!10313 Fix some issues in the docs in the distribution and bijector classes

From: @shallydeng
Reviewed-by: @zichun_ye,@sunnybeike
Signed-off-by: @sunnybeike

mindspore/nn/probability/bijector/gumbel_cdf.py

@@ -30,7 +30,7 @@ class GumbelCDF(Bijector):
     Args:
         loc (float, list, numpy.ndarray, Tensor): The location. Default: 0..
         scale (float, list, numpy.ndarray, Tensor): The scale. Default: 1.0.
-        name (str): The name of the Bijector. Default: 'Gumbel_CDF'.
+        name (str): The name of the Bijector. Default: 'GumbelCDF'.
 
     Supported Platforms:
         ``Ascend`` ``GPU``

mindspore/nn/probability/bijector/invert.py

@@ -19,7 +19,7 @@ from .bijector import Bijector
 
 class Invert(Bijector):
     r"""
-    Invert Bijector.
+    Invert Bijector. Compute the inverse function of the input bijector.
 
     Args:
         bijector (Bijector): Base Bijector.
@@ -59,6 +59,7 @@ class Invert(Bijector):
         param = dict(locals())
         validator.check_value_type('bijector', bijector, [Bijector], "Invert")
         name = name or ('Invert' + bijector.name)
+        param["name"] = name
         super(Invert, self).__init__(is_constant_jacobian=bijector.is_constant_jacobian,
                                      is_injective=bijector.is_injective,
                                      name=name,

mindspore/nn/probability/distribution/bernoulli.py

@@ -164,7 +164,7 @@ class Bernoulli(Distribution):
     def probs(self):
         """
         Return the probability of that the outcome is 1
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._probs
 

mindspore/nn/probability/distribution/beta.py

@@ -191,7 +191,7 @@ class Beta(Distribution):
     def concentration1(self):
         """
         Return the concentration1, also know as the alpha of the Beta distribution,
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._concentration1
 
@@ -199,7 +199,7 @@ class Beta(Distribution):
     def concentration0(self):
         """
         Return the concentration0, also know as the beta of the Beta distribution,
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._concentration0
 

mindspore/nn/probability/distribution/categorical.py

@@ -190,7 +190,7 @@ class Categorical(Distribution):
     @property
     def probs(self):
         """
-        Return the probability after casting to self.dtype.
+        Return the probability after casting to dtype.
         """
         return self._probs
 

mindspore/nn/probability/distribution/cauchy.py

@@ -181,14 +181,14 @@ class Cauchy(Distribution):
     @property
     def loc(self):
         """
-        Return the location of the distribution after casting to self.dtype.
+        Return the location of the distribution after casting to dtype.
         """
         return self._loc
 
     @property
     def scale(self):
         """
-        Return the scale of the distribution after casting to self.dtype.
+        Return the scale of the distribution after casting to dtype.
         """
         return self._scale
 

mindspore/nn/probability/distribution/exponential.py

@@ -167,7 +167,7 @@ class Exponential(Distribution):
     @property
     def rate(self):
         """
-        Return `rate` of the distribution after casting to self.dtype.
+        Return `rate` of the distribution after casting to dtype.
         """
         return self._rate
 

mindspore/nn/probability/distribution/gamma.py

@@ -189,7 +189,7 @@ class Gamma(Distribution):
     def concentration(self):
         """
         Return the concentration, also know as the alpha of the Gamma distribution,
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._concentration
 
@@ -197,7 +197,7 @@ class Gamma(Distribution):
     def rate(self):
         """
         Return the rate, also know as the beta of the Gamma distribution,
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._rate
 

mindspore/nn/probability/distribution/geometric.py

@@ -173,7 +173,7 @@ class Geometric(Distribution):
     def probs(self):
         """
         Return the probability of success of the Bernoulli trail,
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._probs
 

mindspore/nn/probability/distribution/gumbel.py

@@ -128,14 +128,14 @@ class Gumbel(TransformedDistribution):
     @property
     def loc(self):
         """
-        Return the location of the distribution after casting to self.dtype.
+        Return the location of the distribution after casting to dtype.
         """
         return self._loc
 
     @property
     def scale(self):
         """
-        Return the scale of the distribution after casting to self.dtype.
+        Return the scale of the distribution after casting to dtype.
         """
         return self._scale
 

mindspore/nn/probability/distribution/log_normal.py

@@ -184,7 +184,7 @@ class LogNormal(msd.TransformedDistribution):
     def loc(self):
         """
         Distribution parameter for the pre-transformed mean
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._loc
 
@@ -192,7 +192,7 @@ class LogNormal(msd.TransformedDistribution):
     def scale(self):
         """
         Distribution parameter for the pre-transformed standard deviation
-        after casting to self.dtype.
+        after casting to dtype.
         """
         return self._scale
 

mindspore/nn/probability/distribution/logistic.py

@@ -181,14 +181,14 @@ class Logistic(Distribution):
     @property
     def loc(self):
         """
-        Return the location of the distribution after casting to self.dtype.
+        Return the location of the distribution after casting to dtype.
         """
         return self._loc
 
     @property
     def scale(self):
         """
-        Return the scale of the distribution after casting to self.dtype.
+        Return the scale of the distribution after casting to dtype.
         """
         return self._scale
 

mindspore/nn/probability/distribution/poisson.py

@@ -35,7 +35,7 @@ class Poisson(Distribution):
         name (str): The name of the distribution. Default: 'Poisson'.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Note:
         `rate` must be strictly greater than 0.
@@ -157,7 +157,7 @@ class Poisson(Distribution):
     @property
     def rate(self):
         """
-        Return `rate` of the distribution after casting to self.dtype.
+        Return `rate` of the distribution after casting to dtype.
         """
         return self._rate
 

mindspore/nn/probability/distribution/transformed_distribution.py

@@ -31,7 +31,7 @@ class TransformedDistribution(Distribution):
 
     Args:
         bijector (Bijector): The transformation to perform.
-        distribution (Distribution): The original distribution. Must has dtype of mindspore.float_type.
+        distribution (Distribution): The original distribution. Must has a float dtype.
         seed (int): The seed is used in sampling. The global seed is used if it is None. Default:None.
           If this seed is given when a TransformedDistribution object is initialised, the object's sampling function
           will use this seed; elsewise, the underlying distribution's seed will be used.
@@ -42,7 +42,7 @@ class TransformedDistribution(Distribution):
 
     Note:
         The arguments used to initialize the original distribution cannot be None.
-        For example, mynormal = nn.Normal(dtype=dtyple.float32) cannot be used to initialized a
+        For example, mynormal = msd.Normal(dtype=mindspore.float32) cannot be used to initialized a
         TransformedDistribution since `mean` and `sd` are not specified.
         `batch_shape` is the batch_shape of the original distribution.
         `broadcast_shape` is the broadcast shape between the original distribution and bijector.

mindspore/nn/probability/distribution/uniform.py

@@ -181,14 +181,14 @@ class Uniform(Distribution):
     @property
     def low(self):
         """
-        Return the lower bound of the distribution after casting to self.dtype.
+        Return the lower bound of the distribution after casting to dtype.
         """
         return self._low
 
     @property
     def high(self):
         """
-        Return the upper bound of the distribution after casting to self.dtype..
+        Return the upper bound of the distribution after casting to dtype..
         """
         return self._high