diff --git a/python/paddle/v2/fluid/param_attr.py b/python/paddle/v2/fluid/param_attr.py
index 86088fdd7ce17b8b7a9688dc838e69b2aa754013..7952a5ea51c00f72664443fb26faa455e89da7be 100644
--- a/python/paddle/v2/fluid/param_attr.py
+++ b/python/paddle/v2/fluid/param_attr.py
@@ -36,6 +36,8 @@ class ParamAttr(object):
     def to_attr(arg):
         if arg is None:
             return ParamAttr()
+        elif isinstance(arg, list) or isinstance(arg, tuple):
+            return [ParamAttr.to_attr(a) for a in arg]
         elif isinstance(arg, ParamAttr):
             return arg
         elif isinstance(arg, str) or isinstance(arg, unicode):
diff --git a/python/paddle/v2/fluid/tests/test_layers.py b/python/paddle/v2/fluid/tests/test_layers.py
index 57f6a362defa6efbb168f5959c4c391f3b4b7bbe..9b88080158139f267e253c598e60a4d92a0eff68 100644
--- a/python/paddle/v2/fluid/tests/test_layers.py
+++ b/python/paddle/v2/fluid/tests/test_layers.py
@@ -29,7 +29,10 @@ class TestBook(unittest.TestCase):
             label = layers.data(name='label', shape=[1], dtype='int32')
             hidden1 = layers.fc(input=images, size=128, act='relu')
             hidden2 = layers.fc(input=hidden1, size=64, act='relu')
-            predict = layers.fc(input=hidden2, size=10, act='softmax')
+            predict = layers.fc(input=[hidden2, hidden1],
+                                size=10,
+                                act='softmax',
+                                param_attr=["sftmax.w1", "sftmax.w2"])
             cost = layers.cross_entropy(input=predict, label=label)
             avg_cost = layers.mean(x=cost)
             self.assertIsNotNone(avg_cost)