本文整理匯總了Python中net.Net.parameters方法的典型用法代碼示例。如果您正苦於以下問題：Python Net.parameters方法的具體用法？Python Net.parameters怎麽用？Python Net.parameters使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類net.Net的用法示例。
在下文中一共展示了Net.parameters方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: Solver
# 需要導入模塊: from net import Net [as 別名]
# 或者: from net.Net import parameters [as 別名]
class Solver():
def __init__(self, args):
# prepare a datasets
self.train_data = Dataset(train=True,
data_root=args.data_root,
size=args.image_size)
self.test_data = Dataset(train=False,
data_root=args.data_root,
size=args.image_size)
self.train_loader = DataLoader(self.train_data,
batch_size=args.batch_size,
num_workers=1,
shuffle=True, drop_last=True)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.net = Net().to(self.device)
self.loss_fn = torch.nn.L1Loss()
self.optim = torch.optim.Adam(self.net.parameters(), args.lr)
self.args = args
if not os.path.exists(args.ckpt_dir):
os.makedirs(args.ckpt_dir)
def fit(self):
args = self.args
for epoch in range(args.max_epochs):
self.net.train()
for step, inputs in enumerate(self.train_loader):
gt_gray = inputs[0].to(self.device)
gt_ab = inputs[1].to(self.device)
pred_ab = self.net(gt_gray)
loss = self.loss_fn(pred_ab, gt_ab)
self.optim.zero_grad()
loss.backward()
self.optim.step()
if (epoch+1) % args.print_every == 0:
print("Epoch [{}/{}] loss: {:.6f}".format(epoch+1, args.max_epochs, loss.item()))
self.save(args.ckpt_dir, args.ckpt_name, epoch+1)
def save(self, ckpt_dir, ckpt_name, global_step):
save_path = os.path.join(
ckpt_dir, "{}_{}.pth".format(ckpt_name, global_step))
torch.save(self.net.state_dict(), save_path)示例2: train
# 需要導入模塊: from net import Net [as 別名]
# 或者: from net.Net import parameters [as 別名]
def train(args):
# prepare the MNIST dataset
train_dataset = datasets.MNIST(root="./data/",
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = datasets.MNIST(root="./data/",
train=False,
transform=transforms.ToTensor())
# create the data loader
train_loader = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True, drop_last=True)
test_loader = DataLoader(dataset=test_dataset,
batch_size=args.batch_size,
shuffle=False)
# turn on the CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Net().to(device)
loss_op = nn.CrossEntropyLoss()
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
for epoch in range(args.max_epochs):
net.train()
for step, inputs in enumerate(train_loader):
images = inputs[0].to(device)
labels = inputs[1].to(device)
# forward-propagation
outputs = net(images)
loss = loss_op(outputs, labels)
# back-propagation
optim.zero_grad()
loss.backward()
optim.step()
acc = evaluate(net, test_loader, device)
print("Epoch [{}/{}] loss: {:.5f} test acc: {:.3f}"
.format(epoch+1, args.max_epochs, loss.item(), acc))
torch.save(net.state_dict(), "mnist-final.pth")