Fix lint errors and improve docs in fully_connected_reader.py.

PiperOrigin-RevId: 186537109

1 files changed, 15 insertions, 9 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 461fb1c517..307eede5c0 100644
--- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
+++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py
@@ -1,4 +1,4 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -45,6 +45,7 @@ VALIDATION_FILE = 'validation.tfrecords'
 
 
 def decode(serialized_example):
+  """Parses an image and label from the given `serialized_example`."""
   features = tf.parse_single_example(
       serialized_example,
       # Defaults are not specified since both keys are required.
@@ -66,6 +67,7 @@ def decode(serialized_example):
 
 
 def augment(image, label):
+  """Placeholder for data augmentation."""
   # OPTIONAL: Could reshape into a 28x28 image and apply distortions
   # here.  Since we are not applying any distortions in this
   # example, and the next step expects the image to be flattened
@@ -74,9 +76,8 @@ def augment(image, label):
 
 
 def normalize(image, label):
-  # Convert from [0, 255] -> [-0.5, 0.5] floats.
+  """Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
   image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
-
   return image, label
 
 
@@ -106,18 +107,23 @@ def inputs(train, batch_size, num_epochs):
                           if train else VALIDATION_FILE)
 
   with tf.name_scope('input'):
-    # TFRecordDataset opens a protobuf and reads entries line by line
-    # could also be [list, of, filenames]
+    # TFRecordDataset opens a binary file and reads one record at a time.
+    # `filename` could also be a list of filenames, which will be read in order.
     dataset = tf.data.TFRecordDataset(filename)
-    dataset = dataset.repeat(num_epochs)
 
-    # map takes a python function and applies it to every sample
+    # The map transformation takes a function and applies it to every element
+    # of the dataset.
     dataset = dataset.map(decode)
     dataset = dataset.map(augment)
     dataset = dataset.map(normalize)
 
-    #the parameter is the queue size
+    # The shuffle transformation uses a finite-sized buffer to shuffle elements
+    # in memory. The parameter is the number of elements in the buffer. For
+    # completely uniform shuffling, set the parameter to be the same as the
+    # number of elements in the dataset.
     dataset = dataset.shuffle(1000 + 3 * batch_size)
+
+    dataset = dataset.repeat(num_epochs)
     dataset = dataset.batch(batch_size)
 
     iterator = dataset.make_one_shot_iterator()
@@ -153,7 +159,7 @@ 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