我想实现一个基于平衡的准确性评分的模型检查点回调。为此，我实现了以下类：

class BalAccScore(keras.callbacks.Callback):

    def __init__(self, validation_data=None):
        super(BalAccScore, self).__init__()
        self.validation_data = validation_data
        
    def on_train_begin(self, logs={}):
      self.balanced_accuracy = []

    def on_epoch_end(self, epoch, logs={}):
        y_predict = tf.argmax(self.model.predict(self.validation_data[0]), axis=1)
        y_true = tf.argmax(self.validation_data[1], axis=1)
        balacc = balanced_accuracy_score(y_true, y_predict)
        self.balanced_accuracy.append(round(balacc,6))
        logs["val_bal_acc"] = balacc
        keys = list(logs.keys())

        print("\n ------ validation balanced accuracy score: %f ------\n" %balacc)

然后定义以下回调

balAccScore = BalAccScore(validation_data=(X_2, y_2))
mc = ModelCheckpoint(filepath=callback_path, monitor="val_bal_acc", verbose=1, save_best_only=True, save_freq='epoch')

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['val_bal_acc'])

history = model.fit(X_1, y_1, epochs = 5, batch_size = 512,
                    callbacks=[balAccScore,  mc],
                    validation_data = (X_2, y_2)
                    )

然后得到错误
ValueError: Unknown metric function: val_bal_acc
尽管事实上我在使用accuracy时发现它在history下，例如，通过在编译时设置metrics=[“acc”]。在这种情况下，我会得到预期的警告：

WARNING:tensorflow:Can save best model only with val_bal_acc available, skipping.

但除此之外，模型运行完美。不知道为什么它不运行，否则。