From 1700cbdd29af6745bb3fdd266c071e75034f8f18 Mon Sep 17 00:00:00 2001
From: qijun <qijun1994@hotmail.com>
Date: Mon, 17 Apr 2017 14:33:07 +0800
Subject: [PATCH] pass pre-commit

---
 02.recognize_digits/train.py   | 16 ++++++++--------
 08.recommender_system/train.py |  5 +++--
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/02.recognize_digits/train.py b/02.recognize_digits/train.py
index a69548a..d6ea883 100644
--- a/02.recognize_digits/train.py
+++ b/02.recognize_digits/train.py
@@ -44,13 +44,15 @@ def convolutional_neural_network(img):
         input=conv_pool_2, size=10, act=paddle.activation.Softmax())
     return predict
 
+
 def main():
     paddle.init(use_gpu=False, trainer_count=1)
 
     # define network topology
     images = paddle.layer.data(
         name='pixel', type=paddle.data_type.dense_vector(784))
-    label = paddle.layer.data(name='label', type=paddle.data_type.integer_value(10))
+    label = paddle.layer.data(
+        name='label', type=paddle.data_type.integer_value(10))
 
     # Here we can build the prediction network in different ways. Please
     # choose one by uncomment corresponding line.
@@ -72,7 +74,6 @@ def main():
 
     lists = []
 
-
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id % 100 == 0:
@@ -81,12 +82,10 @@ def main():
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=paddle.batch(
                 paddle.dataset.mnist.test(), batch_size=128))
-            print "Test with Pass %d, Cost %f, %s\n" % (event.pass_id, 
-                                                        result.cost,
-                                                        result.metrics)
+            print "Test with Pass %d, Cost %f, %s\n" % (
+                event.pass_id, result.cost, result.metrics)
             lists.append((event.pass_id, result.cost,
-                            result.metrics['classification_error_evaluator']))
-
+                          result.metrics['classification_error_evaluator']))
 
     trainer.train(
         reader=paddle.batch(
@@ -100,5 +99,6 @@ def main():
     print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
     print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
 
+
 if __name__ == '__main__':
-    main()    
+    main()
diff --git a/08.recommender_system/train.py b/08.recommender_system/train.py
index 7c6274f..37d35ed 100644
--- a/08.recommender_system/train.py
+++ b/08.recommender_system/train.py
@@ -3,7 +3,7 @@ import cPickle
 import copy
 
 
-def get_usr_combined_features():    
+def get_usr_combined_features():
     uid = paddle.layer.data(
         name='user_id',
         type=paddle.data_type.integer_value(
@@ -36,6 +36,7 @@ def get_usr_combined_features():
         act=paddle.activation.Tanh())
     return usr_combined_features
 
+
 def get_mov_combined_features():
     movie_title_dict = paddle.dataset.movielens.get_movie_title_dict()
     mov_id = paddle.layer.data(
@@ -63,7 +64,7 @@ def get_mov_combined_features():
         size=200,
         act=paddle.activation.Tanh())
     return mov_combined_features
-    
+
 
 def main():
     paddle.init(use_gpu=False)
-- 
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