early stopping in PyTorch

Question

I tried to implement an early stopping function to avoid my neural network model overfit. I'm pretty sure that the logic is fine, but for some reason, it doesn't work. I want that when the validation loss is greater than the training loss over some epochs, the early stopping function returns True. But it returns False all the time, even though the validation loss becomes a lot greater than the training loss. Could you see where is the problem, please?

early stopping function

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

calling the function during the training

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

EDIT: The train and validation loss:

EDIT2:

def train_validate (model, train_dataloader, validate_dataloader, loss_func, optimiser, device, epochs):
    preds = []
    train_loss =  []
    validation_loss = []
    min_delta = 5
    

    for e in range(epochs):
        
        print(f"Epoch {e+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
        early_stopping = EarlyStopping(tolerance=2, min_delta=5)
        early_stopping(epoch_train_loss, epoch_validate_loss)
        if early_stopping.early_stop:
            print("We are at epoch:", e)
            break

    return train_loss, validation_loss

Answer 1

The problem with your implementation is that whenever you call early_stopping() the counter is re-initialized with 0.

Here is working solution using an oo-oriented approch with __call__() and __init__() instead:

class EarlyStopping:
    def __init__(self, tolerance=5, min_delta=0):

        self.tolerance = tolerance
        self.min_delta = min_delta
        self.counter = 0
        self.early_stop = False

    def __call__(self, train_loss, validation_loss):
        if (validation_loss - train_loss) > self.min_delta:
            self.counter +=1
            if self.counter >= self.tolerance:  
                self.early_stop = True

Call it like that:

early_stopping = EarlyStopping(tolerance=5, min_delta=10)

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
    early_stopping(epoch_train_loss, epoch_validate_loss)
    if early_stopping.early_stop:
      print("We are at epoch:", i)
      break

Example:

early_stopping = EarlyStopping(tolerance=2, min_delta=5)

train_loss = [
    642.14990234,
    601.29278564,
    561.98400879,
    530.01501465,
    497.1098938,
    466.92709351,
    438.2364502,
    413.76028442,
    391.5090332,
    370.79074097,
]
validate_loss = [
    509.13619995,
    497.3125,
    506.17315674,
    497.68960571,
    505.69918823,
    459.78610229,
    480.25592041,
    418.08630371,
    446.42675781,
    372.09902954,
]

for i in range(len(train_loss)):

    early_stopping(train_loss[i], validate_loss[i])
    print(f"loss: {train_loss[i]} : {validate_loss[i]}")
    if early_stopping.early_stop:
        print("We are at epoch:", i)
        break

Output:

loss: 642.14990234 : 509.13619995
loss: 601.29278564 : 497.3125
loss: 561.98400879 : 506.17315674
loss: 530.01501465 : 497.68960571
loss: 497.1098938 : 505.69918823
loss: 466.92709351 : 459.78610229
loss: 438.2364502 : 480.25592041
We are at epoch: 6

Answer 2

It may help someone like myself, I would like to add upon previous answers.

Both of the answers provided have different interpretations of the min_delta parameter. In @KarelZe's answer, min_delta is used as the gap between train_loss and validation_loss:

if (validation_loss - train_loss) > self.min_delta: 
        self.counter +=1

On the other hand, in @isle_of_gods' answer, min_delta is used to increment the counter when the new validation loss is at least min_delta greater than the current minimum validation loss:

elif validation_loss > (self.min_validation_loss + self.min_delta):
        self.counter += 1

Although non of these answers are wrong, since it depends on ones' needs, I think it is more intuitive to consider min_delta as the minimum change required to consider the model as improving. The documentation from Keras, which is equally popular as PyTorch, defines the min_delta parameter in their early stopping mechanism as follows:

min_delta: Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.

That means, any decrease validation loss value will not be counted a decrease unless the decrease is larger than min_delta

To align with the Keras documentation, @isle_of_gods' code can be modified as follows:

class ValidationLossEarlyStopping:
    def __init__(self, patience=1, min_delta=0.0):
        self.patience = patience  # number of times to allow for no improvement before stopping the execution
        self.min_delta = min_delta  # the minimum change to be counted as improvement
        self.counter = 0  # count the number of times the validation accuracy not improving
        self.min_validation_loss = np.inf

    # return True when validation loss is not decreased by the `min_delta` for `patience` times 
    def early_stop_check(self, validation_loss):
        if ((validation_loss+self.min_delta) < self.min_validation_loss):
            self.min_validation_loss = validation_loss
            self.counter = 0  # reset the counter if validation loss decreased at least by min_delta
        elif ((validation_loss+self.min_delta) > self.min_validation_loss):
            self.counter += 1 # increase the counter if validation loss is not decreased by the min_delta
            if self.counter >= self.patience:
                return True
        return False

Answer 3

Although @KarelZe's response solves your problem sufficiently and elegantly, I want to provide an alternative early stopping criterion that is arguably better.

Your early stopping criterion is based on how much (and for how long) the validation loss diverges from the training loss. This will break when the validation loss is indeed decreasing but is generally not close enough to the training loss. The goal of training a model is to encourage the reduction of validation loss and not the reduction in the gap between training loss and validation loss.

Hence, I would argue that a better early stopping criterion would be watch for the trend in validation loss alone, i.e., if the training is not resulting in lowering of the validation loss then terminate it. Here's an example implementation:

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

Here's how you'd use it:

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