diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 637d8dbb856..892cd6765f9 100755
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -139,7 +139,7 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
 
     Args:
         output_size (Union[int, tuple]): The target output size is H x W.
-            ouput_size can be a tuple, or a single H for H x H, and H x W can be int or None
+            ouput_size can be a tuple, or a single H for H x H, and H and W can be int or None
             which means the output size is the same as the input.
 
     Inputs:
@@ -147,13 +147,21 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
           with float16, float32, float64 data type.
 
     Outputs:
-        Tensor, with the same type and same dimensions as the input_x.
+        Tensor, with the same type as the `input_x`.
+        Shape of the output is `input_x_shape[:len(input_x_shape) - len(out_shape)] + out_shape`.
+        If output_size contains None:
+            `out_shape = input_x_shape[-2] + output_size[1]`: If `output_size` is `(None, w)`
+            `out_shape = output_size[1] + input_x_shape[-1]`: If `output_size` is `(h, None)`
+            `out_shape = input_x_shape[-2:]: If output_size` is `(None, None)`
+        If `output_size` dees not contain `None`:
+            `out_shape = (h, h)`: If `output_size` is `h`
+            `out_shape = (h, w)`: If `output_size` is `(h, w)`
 
     Raises:
-        ValueError: if `output_size` is not a tuple and if `output_size` length is not 2.
+        ValueError: If `output_size` is a tuple and if `output_size` length is not 2.
         TypeError: If `input_x` is not a tensor.
         TypeError: If dtype of `input_x` is not float16, float32, float64.
-        ValueError: If `input_x` dimension is less than or more than output_size dimension.
+        ValueError: If `input_x` dimension is less than or equal to output_size dimension.
 
     Supported Platforms:
         ``GPU``
@@ -175,17 +183,18 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
         """Initialize AdaptiveAvgPool2D."""
         validator.check_value_type("output_size", output_size, [int, tuple], self.name)
         if isinstance(output_size, tuple):
-            validator.check_int(len(output_size), 2, Rel.EQ, 'output_size', self.name)
+            validator.check_int(len(output_size), 2, Rel.EQ, 'length of output_size', self.name)
         self.output_size = (output_size, output_size) if isinstance(self.output_size, int) else output_size
 
     def infer_shape(self, x_shape):
         if len(x_shape) <= len(self.output_size):
-            raise ValueError("{} dimension should be larger than {} dimension".format(x_shape, self.output_size))
+            raise ValueError("input_x {} dimension should be larger than output_size {} "
+                             "dimension".format(x_shape, self.output_size))
         validator.check_int(len(x_shape), 5, Rel.LT, 'input_x_dimensions', self.name)
         for input_x_dimension in x_shape:
             validator.check_int(input_x_dimension, 0, Rel.GT, 'input_x dimension', self.name)
         zipped = zip(self.output_size, x_shape[-len(self.output_size):])
-        out_size = [i if i else j for i, j in zipped]
+        out_size = [i if i is not None else j for i, j in zipped]
         for item in out_size:
             validator.check_value_type("item of output_size", item, [int], self.name)
         self.add_prim_attr('output_size', out_size)