diff --git a/research/pate_2017/input.py b/research/pate_2017/input.py
index 65c300b..fd7a78d 100644
--- a/research/pate_2017/input.py
+++ b/research/pate_2017/input.py
@@ -130,7 +130,7 @@ def extract_svhn(local_url):
     data_dict = loadmat(file_obj)
 
     # Extract each dictionary (one for data, one for labels)
-    data, labels = data_dict["X"], data_dict["y"]
+    data, labels = data_dict['X'], data_dict['y']
 
     # Set np type
     data = np.asarray(data, dtype=np.float32)
@@ -197,8 +197,8 @@ def extract_cifar10(local_url, data_dir):
   else:
     # Do everything from scratch
     # Define lists of all files we should extract
-    train_files = ["data_batch_" + str(i) for i in xrange(1, 6)]
-    test_file = ["test_batch"]
+    train_files = ['data_batch_' + str(i) for i in xrange(1, 6)]
+    test_file = ['test_batch']
     cifar10_files = train_files + test_file
 
     # Check if all files have already been extracted
@@ -217,7 +217,7 @@ def extract_cifar10(local_url, data_dir):
     labels = []
     for file in train_files:
       # Construct filename
-      filename = data_dir + "/cifar-10-batches-py/" + file
+      filename = data_dir + '/cifar-10-batches-py/' + file
 
       # Unpickle dictionary and extract images and labels
       images_tmp, labels_tmp = unpickle_cifar_dic(filename)
@@ -236,7 +236,7 @@ def extract_cifar10(local_url, data_dir):
     np.save(data_dir + preprocessed_files[1], train_labels)
 
     # Construct filename for test file
-    filename = data_dir + "/cifar-10-batches-py/" + test_file[0]
+    filename = data_dir + '/cifar-10-batches-py/' + test_file[0]
 
     # Load test images and labels
     test_data, test_images = unpickle_cifar_dic(filename)
@@ -260,7 +260,7 @@ def extract_mnist_data(filename, num_images, image_size, pixel_depth):
   Values are rescaled from [0, 255] down to [-0.5, 0.5].
   """
   # if not os.path.exists(file):
-  if not tf.gfile.Exists(filename+".npy"):
+  if not tf.gfile.Exists(filename+'.npy'):
     with gzip.open(filename) as bytestream:
       bytestream.read(16)
       buf = bytestream.read(image_size * image_size * num_images)
@@ -270,7 +270,7 @@ def extract_mnist_data(filename, num_images, image_size, pixel_depth):
       np.save(filename, data)
       return data
   else:
-    with tf.gfile.Open(filename+".npy", mode='r') as file_obj:
+    with tf.gfile.Open(filename+'.npy', mode='r') as file_obj:
       return np.load(file_obj)
 
 
@@ -279,7 +279,7 @@ def extract_mnist_labels(filename, num_images):
   Extract the labels into a vector of int64 label IDs.
   """
   # if not os.path.exists(file):
-  if not tf.gfile.Exists(filename+".npy"):
+  if not tf.gfile.Exists(filename+'.npy'):
     with gzip.open(filename) as bytestream:
       bytestream.read(8)
       buf = bytestream.read(1 * num_images)
@@ -287,7 +287,7 @@ def extract_mnist_labels(filename, num_images):
       np.save(filename, labels)
     return labels
   else:
-    with tf.gfile.Open(filename+".npy", mode='r') as file_obj:
+    with tf.gfile.Open(filename+'.npy', mode='r') as file_obj:
       return np.load(file_obj)