Merge pull request #2 from tensorflow/master

Sync to tensorflow-master

CODEOWNERS

+adversarial_crypto/* @dave-andersen
+adversarial_text/* @rsepassi
+attention_ocr/* @alexgorban
+autoencoders/* @snurkabill
+cognitive_mapping_and_planning/* @s-gupta
+compression/* @nmjohn
+differential_privacy/* @panyx0718
+domain_adaptation/* @bousmalis @ddohan
+im2txt/* @cshallue
+inception/* @shlens @vincentvanhoucke
+learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum
+lfads/* @jazcollins @susillo
+lm_1b/* @oriolvinyals @panyx0718
+namignizer/* @knathanieltucker
+neural_gpu/* @lukaszkaiser
+neural_programmer/* @arvind2505
+next_frame_prediction/* @panyx0718
+object_detection/* @jch1 @tombstone @derekjchow @jesu9 @dreamdragon
+pcl_rl/* @ofirnachum
+ptn/* @xcyan @arkanath @hellojas @honglaklee
+real_nvp/* @laurent-dinh
+rebar/* @gjtucker
+resnet/* @panyx0718
+skip_thoughts/* @cshallue
+slim/* @sguada @nathansilberman
+street/* @theraysmith
+swivel/* @waterson
+syntaxnet/* @calberti @andorardo
+textsum/* @panyx0718 @peterjliu
+transformer/* @daviddao
+tutorials/embedding/* @zffchen78 @a-dai
+tutorials/image/* @sherrym @shlens
+tutorials/rnn/* @lukaszkaiser @ebrevdo
+video_prediction/* @cbfinn
+

CONTRIBUTING.md

 # Contributing guidelines
 
 If you have created a model and would like to publish it here, please send us a
-pull request. For those just getting started with pull reuests, GitHub has a
+pull request. For those just getting started with pull requests, GitHub has a
 [howto](https://help.github.com/articles/using-pull-requests/).
 
 The code for any model in this repository is licensed under the Apache License

README.md

@@ -22,12 +22,15 @@ running TensorFlow 0.12 or earlier, please
 - [im2txt](im2txt): image-to-text neural network for image captioning.
 - [inception](inception): deep convolutional networks for computer vision.
 - [learning_to_remember_rare_events](learning_to_remember_rare_events):  a large-scale life-long memory module for use in deep learning.
+- [lfads](lfads): sequential variational autoencoder for analyzing neuroscience data.
 - [lm_1b](lm_1b): language modeling on the one billion word benchmark.
 - [namignizer](namignizer): recognize and generate names.
 - [neural_gpu](neural_gpu): highly parallel neural computer.
 - [neural_programmer](neural_programmer): neural network augmented with logic and mathematic operations.
 - [next_frame_prediction](next_frame_prediction): probabilistic future frame synthesis via cross convolutional networks.
+- [object_detection](object_detection): localizing and identifying multiple objects in a single image.
 - [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations.
+- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models.
 - [resnet](resnet): deep and wide residual networks.
 - [skip_thoughts](skip_thoughts): recurrent neural network sentence-to-vector encoder.
 - [slim](slim): image classification models in TF-Slim.

adversarial_crypto/train_eval.py

@@ -118,7 +118,7 @@ class AdversarialCrypto(object):
 
   def model(self, collection, message, key=None):
     """The model for Alice, Bob, and Eve.  If key=None, the first FC layer
-    takes only the Key as inputs.  Otherwise, it uses both the key
+    takes only the message as inputs.  Otherwise, it uses both the key
     and the message.
 
     Args:

adversarial_text/BUILD

+licenses(["notice"])  # Apache 2.0
+
 # Binaries
 # ==============================================================================
 py_binary(
@@ -5,6 +7,8 @@ py_binary(
     srcs = ["evaluate.py"],
     deps = [
         ":graphs",
+        # google3 file dep,
+        # tensorflow dep,
     ],
 )
 
@@ -14,6 +18,8 @@ py_binary(
     deps = [
         ":graphs",
         ":train_utils",
+        # google3 file dep,
+        # tensorflow dep,
     ],
 )
 
@@ -25,6 +31,8 @@ py_binary(
     deps = [
         ":graphs",
         ":train_utils",
+        # google3 file dep,
+        # tensorflow dep,
     ],
 )
 
@@ -37,18 +45,23 @@ py_library(
         ":adversarial_losses",
         ":inputs",
         ":layers",
+        # tensorflow dep,
     ],
 )
 
 py_library(
     name = "adversarial_losses",
     srcs = ["adversarial_losses.py"],
+    deps = [
+        # tensorflow dep,
+    ],
 )
 
 py_library(
     name = "inputs",
     srcs = ["inputs.py"],
     deps = [
+        # tensorflow dep,
         "//adversarial_text/data:data_utils",
     ],
 )
@@ -56,11 +69,18 @@ py_library(
 py_library(
     name = "layers",
     srcs = ["layers.py"],
+    deps = [
+        # tensorflow dep,
+    ],
 )
 
 py_library(
     name = "train_utils",
     srcs = ["train_utils.py"],
+    deps = [
+        # numpy dep,
+        # tensorflow dep,
+    ],
 )
 
 # Tests
@@ -71,6 +91,7 @@ py_test(
     srcs = ["graphs_test.py"],
     deps = [
         ":graphs",
+        # tensorflow dep,
         "//adversarial_text/data:data_utils",
     ],
 )

adversarial_text/README.md

@@ -56,7 +56,6 @@ $ bazel run :pretrain -- \
     --embedding_dims=256 \
     --rnn_cell_size=1024 \
     --num_candidate_samples=1024 \
-    --optimizer=adam \
     --batch_size=256 \
     --learning_rate=0.001 \
     --learning_rate_decay_factor=0.9999 \
@@ -87,7 +86,6 @@ $ bazel run :train_classifier -- \
     --rnn_cell_size=1024 \
     --cl_num_layers=1 \
     --cl_hidden_size=30 \
-    --optimizer=adam \
     --batch_size=64 \
     --learning_rate=0.0005 \
     --learning_rate_decay_factor=0.9998 \
@@ -96,7 +94,8 @@ $ bazel run :train_classifier -- \
     --num_timesteps=400 \
     --keep_prob_emb=0.5 \
     --normalize_embeddings \
-    --adv_training_method=vat
+    --adv_training_method=vat \
+    --perturb_norm_length=5.0
 ```
 
 ### Evaluate on test data
@@ -136,21 +135,21 @@ adversarial training losses). The training loop itself is defined in
 ### Command-Line Flags
 
 Flags related to distributed training and the training loop itself are defined
-in `train_utils.py`.
+in [`train_utils.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/train_utils.py).
 
-Flags related to model hyperparameters are defined in `graphs.py`.
+Flags related to model hyperparameters are defined in [`graphs.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/graphs.py).
 
-Flags related to adversarial training are defined in `adversarial_losses.py`.
+Flags related to adversarial training are defined in [`adversarial_losses.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/adversarial_losses.py).
 
 Flags particular to each job are defined in the main binary files.
 
 ### Data Generation
 
-*   Vocabulary generation: `gen_vocab.py`
-*   Data generation: `gen_data.py`
+*   Vocabulary generation: [`gen_vocab.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/data/gen_vocab.py)
+*   Data generation: [`gen_data.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/data/gen_data.py)
 
-Command-line flags defined in `document_generators.py` control which dataset is
-processed and how.
+Command-line flags defined in [`document_generators.py`](https://github.com/tensorflow/models/tree/master/adversarial_text/data/document_generators.py)
+control which dataset is processed and how.
 
 ## Contact for Issues
 

adversarial_text/adversarial_losses.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,25 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Adversarial losses for text models."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+# Dependency imports
+
 import tensorflow as tf
 
 flags = tf.app.flags
 FLAGS = flags.FLAGS
 
 # Adversarial and virtual adversarial training parameters.
-flags.DEFINE_float('perturb_norm_length', 0.1,
+flags.DEFINE_float('perturb_norm_length', 5.0,
                    'Norm length of adversarial perturbation to be '
-                   'optimized with validation')
+                   'optimized with validation. '
+                   '5.0 is optimal on IMDB with virtual adversarial training. ')
 
 # Virtual adversarial training parameters
 flags.DEFINE_integer('num_power_iteration', 1, 'The number of power iteration')
-flags.DEFINE_float('small_constant_for_finite_diff', 1e-3,
+flags.DEFINE_float('small_constant_for_finite_diff', 1e-1,
                    'Small constant for finite difference method')
 
 # Parameters for building the graph
@@ -83,19 +85,22 @@ def virtual_adversarial_loss(logits, embedded, inputs,
   """
   # Stop gradient of logits. See https://arxiv.org/abs/1507.00677 for details.
   logits = tf.stop_gradient(logits)
+
   # Only care about the KL divergence on the final timestep.
-  weights = _end_of_seq_mask(inputs.labels)
+  weights = inputs.eos_weights
+  assert weights is not None
 
   # Initialize perturbation with random noise.
   # shape(embedded) = (batch_size, num_timesteps, embedding_dim)
-  d = _mask_by_length(tf.random_normal(shape=tf.shape(embedded)), inputs.length)
+  d = tf.random_normal(shape=tf.shape(embedded))
 
   # Perform finite difference method and power iteration.
   # See Eq.(8) in the paper http://arxiv.org/pdf/1507.00677.pdf,
   # Adding small noise to input and taking gradient with respect to the noise
   # corresponds to 1 power iteration.
   for _ in xrange(FLAGS.num_power_iteration):
-    d = _scale_l2(d, FLAGS.small_constant_for_finite_diff)
+    d = _scale_l2(
+        _mask_by_length(d, inputs.length), FLAGS.small_constant_for_finite_diff)
     d_logits = logits_from_embedding_fn(embedded + d)
     kl = _kl_divergence_with_logits(logits, d_logits, weights)
     d, = tf.gradients(
@@ -104,8 +109,7 @@ def virtual_adversarial_loss(logits, embedded, inputs,
         aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
     d = tf.stop_gradient(d)
 
-  perturb = _scale_l2(
-      _mask_by_length(d, inputs.length), FLAGS.perturb_norm_length)
+  perturb = _scale_l2(d, FLAGS.perturb_norm_length)
   vadv_logits = logits_from_embedding_fn(embedded + perturb)
   return _kl_divergence_with_logits(logits, vadv_logits, weights)
 
@@ -136,7 +140,8 @@ def virtual_adversarial_loss_bidir(logits, embedded, inputs,
   """Virtual adversarial loss for bidirectional models."""
   logits = tf.stop_gradient(logits)
   f_inputs, _ = inputs
-  weights = _end_of_seq_mask(f_inputs.labels)
+  weights = f_inputs.eos_weights
+  assert weights is not None
 
   perturbs = [
       _mask_by_length(tf.random_normal(shape=tf.shape(emb)), f_inputs.length)
@@ -155,10 +160,7 @@ def virtual_adversarial_loss_bidir(logits, embedded, inputs,
         aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
     perturbs = [tf.stop_gradient(d) for d in perturbs]
 
-  perturbs = [
-      _scale_l2(_mask_by_length(d, f_inputs.length), FLAGS.perturb_norm_length)
-      for d in perturbs
-  ]
+  perturbs = [_scale_l2(d, FLAGS.perturb_norm_length) for d in perturbs]
   vadv_logits = logits_from_embedding_fn(
       [emb + d for (emb, d) in zip(embedded, perturbs)])
   return _kl_divergence_with_logits(logits, vadv_logits, weights)
@@ -167,7 +169,9 @@ def virtual_adversarial_loss_bidir(logits, embedded, inputs,
 def _mask_by_length(t, length):
   """Mask t, 3-D [batch, time, dim], by length, 1-D [batch,]."""
   maxlen = t.get_shape().as_list()[1]
-  mask = tf.sequence_mask(length, maxlen=maxlen)
+
+  # Subtract 1 from length to prevent the perturbation from going on 'eos'
+  mask = tf.sequence_mask(length - 1, maxlen=maxlen)
   mask = tf.expand_dims(tf.cast(mask, tf.float32), -1)
   # shape(mask) = (batch, num_timesteps, 1)
   return t * mask
@@ -175,32 +179,16 @@ def _mask_by_length(t, length):
 
 def _scale_l2(x, norm_length):
   # shape(x) = (batch, num_timesteps, d)
-
   # Divide x by max(abs(x)) for a numerically stable L2 norm.
   # 2norm(x) = a * 2norm(x/a)
   # Scale over the full sequence, dims (1, 2)
   alpha = tf.reduce_max(tf.abs(x), (1, 2), keep_dims=True) + 1e-12
-  l2_norm = alpha * tf.sqrt(tf.reduce_sum(tf.pow(x / alpha, 2), (1, 2),
-                                          keep_dims=True) + 1e-6)
+  l2_norm = alpha * tf.sqrt(
+      tf.reduce_sum(tf.pow(x / alpha, 2), (1, 2), keep_dims=True) + 1e-6)
   x_unit = x / l2_norm
   return norm_length * x_unit
 
 
-def _end_of_seq_mask(tokens):
-  """Generate a mask for the EOS token (1.0 on EOS, 0.0 otherwise).
-
-  Args:
-    tokens: 1-D integer tensor [num_timesteps*batch_size]. Each element is an
-      id from the vocab.
-
-  Returns:
-    Float tensor same shape as tokens, whose values are 1.0 on the end of
-    sequence and 0.0 on the others.
-  """
-  eos_id = FLAGS.vocab_size - 1
-  return tf.cast(tf.equal(tokens, eos_id), tf.float32)
-
-
 def _kl_divergence_with_logits(q_logits, p_logits, weights):
   """Returns weighted KL divergence between distributions q and p.
 
@@ -218,21 +206,20 @@ def _kl_divergence_with_logits(q_logits, p_logits, weights):
   # For logistic regression
   if FLAGS.num_classes == 2:
     q = tf.nn.sigmoid(q_logits)
-    p = tf.nn.sigmoid(p_logits)
     kl = (-tf.nn.sigmoid_cross_entropy_with_logits(logits=q_logits, labels=q) +
           tf.nn.sigmoid_cross_entropy_with_logits(logits=p_logits, labels=q))
+    kl = tf.squeeze(kl)
 
   # For softmax regression
   else:
     q = tf.nn.softmax(q_logits)
-    p = tf.nn.softmax(p_logits)
-    kl = tf.reduce_sum(q * (tf.log(q) - tf.log(p)), 1)
+    kl = tf.reduce_sum(
+        q * (tf.nn.log_softmax(q_logits) - tf.nn.log_softmax(p_logits)), 1)
 
   num_labels = tf.reduce_sum(weights)
   num_labels = tf.where(tf.equal(num_labels, 0.), 1., num_labels)
 
-  kl.get_shape().assert_has_rank(2)
+  kl.get_shape().assert_has_rank(1)
   weights.get_shape().assert_has_rank(1)
-  loss = tf.identity(tf.reduce_sum(tf.expand_dims(weights, -1) * kl) /
-                     num_labels, name='kl')
+  loss = tf.identity(tf.reduce_sum(weights * kl) / num_labels, name='kl')
   return loss

adversarial_text/data/BUILD

+licenses(["notice"])  # Apache 2.0
+
 package(
     default_visibility = [
         "//adversarial_text:__subpackages__",
@@ -10,6 +12,7 @@ py_binary(
     deps = [
         ":data_utils",
         ":document_generators",
+        # tensorflow dep,
     ],
 )
 
@@ -19,17 +22,24 @@ py_binary(
     deps = [
         ":data_utils",
         ":document_generators",
+        # tensorflow dep,
     ],
 )
 
 py_library(
     name = "document_generators",
     srcs = ["document_generators.py"],
+    deps = [
+        # tensorflow dep,
+    ],
 )
 
 py_library(
     name = "data_utils",
     srcs = ["data_utils.py"],
+    deps = [
+        # tensorflow dep,
+    ],
 )
 
 py_test(
@@ -37,5 +47,6 @@ py_test(
     srcs = ["data_utils_test.py"],
     deps = [
         ":data_utils",
+        # tensorflow dep,
     ],
 )

adversarial_text/data/data_utils.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,13 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Utilities for generating/preprocessing data for adversarial text models."""
 
 import operator
 import os
 import random
 import re
+
+# Dependency imports
+
 import tensorflow as tf
 
 EOS_TOKEN = '</s>'
@@ -215,13 +217,17 @@ def build_lm_sequence(seq):
 
   Returns:
     SequenceWrapper with `seq` tokens copied over to output sequence tokens and
-    labels (offset by 1, i.e. predict next token) with weights set to 1.0.
+    labels (offset by 1, i.e. predict next token) with weights set to 1.0,
+    except for <eos> token.
   """
   lm_seq = SequenceWrapper()
-  for i, timestep in enumerate(seq[:-1]):
+  for i, timestep in enumerate(seq):
+    if i == len(seq) - 1:
+      lm_seq.add_timestep().set_token(timestep.token).set_label(
+          seq[i].token).set_weight(0.0)
+    else:
       lm_seq.add_timestep().set_token(timestep.token).set_label(
           seq[i + 1].token).set_weight(1.0)
-
   return lm_seq
 
 

adversarial_text/data/data_utils_test.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Tests for data_utils."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+# Dependency imports
+
 import tensorflow as tf
 
 from adversarial_text.data import data_utils
@@ -91,6 +92,13 @@ class DataUtilsTest(tf.test.TestCase):
     seq = self._buildDummySequence()
     lm_seq = data.build_lm_sequence(seq)
     for i, ts in enumerate(lm_seq):
+      # For end of sequence, the token and label should be same, and weight
+      # should be 0.0.
+      if i == len(lm_seq) - 1:
+        self.assertEqual(ts.token, i)
+        self.assertEqual(ts.label, i)
+        self.assertEqual(ts.weight, 0.0)
+      else:
         self.assertEqual(ts.token, i)
         self.assertEqual(ts.label, i + 1)
         self.assertEqual(ts.weight, 1.0)

adversarial_text/data/document_generators.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Input readers and document/token generators for datasets."""
 from __future__ import absolute_import
 from __future__ import division
@@ -23,6 +22,8 @@ import csv
 import os
 import random
 
+# Dependency imports
+
 import tensorflow as tf
 
 from adversarial_text.data import data_utils
@@ -60,7 +61,6 @@ flags.DEFINE_string('rcv1_input_dir', '',
 flags.DEFINE_string('rt_input_dir', '',
                     'The Rotten Tomatoes dataset input directory.')
 
-
 # The amazon reviews input file to use in either the RT or IMDB datasets.
 flags.DEFINE_string('amazon_unlabeled_input_file', '',
                     'The unlabeled Amazon Reviews dataset input file. If set, '
@@ -211,8 +211,12 @@ def imdb_documents(dataset='train',
   if FLAGS.amazon_unlabeled_input_file and include_unlabeled:
     with open(FLAGS.amazon_unlabeled_input_file) as rt_f:
       for content in rt_f:
-        yield Document(content=content, is_validation=False, is_test=False,
-                       label=None, add_tokens=False)
+        yield Document(
+            content=content,
+            is_validation=False,
+            is_test=False,
+            label=None,
+            add_tokens=False)
 
 
 def dbpedia_documents(dataset='train',
@@ -265,7 +269,8 @@ def rcv1_documents(dataset='train',
   # pylint:disable=line-too-long
   """Generates Documents for Reuters Corpus (rcv1) dataset.
 
-  Dataset described at http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm
+  Dataset described at
+  http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm
 
   Args:
     dataset: str, identifies the csv file within the rcv1 data directory.
@@ -354,17 +359,25 @@ def rt_documents(dataset='train',
         if class_label is None:
           # Process Amazon Review data for unlabeled dataset
           if content.startswith('review/text'):
-            yield Document(content=content, is_validation=False,
-                           is_test=False, label=None, add_tokens=False)
+            yield Document(
+                content=content,
+                is_validation=False,
+                is_test=False,
+                label=None,
+                add_tokens=False)
         else:
           # 10% of the data is randomly held out for the validation set and
           # another 10% of it is randomly held out for the test set
           random_int = random.randint(1, 10)
           is_validation = random_int == 1
           is_test = random_int == 2
-          if (is_test and dataset != 'test') or (
-              is_validation and not include_validation):
+          if (is_test and dataset != 'test') or (is_validation and
+                                                 not include_validation):
             continue
 
-          yield Document(content=content, is_validation=is_validation,
-                         is_test=is_test, label=class_label, add_tokens=True)
+          yield Document(
+              content=content,
+              is_validation=is_validation,
+              is_test=is_test,
+              label=class_label,
+              add_tokens=True)

adversarial_text/data/gen_data.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Create TFRecord files of SequenceExample protos from dataset.
 
 Constructs 3 datasets:
@@ -31,6 +30,8 @@ from __future__ import print_function
 import os
 import string
 
+# Dependency imports
+
 import tensorflow as tf
 
 from adversarial_text.data import data_utils
@@ -197,6 +198,7 @@ def generate_test_data(vocab_ids, writer_lm_all, writer_seq_ae_all):
 
 
 def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
   tf.logging.info('Assigning vocabulary ids...')
   vocab_ids = make_vocab_ids(
       FLAGS.vocab_file or os.path.join(FLAGS.output_dir, 'vocab.txt'))

adversarial_text/data/gen_vocab.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Generates vocabulary and term frequency files for datasets."""
 from __future__ import absolute_import
 from __future__ import division
@@ -20,6 +19,8 @@ from __future__ import print_function
 
 from collections import defaultdict
 
+# Dependency imports
+
 import tensorflow as tf
 
 from adversarial_text.data import data_utils
@@ -66,6 +67,7 @@ def fill_vocab_from_doc(doc, vocab_freqs, doc_counts):
 
 
 def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
   vocab_freqs = defaultdict(int)
   doc_counts = defaultdict(int)
 

adversarial_text/evaluate.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Evaluates text classification model."""
 
 from __future__ import absolute_import
@@ -22,6 +21,8 @@ from __future__ import print_function
 import math
 import time
 
+# Dependency imports
+
 import tensorflow as tf
 
 import graphs
@@ -100,6 +101,7 @@ def run_eval(eval_ops, summary_writer, saver):
 
 
 def _log_values(sess, value_ops, summary_writer=None):
+  """Evaluate, log, and write summaries of the eval metrics in value_ops."""
   metric_names, value_ops = zip(*value_ops.items())
   values = sess.run(value_ops)
 

adversarial_text/graphs.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Virtual adversarial text models."""
 from __future__ import absolute_import
 from __future__ import division
@@ -20,6 +19,9 @@ from __future__ import print_function
 
 import csv
 import os
+
+# Dependency imports
+
 import tensorflow as tf
 
 import adversarial_losses as adv_lib
@@ -81,7 +83,8 @@ flags.DEFINE_integer('replicas_to_aggregate', 1,
 # Regularization
 flags.DEFINE_float('max_grad_norm', 1.0,
                    'Clip the global gradient norm to this value.')
-flags.DEFINE_float('keep_prob_emb', 1.0, 'keep probability on embedding layer')
+flags.DEFINE_float('keep_prob_emb', 1.0, 'keep probability on embedding layer. '
+                   '0.5 is optimal on IMDB with virtual adversarial training.')
 flags.DEFINE_float('keep_prob_lstm_out', 1.0,
                    'keep probability on lstm output.')
 flags.DEFINE_float('keep_prob_cl_hidden', 1.0,
@@ -249,8 +252,7 @@ class VatxtModel(object):
     eval_ops = {
         'accuracy':
             tf.contrib.metrics.streaming_accuracy(
-                layers_lib.predictions(logits), inputs.labels,
-                inputs.weights)
+                layers_lib.predictions(logits), inputs.labels, inputs.weights)
     }
 
     with tf.control_dependencies([inputs.save_state(next_state)]):
@@ -610,7 +612,8 @@ def _inputs(dataset='train', pretrain=False, bidir=False):
       state_size=FLAGS.rnn_cell_size,
       num_layers=FLAGS.rnn_num_layers,
       batch_size=FLAGS.batch_size,
-      unroll_steps=FLAGS.num_timesteps)
+      unroll_steps=FLAGS.num_timesteps,
+      eos_id=FLAGS.vocab_size - 1)
 
 
 def _get_vocab_freqs():

adversarial_text/graphs_test.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Tests for graphs."""
 from __future__ import absolute_import
 from __future__ import division
@@ -26,6 +25,8 @@ import shutil
 import string
 import tempfile
 
+# Dependency imports
+
 import tensorflow as tf
 
 import graphs

adversarial_text/inputs.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Input utils for virtual adversarial text classification."""
 
 from __future__ import absolute_import
@@ -20,6 +19,9 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+
+# Dependency imports
+
 import tensorflow as tf
 
 from adversarial_text.data import data_utils
@@ -28,7 +30,12 @@ from adversarial_text.data import data_utils
 class VatxtInput(object):
   """Wrapper around NextQueuedSequenceBatch."""
 
-  def __init__(self, batch, state_name=None, tokens=None, num_states=0):
+  def __init__(self,
+               batch,
+               state_name=None,
+               tokens=None,
+               num_states=0,
+               eos_id=None):
     """Construct VatxtInput.
 
     Args:
@@ -36,6 +43,7 @@ class VatxtInput(object):
       state_name: str, name of state to fetch and save.
       tokens: int Tensor, tokens. Defaults to batch's F_TOKEN_ID sequence.
       num_states: int The number of states to store.
+      eos_id: int Id of end of Sequence.
     """
     self._batch = batch
     self._state_name = state_name
@@ -58,6 +66,14 @@ class VatxtInput(object):
     l = tf.reshape(l, [-1])
     self._labels = l
 
+    # eos weights
+    self._eos_weights = None
+    if eos_id:
+      ew = tf.cast(tf.equal(self._tokens, eos_id), tf.float32)
+      ew = tf.transpose(ew, [1, 0])
+      ew = tf.reshape(ew, [-1])
+      self._eos_weights = ew
+
   @property
   def tokens(self):
     return self._tokens
@@ -66,6 +82,10 @@ class VatxtInput(object):
   def weights(self):
     return self._weights
 
+  @property
+  def eos_weights(self):
+    return self._eos_weights
+
   @property
   def labels(self):
     return self._labels
@@ -246,7 +266,8 @@ def inputs(data_dir=None,
            state_size=None,
            num_layers=0,
            batch_size=32,
-           unroll_steps=100):
+           unroll_steps=100,
+           eos_id=None):
   """Inputs for text model.
 
   Args:
@@ -260,7 +281,7 @@ def inputs(data_dir=None,
     num_layers: int, the number of LSTM layers.
     batch_size: int, batch size.
     unroll_steps: int, number of timesteps to unroll for TBTT.
-
+    eos_id: int, id of end of sequence. used for the kl weights on vat
   Returns:
     Instance of VatxtInput (x2 if bidir=True and pretrain=True, i.e. forward and
       reverse).
@@ -280,9 +301,15 @@ def inputs(data_dir=None,
                                       state_size, num_layers, unroll_steps,
                                       batch_size)
       forward_input = VatxtInput(
-          forward_batch, state_name=state_name, num_states=num_layers)
+          forward_batch,
+          state_name=state_name,
+          num_states=num_layers,
+          eos_id=eos_id)
       reverse_input = VatxtInput(
-          reverse_batch, state_name=state_name_rev, num_states=num_layers)
+          reverse_batch,
+          state_name=state_name_rev,
+          num_states=num_layers,
+          eos_id=eos_id)
       return forward_input, reverse_input
 
     elif bidir:
@@ -322,4 +349,5 @@ def inputs(data_dir=None,
           unroll_steps,
           batch_size,
           bidir_input=False)
-      return VatxtInput(batch, state_name=state_name, num_states=num_layers)
+      return VatxtInput(
+          batch, state_name=state_name, num_states=num_layers, eos_id=eos_id)

adversarial_text/layers.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,14 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Layers for VatxtModel."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
+# Dependency imports
 
+import tensorflow as tf
 K = tf.contrib.keras
 
 
@@ -34,7 +34,7 @@ def cl_logits_subgraph(layer_sizes, input_size, num_classes, keep_prob=1.):
       subgraph.add(K.layers.Dense(layer_size, activation='relu'))
 
     if keep_prob < 1.:
-      subgraph.add(K.layers.Dropout(keep_prob))
+      subgraph.add(K.layers.Dropout(1. - keep_prob))
   subgraph.add(K.layers.Dense(1 if num_classes == 2 else num_classes))
   return subgraph
 
@@ -76,7 +76,14 @@ class Embedding(K.layers.Layer):
   def call(self, x):
     embedded = tf.nn.embedding_lookup(self.var, x)
     if self.keep_prob < 1.:
-      embedded = tf.nn.dropout(embedded, self.keep_prob)
+      shape = embedded.get_shape().as_list()
+
+      # Use same dropout masks at each timestep with specifying noise_shape.
+      # This slightly improves performance.
+      # Please see https://arxiv.org/abs/1512.05287 for the theoretical
+      # explanation.
+      embedded = tf.nn.dropout(
+          embedded, self.keep_prob, noise_shape=(shape[0], 1, shape[2]))
     return embedded
 
   def _normalize(self, emb):
@@ -153,11 +160,11 @@ class SoftmaxLoss(K.layers.Layer):
       self.lin_w = self.add_weight(
           shape=(input_shape[-1], self.vocab_size),
           name='lm_lin_w',
-          initializer='glorot_uniform')
+          initializer=K.initializers.glorot_uniform())
       self.lin_b = self.add_weight(
           shape=(self.vocab_size,),
           name='lm_lin_b',
-          initializer='glorot_uniform')
+          initializer=K.initializers.glorot_uniform())
 
     super(SoftmaxLoss, self).build(input_shape)
 

adversarial_text/pretrain.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,18 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Pretrains a recurrent language model.
 
 Computational time:
-  5 days to train 100000 steps on 1 layer 1024 hidden units LSTM,
-  256 embeddings, 400 truncated BP, 64 minibatch and on 4 GPU with
-  SyncReplicasOptimizer, that is the total minibatch is 256.
+  2 days to train 100000 steps on 1 layer 1024 hidden units LSTM,
+  256 embeddings, 400 truncated BP, 256 minibatch and on single GPU (Pascal
+  Titan X, cuDNNv5).
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+# Dependency imports
+
 import tensorflow as tf
 
 import graphs

adversarial_text/train_classifier.py

-# Copyright 2017 Google, Inc. All Rights Reserved.
+# Copyright 2017 Google Inc. 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.
@@ -12,17 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Trains LSTM text classification model.
 
 Model trains with adversarial or virtual adversarial training.
 
 Computational time:
-  6 hours to train 10000 steps without adversarial or virtual adversarial
+  1.8 hours to train 10000 steps without adversarial or virtual adversarial
     training, on 1 layer 1024 hidden units LSTM, 256 embeddings, 400 truncated
-    BP, 64 minibatch and on single GPU.
+    BP, 64 minibatch and on single GPU (Pascal Titan X, cuDNNv5).
 
-  12 hours to train 10000 steps with adversarial or virtual adversarial
+  4 hours to train 10000 steps with adversarial or virtual adversarial
     training, with above condition.
 
 To initialize embedding and LSTM cell weights from a pretrained model, set
@@ -32,6 +31,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+# Dependency imports
+
 import tensorflow as tf
 
 import graphs