pyspark/bigdl/keras/backend.py
def evaluate(self, x, y, batch_size=32, sample_weight=None, is_distributed=False):
        """
        Evaluate a model by the given metrics.
        :param x: ndarray or list of ndarray for local mode.
                  RDD[Sample] for distributed mode
        :param y: ndarray or list of ndarray for local mode and would be None for cluster mode.
        :param batch_size
        :param is_distributed: run in local mode or distributed mode.
               NB: if is_distributed=true, x should be RDD[Sample] and y should be None
        :return:
        """
        if sample_weight:
            unsupport_exp("sample_weight")
        if is_distributed:
            if isinstance(x, np.ndarray):
                input = to_sample_rdd(x, y)
            elif isinstance(x, RDD):
                input = x
            if self.metrics:
                sc = get_spark_context()
                return [r.result for r in
                        self.bmodel.evaluate(input, batch_size, self.metrics)]
            else:
                raise Exception("No Metrics found.")
        else:
            raise Exception("We only support evaluation in distributed mode")
def evaluate(self, x, y, batch_size=32, sample_weight=None, is_distributed=False):
        """
        Evaluate a model by the given metrics.
        :param x: ndarray or list of ndarray for local mode.
                  RDD[Sample] for distributed mode
        :param y: ndarray or list of ndarray for local mode and would be None for cluster mode.
        :param batch_size
        :param is_distributed: run in local mode or distributed mode.
               NB: if is_distributed=true, x should be RDD[Sample] and y should be None
        :return:
        """
        if sample_weight:
            unsupport_exp("sample_weight")
        if is_distributed:
            if isinstance(x, np.ndarray):
                input = to_sample_rdd(x, y)
            elif isinstance(x, RDD):
                input = x
            if self.metrics:
                sc = get_spark_context()
                return [r.result for r in
                        self.bmodel.evaluate(input, batch_size, self.metrics)]
            else:
                raise Exception("No Metrics found.")
        else:
            raise Exception("We only support evaluation in distributed mode")
Evaluate a model by the given metrics.
        :param x: ndarray or list of ndarray for local mode.
                  RDD[Sample] for distributed mode
        :param y: ndarray or list of ndarray for local mode and would be None for cluster mode.
        :param batch_size
        :param is_distributed: run in local mode or distributed mode.
               NB: if is_distributed=true, x should be RDD[Sample] and y should be None
        :return: