diff --git a/python/paddle/v2/fluid/tests/test_profiler.py b/python/paddle/v2/fluid/tests/test_profiler.py
index bb76d13ec58a0d1b3f32acb36393311e8188909b..dfee4e2722ceaf9422bbbcb9e319985f1574c6a3 100644
--- a/python/paddle/v2/fluid/tests/test_profiler.py
+++ b/python/paddle/v2/fluid/tests/test_profiler.py
@@ -41,39 +41,49 @@ class TestProfiler(unittest.TestCase):
                 exe.run(fluid.default_main_program(), feed={'data': input})
         os.remove(output_file)
 
-    def test_profiler(self):
-        image = fluid.layers.data(name='x', shape=[784], dtype='float32')
-        hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
-        hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
-        predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
-        label = fluid.layers.data(name='y', shape=[1], dtype='int64')
-        cost = fluid.layers.cross_entropy(input=predict, label=label)
-        avg_cost = fluid.layers.mean(x=cost)
+    def profiler(self, state):
+        if state == 'GPU' and core.is_compile_gpu():
+            return
+        startup_program = fluid.Program()
+        main_program = fluid.Program()
+
+        with fluid.program_guard(main_program, startup_program):
+            image = fluid.layers.data(name='x', shape=[784], dtype='float32')
+            hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
+            hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
+            predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
+            label = fluid.layers.data(name='y', shape=[1], dtype='int64')
+            cost = fluid.layers.cross_entropy(input=predict, label=label)
+            avg_cost = fluid.layers.mean(x=cost)
+            accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
         optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
-        opts = optimizer.minimize(avg_cost)
-        accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+        opts = optimizer.minimize(avg_cost, startup_program=startup_program)
+
+        place = fluid.CPUPlace() if state == 'CPU' else fluid.CUDAPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
 
-        states = ['CPU', 'GPU'] if core.is_compile_gpu() else ['CPU']
-        for state in states:
-            place = fluid.CPUPlace() if state == 'CPU' else fluid.CUDAPlace(0)
-            exe = fluid.Executor(place)
-            exe.run(fluid.default_startup_program())
+        accuracy.reset(exe)
+        with profiler.profiler(state, 'total') as prof:
+            for iter in range(10):
+                if iter == 2:
+                    profiler.reset_profiler()
+                x = np.random.random((32, 784)).astype("float32")
+                y = np.random.randint(0, 10, (32, 1)).astype("int64")
 
-            accuracy.reset(exe)
+                outs = exe.run(main_program,
+                               feed={'x': x,
+                                     'y': y},
+                               fetch_list=[avg_cost] + accuracy.metrics)
+                acc = np.array(outs[1])
+                pass_acc = accuracy.eval(exe)
 
-            with profiler.profiler(state, 'total') as prof:
-                for iter in range(10):
-                    if iter == 2:
-                        profiler.reset_profiler()
-                    x = np.random.random((32, 784)).astype("float32")
-                    y = np.random.randint(0, 10, (32, 1)).astype("int64")
+    def not_test_cpu_profiler(self):
+        self.profiler('CPU')
 
-                    outs = exe.run(fluid.default_main_program(),
-                                   feed={'x': x,
-                                         'y': y},
-                                   fetch_list=[avg_cost] + accuracy.metrics)
-                    acc = np.array(outs[1])
-                    pass_acc = accuracy.eval(exe)
+    def not_test_cuda_profiler(self):
+        self.profiler('GPU')
 
 
 if __name__ == '__main__':