!13772 fix the bug that the data type of float16 and float32 of SeLU is only supported and update the documentation of Mish operator.

From: @wangshuide2020
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @liangchenghui

mindspore/ops/operations/nn_ops.py

@@ -372,7 +372,7 @@ class ReLU(PrimitiveWithCheck):
 
 class Mish(PrimitiveWithInfer):
     r"""
-    Computes MISH of input tensors element-wise.
+    Computes MISH(A Self Regularized Non-Monotonic Neural Activation Function) of input tensors element-wise.
 
     The function is shown as follows:
 
@@ -380,6 +380,9 @@ class Mish(PrimitiveWithInfer):
 
         \text{output} = x * \tan(\log(1 + \exp(\text{x})))
 
+    See more details in `A Self Regularized Non-Monotonic Neural Activation Function
+    <https://arxiv.org/abs/1908.08681>`_.
+
     Inputs:
         - **x** (Tensor) - The input tensor. Only support float16 and float32.
 
@@ -390,7 +393,7 @@ class Mish(PrimitiveWithInfer):
         ``Ascend``
 
     Raise:
-        TypeError: If num_features data type not float16 and float32 Tensor.
+        TypeError: If dtype of `x` is neither float16 nor float32.
 
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
@@ -428,6 +431,11 @@ class SeLU(PrimitiveWithInfer):
         \text{alpha} * (\exp(x_i) - 1), &\text{otherwise.}
         \end{cases}
 
+    where :math:`alpha` and :math:`scale` are pre-defined constants(:math:`alpha=1.67326324`
+    and :math:`scale=1.05070098`).
+
+    See more details in `Self-Normalizing Neural Networks <https://arxiv.org/abs/1706.02515>`_.
+
     Inputs:
         - **input_x** (Tensor) - The input tensor.
 
@@ -438,7 +446,7 @@ class SeLU(PrimitiveWithInfer):
         ``Ascend``
 
     Raise:
-        TypeError: If num_features data type not int8, int32, float16 and float32 Tensor.
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
 
     Examples:
         >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
@@ -458,7 +466,7 @@ class SeLU(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        valid_dtypes = [mstype.int8, mstype.int32, mstype.float16, mstype.float32]
+        valid_dtypes = [mstype.float16, mstype.float32]
         validator.check_tensor_dtype_valid('x', x_dtype, valid_dtypes, self.name)
         return x_dtype