TensorFlow/Adversarial: Difference between revisions

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Adversarial neural networks use an architecture consisting of two separate neural networks - one network attempts to learn how to accomplish a task, and another network attempts to differentiate between the output of the first network and the "real" output.
Adversarial neural networks use an architecture consisting of two separate neural networks - one network attempts to learn how to accomplish a task, and another network attempts to differentiate between the output of the first network and the "real" output.


=TensorFlow Examples of Adversarial Neural Networks=
=TensorFlow Adversarial Examples=


==Adversarial Crypto==
==Adversarial Crypto==
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This adversarial crypto neural network attempts to learn how to protect communications using the adversarial architecture.
This adversarial crypto neural network attempts to learn how to protect communications using the adversarial architecture.


Link to paper: "Learning to Protect Communications with Adversarial Neural Cryptography": https://arxiv.org/abs/1610.06918
Paper: "Learning to Protect Communications with Adversarial Neural Cryptography"
 
Link to paper: https://arxiv.org/abs/1610.06918


Link to code: https://github.com/tensorflow/models/tree/master/research/adversarial_crypto
Link to code: https://github.com/tensorflow/models/tree/master/research/adversarial_crypto
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===The Model===
===The Model===


We'll step through the code line-by-line again. Here's the link to the code: https://github.com/tensorflow/models/blob/master/research/adversarial_crypto/train_eval.py
We'll step through the code line-by-line. Here's the link to the code: https://github.com/tensorflow/models/blob/master/research/adversarial_crypto/train_eval.py


====License====
Full model walkthrough is on the [[TensorFlow/Adversarial Crypto]] page.


Obligatory license info:
The rundown is:
* Create an AdversarialCrypto class that holds a training optimizer object for the Bob and Alice networks
* Define a method that evaluates the networks as-is and prints the percent losses
* Define a method that trains the network for a specified number of iterations, stopping early if the network reaches its target losses
* Define a method that calls the training function (above), then re-trains Eve several more times from scratch


<pre>
==Adversarial Text==
# Copyright 2016 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.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
</pre>
 
Some info about the network:
* There are actually 3 neural networks involved: Alice, Bob, and Eve
* Alice takes inputs in_m (message), in_k (key) and outputs the ciphertext
* Bob takes inputs in_k (key), ciphertext and attempts to output the plaintext
* Even takes input ciphertext (no key) and also attempts to output the plaintext
 
The file starts with imports/declarations to be compatible with Python 3:
 
<pre>
# TensorFlow Python 3 compatibility
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import signal
import sys
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
</pre>
 
====Input Argument Flags and Parameters====
 
Hyperparameter flags can be set on the command line:
 
<pre>
flags = tf.app.flags
flags.DEFINE_float('learning_rate', 0.0008, 'Constant learning rate')
flags.DEFINE_integer('batch_size', 4096, 'Batch size')
FLAGS = flags.FLAGS
</pre>
 
The FLAGS stuff does not seem to be defined anywhere in the documentation, so the usage is not clear here. But, as an author on TF project states [https://stackoverflow.com/questions/33932901/whats-the-purpose-of-tf-app-flags-in-tensorflow#33938519 here], it is intended to make demos more convenient, and essentially wraps argparse.
 
Also see [[TensorFlow/Command Line Args]].
 
More parameter definitions follow:
 
<pre>
# Input and output configuration.
TEXT_SIZE = 16
KEY_SIZE = 16
 
# Training parameters.
ITERS_PER_ACTOR = 1
EVE_MULTIPLIER = 2  # Train Eve 2x for every step of Alice/Bob
# Train until either max loops or Alice/Bob "good enough":
MAX_TRAINING_LOOPS = 850000
BOB_LOSS_THRESH = 0.02  # Exit when Bob loss < 0.02 and Eve > 7.7 bits
EVE_LOSS_THRESH = 7.7
 
# Logging and evaluation.
PRINT_EVERY = 200  # In training, log every 200 steps.
EVE_EXTRA_ROUNDS = 2000  # At end, train eve a bit more.
RETRAIN_EVE_ITERS = 10000  # Retrain eve up to ITERS*LOOPS times.
RETRAIN_EVE_LOOPS = 25  # With an evaluation each loop
NUMBER_OF_EVE_RESETS = 5  # And do this up to 5 times with a fresh eve.
# Use EVAL_BATCHES samples each time we check accuracy.
EVAL_BATCHES = 1
</pre>
 
 
====Batch of Random Booleans====
 
This is a method to define an array of random booleans - this is used to create the message that Alice encrypts, and to define the key that Alice and Bob use to decrypt the message.
 
====AdversarialCrypto Class====
 
The Adversarial Crypto class defines the set of three neural networks used to do the adversarial network. As part of the training and evaluation process <code>train_and_evaluate()</code>, an instance of this class is created and passed to the evaluation function <code>doeval()</code> in the main body of the code.
 
What does this class do?
* Creates the three networks for Alice, Bob, and Eve
* Creates connections from Alice to Bob and Alice to Eve to pass the correct info to the correct networks
* Defines the loss function for Eve and for Bob
* Defines the optimizers that the networks should use
* Manages the state of each network (i.e., allows you to reset the Eve network)
 
<pre>
class AdversarialCrypto(object):
    """Primary model implementation class for Adversarial Neural Crypto.
    This class contains the code for the model itself,
    and when created, plumbs the pathways from Alice to Bob and
    Eve, creates the optimizers and loss functions, etc.
   
    Attributes:
        eve_loss:  Eve's loss function.
        bob_loss:  Bob's loss function.  Different units from eve_loss.
        eve_optimizer:  A tf op that runs Eve's optimizer.
        bob_optimizer:  A tf op that runs Bob's optimizer.
        bob_reconstruction_loss:  Bob's message reconstruction loss,
          which is comparable to eve_loss.
        reset_eve_vars:  Execute this op to completely reset Eve.
    """
</pre>
 
What does the constructor do?
* The constructor creates the Alice, Bob, and Eve model by calling the model() method with the right parameters
* Creates the optimizer for Bob and for Eve
* Sets up the loss function for Eve, based on <code>tf.reduce_sum()</code> and <code>optimizer.minimize()</code>
* Sets up the loss function for Bob, based on <code>tf.reduce_sum()</code>
 
<pre>
  def __init__(self):
    in_m, in_k = self.get_message_and_key()
    encrypted = self.model('alice', in_m, in_k)
    decrypted = self.model('bob', encrypted, in_k)
    eve_out = self.model('eve', encrypted, None)
 
    self.reset_eve_vars = tf.group(
        *[w.initializer for w in tf.get_collection('eve')])
 
    optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)


    # Eve's goal is to decrypt the entire message:
This trains a neural network model to detect the sentiment in IMDB text. This illustrates semi-supervised learning.
    eve_bits_wrong = tf.reduce_sum(
        tf.abs((eve_out + 1.0) / 2.0 - (in_m + 1.0) / 2.0), [1])
    self.eve_loss = tf.reduce_sum(eve_bits_wrong)
    self.eve_optimizer = optimizer.minimize(
        self.eve_loss, var_list=tf.get_collection('eve'))


    # Alice and Bob want to be accurate...
Link to code: https://github.com/tensorflow/models/tree/master/research/adversarial_text
    self.bob_bits_wrong = tf.reduce_sum(
        tf.abs((decrypted + 1.0) / 2.0 - (in_m + 1.0) / 2.0), [1])
    # ... and to not let Eve do better than guessing.
    self.bob_reconstruction_loss = tf.reduce_sum(self.bob_bits_wrong)
    bob_eve_error_deviation = tf.abs(float(TEXT_SIZE) / 2.0 - eve_bits_wrong)
    # 7-9 bits wrong is OK too, so we squish the error function a bit.
    # Without doing this, we often tend to hang out at 0.25 / 7.5 error,
    # and it seems bad to have continued, high communication error.
    bob_eve_loss = tf.reduce_sum(
        tf.square(bob_eve_error_deviation) / (TEXT_SIZE / 2)**2)


    # Rescale the losses to [0, 1] per example and combine.
==Running==
    self.bob_loss = (self.bob_reconstruction_loss / TEXT_SIZE + bob_eve_loss)


    self.bob_optimizer = optimizer.minimize(
Running this model is slightly more complicated than running the adversarial crypto network.
        self.bob_loss,
        var_list=(tf.get_collection('alice') + tf.get_collection('bob')))
</pre>


====AdversarialCrypto Class - Creation of Neural Network Model====
The adversarial text network steps are as follows:
* fetch data
* generate vocab
* generate training/validation/test data
* pretrain language model
* train classifier
* evaluate classifier on test data


Now, the actual creation of the models for Alice, Bob, and Eve happens in the call to <code>model()</code>. What happens with the method header?
===Get Vocabulary Data===
* We pass in the name of the graph component ('alice', 'bob', or 'eve') to add new model components to
* We pass in the input message (either the plain text, to Alice, or the ciphertext, to Bob and Eve)
* We pass in the key (optional); if no key is passed in, the input to the neural network is just the message


Here's the model method definition:
Start by obtaining the data, which is an 80 MB tar file, and decompress it:


<pre>
<pre>
  def model(self, collection, message, key=None):
$ wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz -O /tmp/imdb.tar.gz
    """The model for Alice, Bob, and Eve. If key=None, the first FC layer
    takes only the message as inputs. Otherwise, it uses both the key
    and the message.
    Args:
      collection:  The graph keys collection to add new vars to.
      message:  The input message to process.
      key:  The input key (if any) to use.
    """


    if key is not None:
$ tar -xf /tmp/imdb.tar.gz -C /tmp
      combined_message = tf.concat(axis=1, values=[message, key])
    else:
      combined_message = message


$ du -hs /tmp/aclImdb
487M /tmp/aclImdb
</pre>
</pre>


If we pass in both a message and a key, we concatenate the inputs using <code>tf.concat()</code>. Otherwise, the only input is the message.
===Build the Vocabulary===


The next step is to call <code>tf.contrib.framework.arg_scope()</code>. The [https://www.tensorflow.org/api_docs/python/tf/contrib/framework/arg_scope documentation] for this function will loop over each TensorFlow model graph passed to it, and add a <code>@add_arg_scope</code> decorator to it.
Use a Bazel job to build the vocabulary from the data:
 
In other words, every time we have a fully_connected layer and a conv2d layer, we set them up to be on the specified graph (Alice, Bob, or Eve):


<pre>
<pre>
    # Ensure that all variables created are in the specified collection.
$ IMDB_DATA_DIR=/tmp/imdb
    with tf.contrib.framework.arg_scope(
        [tf.contrib.layers.fully_connected, tf.contrib.layers.conv2d],
        variables_collections=[collection]):
</pre>


Next, we create a fully connected neural network layer. We pass in the message (and optionally the key), give the layer a size (the text length, and optionally the key length), we initialize the bias of the fully-connected layer as all-zero, and do not set an activation function:
$ bazel run data:gen_vocab -- \
 
    --output_dir=$IMDB_DATA_DIR \
<pre>
     --dataset=imdb \
     fc = tf.contrib.layers.fully_connected(
    --imdb_input_dir=/tmp/aclImdb \
          combined_message,
    --lowercase=False
          TEXT_SIZE + KEY_SIZE,
          biases_initializer=tf.constant_initializer(0.0),
          activation_fn=None)
</pre>
</pre>


Next, we assemble the layers of the neural network model.
This uses a build rule called <code>gen_vocab</code> located in <code>data/BUILD</code>:
 
The model architecture we use is:


<pre>
<pre>
(Fully Connected) -> (Conv2D) -> (Conv2D) -> (Conv2D) -> (Squeeze)
py_binary(
    name = "gen_vocab",
    srcs = ["gen_vocab.py"],
    deps = [
        ":data_utils",
        ":document_generators",
        # tensorflow dep,
    ],
)
</pre>
</pre>


This performs a sequence of 1D convolutions (expands the message out, and squeezes it back down).
This build vocabulary step is, unfortunately, failing. See this Github issue (1917): https://github.com/tensorflow/models/issues/1917
 
<pre>
    fc = tf.contrib.layers.fully_connected(
          combined_message,
          TEXT_SIZE + KEY_SIZE,
          biases_initializer=tf.constant_initializer(0.0),
          activation_fn=None)


      # Perform a sequence of 1D convolutions (by expanding the message out to 2D
==Adversarial Image Network==
      # and then squeezing it back down).
      fc = tf.expand_dims(fc, 2)
      # 2,1 -> 1,2
      conv = tf.contrib.layers.conv2d(
          fc, 2, 2, 2, 'SAME', activation_fn=tf.nn.sigmoid)
      # 1,2 -> 1, 2
      conv = tf.contrib.layers.conv2d(
          conv, 2, 1, 1, 'SAME', activation_fn=tf.nn.sigmoid)
      # 1,2 -> 1, 1
      conv = tf.contrib.layers.conv2d(
          conv, 1, 1, 1, 'SAME', activation_fn=tf.nn.tanh)
      conv = tf.squeeze(conv, 2)
      return conv
</pre>
 
====AdversarialCrypto Class - Creation of Message and Key====
 
In the constructor, the input message and key are generated using a <code>get_message_and_key()</code> method, which in turn calls a <code>batch_of_random_bools()</code> method. This is not complicated, it just constructs a vector of booleans.
 
Here is the method in the AdversarialCrypto class:
 
<pre>
  def get_message_and_key(self):
    """Generate random pseudo-boolean key and message values."""
 
    batch_size = tf.placeholder_with_default(FLAGS.batch_size, shape=[])
 
    in_m = batch_of_random_bools(batch_size, TEXT_SIZE)
    in_k = batch_of_random_bools(batch_size, KEY_SIZE)
    return in_m, in_k
</pre>
 
and the batch_of_random_bools method that it calls:
 
<pre>
def batch_of_random_bools(batch_size, n):
    """Return a batch of random "boolean" numbers.
    Args:
      batch_size:  Batch size dimension of returned tensor.
      n:  number of entries per batch.
    Returns:
      A [batch_size, n] tensor of "boolean" numbers, where each number is
      preresented as -1 or 1.
    """
   
    as_int = tf.random_uniform(
        [batch_size, n], minval=0, maxval=2, dtype=tf.int32)
    expanded_range = (as_int * 2) - 1
    return tf.cast(expanded_range, tf.float32)
</pre>
 
This creates a random uniform tensor of 1s and -1s. Here's a quick interactive iPython session to illustrate:
 
<pre>
In [1]: import tensorflow as tf
 
In [2]: tf.InteractiveSession()
2017-10-26 00:24:11.694267: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-26 00:24:11.694303: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-26 00:24:11.694313: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-26 00:24:11.694321: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Out[2]: <tensorflow.python.client.session.InteractiveSession at 0x11462bba8>
 
In [3]: as_int = tf.random_uniform([10,2], minval=0, maxval=2, dtype=tf.int32)
 
In [4]: as_int.eval()
Out[4]:
array([[0, 0],
      [1, 1],
      [0, 0],
      [0, 1],
      [1, 0],
      [0, 0],
      [0, 1],
      [1, 1],
      [0, 0],
      [1, 0]], dtype=int32)
 
In [5]: expanded_range = (as_int*2)-1
 
In [6]: expanded_range.eval()
Out[6]:
array([[-1,  1],
      [ 1,  1],
      [-1,  1],
      [ 1, -1],
      [ 1,  1],
      [-1,  1],
      [ 1, -1],
      [-1, -1],
      [-1, -1],
      [ 1, -1]], dtype=int32)
</pre>
 
 
====Do Evaluation Method====
 
We now come to the definition of the function <code>doeval()</code> . This function evaluates how good the cryptosystem the Alice/Bob/Eve networks have created are at minimizing Eve's ability to decrypt the message with the ciphertext only and maximizing Bob's ability to decrypt the message with a ciphertext and key.
 
This method evaluates only, and does not train. It runs the networks on a batch of n messages, computes the percentage of bits lost by Bob and Eve, prints the percentages, and returns them. These values are used to determine when to stop training the networks.
 
The method header takes a few arguments:
* The TensorFlow session
* The AdversarialCrypto class instance
* The number of iterations that should be run
* The iteration count to write to the log
 
<pre>
def doeval(s, ac, n, itercount):
    """
    Evaluate the current network on n batches of random examples.
    Args:
        s:  The current TensorFlow session
        ac: an instance of the AdversarialCrypto class
        n:  The number of iterations to run.
        itercount: Iteration count label for logging.
    Returns:
        Bob and Eve's loss, as a percent of bits incorrect.
    """
</pre>
 
The main role of the doeval function is to run the neural network, and compute the losses that result. The TensorFlow session variable <code>s</code> will contain all three neural networks on its graph, so we can just call <code>s.run()</code> without needing to specify all three graphs.
 
Link to Session class documentation: https://www.tensorflow.org/api_docs/python/tf/Session
 
Link to Session.run documentation: https://www.tensorflow.org/api_docs/python/tf/Session#run
 
The run method is passed a list of loss functions owned by the AdversarialCrypto class - these were the sums of incorrect bits defined above (<code>self.bob_reconstruction_loss = tf.reduce_sum(self.bob_bits_wrong)</code>).
 
The reduce_sum function simply sums tensor components along a particular axis, thus reducing the dimensionality of the tensor. In this case we are summing the incorrect bits along the "axis" of the message.
 
Link to reduce_sum documentation: https://www.tensorflow.org/api_docs/python/tf/reduce_sum
 
When we call <code>s.run()</code>, we can pass a single graph element, or an arbitrarily nested collection containing graph elements at the leaves. The graph element can be an operation, a tensor, a tensor handle, or a string naming a tensor or operation in the graph; in this case, we pass a list of two tf.reduce_sum operations.
 
Because the <code>s.run()</code> method is passed two values, it will return two values - the corresponding values returned from the two operations. Thus, the variable bl contains the result of the tf.reduce_sum call for Bob's network (the number of incorrect bits from Bob's network), while el contains the result of the tf.reduce_sum call for Eve's network.
 
<pre>
    bob_loss_accum = 0
    eve_loss_accum = 0
    for _ in xrange(n):
        bl, el = s.run([ac.bob_reconstruction_loss, ac.eve_loss])
        bob_loss_accum += bl
        eve_loss_accum += el
</pre>
 
These error values are set to zero for each batch, and are accumulated across the batch.
 
At the end of the batch of runs, the loss percentages are computed and returned.
 
<pre>
    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))
    sys.stdout.flush()
    return bob_loss_percent, eve_loss_percent
</pre>
 
====Training Methods====
 
There are two training methods:
* train_until_thresh() - trains the network for a specified number of training loops. This method quits early and returns true if the loss function targets are achieved. Otherwise it returns false.
* train_and_evaluate() - trains the network, then trains Eve for extra rounds. Also resets Eve a specified number of times, giving Eve the chance to re-train and alternative ways to break Alice and Bob's cryptosystem.
 
The <code>train_until_thresh()</code> method takes a session and an AdversarialCrypto object. It runs for a specified maximum number of training loops. Within each training loop, it trains Bob's network, then trains Eve's network:
 
<pre>
def train_until_thresh(s, ac):
  for j in xrange(MAX_TRAINING_LOOPS):
    for _ in xrange(ITERS_PER_ACTOR):
      s.run(ac.bob_optimizer)
    for _ in xrange(ITERS_PER_ACTOR * EVE_MULTIPLIER):
      s.run(ac.eve_optimizer)
</pre>
 
It is important to note here that bob_optimizer and eve_optimizer are both calling minimize() on the same AdamOptimizer object, but passing different loss functions to it. From above, the optimizer was defined as:
 
<pre>
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
</pre>
 
and bob_optimizer calls optimizer.minimize() on the sum of incorrect bits from Bob's network, while eve_optimizer calls optimizer.minimize() on the sum of incorrect bits from Eve's network.
 
Next, every M steps, the evaluation method is called. This prints progress, but also checks if the target losses have been achieved. If they have, the network returns true, ending the training early.
 
<pre>
    if j % PRINT_EVERY == 0:
      bob_avg_loss, eve_avg_loss = doeval(s, ac, EVAL_BATCHES, j)
      if (bob_avg_loss < BOB_LOSS_THRESH and eve_avg_loss > EVE_LOSS_THRESH):
        print('Target losses achieved.')
        return True
  return False
</pre>
 
==Adversarial Text==


=Flags=
=Flags=

Latest revision as of 00:13, 27 October 2017

Adversarial Neural Networks

Adversarial neural networks use an architecture consisting of two separate neural networks - one network attempts to learn how to accomplish a task, and another network attempts to differentiate between the output of the first network and the "real" output.

TensorFlow Adversarial Examples

Adversarial Crypto

This adversarial crypto neural network attempts to learn how to protect communications using the adversarial architecture.

Paper: "Learning to Protect Communications with Adversarial Neural Cryptography"

Link to paper: https://arxiv.org/abs/1610.06918

Link to code: https://github.com/tensorflow/models/tree/master/research/adversarial_crypto

Part of the tensorflow models repository (https://github.com/tensorflow/models/tree/master/research).

Running

To train the network: 

$ python train_eval.py

The approach used by the training is to train the "defender" network (representing the Alice-Bob channel) until it is sufficiently well-trained, then reset the "attacker" network (representing the eavesdropper Eve) from scratch to give the eavesdropper multiple opportunities to find weaknesses in the cryptosystem.

The Model

We'll step through the code line-by-line. Here's the link to the code: https://github.com/tensorflow/models/blob/master/research/adversarial_crypto/train_eval.py

Full model walkthrough is on the TensorFlow/Adversarial Crypto page.

The rundown is:

  • Create an AdversarialCrypto class that holds a training optimizer object for the Bob and Alice networks
  • Define a method that evaluates the networks as-is and prints the percent losses
  • Define a method that trains the network for a specified number of iterations, stopping early if the network reaches its target losses
  • Define a method that calls the training function (above), then re-trains Eve several more times from scratch

Adversarial Text

This trains a neural network model to detect the sentiment in IMDB text. This illustrates semi-supervised learning.

Link to code: https://github.com/tensorflow/models/tree/master/research/adversarial_text

Running

Running this model is slightly more complicated than running the adversarial crypto network.

The adversarial text network steps are as follows:

  • fetch data
  • generate vocab
  • generate training/validation/test data
  • pretrain language model
  • train classifier
  • evaluate classifier on test data

Get Vocabulary Data

Start by obtaining the data, which is an 80 MB tar file, and decompress it: 

$ wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz -O /tmp/imdb.tar.gz

$ tar -xf /tmp/imdb.tar.gz -C /tmp

$ du -hs /tmp/aclImdb
487M	/tmp/aclImdb

Build the Vocabulary

Use a Bazel job to build the vocabulary from the data: 

$ IMDB_DATA_DIR=/tmp/imdb

$ bazel run data:gen_vocab -- \
    --output_dir=$IMDB_DATA_DIR \
    --dataset=imdb \
    --imdb_input_dir=/tmp/aclImdb \
    --lowercase=False

This uses a build rule called gen_vocab located in data/BUILD: 

py_binary(
    name = "gen_vocab",
    srcs = ["gen_vocab.py"],
    deps = [
        ":data_utils",
        ":document_generators",
        # tensorflow dep,
    ],
)

This build vocabulary step is, unfortunately, failing. See this Github issue (1917): https://github.com/tensorflow/models/issues/1917

Adversarial Image Network

Flags

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