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):
        """
        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)
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.