pyspark/bigdl/nn/layer.py
def predict_distributed(self, data_rdd, batch_size = -1):
        """
        Model inference base on the given data.
        You need to invoke collect() to trigger those action \
        as the returning result is an RDD.

        :param data_rdd: the data to be predict.
        :param batch_size: total batch size of prediction.
        :return: An RDD represent the predict result.
        """
        result = callBigDlFunc(self.bigdl_type,
                               "modelPredictRDD", self.value, data_rdd, batch_size)
        return result.map(lambda data: data.to_ndarray())
def predict_distributed(self, data_rdd, batch_size = -1):
        """
        Model inference base on the given data.
        You need to invoke collect() to trigger those action \
        as the returning result is an RDD.

        :param data_rdd: the data to be predict.
        :param batch_size: total batch size of prediction.
        :return: An RDD represent the predict result.
        """
        result = callBigDlFunc(self.bigdl_type,
                               "modelPredictRDD", self.value, data_rdd, batch_size)
        return result.map(lambda data: data.to_ndarray())
def predict_distributed ( self , data_rdd , batch_size = - 1 ) : result = callBigDlFunc ( self . bigdl_type , "modelPredictRDD" , self . value , data_rdd , batch_size ) return result . map ( lambda data : data . to_ndarray ( ) )
Model inference base on the given data.
        You need to invoke collect() to trigger those action \
        as the returning result is an RDD.

        :param data_rdd: the data to be predict.
        :param batch_size: total batch size of prediction.
        :return: An RDD represent the predict result.
Model inference base on the given data . You need to invoke collect () to trigger those action \ as the returning result is an RDD .