diff --git a/python/paddle/fluid/tests/unittests/test_recordio_reader.py b/python/paddle/fluid/tests/unittests/test_recordio_reader.py
index 1a135fcdd0b54b5e8fa876d7016a7a08bb7fdcb1..6ec833f6c1a3d0440d3529f6c6e03e47935e881c 100644
--- a/python/paddle/fluid/tests/unittests/test_recordio_reader.py
+++ b/python/paddle/fluid/tests/unittests/test_recordio_reader.py
@@ -21,7 +21,7 @@ import paddle.v2 as paddle
 class TestRecordIO(unittest.TestCase):
     def setUp(self):
         # Convert mnist to recordio file
-        with fluid.program_guard(fluid.Program()):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
             reader = paddle.batch(mnist.train(), batch_size=32)
             feeder = fluid.DataFeeder(
                 feed_list=[  # order is image and label
@@ -35,24 +35,26 @@ class TestRecordIO(unittest.TestCase):
                 './mnist.recordio', reader, feeder)
 
     def test_main(self):
-        data_file = fluid.layers.open_recordio_file(
-            './mnist.recordio',
-            shapes=[[-1, 784], [-1, 1]],
-            lod_levels=[0, 0],
-            dtypes=['float32', 'int64'])
-        img, label = fluid.layers.read_file(data_file)
+        # use new program
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data_file = fluid.layers.open_recordio_file(
+                './mnist.recordio',
+                shapes=[[-1, 784], [-1, 1]],
+                lod_levels=[0, 0],
+                dtypes=['float32', 'int64'])
+            img, label = fluid.layers.read_file(data_file)
 
-        hidden = fluid.layers.fc(input=img, size=100, act='tanh')
-        prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
-        loss = fluid.layers.cross_entropy(input=prediction, label=label)
-        avg_loss = fluid.layers.mean(loss)
+            hidden = fluid.layers.fc(input=img, size=100, act='tanh')
+            prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
+            loss = fluid.layers.cross_entropy(input=prediction, label=label)
+            avg_loss = fluid.layers.mean(loss)
 
-        fluid.optimizer.Adam(learning_rate=1e-3).minimize(avg_loss)
+            fluid.optimizer.Adam(learning_rate=1e-3).minimize(avg_loss)
 
-        exe = fluid.Executor(fluid.CPUPlace())
-        exe.run(fluid.default_startup_program())
-        avg_loss_np = []
-        for i in xrange(100):  # train 100 mini-batch
-            tmp, = exe.run(fetch_list=[avg_loss])
-            avg_loss_np.append(tmp)
-        self.assertLess(avg_loss_np[-1], avg_loss_np[0])
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_startup_program())
+            avg_loss_np = []
+            for i in xrange(100):  # train 100 mini-batch
+                tmp, = exe.run(fetch_list=[avg_loss])
+                avg_loss_np.append(tmp)
+            self.assertLess(avg_loss_np[-1], avg_loss_np[0])