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import torch
import numpy as np
class EarlyStopping:
def __init__(self, patience=5, verbose=False, path='checkpoint_model.pth'):
self.patience = patience # stop cpunter
self.verbose = verbose
self.counter = 0 # current counter
self.best_score = None # best score
self.early_stop = False # stop flag
self.val_loss_min = np.Inf # to memorize previous best score
self.path = path # path to save the best model
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None: #1Epoch
self.best_score = score
self.checkpoint(val_loss, model) # save model and show score
elif score < self.best_score: # if it can not update best score
self.counter += 1 # stop 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: # if it update best score
self.best_score = score
self.checkpoint(val_loss, model) # save model and show score
self.counter = 0 # stop counter is reset
def checkpoint(self, val_loss, model):
if self.verbose:
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.state_dict(), self.path)
self.val_loss_min = val_loss |