fix the validation of Softmax, Tanh, Elu operators.

mindspore/nn/layer/activation.py

@@ -175,7 +175,7 @@ class ELU(Cell):
         ValueError: If `alpha` is not equal to 1.0.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float32)

mindspore/ops/operations/nn_ops.py

@@ -172,7 +172,7 @@ class Softmax(PrimitiveWithInfer):
         return logits
 
     def infer_dtype(self, logits):
-        validator.check_tensor_dtype_valid("logits", logits, (mstype.float16, mstype.float32), self.name)
+        validator.check_tensor_dtype_valid("logits", logits, mstype.float_type, self.name)
         return logits
 
 
@@ -603,7 +603,7 @@ class Elu(PrimitiveWithInfer):
         return input_x
 
     def infer_dtype(self, input_x):
-        validator.check_tensor_dtype_valid('input_x', input_x, (mstype.float16, mstype.float32), self.name)
+        validator.check_tensor_dtype_valid('input_x', input_x, mstype.float_type, self.name)
         return input_x
 
 
@@ -761,7 +761,7 @@ class Tanh(PrimitiveWithInfer):
         TypeError: If dtype of `input_x` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU``  ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32)
@@ -779,7 +779,7 @@ class Tanh(PrimitiveWithInfer):
         return input_x
 
     def infer_dtype(self, input_x):
-        validator.check_tensor_dtype_valid("input_x", input_x, (mstype.float16, mstype.float32), self.name)
+        validator.check_tensor_dtype_valid("input_x", input_x, mstype.float_type, self.name)
         return input_x