diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py
index 0d5ad46a4fb5b3e6eb057976cdaf3885d7be3209..d08821e510c2b547ddd42e78137edaffd3d850fe 100755
--- a/python/paddle/nn/functional/loss.py
+++ b/python/paddle/nn/functional/loss.py
@@ -392,20 +392,24 @@ def hsigmoid_loss(input,
 
             paddle.set_device('cpu')
 
-            input = paddle.uniform([2, 3])
-            # [[-0.8018668   0.8736385  -0.9064771 ] # random
-            #  [-0.10228515 -0.87188244 -0.8783718 ]] # random
+            input = paddle.uniform([4, 3])
+            # [[0.45424712  -0.77296764  0.82943869] # random
+            #  [0.85062802  0.63303483  0.35312140] # random
+            #  [0.57170701  0.16627562  0.21588242] # random
+            #  [0.27610803  -0.99303514  -0.17114788]] # random
             label = paddle.to_tensor([0, 1, 4, 5])
             num_classes = 5
             weight=paddle.uniform([num_classes-1, 3])
-            # [[-0.24148715  0.8449961  -0.7399121 ] # random
-            #  [-0.9800559   0.43509364  0.9091208 ] # random
-            #  [ 0.60194826  0.10430074 -0.4521166 ] # random
-            #  [-0.4469818  -0.01536179 -0.604454  ]] # random
+            # [[-0.64477652  0.24821866  -0.17456549] # random
+            #  [-0.04635394  0.07473493  -0.25081766] # random
+            #  [ 0.05986035  -0.12185556  0.45153677] # random
+            #  [-0.66236806  0.91271877  -0.88088769]] # random
 
             out=F.hsigmoid_loss(input, label, num_classes, weight)
-            # [[3.0159328]
-            #  [2.2407534]]
+            # [[1.96709502]
+            #  [2.40019274]
+            #  [2.11009121]
+            #  [1.92374969]]
     """
 
     if _non_static_mode():
diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py
index d4e059b6dfa493dcd5fd2959bd8b689046006ee4..a20e7de751d164ca5ab849bbc5efb0b8f970c87b 100644
--- a/python/paddle/nn/layer/loss.py
+++ b/python/paddle/nn/layer/loss.py
@@ -465,14 +465,18 @@ class HSigmoidLoss(Layer):
             import paddle
             paddle.set_device('cpu')
 
-            input = paddle.uniform([2, 3])
-            # [[-0.2820413   0.9528898  -0.81638825] # random
-            #  [-0.6733154  -0.33866507  0.25770962]] # random
+            input = paddle.uniform([4, 3])
+            # [[0.56194401  -0.22450298  -0.10741806] # random
+            #  [0.36136317  0.23556745  0.88748658] # random
+            #  [0.18151939  0.80947340  -0.31078976] # random
+            #  [0.68886101  -0.14239830  -0.41297770]] # random
             label = paddle.to_tensor([0, 1, 4, 5])
             m = paddle.nn.HSigmoidLoss(3, 5)
             out = m(input, label)
-            # [[2.4543471]
-            #  [1.9359267]]
+            # [[2.42524505]
+            #  [1.74917245]
+            #  [3.14571381]
+            #  [2.34564662]]
     """
 
     def __init__(self,