Add C0330 bad-continuation check to pylint.

PiperOrigin-RevId: 183270896

1 files changed, 21 insertions, 28 deletions

diff --git a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
index fa4c1c0da5..461fb1c517 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Train and Eval the MNIST network.
 
 This version is like fully_connected_feed.py but uses data converted
@@ -65,6 +64,7 @@ def decode(serialized_example):
 
   return image, label
 
+
 def augment(image, label):
   # OPTIONAL: Could reshape into a 28x28 image and apply distortions
   # here.  Since we are not applying any distortions in this
@@ -72,12 +72,14 @@ def augment(image, label):
   # into a vector, we don't bother.
   return image, label
 
+
 def normalize(image, label):
   # Convert from [0, 255] -> [-0.5, 0.5] floats.
   image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
 
   return image, label
 
+
 def inputs(train, batch_size, num_epochs):
   """Reads input data num_epochs times.
 
@@ -98,9 +100,10 @@ def inputs(train, batch_size, num_epochs):
     over the dataset once. On the other hand there is no special initialization
     required.
   """
-  if not num_epochs: num_epochs = None
-  filename = os.path.join(FLAGS.train_dir,
-                          TRAIN_FILE if train else VALIDATION_FILE)
+  if not num_epochs:
+    num_epochs = None
+  filename = os.path.join(FLAGS.train_dir, TRAIN_FILE
+                          if train else VALIDATION_FILE)
 
   with tf.name_scope('input'):
     # TFRecordDataset opens a protobuf and reads entries line by line
@@ -127,13 +130,11 @@ def run_training():
   # Tell TensorFlow that the model will be built into the default Graph.
   with tf.Graph().as_default():
     # Input images and labels.
-    image_batch, label_batch = inputs(train=True, batch_size=FLAGS.batch_size,
-                               num_epochs=FLAGS.num_epochs)
+    image_batch, label_batch = inputs(
+        train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs)
 
     # Build a Graph that computes predictions from the inference model.
-    logits = mnist.inference(image_batch,
-                             FLAGS.hidden1,
-                             FLAGS.hidden2)
+    logits = mnist.inference(image_batch, FLAGS.hidden1, FLAGS.hidden2)
 
     # Add to the Graph the loss calculation.
     loss = mnist.loss(logits, label_batch)
@@ -152,7 +153,7 @@ def run_training():
       sess.run(init_op)
       try:
         step = 0
-        while True: #train until OutOfRangeError
+        while True:  #train until OutOfRangeError
           start_time = time.time()
 
           # Run one step of the model.  The return values are
@@ -168,10 +169,12 @@ def run_training():
           # Print an overview fairly often.
           if step % 100 == 0:
             print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
-                                                     duration))
+                                                       duration))
           step += 1
       except tf.errors.OutOfRangeError:
-        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
+        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs,
+                                                          step))
+
 
 def main(_):
   run_training()
@@ -183,37 +186,27 @@ if __name__ == '__main__':
       '--learning_rate',
       type=float,
       default=0.01,
-      help='Initial learning rate.'
-  )
+      help='Initial learning rate.')
   parser.add_argument(
       '--num_epochs',
       type=int,
       default=2,
-      help='Number of epochs to run trainer.'
-  )
+      help='Number of epochs to run trainer.')
   parser.add_argument(
       '--hidden1',
       type=int,
       default=128,
-      help='Number of units in hidden layer 1.'
-  )
+      help='Number of units in hidden layer 1.')
   parser.add_argument(
       '--hidden2',
       type=int,
       default=32,
-      help='Number of units in hidden layer 2.'
-  )
-  parser.add_argument(
-      '--batch_size',
-      type=int,
-      default=100,
-      help='Batch size.'
-  )
+      help='Number of units in hidden layer 2.')
+  parser.add_argument('--batch_size', type=int, default=100, help='Batch size.')
   parser.add_argument(
       '--train_dir',
       type=str,
       default='/tmp/data',
-      help='Directory with the training data.'
-  )
+      help='Directory with the training data.')
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)