训练和评估 ¶
在 PyTorch 中，训练和评估的流程是一致的，只是在训练过程中需要将模型的参数进行更新，而在评估过程中则不需要更新参数。
model.train() # 训练状态
model.eval() # 验证/测试状态
In [ ]:
Copied!
def train(model, train_loader, epoch):
model.train()
train_loss = 0
for data, label in train_loader:
data, label = data.cuda(), label.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()
train_loss += loss.item()*data.size(0)
train_loss = train_loss/len(train_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f}'.format(epoch, train_loss))
def train(model, train_loader, epoch):
model.train()
train_loss = 0
for data, label in train_loader:
data, label = data.cuda(), label.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, label)
loss.backward()
optimizer.step()
train_loss += loss.item()*data.size(0)
train_loss = train_loss/len(train_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f}'.format(epoch, train_loss))
In [ ]:
Copied!
def val(epoch):
model.eval()
val_loss = 0
with torch.no_grad():
for data, label in val_loader:
data, label = data.cuda(), label.cuda()
output = model(data)
preds = torch.argmax(output, 1)
loss = criterion(output, label)
val_loss += loss.item()*data.size(0)
running_accu += torch.sum(preds == label.data)
val_loss = val_loss/len(val_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f}'.format(epoch, val_loss))
def val(epoch):
model.eval()
val_loss = 0
with torch.no_grad():
for data, label in val_loader:
data, label = data.cuda(), label.cuda()
output = model(data)
preds = torch.argmax(output, 1)
loss = criterion(output, label)
val_loss += loss.item()*data.size(0)
running_accu += torch.sum(preds == label.data)
val_loss = val_loss/len(val_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f}'.format(epoch, val_loss))