x = self.fc2(x)
return x, F.log_softmax(x)
model = Net()
model = nn.DataParallel(model)
elif args.network == 'Alexnet':
model = alexnet.AlexNet()
elif args.network == 'Vgg':
model = vgg.vgg16_mnist_bn()
print(model)
elif args.network == 'Resnet34':
model = resnet.ResNet34()
elif args.network == 'Resnet':
model = resnet.ResNet50()
elif args.network == 'Densenet':
model = densenet.densenet_cifar()
#print(model)
if args.cuda:
model.cuda(args.gpu)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
def adjust_learning_rate(optimizer, epoch):
x = x.view(-1, 500)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return x, F.log_softmax(x)
model = Net()
elif args.network == 'Alexnet':
model = alexnet.AlexNet(num_classes=100)
elif args.network == 'Vgg':
model = vgg.vgg16()
print(model)
elif args.network == 'Resnet':
model = resnet.ResNet50(num_classes=100)
elif args.network == 'Densenet':
model = densenet.densenet_cifar(num_classes=100)
#print(model)
if args.cuda:
model.cuda(args.gpu)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
def adjust_learning_rate(optimizer, epoch):
lr = args.lr * (0.1 ** (epoch // args.lr_decay))