diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py
index ededf767bcab46028dbfd883b52e66827e86d4be..e47de2436ff7379f56739a2458763a0845cd5fe5 100644
--- a/python/paddle/v2/framework/tests/mnist.py
+++ b/python/paddle/v2/framework/tests/mnist.py
@@ -52,7 +52,7 @@ def grad_var_name(var_name):
     return var_name + "@GRAD"
 
 
-def sgd_optimizer(net, param_name, learning_rate=0.01):
+def sgd_optimizer(net, param_name, learning_rate=0.005):
     grad_name = grad_var_name(param_name)
     optimize_op = Operator(
         "sgd",
@@ -166,9 +166,9 @@ def set_cost():
     cost_grad.set(numpy.ones(cost_shape).astype("float32"), place)
 
 
-def print_cost():
+def mean_cost():
     cost_data = numpy.array(scope.find_var("cross_entropy_3").get_tensor())
-    print(cost_data.sum() / len(cost_data))
+    return cost_data.sum() / len(cost_data)
 
 
 def error_rate(predict, label):
@@ -176,7 +176,7 @@ def error_rate(predict, label):
         axis=1)
     label = numpy.array(scope.find_var(label).get_tensor())
     error_num = numpy.sum(predict_var != label)
-    print(error_num / float(len(label)))
+    return error_num / float(len(label))
 
 
 images = data_layer(name='pixel', dims=[BATCH_SIZE, 784])
@@ -198,16 +198,35 @@ print_inputs_outputs(forward_network)
 print_inputs_outputs(backward_net)
 print_inputs_outputs(optimize_net)
 
-reader = paddle.batch(
+train_reader = paddle.batch(
     paddle.reader.shuffle(
         paddle.dataset.mnist.train(), buf_size=8192),
     batch_size=BATCH_SIZE)
 
+
+def test():
+    test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
+    cost = []
+    error = []
+    for data in test_reader():
+        image = numpy.array(map(lambda x: x[0], data)).astype("float32")
+        label = numpy.array(map(lambda x: x[1], data)).astype("int32")
+        feed_data("pixel", image)
+        feed_data("label", label)
+
+        forward_network.infer_shape(scope)
+        forward_network.run(scope, dev_ctx)
+        cost.append(mean_cost())
+        error.append(error_rate(predict, "label"))
+    print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str(
+        sum(error) / float(len(error))))
+
+
 PASS_NUM = 1000
 for pass_id in range(PASS_NUM):
     batch_id = 0
 
-    for data in reader():
+    for data in train_reader():
         image = numpy.array(map(lambda x: x[0], data)).astype("float32")
         label = numpy.array(map(lambda x: x[1], data)).astype("int32")
         feed_data("pixel", image)
@@ -222,7 +241,8 @@ for pass_id in range(PASS_NUM):
         optimize_net.run(scope, dev_ctx)
         if batch_id % 100 == 0:
             print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]")
-            print_cost()
-            error_rate(predict, "label")
+            test()
+            # print(mean_cost())
+            # print(error_rate(predict, "label"))
 
         batch_id = batch_id + 1