fix some error of distribution and bijector

mindspore/nn/probability/bijector/exp.py

@@ -34,8 +34,6 @@ class Exp(PowerTransform):
         >>> import mindspore
         >>> import mindspore.nn as nn
         >>> from mindspore import Tensor
-        >>> import mindspore.context as context
-        >>> context.set_context(mode=context.GRAPH_MODE)
         >>>
         >>> # To initialize an Exp bijector.
         >>> exp_bijector = nn.probability.bijector.Exp()

mindspore/nn/probability/distribution/geometric.py

@@ -26,7 +26,7 @@ from ._utils.custom_ops import exp_generic, log_generic
 class Geometric(Distribution):
     """
     Geometric Distribution.
-    It represents that there are k failures before the first sucess, namely taht there are in total k+1 Bernoulli trails
+    It represents that there are k failures before the first sucess, namely taht there are in total k+1 Bernoulli trials
     when the first success is achieved.
 
     Args:
@@ -62,7 +62,7 @@ class Geometric(Distribution):
         >>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments as follows.
         >>> # Args:
         >>> #     value (Tensor): the value to be evaluated.
-        >>> #     probs1 (Tensor): the probability of success of a Bernoulli trail. Default: self.probs.
+        >>> #     probs1 (Tensor): the probability of success of a Bernoulli trial. Default: self.probs.
         >>> # Examples of `prob`.
         >>> # Similar calls can be made to other probability functions
         >>> # by replacing `prob` by the name of the function.
@@ -79,7 +79,7 @@ class Geometric(Distribution):
         (3,)
         >>> # Functions `mean`, `sd`, `var`, and `entropy` have the same arguments.
         >>> # Args:
-        >>> #     probs1 (Tensor): the probability of success of a Bernoulli trail. Default: self.probs.
+        >>> #     probs1 (Tensor): the probability of success of a Bernoulli trial. Default: self.probs.
         >>> # Examples of `mean`. `sd`, `var`, and `entropy` are similar.
         >>> ans = g1.mean() # return 1.0
         >>> print(ans.shape)
@@ -94,8 +94,8 @@ class Geometric(Distribution):
         >>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same.
         >>> # Args:
         >>> #     dist (str): the name of the distribution. Only 'Geometric' is supported.
-        >>> #     probs1_b (Tensor): the probability of success of a Bernoulli trail of distribution b.
-        >>> #     probs1_a (Tensor): the probability of success of a Bernoulli trail of distribution a. Default: self.probs.
+        >>> #     probs1_b (Tensor): the probability of success of a Bernoulli trial of distribution b.
+        >>> #     probs1_a (Tensor): the probability of success of a Bernoulli trial of distribution a. Default: self.probs.
         >>> # Examples of `kl_loss`. `cross_entropy` is similar.
         >>> ans = g1.kl_loss('Geometric', probs_b)
         >>> print(ans.shape)
@@ -110,7 +110,7 @@ class Geometric(Distribution):
         >>> # Examples of `sample`.
         >>> # Args:
         >>> #     shape (tuple): the shape of the sample. Default: ()
-        >>> #     probs1 (Tensor): the probability of success of a Bernoulli trail. Default: self.probs.
+        >>> #     probs1 (Tensor): the probability of success of a Bernoulli trial. Default: self.probs.
         >>> ans = g1.sample()
         >>> print(ans.shape)
         ()
@@ -172,7 +172,7 @@ class Geometric(Distribution):
     @property
     def probs(self):
         """
-        Return the probability of success of the Bernoulli trail,
+        Return the probability of success of the Bernoulli trial,
         after casting to dtype.
         """
         return self._probs

mindspore/nn/probability/distribution/uniform.py

@@ -44,11 +44,9 @@ class Uniform(Distribution):
 
     Examples:
         >>> import mindspore
-        >>> import mindspore.context as context
         >>> import mindspore.nn as nn
         >>> import mindspore.nn.probability.distribution as msd
         >>> from mindspore import Tensor
-        >>> context.set_context(mode=context.GRAPH_MODE)
         >>> # To initialize a Uniform distribution of the lower bound 0.0 and the higher bound 1.0.
         >>> u1 = msd.Uniform(0.0, 1.0, dtype=mindspore.float32)
         >>> # A Uniform distribution can be initialized without arguments.