pass pre-commit

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:
@@ -81,13 +82,11 @@ 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']))
 
-
     trainer.train(
         reader=paddle.batch(
             paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=8192),
@@ -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()

08.recommender_system/train.py

@@ -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(