[Python] Pytorch : Early stopping Mechanism

I work with Pytorch and CIFAR100 dataset, While I'm newer, I would like to incorporate the early stopping mechanism in my code,

def train(net,trainloader,epochs,use_gpu = True):
...
net.train()
for epoch in range(epochs):
print ("Epoch {}/{}".format(epoch+1, epochs))
running_loss = 0.0
running_corrects = 0
for i, data in enumerate(trainloader, 0):
images, labels = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
outputs = net(images)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()

epoch_loss = running_loss/len(trainloader.dataset)
print('Loss: {}'.format(epoch_loss))


This is the class of early stopping:

class EarlyStopper:
def __init__(self, patience=1, min_delta=0):
self.patience = patience
self.min_delta = min_delta
self.counter = 0
self.min_validation_loss = float('inf')

def early_stop(self, validation_loss):
if validation_loss < self.min_validation_loss:
self.min_validation_loss = validation_loss
self.counter = 0
elif validation_loss > (self.min_validation_loss + self.min_delta):
self.counter += 1
if self.counter >= self.patience:
return True
return False


I don't have a validation set, I suppose that test set is same as validation set Now, I would like to know where I must make those lines:

early_stopper = EarlyStopper(patience=3, min_delta=10)
for epoch in np.arange(n_epochs):
train_loss = train_one_epoch(model, train_loader)
validation_loss = validate_one_epoch(model, validation_loader)
if early_stopper.early_stop(validation_loss):
break

Continue reading...