diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index e2c1a65411d7d33ff45a042504bbb8ea95be77e5..8e1e4813247bdf1ed0d725591f789f8640f3fc49 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -10567,17 +10567,17 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
     '''
   **Npair Loss Layer**
   
-  see http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
-
-  Npair loss requires paired data. Npair loss has two parts, the first part is L2 
-  regularizer on the embedding vector, the second part is cross entropy loss which
+  Read `Improved Deep Metric Learning with Multi class N pair Loss Objective <http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf>`_ .
+  
+  Npair loss requires paired data. Npair loss has two parts: the first part is L2 
+  regularizer on the embedding vector; the second part is cross entropy loss which
   takes the similarity matrix of anchor and positive as logits.
 
   Args:
     anchor(Variable): embedding vector for the anchor image. shape=[batch_size, embedding_dims]
     positive(Variable): embedding vector for the positive image. shape=[batch_size, embedding_dims]
-    labels(Varieble): 1-D tensor. shape=[batch_size]
-    l2_res(float32): L2 regularization term on embedding vector, default: 0.02
+    labels(Variable): 1-D tensor. shape=[batch_size]
+    l2_reg(float32): L2 regularization term on embedding vector, default: 0.002
 
   Returns:
     npair loss(Variable): return npair loss, shape=[1]