diff --git a/fluid/image_classification/se_resnext.py b/fluid/image_classification/se_resnext.py
index 99a62347dadd9fb0ca3d3a85808edc5e5976cc5b..9d7b4ca2be6979ef0ceaf2a5636d8162fe3fc821 100644
--- a/fluid/image_classification/se_resnext.py
+++ b/fluid/image_classification/se_resnext.py
@@ -103,66 +103,87 @@ def train(learning_rate,
           batch_size,
           num_passes,
           init_model=None,
-          model_save_dir='model'):
+          model_save_dir='model',
+          parallel=True):
     class_dim = 1000
     image_shape = [3, 224, 224]
 
     image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
-    out = SE_ResNeXt(input=image, class_dim=class_dim)
-
-    cost = fluid.layers.cross_entropy(input=out, label=label)
-    avg_cost = fluid.layers.mean(x=cost)
+    if parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+
+        with pd.do():
+            image_ = pd.read_input(image)
+            label_ = pd.read_input(label)
+            out = SE_ResNeXt(input=image_, class_dim=class_dim)
+            cost = fluid.layers.cross_entropy(input=out, label=label_)
+            avg_cost = fluid.layers.mean(x=cost)
+            accuracy = fluid.layers.accuracy(input=out, label=label_)
+            pd.write_output(avg_cost)
+            pd.write_output(accuracy)
+
+        avg_cost, accuracy = pd()
+        avg_cost = fluid.layers.mean(x=avg_cost)
+        accuracy = fluid.layers.mean(x=accuracy)
+    else:
+        out = SE_ResNeXt(input=image, class_dim=class_dim)
+        cost = fluid.layers.cross_entropy(input=out, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+        accuracy = fluid.layers.accuracy(input=out, label=label)
 
     optimizer = fluid.optimizer.Momentum(
         learning_rate=learning_rate,
         momentum=0.9,
         regularization=fluid.regularizer.L2Decay(1e-4))
     opts = optimizer.minimize(avg_cost)
-    accuracy = fluid.evaluator.Accuracy(input=out, label=label)
 
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):
-        test_accuracy = fluid.evaluator.Accuracy(input=out, label=label)
-        test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
-        inference_program = fluid.io.get_inference_program(test_target)
+        inference_program = fluid.io.get_inference_program([avg_cost, accuracy])
 
     place = fluid.CUDAPlace(0)
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
     if init_model is not None:
-        fluid.io.load_persistables_if_exist(exe, init_model)
+        fluid.io.load_persistables(exe, init_model)
 
     train_reader = paddle.batch(reader.train(), batch_size=batch_size)
     test_reader = paddle.batch(reader.test(), batch_size=batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
 
     for pass_id in range(num_passes):
-        accuracy.reset(exe)
         for batch_id, data in enumerate(train_reader()):
-            loss, acc = exe.run(fluid.default_main_program(),
-                                feed=feeder.feed(data),
-                                fetch_list=[avg_cost] + accuracy.metrics)
-            print("Pass {0}, batch {1}, loss {2}, acc {3}".format(
-                pass_id, batch_id, loss[0], acc[0]))
-        pass_acc = accuracy.eval(exe)
-
-        test_accuracy.reset(exe)
+            loss = exe.run(fluid.default_main_program(),
+                           feed=feeder.feed(data),
+                           fetch_list=[avg_cost])
+            print("Pass {0}, batch {1}, loss {2}".format(pass_id, batch_id,
+                                                         float(loss[0])))
+
+        total_loss = 0.0
+        total_acc = 0.0
+        total_batch = 0
         for data in test_reader():
             loss, acc = exe.run(inference_program,
                                 feed=feeder.feed(data),
-                                fetch_list=[avg_cost] + test_accuracy.metrics)
-        test_pass_acc = test_accuracy.eval(exe)
-        print("End pass {0}, train_acc {1}, test_acc {2}".format(
-            pass_id, pass_acc, test_pass_acc))
+                                fetch_list=[avg_cost, accuracy])
+            total_loss += float(loss)
+            total_acc += float(acc)
+            total_batch += 1
+        print("End pass {0}, test_loss {1}, test_acc {2}".format(
+            pass_id, total_loss / total_batch, total_acc / total_batch))
 
         model_path = os.path.join(model_save_dir, str(pass_id))
-        if not os.path.isdir(model_path):
-            os.makedirs(model_path)
-        fluid.io.save_persistables(exe, model_path)
+        fluid.io.save_inference_model(model_path, ['image'], [out], exe)
 
 
 if __name__ == '__main__':
-    train(learning_rate=0.1, batch_size=8, num_passes=100, init_model=None)
+    train(
+        learning_rate=0.1,
+        batch_size=8,
+        num_passes=100,
+        init_model=None,
+        parallel=False)