diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py
index 40166f4d36e94ec74614e7c81c8c9b20f7c09a72..829056f5767d7c6ea5f29027f4974dc0ccf8dc12 100755
--- a/python/paddle/nn/functional/pooling.py
+++ b/python/paddle/nn/functional/pooling.py
@@ -200,7 +200,6 @@ def avg_pool1d(x,
         .. code-block:: python
           import paddle
           import paddle.nn.functional as F
-          paddle.disable_static()
           data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
           out = F.avg_pool1d(data, kernel_size=2, stride=2, padding=0)
           # out shape: [1, 3, 16]
@@ -253,7 +252,7 @@ def avg_pool1d(x,
             "use_cudnn": True,
             "ceil_mode": ceil_mode,
             "use_mkldnn": False,
-            "exclusive": not exclusive,
+            "exclusive": exclusive,
             "data_format": data_format,
         })
 
@@ -314,7 +313,6 @@ def avg_pool2d(x,
           import paddle
           import paddle.nn.functional as F
           import numpy as np
-          paddle.disable_static()
           # avg pool2d
           x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32))
           out = F.avg_pool2d(x,
@@ -365,7 +363,7 @@ def avg_pool2d(x,
             "use_cudnn": True,
             "ceil_mode": ceil_mode,
             "use_mkldnn": False,
-            "exclusive": not exclusive,
+            "exclusive": exclusive,
             "data_format": data_format,
         })
 
@@ -481,7 +479,7 @@ def avg_pool3d(x,
             "use_cudnn": True,
             "ceil_mode": ceil_mode,
             "use_mkldnn": False,
-            "exclusive": not exclusive,
+            "exclusive": exclusive,
             "data_format": data_format,
         })
 
@@ -538,7 +536,6 @@ def max_pool1d(x,
         .. code-block:: python
           import paddle
           import paddle.nn.functional as F
-          paddle.disable_static()
           data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
           pool_out = F.max_pool1d(data, kernel_size=2, stride=2, padding=0)
           # pool_out shape: [1, 3, 16]
@@ -661,7 +658,6 @@ def max_pool2d(x,
           import paddle
           import paddle.nn.functional as F
           import numpy as np
-          paddle.disable_static()
           # max pool2d
           x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32]).astype(np.float32))
           out = F.max_pool2d(x,
@@ -791,7 +787,7 @@ def max_pool3d(x,
           import paddle
           import paddle.nn.functional as F
           import numpy as np
-          paddle.disable_static()
+
           # max pool3d
           x = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32, 32, 32]).astype(np.float32))
           output = F.max_pool2d(x,
@@ -905,7 +901,7 @@ def adaptive_avg_pool1d(x, output_size, name=None):
               #
               import paddle
               import paddle.nn.functional as F
-              paddle.disable_static()
+
               data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
               pool_out = F.adaptive_average_pool1d(data, output_size=16)
               # pool_out shape: [1, 3, 16])
@@ -982,7 +978,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
             #
             import paddle
             import numpy as np
-            paddle.disable_static()
+
             input_data = np.random.rand(2, 3, 32, 32)
             x = paddle.to_tensor(input_data)
             # x.shape is [2, 3, 32, 32]
@@ -1086,7 +1082,7 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
             #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
             import paddle
             import numpy as np
-            paddle.disable_static()
+
             input_data = np.random.rand(2, 3, 8, 32, 32)
             x = paddle.to_tensor(input_data)
             # x.shape is [2, 3, 8, 32, 32]
@@ -1186,7 +1182,7 @@ def adaptive_max_pool1d(x, output_size, return_mask=False, name=None):
               #
               import paddle
               import paddle.nn.functional as F
-              paddle.disable_static()
+
               data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32))
               pool_out = F.adaptive_max_pool1d(data, output_size=16)
               # pool_out shape: [1, 3, 16])
@@ -1266,7 +1262,7 @@ def adaptive_max_pool2d(x, output_size, return_mask=False, name=None):
               #
               import paddle
               import numpy as np
-              paddle.disable_static()
+
               input_data = np.random.rand(2, 3, 32, 32)
               x = paddle.to_tensor(input_data)
               # x.shape is [2, 3, 32, 32]
@@ -1356,7 +1352,7 @@ def adaptive_max_pool3d(x, output_size, return_mask=False, name=None):
               #
               import paddle
               import numpy as np
-              paddle.disable_static()
+
               input_data = np.random.rand(2, 3, 8, 32, 32)
               x = paddle.to_tensor(input_data)
               # x.shape is [2, 3, 8, 32, 32]