pyspark/bigdl/optim/optimizer.py
def set_validation(self, batch_size, X_val, Y_val, trigger, val_method=None):
        """
        Configure validation settings.

        :param batch_size: validation batch size
        :param X_val: features of validation dataset
        :param Y_val: label of validation dataset
        :param trigger: validation interval
        :param val_method: the ValidationMethod to use,e.g. "Top1Accuracy", "Top5Accuracy", "Loss"
        """
        if val_method is None:
            val_method = [Top1Accuracy()]
        callBigDlFunc(self.bigdl_type, "setValidation", self.value, batch_size,
                      trigger, [JTensor.from_ndarray(X) for X in to_list(X_val)],
                      JTensor.from_ndarray(Y_val), to_list(val_method))
def set_validation(self, batch_size, X_val, Y_val, trigger, val_method=None):
        """
        Configure validation settings.

        :param batch_size: validation batch size
        :param X_val: features of validation dataset
        :param Y_val: label of validation dataset
        :param trigger: validation interval
        :param val_method: the ValidationMethod to use,e.g. "Top1Accuracy", "Top5Accuracy", "Loss"
        """
        if val_method is None:
            val_method = [Top1Accuracy()]
        callBigDlFunc(self.bigdl_type, "setValidation", self.value, batch_size,
                      trigger, [JTensor.from_ndarray(X) for X in to_list(X_val)],
                      JTensor.from_ndarray(Y_val), to_list(val_method))
Configure validation settings.

        :param batch_size: validation batch size
        :param X_val: features of validation dataset
        :param Y_val: label of validation dataset
        :param trigger: validation interval
        :param val_method: the ValidationMethod to use,e.g. "Top1Accuracy", "Top5Accuracy", "Loss"