Revert

Author: reyoung

python/paddle/v2/fluid/tests/book/test_image_classification_train.py

@@ -5,7 +5,6 @@
 import paddle.v2.fluid.layers as layers
 import paddle.v2.fluid.nets as nets
 import paddle.v2.fluid.evaluator as evaluator
-from paddle.v2.fluid.io import get_inference_program
 from paddle.v2.fluid.executor import Executor
 from paddle.v2.fluid.initializer import XavierInitializer
 from paddle.v2.fluid.optimizer import AdamOptimizer
@@ -117,11 +116,9 @@ def conv_block(input, num_filter, groups, dropouts):
 
 train_reader = paddle.batch(
     paddle.reader.shuffle(
-        paddle.dataset.cifar.train10(), buf_size=BATCH_SIZE * 10),
+        paddle.dataset.cifar.train10(), buf_size=128 * 10),
     batch_size=BATCH_SIZE)
 
-test_reader = paddle.batch(paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
-
 place = core.CPUPlace()
 exe = Executor(place)
 
@@ -152,41 +149,10 @@ def conv_block(input, num_filter, groups, dropouts):
         loss = np.array(outs[0])
         acc = np.array(outs[1])
         pass_acc = accuracy.eval(exe)
-
-        batch_id = batch_id + 1
-
-        test_accuracy, test_acc_out = evaluator.accuracy(
-            input=predict, label=label)
-
-        test_target = [avg_cost, test_acc_out] + test_accuracy.states().values()
-        inference_program = get_inference_program(test_target)
-
-        test_accuracy.reset(exe)
-
-        for data in test_reader():
-            x_data = np.array(map(lambda x: x[0].reshape(data_shape),
-                                  data)).astype("float32")
-            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-            y_data = np.expand_dims(y_data, axis=1)
-
-            tensor_x = core.LoDTensor()
-            tensor_x.set(x_data, place)
-
-            tensor_y = core.LoDTensor()
-            tensor_y.set(y_data, place)
-
-            outs = exe.run(inference_program,
-                           feed={'pixel': tensor_x,
-                                 'label': tensor_y},
-                           fetch_list=[avg_cost, test_acc_out])
-            out = np.array(outs[0])
-            acc = np.array(outs[1])
-
-        test_pass_acc = test_accuracy.eval(exe)
-
         print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
               " loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
-                  pass_acc) + " test_pass_acc:" + str(test_pass_acc))
+                  pass_acc))
+        batch_id = batch_id + 1
 
         if batch_id > 1:
             # this model is slow, so if we can train two mini batch, we think it works properly.