diff --git a/paddle/fluid/operators/pool_op.cc b/paddle/fluid/operators/pool_op.cc
index fc3636e0b24765f681d3260b07fe854309774a40..4f6d31efb41c0bd1f4feddb3760cf53d27a2de2f 100644
--- a/paddle/fluid/operators/pool_op.cc
+++ b/paddle/fluid/operators/pool_op.cc
@@ -262,28 +262,52 @@ Example:
   For exclusive = false:
        $$
        hstart = i * strides[0] - paddings[0]
+       $$
+       $$
        hend = hstart + ksize[0]
+       $$
+       $$
        wstart = j * strides[1] - paddings[1]
+       $$
+       $$
        wend = wstart + ksize[1]
+       $$
+       $$
        Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
        $$
   For exclusive = true:
        $$
        hstart = max(0, i * strides[0] - paddings[0])
+       $$
+       $$
        hend = min(H, hstart + ksize[0])
+       $$
+       $$
        wstart = max(0, j * strides[1] - paddings[1])
+       $$
+       $$
        wend = min(W, wstart + ksize[1])
+       $$
+       $$
        Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
        $$
 
   For adaptive = true:
-      $$
-      hstart = floor(i * H_{in} / H_{out})
-      hend = ceil((i + 1) * H_{in} / H_{out})
-      wstart = floor(j * W_{in} / W_{out})
-      wend = ceil((j + 1) * W_{in} / W_{out})
-      Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
-      $$
+       $$
+       hstart = floor(i * H_{in} / H_{out})
+       $$
+       $$
+       hend = ceil((i + 1) * H_{in} / H_{out})
+       $$
+       $$
+       wstart = floor(j * W_{in} / W_{out})
+       $$
+       $$
+       wend = ceil((j + 1) * W_{in} / W_{out})
+       $$
+       $$
+       Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
+       $$
 )DOC");
 }
 
@@ -403,35 +427,72 @@ Example:
        H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
        W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
   $$
+
   For exclusive = false:
   $$
   dstart = i * strides[0] - paddings[0]
+  $$
+  $$
   dend = dstart + ksize[0]
+  $$
+  $$
   hstart = j * strides[1] - paddings[1]
+  $$
+  $$
   hend = hstart + ksize[1]
+  $$
+  $$
   wstart = k * strides[2] - paddings[2]
+  $$
+  $$
   wend = wstart + ksize[2]
+  $$
+  $$
   Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
   $$
   For exclusive = true:
   $$
   dstart = max(0, i * strides[0] - paddings[0])
+  $$
+  $$
   dend = min(D, dstart + ksize[0])
+  $$
+  $$
   hstart = max(0, j * strides[1] - paddings[1])
+  $$
+  $$
   hend = min(H, hstart + ksize[1])
+  $$
+  $$
   wstart = max(0, k * strides[2] - paddings[2])
+  $$
+  $$
   wend = min(W, wstart + ksize[2])
+  $$
+  $$
   Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
   $$
 
   For adaptive = true:
   $$
   dstart = floor(i * D_{in} / D_{out})
+  $$
+  $$
   dend = ceil((i + 1) * D_{in} / D_{out})
+  $$
+  $$
   hstart = floor(j * H_{in} / H_{out})
+  $$
+  $$
   hend = ceil((j + 1) * H_{in} / H_{out})
+  $$
+  $$
   wstart = floor(k * W_{in} / W_{out})
+  $$
+  $$
   wend = ceil((k + 1) * W_{in} / W_{out})
+  $$
+  $$
   Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
   $$
 
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 1a7d076835841e3c1b8a90a43437eff13645fb8a..1ae9f6fc3b360c90b50b96a2cedf7f2949cbe451 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -2569,7 +2569,13 @@ def adaptive_pool2d(input,
                     require_index=False,
                     name=None):
     """
-    ${comment}
+    **Adaptive Pool2d Operator**
+    The adaptive_pool2d operation calculates the output based on the input, pool_size,
+    pool_type parameters. Input(X) and output(Out) are in NCHW format, where N is batch
+    size, C is the number of channels, H is the height of the feature, and W is
+    the width of the feature. Parameters(pool_size) should contain two elements which
+    represent height and width, respectively. Also the H and W dimensions of output(Out)
+    is same as Parameter(pool_size).
 
     Args:
         input (Variable): The input tensor of pooling operator. The format of
@@ -2579,8 +2585,8 @@ def adaptive_pool2d(input,
         pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
             it must contain two integers, (pool_size_Height, pool_size_Width).
         pool_type: ${pooling_type_comment}
-        require_index (bool): If true, the index of max pooling point along with outputs.
-            it cannot be set in average pooling type.
+        require_index (bool): If true, the index of max pooling point will be returned along
+            with outputs. It cannot be set in average pooling type.
         name (str|None): A name for this layer(optional). If set None, the
                         layer will be named automatically.
 
@@ -2661,18 +2667,24 @@ def adaptive_pool3d(input,
                     require_index=False,
                     name=None):
     """
-    ${comment}
+    **Adaptive Pool3d Operator**
+    The adaptive_pool3d operation calculates the output based on the input, pool_size,
+    pool_type parameters. Input(X) and output(Out) are in NCDHW format, where N is batch
+    size, C is the number of channels, D is the depth of the feature, H is the height of
+    the feature, and W is the width of the feature. Parameters(pool_size) should contain
+    three elements which represent height and width, respectively. Also the D, H and W
+    dimensions of output(Out) is same as Parameter(pool_size).
 
     Args:
         input (Variable): The input tensor of pooling operator. The format of
-                          input tensor is NCHW, where N is batch size, C is
-                          the number of channels, H is the height of the
-                          feature, and W is the width of the feature.
+                          input tensor is NCDHW, where N is batch size, C is
+                          the number of channels, D is the depth of the feature,
+                          H is the height of the feature, and W is the width of the feature.
         pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
-            it must contain two integers, (Depth, Height, Width).
+            it must contain three integers, (Depth, Height, Width).
         pool_type: ${pooling_type_comment}
-        require_index (bool): If true, the index of max pooling point along with outputs.
-            it cannot be set in average pooling type.
+        require_index (bool): If true, the index of max pooling point will be returned along
+            with outputs. It cannot be set in average pooling type.
         name (str|None): A name for this layer(optional). If set None, the
                         layer will be named automatically.
 
@@ -2709,7 +2721,7 @@ def adaptive_pool3d(input,
               name='data', shape=[3, 32, 32], dtype='float32')
           pool_out, mask = fluid.layers.adaptive_pool3d(
                             input=data,
-                            pool_size=[3, 3],
+                            pool_size=[3, 3, 3],
                             pool_type='avg')
     """
     if pool_type not in ["max", "avg"]: