diff --git a/mindspore/explainer/_runner.py b/mindspore/explainer/_runner.py
index 1bc062a46d..abe7ba8a66 100644
--- a/mindspore/explainer/_runner.py
+++ b/mindspore/explainer/_runner.py
@@ -159,13 +159,14 @@ class ExplainRunner:
                 label probability distribution :math:`P(y|x)`. Default: Softmax().
 
         Examples:
+            >>> from mindspore.explainer import ExplainRunner
             >>> from mindspore.explainer.explanation import GuidedBackprop, Gradient
             >>> from mindspore.nn import Sigmoid
             >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
-            >>> # obtain dataset object
-            >>> dataset = get_dataset()
-            >>> classes = ["cat", "dog", ...]
-            >>> # load checkpoint to a network, e.g. resnet50
+            >>> # Prepare the dataset for explaining and evaluation, e.g., Cifar10
+            >>> dataset = get_dataset('/path/to/Cifar10_dataset')
+            >>> classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'turck']
+            >>> # load checkpoint to a network, e.g. checkpoint of resnet50 trained on Cifar10
             >>> param_dict = load_checkpoint("checkpoint.ckpt")
             >>> net = resnet50(len(classes))
             >>> load_param_into_net(net, param_dict)
@@ -204,7 +205,7 @@ class ExplainRunner:
         check_value_type("activation_fn", activation_fn, Cell)
 
         self._model = ms.nn.SequentialCell([explainers[0].model, activation_fn])
-        next_element = dataset.create_tuple_iterator().get_next()
+        next_element = next(dataset.create_tuple_iterator())
         inputs, _, _ = self._unpack_next_element(next_element)
         prop_test = self._model(inputs)
         check_value_type("output of model im explainer", prop_test, ms.Tensor)
@@ -314,7 +315,7 @@ class ExplainRunner:
             dataset (`ds`): the user parsed dataset.
             benchmarkers (list[`AttributionMetric`]): the user parsed benchmarkers.
         """
-        next_element = dataset.create_tuple_iterator().get_next()
+        next_element = next(dataset.create_tuple_iterator())
 
         if len(next_element) not in [1, 2, 3]:
             raise ValueError("The dataset should provide [images] or [images, labels], [images, labels, bboxes]"
@@ -611,29 +612,27 @@ class ExplainRunner:
         inputs, labels, _ = self._unpack_next_element(next_element)
         for idx, inp in enumerate(inputs):
             inp = _EXPAND_DIMS(inp, 0)
-            saliency_dict = saliency_dict_lst[idx]
-            for label, saliency in saliency_dict.items():
-                if isinstance(benchmarker, Localization):
-                    _, _, bboxes = self._unpack_next_element(next_element, True)
-                    if label in labels[idx]:
-                        res = benchmarker.evaluate(explainer, inp, targets=label, mask=bboxes[idx][label],
-                                                   saliency=saliency)
-                        if np.any(res == np.nan):
-                            res = np.zeros_like(res)
+            if isinstance(benchmarker, LabelAgnosticMetric):
+                res = benchmarker.evaluate(explainer, inp)
+                res[np.isnan(res)] = 0.0
+                benchmarker.aggregate(res)
+            else:
+                saliency_dict = saliency_dict_lst[idx]
+                for label, saliency in saliency_dict.items():
+                    if isinstance(benchmarker, Localization):
+                        _, _, bboxes = self._unpack_next_element(next_element, True)
+                        if label in labels[idx]:
+                            res = benchmarker.evaluate(explainer, inp, targets=label, mask=bboxes[idx][label],
+                                                       saliency=saliency)
+                            res[np.isnan(res)] = 0.0
+                            benchmarker.aggregate(res, label)
+                    elif isinstance(benchmarker, LabelSensitiveMetric):
+                        res = benchmarker.evaluate(explainer, inp, targets=label, saliency=saliency)
+                        res[np.isnan(res)] = 0.0
                         benchmarker.aggregate(res, label)
-                elif isinstance(benchmarker, LabelSensitiveMetric):
-                    res = benchmarker.evaluate(explainer, inp, targets=label, saliency=saliency)
-                    if np.any(res == np.nan):
-                        res = np.zeros_like(res)
-                    benchmarker.aggregate(res, label)
-                elif isinstance(benchmarker, LabelAgnosticMetric):
-                    res = benchmarker.evaluate(explainer, inp)
-                    if np.any(res == np.nan):
-                        res = np.zeros_like(res)
-                    benchmarker.aggregate(res)
-                else:
-                    raise TypeError('Benchmarker must be one of LabelSensitiveMetric or LabelAgnosticMetric, but'
-                                    'receive {}'.format(type(benchmarker)))
+                    else:
+                        raise TypeError('Benchmarker must be one of LabelSensitiveMetric or LabelAgnosticMetric, but'
+                                        'receive {}'.format(type(benchmarker)))
 
     def _save_original_image(self, sample_id: int, image):
         """Save an image to summary directory."""