diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 56a3537923c..5ce2830113f 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -3423,7 +3423,7 @@ class ComputeAccidentalHits(PrimitiveWithCheck):
         - **true_classes** (Tensor) - The target classes. With data type of int32 or int64
           and shape [batch_size, num_true].
         - **sampled_candidates** (Tensor) - The sampled_candidates output of CandidateSampler,
-          with shape [num_sampled] and the same type as true_classes.
+          with data type of int32 or int64 and shape [num_sampled].
 
     Outputs:
         Tuple of 3 Tensors.
diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py
index 3a477da37c9..8fbcd355bd8 100644
--- a/mindspore/ops/operations/random_ops.py
+++ b/mindspore/ops/operations/random_ops.py
@@ -602,9 +602,10 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
     Args:
         num_true (int): The number of target classes per training example. Default: 1.
         num_sampled (int): The number of classes to randomly sample. Default: 5.
-        unique (bool): Determines whether sample with rejection. If unique is True,
-            all sampled classes in a batch are unique. Default: True.
-        range_max (int): The number of possible classes. Default: 5.
+        unique (bool): Determines whether sample with rejection. If `unique` is True,
+          all sampled classes in a batch are unique. Default: True.
+        range_max (int): The number of possible classes. When `unique` is True,
+          `range_max` must be greater than or equal to `num_sampled`. Default: 5.
         seed (int): Random seed, must be non-negative.
 
     Inputs:
@@ -644,6 +645,7 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
         Validator.check_value_type("seed", seed, [int], self.name)
         self.num_true = Validator.check_number("num_true", num_true, 1, Rel.GE, self.name)
         self.num_sampled = Validator.check_number("num_sampled", num_sampled, 1, Rel.GE, self.name)
+        Validator.check_number("range_max", range_max, 1, Rel.GE, self.name)
         if unique:
             Validator.check("range_max", range_max, "num_sampled", num_sampled, Rel.GE, self.name)
         self.range_max = range_max