EarlyStopping、torch.saveは、モデルファイルを指定されたフォルダーに保存し、動的に名前を付けます

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問題の説明

ニューラルネットワークのトレーニングプロセス中に過剰適合が発生する可能性があります。早期停止の方法を使用すると、検証セットの損失が増加しないか、負の増加が増加した後のいくつかのエポックでのトレーニングの早期終了により、過剰適合を効果的に回避できます。このプロセスでは、保存の合計モデルファイル。

解決

class EarlyStopping:
    """Early stops the training if validation loss doesn't improve after a given patience."""
    def __init__(self, patience=7, verbose=False, delta=0,layer=1):
        """
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement. 
                            Default: False
            delta (float): Minimum change in the monitored quantity to qualify as an improvement.
                            Default: 0
        """
        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        self.delta = delta
        self.layer = layer
        

    def __call__(self, val_loss,model,layer):

        score = -val_loss

        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(val_loss, model, layer)
        elif score < self.best_score + self.delta:
            self.counter += 1
            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, layer)
            self.counter = 0

    def save_checkpoint(self, val_loss, model,layer):
        '''Saves model when validation loss decrease.'''
        if self.verbose:
            print(f'Validation loss decreased ({
      
      self.val_loss_min:.6f} --> {
      
      val_loss:.6f}).  Saving model ...')
        save_path = './ResultData_earlystop/savemodel/'
        filepath = os.path.join(save_path, 'checkpoint_model_layer{}.pt'.format(self.layer))#
        torch.save(model.state_dict(), filepath)	# 这里会存储迄今最优模型的参数
        self.val_loss_min = val_loss

early_stopping = EarlyStopping(patience=patience, verbose=True,layer=1)

# early_stopping needs the validation loss to check if it has decresed, 
            # and if it has, it will make a checkpoint of the current model
            # early_stopping = EarlyStopping(patience=patience, verbose=True,layer=1)
            early_stopping(EvalLoss,model,1)
            
            if early_stopping.early_stop:
                print("Early stopping")
                break
            
            # load the last checkpoint with the best model
            model.load_state_dict(torch.load('./ResultData_earlystop/savemodel/checkpoint_model_layer1.pt'))