Small style fixes

tutorials/image/cifar10_estimator/cifar10_main.py

@@ -33,6 +33,8 @@ import functools
 import operator
 import os
 
+import cifar10
+import cifar10_model
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
@@ -41,8 +43,6 @@ from tensorflow.python.training import basic_session_run_hooks
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training import training_util
 
-import cifar10
-import cifar10_model
 
 tf.logging.set_verbosity(tf.logging.INFO)
 
@@ -80,9 +80,10 @@ tf.flags.DEFINE_boolean('run_experiment', False,
                         'If True will run an experiment,'
                         'otherwise will run training and evaluation'
                         'using the estimator interface.'
-                        'Experiments perform training on several workers in parallel'
-                        ', in other words experiments know how to invoke train and'
-                        ' eval in a sensible fashion for distributed training.')
+                        'Experiments perform training on several workers in'
+                        'parallel, in other words experiments know how to'
+                        ' invoke train and eval in a sensible fashion for'
+                        ' distributed training.')
 
 tf.flags.DEFINE_boolean('sync', False,
                         'If true when running in a distributed environment'
@@ -117,8 +118,8 @@ tf.flags.DEFINE_boolean('log_device_placement', False,
 
 
 class ExamplesPerSecondHook(session_run_hook.SessionRunHook):
-  """Hook to print out examples per second
-    
+  """Hook to print out examples per second.
+
     Total time is tracked and then divided by the total number of steps
     to get the average step time and then batch_size is used to determine
     the running average of examples per second. The examples per second for the
@@ -131,15 +132,16 @@ class ExamplesPerSecondHook(session_run_hook.SessionRunHook):
       every_n_steps=100,
       every_n_secs=None,):
     """Initializer for ExamplesPerSecondHook.
-    Args:
+
+      Args:
       batch_size: Total batch size used to calculate examples/second from
       global time.
       every_n_steps: Log stats every n steps.
       every_n_secs: Log stats every n seconds.
-      """
+    """
     if (every_n_steps is None) == (every_n_secs is None):
-      raise ValueError(
-          'exactly one of every_n_steps and every_n_secs should be provided.')
+      raise ValueError('exactly one of every_n_steps'
+                       ' and every_n_secs should be provided.')
     self._timer = basic_session_run_hooks.SecondOrStepTimer(
         every_steps=every_n_steps, every_secs=every_n_secs)
 
@@ -188,6 +190,7 @@ class GpuParamServerDeviceSetter(object):
 
   def __init__(self, worker_device, ps_devices):
     """Initializer for GpuParamServerDeviceSetter.
+
     Args:
       worker_device: the device to use for computation Ops.
       ps_devices: a list of devices to use for Variable Ops. Each variable is
@@ -202,7 +205,7 @@ class GpuParamServerDeviceSetter(object):
       return op.device
     if op.type not in ['Variable', 'VariableV2', 'VarHandleOp']:
       return self.worker_device
-      
+
     # Gets the least loaded ps_device
     device_index, _ = min(enumerate(self.ps_sizes), key=operator.itemgetter(1))
     device_name = self.ps_devices[device_index]
@@ -211,6 +214,7 @@ class GpuParamServerDeviceSetter(object):
 
     return device_name
 
+
 def _create_device_setter(is_cpu_ps, worker, num_gpus):
   """Create device setter object."""
   if is_cpu_ps:
@@ -400,7 +404,8 @@ def input_fn(subset, num_shards):
   elif subset == 'validate' or subset == 'eval':
     batch_size = FLAGS.eval_batch_size
   else:
-    raise ValueError('Subset must be one of \'train\', \'validate\' and \'eval\'')
+    raise ValueError('Subset must be one of \'train\''
+                     ', \'validate\' and \'eval\'')
   with tf.device('/cpu:0'):
     use_distortion = subset == 'train' and FLAGS.use_distortion_for_training
     dataset = cifar10.Cifar10DataSet(FLAGS.data_dir, subset, use_distortion)
@@ -429,7 +434,14 @@ def input_fn(subset, num_shards):
 # create experiment
 def get_experiment_fn(train_input_fn, eval_input_fn, train_steps, eval_steps,
                       train_hooks):
+  """Returns an Experiment function.
+
+  Experiments perform training on several workers in parallel,
+  in other words experiments know how to invoke train and eval in a sensible
+  fashion for distributed training.
+  """
   def _experiment_fn(run_config, hparams):
+    """Returns an Experiment."""
     del hparams  # unused arg
     # create estimator
     classifier = tf.estimator.Estimator(model_fn=_resnet_model_fn,
@@ -491,7 +503,7 @@ def main(unused_argv):
 
   logging_hook = tf.train.LoggingTensorHook(
       tensors=tensors_to_log, every_n_iter=100)
-  
+
   examples_sec_hook = ExamplesPerSecondHook(
       FLAGS.train_batch_size, every_n_steps=10)