"fix based on comments"

doc/design/evaluator.md

@@ -42,14 +42,14 @@ class Evaluator(object):
        """
     
 
-    def reset(self, executor, program=None):
+    def reset(self, executor, reset_program=None):
       """
       Reset metric states at the begin of each pass/user specified batch number.
       Execute the reset_program to reset the states.
       """
       
 
-    def eval(self, executor, program=None):
+    def eval(self, executor, eval_program=None):
       """
       Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
       Execute the eval_program and return the result.

python/paddle/v2/framework/evaluator.py

@@ -39,7 +39,7 @@ class Evaluator(object):
         """
         raise NotImplementedError()
 
-    def reset(self, executor, program=None):
+    def reset(self, executor, reset_program=None):
         """
         Clear metric states at the begin of each pass/user specified batch
         """
@@ -63,7 +63,7 @@ class Evaluator(object):
                 type="scale", inputs={"X": zeros}, outputs={"Out": g_var})
         executor.run(reset_program, fetch_list=self._states.values())
 
-    def eval(self, executor, program=None):
+    def eval(self, executor, eval_program=None):
         """
         Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
         """

python/paddle/v2/framework/tests/test_fit_a_line.py

@@ -6,7 +6,6 @@ import paddle.v2.framework.optimizer as optimizer
 from paddle.v2.framework.framework import Program, g_main_program
 from paddle.v2.framework.io import save_persistables, load_persistables
 from paddle.v2.framework.executor import Executor
-from paddle.v2.framework.evaluator import Accuracy
 
 import numpy as np
 
@@ -32,8 +31,6 @@ y = layers.data(
     main_program=main_program,
     startup_program=startup_program)
 
-accuracy = evaluator.Accuracy(input=y_predict, label=y)
-
 cost = layers.square_error_cost(
     input=y_predict,
     label=y,
@@ -61,7 +58,6 @@ 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")
@@ -76,10 +72,8 @@ for pass_id in range(PASS_NUM):
         outs = exe.run(main_program,
                        feed={'x': tensor_x,
                              'y': tensor_y},
-                       fetch_list=[avg_cost, accuracy])
+                       fetch_list=[avg_cost])
         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.