diff --git a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
index fdc60861760163d2ebad3b050e551929321baafd..593d0013c9d16c0f10fbb2317ff2b0bab709609f 100644
--- a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
+++ b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
@@ -145,7 +145,7 @@ def seq_to_seq_net():
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(x=cost)
 
-    return avg_cost
+    return avg_cost, prediction
 
 
 def to_lodtensor(data, place):
@@ -163,8 +163,8 @@ def to_lodtensor(data, place):
     return res
 
 
-def main():
-    avg_cost = seq_to_seq_net()
+def train(save_dirname=None):
+    [avg_cost, prediction] = seq_to_seq_net()
 
     optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
     optimizer.minimize(avg_cost)
@@ -196,9 +196,52 @@ def main():
             print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
                   " avg_cost=" + str(avg_cost_val))
             if batch_id > 3:
+                if save_dirname is not None:
+                    fluid.io.save_inference_model(save_dirname, [
+                        'source_sequence', 'target_sequence', 'label_sequence'
+                    ], [prediction], exe)
                 exit(0)
             batch_id += 1
 
 
+def inference(save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # Use fluid.io.load_inference_model to obtain the inference program desc,
+    # the feed_target_names (the names of variables that will be feeded 
+    # data using feed operators), and the fetch_targets (variables that 
+    # we want to obtain data from using fetch operators).
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+    data = [[0, 1, 0, 1], [0, 1, 1, 0, 0, 1]]
+    word_data = to_lodtensor(data, place)
+    trg_word = to_lodtensor(data, place)
+    trg_word_next = to_lodtensor(data, place)
+
+    # Construct feed as a dictionary of {feed_target_name: feed_target_data}
+    # and results will contain a list of data corresponding to fetch_targets.
+    print(feed_target_names)
+    assert feed_target_names[0] == 'source_sequence'
+    assert feed_target_names[1] == 'target_sequence'
+    assert feed_target_names[2] == 'label_sequence'
+    results = exe.run(inference_program,
+                      feed={
+                          feed_target_names[0]: word_data,
+                          feed_target_names[1]: trg_word,
+                          feed_target_names[2]: trg_word_next
+                      },
+                      fetch_list=fetch_targets)
+
+    print("Inference Shape: ", results[0].shape)
+    print("infer results: ", results[0])
+
+
 if __name__ == '__main__':
-    main()
+    save_dirname = "rnn_encoder_decoder.inference.model"
+    train(save_dirname)
+    infer(save_dirname)