diff --git a/python/paddle/v2/tests/test_data_feeder.py b/python/paddle/v2/tests/test_data_feeder.py
index 4d5df6e8931640e1e801b88c12252ace07a53c5d..5f67da6a5b32d74228d727d94ec79b9f7a06dab7 100644
--- a/python/paddle/v2/tests/test_data_feeder.py
+++ b/python/paddle/v2/tests/test_data_feeder.py
@@ -176,7 +176,7 @@ class DataFeederTest(unittest.TestCase):
             self.assertEqual(output_sparse.getSparseRowCols(i), data[i][1])
             self.assertEqual(output_index[i], data[i][0])
 
-        # reader returns 3 featreus, but only use 2 features
+        # reader returns 3 features, but only use 2 features
         data_types = [('fea0', data_type.dense_vector(100)),
                       ('fea2', data_type.integer_value(10))]
         feeder = DataFeeder(data_types, {'fea0': 2, 'fea2': 0})
@@ -187,6 +187,27 @@ class DataFeederTest(unittest.TestCase):
             self.assertEqual(output_dense[i].all(), data[i][2].all())
             self.assertEqual(output_index[i], data[i][0])
 
+        # reader returns 3 featreus, one is duplicate data
+        data_types = [('fea0', data_type.dense_vector(100)),
+                      ('fea1', data_type.sparse_binary_vector(20000)),
+                      ('fea2', data_type.integer_value(10)),
+                      ('fea3', data_type.dense_vector(100))]
+        feeder = DataFeeder(data_types,
+                            {'fea0': 2,
+                             'fea1': 1,
+                             'fea2': 0,
+                             'fea3': 2})
+        arg = feeder(data)
+        fea0 = arg.getSlotValue(0).copyToNumpyMat()
+        fea1 = arg.getSlotValue(1)
+        fea2 = arg.getSlotIds(2).copyToNumpyArray()
+        fea3 = arg.getSlotValue(3).copyToNumpyMat()
+        for i in xrange(batch_size):
+            self.assertEqual(fea0[i].all(), data[i][2].all())
+            self.assertEqual(fea1.getSparseRowCols(i), data[i][1])
+            self.assertEqual(fea2[i], data[i][0])
+            self.assertEqual(fea3[i].all(), data[i][2].all())
+
     def test_multiple_features_tuple(self):
         batch_size = 2
         data = []