pyspark/bigdl/keras/backend.py
def predict(self, x, batch_size=None, verbose=None, is_distributed=False):
        """Generates output predictions for the input samples,
        processing the samples in a batched way.

        # Arguments
            x: the input data, as a Numpy array or list of Numpy array for local mode.
               as RDD[Sample] for distributed mode
            is_distributed: used to control run in local or cluster. the default value is False
        # Returns
            A Numpy array or RDD[Sample] of predictions.
        """
        if batch_size or verbose:
            raise Exception("we don't support batch_size or verbose for now")
        if is_distributed:
            if isinstance(x, np.ndarray):
                input = to_sample_rdd(x, np.zeros([x.shape[0]]))
            #  np.asarray(self.bmodel.predict(x_rdd).collect())
            elif isinstance(x, RDD):
                input = x
            return self.bmodel.predict(input)
        else:
            if isinstance(x, np.ndarray):
                return self.bmodel.predict_local(x)
        raise Exception("not supported type: %s" % x)
def predict(self, x, batch_size=None, verbose=None, is_distributed=False):
        """Generates output predictions for the input samples,
        processing the samples in a batched way.

        # Arguments
            x: the input data, as a Numpy array or list of Numpy array for local mode.
               as RDD[Sample] for distributed mode
            is_distributed: used to control run in local or cluster. the default value is False
        # Returns
            A Numpy array or RDD[Sample] of predictions.
        """
        if batch_size or verbose:
            raise Exception("we don't support batch_size or verbose for now")
        if is_distributed:
            if isinstance(x, np.ndarray):
                input = to_sample_rdd(x, np.zeros([x.shape[0]]))
            #  np.asarray(self.bmodel.predict(x_rdd).collect())
            elif isinstance(x, RDD):
                input = x
            return self.bmodel.predict(input)
        else:
            if isinstance(x, np.ndarray):
                return self.bmodel.predict_local(x)
        raise Exception("not supported type: %s" % x)
def predict ( self , x , batch_size = None , verbose = None , is_distributed = False ) : if batch_size or verbose : raise Exception ( "we don't support batch_size or verbose for now" ) if is_distributed : if isinstance ( x , np . ndarray ) : input = to_sample_rdd ( x , np . zeros ( [ x . shape [ 0 ] ] ) ) #  np.asarray(self.bmodel.predict(x_rdd).collect()) elif isinstance ( x , RDD ) : input = x return self . bmodel . predict ( input ) else : if isinstance ( x , np . ndarray ) : return self . bmodel . predict_local ( x ) raise Exception ( "not supported type: %s" % x )
Generates output predictions for the input samples,
        processing the samples in a batched way.

        # Arguments
            x: the input data, as a Numpy array or list of Numpy array for local mode.
               as RDD[Sample] for distributed mode
            is_distributed: used to control run in local or cluster. the default value is False
        # Returns
            A Numpy array or RDD[Sample] of predictions.
Generates output predictions for the input samples processing the samples in a batched way .