diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 674b5ac58b6febd914cb36c75356d8aa70a908b1..8e9c40877bac59378fb352b5bd2448c5ae3f5d29 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -2628,7 +2628,7 @@ class AverageLayer(LayerBase):
 
 @config_layer('cos')
 class CosSimLayer(LayerBase):
-    def __init__(self, name, inputs, cos_scale=5, device=None):
+    def __init__(self, name, inputs, cos_scale=1, device=None):
         super(CosSimLayer, self).__init__(
             name, 'cos', 1, inputs=inputs, device=device)
         config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 9b6e5774bc82dc05e14a2565fa9cce98764adf04..23c0e9174ff958cbca8963de12cb39bac0b2583d 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -1673,7 +1673,7 @@ def trans_layer(input, name=None, layer_attr=None):
 
 @wrap_name_default()
 @layer_support()
-def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None):
+def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
     """
     Cosine Similarity Layer. The cosine similarity equation is here.
 
diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr
index b30bbb2a4e24d74ebe1d6c8eda8be5aa09217f6d..c1bfdf1b19c61d096c25af061c6fbb3bbfc50265 100644
--- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr
+++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr
@@ -79,7 +79,7 @@ layers {
   inputs {
     input_layer_name: "b"
   }
-  cos_scale: 5
+  cos_scale: 1
 }
 layers {
   name: "__cos_sim_1__"
@@ -92,7 +92,7 @@ layers {
   inputs {
     input_layer_name: "c"
   }
-  cos_scale: 5
+  cos_scale: 1
 }
 layers {
   name: "__sum_to_one_norm_layer_0__"