!6228 Restrict the range of the parameters in random ops

Merge pull request !6228 from peixu_ren/custom_bijector

mindspore/ops/composite/random_ops.py

@@ -49,7 +49,8 @@ def normal(shape, mean, stddev, seed=0):
         shape (tuple): The shape of random tensor to be generated.
         mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak.
           With float32 data type.
-        stddev (Tensor): The deviation σ distribution parameter. With float32 data type.
+        stddev (Tensor): The deviation σ distribution parameter. It should be greater than 0.
+          With float32 data type.
         seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
           Must be non-negative. Default: 0.
 
@@ -136,8 +137,8 @@ def gamma(shape, alpha, beta, seed=0):
 
     Args:
         shape (tuple): The shape of random tensor to be generated.
-        alpha (Tensor): The alpha α distribution parameter. With float32 data type.
-        beta (Tensor): The beta β distribution parameter. With float32 data type.
+        alpha (Tensor): The alpha α distribution parameter. It should be greater than 0. With float32 data type.
+        beta (Tensor): The beta β distribution parameter. It should be greater than 0. With float32 data type.
         seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
           Must be non-negative. Default: 0.
 
@@ -164,7 +165,7 @@ def poisson(shape, mean, seed=0):
 
     Args:
         shape (tuple): The shape of random tensor to be generated.
-        mean (Tensor): The mean μ distribution parameter. With float32 data type.
+        mean (Tensor): The mean μ distribution parameter. It should be greater than 0. With float32 data type.
         seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
           Must be non-negative. Default: 0.
 

mindspore/ops/operations/random_ops.py

@@ -130,9 +130,9 @@ class Gamma(PrimitiveWithInfer):
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
-        - **alpha** (Tensor) - The α distribution parameter.
+        - **alpha** (Tensor) - The α distribution parameter. It should be greater than 0.
           It is also known as the shape parameter with float32 data type.
-        - **beta** (Tensor) - The β distribution parameter.
+        - **beta** (Tensor) - The β distribution parameter. It should be greater than 0.
           It is also known as the scale parameter with float32 data type.
 
     Outputs:
@@ -185,8 +185,8 @@ class Poisson(PrimitiveWithInfer):
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
-        - **mean** (Tensor) - μ, parameter which the distribution was constructed with.
-          The parameter defines the mean number of occurrences of the event, with float32 data type.
+        - **mean** (Tensor) - μ parameter the distribution was constructed with. The parameter defines mean number
+          of occurrences of the event. It should be greater than 0. With float32 data type.
 
     Outputs:
         Tensor. Its shape should be the broadcasted shape of `shape` and the shape of `mean`.