diff --git a/python/paddle/v2/framework/evaluator.py b/python/paddle/v2/framework/evaluator.py
index ba2a06187890e2016872f7464639df1047a0b7cf..4f8e6fd4884307eeddfd6eda88019f5bb72a6cd6 100644
--- a/python/paddle/v2/framework/evaluator.py
+++ b/python/paddle/v2/framework/evaluator.py
@@ -121,18 +121,14 @@ class Accuracy(Evaluator):
         return executor.run(eval_program, fetch_list=[eval_out])
 
 
-# This is demo for composing low level op to compute metric
+# Demo for composing low level op to compute the F1 metric
 class F1(Evaluator):
     def __init__(self, input, label, **kwargs):
         super(F1, self).__init__("F1", **kwargs)
-        super(Accuracy, self).__init__("accuracy", **kwargs)
-        g_total = helper.create_global_variable(
-            name=unique_name("Total"),
-            persistable=True,
-            dtype="int64",
-            shape=[1])
-        g_correct = helper.create_global_variable(
-            name=unique_name("Correct"),
-            persistable=True,
-            dtype="int64",
-            shape=[1])
+        g_tp = helper.create_global_variable(
+            name=unique_name("Tp"), persistable=True, dtype="int64", shape=[1])
+        g_fp = helper.create_global_variable(
+            name=unique_name("Fp"), persistable=True, dtype="int64", shape=[1])
+
+        self._states["Tp"] = g_tp
+        self._states["Fp"] = g_fp
diff --git a/python/paddle/v2/framework/tests/test_fit_a_line.py b/python/paddle/v2/framework/tests/test_fit_a_line.py
index aba1f27ad64b2e510a957736fb566c7616d2b435..28588506a675bd6fbf121a2f95c0fda13a8b8934 100644
--- a/python/paddle/v2/framework/tests/test_fit_a_line.py
+++ b/python/paddle/v2/framework/tests/test_fit_a_line.py
@@ -61,6 +61,7 @@ PASS_NUM = 100
 for pass_id in range(PASS_NUM):
     save_persistables(exe, "./fit_a_line.model/", main_program=main_program)
     load_persistables(exe, "./fit_a_line.model/", main_program=main_program)
+    accuracy.reset(exe)
     for data in train_reader():
         x_data = np.array(map(lambda x: x[0], data)).astype("float32")
         y_data = np.array(map(lambda x: x[1], data)).astype("float32")
@@ -75,8 +76,10 @@ for pass_id in range(PASS_NUM):
         outs = exe.run(main_program,
                        feed={'x': tensor_x,
                              'y': tensor_y},
-                       fetch_list=[avg_cost])
+                       fetch_list=[avg_cost, accuracy])
         out = np.array(outs[0])
+        pass_acc = accuracy.eval(exe)
+        print pass_acc
 
         if out[0] < 10.0:
             exit(0)  # if avg cost less than 10.0, we think our code is good.