我尝试实现提前停止功能以避免我的神经网络模型过度拟合。我很确定逻辑是正确的，但由于某种原因，它不起作用。我希望当验证损失大于某些时期的训练损失时，早期停止函数返回 True。但它始终返回 False，即使验证损失变得比训练损失大得多。请问您能看出问题出在哪里吗？

早停功能

def early_stopping(train_loss, validation_loss, min_delta, tolerance):

    counter = 0
    if (validation_loss - train_loss) > min_delta:
        counter +=1
        if counter >= tolerance:
          return True

在训练期间调用该函数

for i in range(epochs):
    
    print(f"Epoch {i+1}")
    epoch_train_loss, pred = train_one_epoch(model, train_dataloader, loss_func, optimiser, device)
    train_loss.append(epoch_train_loss)

    # validation 

    with torch.no_grad(): 
       epoch_validate_loss = validate_one_epoch(model, validate_dataloader, loss_func, device)
       validation_loss.append(epoch_validate_loss)
    
    # early stopping
    if early_stopping(epoch_train_loss, epoch_validate_loss, min_delta=10, tolerance = 20):
      print("We are at epoch:", i)
      break

编辑：训练和验证损失：

编辑2：

def train_validate (model, train_dataloader, validate_dataloader, loss_func, optimiser, device, epochs): …