diff --git a/paddle/fluid/operators/clip_by_norm_op.cc b/paddle/fluid/operators/clip_by_norm_op.cc
index c87bded034e382c981d119e8499d6780e288031f..eae86a373be278cbb3ea9425b2ff0169f8faa99e 100644
--- a/paddle/fluid/operators/clip_by_norm_op.cc
+++ b/paddle/fluid/operators/clip_by_norm_op.cc
@@ -54,10 +54,19 @@ be linearly scaled to make the L2 norm of $Out$ equal to $max\_norm$, as
 shown in the following formula:
 
 $$
-Out = \frac{max\_norm * X}{norm(X)},
+Out = \\frac{max\\_norm * X}{norm(X)},
 $$
 
 where $norm(X)$ represents the L2 norm of $X$.
+
+Examples:
+        .. code-block:: python
+
+            data = fluid.layer.data(
+                name='data', shape=[2, 4, 6], dtype='float32')
+            reshaped = fluid.layers.clip_by_norm(
+                x=data, max_norm=0.5)
+
 )DOC");
   }
 };
diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py
index 80e8ff484a4c04df1b41bbca284d7c604962934c..be4dd41577cd7df97151b2dd7b1cf8aa4e2d25ff 100644
--- a/python/paddle/fluid/layers/control_flow.py
+++ b/python/paddle/fluid/layers/control_flow.py
@@ -866,6 +866,7 @@ def array_write(x, i, array=None):
         Variable: The output LOD_TENSOR_ARRAY where the input tensor is written.
 
     Examples:
+
         .. code-block::python
 
           tmp = fluid.layers.zeros(shape=[10], dtype='int32')
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 2c1f9888282188b8066924cef0108ee697f91da6..2c7e04c1e68d770ecbef6b4deee6c3dff79051c0 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -3159,8 +3159,6 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
 
     Examples:
 
-    As an example:
-
         .. code-block:: text
 
             Given:
@@ -3204,7 +3202,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
 
             output.lod = [[0, 4, 8]]
 
-        The simple usage is:
+     Examples:
 
         .. code-block:: python
 
@@ -3738,7 +3736,7 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
 
         Output(i, x, y) = Input(i, x, y) / \left(
         k + \alpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)}
-        (Input(j, x, y))^2 \right)^{\beta}
+        (Input(j, x, y))^2\right)^{\beta}
 
     In the above equation: