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")
def evaluate ( self , x , y , batch_size = 32 , sample_weight = None , is_distributed = False ) : 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:
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 :