Merge pull request #2603 from charlesreid1/master

Consistent, fixed-width printing from adversarial crypto example.

research/adversarial_crypto/README.md

@@ -16,16 +16,16 @@ Cryptography"](https://arxiv.org/abs/1610.06918).
 > encryption and decryption, and also how to apply these operations
 > selectively in order to meet confidentiality goals.
 
-This code allows you to train an encoder/decoder/adversary triplet
+This code allows you to train encoder/decoder/adversary network triplets
 and evaluate their effectiveness on randomly generated input and key
 pairs.
 
 ## Prerequisites
 
 The only software requirements for running the encoder and decoder is having
-Tensorflow installed.
+TensorFlow installed.
 
-Requires Tensorflow r0.12 or later.
+Requires TensorFlow r0.12 or later.
 
 ## Training and evaluating
 
@@ -49,8 +49,8 @@ of two.  In the version in the paper, there was a nonlinear unit
 after the fully-connected layer;  that nonlinear has been removed
 here.  These changes improve the robustness of training.  The
 initializer for the convolution layers has switched to the
-tf.contrib.layers default of xavier_initializer instead of
-a simpler truncated_normal.
+`tf.contrib.layers default` of `xavier_initializer` instead of
+a simpler `truncated_normal`.
 
 ## Contact information
 

research/adversarial_crypto/train_eval.py

@@ -117,7 +117,7 @@ class AdversarialCrypto(object):
     return in_m, in_k
 
   def model(self, collection, message, key=None):
-    """The model for Alice, Bob, and Eve.  If key=None, the first FC layer
+    """The model for Alice, Bob, and Eve.  If key=None, the first fully connected layer
     takes only the message as inputs.  Otherwise, it uses both the key
     and the message.
 
@@ -206,7 +206,7 @@ def doeval(s, ac, n, itercount):
     itercount: Iteration count label for logging.
 
   Returns:
-    Bob and eve's loss, as a percent of bits incorrect.
+    Bob and Eve's loss, as a percent of bits incorrect.
   """
 
   bob_loss_accum = 0
@@ -217,7 +217,7 @@ def doeval(s, ac, n, itercount):
     eve_loss_accum += el
   bob_loss_percent = bob_loss_accum / (n * FLAGS.batch_size)
   eve_loss_percent = eve_loss_accum / (n * FLAGS.batch_size)
-  print('%d %.2f %.2f' % (itercount, bob_loss_percent, eve_loss_percent))
+  print('%10d\t%20.2f\t%20.2f'%(itercount, bob_loss_percent, eve_loss_percent))
   sys.stdout.flush()
   return bob_loss_percent, eve_loss_percent
 
@@ -245,7 +245,7 @@ def train_and_evaluate():
   with tf.Session() as s:
     s.run(init)
     print('# Batch size: ', FLAGS.batch_size)
-    print('# Iter Bob_Recon_Error Eve_Recon_Error')
+    print('# %10s\t%20s\t%20s'%("Iter","Bob_Recon_Error","Eve_Recon_Error"))
 
     if train_until_thresh(s, ac):
       for _ in xrange(EVE_EXTRA_ROUNDS):