pyspark/bigdl/nn/keras/topology.py
def evaluate(self, x, y=None, batch_size=32):
        """
        Evaluate a model on a given dataset in distributed mode.

        # Arguments
        x: Input data. A Numpy array or RDD of Sample.
        y: Labels. A Numpy array. Default is None if x is already RDD of Sample.
        batch_size: Number of samples per gradient update.
        """
        if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
            evaluation_data = to_sample_rdd(x, y)
        elif isinstance(x, RDD) and not y:
            evaluation_data = x
        else:
            raise TypeError("Unsupported evaluation data type: %s" % type(x))
        return callBigDlFunc(self.bigdl_type, "evaluate",
                             self.value,
                             evaluation_data,
                             batch_size)
def evaluate(self, x, y=None, batch_size=32):
        if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
            evaluation_data = to_sample_rdd(x, y)
        elif isinstance(x, RDD) and not y:
            evaluation_data = x
        else:
            raise TypeError("Unsupported evaluation data type: %s" % type(x))
        return callBigDlFunc(self.bigdl_type, "evaluate",
                             self.value,
                             evaluation_data,
                             batch_size)
def evaluate ( self , x , y = None , batch_size = 32 ) : if isinstance ( x , np . ndarray ) and isinstance ( y , np . ndarray ) : evaluation_data = to_sample_rdd ( x , y ) elif isinstance ( x , RDD ) and not y : evaluation_data = x else : raise TypeError ( "Unsupported evaluation data type: %s" % type ( x ) ) return callBigDlFunc ( self . bigdl_type , "evaluate" , self . value , evaluation_data , batch_size )
Evaluate a model on a given dataset in distributed mode.

        # Arguments
        x: Input data. A Numpy array or RDD of Sample.
        y: Labels. A Numpy array. Default is None if x is already RDD of Sample.
        batch_size: Number of samples per gradient update.
Evaluate a model on a given dataset in distributed mode .