diff --git a/settings.py b/settings.py
index 482724e..9581966 100644
--- a/settings.py
+++ b/settings.py
@@ -1,15 +1,15 @@
 import tensorflow as tf
 import numpy as np
+
 flags = tf.app.flags
 FLAGS = flags.FLAGS
 
-
 flags.DEFINE_integer('hidden3', 64, 'Number of units in hidden layer 3.')
 flags.DEFINE_integer('discriminator_out', 0, 'discriminator_out.')
 flags.DEFINE_float('discriminator_learning_rate', 0.001, 'Initial learning rate.')
-flags.DEFINE_float('learning_rate', .6*0.001, 'Initial learning rate.')
-flags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.')#64 for Citeseer and Pubmed
-flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')#64 for Citeseer and Pubmed
+flags.DEFINE_float('learning_rate', .6 * 0.001, 'Initial learning rate.')
+flags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.')  # 64 for Citeseer and Pubmed
+flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')  # 64 for Citeseer and Pubmed
 flags.DEFINE_float('weight_decay', 0., 'Weight for L2 loss on embedding matrix.')
 flags.DEFINE_float('dropout', 0., 'Dropout rate (1 - keep probability).')
 flags.DEFINE_integer('features', 1, 'Whether to use features (1) or not (0).')
@@ -19,8 +19,7 @@ flags.DEFINE_integer('iterations', 60, 'number of iterations.')
 '''
 infor: number of clusters 
 '''
-infor = {'cora': 7, 'citeseer': 6, 'pubmed':3}
-
+infor = {'cora': 7, 'citeseer': 6, 'pubmed': 3}
 
 '''
 We did not set any seed when we conducted the experiments described in the paper;
@@ -30,6 +29,7 @@ seed = 7
 np.random.seed(seed)
 tf.set_random_seed(seed)
 
+
 def get_settings(dataname, model, task):
     if dataname != 'citeseer' and dataname != 'cora' and dataname != 'pubmed':
         print('error: wrong data set name')
@@ -39,11 +39,10 @@ def get_settings(dataname, model, task):
     if task == 'clustering':
         iterations = FLAGS.iterations
         clustering_num = infor[dataname]
-        re = {'data_name': dataname, 'iterations' : iterations, 'clustering_num' :clustering_num, 'model' : model}
+        re = {'data_name': dataname, 'iterations': iterations, 'clustering_num': clustering_num, 'model': model}
     elif task == 'link_prediction':
         iterations = 4 * FLAGS.iterations
         print('epoch is', iterations)
-        re = {'data_name': dataname, 'iterations' : iterations,'model' : model}
+        re = {'data_name': dataname, 'iterations': iterations, 'model': model}
 
     return re
-