diff --git a/mindspore/nn/probability/distribution/_utils/utils.py b/mindspore/nn/probability/distribution/_utils/utils.py
index 729c42634d..2908f33b6b 100644
--- a/mindspore/nn/probability/distribution/_utils/utils.py
+++ b/mindspore/nn/probability/distribution/_utils/utils.py
@@ -370,7 +370,7 @@ class CheckTensor(PrimitiveWithInfer):
     def __call__(self, x, name):
         if isinstance(x, Tensor):
             return x
-        raise TypeError(f"For {name}, input type should be a Tensor.")
+        raise TypeError(f"For {name}, input type should be a Tensor or Parameter.")
 
 def common_dtype(arg_a, name_a, arg_b, name_b, hint_type):
     """
diff --git a/mindspore/nn/probability/distribution/bernoulli.py b/mindspore/nn/probability/distribution/bernoulli.py
index 0dcbc59689..7fce4b7802 100644
--- a/mindspore/nn/probability/distribution/bernoulli.py
+++ b/mindspore/nn/probability/distribution/bernoulli.py
@@ -186,8 +186,8 @@ class Bernoulli(Distribution):
             H(B) = -probs0 * \log(probs0) - probs1 * \log(probs1)
         """
         probs1 = self._check_param(probs1)
-        probs0 = 1 - probs1
-        return -1 * (probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
+        probs0 = 1.0 - probs1
+        return -(probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
 
     def _cross_entropy(self, dist, probs1_b, probs1=None):
         """
diff --git a/mindspore/nn/probability/distribution/distribution.py b/mindspore/nn/probability/distribution/distribution.py
index dcb904aeac..943b022057 100644
--- a/mindspore/nn/probability/distribution/distribution.py
+++ b/mindspore/nn/probability/distribution/distribution.py
@@ -27,7 +27,7 @@ class Distribution(Cell):
 
     Args:
         seed (int): random seed used in sampling.
-        dtype (mindspore.dtype): type of the distribution.
+        dtype (mindspore.dtype): the type of the event samples. Default: subclass dtype.
         name (str): Python str name prefixed to Ops created by this class. Default: subclass name.
         param (dict): parameters used to initialize the distribution.