follow comments.

python/paddle/trainer/config_parser.py

@@ -3338,7 +3338,7 @@ class CosSimLayer(LayerBase):
         config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
         config_assert(
             self.get_input_layer(0).size == self.get_input_layer(1).size,
-            'inputs of CosSimLayer must have same dim')
+            'The two inputs of CosSimLayer must have the same dimensionality.')
         self.config.cos_scale = cos_scale
 
 

python/paddle/trainer_config_helpers/layers.py

@@ -2338,7 +2338,7 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
 @layer_support()
 def l2_distance_layer(x, y, name=None, layer_attr=None):
     """
-    This layer calculate and return the Euclidean distance between two input
+    This layer calculates and returns the Euclidean distance between two input
     vectors a and b. The equation is as follows:
 
     ..  math::
@@ -2374,7 +2374,7 @@ def l2_distance_layer(x, y, name=None, layer_attr=None):
     Layer(
         name=name,
         type=LayerType.L2_DISTANCE,
-        inputs=[x.name, x.name],
+        inputs=[x.name, y.name],
         **ExtraLayerAttribute.to_kwargs(layer_attr))
     return LayerOutput(name, LayerType.L2_DISTANCE, parents=[x, y], size=1)
 

python/paddle/trainer_config_helpers/tests/configs/protostr/test_l2_distance_layer.protostr

@@ -20,7 +20,7 @@ layers {
     input_layer_name: "x"
   }
   inputs {
-    input_layer_name: "x"
+    input_layer_name: "y"
   }
 }
 input_layer_names: "x"