pyspark/bigdl/nn/layer.py
def predict_class(self, features):
        """
        Model inference base on the given data which returning label
        :param features: it can be a ndarray or list of ndarray for locally inference
                         or RDD[Sample] for running in distributed fashion
        :return: ndarray or RDD[Sample] depend on the the type of features.
        """
        if isinstance(features, RDD):
            return self.predict_class_distributed(features)
        else:
            return self.predict_class_local(features)
def predict_class(self, features):
        """
        Model inference base on the given data which returning label
        :param features: it can be a ndarray or list of ndarray for locally inference
                         or RDD[Sample] for running in distributed fashion
        :return: ndarray or RDD[Sample] depend on the the type of features.
        """
        if isinstance(features, RDD):
            return self.predict_class_distributed(features)
        else:
            return self.predict_class_local(features)
def predict_class ( self , features ) : if isinstance ( features , RDD ) : return self . predict_class_distributed ( features ) else : return self . predict_class_local ( features )
Model inference base on the given data which returning label
        :param features: it can be a ndarray or list of ndarray for locally inference
                         or RDD[Sample] for running in distributed fashion
        :return: ndarray or RDD[Sample] depend on the the type of features.
Model inference base on the given data which returning label : param features : it can be a ndarray or list of ndarray for locally inference or RDD [ Sample ] for running in distributed fashion : return : ndarray or RDD [ Sample ] depend on the the type of features .