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from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.metrics import accuracy_score
class EarlyStopping:
def __init__(self, patience=5, verbose=False, delta=0):
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.delta = delta
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model)
elif score < self.best_score + self.delta:
self.counter += 1
if self.verbose:
print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(val_loss, model)
self.counter = 0
def save_checkpoint(self, val_loss, model):
if self.verbose:
print(f'Validation loss decreased ({self.best_score:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.state_dict(), 'checkpoint.pt')
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