Merge pull request #2101 from mari-linhares/patch-9

Fixing style and typos

tutorials/image/cifar10_estimator/cifar10.py

@@ -18,8 +18,6 @@ See http://www.cs.toronto.edu/~kriz/cifar.html.
 """
 import os
 
-from six.moves import xrange  # pylint: disable=redefined-builtin
-
 import tensorflow as tf
 
 HEIGHT = 32

tutorials/image/cifar10_estimator/cifar10_main.py

@@ -78,7 +78,7 @@ tf.flags.DEFINE_float('learning_rate', 0.1,
                       The learning rate will decrease during training.
                       For more details check the model_fn implementation
                       in this file.
-                      """.)
+                      """)
 
 tf.flags.DEFINE_boolean('use_distortion_for_training', True,
                         'If doing image distortion for training.')
@@ -317,9 +317,8 @@ def _resnet_model_fn(features, labels, mode):
         num_batches_per_epoch * x
         for x in np.array([82, 123, 300], dtype=np.int64)
     ]
-    staged_lr = [FLAGS.learning_rate * x 
-        for x in [1, 0.1, 0.01, 0.002]]
-    
+    staged_lr = [FLAGS.learning_rate * x for x in [1, 0.1, 0.01, 0.002]]
+
     learning_rate = tf.train.piecewise_constant(tf.train.get_global_step(),
                                                 boundaries, staged_lr)
     # Create a nicely-named tensor for logging

tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py

@@ -46,8 +46,7 @@ def _bytes_feature(value):
 
 
 def _get_file_names():
-  """Returns the file names expected to exist for training, validation
-  and evaluation in the input_dir."""
+  """Returns the file names expected to exist in the input_dir."""
   file_names = {}
   file_names['train'] = ['data_batch_%d' % i for i in xrange(1, 5)]
   file_names['validation'] = ['data_batch_5']
@@ -81,9 +80,8 @@ def convert_to_tfrecord(input_files, output_file):
       record_writer.write(example.SerializeToString())
   record_writer.close()
 
-def main(argv):
-  del argv  # Unused.
 
+def main(unused_argv):
   file_names = _get_file_names()
   for mode, files in file_names.items():
     input_files = [