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)
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.