Make notest_dist_image_classification consistent with distributed...

Make notest_dist_image_classification  consistent with distributed implementation in others. (#7899)

* Make this file consistent with others

* fixed style

python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py

@@ -14,8 +14,6 @@
 
 from __future__ import print_function
 
-import sys
-
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import os
@@ -106,10 +104,10 @@ if len(sys.argv) >= 2:
     net_type = sys.argv[1]
 
 if net_type == "vgg":
-    print("train vgg net")
+    print("training vgg net")
     net = vgg16_bn_drop(images)
 elif net_type == "resnet":
-    print("train resnet")
+    print("training resnet")
     net = resnet_cifar10(images, 32)
 else:
     raise ValueError("%s network is not supported" % net_type)
@@ -129,6 +127,7 @@ train_reader = paddle.batch(
     batch_size=BATCH_SIZE)
 
 place = fluid.CPUPlace()
+feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
 exe = fluid.Executor(place)
 
 t = fluid.DistributeTranspiler()
@@ -146,17 +145,14 @@ if training_role == "PSERVER":
     if not current_endpoint:
         print("need env SERVER_ENDPOINT")
         exit(1)
-    print("start pserver at:", current_endpoint)
     pserver_prog = t.get_pserver_program(current_endpoint)
     pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
     exe.run(pserver_startup)
     exe.run(pserver_prog)
-    print("pserver run end")
 elif training_role == "TRAINER":
-    print("start trainer")
     trainer_prog = t.get_trainer_program()
-    feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
     exe.run(fluid.default_startup_program())
+
     for pass_id in range(PASS_NUM):
         accuracy.reset(exe)
         for data in train_reader():
@@ -164,9 +160,10 @@ elif training_role == "TRAINER":
                                 feed=feeder.feed(data),
                                 fetch_list=[avg_cost] + accuracy.metrics)
             pass_acc = accuracy.eval(exe)
-            print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
-                pass_acc))
-            # this model is slow, so if we can train two mini batch, we think it works properly.
+            print("pass_id:" + str(pass_id) + "loss:" + str(loss) + " pass_acc:"
+                  + str(pass_acc))
+            # this model is slow, so if we can train two mini batches,
+            # we think it works properly.
     print("trainer run end")
 else:
     print("environment var TRAINER_ROLE should be TRAINER os PSERVER")