53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
import numpy as np
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class EarlyStopping:
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"""Early stops the training if validation loss doesn't improve after a given patience."""
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def __init__(self, patience=7, verbose=False, delta=0, save_path="."):
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"""
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Args:
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patience (int): How long to wait after last time validation loss improved.
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Default: 7
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verbose (bool): If True, prints a message for each validation loss improvement.
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Default: False
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delta (float): Minimum change in the monitored quantity to qualify as an improvement.
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Default: 0
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"""
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self.patience = patience
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self.verbose = verbose
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self.counter = 0
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self.best_score = None
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self.early_stop = False
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self.val_loss_min = np.Inf
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self.delta = delta
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self.save_path = save_path
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def __call__(self, val_loss, model):
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score = -val_loss
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if self.best_score is None:
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self.best_score = score
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self.save_checkpoint(val_loss, model)
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elif score < self.best_score + self.delta:
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self.counter += 1
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print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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if self.counter >= self.patience:
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self.early_stop = True
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else:
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self.best_score = score
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self.save_checkpoint(val_loss, model)
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self.counter = 0
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def save_checkpoint(self, val_loss, model):
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'''Saves model when validation loss decrease.'''
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if self.verbose:
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print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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# save_path = join(self.save_path, "best_model")
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# if not os.path.exists(save_path):
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# os.mkdir(save_path)
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# model_to_save = model.module if hasattr(model, 'module') else model
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# model_to_save.save_pretrained(save_path)
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self.val_loss_min = val_loss
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