diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 31652613fb3a55636b32babbc4bde60d65776c61..52c7a57a1f88ebfbe4cdc73e8b7ba4cd4759c7d2 100755
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -4921,12 +4921,14 @@ def crf_decoding_layer(input,
 
 @wrap_act_default(act=SigmoidActivation())
 @wrap_bias_attr_default(has_bias=True)
+@wrap_param_attr_default()
 @wrap_name_default()
 @layer_support()
 def nce_layer(input,
               label,
               num_classes,
               act=None,
+              param_attr=None,
               weight=None,
               num_neg_samples=10,
               neg_distribution=None,
@@ -4957,6 +4959,8 @@ def nce_layer(input,
     :type num_classes: int
     :param act: Activation, default is Sigmoid.
     :type act: BaseActivation
+    :param param_attr: The Parameter Attribute|list.
+    :type param_attr: ParameterAttribute
     :param num_neg_samples: number of negative samples. Default is 10.
     :type num_neg_samples: int
     :param neg_distribution: The distribution for generating the random negative labels.
@@ -4972,7 +4976,16 @@ def nce_layer(input,
     """
     if isinstance(input, LayerOutput):
         input = [input]
+        assert not isinstance(param_attr, collections.Sequence)
+        param_attr = [param_attr]
+    else:
+        if isinstance(param_attr, collections.Sequence):
+            assert len(input) == len(param_attr)
+        else:
+            param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]
+
     assert isinstance(input, collections.Sequence)
+
     assert isinstance(label, LayerOutput)
     assert label.layer_type == LayerType.DATA
     if neg_distribution is not None:
@@ -4984,9 +4997,9 @@ def nce_layer(input,
 
     ipts_for_layer = []
     parents = []
-    for each_input in input:
+    for each_input, attr in zip(input, param_attr):
         assert isinstance(each_input, LayerOutput)
-        ipts_for_layer.append(each_input.name)
+        ipts_for_layer.append(Input(each_input.name, **attr.attr))
         parents.append(each_input)
     ipts_for_layer.append(label.name)
     parents.append(label)