_train=True)
device = torch.device("cuda")
if (model == 'CNN'):
import deeprobust.image.netmodels.CNN as MODEL
#from deeprobust.image.netmodels.CNN import Net
train_net = MODEL.Net(ensemble_size=ensemble_size,
t=t).to(device=device)
elif (model == 'ResNet18'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet18().to(device=device)
elif (model == 'ResNet34'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet34().to(device=device)
elif (model == 'ResNet50'):
import deeprobust.image.netmodels.resnet as MODEL
train_net = MODEL.ResNet50().to(device=device)
elif (model == 'densenet'):
import deeprobust.image.netmodels.densenet as MODEL
train_net = MODEL.densenet_cifar().to(device=device)
elif (model == 'vgg11'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG11').to(device=device)
elif (model == 'vgg13'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG13').to(device=device)
elif (model == 'vgg16'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG16').to(device=device)
elif (model == 'vgg19'):
import deeprobust.image.netmodels.vgg as MODEL
train_net = MODEL.VGG('VGG19').to(device=device)
optimizer = optim.SGD(train_net.parameters(), lr=0.01, momentum=0.5)
save_model = True
train_loss = 0
test_loss = 0
for epoch in range(1, maxepoch + 1): ## 5 batches
print(epoch)
train_loss += MODEL.train(train_net, device, train_loader, optimizer,
epoch)
test_loss += MODEL.test(train_net, device, test_loader)
if (save_model
and (epoch % (save_per_epoch) == 0 or epoch == maxepoch)):
if os.path.isdir('./trained_models/'):
print('Save model.')
torch.save(
train_net.state_dict(), './trained_models/' + data + "_" +
model + "_epoch_" + str(epoch) + ".pt")
else:
os.mkdir('./trained_models/')
print('Make directory and save model.')
torch.save(
train_net.state_dict(), './trained_models/' + data + "_" +
model + "_epoch_" + str(epoch) + ".pt")
empirical_error = train_loss / (len(train_loader.dataset) * maxepoch)
expected_error = (train_loss + test_loss) / (
(len(train_loader) + len(test_loader)) * maxepoch)
generalization_error = abs(expected_error - empirical_error)
print("========Expected Error========")
print('Expected Error over the whole set: {:.4f}'.format(expected_error))
print("========Empirical Error========")
print('Empirical Error over the training set: {:.4f}'.format(
empirical_error))
print("========Generalization Error========")
print('Generalization Error: {:.4f}\n'.format(generalization_error))
return generalization_error