diff --git a/python/paddle/fluid/layers/learning_rate_scheduler.py b/python/paddle/fluid/layers/learning_rate_scheduler.py
index 716cc7824eff0c56cc55a055310fa8b1913ac5e6..2e5cff74c1dc1ddb4346c0c5bc6417f5293db14a 100644
--- a/python/paddle/fluid/layers/learning_rate_scheduler.py
+++ b/python/paddle/fluid/layers/learning_rate_scheduler.py
@@ -162,22 +162,27 @@ def polynomial_decay(learning_rate,
                      end_learning_rate=0.0001,
                      power=1.0,
                      cycle=False):
-    """Applies polynomial decay to the initial learning rate.
+    """
+    **polynomial_decay**
+
+    Applies polynomial decay to the initial learning rate.
+
+    .. code-block::python
+
+     if cycle:
+       decay_steps = decay_steps * ceil(global_step / decay_steps)
+     else:
+       global_step = min(global_step, decay_steps)
+       decayed_learning_rate = (learning_rate - end_learning_rate) *
+            (1 - global_step / decay_steps) ^ power + end_learning_rate
 
-    >>> if cycle:
-    >>>     decay_steps = decay_steps * ceil(global_step / decay_steps)
-    >>> else:
-    >>>     global_step = min(global_step, decay_steps)
-    >>> decayed_learning_rate = (learning_rate - end_learning_rate) *
-    >>>                   (1 - global_step / decay_steps) ^ power +
-    >>>                   end_learning_rate
     Args:
-        learning_rate: A scalar float32 value or a Variable. This
+        learning_rate(Variable|float32): A scalar float32 value or a Variable. This
           will be the initial learning rate during training
-        decay_steps: A Python `int32` number.
-        end_learning_rate: A Python `float` number.
-        power: A Python `float` number
-        cycle: Boolean. If set true, decay the learning rate every decay_steps.
+        decay_steps(int32): A Python `int32` number.
+        end_learning_rate(float): A Python `float` number.
+        power(float): A Python `float` number
+        cycle(bool, Default False): Boolean. If set true, decay the learning rate every decay_steps.
 
     Returns:
         The decayed learning rate