diff --git a/.travis.yml b/.travis.yml
index c6f4a57e9..2cdcd85bf 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,23 +6,33 @@ before_install:
   - sudo apt-get update -qq
   - sudo apt-get install -qq libhdf5-dev
 install:
-  - pip install -q tensorflow
+  - pip install -q .[tensorflow]
   - pip install -q .[tests]
+  # Make sure we have the latest version of numpy - avoid problems we were
+  # seeing with Python 3
+  - pip install -q -U numpy
 env:
   global:
     - T2T_PROBLEM=algorithmic_reverse_binary40_test
     - T2T_DATA_DIR=/tmp/t2t-data
     - T2T_TRAIN_DIR=/tmp/t2t-train
 script:
+  # Check import
+  - python -c "from tensor2tensor.models import transformer; print(transformer.Transformer.__name__)"
+
+  # Run tests
   - pytest --ignore=tensor2tensor/utils/registry_test.py --ignore=tensor2tensor/problems_test.py --ignore=tensor2tensor/utils/trainer_lib_test.py --ignore=tensor2tensor/data_generators/algorithmic_math_test.py
   - pytest tensor2tensor/utils/registry_test.py
   - pytest tensor2tensor/utils/trainer_lib_test.py
+
+  # Run installed scripts
   - t2t-datagen 2>&1 | grep translate && echo passed
-  - t2t-trainer --registry_help --t2t_usr_dir=./tensor2tensor/test_data/example_usr_dir 2>&1 | grep my_very_own_hparams && echo passed
-  - python -c "from tensor2tensor.models import transformer; print(transformer.Transformer.__name__)"
   - t2t-trainer --registry_help
-  - mkdir $T2T_DATA_DIR
-  - mkdir $T2T_TRAIN_DIR
+
+  # Test --t2t_usr_dir
+  - t2t-trainer --registry_help --t2t_usr_dir=./tensor2tensor/test_data/example_usr_dir 2>&1 | grep my_very_own_hparams && echo passed
+
+  # Run data generation, training, and decoding on a dummy problem
   - t2t-datagen --problem=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR
   - t2t-trainer --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --train_steps=5 --eval_steps=5 --output_dir=$T2T_TRAIN_DIR
   - t2t-decoder --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --output_dir=$T2T_TRAIN_DIR --decode_hparams='num_samples=10'
diff --git a/README.md b/README.md
index ed0f89b5f..de8de0bfe 100644
--- a/README.md
+++ b/README.md
@@ -148,8 +148,11 @@ t2t-decoder \
   --decode_from_file=$DECODE_FILE \
   --decode_to_file=translation.en
 
-# Eval BLEU
-# (Always report proper BLEU in papers, not the internal approx_bleu.)
+# See the translations
+cat translation.en
+
+# Evaluate the BLEU score
+# Note: Report this BLEU score in papers, not the internal approx_bleu metric.
 t2t-bleu --translation=translation.en --reference=ref-translation.de
 ```
 
diff --git a/docs/cloud_tpu.md b/docs/cloud_tpu.md
index 55144e69c..d375c6741 100644
--- a/docs/cloud_tpu.md
+++ b/docs/cloud_tpu.md
@@ -10,54 +10,22 @@ Models and hparams that are known to work on TPU:
 * `resnet50` with `resnet_base`
 * `revnet104` with `revnet_base`
 
-To run on TPUs, you need to be part of the alpha program; if you're not, these
-commands won't work for you currently, but access will expand soon, so get
-excited for your future ML supercomputers in the cloud.
+TPU access is currently limited, but access will expand soon, so get excited for
+your future ML supercomputers in the cloud.
 
 ## Tutorial: Transformer En-De translation on TPU
 
-Update `gcloud`: `gcloud components update`
+**Note**: You'll need TensorFlow 1.5+.
 
-Set your default zone to a TPU-enabled zone. TPU machines are only available in
-certain zones for now.
+Configure the `gcloud` CLI:
 ```
+gcloud components update
+gcloud auth application-default login
+# Set your default zone to a TPU-enabled zone.
 gcloud config set compute/zone us-central1-f
 ```
 
-Launch a GCE instance; this will run the Python trainer.
-```
-gcloud compute instances create $USER-vm \
-  --machine-type=n1-standard-8 \
-  --image-family=tf-nightly \
-  --image-project=ml-images \
-  --scopes=https://www.googleapis.com/auth/cloud-platform
-```
-
-Launch the TPU instance; the Python program will connect to this to train on the
-TPU device.
-```
-gcloud alpha compute tpus list
-# Make an IP with structure 10.240.X.2 that’s unique in the list
-TPU_IP=10.240.0.2
-gcloud alpha compute tpus create \
-  $USER-tpu \
-  --range=${TPU_IP/%2/0}/29 \
-  --version=nightly
-```
-
-SSH in with port forwarding for TensorBoard
-```
-gcloud compute ssh $USER-vm -- -L 6006:localhost:6006
-```
-
-Now that you're on the cloud instance, install T2T:
-```
-pip install tensor2tensor --user
-# Add the python bin dir to your path
-export PATH=$HOME/.local/bin:$PATH
-```
-
-Generate data to GCS
+Generate data to GCS.
 If you already have the data, use `gsutil cp` to copy to GCS.
 ```
 GCS_BUCKET=gs://my-bucket
@@ -65,21 +33,16 @@ DATA_DIR=$GCS_BUCKET/t2t/data/
 t2t-datagen --problem=translate_ende_wmt8k --data_dir=$DATA_DIR
 ```
 
-Setup some vars used below. `TPU_IP` and `DATA_DIR` should be the same as what
-was used above. Note that the `DATA_DIR` and `OUT_DIR` must be GCS buckets.
+Specify an output directory and launch TensorBoard to monitor training:
 ```
-TPU_IP=10.240.0.2
-DATA_DIR=$GCS_BUCKET/t2t/data/
-OUT_DIR=$GCS_BUCKET/t2t/training/transformer_ende_1
-TPU_MASTER=grpc://$TPU_IP:8470
+OUT_DIR=$GCS_BUCKET/t2t/training/transformer_v1
+tensorboard --logdir=$OUT_DIR
 ```
 
-Launch TensorBoard in the background so you can monitor training:
-```
-tensorboard --logdir=$OUT_DIR > /tmp/tensorboard_logs.txt 2>&1 &
-```
+Note that both the data and output directories must be Google Cloud Storage
+buckets (i.e. start with `gs://`).
 
-Train and evaluate.
+Launch! It's as simple as adding the `--cloud_tpu` flag.
 ```
 t2t-trainer \
   --model=transformer \
@@ -88,21 +51,27 @@ t2t-trainer \
   --train_steps=10 \
   --eval_steps=10 \
   --local_eval_frequency=10 \
-  --iterations_per_loop=10 \
-  --master=$TPU_MASTER \
-  --use_tpu=True \
   --data_dir=$DATA_DIR \
-  --output_dir=$OUT_DIR
+  --output_dir=$OUT_DIR \
+  --cloud_tpu \
+  --cloud_delete_on_done
 ```
 
 The above command will train for 10 steps, then evaluate for 10 steps. You can
 (and should) increase the number of total training steps with the
 `--train_steps` flag. Evaluation will happen every `--local_eval_frequency`
-steps, each time for `--eval_steps`. When you increase then number of training
-steps, also increase `--iterations_per_loop`, which controls how frequently the
-TPU machine returns control to the host machine (1000 seems like a fine number).
-
-Back on your local machine, open your browser and navigate to `localhost:6006`
-for TensorBoard.
+steps, each time for `--eval_steps`. The `--cloud_delete_on_done` flag has the
+trainer delete the VMs on completion.
 
 Voila. Enjoy your new supercomputer.
+
+Note that checkpoints are compatible between CPU, GPU, and TPU models so you can
+switch between hardware at will.
+
+## Additional flags
+
+* `--cloud_vm_name`: The name of the VM to use or create. This can be reused
+  across multiple concurrent runs.
+* `--cloud_tpu_name`: The name of the TPU instance to use or create. If you want
+  to launch multiple jobs on TPU, provide different names here for each one.
+  Each TPU instance can only be training one model at a time.
diff --git a/docs/walkthrough.md b/docs/walkthrough.md
index 06a15d1c8..de8de0bfe 100644
--- a/docs/walkthrough.md
+++ b/docs/walkthrough.md
@@ -133,6 +133,7 @@ t2t-trainer \
 DECODE_FILE=$DATA_DIR/decode_this.txt
 echo "Hello world" >> $DECODE_FILE
 echo "Goodbye world" >> $DECODE_FILE
+echo -e 'Hallo Welt\nAuf Wiedersehen Welt' > ref-translation.de
 
 BEAM_SIZE=4
 ALPHA=0.6
@@ -144,9 +145,15 @@ t2t-decoder \
   --hparams_set=$HPARAMS \
   --output_dir=$TRAIN_DIR \
   --decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA" \
-  --decode_from_file=$DECODE_FILE
+  --decode_from_file=$DECODE_FILE \
+  --decode_to_file=translation.en
 
-cat $DECODE_FILE.$MODEL.$HPARAMS.beam$BEAM_SIZE.alpha$ALPHA.decodes
+# See the translations
+cat translation.en
+
+# Evaluate the BLEU score
+# Note: Report this BLEU score in papers, not the internal approx_bleu metric.
+t2t-bleu --translation=translation.en --reference=ref-translation.de
 ```
 
 ---
diff --git a/setup.py b/setup.py
index ede08f6ae..1d3f14a94 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.4.2',
+    version='1.4.3',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='no-reply@google.com',
@@ -36,7 +36,6 @@
         'future',
         'gevent',
         'gunicorn',
-        'gym',
         'numpy',
         'requests',
         'scipy',
@@ -44,8 +43,8 @@
         'six',
     ],
     extras_require={
-        'tensorflow': ['tensorflow>=1.4.1'],
-        'tensorflow_gpu': ['tensorflow-gpu>=1.4.1'],
+        'tensorflow': ['tensorflow>=1.5.0'],
+        'tensorflow_gpu': ['tensorflow-gpu>=1.5.0'],
         'tests': ['pytest', 'h5py', 'mock'],
     },
     classifiers=[
diff --git a/tensor2tensor/bin/t2t_datagen.py b/tensor2tensor/bin/t2t_datagen.py
index 451b99a3a..8c91b7ee0 100644
--- a/tensor2tensor/bin/t2t_datagen.py
+++ b/tensor2tensor/bin/t2t_datagen.py
@@ -168,6 +168,8 @@ def main(_):
     tf.logging.warning("It is strongly recommended to specify --data_dir. "
                        "Data will be written to default data_dir=%s.",
                        FLAGS.data_dir)
+  FLAGS.data_dir = os.path.expanduser(FLAGS.data_dir)
+  tf.gfile.MakeDirs(FLAGS.data_dir)
 
   tf.logging.info("Generating problems:\n%s"
                   % registry.display_list_by_prefix(problems,
diff --git a/tensor2tensor/bin/t2t_decoder.py b/tensor2tensor/bin/t2t_decoder.py
index bf87949f5..e7ae6b74a 100644
--- a/tensor2tensor/bin/t2t_decoder.py
+++ b/tensor2tensor/bin/t2t_decoder.py
@@ -53,8 +53,9 @@
                     "Path to the source file for decoding")
 flags.DEFINE_string("decode_to_file", None,
                     "Path to the decoded (output) file")
-flags.DEFINE_bool("keep_timestamp", True,
-                  "Set the mtime of the decoded file to the checkpoint_path+'.index' mtime.")
+flags.DEFINE_bool("keep_timestamp", False,
+                  "Set the mtime of the decoded file to the "
+                  "checkpoint_path+'.index' mtime.")
 flags.DEFINE_bool("decode_interactive", False,
                   "Interactive local inference mode.")
 flags.DEFINE_integer("decode_shards", 1, "Number of decoding replicas.")
@@ -83,7 +84,7 @@ def decode(estimator, hparams, decode_hp):
                               decode_hp, FLAGS.decode_to_file,
                               checkpoint_path=FLAGS.checkpoint_path)
     if FLAGS.checkpoint_path and FLAGS.keep_timestamp:
-      ckpt_time = os.path.getmtime(FLAGS.checkpoint_path + '.index')
+      ckpt_time = os.path.getmtime(FLAGS.checkpoint_path + ".index")
       os.utime(FLAGS.decode_to_file, (ckpt_time, ckpt_time))
   else:
     decoding.decode_from_dataset(
diff --git a/tensor2tensor/bin/t2t_trainer.py b/tensor2tensor/bin/t2t_trainer.py
index 77cfa98d5..8f1f0dfdc 100644
--- a/tensor2tensor/bin/t2t_trainer.py
+++ b/tensor2tensor/bin/t2t_trainer.py
@@ -26,6 +26,7 @@
 
 from tensor2tensor import models  # pylint: disable=unused-import
 from tensor2tensor import problems as problems_lib  # pylint: disable=unused-import
+from tensor2tensor.utils import cloud
 from tensor2tensor.utils import decoding
 from tensor2tensor.utils import flags as t2t_flags  # pylint: disable=unused-import
 from tensor2tensor.utils import registry
@@ -46,7 +47,7 @@
                     "available to the t2t-trainer.")
 flags.DEFINE_integer("random_seed", 1234, "Random seed.")
 flags.DEFINE_integer("tpu_num_shards", 8, "Number of tpu shards.")
-flags.DEFINE_integer("iterations_per_loop", 1000,
+flags.DEFINE_integer("iterations_per_loop", 100,
                      "Number of iterations in a TPU training loop.")
 flags.DEFINE_bool("use_tpu", False, "Whether to use TPU.")
 flags.DEFINE_integer("tpu_infeed_sleep_secs", None,
@@ -71,6 +72,15 @@
 except:  # pylint: disable=bare-except
   pass
 
+# Google Cloud TPUs
+flags.DEFINE_bool("cloud_tpu", False, "Whether to launch on Cloud TPUs.")
+flags.DEFINE_string("cloud_vm_name", "%s-vm" % os.getenv("USER"),
+                    "Name of Cloud VM to use or create.")
+flags.DEFINE_string("cloud_tpu_name", "%s-tpu" % os.getenv("USER"),
+                    "Name of Cloud TPU instance to use or create.")
+flags.DEFINE_bool("cloud_delete_on_done", False,
+                  "Whether to delete the VM and TPU instance when done.")
+
 
 def get_problem_name():
   problems = FLAGS.problems.split("-")
@@ -79,10 +89,10 @@ def get_problem_name():
 
 
 def create_hparams():
-  if FLAGS.use_tpu and "tpu" not in FLAGS.hparams_set:
-    tf.logging.warn("Not all hyperparameter sets work on TPU. When available "
-                    "for a given model, prefer hparams_sets with a '_tpu' "
-                    "suffix, e.g. transformer_tpu.")
+  if (FLAGS.cloud_tpu or FLAGS.use_tpu) and "tpu" not in FLAGS.hparams_set:
+    tf.logging.warn("Not all hyperparameter sets work on TPU. "
+                    "Prefer hparams_sets with a '_tpu' suffix, "
+                    "e.g. transformer_tpu, if available for your model.")
   return trainer_lib.create_hparams(FLAGS.hparams_set, FLAGS.hparams)
 
 
@@ -109,7 +119,8 @@ def create_experiment_fn():
 
 def create_run_config(hp):
   save_ckpt_steps = max(FLAGS.iterations_per_loop, FLAGS.local_eval_frequency)
-  if FLAGS.save_checkpoints_secs:
+  save_ckpt_secs = FLAGS.save_checkpoints_secs or None
+  if save_ckpt_secs:
     save_ckpt_steps = None
   return trainer_lib.create_run_config(
       model_dir=os.path.expanduser(FLAGS.output_dir),
@@ -118,7 +129,7 @@ def create_run_config(hp):
       num_shards=FLAGS.tpu_num_shards,
       log_device_placement=FLAGS.log_device_placement,
       save_checkpoints_steps=save_ckpt_steps,
-      save_checkpoints_secs=FLAGS.save_checkpoints_secs,
+      save_checkpoints_secs=save_ckpt_secs,
       keep_checkpoint_max=FLAGS.keep_checkpoint_max,
       keep_checkpoint_every_n_hours=FLAGS.keep_checkpoint_every_n_hours,
       num_gpus=FLAGS.worker_gpu,
@@ -126,7 +137,7 @@ def create_run_config(hp):
       shard_to_cpu=FLAGS.locally_shard_to_cpu,
       num_async_replicas=FLAGS.worker_replicas,
       gpu_mem_fraction=FLAGS.worker_gpu_memory_fraction,
-      enable_graph_rewriter=FLAGS.experimental_optimize_placement,
+      enable_graph_rewriter=FLAGS.enable_graph_rewriter,
       use_tpu=FLAGS.use_tpu,
       schedule=FLAGS.schedule,
       no_data_parallelism=hp.no_data_parallelism,
@@ -156,9 +167,8 @@ def generate_data():
 @contextlib.contextmanager
 def profile_context():
   if FLAGS.profile:
-    with tf.contrib.tfprof.ProfileContext("t2tprof",
-                                          trace_steps=range(100),
-                                          dump_steps=range(100)) as pctx:
+    with tf.contrib.tfprof.ProfileContext(
+        "t2tprof", trace_steps=range(100), dump_steps=range(100)) as pctx:
       opts = tf.profiler.ProfileOptionBuilder.time_and_memory()
       pctx.add_auto_profiling("op", opts, range(100))
       yield
@@ -188,8 +198,7 @@ def save_metadata(hparams):
     flags_str = FLAGS.flags_into_string()
     t2t_flags_str = "\n".join([
         "--%s=%s" % (f.name, f.value)
-        for f in FLAGS.flags_by_module_dict()[
-            "tensor2tensor.utils.flags"]
+        for f in FLAGS.flags_by_module_dict()["tensor2tensor.utils.flags"]
     ])
   else:
     flags_dict = FLAGS.__dict__["__flags"]
@@ -220,6 +229,29 @@ def execute_schedule(exp):
     getattr(exp, FLAGS.schedule)()
 
 
+@contextlib.contextmanager
+def maybe_cloud_tpu():
+  """If FLAGS.cloud_tpu is set, setup Cloud instances."""
+  if not FLAGS.cloud_tpu:
+    yield
+    return
+
+  tf.logging.info("Running on Cloud TPU")
+
+  if (not FLAGS.data_dir.startswith("gs://") or
+      not FLAGS.output_dir.startswith("gs://")):
+    raise ValueError("To run on Cloud TPUs, data_dir and output_dir need to "
+                     "be gs:// paths, i.e. on Google Cloud Storage.")
+
+  FLAGS.use_tpu = True
+  with cloud.cloud_tpu(
+      FLAGS.cloud_vm_name,
+      FLAGS.cloud_tpu_name,
+      delete_on_done=FLAGS.cloud_delete_on_done) as tpu_master:
+    FLAGS.master = tpu_master
+    yield
+
+
 def main(_):
   tf.logging.set_verbosity(tf.logging.INFO)
   trainer_lib.set_random_seed(FLAGS.random_seed)
@@ -230,14 +262,13 @@ def main(_):
     generate_data()
 
   hparams = create_hparams()
-  run_config = create_run_config(hparams)
-
   if is_chief():
     save_metadata(hparams)
 
-  exp_fn = create_experiment_fn()
-  exp = exp_fn(run_config, hparams)
-  execute_schedule(exp)
+  with maybe_cloud_tpu():
+    exp_fn = create_experiment_fn()
+    exp = exp_fn(create_run_config(hparams), hparams)
+    execute_schedule(exp)
 
 
 if __name__ == "__main__":
diff --git a/tensor2tensor/bin/t2t_translate_all.py b/tensor2tensor/bin/t2t_translate_all.py
index 90d078575..40046e883 100644
--- a/tensor2tensor/bin/t2t_translate_all.py
+++ b/tensor2tensor/bin/t2t_translate_all.py
@@ -81,7 +81,7 @@ def main(_):
   if not os.path.exists(flags_path):
     shutil.copy2(os.path.join(model_dir, "flags.txt"), flags_path)
 
-  locals_and_flags = {'FLAGS': FLAGS}
+  locals_and_flags = {"FLAGS": FLAGS}
   for model in bleu_hook.stepfiles_iterator(model_dir, FLAGS.wait_minutes,
                                             FLAGS.min_steps):
     tf.logging.info("Translating " + model.filename)
diff --git a/tensor2tensor/data_generators/all_problems.py b/tensor2tensor/data_generators/all_problems.py
index ba91965af..80734e06d 100644
--- a/tensor2tensor/data_generators/all_problems.py
+++ b/tensor2tensor/data_generators/all_problems.py
@@ -22,16 +22,22 @@
 from tensor2tensor.data_generators import algorithmic
 from tensor2tensor.data_generators import algorithmic_math
 from tensor2tensor.data_generators import audio
+from tensor2tensor.data_generators import celeba
+from tensor2tensor.data_generators import cifar
 from tensor2tensor.data_generators import cipher
 from tensor2tensor.data_generators import cnn_dailymail
 from tensor2tensor.data_generators import desc2code
+from tensor2tensor.data_generators import fsns
 from tensor2tensor.data_generators import gym
 from tensor2tensor.data_generators import ice_parsing
-from tensor2tensor.data_generators import image
+from tensor2tensor.data_generators import imagenet
 from tensor2tensor.data_generators import imdb
 from tensor2tensor.data_generators import librispeech
 from tensor2tensor.data_generators import lm1b
+from tensor2tensor.data_generators import mnist
+from tensor2tensor.data_generators import mscoco
 from tensor2tensor.data_generators import multinli
+from tensor2tensor.data_generators import ocr
 from tensor2tensor.data_generators import problem_hparams
 from tensor2tensor.data_generators import ptb
 from tensor2tensor.data_generators import snli
@@ -40,6 +46,7 @@
 from tensor2tensor.data_generators import translate_enfr
 from tensor2tensor.data_generators import translate_enmk
 from tensor2tensor.data_generators import translate_enzh
+from tensor2tensor.data_generators import twentybn
 from tensor2tensor.data_generators import wiki
 from tensor2tensor.data_generators import wsj_parsing
 
diff --git a/tensor2tensor/data_generators/celeba.py b/tensor2tensor/data_generators/celeba.py
new file mode 100644
index 000000000..5b85c2590
--- /dev/null
+++ b/tensor2tensor/data_generators/celeba.py
@@ -0,0 +1,170 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""CelebA."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import zipfile
+
+# Dependency imports
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+
+@registry.register_problem
+class ImageCeleba(image_utils.ImageProblem):
+  """CelebA dataset, aligned and cropped images."""
+  IMG_DATA = ("img_align_celeba.zip",
+              "https://drive.google.com/uc?export=download&"
+              "id=0B7EVK8r0v71pZjFTYXZWM3FlRnM")
+  LANDMARKS_DATA = ("celeba_landmarks_align",
+                    "https://drive.google.com/uc?export=download&"
+                    "id=0B7EVK8r0v71pd0FJY3Blby1HUTQ")
+  ATTR_DATA = ("celeba_attr", "https://drive.google.com/uc?export=download&"
+               "id=0B7EVK8r0v71pblRyaVFSWGxPY0U")
+
+  LANDMARK_HEADINGS = ("lefteye_x lefteye_y righteye_x righteye_y "
+                       "nose_x nose_y leftmouth_x leftmouth_y rightmouth_x "
+                       "rightmouth_y").split()
+  ATTR_HEADINGS = (
+      "5_o_Clock_Shadow Arched_Eyebrows Attractive Bags_Under_Eyes Bald Bangs "
+      "Big_Lips Big_Nose Black_Hair Blond_Hair Blurry Brown_Hair "
+      "Bushy_Eyebrows Chubby Double_Chin Eyeglasses Goatee Gray_Hair "
+      "Heavy_Makeup High_Cheekbones Male Mouth_Slightly_Open Mustache "
+      "Narrow_Eyes No_Beard Oval_Face Pale_Skin Pointy_Nose Receding_Hairline "
+      "Rosy_Cheeks Sideburns Smiling Straight_Hair Wavy_Hair Wearing_Earrings "
+      "Wearing_Hat Wearing_Lipstick Wearing_Necklace Wearing_Necktie Young"
+  ).split()
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": ("image:identity", 256)}
+    p.target_modality = ("image:identity", 256)
+    p.batch_size_multiplier = 256
+    p.max_expected_batch_size_per_shard = 4
+    p.input_space_id = 1
+    p.target_space_id = 1
+
+  def generator(self, tmp_dir, how_many, start_from=0):
+    """Image generator for CELEBA dataset.
+
+    Args:
+      tmp_dir: path to temporary storage directory.
+      how_many: how many images and labels to generate.
+      start_from: from which image to start.
+
+    Yields:
+      A dictionary representing the images with the following fields:
+      * image/encoded: the string encoding the image as JPEG,
+      * image/format: the string "jpeg" representing image format,
+    """
+    out_paths = []
+    for fname, url in [self.IMG_DATA, self.LANDMARKS_DATA, self.ATTR_DATA]:
+      path = generator_utils.maybe_download_from_drive(tmp_dir, fname, url)
+      out_paths.append(path)
+
+    img_path, landmarks_path, attr_path = out_paths  # pylint: disable=unbalanced-tuple-unpacking
+    unzipped_folder = img_path[:-4]
+    if not tf.gfile.Exists(unzipped_folder):
+      zipfile.ZipFile(img_path, "r").extractall(tmp_dir)
+
+    with tf.gfile.Open(landmarks_path) as f:
+      landmarks_raw = f.read()
+
+    with tf.gfile.Open(attr_path) as f:
+      attr_raw = f.read()
+
+    def process_landmarks(raw_data):
+      landmarks = {}
+      lines = raw_data.split("\n")
+      headings = lines[1].strip().split()
+      for line in lines[2:-1]:
+        values = line.strip().split()
+        img_name = values[0]
+        landmark_values = [int(v) for v in values[1:]]
+        landmarks[img_name] = landmark_values
+      return landmarks, headings
+
+    def process_attrs(raw_data):
+      attrs = {}
+      lines = raw_data.split("\n")
+      headings = lines[1].strip().split()
+      for line in lines[2:-1]:
+        values = line.strip().split()
+        img_name = values[0]
+        attr_values = [int(v) for v in values[1:]]
+        attrs[img_name] = attr_values
+      return attrs, headings
+
+    img_landmarks, _ = process_landmarks(landmarks_raw)
+    img_attrs, _ = process_attrs(attr_raw)
+
+    image_files = tf.gfile.Glob(unzipped_folder + "/*.jpg")
+    for filename in image_files[start_from:start_from + how_many]:
+      img_name = os.path.basename(filename)
+      landmarks = img_landmarks[img_name]
+      attrs = img_attrs[img_name]
+
+      with tf.gfile.Open(filename, "r") as f:
+        encoded_image_data = f.read()
+        yield {
+            "image/encoded": [encoded_image_data],
+            "image/format": ["jpeg"],
+            "attributes": attrs,
+            "landmarks": landmarks,
+        }
+
+  @property
+  def train_shards(self):
+    return 100
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    generator_utils.generate_dataset_and_shuffle(
+        self.generator(tmp_dir, 162770),  # train
+        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
+        self.generator(tmp_dir, 19867, 162770),  # dev
+        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
+
+
+@registry.register_problem
+class Img2imgCeleba(ImageCeleba):
+  """8px to 32px problem."""
+
+  def dataset_filename(self):
+    return "image_celeba"
+
+  def preprocess_example(self, example, unused_mode, unused_hparams):
+    image = example["inputs"]
+    # Remove boundaries in CelebA images. Remove 40 pixels each side
+    # vertically and 20 pixels each side horizontally.
+    image = tf.image.crop_to_bounding_box(image, 40, 20, 218 - 80, 178 - 40)
+    image_8 = image_utils.resize_by_area(image, 8)
+    image_32 = image_utils.resize_by_area(image, 32)
+
+    example["inputs"] = image_8
+    example["targets"] = image_32
+    return example
diff --git a/tensor2tensor/data_generators/cifar.py b/tensor2tensor/data_generators/cifar.py
new file mode 100644
index 000000000..0c03ebc30
--- /dev/null
+++ b/tensor2tensor/data_generators/cifar.py
@@ -0,0 +1,191 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""CIFAR."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import tarfile
+
+# Dependency imports
+
+import numpy as np
+
+from six.moves import cPickle
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.data_generators import mnist
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+# URLs and filenames for CIFAR data.
+_CIFAR10_URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+_CIFAR10_PREFIX = "cifar-10-batches-py/"
+_CIFAR10_TRAIN_FILES = [
+    "data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4",
+    "data_batch_5"
+]
+_CIFAR10_TEST_FILES = ["test_batch"]
+_CIFAR10_IMAGE_SIZE = 32
+
+
+def _get_cifar10(directory):
+  """Download and extract CIFAR to directory unless it is there."""
+  filename = os.path.basename(_CIFAR10_URL)
+  path = generator_utils.maybe_download(directory, filename, _CIFAR10_URL)
+  tarfile.open(path, "r:gz").extractall(directory)
+
+
+def cifar10_generator(tmp_dir, training, how_many, start_from=0):
+  """Image generator for CIFAR-10.
+
+  Args:
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the test set.
+    how_many: how many images and labels to generate.
+    start_from: from which image to start.
+
+  Returns:
+    An instance of image_generator that produces CIFAR-10 images and labels.
+  """
+  _get_cifar10(tmp_dir)
+  data_files = _CIFAR10_TRAIN_FILES if training else _CIFAR10_TEST_FILES
+  all_images, all_labels = [], []
+  for filename in data_files:
+    path = os.path.join(tmp_dir, _CIFAR10_PREFIX, filename)
+    with tf.gfile.Open(path, "r") as f:
+      data = cPickle.load(f)
+    images = data["data"]
+    num_images = images.shape[0]
+    images = images.reshape((num_images, 3, _CIFAR10_IMAGE_SIZE,
+                             _CIFAR10_IMAGE_SIZE))
+    all_images.extend([
+        np.squeeze(images[j]).transpose((1, 2, 0)) for j in xrange(num_images)
+    ])
+    labels = data["labels"]
+    all_labels.extend([labels[j] for j in xrange(num_images)])
+  return image_utils.image_generator(
+      all_images[start_from:start_from + how_many],
+      all_labels[start_from:start_from + how_many])
+
+
+@registry.register_problem
+class ImageCifar10Tune(mnist.ImageMnistTune):
+  """Cifar-10 Tune."""
+
+  @property
+  def num_channels(self):
+    return 3
+
+  @property
+  def class_labels(self):
+    return [
+        "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse",
+        "ship", "truck"
+    ]
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    image = example["inputs"]
+    image.set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      image = image_utils.cifar_image_augmentation(image)
+    image = tf.image.per_image_standardization(image)
+    example["inputs"] = image
+    return example
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return cifar10_generator(tmp_dir, True, 48000)
+    else:
+      return cifar10_generator(tmp_dir, True, 2000, 48000)
+
+
+@registry.register_problem
+class ImageCifar10(ImageCifar10Tune):
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return cifar10_generator(tmp_dir, True, 50000)
+    else:
+      return cifar10_generator(tmp_dir, False, 10000)
+
+
+@registry.register_problem
+class ImageCifar10Plain(ImageCifar10):
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    image = example["inputs"]
+    image.set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
+    image = tf.image.per_image_standardization(image)
+    example["inputs"] = image
+    return example
+
+
+@registry.register_problem
+class ImageCifar10PlainGen(ImageCifar10Plain):
+  """CIFAR-10 32x32 for image generation without standardization preprep."""
+
+  def dataset_filename(self):
+    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"].set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
+    example["inputs"] = tf.to_int64(example["inputs"])
+    return example
+
+
+@registry.register_problem
+class ImageCifar10Plain8(ImageCifar10):
+  """CIFAR-10 rescaled to 8x8 for output: Conditional image generation."""
+
+  def dataset_filename(self):
+    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    image = example["inputs"]
+    image = image_utils.resize_by_area(image, 8)
+    image = tf.image.per_image_standardization(image)
+    example["inputs"] = image
+    return example
+
+
+@registry.register_problem
+class Img2imgCifar10(ImageCifar10):
+  """CIFAR-10 rescaled to 8x8 for input and 32x32 for output."""
+
+  def dataset_filename(self):
+    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
+
+  def preprocess_example(self, example, unused_mode, unused_hparams):
+
+    inputs = example["inputs"]
+    # For Img2Img resize input and output images as desired.
+    example["inputs"] = image_utils.resize_by_area(inputs, 8)
+    example["targets"] = image_utils.resize_by_area(inputs, 32)
+    return example
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": ("image:identity", 256)}
+    p.target_modality = ("image:identity", 256)
+    p.batch_size_multiplier = 256
+    p.max_expected_batch_size_per_shard = 4
+    p.input_space_id = 1
+    p.target_space_id = 1
diff --git a/tensor2tensor/data_generators/fsns.py b/tensor2tensor/data_generators/fsns.py
new file mode 100644
index 000000000..938931c4c
--- /dev/null
+++ b/tensor2tensor/data_generators/fsns.py
@@ -0,0 +1,82 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""FSNS."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+# Dependency imports
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.data_generators import problem
+from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+
+@registry.register_problem
+class ImageFSNS(image_utils.ImageProblem):
+  """Problem spec for French Street Name recognition."""
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    list_url = ("https://raw.githubusercontent.com/tensorflow/models/master/"
+                "street/python/fsns_urls.txt")
+    fsns_urls = generator_utils.maybe_download(tmp_dir, "fsns_urls.txt",
+                                               list_url)
+    fsns_files = [
+        f.strip() for f in open(fsns_urls, "r") if f.startswith("http://")
+    ]
+    for url in fsns_files:
+      if "/train/train" in url:
+        generator_utils.maybe_download(
+            data_dir, "image_fsns-train" + url[-len("-00100-of-00512"):], url)
+      elif "/validation/validation" in url:
+        generator_utils.maybe_download(
+            data_dir, "image_fsns-dev" + url[-len("-00100-of-00512"):], url)
+      elif "charset" in url:
+        generator_utils.maybe_download(data_dir, "charset_size134.txt", url)
+
+  def feature_encoders(self, data_dir):
+    # This vocab file must be present within the data directory.
+    vocab_filename = os.path.join(data_dir, "charset_size134.txt")
+    return {
+        "inputs": text_encoder.ImageEncoder(),
+        "targets": text_encoder.SubwordTextEncoder(vocab_filename)
+    }
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": (registry.Modalities.IMAGE, 256)}
+    vocab_size = self._encoders["targets"].vocab_size
+    p.target_modality = (registry.Modalities.SYMBOL, vocab_size)
+    p.batch_size_multiplier = 256
+    p.max_expected_batch_size_per_shard = 2
+    p.input_space_id = problem.SpaceID.IMAGE
+    p.target_space_id = problem.SpaceID.EN_TOK
+
+  def example_reading_spec(self):
+    label_key = "image/unpadded_label"
+    data_fields, data_items_to_decoders = (
+        super(ImageFSNS, self).example_reading_spec())
+    data_fields[label_key] = tf.VarLenFeature(tf.int64)
+    data_items_to_decoders[
+        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
+    return data_fields, data_items_to_decoders
diff --git a/tensor2tensor/data_generators/gym.py b/tensor2tensor/data_generators/gym.py
index 631c2b281..27be9a71b 100644
--- a/tensor2tensor/data_generators/gym.py
+++ b/tensor2tensor/data_generators/gym.py
@@ -23,8 +23,6 @@
 
 # Dependency imports
 
-import gym
-
 from tensor2tensor.data_generators import generator_utils
 from tensor2tensor.data_generators import problem
 from tensor2tensor.utils import registry
@@ -33,6 +31,15 @@
 
 
 
+def gym_lib():
+  """Access to gym to allow for import of this file without a gym install."""
+  try:
+    import gym  # pylint: disable=g-import-not-at-top
+  except ImportError:
+    raise ImportError("pip install gym to use gym-based Problems")
+  return gym
+
+
 class GymDiscreteProblem(problem.Problem):
   """Gym environment with discrete actions and rewards."""
 
@@ -48,7 +55,7 @@ def env_name(self):
   @property
   def env(self):
     if self._env is None:
-      self._env = gym.make(self.env_name)
+      self._env = gym_lib().make(self.env_name)
     return self._env
 
   @property
diff --git a/tensor2tensor/data_generators/image.py b/tensor2tensor/data_generators/image.py
deleted file mode 100644
index 794d6615a..000000000
--- a/tensor2tensor/data_generators/image.py
+++ /dev/null
@@ -1,1148 +0,0 @@
-# coding=utf-8
-# Copyright 2017 The Tensor2Tensor Authors.
-#
-# 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.
-
-"""Data generators for image data-sets."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import gzip
-import io
-import json
-import os
-import random
-import struct
-import tarfile
-import zipfile
-
-# Dependency imports
-
-import numpy as np
-from six.moves import cPickle
-from six.moves import xrange  # pylint: disable=redefined-builtin
-from six.moves import zip  # pylint: disable=redefined-builtin
-from tensor2tensor.data_generators import generator_utils
-from tensor2tensor.data_generators import problem
-from tensor2tensor.data_generators import text_encoder
-from tensor2tensor.layers import common_layers
-from tensor2tensor.utils import registry
-
-import tensorflow as tf
-
-from tensorflow.python.eager import context
-
-
-def resize_by_area(img, size):
-  """image resize function used by quite a few image problems."""
-  return tf.to_int64(
-      tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA))
-
-
-class ImageProblem(problem.Problem):
-
-  def example_reading_spec(self, label_repr=None):
-    data_fields = {
-        "image/encoded": tf.FixedLenFeature((), tf.string),
-        "image/format": tf.FixedLenFeature((), tf.string),
-    }
-
-    data_items_to_decoders = {
-        "inputs":
-            tf.contrib.slim.tfexample_decoder.Image(
-                image_key="image/encoded",
-                format_key="image/format",
-                channels=3),
-    }
-
-    return data_fields, data_items_to_decoders
-
-
-@registry.register_problem("image_celeba_tune")
-class ImageCeleba(ImageProblem):
-  """CelebA dataset, aligned and cropped images."""
-  IMG_DATA = ("img_align_celeba.zip",
-              "https://drive.google.com/uc?export=download&"
-              "id=0B7EVK8r0v71pZjFTYXZWM3FlRnM")
-  LANDMARKS_DATA = ("celeba_landmarks_align",
-                    "https://drive.google.com/uc?export=download&"
-                    "id=0B7EVK8r0v71pd0FJY3Blby1HUTQ")
-  ATTR_DATA = ("celeba_attr", "https://drive.google.com/uc?export=download&"
-               "id=0B7EVK8r0v71pblRyaVFSWGxPY0U")
-
-  LANDMARK_HEADINGS = ("lefteye_x lefteye_y righteye_x righteye_y "
-                       "nose_x nose_y leftmouth_x leftmouth_y rightmouth_x "
-                       "rightmouth_y").split()
-  ATTR_HEADINGS = (
-      "5_o_Clock_Shadow Arched_Eyebrows Attractive Bags_Under_Eyes Bald Bangs "
-      "Big_Lips Big_Nose Black_Hair Blond_Hair Blurry Brown_Hair "
-      "Bushy_Eyebrows Chubby Double_Chin Eyeglasses Goatee Gray_Hair "
-      "Heavy_Makeup High_Cheekbones Male Mouth_Slightly_Open Mustache "
-      "Narrow_Eyes No_Beard Oval_Face Pale_Skin Pointy_Nose Receding_Hairline "
-      "Rosy_Cheeks Sideburns Smiling Straight_Hair Wavy_Hair Wearing_Earrings "
-      "Wearing_Hat Wearing_Lipstick Wearing_Necklace Wearing_Necktie Young"
-  ).split()
-
-  def preprocess_example(self, example, unused_mode, unused_hparams):
-
-    inputs = example["inputs"]
-    # Remove boundaries in CelebA images. Remove 40 pixels each side
-    # vertically and 20 pixels each side horizontally.
-    inputs = tf.image.crop_to_bounding_box(inputs, 40, 20, 218 - 80, 178 - 40)
-    example["inputs"] = resize_by_area(inputs, 8)
-    example["targets"] = resize_by_area(inputs, 32)
-    return example
-
-  def hparams(self, defaults, unused_model_hparams):
-    p = defaults
-    p.input_modality = {"inputs": ("image:identity", 256)}
-    p.target_modality = ("image:identity", 256)
-    p.batch_size_multiplier = 256
-    p.max_expected_batch_size_per_shard = 4
-    p.input_space_id = 1
-    p.target_space_id = 1
-
-  def generator(self, tmp_dir, how_many, start_from=0):
-    """Image generator for CELEBA dataset.
-
-    Args:
-      tmp_dir: path to temporary storage directory.
-      how_many: how many images and labels to generate.
-      start_from: from which image to start.
-
-    Yields:
-      A dictionary representing the images with the following fields:
-      * image/encoded: the string encoding the image as JPEG,
-      * image/format: the string "jpeg" representing image format,
-    """
-    out_paths = []
-    for fname, url in [self.IMG_DATA, self.LANDMARKS_DATA, self.ATTR_DATA]:
-      path = generator_utils.maybe_download_from_drive(tmp_dir, fname, url)
-      out_paths.append(path)
-
-    img_path, landmarks_path, attr_path = out_paths  # pylint: disable=unbalanced-tuple-unpacking
-    unzipped_folder = img_path[:-4]
-    if not tf.gfile.Exists(unzipped_folder):
-      zipfile.ZipFile(img_path, "r").extractall(tmp_dir)
-
-    with tf.gfile.Open(landmarks_path) as f:
-      landmarks_raw = f.read()
-
-    with tf.gfile.Open(attr_path) as f:
-      attr_raw = f.read()
-
-    def process_landmarks(raw_data):
-      landmarks = {}
-      lines = raw_data.split("\n")
-      headings = lines[1].strip().split()
-      for line in lines[2:-1]:
-        values = line.strip().split()
-        img_name = values[0]
-        landmark_values = [int(v) for v in values[1:]]
-        landmarks[img_name] = landmark_values
-      return landmarks, headings
-
-    def process_attrs(raw_data):
-      attrs = {}
-      lines = raw_data.split("\n")
-      headings = lines[1].strip().split()
-      for line in lines[2:-1]:
-        values = line.strip().split()
-        img_name = values[0]
-        attr_values = [int(v) for v in values[1:]]
-        attrs[img_name] = attr_values
-      return attrs, headings
-
-    img_landmarks, _ = process_landmarks(landmarks_raw)
-    img_attrs, _ = process_attrs(attr_raw)
-
-    image_files = tf.gfile.Glob(unzipped_folder + "/*.jpg")
-    for filename in image_files[start_from:start_from + how_many]:
-      img_name = os.path.basename(filename)
-      landmarks = img_landmarks[img_name]
-      attrs = img_attrs[img_name]
-
-      with tf.gfile.Open(filename, "r") as f:
-        encoded_image_data = f.read()
-        yield {
-            "image/encoded": [encoded_image_data],
-            "image/format": ["jpeg"],
-            "attributes": attrs,
-            "landmarks": landmarks,
-        }
-
-  @property
-  def train_shards(self):
-    return 100
-
-  @property
-  def dev_shards(self):
-    return 10
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    generator_utils.generate_dataset_and_shuffle(
-        self.generator(tmp_dir, 162770),  # train
-        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
-        self.generator(tmp_dir, 19867, 162770),  # dev
-        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
-
-
-@registry.register_problem
-class ImageFSNS(ImageProblem):
-  """Problem spec for French Street Name recognition."""
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    list_url = ("https://raw.githubusercontent.com/tensorflow/models/master/"
-                "street/python/fsns_urls.txt")
-    fsns_urls = generator_utils.maybe_download(tmp_dir, "fsns_urls.txt",
-                                               list_url)
-    fsns_files = [
-        f.strip() for f in open(fsns_urls, "r") if f.startswith("http://")
-    ]
-    for url in fsns_files:
-      if "/train/train" in url:
-        generator_utils.maybe_download(
-            data_dir, "image_fsns-train" + url[-len("-00100-of-00512"):], url)
-      elif "/validation/validation" in url:
-        generator_utils.maybe_download(
-            data_dir, "image_fsns-dev" + url[-len("-00100-of-00512"):], url)
-      elif "charset" in url:
-        generator_utils.maybe_download(data_dir, "charset_size134.txt", url)
-
-  def feature_encoders(self, data_dir):
-    # This vocab file must be present within the data directory.
-    vocab_filename = os.path.join(data_dir, "charset_size134.txt")
-    return {
-        "inputs": text_encoder.ImageEncoder(),
-        "targets": text_encoder.SubwordTextEncoder(vocab_filename)
-    }
-
-  def hparams(self, defaults, unused_model_hparams):
-    p = defaults
-    p.input_modality = {"inputs": (registry.Modalities.IMAGE, 256)}
-    vocab_size = self._encoders["targets"].vocab_size
-    p.target_modality = (registry.Modalities.SYMBOL, vocab_size)
-    p.batch_size_multiplier = 256
-    p.max_expected_batch_size_per_shard = 2
-    p.input_space_id = problem.SpaceID.IMAGE
-    p.target_space_id = problem.SpaceID.EN_TOK
-
-  def example_reading_spec(self):
-    label_key = "image/unpadded_label"
-    data_fields, data_items_to_decoders = (
-        super(ImageFSNS, self).example_reading_spec())
-    data_fields[label_key] = tf.VarLenFeature(tf.int64)
-    data_items_to_decoders[
-        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
-    return data_fields, data_items_to_decoders
-
-
-class Image2ClassProblem(ImageProblem):
-  """Base class for image classification problems."""
-
-  @property
-  def is_small(self):
-    raise NotImplementedError()
-
-  @property
-  def num_classes(self):
-    raise NotImplementedError()
-
-  @property
-  def train_shards(self):
-    raise NotImplementedError()
-
-  @property
-  def dev_shards(self):
-    return 1
-
-  @property
-  def class_labels(self):
-    return ["ID_%d" % i for i in range(self.num_classes)]
-
-  def feature_encoders(self, data_dir):
-    del data_dir
-    return {
-        "inputs": text_encoder.ImageEncoder(),
-        "targets": text_encoder.ClassLabelEncoder(self.class_labels)
-    }
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    raise NotImplementedError()
-
-  def example_reading_spec(self):
-    label_key = "image/class/label"
-    data_fields, data_items_to_decoders = (
-        super(Image2ClassProblem, self).example_reading_spec())
-    data_fields[label_key] = tf.FixedLenFeature((1,), tf.int64)
-
-    data_items_to_decoders[
-        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
-    return data_fields, data_items_to_decoders
-
-  def hparams(self, defaults, unused_model_hparams):
-    p = defaults
-    p.input_modality = {"inputs": (registry.Modalities.IMAGE, 256)}
-    p.target_modality = (registry.Modalities.CLASS_LABEL, self.num_classes)
-    p.batch_size_multiplier = 4 if self.is_small else 256
-    p.max_expected_batch_size_per_shard = 8 if self.is_small else 2
-    p.loss_multiplier = 3.0 if self.is_small else 1.0
-    if self._was_reversed:
-      p.loss_multiplier = 1.0
-    p.input_space_id = problem.SpaceID.IMAGE
-    p.target_space_id = problem.SpaceID.IMAGE_LABEL
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    generator_utils.generate_dataset_and_shuffle(
-        self.generator(data_dir, tmp_dir, True),
-        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
-        self.generator(data_dir, tmp_dir, False),
-        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
-
-
-def imagenet_preprocess_example(example, mode, resize_size=None):
-  """Preprocessing used for Imagenet and similar problems."""
-  if resize_size is None:
-    resize_size = [299, 299]
-
-  def preprocess(img):
-    img = tf.image.resize_images(img, [360, 360])
-    img = common_layers.image_augmentation(
-        tf.to_float(img) / 255., crop_size=resize_size)
-    return tf.to_int64(img * 255.)
-
-  def resize(img):
-    return tf.to_int64(tf.image.resize_images(img, resize_size))
-
-  inputs = tf.cast(example["inputs"], tf.int64)
-  if mode == tf.estimator.ModeKeys.TRAIN:
-    example["inputs"] = tf.cond(  # Preprocess 90% of the time.
-        tf.less(tf.random_uniform([]), 0.9),
-        lambda img=inputs: preprocess(img),
-        lambda img=inputs: resize(img))
-  else:
-    example["inputs"] = resize(inputs)
-  return example
-
-
-@registry.register_problem
-class ImageImagenet(Image2ClassProblem):
-  """Imagenet."""
-
-  @property
-  def is_small(self):
-    return False
-
-  @property
-  def num_classes(self):
-    return 1000
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    # TODO(lukaszkaiser): find a better way than printing this.
-    print("To generate the ImageNet dataset in the proper format, follow "
-          "instructions at https://github.com/tensorflow/models/blob/master"
-          "/inception/README.md#getting-started")
-
-  def preprocess_example(self, example, mode, _):
-    return imagenet_preprocess_example(example, mode)
-
-
-@registry.register_problem
-class ImageImagenet224(ImageImagenet):
-  """Imagenet rescaled to 224x224."""
-
-  def dataset_filename(self):
-    return "image_imagenet"  # Reuse Imagenet data.
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    tf.logging.warning(
-        "Generate data for image_imagenet224 with image_imagenet")
-
-  def preprocess_example(self, example, mode, _):
-    return imagenet_preprocess_example(example, mode, resize_size=[224, 224])
-
-
-@registry.register_problem
-class ImageImagenet32(Image2ClassProblem):
-  """Imagenet rescaled to 32x32."""
-
-  def dataset_filename(self):
-    return "image_imagenet"  # Reuse Imagenet data.
-
-  @property
-  def is_small(self):
-    return True  # Modalities like for CIFAR.
-
-  @property
-  def num_classes(self):
-    return 1000
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    # TODO(lukaszkaiser): find a better way than printing this.
-    print("To generate the ImageNet dataset in the proper format, follow "
-          "instructions at https://github.com/tensorflow/models/blob/master"
-          "/inception/README.md#getting-started")
-
-  def preprocess_example(self, example, mode, unused_hparams):
-    # Just resize with area.
-    if self._was_reversed:
-      example["inputs"] = tf.to_int64(
-          tf.image.resize_images(example["inputs"], [32, 32],
-                                 tf.image.ResizeMethod.AREA))
-    else:
-      example = imagenet_preprocess_example(example, mode)
-      example["inputs"] = tf.to_int64(
-          tf.image.resize_images(example["inputs"], [32, 32]))
-    return example
-
-
-@registry.register_problem
-class ImageImagenet64(Image2ClassProblem):
-  """Imagenet rescaled to 64x64."""
-
-  def dataset_filename(self):
-    return "image_imagenet"  # Reuse Imagenet data.
-
-  @property
-  def is_small(self):
-    return True  # Modalities like for CIFAR.
-
-  @property
-  def num_classes(self):
-    return 1000
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    # TODO(lukaszkaiser): find a better way than printing this.
-    print("To generate the ImageNet dataset in the proper format, follow "
-          "instructions at https://github.com/tensorflow/models/blob/master"
-          "/inception/README.md#getting-started")
-
-  def preprocess_example(self, example, mode, unused_hparams):
-    inputs = example["inputs"]
-    # Just resize with area.
-    if self._was_reversed:
-      example["inputs"] = resize_by_area(inputs, 64)
-    else:
-      example = imagenet_preprocess_example(example, mode)
-      example["inputs"] = example["inputs"] = resize_by_area(inputs, 64)
-    return example
-
-
-@registry.register_problem
-class Img2imgImagenet(ImageProblem):
-  """Imagenet rescaled to 8x8 for input and 32x32 for output."""
-
-  def dataset_filename(self):
-    return "image_imagenet"  # Reuse Imagenet data.
-
-  def preprocess_example(self, example, unused_mode, unused_hparams):
-
-    inputs = example["inputs"]
-    # For Img2Img resize input and output images as desired.
-    example["inputs"] = resize_by_area(inputs, 8)
-    example["targets"] = resize_by_area(inputs, 32)
-    return example
-
-  def hparams(self, defaults, unused_model_hparams):
-    p = defaults
-    p.input_modality = {"inputs": ("image:identity", 256)}
-    p.target_modality = ("image:identity", 256)
-    p.batch_size_multiplier = 256
-    p.max_expected_batch_size_per_shard = 4
-    p.input_space_id = 1
-    p.target_space_id = 1
-
-
-def _encoded_images(images):
-  if context.in_eager_mode():
-    for image in images:
-      yield tf.image.encode_png(image).numpy()
-  else:
-    (width, height, channels) = images[0].shape
-    with tf.Graph().as_default():
-      image_t = tf.placeholder(dtype=tf.uint8, shape=(width, height, channels))
-      encoded_image_t = tf.image.encode_png(image_t)
-      with tf.Session() as sess:
-        for image in images:
-          enc_string = sess.run(encoded_image_t, feed_dict={image_t: image})
-          yield enc_string
-
-
-def image_generator(images, labels):
-  """Generator for images that takes image and labels lists and creates pngs.
-
-  Args:
-    images: list of images given as [width x height x channels] numpy arrays.
-    labels: list of ints, same length as images.
-
-  Yields:
-    A dictionary representing the images with the following fields:
-    * image/encoded: the string encoding the image as PNG,
-    * image/format: the string "png" representing image format,
-    * image/class/label: an integer representing the label,
-    * image/height: an integer representing the height,
-    * image/width: an integer representing the width.
-    Every field is actually a singleton list of the corresponding type.
-
-  Raises:
-    ValueError: if images is an empty list.
-  """
-  if not images:
-    raise ValueError("Must provide some images for the generator.")
-  width, height, _ = images[0].shape
-  for (enc_image, label) in zip(_encoded_images(images), labels):
-    yield {
-        "image/encoded": [enc_image],
-        "image/format": ["png"],
-        "image/class/label": [int(label)],
-        "image/height": [height],
-        "image/width": [width]
-    }
-
-
-# URLs and filenames for MNIST data.
-_MNIST_URL = "http://yann.lecun.com/exdb/mnist/"
-_MNIST_TRAIN_DATA_FILENAME = "train-images-idx3-ubyte.gz"
-_MNIST_TRAIN_LABELS_FILENAME = "train-labels-idx1-ubyte.gz"
-_MNIST_TEST_DATA_FILENAME = "t10k-images-idx3-ubyte.gz"
-_MNIST_TEST_LABELS_FILENAME = "t10k-labels-idx1-ubyte.gz"
-_MNIST_IMAGE_SIZE = 28
-
-
-def _get_mnist(directory):
-  """Download all MNIST files to directory unless they are there."""
-  for filename in [
-      _MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_FILENAME,
-      _MNIST_TEST_DATA_FILENAME, _MNIST_TEST_LABELS_FILENAME
-  ]:
-    generator_utils.maybe_download(directory, filename, _MNIST_URL + filename)
-
-
-def _extract_mnist_images(filename, num_images):
-  """Extract images from an MNIST file into a numpy array.
-
-  Args:
-    filename: The path to an MNIST images file.
-    num_images: The number of images in the file.
-
-  Returns:
-    A numpy array of shape [number_of_images, height, width, channels].
-  """
-  with gzip.open(filename) as bytestream:
-    bytestream.read(16)
-    buf = bytestream.read(_MNIST_IMAGE_SIZE * _MNIST_IMAGE_SIZE * num_images)
-    data = np.frombuffer(buf, dtype=np.uint8)
-    data = data.reshape(num_images, _MNIST_IMAGE_SIZE, _MNIST_IMAGE_SIZE, 1)
-  return data
-
-
-def _extract_mnist_labels(filename, num_labels):
-  """Extract labels from an MNIST file into integers.
-
-  Args:
-    filename: The path to an MNIST labels file.
-    num_labels: The number of labels in the file.
-
-  Returns:
-    A int64 numpy array of shape [num_labels]
-  """
-  with gzip.open(filename) as bytestream:
-    bytestream.read(8)
-    buf = bytestream.read(num_labels)
-    labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
-  return labels
-
-
-def mnist_common_generator(tmp_dir, training, how_many, data_filename,
-                           label_filename, start_from=0):
-  """Image generator for MNIST.
-
-  Args:
-    tmp_dir: path to temporary storage directory.
-    training: a Boolean; if true, we use the train set, otherwise the test set.
-    how_many: how many images and labels to generate.
-    data_filename: file that contains features data.
-    label_filename: file that contains labels.
-    start_from: from which image to start.
-
-  Returns:
-    An instance of image_generator that produces MNIST images.
-  """
-  data_path = os.path.join(tmp_dir, data_filename)
-  labels_path = os.path.join(tmp_dir, label_filename)
-  images = _extract_mnist_images(data_path, 60000 if training else 10000)
-  labels = _extract_mnist_labels(labels_path, 60000 if training else 10000)
-  # Shuffle the data to make sure classes are well distributed.
-  data = list(zip(images, labels))
-  random.shuffle(data)
-  images, labels = list(zip(*data))
-  return image_generator(images[start_from:start_from + how_many],
-                         labels[start_from:start_from + how_many])
-
-
-def mnist_generator(tmp_dir, training, how_many, start_from=0):
-  """Image generator for MNIST.
-
-  Args:
-    tmp_dir: path to temporary storage directory.
-    training: a Boolean; if true, we use the train set, otherwise the test set.
-    how_many: how many images and labels to generate.
-    start_from: from which image to start.
-
-  Returns:
-    An instance of image_generator that produces MNIST images.
-  """
-  _get_mnist(tmp_dir)
-  d = _MNIST_TRAIN_DATA_FILENAME if training else _MNIST_TEST_DATA_FILENAME
-  l = _MNIST_TRAIN_LABELS_FILENAME if training else _MNIST_TEST_LABELS_FILENAME
-  return mnist_common_generator(tmp_dir, training, how_many, d, l, start_from)
-
-
-@registry.register_problem
-class ImageMnistTune(Image2ClassProblem):
-  """MNIST, tuning data."""
-
-  @property
-  def is_small(self):
-    return True
-
-  @property
-  def num_classes(self):
-    return 10
-
-  @property
-  def class_labels(self):
-    return [str(c) for c in range(self.num_classes)]
-
-  @property
-  def train_shards(self):
-    return 10
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    if is_training:
-      return mnist_generator(tmp_dir, True, 55000)
-    else:
-      return mnist_generator(tmp_dir, True, 5000, 55000)
-
-
-@registry.register_problem
-class ImageMnist(ImageMnistTune):
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    if is_training:
-      return mnist_generator(tmp_dir, True, 60000)
-    else:
-      return mnist_generator(tmp_dir, False, 10000)
-
-# URLs and filenames for MNIST data.
-_FASHION_MNIST_URL = ("http://fashion-mnist.s3-website.eu-central-1"
-                      ".amazonaws.com/")
-_FASHION_MNIST_LOCAL_FILE_PREFIX = "fashion-"
-_FASHION_MNIST_IMAGE_SIZE = 28
-
-
-def _get_fashion_mnist(directory):
-  """Download all FashionMNIST files to directory unless they are there."""
-  # Fashion mnist files have the same names as MNIST.
-  # We must choose a separate name (by adding 'fashion-' prefix) in the tmp_dir.
-  for filename in [
-      _MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_FILENAME,
-      _MNIST_TEST_DATA_FILENAME, _MNIST_TEST_LABELS_FILENAME
-  ]:
-    generator_utils.maybe_download(directory,
-                                   _FASHION_MNIST_LOCAL_FILE_PREFIX + filename,
-                                   _FASHION_MNIST_URL + filename)
-
-
-def fashion_mnist_generator(tmp_dir, training, how_many, start_from=0):
-  """Image generator for FashionMNIST.
-
-  Args:
-    tmp_dir: path to temporary storage directory.
-    training: a Boolean; if true, we use the train set, otherwise the test set.
-    how_many: how many images and labels to generate.
-    start_from: from which image to start.
-
-  Returns:
-    An instance of image_generator that produces MNIST images.
-  """
-  _get_fashion_mnist(tmp_dir)
-  d = _FASHION_MNIST_LOCAL_FILE_PREFIX + (
-      _MNIST_TRAIN_DATA_FILENAME if training else _MNIST_TEST_DATA_FILENAME)
-  l = _FASHION_MNIST_LOCAL_FILE_PREFIX + (
-      _MNIST_TRAIN_LABELS_FILENAME if training else
-      _MNIST_TEST_LABELS_FILENAME)
-  return mnist_common_generator(tmp_dir, training, how_many, d, l, start_from)
-
-
-@registry.register_problem
-class ImageFashionMnist(Image2ClassProblem):
-  """Fashion MNIST."""
-
-  @property
-  def is_small(self):
-    return True
-
-  @property
-  def num_classes(self):
-    return 10
-
-  @property
-  def class_labels(self):
-    return [str(c) for c in range(self.num_classes)]
-
-  @property
-  def train_shards(self):
-    return 10
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    if is_training:
-      return fashion_mnist_generator(tmp_dir, True, 60000)
-    else:
-      return fashion_mnist_generator(tmp_dir, False, 10000)
-
-
-# URLs and filenames for CIFAR data.
-_CIFAR10_URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
-_CIFAR10_PREFIX = "cifar-10-batches-py/"
-_CIFAR10_TRAIN_FILES = [
-    "data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4",
-    "data_batch_5"
-]
-_CIFAR10_TEST_FILES = ["test_batch"]
-_CIFAR10_IMAGE_SIZE = 32
-
-
-def _get_cifar10(directory):
-  """Download and extract CIFAR to directory unless it is there."""
-  filename = os.path.basename(_CIFAR10_URL)
-  path = generator_utils.maybe_download(directory, filename, _CIFAR10_URL)
-  tarfile.open(path, "r:gz").extractall(directory)
-
-
-def cifar10_generator(tmp_dir, training, how_many, start_from=0):
-  """Image generator for CIFAR-10.
-
-  Args:
-    tmp_dir: path to temporary storage directory.
-    training: a Boolean; if true, we use the train set, otherwise the test set.
-    how_many: how many images and labels to generate.
-    start_from: from which image to start.
-
-  Returns:
-    An instance of image_generator that produces CIFAR-10 images and labels.
-  """
-  _get_cifar10(tmp_dir)
-  data_files = _CIFAR10_TRAIN_FILES if training else _CIFAR10_TEST_FILES
-  all_images, all_labels = [], []
-  for filename in data_files:
-    path = os.path.join(tmp_dir, _CIFAR10_PREFIX, filename)
-    with tf.gfile.Open(path, "r") as f:
-      data = cPickle.load(f)
-    images = data["data"]
-    num_images = images.shape[0]
-    images = images.reshape((num_images, 3, _CIFAR10_IMAGE_SIZE,
-                             _CIFAR10_IMAGE_SIZE))
-    all_images.extend([
-        np.squeeze(images[j]).transpose((1, 2, 0)) for j in xrange(num_images)
-    ])
-    labels = data["labels"]
-    all_labels.extend([labels[j] for j in xrange(num_images)])
-  return image_generator(all_images[start_from:start_from + how_many],
-                         all_labels[start_from:start_from + how_many])
-
-
-@registry.register_problem
-class ImageCifar10Tune(ImageMnistTune):
-  """Cifar-10 Tune."""
-
-  @property
-  def class_labels(self):
-    return [
-        "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse",
-        "ship", "truck"
-    ]
-
-  def preprocess_example(self, example, mode, unused_hparams):
-    example["inputs"].set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
-    if mode == tf.estimator.ModeKeys.TRAIN:
-      example["inputs"] = common_layers.cifar_image_augmentation(
-          example["inputs"])
-    example["inputs"] = tf.to_int64(example["inputs"])
-    return example
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    if is_training:
-      return cifar10_generator(tmp_dir, True, 48000)
-    else:
-      return cifar10_generator(tmp_dir, True, 2000, 48000)
-
-
-@registry.register_problem
-class ImageCifar10(ImageCifar10Tune):
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    if is_training:
-      return cifar10_generator(tmp_dir, True, 50000)
-    else:
-      return cifar10_generator(tmp_dir, False, 10000)
-
-
-@registry.register_problem
-class ImageCifar10Plain(ImageCifar10):
-
-  def preprocess_example(self, example, mode, unused_hparams):
-    example["inputs"].set_shape([_CIFAR10_IMAGE_SIZE, _CIFAR10_IMAGE_SIZE, 3])
-    example["inputs"] = tf.to_int64(example["inputs"])
-    return example
-
-
-@registry.register_problem
-class ImageCifar10Plain8(ImageCifar10):
-  """CIFAR-10 rescaled to 8x8 for output: Conditional image generation."""
-
-  def dataset_filename(self):
-    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
-
-  def preprocess_example(self, example, mode, unused_hparams):
-    example["inputs"] = resize_by_area(example["inputs"], 8)
-    return example
-
-
-@registry.register_problem
-class Img2imgCifar10(ImageCifar10):
-  """CIFAR-10 rescaled to 8x8 for input and 32x32 for output."""
-
-  def dataset_filename(self):
-    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
-
-  def preprocess_example(self, example, unused_mode, unused_hparams):
-
-    inputs = example["inputs"]
-    # For Img2Img resize input and output images as desired.
-    example["inputs"] = resize_by_area(inputs, 8)
-    example["targets"] = resize_by_area(inputs, 32)
-    return example
-
-  def hparams(self, defaults, unused_model_hparams):
-    p = defaults
-    p.input_modality = {"inputs": ("image:identity", 256)}
-    p.target_modality = ("image:identity", 256)
-    p.batch_size_multiplier = 256
-    p.max_expected_batch_size_per_shard = 4
-    p.input_space_id = 1
-    p.target_space_id = 1
-
-
-# URLs and filenames for MSCOCO data.
-_MSCOCO_ROOT_URL = "http://msvocds.blob.core.windows.net/"
-_MSCOCO_URLS = [
-    "coco2014/train2014.zip", "coco2014/val2014.zip", "coco2014/test2014.zip",
-    "annotations-1-0-3/captions_train-val2014.zip"
-]
-_MSCOCO_TRAIN_PREFIX = "train2014"
-_MSCOCO_EVAL_PREFIX = "val2014"
-_MSCOCO_TRAIN_CAPTION_FILE = "annotations/captions_train2014.json"
-_MSCOCO_EVAL_CAPTION_FILE = "annotations/captions_val2014.json"
-
-
-def _get_mscoco(directory):
-  """Download and extract MSCOCO datasets to directory unless it is there."""
-  for url in _MSCOCO_URLS:
-    filename = os.path.basename(url)
-    download_url = os.path.join(_MSCOCO_ROOT_URL, url)
-    path = generator_utils.maybe_download(directory, filename, download_url)
-    unzip_dir = os.path.join(directory, filename.strip(".zip"))
-    if not tf.gfile.Exists(unzip_dir):
-      zipfile.ZipFile(path, "r").extractall(directory)
-
-
-def mscoco_generator(data_dir,
-                     tmp_dir,
-                     training,
-                     how_many,
-                     start_from=0,
-                     eos_list=None,
-                     vocab_filename=None,
-                     vocab_size=0):
-  """Image generator for MSCOCO captioning problem with token-wise captions.
-
-  Args:
-    data_dir: path to the data directory.
-    tmp_dir: path to temporary storage directory.
-    training: a Boolean; if true, we use the train set, otherwise the test set.
-    how_many: how many images and labels to generate.
-    start_from: from which image to start.
-    eos_list: optional list of end of sentence tokens, otherwise use default
-      value `1`.
-    vocab_filename: file within `tmp_dir` to read vocabulary from.
-    vocab_size: integer target to generate vocabulary size to.
-
-  Yields:
-    A dictionary representing the images with the following fields:
-    * image/encoded: the string encoding the image as JPEG,
-    * image/format: the string "jpeg" representing image format,
-    * image/class/label: a list of integers representing the caption,
-    * image/height: an integer representing the height,
-    * image/width: an integer representing the width.
-    Every field is actually a list of the corresponding type.
-  """
-  eos_list = [1] if eos_list is None else eos_list
-  if vocab_filename is not None:
-    vocab_symbolizer = generator_utils.get_or_generate_vocab(
-        data_dir, tmp_dir, vocab_filename, vocab_size)
-  _get_mscoco(tmp_dir)
-  caption_filepath = (
-      _MSCOCO_TRAIN_CAPTION_FILE if training else _MSCOCO_EVAL_CAPTION_FILE)
-  caption_filepath = os.path.join(tmp_dir, caption_filepath)
-  prefix = _MSCOCO_TRAIN_PREFIX if training else _MSCOCO_EVAL_PREFIX
-  caption_file = io.open(caption_filepath)
-  caption_json = json.load(caption_file)
-  # Dictionary from image_id to ((filename, height, width), captions).
-  image_dict = dict()
-  for image in caption_json["images"]:
-    image_dict[image["id"]] = [(image["file_name"], image["height"],
-                                image["width"]), []]
-  annotations = caption_json["annotations"]
-  annotation_count = len(annotations)
-  image_count = len(image_dict)
-  tf.logging.info("Processing %d images and %d labels\n" % (image_count,
-                                                            annotation_count))
-  for annotation in annotations:
-    image_id = annotation["image_id"]
-    image_dict[image_id][1].append(annotation["caption"])
-
-  data = list(image_dict.values())[start_from:start_from + how_many]
-  random.shuffle(data)
-  for image_info, labels in data:
-    image_filename = image_info[0]
-    image_filepath = os.path.join(tmp_dir, prefix, image_filename)
-    with tf.gfile.Open(image_filepath, "r") as f:
-      encoded_image_data = f.read()
-      height, width = image_info[1], image_info[2]
-      for label in labels:
-        if vocab_filename is None:
-          label = [ord(c) for c in label] + eos_list
-        else:
-          label = vocab_symbolizer.encode(label) + eos_list
-        yield {
-            "image/encoded": [encoded_image_data],
-            "image/format": ["jpeg"],
-            "image/class/label": label,
-            "image/height": [height],
-            "image/width": [width]
-        }
-
-
-class Image2TextProblem(ImageProblem):
-  """Base class for image-to-text problems."""
-
-  @property
-  def is_character_level(self):
-    raise NotImplementedError()
-
-  @property
-  def targeted_vocab_size(self):
-    raise NotImplementedError()  # Not needed if self.is_character_level.
-
-  @property
-  def target_space_id(self):
-    raise NotImplementedError()
-
-  @property
-  def train_shards(self):
-    raise NotImplementedError()
-
-  @property
-  def dev_shards(self):
-    raise NotImplementedError()
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    raise NotImplementedError()
-
-  def example_reading_spec(self):
-    label_key = "image/class/label"
-    data_fields, data_items_to_decoders = (
-        super(Image2TextProblem, self).example_reading_spec())
-    data_fields[label_key] = tf.FixedLenFeature((1,), tf.int64)
-    data_items_to_decoders[
-        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
-    return data_fields, data_items_to_decoders
-
-  def feature_encoders(self, data_dir):
-    if self.is_character_level:
-      encoder = text_encoder.ByteTextEncoder()
-    else:
-      vocab_filename = os.path.join(
-          data_dir, "vocab.endefr.%d" % self.targeted_vocab_size)
-      encoder = text_encoder.SubwordTextEncoder(vocab_filename)
-    return {"targets": encoder}
-
-  def hparams(self, defaults, unused_model_hparams):
-    p = defaults
-    p.input_modality = {"inputs": (registry.Modalities.IMAGE, 256)}
-    encoder = self._encoders["targets"]
-    p.target_modality = (registry.Modalities.SYMBOL, encoder.vocab_size)
-    p.batch_size_multiplier = 256
-    p.max_expected_batch_size_per_shard = 2
-    p.loss_multiplier = 1.0
-    p.input_space_id = problem.SpaceID.IMAGE
-    p.target_space_id = self.target_space_id
-
-  def generate_data(self, data_dir, tmp_dir, task_id=-1):
-    generator_utils.generate_dataset_and_shuffle(
-        self.generator(data_dir, tmp_dir, True),
-        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
-        self.generator(data_dir, tmp_dir, False),
-        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
-
-
-@registry.register_problem
-class ImageMsCocoCharacters(Image2TextProblem):
-  """MSCOCO, character level."""
-
-  @property
-  def is_character_level(self):
-    return True
-
-  @property
-  def target_space_id(self):
-    return problem.SpaceID.EN_CHR
-
-  @property
-  def train_shards(self):
-    return 100
-
-  @property
-  def dev_shards(self):
-    return 10
-
-  def preprocess_example(self, example, mode, _):
-    return imagenet_preprocess_example(example, mode)
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    if is_training:
-      return mscoco_generator(data_dir, tmp_dir, True, 80000)
-    else:
-      return mscoco_generator(data_dir, tmp_dir, False, 40000)
-    raise NotImplementedError()
-
-
-@registry.register_problem
-class ImageMsCocoTokens8k(ImageMsCocoCharacters):
-  """MSCOCO, 8k tokens vocab."""
-
-  @property
-  def is_character_level(self):
-    return False
-
-  @property
-  def targeted_vocab_size(self):
-    return 2**13  # 8192
-
-  @property
-  def target_space_id(self):
-    return problem.SpaceID.EN_TOK
-
-  @property
-  def train_shards(self):
-    return 100
-
-  @property
-  def dev_shards(self):
-    return 10
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    vocab_filename = "vocab.endefr.%d" % self.targeted_vocab_size
-    if is_training:
-      return mscoco_generator(
-          data_dir,
-          tmp_dir,
-          True,
-          80000,
-          vocab_filename=vocab_filename,
-          vocab_size=self.targeted_vocab_size)
-    else:
-      return mscoco_generator(
-          data_dir,
-          tmp_dir,
-          False,
-          40000,
-          vocab_filename=vocab_filename,
-          vocab_size=self.targeted_vocab_size)
-
-
-@registry.register_problem
-class ImageMsCocoTokens32k(ImageMsCocoTokens8k):
-  """MSCOCO, 32k tokens vocab."""
-
-  @property
-  def targeted_vocab_size(self):
-    return 2**15  # 32768
-
-
-@registry.register_problem
-class OcrTest(Image2TextProblem):
-  """OCR test problem."""
-
-  @property
-  def is_small(self):
-    return True
-
-  @property
-  def is_character_level(self):
-    return True
-
-  @property
-  def target_space_id(self):
-    return problem.SpaceID.EN_CHR
-
-  @property
-  def train_shards(self):
-    return 1
-
-  @property
-  def dev_shards(self):
-    return 1
-
-  def preprocess_example(self, example, mode, _):
-    # Resize from usual size ~1350x60 to 90x4 in this test.
-    img = example["inputs"]
-    example["inputs"] = tf.to_int64(
-        tf.image.resize_images(img, [90, 4], tf.image.ResizeMethod.AREA))
-    return example
-
-  def generator(self, data_dir, tmp_dir, is_training):
-    # In this test problem, we assume that the data is in tmp_dir/ocr/ in
-    # files names 0.png, 0.txt, 1.png, 1.txt and so on until num_examples.
-    num_examples = 2
-    ocr_dir = os.path.join(tmp_dir, "ocr/")
-    tf.logging.info("Looking for OCR data in %s." % ocr_dir)
-    for i in xrange(num_examples):
-      image_filepath = os.path.join(ocr_dir, "%d.png" % i)
-      text_filepath = os.path.join(ocr_dir, "%d.txt" % i)
-      with tf.gfile.Open(text_filepath, "rb") as f:
-        label = f.read()
-      with tf.gfile.Open(image_filepath, "rb") as f:
-        encoded_image_data = f.read()
-      # In PNG files width and height are stored in these bytes.
-      width, height = struct.unpack(">ii", encoded_image_data[16:24])
-      yield {
-          "image/encoded": [encoded_image_data],
-          "image/format": ["png"],
-          "image/class/label": label.strip(),
-          "image/height": [height],
-          "image/width": [width]
-      }
diff --git a/tensor2tensor/data_generators/image_utils.py b/tensor2tensor/data_generators/image_utils.py
new file mode 100644
index 000000000..78fdde262
--- /dev/null
+++ b/tensor2tensor/data_generators/image_utils.py
@@ -0,0 +1,272 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""Base classes and utilities for image datasets."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+# Dependency imports
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import problem
+from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+from tensorflow.python.eager import context
+
+
+def resize_by_area(img, size):
+  """image resize function used by quite a few image problems."""
+  return tf.to_int64(
+      tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA))
+
+
+class ImageProblem(problem.Problem):
+  """Base class for problems with images."""
+
+  @property
+  def num_channels(self):
+    """Number of color channels."""
+    return 3
+
+  def example_reading_spec(self, label_repr=None):
+    data_fields = {
+        "image/encoded": tf.FixedLenFeature((), tf.string),
+        "image/format": tf.FixedLenFeature((), tf.string),
+    }
+
+    data_items_to_decoders = {
+        "inputs":
+            tf.contrib.slim.tfexample_decoder.Image(
+                image_key="image/encoded",
+                format_key="image/format",
+                channels=self.num_channels),
+    }
+
+    return data_fields, data_items_to_decoders
+
+  def preprocess_example(self, example, mode, hparams):
+    example["inputs"] = tf.image.per_image_standardization(example["inputs"])
+    return example
+
+
+class Image2ClassProblem(ImageProblem):
+  """Base class for image classification problems."""
+
+  @property
+  def is_small(self):
+    raise NotImplementedError()
+
+  @property
+  def num_classes(self):
+    raise NotImplementedError()
+
+  @property
+  def train_shards(self):
+    raise NotImplementedError()
+
+  @property
+  def dev_shards(self):
+    return 1
+
+  @property
+  def class_labels(self):
+    return ["ID_%d" % i for i in range(self.num_classes)]
+
+  def feature_encoders(self, data_dir):
+    del data_dir
+    return {
+        "inputs": text_encoder.ImageEncoder(),
+        "targets": text_encoder.ClassLabelEncoder(self.class_labels)
+    }
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    raise NotImplementedError()
+
+  def example_reading_spec(self):
+    label_key = "image/class/label"
+    data_fields, data_items_to_decoders = (
+        super(Image2ClassProblem, self).example_reading_spec())
+    data_fields[label_key] = tf.FixedLenFeature((1,), tf.int64)
+
+    data_items_to_decoders[
+        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
+    return data_fields, data_items_to_decoders
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": (registry.Modalities.IMAGE, 256)}
+    p.target_modality = (registry.Modalities.CLASS_LABEL, self.num_classes)
+    p.batch_size_multiplier = 4 if self.is_small else 256
+    p.max_expected_batch_size_per_shard = 8 if self.is_small else 2
+    p.loss_multiplier = 3.0 if self.is_small else 1.0
+    if self._was_reversed:
+      p.loss_multiplier = 1.0
+    p.input_space_id = problem.SpaceID.IMAGE
+    p.target_space_id = problem.SpaceID.IMAGE_LABEL
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    generator_utils.generate_dataset_and_shuffle(
+        self.generator(data_dir, tmp_dir, True),
+        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
+        self.generator(data_dir, tmp_dir, False),
+        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
+
+
+def _encoded_images(images):
+  if context.in_eager_mode():
+    for image in images:
+      yield tf.image.encode_png(image).numpy()
+  else:
+    (width, height, channels) = images[0].shape
+    with tf.Graph().as_default():
+      image_t = tf.placeholder(dtype=tf.uint8, shape=(width, height, channels))
+      encoded_image_t = tf.image.encode_png(image_t)
+      with tf.Session() as sess:
+        for image in images:
+          enc_string = sess.run(encoded_image_t, feed_dict={image_t: image})
+          yield enc_string
+
+
+def image_generator(images, labels):
+  """Generator for images that takes image and labels lists and creates pngs.
+
+  Args:
+    images: list of images given as [width x height x channels] numpy arrays.
+    labels: list of ints, same length as images.
+
+  Yields:
+    A dictionary representing the images with the following fields:
+    * image/encoded: the string encoding the image as PNG,
+    * image/format: the string "png" representing image format,
+    * image/class/label: an integer representing the label,
+    * image/height: an integer representing the height,
+    * image/width: an integer representing the width.
+    Every field is actually a singleton list of the corresponding type.
+
+  Raises:
+    ValueError: if images is an empty list.
+  """
+  if not images:
+    raise ValueError("Must provide some images for the generator.")
+  width, height, _ = images[0].shape
+  for (enc_image, label) in zip(_encoded_images(images), labels):
+    yield {
+        "image/encoded": [enc_image],
+        "image/format": ["png"],
+        "image/class/label": [int(label)],
+        "image/height": [height],
+        "image/width": [width]
+    }
+
+
+class Image2TextProblem(ImageProblem):
+  """Base class for image-to-text problems."""
+
+  @property
+  def is_character_level(self):
+    raise NotImplementedError()
+
+  @property
+  def targeted_vocab_size(self):
+    raise NotImplementedError()  # Not needed if self.is_character_level.
+
+  @property
+  def target_space_id(self):
+    raise NotImplementedError()
+
+  @property
+  def train_shards(self):
+    raise NotImplementedError()
+
+  @property
+  def dev_shards(self):
+    raise NotImplementedError()
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    raise NotImplementedError()
+
+  def example_reading_spec(self):
+    label_key = "image/class/label"
+    data_fields, data_items_to_decoders = (
+        super(Image2TextProblem, self).example_reading_spec())
+    data_fields[label_key] = tf.VarLenFeature(tf.int64)
+    data_items_to_decoders[
+        "targets"] = tf.contrib.slim.tfexample_decoder.Tensor(label_key)
+    return data_fields, data_items_to_decoders
+
+  def feature_encoders(self, data_dir):
+    if self.is_character_level:
+      encoder = text_encoder.ByteTextEncoder()
+    else:
+      vocab_filename = os.path.join(
+          data_dir, "vocab.ende.%d" % self.targeted_vocab_size)
+      encoder = text_encoder.SubwordTextEncoder(vocab_filename)
+    input_encoder = text_encoder.ImageEncoder()
+    return {"inputs": input_encoder, "targets": encoder}
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": (registry.Modalities.IMAGE, 256)}
+    encoder = self._encoders["targets"]
+    p.target_modality = (registry.Modalities.SYMBOL, encoder.vocab_size)
+    p.batch_size_multiplier = 256
+    p.max_expected_batch_size_per_shard = 4
+    p.loss_multiplier = 1.0
+    p.input_space_id = problem.SpaceID.IMAGE
+    p.target_space_id = self.target_space_id
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    generator_utils.generate_dataset_and_shuffle(
+        self.generator(data_dir, tmp_dir, True),
+        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
+        self.generator(data_dir, tmp_dir, False),
+        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
+
+
+def image_augmentation(images, do_colors=False, crop_size=None):
+  """Image augmentation: cropping, flipping, and color transforms."""
+  if crop_size is None:
+    crop_size = [299, 299]
+  images = tf.random_crop(images, crop_size + [3])
+  images = tf.image.random_flip_left_right(images)
+  if do_colors:  # More augmentation, but might be slow.
+    images = tf.image.random_brightness(images, max_delta=32. / 255.)
+    images = tf.image.random_saturation(images, lower=0.5, upper=1.5)
+    images = tf.image.random_hue(images, max_delta=0.2)
+    images = tf.image.random_contrast(images, lower=0.5, upper=1.5)
+  return images
+
+
+def cifar_image_augmentation(images):
+  """Image augmentation suitable for CIFAR-10/100.
+
+  As described in https://arxiv.org/pdf/1608.06993v3.pdf (page 5).
+
+  Args:
+    images: a Tensor.
+  Returns:
+    Tensor of the same shape as images.
+  """
+  images = tf.image.resize_image_with_crop_or_pad(images, 40, 40)
+  images = tf.random_crop(images, [32, 32, 3])
+  images = tf.image.random_flip_left_right(images)
+  return images
diff --git a/tensor2tensor/data_generators/image_test.py b/tensor2tensor/data_generators/image_utils_test.py
similarity index 86%
rename from tensor2tensor/data_generators/image_test.py
rename to tensor2tensor/data_generators/image_utils_test.py
index 59cad4226..5a7afc3b7 100644
--- a/tensor2tensor/data_generators/image_test.py
+++ b/tensor2tensor/data_generators/image_utils_test.py
@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Image generators test."""
+"""image_utils test."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -22,13 +22,20 @@
 # Dependency imports
 
 import numpy as np
-from tensor2tensor.data_generators import image
+from tensor2tensor.data_generators import image_utils
 
 import tensorflow as tf
 
 
 class ImageTest(tf.test.TestCase):
 
+  def testImageAugmentation(self):
+    x = np.random.rand(500, 500, 3)
+    with self.test_session() as session:
+      y = image_utils.image_augmentation(tf.constant(x))
+      res = session.run(y)
+    self.assertEqual(res.shape, (299, 299, 3))
+
   def testImageGenerator(self):
     # 2 random images
     np.random.seed(1111)  # To avoid any flakiness.
@@ -36,7 +43,7 @@ def testImageGenerator(self):
     image2 = np.random.randint(0, 255, size=(10, 12, 3))
     # Call image generator on the 2 images with labels [1, 2].
     encoded_imgs, labels = [], []
-    for dictionary in image.image_generator([image1, image2], [1, 2]):
+    for dictionary in image_utils.image_generator([image1, image2], [1, 2]):
       self.assertEqual(
           sorted(list(dictionary)), [
               "image/class/label", "image/encoded", "image/format",
diff --git a/tensor2tensor/data_generators/imagenet.py b/tensor2tensor/data_generators/imagenet.py
new file mode 100644
index 000000000..1ca2ec3e2
--- /dev/null
+++ b/tensor2tensor/data_generators/imagenet.py
@@ -0,0 +1,363 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""ImageNet."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+
+# Derived from ImageNet data
+MEAN_RGB = [0.485, 0.456, 0.406]
+STDDEV_RGB = [0.229, 0.224, 0.225]
+
+
+def imagenet_preprocess_example(example, mode, resize_size=None):
+  """Preprocessing used for Imagenet and similar problems."""
+  resize_size = resize_size or [299, 299]
+  assert resize_size[0] == resize_size[1]
+
+  image = example["inputs"]
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    image = preprocess_for_train(image, image_size=resize_size[0])
+  else:
+    image = preprocess_for_eval(image, image_size=resize_size[0])
+
+  example["inputs"] = image
+  return example
+
+
+@registry.register_problem
+class ImageImagenet(image_utils.Image2ClassProblem):
+  """Imagenet."""
+
+  @property
+  def is_small(self):
+    return False
+
+  @property
+  def num_classes(self):
+    return 1000
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    # TODO(lukaszkaiser): find a better way than printing this.
+    print("To generate the ImageNet dataset in the proper format, follow "
+          "instructions at https://github.com/tensorflow/models/blob/master"
+          "/inception/README.md#getting-started")
+
+  def preprocess_example(self, example, mode, _):
+    return imagenet_preprocess_example(example, mode)
+
+
+class ImageImagenetRescaled(ImageImagenet):
+  """Imagenet rescaled to rescale_size."""
+
+  @property
+  def rescale_size(self):
+    # return [224, 224]
+    raise NotImplementedError()
+
+  def dataset_filename(self):
+    return "image_imagenet"  # Reuse Imagenet data.
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    tf.logging.warning(
+        "Generate data for rescaled ImageNet problems with image_imagenet")
+
+  def preprocess_example(self, example, mode, _):
+    return imagenet_preprocess_example(
+        example, mode, resize_size=self.rescale_size)
+
+
+@registry.register_problem
+class ImageImagenet224(ImageImagenetRescaled):
+  """Imagenet rescaled to 224x224."""
+
+  @property
+  def rescale_size(self):
+    return [224, 224]
+
+
+@registry.register_problem
+class ImageImagenet32(ImageImagenetRescaled):
+  """Imagenet rescaled to 32x32."""
+
+  @property
+  def rescale_size(self):
+    return [32, 32]
+
+  @property
+  def is_small(self):
+    return True  # Modalities like for CIFAR.
+
+  def preprocess_example(self, example, mode, _):
+    # Just resize with area.
+    if self._was_reversed:
+      example["inputs"] = tf.to_int64(
+          tf.image.resize_images(example["inputs"], self.rescale_size,
+                                 tf.image.ResizeMethod.AREA))
+    else:
+      example = imagenet_preprocess_example(example, mode)
+      example["inputs"] = tf.to_int64(
+          tf.image.resize_images(example["inputs"], self.rescale_size))
+    return example
+
+
+@registry.register_problem
+class ImageImagenet64(ImageImagenet32):
+  """Imagenet rescaled to 64x64."""
+
+  @property
+  def rescale_size(self):
+    return [64, 64]
+
+
+@registry.register_problem
+class Img2imgImagenet(image_utils.ImageProblem):
+  """Imagenet rescaled to 8x8 for input and 32x32 for output."""
+
+  def dataset_filename(self):
+    return "image_imagenet"  # Reuse Imagenet data.
+
+  def preprocess_example(self, example, unused_mode, unused_hparams):
+
+    inputs = example["inputs"]
+    # For Img2Img resize input and output images as desired.
+    example["inputs"] = image_utils.resize_by_area(inputs, 8)
+    example["targets"] = image_utils.resize_by_area(inputs, 32)
+    return example
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    tf.logging.warning("Generate data for img2img_imagenet with image_imagenet")
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": ("image:identity", 256)}
+    p.target_modality = ("image:identity", 256)
+    p.batch_size_multiplier = 256
+    p.max_expected_batch_size_per_shard = 4
+    p.input_space_id = 1
+    p.target_space_id = 1
+
+
+# The following preprocessing functions were taken from
+# cloud_tpu/models/resnet/resnet_preprocessing.py
+# ==============================================================================
+def _crop(image, offset_height, offset_width, crop_height, crop_width):
+  """Crops the given image using the provided offsets and sizes.
+
+  Note that the method doesn't assume we know the input image size but it does
+  assume we know the input image rank.
+
+  Args:
+    image: `Tensor` image of shape [height, width, channels].
+    offset_height: `Tensor` indicating the height offset.
+    offset_width: `Tensor` indicating the width offset.
+    crop_height: the height of the cropped image.
+    crop_width: the width of the cropped image.
+
+  Returns:
+    the cropped (and resized) image.
+
+  Raises:
+    InvalidArgumentError: if the rank is not 3 or if the image dimensions are
+      less than the crop size.
+  """
+  original_shape = tf.shape(image)
+
+  rank_assertion = tf.Assert(
+      tf.equal(tf.rank(image), 3), ["Rank of image must be equal to 3."])
+  with tf.control_dependencies([rank_assertion]):
+    cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])
+
+  size_assertion = tf.Assert(
+      tf.logical_and(
+          tf.greater_equal(original_shape[0], crop_height),
+          tf.greater_equal(original_shape[1], crop_width)),
+      ["Crop size greater than the image size."])
+
+  offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))
+
+  # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
+  # define the crop size.
+  with tf.control_dependencies([size_assertion]):
+    image = tf.slice(image, offsets, cropped_shape)
+  return tf.reshape(image, cropped_shape)
+
+
+def distorted_bounding_box_crop(image,
+                                bbox,
+                                min_object_covered=0.1,
+                                aspect_ratio_range=(0.75, 1.33),
+                                area_range=(0.05, 1.0),
+                                max_attempts=100,
+                                scope=None):
+  """Generates cropped_image using a one of the bboxes randomly distorted.
+
+  See `tf.image.sample_distorted_bounding_box` for more documentation.
+
+  Args:
+    image: `Tensor` of image (it will be converted to floats in [0, 1]).
+    bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
+        where each coordinate is [0, 1) and the coordinates are arranged
+        as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
+        image.
+    min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
+        area of the image must contain at least this fraction of any bounding
+        box supplied.
+    aspect_ratio_range: An optional list of `float`s. The cropped area of the
+        image must have an aspect ratio = width / height within this range.
+    area_range: An optional list of `float`s. The cropped area of the image
+        must contain a fraction of the supplied image within in this range.
+    max_attempts: An optional `int`. Number of attempts at generating a cropped
+        region of the image of the specified constraints. After `max_attempts`
+        failures, return the entire image.
+    scope: Optional `str` for name scope.
+  Returns:
+    (cropped image `Tensor`, distorted bbox `Tensor`).
+  """
+  with tf.name_scope(scope, "distorted_bounding_box_crop", [image, bbox]):
+    # Each bounding box has shape [1, num_boxes, box coords] and
+    # the coordinates are ordered [ymin, xmin, ymax, xmax].
+
+    # A large fraction of image datasets contain a human-annotated bounding
+    # box delineating the region of the image containing the object of interest.
+    # We choose to create a new bounding box for the object which is a randomly
+    # distorted version of the human-annotated bounding box that obeys an
+    # allowed range of aspect ratios, sizes and overlap with the human-annotated
+    # bounding box. If no box is supplied, then we assume the bounding box is
+    # the entire image.
+    sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
+        tf.shape(image),
+        bounding_boxes=bbox,
+        min_object_covered=min_object_covered,
+        aspect_ratio_range=aspect_ratio_range,
+        area_range=area_range,
+        max_attempts=max_attempts,
+        use_image_if_no_bounding_boxes=True)
+    bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box
+
+    # Crop the image to the specified bounding box.
+    cropped_image = tf.slice(image, bbox_begin, bbox_size)
+    return cropped_image, distort_bbox
+
+
+def _random_crop(image, size):
+  """Make a random crop of (`size` x `size`)."""
+  bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
+  random_image, bbox = distorted_bounding_box_crop(
+      image,
+      bbox,
+      min_object_covered=0.1,
+      aspect_ratio_range=(3. / 4, 4. / 3.),
+      area_range=(0.08, 1.0),
+      max_attempts=1,
+      scope=None)
+  bad = _at_least_x_are_true(tf.shape(image), tf.shape(random_image), 3)
+
+  image = tf.cond(
+      bad, lambda: _center_crop(_do_scale(image, size), size),
+      lambda: tf.image.resize_bicubic([random_image], [size, size])[0])
+  return image
+
+
+def _flip(image):
+  """Random horizontal image flip."""
+  image = tf.image.random_flip_left_right(image)
+  return image
+
+
+def _at_least_x_are_true(a, b, x):
+  """At least `x` of `a` and `b` `Tensors` are true."""
+  match = tf.equal(a, b)
+  match = tf.cast(match, tf.int32)
+  return tf.greater_equal(tf.reduce_sum(match), x)
+
+
+def _do_scale(image, size):
+  """Rescale the image by scaling the smaller spatial dimension to `size`."""
+  shape = tf.cast(tf.shape(image), tf.float32)
+  w_greater = tf.greater(shape[0], shape[1])
+  shape = tf.cond(w_greater,
+                  lambda: tf.cast([shape[0] / shape[1] * size, size], tf.int32),
+                  lambda: tf.cast([size, shape[1] / shape[0] * size], tf.int32))
+
+  return tf.image.resize_bicubic([image], shape)[0]
+
+
+def _center_crop(image, size):
+  """Crops to center of image with specified `size`."""
+  image_height = tf.shape(image)[0]
+  image_width = tf.shape(image)[1]
+
+  offset_height = ((image_height - size) + 1) / 2
+  offset_width = ((image_width - size) + 1) / 2
+  image = _crop(image, offset_height, offset_width, size, size)
+  return image
+
+
+def _normalize(image):
+  """Normalize the image to zero mean and unit variance."""
+  offset = tf.constant(MEAN_RGB, shape=[1, 1, 3])
+  image -= offset
+
+  scale = tf.constant(STDDEV_RGB, shape=[1, 1, 3])
+  image /= scale
+  return image
+
+
+def preprocess_for_train(image, image_size=224):
+  """Preprocesses the given image for evaluation.
+
+  Args:
+    image: `Tensor` representing an image of arbitrary size.
+    image_size: int, how large the output image should be.
+
+  Returns:
+    A preprocessed image `Tensor`.
+  """
+  image = _random_crop(image, image_size)
+  image = _normalize(image)
+  image = _flip(image)
+  image = tf.reshape(image, [image_size, image_size, 3])
+  return image
+
+
+def preprocess_for_eval(image, image_size=224):
+  """Preprocesses the given image for evaluation.
+
+  Args:
+    image: `Tensor` representing an image of arbitrary size.
+    image_size: int, how large the output image should be.
+
+  Returns:
+    A preprocessed image `Tensor`.
+  """
+  image = _do_scale(image, image_size + 32)
+  image = _normalize(image)
+  image = _center_crop(image, image_size)
+  image = tf.reshape(image, [image_size, image_size, 3])
+  return image
+
+
+# ==============================================================================
diff --git a/tensor2tensor/data_generators/imdb.py b/tensor2tensor/data_generators/imdb.py
index 95d728b1e..22e674098 100644
--- a/tensor2tensor/data_generators/imdb.py
+++ b/tensor2tensor/data_generators/imdb.py
@@ -48,6 +48,10 @@ def num_shards(self):
   def vocab_file(self):
     return "sentiment_imdb.vocab"
 
+  @property
+  def batch_size_means_tokens(self):
+    return True
+
   @property
   def targeted_vocab_size(self):
     return 2**13  # 8k vocab suffices for this small dataset.
diff --git a/tensor2tensor/data_generators/mnist.py b/tensor2tensor/data_generators/mnist.py
new file mode 100644
index 000000000..9a6792b62
--- /dev/null
+++ b/tensor2tensor/data_generators/mnist.py
@@ -0,0 +1,250 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""MNIST."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gzip
+import os
+import random
+
+# Dependency imports
+
+import numpy as np
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+# URLs and filenames for MNIST data.
+_MNIST_URL = "http://yann.lecun.com/exdb/mnist/"
+_MNIST_TRAIN_DATA_FILENAME = "train-images-idx3-ubyte.gz"
+_MNIST_TRAIN_LABELS_FILENAME = "train-labels-idx1-ubyte.gz"
+_MNIST_TEST_DATA_FILENAME = "t10k-images-idx3-ubyte.gz"
+_MNIST_TEST_LABELS_FILENAME = "t10k-labels-idx1-ubyte.gz"
+_MNIST_IMAGE_SIZE = 28
+
+
+def _get_mnist(directory):
+  """Download all MNIST files to directory unless they are there."""
+  for filename in [
+      _MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_FILENAME,
+      _MNIST_TEST_DATA_FILENAME, _MNIST_TEST_LABELS_FILENAME
+  ]:
+    generator_utils.maybe_download(directory, filename, _MNIST_URL + filename)
+
+
+def _extract_mnist_images(filename, num_images):
+  """Extract images from an MNIST file into a numpy array.
+
+  Args:
+    filename: The path to an MNIST images file.
+    num_images: The number of images in the file.
+
+  Returns:
+    A numpy array of shape [number_of_images, height, width, channels].
+  """
+  with gzip.open(filename) as bytestream:
+    bytestream.read(16)
+    buf = bytestream.read(_MNIST_IMAGE_SIZE * _MNIST_IMAGE_SIZE * num_images)
+    data = np.frombuffer(buf, dtype=np.uint8)
+    data = data.reshape(num_images, _MNIST_IMAGE_SIZE, _MNIST_IMAGE_SIZE, 1)
+  return data
+
+
+def _extract_mnist_labels(filename, num_labels):
+  """Extract labels from an MNIST file into integers.
+
+  Args:
+    filename: The path to an MNIST labels file.
+    num_labels: The number of labels in the file.
+
+  Returns:
+    A int64 numpy array of shape [num_labels]
+  """
+  with gzip.open(filename) as bytestream:
+    bytestream.read(8)
+    buf = bytestream.read(num_labels)
+    labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int64)
+  return labels
+
+
+def mnist_common_generator(tmp_dir,
+                           training,
+                           how_many,
+                           data_filename,
+                           label_filename,
+                           start_from=0):
+  """Image generator for MNIST.
+
+  Args:
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the test set.
+    how_many: how many images and labels to generate.
+    data_filename: file that contains features data.
+    label_filename: file that contains labels.
+    start_from: from which image to start.
+
+  Returns:
+    An instance of image_generator that produces MNIST images.
+  """
+  data_path = os.path.join(tmp_dir, data_filename)
+  labels_path = os.path.join(tmp_dir, label_filename)
+  images = _extract_mnist_images(data_path, 60000 if training else 10000)
+  labels = _extract_mnist_labels(labels_path, 60000 if training else 10000)
+  # Shuffle the data to make sure classes are well distributed.
+  data = list(zip(images, labels))
+  random.shuffle(data)
+  images, labels = list(zip(*data))
+  return image_utils.image_generator(images[start_from:start_from + how_many],
+                                     labels[start_from:start_from + how_many])
+
+
+def mnist_generator(tmp_dir, training, how_many, start_from=0):
+  """Image generator for MNIST.
+
+  Args:
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the test set.
+    how_many: how many images and labels to generate.
+    start_from: from which image to start.
+
+  Returns:
+    An instance of image_generator that produces MNIST images.
+  """
+  _get_mnist(tmp_dir)
+  d = _MNIST_TRAIN_DATA_FILENAME if training else _MNIST_TEST_DATA_FILENAME
+  l = _MNIST_TRAIN_LABELS_FILENAME if training else _MNIST_TEST_LABELS_FILENAME
+  return mnist_common_generator(tmp_dir, training, how_many, d, l, start_from)
+
+
+@registry.register_problem
+class ImageMnistTune(image_utils.Image2ClassProblem):
+  """MNIST, tuning data."""
+
+  @property
+  def num_channels(self):
+    return 1
+
+  @property
+  def is_small(self):
+    return True
+
+  @property
+  def num_classes(self):
+    return 10
+
+  @property
+  def class_labels(self):
+    return [str(c) for c in range(self.num_classes)]
+
+  @property
+  def train_shards(self):
+    return 10
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    image = example["inputs"]
+    image.set_shape([_MNIST_IMAGE_SIZE, _MNIST_IMAGE_SIZE, 1])
+    image = tf.image.per_image_standardization(image)
+    example["inputs"] = image
+    return example
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return mnist_generator(tmp_dir, True, 55000)
+    else:
+      return mnist_generator(tmp_dir, True, 5000, 55000)
+
+
+@registry.register_problem
+class ImageMnist(ImageMnistTune):
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return mnist_generator(tmp_dir, True, 60000)
+    else:
+      return mnist_generator(tmp_dir, False, 10000)
+
+
+# URLs and filenames for MNIST data.
+_FASHION_MNIST_URL = ("http://fashion-mnist.s3-website.eu-central-1"
+                      ".amazonaws.com/")
+_FASHION_MNIST_LOCAL_FILE_PREFIX = "fashion-"
+_FASHION_MNIST_IMAGE_SIZE = 28
+
+
+def _get_fashion_mnist(directory):
+  """Download all FashionMNIST files to directory unless they are there."""
+  # Fashion mnist files have the same names as MNIST.
+  # We must choose a separate name (by adding 'fashion-' prefix) in the tmp_dir.
+  for filename in [
+      _MNIST_TRAIN_DATA_FILENAME, _MNIST_TRAIN_LABELS_FILENAME,
+      _MNIST_TEST_DATA_FILENAME, _MNIST_TEST_LABELS_FILENAME
+  ]:
+    generator_utils.maybe_download(directory,
+                                   _FASHION_MNIST_LOCAL_FILE_PREFIX + filename,
+                                   _FASHION_MNIST_URL + filename)
+
+
+def fashion_mnist_generator(tmp_dir, training, how_many, start_from=0):
+  """Image generator for FashionMNIST.
+
+  Args:
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the test set.
+    how_many: how many images and labels to generate.
+    start_from: from which image to start.
+
+  Returns:
+    An instance of image_generator that produces MNIST images.
+  """
+  _get_fashion_mnist(tmp_dir)
+  d = _FASHION_MNIST_LOCAL_FILE_PREFIX + (
+      _MNIST_TRAIN_DATA_FILENAME if training else _MNIST_TEST_DATA_FILENAME)
+  l = _FASHION_MNIST_LOCAL_FILE_PREFIX + (
+      _MNIST_TRAIN_LABELS_FILENAME if training else _MNIST_TEST_LABELS_FILENAME)
+  return mnist_common_generator(tmp_dir, training, how_many, d, l, start_from)
+
+
+@registry.register_problem
+class ImageFashionMnist(image_utils.Image2ClassProblem):
+  """Fashion MNIST."""
+
+  @property
+  def is_small(self):
+    return True
+
+  @property
+  def num_classes(self):
+    return 10
+
+  @property
+  def class_labels(self):
+    return [str(c) for c in range(self.num_classes)]
+
+  @property
+  def train_shards(self):
+    return 10
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return fashion_mnist_generator(tmp_dir, True, 60000)
+    else:
+      return fashion_mnist_generator(tmp_dir, False, 10000)
diff --git a/tensor2tensor/data_generators/mscoco.py b/tensor2tensor/data_generators/mscoco.py
new file mode 100644
index 000000000..8dc1e1bba
--- /dev/null
+++ b/tensor2tensor/data_generators/mscoco.py
@@ -0,0 +1,234 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""MS COCO."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import io
+import json
+import os
+import random
+import zipfile
+
+# Dependency imports
+
+from tensor2tensor.data_generators import generator_utils
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.data_generators import imagenet
+from tensor2tensor.data_generators import problem
+from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+# URLs and filenames for MSCOCO data.
+_MSCOCO_ROOT_URL = "http://msvocds.blob.core.windows.net/"
+_MSCOCO_URLS = [
+    "coco2014/train2014.zip", "coco2014/val2014.zip", "coco2014/test2014.zip",
+    "annotations-1-0-3/captions_train-val2014.zip"
+]
+_MSCOCO_TRAIN_PREFIX = "train2014"
+_MSCOCO_EVAL_PREFIX = "val2014"
+_MSCOCO_TRAIN_CAPTION_FILE = "annotations/captions_train2014.json"
+_MSCOCO_EVAL_CAPTION_FILE = "annotations/captions_val2014.json"
+
+
+def _get_mscoco(directory):
+  """Download and extract MSCOCO datasets to directory unless it is there."""
+  for url in _MSCOCO_URLS:
+    filename = os.path.basename(url)
+    download_url = os.path.join(_MSCOCO_ROOT_URL, url)
+    path = generator_utils.maybe_download(directory, filename, download_url)
+    unzip_dir = os.path.join(directory, filename.strip(".zip"))
+    if not tf.gfile.Exists(unzip_dir):
+      zipfile.ZipFile(path, "r").extractall(directory)
+
+
+def mscoco_generator(data_dir,
+                     tmp_dir,
+                     training,
+                     how_many,
+                     start_from=0,
+                     eos_list=None,
+                     vocab_filename=None):
+  """Image generator for MSCOCO captioning problem with token-wise captions.
+
+  Args:
+    data_dir: path to the data directory.
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the test set.
+    how_many: how many images and labels to generate.
+    start_from: from which image to start.
+    eos_list: optional list of end of sentence tokens, otherwise use default
+      value `1`.
+    vocab_filename: file within `tmp_dir` to read vocabulary from.
+
+  Yields:
+    A dictionary representing the images with the following fields:
+    * image/encoded: the string encoding the image as JPEG,
+    * image/format: the string "jpeg" representing image format,
+    * image/class/label: a list of integers representing the caption,
+    * image/height: an integer representing the height,
+    * image/width: an integer representing the width.
+    Every field is actually a list of the corresponding type.
+  """
+  eos_list = [1] if eos_list is None else eos_list
+  def get_vocab():
+    """Get vocab for caption text encoder."""
+    if data_dir is not None and vocab_filename is not None:
+      vocab_filepath = os.path.join(data_dir, vocab_filename)
+      if tf.gfile.Exists(vocab_filepath):
+        tf.logging.info("Found vocab file: %s", vocab_filepath)
+        vocab_symbolizer = text_encoder.SubwordTextEncoder(vocab_filepath)
+        return vocab_symbolizer
+      else:
+        raise ValueError("Vocab file does not exist: %s", vocab_filepath)
+    return None
+
+  vocab_symbolizer = get_vocab()
+  _get_mscoco(tmp_dir)
+  caption_filepath = (
+      _MSCOCO_TRAIN_CAPTION_FILE if training else _MSCOCO_EVAL_CAPTION_FILE)
+  caption_filepath = os.path.join(tmp_dir, caption_filepath)
+  prefix = _MSCOCO_TRAIN_PREFIX if training else _MSCOCO_EVAL_PREFIX
+  caption_file = io.open(caption_filepath)
+  caption_json = json.load(caption_file)
+  # Dictionary from image_id to ((filename, height, width), captions).
+  image_dict = dict()
+  for image in caption_json["images"]:
+    image_dict[image["id"]] = [(image["file_name"], image["height"],
+                                image["width"]), []]
+  annotations = caption_json["annotations"]
+  annotation_count = len(annotations)
+  image_count = len(image_dict)
+  tf.logging.info("Processing %d images and %d labels\n" % (image_count,
+                                                            annotation_count))
+  for annotation in annotations:
+    image_id = annotation["image_id"]
+    image_dict[image_id][1].append(annotation["caption"])
+
+  data = list(image_dict.values())[start_from:start_from + how_many]
+  random.shuffle(data)
+  for image_info, labels in data:
+    image_filename = image_info[0]
+    image_filepath = os.path.join(tmp_dir, prefix, image_filename)
+    with tf.gfile.Open(image_filepath, "r") as f:
+      encoded_image_data = f.read()
+      height, width = image_info[1], image_info[2]
+      for label in labels:
+        if vocab_filename is None or vocab_symbolizer is None:
+          label = [ord(c) for c in label] + eos_list
+        else:
+          label = vocab_symbolizer.encode(label) + eos_list
+        yield {
+            "image/encoded": [encoded_image_data],
+            "image/format": ["jpeg"],
+            "image/class/label": label,
+            "image/height": [height],
+            "image/width": [width]
+        }
+
+
+@registry.register_problem
+class ImageMsCocoCharacters(image_utils.Image2TextProblem):
+  """MSCOCO, character level."""
+
+  @property
+  def is_character_level(self):
+    return True
+
+  @property
+  def target_space_id(self):
+    return problem.SpaceID.EN_CHR
+
+  @property
+  def train_shards(self):
+    return 100
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def preprocess_example(self, example, mode, _):
+    return imagenet.imagenet_preprocess_example(example, mode)
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return mscoco_generator(data_dir, tmp_dir, True, 80000)
+    else:
+      return mscoco_generator(data_dir, tmp_dir, False, 40000)
+    raise NotImplementedError()
+
+
+@registry.register_problem
+class ImageMsCocoTokens32k(ImageMsCocoCharacters):
+  """MSCOCO, 8k tokens vocab."""
+
+  @property
+  def is_character_level(self):
+    return False
+
+  @property
+  def targeted_vocab_size(self):
+    return 2**15  # 32768
+
+  @property
+  def target_space_id(self):
+    return problem.SpaceID.EN_TOK
+
+  @property
+  def train_shards(self):
+    return 100
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    # We use the translate vocab file as the vocabulary for captions.
+    # This requires having the vocab file present in the data_dir for the
+    # generation pipeline to succeed.
+    vocab_filename = "vocab.ende.%d" % self.targeted_vocab_size
+    if is_training:
+      return mscoco_generator(
+          data_dir,
+          tmp_dir,
+          True,
+          80000,
+          vocab_filename=vocab_filename)
+    else:
+      return mscoco_generator(
+          data_dir,
+          tmp_dir,
+          False,
+          40000,
+          vocab_filename=vocab_filename)
+
+
+@registry.register_problem
+class ImageTextMsCoco(ImageMsCocoTokens32k):
+  """Problem for using MsCoco for generating images from text."""
+  _MSCOCO_IMAGE_SIZE = 32
+
+  def dataset_filename(self):
+    return "image_ms_coco_tokens32k"
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"] = image_utils.resize_by_area(
+        example["inputs"], self._MSCOCO_IMAGE_SIZE)
+    return example
diff --git a/tensor2tensor/data_generators/ocr.py b/tensor2tensor/data_generators/ocr.py
new file mode 100644
index 000000000..8e92165bd
--- /dev/null
+++ b/tensor2tensor/data_generators/ocr.py
@@ -0,0 +1,87 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""OCR."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import struct
+
+# Dependency imports
+
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.data_generators import problem
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+
+@registry.register_problem
+class OcrTest(image_utils.Image2TextProblem):
+  """OCR test problem."""
+
+  @property
+  def is_small(self):
+    return True
+
+  @property
+  def is_character_level(self):
+    return True
+
+  @property
+  def target_space_id(self):
+    return problem.SpaceID.EN_CHR
+
+  @property
+  def train_shards(self):
+    return 1
+
+  @property
+  def dev_shards(self):
+    return 1
+
+  def preprocess_example(self, example, mode, _):
+    # Resize from usual size ~1350x60 to 90x4 in this test.
+    img = example["inputs"]
+    img = tf.to_int64(
+        tf.image.resize_images(img, [90, 4], tf.image.ResizeMethod.AREA))
+    img = tf.image.per_image_standardization(img)
+    example["inputs"] = img
+    return example
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    # In this test problem, we assume that the data is in tmp_dir/ocr/ in
+    # files names 0.png, 0.txt, 1.png, 1.txt and so on until num_examples.
+    num_examples = 2
+    ocr_dir = os.path.join(tmp_dir, "ocr/")
+    tf.logging.info("Looking for OCR data in %s." % ocr_dir)
+    for i in xrange(num_examples):
+      image_filepath = os.path.join(ocr_dir, "%d.png" % i)
+      text_filepath = os.path.join(ocr_dir, "%d.txt" % i)
+      with tf.gfile.Open(text_filepath, "rb") as f:
+        label = f.read()
+      with tf.gfile.Open(image_filepath, "rb") as f:
+        encoded_image_data = f.read()
+      # In PNG files width and height are stored in these bytes.
+      width, height = struct.unpack(">ii", encoded_image_data[16:24])
+      yield {
+          "image/encoded": [encoded_image_data],
+          "image/format": ["png"],
+          "image/class/label": label.strip(),
+          "image/height": [height],
+          "image/width": [width]
+      }
diff --git a/tensor2tensor/data_generators/problem.py b/tensor2tensor/data_generators/problem.py
index 53fa48740..890271dbe 100644
--- a/tensor2tensor/data_generators/problem.py
+++ b/tensor2tensor/data_generators/problem.py
@@ -119,8 +119,8 @@ def preprocess_example_common(example, hparams, mode):
       example["targets"] = tf.concat(
           [example["inputs"], [0], example["targets"]], 0)
   if hparams.split_to_length:
-    example["targets"] = tf.reshape(
-        example["targets"], [-1, hparams.split_to_length, 1, 1])
+    example["targets"] = tf.reshape(example["targets"],
+                                    [-1, hparams.split_to_length, 1, 1])
     if len(example) != 1:
       raise ValueError("split_to_length only works for LM problems")
     return tf.data.Dataset.from_tensor_slices(example)
@@ -239,10 +239,21 @@ def max_length(self, model_hparams):
     Returns:
       an integer
     """
-    return (
-        model_hparams.split_to_length or
-        model_hparams.max_length or
-        model_hparams.batch_size)
+    return (model_hparams.split_to_length or model_hparams.max_length or
+            model_hparams.batch_size)
+
+  def tpu_batch_size_per_shard(self, model_hparams):
+    """Batch size in examples per TPU core.
+
+    Args:
+      model_hparams: model hyperparameters
+    Returns:
+      an integer
+    """
+    if self.batch_size_means_tokens:
+      return model_hparams.batch_size // self.max_length(model_hparams)
+    else:
+      return model_hparams.batch_size
 
   @property
   def batch_size_means_tokens(self):
@@ -425,7 +436,9 @@ def dataset(self,
               hparams=None,
               preprocess=True,
               dataset_split=None,
-              shard=None):
+              shard=None,
+              partition_id=0,
+              num_partitions=1):
     """Build a Dataset for this problem.
 
     Args:
@@ -433,8 +446,7 @@ def dataset(self,
       data_dir: directory that contains data files.
       num_threads: int, number of threads to use for decode and preprocess
         Dataset.map calls.
-      output_buffer_size: int, how many elements to prefetch in Dataset.map
-        calls.
+      output_buffer_size: int, how many elements to prefetch at end of pipeline.
       shuffle_files: whether to shuffle input files. Default behavior (i.e. when
         shuffle_files=None) is to shuffle if mode == TRAIN.
       repeat: whether to repeat the Dataset. Default behavior is to repeat if
@@ -447,9 +459,14 @@ def dataset(self,
       dataset_split: tf.estimator.ModeKeys + ["test"], which split to read data
         from (TRAIN:"-train", EVAL:"-dev", "test":"-test"). Defaults to mode.
       shard: int, if provided, will only read data from the specified shard.
+      partition_id: integer - which partition of the dataset to read from
+      num_partitions: how many partitions in the dataset
 
     Returns:
       Dataset containing dict<feature name, Tensor>.
+
+    Raises:
+      ValueError: if num_partitions is greater than the number of data files.
     """
     is_training = mode == tf.estimator.ModeKeys.TRAIN
     repeat = repeat or repeat is None and is_training
@@ -470,39 +487,13 @@ def dataset(self,
 
     data_filepattern = self.filepattern(data_dir, dataset_split, shard=shard)
     tf.logging.info("Reading data files from %s", data_filepattern)
-    dataset = tf.data.Dataset.list_files(data_filepattern)
-
-    if shuffle_files:
-      dataset = dataset.shuffle(buffer_size=1024)
+    data_files = tf.contrib.slim.parallel_reader.get_data_files(
+        data_filepattern)
 
+    # Functions used in dataset transforms below
     def _load_records(filename):
-      return tf.data.TFRecordDataset(filename, buffer_size=16 * 1000 * 1000)
-
-    if hasattr(tf.contrib.data, "parallel_interleave"):
-      interleave = lambda ds, fn: ds.apply(  # pylint: disable=g-long-lambda
-          tf.contrib.data.parallel_interleave(
-              fn, sloppy=is_training, cycle_length=16))
-    else:
-      interleave = lambda ds, fn: ds.interleave(fn, cycle_length=16)
-
-    dataset = interleave(dataset, _load_records)
-
-    if repeat:
-      dataset = dataset.repeat()
-
-    if shuffle_files:
-      # Skip a random fraction at the beginning of the stream.  The skip is
-      # essential for synchronous highly-parallel training to avoid multiple
-      # replicas reading the same data in lock-step.
-      data_files = tf.contrib.slim.parallel_reader.get_data_files(
-          data_filepattern)
-      num_skip = random.randint(0, _file_num_records_cached(data_files[0]))
-      dataset = dataset.skip(num_skip)
-
-    def _maybe_reverse_and_copy(example):
-      self.maybe_reverse_features(example)
-      self.maybe_copy_features(example)
-      return example
+      # Load records from file with an 8MiB read buffer.
+      return tf.data.TFRecordDataset(filename, buffer_size=8 * 1024 * 1024)
 
     def _preprocess(example):
       examples = self.preprocess_example(example, mode, hparams)
@@ -510,11 +501,32 @@ def _preprocess(example):
         examples = tf.data.Dataset.from_tensors(examples)
       return examples
 
-    dataset = dataset.map(self.decode_example, num_parallel_calls=num_threads)
+    def _maybe_reverse_and_copy(example):
+      self.maybe_reverse_features(example)
+      self.maybe_copy_features(example)
+      return example
 
+    if len(data_files) < num_partitions:
+      raise ValueError(
+          "number of data files (%d) must be at least the number of hosts (%d)"
+          % (len(data_files), num_partitions))
+    data_files = [f for (i, f) in enumerate(data_files)
+                  if i % num_partitions == partition_id]
+    tf.logging.info(
+        "partition: %d num_data_files: %d" % (partition_id, len(data_files)))
+    if shuffle_files:
+      random.shuffle(data_files)
+    dataset = tf.data.Dataset.from_tensor_slices(tf.constant(data_files))
+    dataset = dataset.apply(
+        tf.contrib.data.parallel_interleave(
+            _load_records, sloppy=is_training, cycle_length=8))
+    if repeat:
+      dataset = dataset.repeat()
+    dataset = dataset.map(self.decode_example, num_parallel_calls=num_threads)
     if preprocess:
-      dataset = interleave(dataset, _preprocess)
-
+      dataset = dataset.apply(
+          tf.contrib.data.parallel_interleave(
+              _preprocess, sloppy=is_training, cycle_length=8))
     dataset = dataset.map(
         _maybe_reverse_and_copy, num_parallel_calls=num_threads)
 
@@ -588,17 +600,57 @@ def feature_info(self):
     self._feature_info = features
     return features
 
-  def make_estimator_input_fn(self, mode, hparams, data_dir=None,
+  def make_estimator_input_fn(self,
+                              mode,
+                              hparams,
+                              data_dir=None,
                               dataset_kwargs=None):
     """Return input_fn wrapped for Estimator."""
 
     def estimator_input_fn(params, config):
-      return self.input_fn(mode, hparams, data_dir=data_dir, params=params,
-                           config=config, dataset_kwargs=dataset_kwargs)
+      return self.input_fn(
+          mode,
+          hparams,
+          data_dir=data_dir,
+          params=params,
+          config=config,
+          dataset_kwargs=dataset_kwargs)
 
     return estimator_input_fn
 
-  def input_fn(self, mode, hparams, data_dir=None, params=None, config=None,
+  def _dataset_partition(self, mode, config):
+    """Which part of the training data to read.
+
+    If there are multiple parallel calls to input_fn (multiple TPU hosts),
+    then we want each one to read from a separate partition of the training
+    data.
+
+    Args:
+      mode: tf.estimator.ModeKeys
+      config: RunConfig
+    Returns:
+      partition_id: an integer
+      num_partitions: an integer
+    """
+    if mode != tf.estimator.ModeKeys.TRAIN or not hasattr(config, "tpu_config"):
+      return 0, 1
+    if config.tpu_config.per_host_input_for_training:
+      num_partitions = max(config.tpu_config.num_shards // 8, 1)
+    else:
+      num_partitions = config.tpu_config.num_shards
+    partition_id = getattr(self, "_next_partition_id", 0)
+    self._next_partition_id = partition_id + 1
+    tf.logging.info("num_partitions = %d partition_id = %d" %
+                    (num_partitions, partition_id))
+    assert partition_id < num_partitions
+    return partition_id, num_partitions
+
+  def input_fn(self,
+               mode,
+               hparams,
+               data_dir=None,
+               params=None,
+               config=None,
                dataset_kwargs=None):
     """Builds input pipeline for problem.
 
@@ -615,9 +667,11 @@ def input_fn(self, mode, hparams, data_dir=None, params=None, config=None,
     Returns:
       (features_dict<str name, Tensor feature>, Tensor targets)
     """
+    partition_id, num_partitions = self._dataset_partition(mode, config)
+
     is_training = mode == tf.estimator.ModeKeys.TRAIN
     if config.use_tpu:
-      num_threads = 32
+      num_threads = 64
     else:
       num_threads = 4 if is_training else 1
 
@@ -629,10 +683,9 @@ def tpu_valid_size(example):
 
     def gpu_valid_size(example):
       drop_long_sequences = is_training or hparams.eval_drop_long_sequences
-      return data_reader.example_valid_size(
-          example,
-          hparams.min_length,
-          max_length if drop_long_sequences else 10**9)
+      return data_reader.example_valid_size(example, hparams.min_length,
+                                            max_length
+                                            if drop_long_sequences else 10**9)
 
     def define_shapes(example):
       batch_size = config and config.use_tpu and params["batch_size"]
@@ -646,7 +699,10 @@ def define_shapes(example):
         "mode": mode,
         "data_dir": data_dir,
         "num_threads": num_threads,
-        "hparams": hparams})
+        "hparams": hparams,
+        "partition_id": partition_id,
+        "num_partitions": num_partitions,
+    })
 
     dataset = self.dataset(**dataset_kwargs)
     dataset = dataset.map(
@@ -662,8 +718,8 @@ def define_shapes(example):
       else:
         tf.logging.warning(
             "Shapes are not fully defined. Assuming batch_size means tokens. "
-            "You should probably override batch_size_means_tokens() "
-            "in your problem subclass")
+            "Override batch_size_means_tokens() "
+            "in your problem subclass if this is undesired behavior.")
         batch_size_means_tokens = True
 
     # Batching
@@ -672,12 +728,13 @@ def define_shapes(example):
       if config and config.use_tpu:
         # on TPU, we use params["batch_size"], which specifies the number of
         # examples across all datashards
-        tpu_batch_size = params["batch_size"]
+        batch_size = params["batch_size"]
         dataset = dataset.apply(
-            tf.contrib.data.batch_and_drop_remainder(tpu_batch_size))
+            tf.contrib.data.batch_and_drop_remainder(batch_size))
       else:
         num_shards = (config and config.data_parallelism.n) or 1
-        dataset = dataset.batch(hparams.batch_size * num_shards)
+        batch_size = hparams.batch_size * num_shards
+        dataset = dataset.batch(batch_size)
     else:
       # batch_size means tokens per datashard
       if config and config.use_tpu:
@@ -687,9 +744,10 @@ def define_shapes(example):
             dataset.output_shapes, none_filler=max_length)
         # on TPU, we use params["batch_size"], which specifies the number of
         # examples across all datashards
+        batch_size = params["batch_size"]
         dataset = dataset.apply(
             tf.contrib.data.padded_batch_and_drop_remainder(
-                params["batch_size"], padded_shapes))
+                batch_size, padded_shapes))
       else:
         # On GPU, bucket by length
         dataset = dataset.filter(gpu_valid_size)
@@ -702,12 +760,11 @@ def define_shapes(example):
           batching_scheme["batch_sizes"] = [hparams.batch_size]
           batching_scheme["boundaries"] = []
         dataset = data_reader.bucket_by_sequence_length(
-            dataset,
-            data_reader.example_length,
-            batching_scheme["boundaries"],
+            dataset, data_reader.example_length, batching_scheme["boundaries"],
             batching_scheme["batch_sizes"])
 
         if not is_training:
+
           def _pad_batch(features):
             if not config or config.data_parallelism.n <= 1:
               return features
@@ -946,7 +1003,9 @@ def _maybe_pack_examples(self, generator):
     """Helper to generate_data()."""
     if self.packed_length:
       return generator_utils.pack_examples(
-          generator, self.has_inputs, self.packed_length,
+          generator,
+          self.has_inputs,
+          self.packed_length,
           chop_long_sequences=not self.has_inputs)
     else:
       return generator
@@ -964,8 +1023,8 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
       generator_utils.shuffle_dataset(all_paths)
     else:
       generator_utils.generate_dataset_and_shuffle(
-          self._maybe_pack_examples(self.generator(data_dir, tmp_dir, True)),
-          train_paths,
+          self._maybe_pack_examples(self.generator(data_dir, tmp_dir,
+                                                   True)), train_paths,
           self._maybe_pack_examples(self.generator(data_dir, tmp_dir, False)),
           dev_paths)
 
@@ -1007,9 +1066,7 @@ def hparams(self, defaults, unused_model_hparams):
       p.input_modality["targets_position"] = identity
 
   def example_reading_spec(self):
-    data_fields = {
-        "targets": tf.VarLenFeature(tf.int64)
-    }
+    data_fields = {"targets": tf.VarLenFeature(tf.int64)}
     if self.has_inputs:
       data_fields["inputs"] = tf.VarLenFeature(tf.int64)
 
@@ -1090,11 +1147,15 @@ def text_filepaths_for_task(self, tmp_dir, task_id):
     assert task_id >= 0
     assert task_id < self.num_train_shards + self.num_dev_shards
     if task_id < self.num_train_shards:
-      return [f for i, f in enumerate(self.train_text_filepaths(tmp_dir))
-              if i % self.num_train_shards == task_id]
+      return [
+          f for i, f in enumerate(self.train_text_filepaths(tmp_dir))
+          if i % self.num_train_shards == task_id
+      ]
     else:
-      return [f for i, f in enumerate(self.dev_text_filepaths(tmp_dir))
-              if i % self.num_dev_shards == task_id - self.num_train_shards]
+      return [
+          f for i, f in enumerate(self.dev_text_filepaths(tmp_dir))
+          if i % self.num_dev_shards == task_id - self.num_train_shards
+      ]
 
   def filepath_to_unicode_strings(self, filepath):
     """Read text out of an input file.
@@ -1140,8 +1201,8 @@ def file_generator(self,
       chars_this_file = 0
       tf.logging.info("reading file %s" % fname)
       for text in self.filepath_to_unicode_strings(fname):
-        if (max_chars_per_file and chars_this_file + len(text)
-            > max_chars_per_file):
+        if (max_chars_per_file and
+            chars_this_file + len(text) > max_chars_per_file):
           text = text[:max_chars_per_file - chars_this_file]
         if max_chars_total and chars_total + len(text) > max_chars_total:
           text = text[:max_chars_total - chars_total]
@@ -1237,7 +1298,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
   @property
   def max_chars_for_vocab(self):
     """Number of characters of training data to use for generating vocab."""
-    return 10 ** 7
+    return 10**7
 
   @property
   def target_space_id(self):
@@ -1254,7 +1315,7 @@ def num_dev_shards(self):
   @property
   def max_dev_chars(self):
     """Limit dev set to at most this many characters (default 10M)."""
-    return 10 ** 7
+    return 10**7
 
   @property
   def multiprocess_generate(self):
@@ -1277,14 +1338,12 @@ def has_inputs(self):
     return False
 
   def eval_metrics(self):
-    return [
-        metrics.Metrics.ACC, metrics.Metrics.NEG_LOG_PERPLEXITY
-    ]
+    return [metrics.Metrics.ACC, metrics.Metrics.NEG_LOG_PERPLEXITY]
 
 
 def to_unicode_ignore_erros(s):
-  return (unicode(s, "utf-8", errors="ignore") if six.PY2 else
-          s.decode("utf-8", "ignore"))
+  return (unicode(s, "utf-8", errors="ignore")
+          if six.PY2 else s.decode("utf-8", "ignore"))
 
 
 def _are_shapes_fully_defined(shapes_dict):
@@ -1359,3 +1418,23 @@ def pad_batch(features, batch_multiple):
     padded_feature = tf.pad(feature, paddings)
     padded_features[k] = padded_feature
   return padded_features
+
+
+def problem_hparams_to_features(problem_hparams):
+  input_space_id, target_space_id = 0, 0
+  if problem_hparams:
+    input_space_id = problem_hparams.input_space_id
+    target_space_id = problem_hparams.target_space_id
+  return {
+      "problem_choice": 0,
+      "input_space_id": input_space_id,
+      "target_space_id": target_space_id,
+  }
+
+
+def skip_random_fraction(dataset, data_file):
+  # Skip a random fraction at the beginning of the stream.  The skip is
+  # essential for synchronous highly-parallel training to avoid multiple
+  # replicas reading the same data in lock-step.
+  num_skip = random.randint(0, _file_num_records_cached(data_file))
+  return dataset.skip(num_skip)
diff --git a/tensor2tensor/data_generators/speech_recognition.py b/tensor2tensor/data_generators/speech_recognition.py
index c54878045..01a3db564 100644
--- a/tensor2tensor/data_generators/speech_recognition.py
+++ b/tensor2tensor/data_generators/speech_recognition.py
@@ -33,6 +33,7 @@
 from tensor2tensor.data_generators import problem
 from tensor2tensor.data_generators import text_encoder
 from tensor2tensor.layers import common_layers
+from tensor2tensor.utils import metrics
 from tensor2tensor.utils import modality
 from tensor2tensor.utils import registry
 
@@ -93,9 +94,7 @@ def compute_mel_filterbank_features(
     num_mel_bins: filterbank size
     log_noise_floor: clip small values to prevent numeric overflow in log
   Returns:
-    tuple of (filterbanks, filterbank_lens) where:
-      filterbanks are float32 tensor with shape [batch_size, len, num_bins, 1]
-      filterbank_lens are int64 tensor with shape [batch_size]
+    filterbanks: a float32 tensor with shape [batch_size, len, num_bins, 1]
   """
   # `stfts` is a complex64 Tensor representing the short-time Fourier
   # Transform of each signal in `signals`. Its shape is
@@ -214,6 +213,8 @@ class SpeechRecognitionProblem(problem.Problem):
   def hparams(self, defaults, model_hparams):
     p = model_hparams
     # Filterbank extraction
+    # The trainer seems to reserve memory for all members of the input dict
+    p.add_hparam("audio_keep_example_waveforms", False)
     p.add_hparam("audio_sample_rate", 16000)
     p.add_hparam("audio_preemphasis", 0.97)
     p.add_hparam("audio_dither", 1.0 / np.iinfo(np.int16).max)
@@ -243,6 +244,9 @@ def target_space_id(self):
 
   def feature_encoders(self, _):
     return {
+        "inputs": None,  # Put None to make sure that the logic in
+                         # decoding.py doesn't try to convert the floats
+                         # into text...
         "waveforms": AudioEncoder(),
         "targets": text_encoder.ByteTextEncoder(),
     }
@@ -274,13 +278,19 @@ def preprocess_example(self, example, mode, hparams):
       mel_fbanks = add_delta_deltas(mel_fbanks)
     fbank_size = common_layers.shape_list(mel_fbanks)
     assert fbank_size[0] == 1
-    # Later models like to flatten the two spatial dims. Instead, we add a unit
-    # spatial dim and flatten the frequencies and channels.
+    # Later models like to flatten the two spatial dims. Instead, we add a
+    # unit spatial dim and flatten the frequencies and channels.
     example["inputs"] = tf.reshape(
         mel_fbanks, [fbank_size[1], 1, fbank_size[2] * fbank_size[3]])
+    if not p.audio_keep_example_waveforms:
+      del example["waveforms"]
     return super(SpeechRecognitionProblem, self
                 ).preprocess_example(example, mode, hparams)
 
+  def eval_metrics(self):
+    defaults = super(SpeechRecognitionProblem, self).eval_metrics()
+    return defaults + [metrics.Metrics.EDIT_DISTANCE]
+
 
 @registry.register_audio_modality
 class SpeechRecognitionModality(modality.Modality):
@@ -303,8 +313,8 @@ def bottom(self, inputs):
       num_mel_bins = p.audio_num_mel_bins
       num_channels = 3 if p.audio_add_delta_deltas else 1
       # The convention is that the models are flattened along the spatial,
-      # dimensions, thus the speech preprocessor treats frequencies and channels
-      # as image colors (last axis)
+      # dimensions, thus the speech preprocessor treats frequencies and
+      # channels as image colors (last axis)
       x.set_shape([None, None, 1, num_mel_bins * num_channels])
 
       # This replaces CMVN estimation on data
diff --git a/tensor2tensor/data_generators/twentybn.py b/tensor2tensor/data_generators/twentybn.py
new file mode 100644
index 000000000..c70313767
--- /dev/null
+++ b/tensor2tensor/data_generators/twentybn.py
@@ -0,0 +1,131 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""Data generator for twenty bn video data-set."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+# Dependency imports
+
+from tensor2tensor.data_generators import image_utils
+from tensor2tensor.utils import registry
+
+import tensorflow as tf
+
+
+_FILE_VIDEO_PATTERN = '20bn-something-something-v1'
+_FILE_LABEL_PATTERN = 'something-something-v1-'
+
+_TWENTYBN_IMAGE_SIZE = 32
+
+
+def resize_video_frames(images, size):
+  resized_images = []
+  for image in images:
+    resized_images.append(
+        tf.to_int64(tf.image.resize_images(
+            image, [size, size], tf.image.ResizeMethod.BILINEAR)))
+  return resized_images
+
+
+def twentybn_generator(tmp_dir, training):
+  """Video generator for twenty-bn dataset.
+
+  Args:
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the dev set.
+
+
+  Yields:
+    A dictionary representing the images with the following fields:
+    * image/encoded: the string encoding the images of a video as JPG,
+    * image/format: the string "jpg" representing image format,
+    * image/class/label: an integer representing the label,
+  """
+  data_suffix = 'train' if training else 'validation'
+
+  def process_labels():
+    all_labels = {}
+    with tf.gfile.Open(tmp_dir + _FILE_LABEL_PATTERN + 'labels.csv') as f:
+      for (i, label) in enumerate(f):
+        all_labels[label] = i+1
+    return all_labels
+
+  def read_id_to_labels():
+    id_to_label = {}
+    with tf.gfile.Open(tmp_dir + _FILE_LABEL_PATTERN +
+                       data_suffix + '.csv') as f:
+      for line in f:
+        values = line.split(';')
+        id_to_label[int(values[0])] = values[1]
+    return id_to_label
+
+  # Get the label string to class id dictionary.
+  all_labels = process_labels()
+  # Get the video ids to label string dictionary.
+  id_to_labels = read_id_to_labels()
+
+  # Read video frames as images.
+  for vname, label_id in id_to_labels.items():
+    path = os.path.join(os.path.join(tmp_dir, _FILE_VIDEO_PATTERN), str(vname))
+    label = all_labels[label_id]
+    images = []
+    image_files = tf.gfile.Glob(os.path.join(path, '*.jpg'))
+
+    for filename in image_files:
+      with tf.gfile.Open(filename, 'rb') as f:
+        encoded_image_data = f.read()
+        images.append(encoded_image_data)
+    yield {
+        'image/encoded': images,
+        'image/format': ['jpg'],
+        'image/class/label': [int(label)]
+    }
+
+
+@registry.register_problem
+class VideoTwentybn(image_utils.Image2ClassProblem):
+  """Videonet."""
+
+  @property
+  def is_small(self):
+    return True
+
+  @property
+  def num_classes(self):
+    return 174
+
+  @property
+  def train_shards(self):
+    return 100
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def preprocess_example(self, example, unused_mode, unused_hparams):
+    example['inputs'] = resize_video_frames(example['inputs'],
+                                            _TWENTYBN_IMAGE_SIZE)
+    return example
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return twentybn_generator(tmp_dir, True)
+    else:
+      return twentybn_generator(tmp_dir, False)
diff --git a/tensor2tensor/layers/common_attention.py b/tensor2tensor/layers/common_attention.py
index d7874ceff..e82e6d471 100644
--- a/tensor2tensor/layers/common_attention.py
+++ b/tensor2tensor/layers/common_attention.py
@@ -70,30 +70,40 @@ def partial(fct, *args, **kwargs):
     return functools.wraps(fct)(functools.partial(fct, *args, **kwargs))
 
   def register_layer(
-      fct,
+      fct_in,
       default_args=None,
       default_kwargs=None,
       use_dp=True,
+      recompute_grad=False,
   ):
     """Turn a function into its standardized version.
 
     Args:
-      fct (fct): The function to register
+      fct_in (fct): The function to register
       default_args (list): The default parameters to add to the function.
       default_kwargs (dict): The default parameters to add to the function.
         Those arguments can be overwriten when calling the function.
       use_dp (bool): Wrap the function call within a dataparalellism object if
         dp is available. Some layers (like moe) must be called without dp.
+      recompute_grad (bool): If True, recompute the function during the
+        backward pass to save memory
 
     Returns:
       fct: the standardized layer function.
     """
     # The kwargs given when calling the function overwrite the default ones
-    fct = partial(fct, *(default_args or []), **(default_kwargs or {}))
+    fct_in = partial(fct_in, *(default_args or []), **(default_kwargs or {}))
 
-    @functools.wraps(fct)
+    @functools.wraps(fct_in)
     def decorator(x, *args, **kwargs):
       """Call the layer function."""
+      fct = fct_in  # For closure. Could use nonlocal with Python 3
+      # Eventually create the memory optimized version of the function
+      if recompute_grad:
+        fct = partial(fct, **kwargs)  # recompute_grad only accept args
+        fct = common_layers.recompute_grad(fct)
+        kwargs = {}
+
       # Eventually use dp (if given and not MoE)
       if use_dp and dp is not None:
         y = dp(fct, x, *args, **kwargs)
@@ -135,10 +145,48 @@ def decorator(x, *args, **kwargs):
           dropout_rate=hparams.attention_dropout,
       ))
 
-  # === Local attention layer ===
+  # === Memory efficient full-attention layer ===
+  # Save memory by not storing the activations and
+  # recomputing them during the backward pass
+  memeff_attention_base_fn = register_layer(
+      multihead_attention,
+      default_kwargs=dict(
+          total_key_depth=total_key_depth,
+          total_value_depth=total_value_depth,
+          output_depth=hparams.hidden_size,
+          num_heads=hparams.num_heads,
+          dropout_rate=hparams.attention_dropout,
+      ),
+      recompute_grad=True,
+  )
+  def memeff_attention_fn(*args, **kwargs):
+    """Modify args/kwargs for compatibility with recompute_grad."""
+    kwargs = kwargs.copy()
+    assert len(args) == 1
+    x = args[0]
+    memory_antecedent = kwargs.pop("memory_antecedent", x)  # Same as x if None
+    if kwargs.get("bias", None) is not None:  # Case where bias has been set
+      args = (x, memory_antecedent, kwargs.pop("bias"))
+    else:
+      # Otherwise, only 2 args. This is necessary as recompute_grad does not
+      # support None values.
+      args = (x, memory_antecedent)
+    return memeff_attention_base_fn(*args, **kwargs)
+
+  # === Local attention (unmasked) layer ===
   # Reuse same parameters as multihead_attention
-  # Only works for self attention. Always mask the future.
+  # Don't mask the future
   local_attention_fn = partial(
+      multihead_attention_fn,
+      block_length=hparams.attention_loc_block_length,
+      block_width=hparams.attention_loc_block_width,
+      attention_type="local_unmasked",
+  )
+
+  # === Local attention (masked) layer ===
+  # Reuse same parameters as multihead_attention
+  # Only works for self attention. Always mask the future.
+  local_attention_masked_fn = partial(
       multihead_attention_fn,
       block_length=hparams.attention_loc_block_length,
       attention_type="local_mask_right",
@@ -213,8 +261,9 @@ def decorator(x, *args, **kwargs):
   layers = dict(
       a=multihead_attention_fn,  # Multihead full attention
       loc=local_attention_fn,  # Local attention
+      locm=local_attention_masked_fn,  # Local masked attention
       red=compressed_attention_fn,  # Memory-compressed attention
-      mem=None,  # Memory efficient
+      mem=memeff_attention_fn,  # Memory efficient
       fc=conv_hidden_relu,
       sep=sep_conv_relu,  # Fully connected
       sepm=sep_conv_relu_masked,  # masked separable convolution
@@ -252,6 +301,8 @@ def add_standard_attention_hparams(hparams):
   hparams.add_hparam("attention_dropout", 0.0)
   # Attention: Local
   hparams.add_hparam("attention_loc_block_length", 256)
+  # Attention: Local (unmasked only): How much to look left.
+  hparams.add_hparam("attention_loc_block_width", 128)
   # Attention: Memory-compressed
   hparams.add_hparam("attention_red_factor", 3)
   hparams.add_hparam("attention_red_type", "conv")
@@ -986,9 +1037,9 @@ def grouped_attention_multihead(query_antecedent,
       name,
       default_name="multihead_attention_sparse",
       values=[query_antecedent, memory_antecedent]):
-    q = tf.layers.dense(
+    q = common_layers.dense(
         query_antecedent, total_key_depth, use_bias=False, name="q_transform")
-    kv = tf.layers.dense(
+    kv = common_layers.dense(
         memory_antecedent,
         total_key_depth + total_value_depth,
         use_bias=False,
@@ -1000,7 +1051,7 @@ def grouped_attention_multihead(query_antecedent,
     # We will train these by auxiliary losses.  We use stop_gradient here
     # to keep these losses from back-propagating to the rest of the model.
     # We add biases that help balance the usage of the experts.
-    q_pred = tf.layers.dense(
+    q_pred = common_layers.dense(
         tf.stop_gradient(query_antecedent),
         num_heads * num_groups,
         use_bias=False,
@@ -1008,7 +1059,7 @@ def grouped_attention_multihead(query_antecedent,
     q_pred = split_heads(q_pred, num_heads)
     q_bias = tf.get_variable("q_bias", [1, num_heads, 1, num_groups])
     q_pred_biased = q_pred + q_bias
-    m_pred = tf.layers.dense(
+    m_pred = common_layers.dense(
         tf.stop_gradient(memory_antecedent),
         num_heads * num_groups,
         use_bias=False,
@@ -1077,7 +1128,7 @@ def grouped_attention_multihead(query_antecedent,
 
     o = tf.reshape(o, [batch, num_heads, length_q, depth_v])
     o = combine_heads(o)
-    o = tf.layers.dense(
+    o = common_layers.dense(
         o, output_depth, use_bias=False, name="output_transform")
 
     m_total = m_dispatcher.combine(m_total)
@@ -1180,7 +1231,8 @@ def dot_product_attention(q,
                           image_shapes=None,
                           name=None,
                           make_image_summary=True,
-                          save_weights_to=None):
+                          save_weights_to=None,
+                          dropout_broadcast_dims=None):
   """dot-product attention.
 
   Args:
@@ -1196,6 +1248,9 @@ def dot_product_attention(q,
     save_weights_to: an optional dictionary to capture attention weights
       for vizualization; the weights tensor will be appended there under
       a string key created from the variable scope (including name).
+    dropout_broadcast_dims:  an optional list of integers less than 4
+      specifying in which dimensions to broadcast the dropout decisions.
+      saves memory.
 
   Returns:
     A Tensor.
@@ -1210,7 +1265,8 @@ def dot_product_attention(q,
     if save_weights_to is not None:
       save_weights_to[scope.name] = weights
     # dropping out the attention links for each of the heads
-    weights = tf.nn.dropout(weights, 1.0 - dropout_rate)
+    weights = common_layers.dropout_with_broadcast_dims(
+        weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
     if expert_utils.should_generate_summaries() and make_image_summary:
       attention_image_summary(weights, image_shapes)
     return tf.matmul(weights, v)
@@ -1229,21 +1285,16 @@ def _generate_relative_positions_matrix(length, max_relative_position):
   return final_mat
 
 
-def _generate_relative_positions_embeddings(heads, length, depth,
+def _generate_relative_positions_embeddings(length, depth,
                                             max_relative_position, name):
-  """Generates tensor of size [heads, length, length, depth]."""
+  """Generates tensor of size [length, length, depth]."""
   with tf.variable_scope(name):
     relative_positions_matrix = _generate_relative_positions_matrix(
         length, max_relative_position)
     vocab_size = max_relative_position * 2 + 1
-    # Generates embedding for each relative position of dimension heads * depth.
-    embeddings_table = tf.get_variable("embeddings",
-                                       [vocab_size, heads * depth])
+    # Generates embedding for each relative position of dimension depth.
+    embeddings_table = tf.get_variable("embeddings", [vocab_size, depth])
     embeddings = tf.gather(embeddings_table, relative_positions_matrix)
-    # Split embeddings per head.
-    embeddings = tf.reshape(embeddings, [length, length, heads, depth])
-    # Transpose to shape [heads, length, length, depth].
-    embeddings = tf.transpose(embeddings, [2, 0, 1, 3])
     return embeddings
 
 
@@ -1255,18 +1306,30 @@ def _relative_attention_inner(x, y, z, transpose):
   Args:
     x: Tensor with shape [batch_size, heads, length, length or depth].
     y: Tensor with shape [batch_size, heads, length, depth].
-    z: Tensor with shape [heads, length, length, depth].
+    z: Tensor with shape [length, length, depth].
     transpose: Whether to tranpose inner matrices of y and z. Should be true if
         last dimension of x is depth, not length.
 
   Returns:
-    A Tensor with shape [batch_size, heads, length, a].
+    A Tensor with shape [batch_size, heads, length, length or depth].
   """
+  batch_size = tf.shape(x)[0]
+  heads = x.get_shape().as_list()[1]
+  length = tf.shape(x)[2]
+
+  # xy_matmul is [batch_size, heads, length, length or depth]
   xy_matmul = tf.matmul(x, y, transpose_b=transpose)
-  x_t = tf.transpose(x, [1, 2, 0, 3])
-  x_tz_matmul = tf.matmul(x_t, z, transpose_b=transpose)
-  x_tz_matmul_t = tf.transpose(x_tz_matmul, [2, 0, 1, 3])
-  return xy_matmul + x_tz_matmul_t
+  # x_t is [length, batch_size, heads, length or depth]
+  x_t = tf.transpose(x, [2, 0, 1, 3])
+  # x_t_r is [length, batch_size * heads, length or depth]
+  x_t_r = tf.reshape(x_t, [length, heads * batch_size, -1])
+  # x_tz_matmul is [length, batch_size * heads, length or depth]
+  x_tz_matmul = tf.matmul(x_t_r, z, transpose_b=transpose)
+  # x_tz_matmul_r is [length, batch_size, heads, length or depth]
+  x_tz_matmul_r = tf.reshape(x_tz_matmul, [length, batch_size, heads, -1])
+  # x_tz_matmul_r_t is [batch_size, heads, length, length or depth]
+  x_tz_matmul_r_t = tf.transpose(x_tz_matmul_r, [1, 2, 0, 3])
+  return xy_matmul + x_tz_matmul_r_t
 
 
 def dot_product_attention_relative(q,
@@ -1276,7 +1339,8 @@ def dot_product_attention_relative(q,
                                    max_relative_position,
                                    dropout_rate=0.0,
                                    image_shapes=None,
-                                   name=None):
+                                   name=None,
+                                   make_image_summary=True):
   """Calculate relative position-aware dot-product self-attention.
 
   The attention calculation is augmented with learned representations for the
@@ -1292,6 +1356,7 @@ def dot_product_attention_relative(q,
     dropout_rate: a floating point number.
     image_shapes: optional tuple of integer scalars.
     name: an optional string.
+    make_image_summary: Whether to make an attention image summary.
 
   Returns:
     A Tensor.
@@ -1311,14 +1376,12 @@ def dot_product_attention_relative(q,
     q.get_shape().assert_is_compatible_with(v.get_shape())
 
     # Use separate embeddings suitable for keys and values.
-    heads = q.get_shape().as_list()[1]
     depth = q.get_shape().as_list()[3]
     length = common_layers.shape_list(q)[2]
     relations_keys = _generate_relative_positions_embeddings(
-        heads, length, depth, max_relative_position, "relative_positions_keys")
+        length, depth, max_relative_position, "relative_positions_keys")
     relations_values = _generate_relative_positions_embeddings(
-        heads, length, depth, max_relative_position,
-        "relative_positions_values")
+        length, depth, max_relative_position, "relative_positions_values")
 
     # Compute self attention considering the relative position embeddings.
     logits = _relative_attention_inner(q, k, relations_keys, True)
@@ -1326,7 +1389,7 @@ def dot_product_attention_relative(q,
       logits += bias
     weights = tf.nn.softmax(logits, name="attention_weights")
     weights = tf.nn.dropout(weights, 1.0 - dropout_rate)
-    if not tf.get_variable_scope().reuse:
+    if not tf.get_variable_scope().reuse and make_image_summary:
       attention_image_summary(weights, image_shapes)
     return _relative_attention_inner(weights, v, relations_values, False)
 
@@ -2212,7 +2275,7 @@ def compute_qkv(query_antecedent,
     memory_antecedent = query_antecedent
   def _compute(inp, depth, filter_width, padding, name):
     if filter_width == 1:
-      return tf.layers.dense(inp, depth, use_bias=False, name=name)
+      return common_layers.dense(inp, depth, use_bias=False, name=name)
     else:
       return common_layers.conv1d(inp, depth, filter_width, padding, name=name)
   q = _compute(
@@ -2246,6 +2309,8 @@ def multihead_attention(query_antecedent,
                         num_memory_blocks=2,
                         name=None,
                         save_weights_to=None,
+                        make_image_summary=True,
+                        dropout_broadcast_dims=None,
                         **kwargs):
   """Multihead scaled-dot-product attention with input/output transformations.
 
@@ -2289,6 +2354,10 @@ def multihead_attention(query_antecedent,
     save_weights_to: an optional dictionary to capture attention weights
       for vizualization; the weights tensor will be appended there under
       a string key created from the variable scope (including name).
+    make_image_summary: Whether to make an attention image summary.
+    dropout_broadcast_dims:  an optional list of integers less than 4
+      specifying in which dimensions to broadcast the dropout decisions.
+      saves memory.
     **kwargs (dict): Parameters for the attention function
 
   Caching:
@@ -2350,10 +2419,13 @@ def multihead_attention(query_antecedent,
         x, additional_returned_value = x  # Unpack
     elif attention_type == "dot_product":
       x = dot_product_attention(q, k, v, bias, dropout_rate, image_shapes,
-                                save_weights_to=save_weights_to)
+                                save_weights_to=save_weights_to,
+                                make_image_summary=make_image_summary,
+                                dropout_broadcast_dims=dropout_broadcast_dims)
     elif attention_type == "dot_product_relative":
       x = dot_product_attention_relative(q, k, v, bias, max_relative_position,
-                                         dropout_rate, image_shapes)
+                                         dropout_rate, image_shapes,
+                                         make_image_summary=make_image_summary)
     elif attention_type == "local_mask_right":
       x = masked_local_attention_1d(q, k, v, block_length=block_length)
     elif attention_type == "local_unmasked":
@@ -2367,7 +2439,7 @@ def multihead_attention(query_antecedent,
       x = dilated_self_attention_1d(q, k, v, block_length, block_width,
                                     gap_size, num_memory_blocks)
     x = combine_heads(x)
-    x = tf.layers.dense(
+    x = common_layers.dense(
         x, output_depth, use_bias=False, name="output_transform")
     if additional_returned_value is not None:
       return x, additional_returned_value
@@ -2431,7 +2503,7 @@ def multihead_attention_2d(query_antecedent,
       x = masked_local_attention_2d(
           q, k, v, query_shape=query_shape, memory_flange=memory_flange)
     x = combine_heads_2d(x)
-    x = tf.layers.dense(
+    x = common_layers.dense(
         x, output_depth, use_bias=False, name="output_transform")
     return x
 
@@ -2471,16 +2543,16 @@ def ffn_self_attention_layer(x,
     x_shape = common_layers.shape_list(x)
     part_depth = filter_depth // num_parts
     if not share_kv:
-      combined = tf.layers.dense(
+      combined = common_layers.dense(
           x, filter_depth * 3, use_bias=False, name="qkv_transform")
       combined = tf.expand_dims(combined, axis=2)
       q, k, v = tf.split(combined, 3, axis=3)
     else:
       q = tf.expand_dims(
-          tf.layers.dense(
+          common_layers.dense(
               x, filter_depth, use_bias=False, name="q_transform"), axis=2)
       kv_combined = tf.expand_dims(
-          tf.layers.dense(
+          common_layers.dense(
               tf.concat([x, x], axis=1), filter_depth, use_bias=False,
               name="kv_transform"),
           axis=2)
@@ -2495,7 +2567,7 @@ def ffn_self_attention_layer(x,
     bias = None
     x = dot_product_attention(batch_q, batch_k, batch_v, bias, dropout_rate)
     x = tf.reshape(x, [x_shape[0], x_shape[1], filter_depth])
-    x = tf.layers.dense(
+    x = common_layers.dense(
         x, output_depth, use_bias=False, name="output_transform")
     return x
 
@@ -2547,7 +2619,8 @@ def parameter_attention(x,
             output_depth**0.5)
     batch_size = common_layers.shape_list(x)[0]
     length = common_layers.shape_list(x)[1]
-    q = tf.layers.dense(x, total_key_depth, use_bias=False, name="q_transform")
+    q = common_layers.dense(
+        x, total_key_depth, use_bias=False, name="q_transform")
     if dropout_rate:
       # This is a cheaper form of attention dropout where we use to use
       # the same dropout decisions across batch elemets and query positions,
@@ -2566,7 +2639,7 @@ def parameter_attention(x,
     y = tf.transpose(y, [1, 2, 0, 3])
     y = tf.reshape(y, [batch_size, length, total_value_depth])
     y.set_shape([None, None, total_value_depth])
-    y = tf.layers.dense(
+    y = common_layers.dense(
         y, output_depth, use_bias=False, name="output_transform")
     return y
 
@@ -3319,7 +3392,7 @@ def pad_and_reshape(x):
             block_length,
             block_length,  # Restore the block length dimension
         ])
-    weights = tf.reduce_sum(weights, axis=3, keep_dims=True)  # Compress block
+    weights = tf.reduce_sum(weights, axis=3, keepdims=True)  # Compress block
     v_out = tf.matmul(weights, v)  # [1, block_length] @ [block_length, depth]
     v_out = tf.squeeze(v_out, axis=3)
     return v_out
diff --git a/tensor2tensor/layers/common_hparams.py b/tensor2tensor/layers/common_hparams.py
index f6728ed86..02a5df2f3 100644
--- a/tensor2tensor/layers/common_hparams.py
+++ b/tensor2tensor/layers/common_hparams.py
@@ -32,9 +32,10 @@
 def basic_params1():
   """A set of basic hyperparameters."""
   return tf.contrib.training.HParams(
-      # If the features are variable length, this is in tokens per batch per
-      # GPU. If the features are of known shape (e.g. image problems), this is
-      # the actual batch size.
+      # If the problem consists of variable-length sequences
+      # (see problem.batch_size_means_tokens()), then this is the number
+      # of tokens per batch per GPU or per TPU core.  Otherwise, this is
+      # the number of examples per GPU or per TPU core.
       batch_size=4096,
       # If True, then if the features are of variable length, the batch_size is
       # used as the actual batch size (and not tokens per batch).
@@ -50,6 +51,8 @@ def basic_params1():
       clip_grad_norm=2.0,
       grad_noise_scale=0.0,
       summarize_grads=False,
+      # Whether to log the name and size of every variable
+      summarize_vars=False,
       initializer="orthogonal",
       initializer_gain=1.5,
       label_smoothing=0.1,
@@ -58,9 +61,13 @@ def basic_params1():
       optimizer_adam_beta1=0.85,
       optimizer_adam_beta2=0.997,
       optimizer_momentum_momentum=0.9,
-      weight_decay=0.1,
+      optimizer_momentum_nesterov=False,
+      weight_decay=1e-6,
       weight_noise=0.0,
       learning_rate_decay_scheme="none",
+      # decay_steps and decay_staircase for learning_rate_decay_scheme=="exp"
+      learning_rate_decay_steps=5000,
+      learning_rate_decay_staircase=False,
       learning_rate_minimum=None,
       learning_rate_decay_rate=1.0,
       learning_rate_warmup_steps=100,
@@ -94,6 +101,13 @@ def basic_params1():
       layer_postprocess_sequence="dan",
       # dropout rate to use during layer_preprocess and layer_postprocess
       layer_prepostprocess_dropout=0.1,
+      # broadcast dimensions for layer_prepostprocess_dropout
+      # a comma-separated list of integers.
+      # see common_layers.dropout_with_broadcast_dims()
+      # Change this to "1" to save memory.
+      layer_prepostprocess_dropout_broadcast_dims="",
+      # dropout some symbols (set them to 0) before embedding.
+      symbol_dropout=0.0,
       # What type of normalization to use
       norm_type="layer",  # "batch", layer", "noam", "none".
       # epsilon parameter to normalization function
@@ -194,12 +208,6 @@ def basic_params1():
       # device training and mostly should be turned on for performance. One
       # exception are recurrent models: with dynamic loops it must be off.
       daisy_chain_variables=True,
-      # This is the actual batch size, *not* tokens per batch (i.e. for
-      # language models this is the number of sentences in the batch)
-      tpu_batch_size_per_shard=24,
-      # Set by t2t_trainer if --use_tpu to let the model know whether we are on
-      # TPU. Switching on/off tpu should not invalidate checkpoints.
-      use_tpu=False,
       # If True in PREDICT mode, then last-position-only optimizations are not
       # used.
       force_full_predict=False,
@@ -340,7 +348,7 @@ def basic_range1(ranged_hparams):
                       ["uniform", "orthogonal", "uniform_unit_scaling"])
   rhp.set_float("initializer_gain", 0.5, 3.5)
   rhp.set_categorical("learning_rate_decay_scheme",
-                      ["none", "sqrt", "noam", "exp10k"])
+                      ["none", "sqrt", "noam", "exp"])
   rhp.set_float("optimizer_adam_epsilon", 1e-7, 1e-2, scale=rhp.LOG_SCALE)
   rhp.set_float("optimizer_adam_beta1", 0.8, 0.9)
   rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
diff --git a/tensor2tensor/layers/common_layers.py b/tensor2tensor/layers/common_layers.py
index 5e9d9cd45..c8d54fb99 100644
--- a/tensor2tensor/layers/common_layers.py
+++ b/tensor2tensor/layers/common_layers.py
@@ -40,6 +40,40 @@
 allow_defun = False
 
 
+def is_on_tpu():
+  return tf.contrib.framework.get_name_scope().startswith("TPUReplicate")
+
+
+def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
+  """Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
+
+  Instead of specifying noise_shape, this function takes broadcast_dims -
+  a list of dimension numbers in which noise_shape should be 1.  The random
+  keep/drop tensor has dimensionality 1 along these dimensions.
+
+  Args:
+    x: a floating point tensor.
+    keep_prob: A scalar Tensor with the same type as x.
+      The probability that each element is kept.
+    broadcast_dims: an optional list of integers
+      the dimensions along which to broadcast the keep/drop flags.
+    **kwargs: keyword arguments to tf.nn.dropout other than "noise_shape".
+  Returns:
+    A Tensor with the same size and shape as x.
+  """
+  assert "noise_shape" not in kwargs
+  if broadcast_dims:
+    shape = tf.shape(x)
+    ndims = len(x.get_shape())
+    kwargs["noise_shape"] = [
+        1 if i in broadcast_dims else shape[i] for i in xrange(ndims)]
+  return tf.nn.dropout(x, keep_prob, **kwargs)
+
+
+def comma_separated_string_to_integer_list(s):
+  return [int(i) for i in s.split(",") if i]
+
+
 def saturating_sigmoid(x):
   """Saturating sigmoid: 1.2 * sigmoid(x) - 0.1 cut to [0, 1]."""
   with tf.name_scope("saturating_sigmoid", [x]):
@@ -137,23 +171,6 @@ def shakeshake(xs, equal_grad=False):
   return shakeshake2(arg1, arg2)
 
 
-def standardize_images(x):
-  """Image standardization on batches (tf.image.per_image_standardization)."""
-  with tf.name_scope("standardize_images", [x]):
-    x = tf.to_float(x)
-    x_mean = tf.reduce_mean(x, axis=[1, 2, 3], keep_dims=True)
-    x_variance = tf.reduce_mean(
-        tf.square(x - x_mean), axis=[1, 2, 3], keep_dims=True)
-    x_shape = shape_list(x)
-    num_pixels = tf.to_float(x_shape[1] * x_shape[2] * 3)
-    x = (x - x_mean) / tf.maximum(tf.sqrt(x_variance), tf.rsqrt(num_pixels))
-    # TODO(lukaszkaiser): remove hack below, needed for greedy decoding for now.
-    if x.shape and len(x.shape) == 4 and x.shape[3] == 1:
-      x = tf.concat([x, x, x], axis=3)  # Not used, just a dead tf.cond branch.
-    x.set_shape([None, None, None, 3])
-    return x
-
-
 def convert_rgb_to_real(x):
   """Conversion of pixel values to real numbers."""
   with tf.name_scope("rgb_to_real", [x]):
@@ -164,44 +181,42 @@ def convert_rgb_to_real(x):
     return x
 
 
-def image_augmentation(images, do_colors=False, crop_size=None):
-  """Image augmentation: cropping, flipping, and color transforms."""
-  if crop_size is None:
-    crop_size = [299, 299]
-  images = tf.random_crop(images, crop_size + [3])
-  images = tf.image.random_flip_left_right(images)
-  if do_colors:  # More augmentation, but might be slow.
-    images = tf.image.random_brightness(images, max_delta=32. / 255.)
-    images = tf.image.random_saturation(images, lower=0.5, upper=1.5)
-    images = tf.image.random_hue(images, max_delta=0.2)
-    images = tf.image.random_contrast(images, lower=0.5, upper=1.5)
-  return images
+def flatten4d3d(x):
+  """Flatten a 4d-tensor into a 3d-tensor by joining width and height."""
+  xshape = shape_list(x)
+  result = tf.reshape(x, [xshape[0], xshape[1] * xshape[2], xshape[3]])
+  return result
+
 
+# TODO(noam): remove this function after TPUs do gather faster.
+def gather(params, indices):
+  """Version of tf.gather that works faster on tpu."""
+  if not is_on_tpu():
+    return tf.gather(params, indices)
+  vocab_size = params.get_shape().as_list()[0]
+  indices_flat = tf.reshape(indices, [-1])
+  out = tf.matmul(tf.one_hot(indices_flat, vocab_size), params)
+  out = eu.reshape_like(out, tf.expand_dims(indices, -1))
+  return out
 
-def cifar_image_augmentation(images):
-  """Image augmentation suitable for CIFAR-10/100.
 
-  As described in https://arxiv.org/pdf/1608.06993v3.pdf (page 5).
+def dropout_no_scaling(x, keep_prob):
+  """Like tf.nn.dropout, but does not scale up.  Works on integers also.
 
   Args:
-    images: a Tensor.
+    x: a Tensor
+    keep_prob: a floating point number
   Returns:
-    Tensor of the same shape as images.
+    a Tensor of the same size and shape as x
   """
-  images = tf.image.resize_image_with_crop_or_pad(images, 40, 40)
-  images = tf.random_crop(images, [32, 32, 3])
-  images = tf.image.random_flip_left_right(images)
-  return images
-
-
-def flatten4d3d(x):
-  """Flatten a 4d-tensor into a 3d-tensor by joining width and height."""
-  xshape = shape_list(x)
-  result = tf.reshape(x, [xshape[0], xshape[1] * xshape[2], xshape[3]])
-  return result
+  if keep_prob == 1.0:
+    return x
+  return x * tf.cast(
+      tf.less(tf.random_uniform(tf.shape(x)), keep_prob), x.dtype)
 
 
-def embedding(x, vocab_size, dense_size, name=None, reuse=None, multiplier=1.0):
+def embedding(x, vocab_size, dense_size, name=None, reuse=None, multiplier=1.0,
+              symbol_dropout_rate=0.0):
   """Embed x of type int64 into dense vectors, reducing to max 4 dimensions."""
   with tf.variable_scope(
       name, default_name="embedding", values=[x], reuse=reuse):
@@ -211,7 +226,8 @@ def embedding(x, vocab_size, dense_size, name=None, reuse=None, multiplier=1.0):
     # parameter server. This avoids excess computation on the parameter server.
     if not tfe_context.in_eager_mode():
       embedding_var = eu.convert_gradient_to_tensor(embedding_var)
-    emb_x = tf.gather(embedding_var, x)
+    x = dropout_no_scaling(x, 1.0 - symbol_dropout_rate)
+    emb_x = gather(embedding_var, x)
     if multiplier != 1.0:
       emb_x *= multiplier
     static_shape = emb_x.shape.as_list()
@@ -462,7 +478,7 @@ def tpu_conv1d(inputs, filters, kernel_size, padding="SAME", name="tpu_conv1d"):
     a Tensor with shape [batch, length, filters].
   """
   if kernel_size == 1:
-    return tf.layers.dense(inputs, filters, name=name, use_bias=True)
+    return dense(inputs, filters, name=name, use_bias=True)
   if padding == "SAME":
     assert kernel_size % 2 == 1
     first_offset = -((kernel_size - 1) // 2)
@@ -475,7 +491,7 @@ def tpu_conv1d(inputs, filters, kernel_size, padding="SAME", name="tpu_conv1d"):
   for i in xrange(kernel_size):
     shifted = tf.slice(padded, [0, i, 0], tf.shape(inputs)) if i else inputs
     shifted.set_shape(inputs.get_shape())
-    results.append(tf.layers.dense(
+    results.append(dense(
         shifted, filters, use_bias=(i == 0), name=name + "_%d" % i))
   ret = tf.add_n(results)
   ret *= kernel_size ** -0.5
@@ -493,8 +509,8 @@ def layer_norm_vars(filters):
 
 def layer_norm_compute_python(x, epsilon, scale, bias):
   """Layer norm raw computation."""
-  mean = tf.reduce_mean(x, axis=[-1], keep_dims=True)
-  variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keep_dims=True)
+  mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
+  variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keepdims=True)
   norm_x = (x - mean) * tf.rsqrt(variance + epsilon)
   return norm_x * scale + bias
 
@@ -563,7 +579,8 @@ def layer_prepostprocess(previous_value,
                          depth,
                          epsilon,
                          default_name,
-                         name=None):
+                         name=None,
+                         dropout_broadcast_dims=None):
   """Apply a sequence of functions to the input or output of a layer.
 
   The sequence is specified as a string which may contain the following
@@ -585,6 +602,9 @@ def layer_prepostprocess(previous_value,
     epsilon: a float (parameter for normalization)
     default_name: a string
     name: a string
+    dropout_broadcast_dims:  an optional list of integers less than 3
+      specifying in which dimensions to broadcast the dropout decisions.
+      saves memory.
 
   Returns:
     a Tensor
@@ -599,7 +619,8 @@ def layer_prepostprocess(previous_value,
         x = apply_norm(x, norm_type, depth, epsilon)
       else:
         assert c == "d", ("Unknown sequence step %s" % c)
-        x = tf.nn.dropout(x, 1.0 - dropout_rate)
+        x = dropout_with_broadcast_dims(
+            x, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
     return x
 
 
@@ -634,6 +655,8 @@ def layer_preprocess(layer_input, hparams):
       norm_type=hparams.norm_type,
       depth=None,
       epsilon=hparams.norm_epsilon,
+      dropout_broadcast_dims=comma_separated_string_to_integer_list(
+          getattr(hparams, "layer_prepostprocess_dropout_broadcast_dims", "")),
       default_name="layer_prepostprocess")
 
 
@@ -667,6 +690,8 @@ def layer_postprocess(layer_input, layer_output, hparams):
       norm_type=hparams.norm_type,
       depth=None,
       epsilon=hparams.norm_epsilon,
+      dropout_broadcast_dims=comma_separated_string_to_integer_list(
+          getattr(hparams, "layer_prepostprocess_dropout_broadcast_dims", "")),
       default_name="layer_postprocess")
 
 
@@ -907,7 +932,7 @@ def decompress_seqcnn(x,
         targets_shape[0], targets_shape[1], targets_shape[2], channels,
         hidden_size
     ])
-    return tf.layers.dense(outputs, targets_vocab_size)
+    return dense(outputs, targets_vocab_size)
 
 
 def simple_attention(target, source, bias=None):
@@ -1146,7 +1171,7 @@ def mask_from_embedding(emb):
   Returns:
     a 0.0/1.0 Tensor with shape [batch, width, height, 1].
   """
-  return weights_nonzero(tf.reduce_sum(tf.abs(emb), axis=3, keep_dims=True))
+  return weights_nonzero(tf.reduce_sum(tf.abs(emb), axis=3, keepdims=True))
 
 
 def mask_leq(target_length, source_length):
@@ -1295,13 +1320,15 @@ def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask):
     return inputs
 
 
-def dense_relu_dense(inputs, filter_size, output_size, dropout=0.0):
+def dense_relu_dense(inputs, filter_size, output_size, dropout=0.0,
+                     dropout_broadcast_dims=None):
   """Hidden layer with RELU activation followed by linear projection."""
-  h = tf.layers.dense(
+  h = dense(
       inputs, filter_size, use_bias=True, activation=tf.nn.relu, name="conv1")
   if dropout != 0.0:
-    h = tf.nn.dropout(h, 1.0 - dropout)
-  o = tf.layers.dense(h, output_size, use_bias=True, name="conv2")
+    h = dropout_with_broadcast_dims(
+        h, 1.0 - dropout, broadcast_dims=dropout_broadcast_dims)
+  o = dense(h, output_size, use_bias=True, name="conv2")
   return o
 
 
@@ -1347,7 +1374,7 @@ def sepconv_relu_sepconv(inputs,
     else:
       is_3d = False
     h = separable_conv(
-        inputs, filter_size, first_kernel_size, ctivation=tf.nn.relu,
+        inputs, filter_size, first_kernel_size, activation=tf.nn.relu,
         padding=padding, name="conv1")
     if dropout != 0.0:
       h = tf.nn.dropout(h, 1.0 - dropout)
@@ -1676,7 +1703,7 @@ def smoothing_cross_entropy(logits,
           depth=vocab_size,
           on_value=confidence,
           off_value=low_confidence)
-    xentropy = tf.nn.softmax_cross_entropy_with_logits(
+    xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(
         logits=logits, labels=soft_targets)
     return xentropy - normalizing
 
@@ -1710,7 +1737,7 @@ def global_pool_1d(inputs, pooling_type="MAX", mask=None):
       if mask is not None:
         # Some elems are dummy elems so we can't just reduce the average.
         output = tf.reduce_sum(inputs, axis=1)
-        num_elems = tf.reduce_sum(mask, axis=1, keep_dims=True)
+        num_elems = tf.reduce_sum(mask, axis=1, keepdims=True)
         output = tf.div(output, tf.maximum(num_elems, 1))
       else:
         output = tf.reduce_mean(inputs, axis=1)
@@ -1746,6 +1773,29 @@ def running_global_pool_1d(inputs, pooling_type="MAX"):
   return output
 
 
+def gated_linear_unit_layer(x, name=None):
+  """Gated linear unit layer.
+
+  Paper: Language Modeling with Gated Convolutional Networks.
+  Link: https://arxiv.org/abs/1612.08083
+  x = Wx * sigmoid(W'x).
+
+  Args:
+    x: A tensor
+    name: A string
+
+  Returns:
+    x: A tensor
+  """
+
+  with tf.variable_scope(
+      name, default_name="glu_layer", values=[x]):
+    depth = shape_list(x)[-1]
+    x = tf.layers.dense(x, depth * 2, activation=None)
+    x, gating_x = tf.split(x, 2, axis=-1)
+    return x * tf.nn.sigmoid(gating_x)
+
+
 def linear_set_layer(layer_size,
                      inputs,
                      context=None,
@@ -2484,3 +2534,97 @@ def _step(source_replica, target_replica, x_split, op="plus_eq"):
   if maybe_reduce:
     y = expand_by_device(original_parallelism, parallelism, y)
   return y
+
+
+def recompute_grad(fn):
+  """Decorator that recomputes the function on the backwards pass.
+
+  Args:
+    fn: a function that takes Tensors (all as positional arguments) and returns
+      a tuple of Tensors.
+
+  Returns:
+    A wrapped fn that is identical to fn when called, but its activations will
+    be discarded and recomputed on the backwards pass (i.e. on a call to
+    tf.gradients).
+  """
+
+  @functools.wraps(fn)
+  def wrapped(*args):
+    return _recompute_grad(fn, args)
+
+  return wrapped
+
+
+def _recompute_grad(fn, args):
+  """See recompute_grad."""
+
+  cached_vs = []
+  cached_arg_scope = []
+
+  def grad_fn(inputs, variables, outputs, output_grads):
+    """Recompute outputs for gradient computation."""
+    del outputs
+    variables = [underlying_variable_ref(v) for v in variables]
+    # Recompute outputs
+    with tf.control_dependencies(output_grads):
+      with tf.contrib.framework.arg_scope(cached_arg_scope[0]):
+        with tf.variable_scope(cached_vs[0], reuse=True):
+          outputs = fn(*inputs)
+
+    if not (isinstance(outputs, list) or isinstance(outputs, tuple)):
+      outputs = [outputs]
+    outputs = list(outputs)
+    grads = tf.gradients(outputs, inputs + variables, output_grads)
+    grad_inputs = grads[:len(inputs)]
+    grad_vars = grads[len(inputs):]
+    if is_on_tpu():
+      # TODO(noam): remove this hack once XLA does the right thing.
+      # Force the gradinets on the inputs to be computed before the variables
+      # are updated.  This saves memory by preventing XLA from making an extra
+      # copy of the variables.
+      grad_vars = force_dependency(grad_vars, grad_inputs)
+    return grad_inputs, grad_vars
+
+  @fn_with_custom_grad(grad_fn)
+  def fn_with_recompute(*args):
+    cached_vs.append(tf.get_variable_scope())
+    # TODO(rsepassi): Rm conditional in TF 1.5
+    if hasattr(tf.contrib.framework, "current_arg_scope"):
+      cached_arg_scope.append(tf.contrib.framework.current_arg_scope())
+    else:
+      cached_arg_scope.append({})
+    return fn(*args)
+
+  return fn_with_recompute(*args)
+
+
+def force_dependency(xs, ys):
+  """Force all of xs to depend on all of ys, using a false data dependency.
+
+  XLA seems to ignore control dependencies.
+
+  Args:
+    xs: a list of tensors
+    ys: a list of tensors:
+  Returns:
+    a list of tensors of the same length as xs
+  """
+  def _first_element(x):
+    ndims = x.get_shape().ndims
+    return tf.reshape(tf.slice(x, [0] * ndims, [1] * ndims), [])
+  my_zero = tf.add_n([_first_element(y) for y  in ys if y is not None]) * 1e-30
+  return [x + my_zero for x in xs]
+
+
+def dense(x, units, **kwargs):
+  """Identical to tf.layers.dense, Memory optimization on tpu."""
+  fn = lambda x: tf.layers.dense(x, units, **kwargs)
+  if is_on_tpu():
+    # TODO(noam): remove this hack once XLA does the right thing.
+    # Forces the gradinets on the inputs to be computed before the variables
+    # are updated.  This saves memory by preventing XLA from making an extra
+    # copy of the variables.
+    return _recompute_grad(fn, [x])
+  else:
+    return fn(x)
diff --git a/tensor2tensor/layers/common_layers_test.py b/tensor2tensor/layers/common_layers_test.py
index ee07c48d3..38c009926 100644
--- a/tensor2tensor/layers/common_layers_test.py
+++ b/tensor2tensor/layers/common_layers_test.py
@@ -29,20 +29,6 @@
 
 class CommonLayersTest(tf.test.TestCase):
 
-  def testStandardizeImages(self):
-    x = np.random.rand(5, 7, 7, 3)
-    with self.test_session() as session:
-      y = common_layers.standardize_images(tf.constant(x))
-      res = session.run(y)
-    self.assertEqual(res.shape, (5, 7, 7, 3))
-
-  def testImageAugmentation(self):
-    x = np.random.rand(500, 500, 3)
-    with self.test_session() as session:
-      y = common_layers.image_augmentation(tf.constant(x))
-      res = session.run(y)
-    self.assertEqual(res.shape, (299, 299, 3))
-
   def testSaturatingSigmoid(self):
     x = np.array([-120.0, -100.0, 0.0, 100.0, 120.0], dtype=np.float32)
     with self.test_session() as session:
@@ -589,5 +575,52 @@ def grad_fn(inputs, variables, unused_outputs, unused_grad_outputs):
         self.assertAllClose(g1, g2)
 
 
+class RecomputeTest(tf.test.TestCase):
+
+  def testRecompute(self):
+
+    def layer(x, name=None):
+      with tf.variable_scope(name, default_name="layer"):
+        x = tf.contrib.layers.layer_norm(x)
+        x = tf.layers.conv1d(
+            x,
+            10,
+            1,
+            use_bias=False,
+            kernel_initializer=tf.constant_initializer(42.42))
+        x = tf.nn.relu(x)
+        return x
+
+    def fn(x):
+      out = x
+      for _ in range(3):
+        out = layer(out)
+      return out
+
+    @common_layers.recompute_grad
+    def fn_recompute(x):
+      return fn(x)
+
+    x = tf.random_uniform((3, 1, 3))
+    recompute_vars = None
+    with tf.variable_scope("recompute") as vs:
+      out1 = tf.reduce_sum(fn_recompute(x))
+      recompute_vars = vs.trainable_variables()
+    reg_vars = None
+    with tf.variable_scope("regular") as vs:
+      out2 = tf.reduce_sum(fn(x))
+      reg_vars = vs.trainable_variables()
+
+    grad1 = tf.gradients(out1, recompute_vars)
+    grad2 = tf.gradients(out2, reg_vars)
+
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      outs = sess.run([out1, out2, grad1, grad2])
+      self.assertAllClose(outs[0], outs[1])
+      for g1, g2 in zip(outs[2], outs[3]):
+        self.assertAllClose(g1, g2)
+
+
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensor2tensor/layers/modalities.py b/tensor2tensor/layers/modalities.py
index 0e41dd086..26063388b 100644
--- a/tensor2tensor/layers/modalities.py
+++ b/tensor2tensor/layers/modalities.py
@@ -32,15 +32,6 @@
 from tensorflow.python.eager import context
 
 
-# TODO(noam): remove this function after TPUs do gather faster.
-def tpu_gather(params, indices):
-  vocab_size = params.get_shape().as_list()[0]
-  indices_flat = tf.reshape(indices, [-1])
-  out = tf.matmul(tf.one_hot(indices_flat, vocab_size), params)
-  out = eu.reshape_like(out, tf.expand_dims(indices, -1))
-  return out
-
-
 @registry.register_symbol_modality("default")
 class SymbolModality(modality.Modality):
   """Modality for sets of discrete symbols.
@@ -107,8 +98,9 @@ def bottom_simple(self, x, name, reuse):
       # Squeeze out the channels dimension.
       x = tf.squeeze(x, axis=3)
       var = self._get_weights()
-      ret = (tpu_gather(var, x) if self._model_hparams.use_tpu
-             else tf.gather(var, x))
+      x = common_layers.dropout_no_scaling(
+          x, 1.0 - self._model_hparams.symbol_dropout)
+      ret = common_layers.gather(var, x)
       if self._model_hparams.multiply_embedding_mode == "sqrt_depth":
         ret *= self._body_input_depth**0.5
       ret *= tf.expand_dims(tf.to_float(tf.not_equal(x, 0)), -1)
@@ -160,7 +152,7 @@ def top(self, body_output, _):
       else:
         body_output = tf.reshape(body_output, [-1, body_output_shape[-1]])
         logits = tf.matmul(body_output, var, transpose_b=True)
-        if (self._model_hparams.use_tpu and
+        if (common_layers.is_on_tpu() and
             self._model_hparams.mode == tf.estimator.ModeKeys.TRAIN):
           # TPU does not react kindly to extra dimensions.
           # TODO(noam): remove this once TPU is more forgiving of extra dims.
@@ -206,10 +198,10 @@ class ImageModality(modality.Modality):
 
   def bottom(self, inputs):
     with tf.variable_scope(self.name):
-      inputs = common_layers.standardize_images(inputs)
+      inputs = tf.to_float(inputs)
       if not context.in_eager_mode():
         tf.summary.image("inputs", inputs, max_outputs=2)
-      return tf.to_float(inputs)
+      return inputs
 
   def targets_bottom(self, inputs):
     with tf.variable_scope(self.name):
@@ -243,36 +235,38 @@ def top(self, body_output, _):
       return res
 
 
-@registry.register_image_modality("image_identity_compress")
-class ImageIdentityCompressModality(modality.Modality):
-  """Modality for images used in generation."""
+@registry.register_image_modality("image_channel_compress")
+class ImageChannelCompressModality(modality.Modality):
+  """Modality for images using channel compression for generation."""
 
   def bottom_compress(self, inputs, name="bottom"):
     """Transform input from data space to model space.
 
-    Perform conversion of RGB pixel values to a real number and combine values
-    for each pixel to form representation of image_length x image_length dims.
+    Perform conversion of RGB pixel values to a real number in the range -1 to 1
+    and combine channel values for each pixel to form a representation of
+    size image_length x image_length dims.
 
     Args:
-      inputs: A Tensor with shape [batch, ...]
+      inputs: A Tensor representing pixel intensities as integers. [batch, ...]
       name: string, scope.
     Returns:
       body_input: A Tensor with shape [batch, ?, ?, body_input_depth].
     """
     with tf.variable_scope(name):
+      inputs = tf.to_float(inputs)
+      tf.summary.image("inputs", inputs, max_outputs=2)
       inputs = common_layers.convert_rgb_to_real(inputs)
       ishape = common_layers.shape_list(inputs)
       inputs = tf.reshape(inputs, [-1, ishape[1], ishape[2] * ishape[3], 1])
       inputs.set_shape([None, None, None, 1])
       # We compress RGB intensities for each pixel using a conv.
-      x = common_layers.conv_block(
-          inputs,
-          self._body_input_depth, [((1, 1), (1, 3))],
-          first_relu=False,
-          padding="VALID",
-          strides=(1, 3),
-          force2d=True,
-          name="conv_input")
+      x = tf.layers.conv2d(inputs,
+                           self._body_input_depth, (1, 3),
+                           padding="VALID",
+                           strides=(1, 3),
+                           activation=tf.nn.relu,
+                           name="conv_input")
+      x.set_shape([None, None, None, self._body_input_depth])
       return x
 
   def bottom(self, inputs):
@@ -287,16 +281,18 @@ def top(self, body_output, _):
       img_len = self._model_hparams.img_len
       channels = self._model_hparams.num_channels
       batch = common_layers.shape_list(body_output)[0]
-      x = common_layers.conv(
+      x = tf.layers.conv2d(
           body_output,
-          hidden_dim * channels, (1, 1),
+          hidden_dim*channels, (1, 1),
+          strides=(1, 1),
           padding="VALID",
           activation=tf.nn.relu,
           name="decompress_conv")
       x = tf.reshape(x, [batch, img_len, img_len * channels, hidden_dim])
-      x.set_shape([None, None, None, hidden_dim])
-      x = common_layers.conv(
-          x, self.top_dimensionality, (1, 1), name="output_conv")
+      x = common_layers.layer_preprocess(x, self._model_hparams)
+      x = tf.layers.dense(x, 256,
+                          use_bias=True, activation=None,
+                          name="output_conv")
       x = tf.reshape(x,
                      [-1, img_len, img_len, channels, self.top_dimensionality])
       return x
@@ -425,7 +421,7 @@ def top(self, body_output, _):
     """
     with tf.variable_scope(self.name):
       x = body_output
-      x = tf.reduce_mean(x, axis=[1, 2], keep_dims=True)
+      x = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
       res = tf.layers.dense(x, self._vocab_size)
       return tf.expand_dims(res, 3)
 
@@ -490,19 +486,6 @@ def loss(self, top_out, targets):
       return tf.reduce_sum(lp_loss * weights), tf.reduce_sum(weights)
 
 
-@registry.register_generic_modality("zero_loss")
-@registry.register_audio_modality("zero_loss")
-@registry.register_image_modality("zero_loss")
-@registry.register_symbol_modality("zero_loss")
-@registry.register_class_label_modality("zero_loss")
-@registry.register_real_modality("zero_loss")
-class IdentityZeroLossModality(IdentityModality):
-  """Identity with 0 loss."""
-
-  def loss(self, top_out, targets):
-    return tf.constant(0., tf.float32), tf.constant(0., tf.float32)
-
-
 @registry.register_symbol_modality("identity")
 class IdentitySymbolModality(SymbolModality):
   """Symbol modality with identity top and bottom transformations.
@@ -515,3 +498,7 @@ def bottom(self, x):
 
   def top(self, body_output, _):
     return body_output
+
+  def targets_bottom(self, x):
+    """SymbolModality overrides targets_bottom, so need to override here too."""
+    return self.bottom(x)
diff --git a/tensor2tensor/layers/rev_block.py b/tensor2tensor/layers/rev_block.py
index 88bf622ab..1684ebf2c 100644
--- a/tensor2tensor/layers/rev_block.py
+++ b/tensor2tensor/layers/rev_block.py
@@ -18,15 +18,11 @@
 From
 [The Reversible Residual Network: Backpropagation Without Storing
 Activations](https://arxiv.org/abs/1707.04585).
-
-Also contains the @recompute_grad decorator, which recomputes the forward
-function on the backwards pass.
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import functools
 import re
 
 # Dependency imports
@@ -351,59 +347,3 @@ def rev_block(x1,
   """
   block = RevBlock(f, g, num_layers, f_side_input, g_side_input, is_training)
   return block.forward(x1, x2)
-
-
-def recompute_grad(fn):
-  """Decorator that recomputes the function on the backwards pass.
-
-  Args:
-    fn: a function that takes Tensors (all as positional arguments) and returns
-      a tuple of Tensors.
-
-  Returns:
-    A wrapped fn that is identical to fn when called, but its activations will
-    be discarded and recomputed on the backwards pass (i.e. on a call to
-    tf.gradients).
-  """
-
-  @functools.wraps(fn)
-  def wrapped(*args):
-    return _recompute_grad(fn, args)
-
-  return wrapped
-
-
-def _recompute_grad(fn, args):
-  """See recompute_grad."""
-
-  cached_vs = []
-  cached_arg_scope = []
-
-  def grad_fn(inputs, variables, outputs, output_grads):
-    """Recompute outputs for gradient computation."""
-    del outputs
-    # Recompute outputs
-    with tf.control_dependencies(output_grads):
-      with tf.contrib.framework.arg_scope(cached_arg_scope[0]):
-        with tf.variable_scope(cached_vs[0], reuse=True):
-          outputs = fn(*inputs)
-
-    if not (isinstance(outputs, list) or isinstance(outputs, tuple)):
-      outputs = [outputs]
-    outputs = list(outputs)
-    grads = tf.gradients(outputs, inputs + variables, output_grads)
-    grad_inputs = grads[:len(inputs)]
-    grad_vars = grads[len(inputs):]
-    return grad_inputs, grad_vars
-
-  @common_layers.fn_with_custom_grad(grad_fn)
-  def fn_with_recompute(*args):
-    cached_vs.append(tf.get_variable_scope())
-    # TODO(rsepassi): Rm conditional in TF 1.5
-    if hasattr(tf.contrib.framework, "current_arg_scope"):
-      cached_arg_scope.append(tf.contrib.framework.current_arg_scope())
-    else:
-      cached_arg_scope.append({})
-    return fn(*args)
-
-  return fn_with_recompute(*args)
diff --git a/tensor2tensor/layers/rev_block_test.py b/tensor2tensor/layers/rev_block_test.py
index acc68f9bd..de7a9bca2 100644
--- a/tensor2tensor/layers/rev_block_test.py
+++ b/tensor2tensor/layers/rev_block_test.py
@@ -183,52 +183,5 @@ def f(x):
     self._testRevBlock(x=x, f=f)
 
 
-class RecomputeTest(tf.test.TestCase):
-
-  def testRecompute(self):
-
-    def layer(x, name=None):
-      with tf.variable_scope(name, default_name="layer"):
-        x = tf.contrib.layers.layer_norm(x)
-        x = tf.layers.conv1d(
-            x,
-            10,
-            1,
-            use_bias=False,
-            kernel_initializer=tf.constant_initializer(42.42))
-        x = tf.nn.relu(x)
-        return x
-
-    def fn(x):
-      out = x
-      for _ in range(3):
-        out = layer(out)
-      return out
-
-    @rev_block.recompute_grad
-    def fn_recompute(x):
-      return fn(x)
-
-    x = tf.random_uniform((3, 1, 3))
-    recompute_vars = None
-    with tf.variable_scope("recompute") as vs:
-      out1 = tf.reduce_sum(fn_recompute(x))
-      recompute_vars = vs.trainable_variables()
-    reg_vars = None
-    with tf.variable_scope("regular") as vs:
-      out2 = tf.reduce_sum(fn(x))
-      reg_vars = vs.trainable_variables()
-
-    grad1 = tf.gradients(out1, recompute_vars)
-    grad2 = tf.gradients(out2, reg_vars)
-
-    with self.test_session() as sess:
-      sess.run(tf.global_variables_initializer())
-      outs = sess.run([out1, out2, grad1, grad2])
-      self.assertAllClose(outs[0], outs[1])
-      for g1, g2 in zip(outs[2], outs[3]):
-        self.assertAllClose(g1, g2)
-
-
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensor2tensor/models/__init__.py b/tensor2tensor/models/__init__.py
index ef92ccaff..982327b92 100644
--- a/tensor2tensor/models/__init__.py
+++ b/tensor2tensor/models/__init__.py
@@ -26,7 +26,7 @@
 from tensor2tensor.models import aligned
 from tensor2tensor.models import attention_lm
 from tensor2tensor.models import attention_lm_moe
-from tensor2tensor.models import bluenet
+from tensor2tensor.models import basic
 from tensor2tensor.models import bytenet
 from tensor2tensor.models import cycle_gan
 from tensor2tensor.models import gene_expression
diff --git a/tensor2tensor/models/basic.py b/tensor2tensor/models/basic.py
new file mode 100644
index 000000000..1354259b5
--- /dev/null
+++ b/tensor2tensor/models/basic.py
@@ -0,0 +1,59 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""Basic models for testing simple tasks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+from tensor2tensor.layers import common_hparams
+from tensor2tensor.layers import common_layers
+from tensor2tensor.utils import registry
+from tensor2tensor.utils import t2t_model
+
+import tensorflow as tf
+
+
+@registry.register_model
+class BasicFcRelu(t2t_model.T2TModel):
+
+  def body(self, features):
+    hparams = self._hparams
+    x = features["inputs"]
+    shape = common_layers.shape_list(x)
+    x = tf.reshape(x, [-1, shape[1] * shape[2] * shape[3]])
+    for i in xrange(hparams.num_hidden_layers):
+      x = tf.layers.dense(x, hparams.hidden_size, name="layer_%d" % i)
+      x = tf.nn.dropout(x, keep_prob=1.0 - hparams.dropout)
+      x = tf.nn.relu(x)
+    return tf.expand_dims(tf.expand_dims(x, axis=1), axis=1)  # 4D For T2T.
+
+
+@registry.register_hparams
+def basic_fc_small():
+  """Small fully connected model."""
+  hparams = common_hparams.basic_params1()
+  hparams.learning_rate = 0.1
+  hparams.batch_size = 128
+  hparams.hidden_size = 256
+  hparams.num_hidden_layers = 2
+  hparams.initializer = "uniform_unit_scaling"
+  hparams.initializer_gain = 1.0
+  hparams.weight_decay = 0.0
+  hparams.dropout = 0.0
+  return hparams
diff --git a/tensor2tensor/models/bluenet.py b/tensor2tensor/models/bluenet.py
deleted file mode 100644
index 86625a834..000000000
--- a/tensor2tensor/models/bluenet.py
+++ /dev/null
@@ -1,545 +0,0 @@
-# coding=utf-8
-# Copyright 2017 The Tensor2Tensor Authors.
-#
-# 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.
-
-"""BlueNet: and out of the blue network to experiment with shake-shake."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-
-# Dependency imports
-
-import numpy as np
-
-from six.moves import xrange  # pylint: disable=redefined-builtin
-
-from tensor2tensor.layers import common_hparams
-from tensor2tensor.layers import common_layers
-from tensor2tensor.utils import registry
-from tensor2tensor.utils import t2t_model
-
-import tensorflow as tf
-
-# var:          1d tensor, raw weights for each choice
-# tempered_var: raw weights with temperature applied
-# inv_t:        inverse of the temperature to use when normalizing `var`
-# normalized:   same shape as var, but where each item is between 0 and 1, and
-#               the sum is 1
-SelectionWeights = collections.namedtuple(
-    "SelectionWeights", ["var", "tempered_var", "inv_t", "normalized"])
-
-
-def create_selection_weights(name,
-                             type_,
-                             shape,
-                             inv_t=1,
-                             initializer=tf.zeros_initializer(),
-                             regularizer=None,
-                             names=None):
-  """Create a SelectionWeights tuple.
-
-  Args:
-    name: Name for the underlying variable containing the unnormalized weights.
-    type_: "softmax" or "sigmoid" or ("softmax_topk", k) where k is an int.
-    shape: Shape for the variable.
-    inv_t: Inverse of the temperature to use in normalization.
-    initializer: Initializer for the variable, passed to `tf.get_variable`.
-    regularizer: Regularizer for the variable. A callable which accepts
-      `tempered_var` and `normalized`.
-    names: Name of each selection.
-
-  Returns:
-    The created SelectionWeights tuple.
-
-  Raises:
-    ValueError: if type_ is not in the supported range.
-  """
-  var = tf.get_variable(name, shape, initializer=initializer)
-
-  if callable(inv_t):
-    inv_t = inv_t(var)
-  if inv_t == 1:
-    tempered_var = var
-  else:
-    tempered_var = var * inv_t
-
-  if type_ == "softmax":
-    weights = tf.nn.softmax(tempered_var)
-  elif type_ == "sigmoid":
-    weights = tf.nn.sigmoid(tempered_var)
-  elif isinstance(type_, (list, tuple)) and type_[0] == "softmax_topk":
-    assert len(shape) == 1
-    # TODO(rshin): Change this to select without replacement?
-    selection = tf.multinomial(tf.expand_dims(var, axis=0), 4)
-    selection = tf.squeeze(selection, axis=0)  # [k] selected classes.
-    to_run = tf.one_hot(selection, shape[0])  # [k x nmodules] one-hot.
-    # [nmodules], 0=not run, 1=run.
-    to_run = tf.minimum(tf.reduce_sum(to_run, axis=0), 1)
-    weights = tf.nn.softmax(tempered_var - 1e9 * (1.0 - to_run))
-  else:
-    raise ValueError("Unknown type: %s" % type_)
-
-  if regularizer is not None:
-    loss = regularizer(tempered_var, weights)
-    if loss is not None:
-      tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, loss)
-
-  if names is not None:
-    tf.get_collection_ref("selection_weight_names/" + var.name).extend(
-        names.flatten() if isinstance(names, np.ndarray) else names)
-    tf.add_to_collection("selection_weight_names_tensor/" + var.name,
-                         tf.constant(names))
-
-  return SelectionWeights(
-      var=var, tempered_var=tempered_var, inv_t=inv_t, normalized=weights)
-
-
-def kernel_premultiplier(max_kernel_size, kernel_sizes, input_channels,
-                         kernel_selection_weights, channel_selection_weights):
-  """Get weights to multiply the kernel with, before convolving.
-
-  Args:
-    max_kernel_size: (int, int) tuple giving the largest kernel size.
-    kernel_sizes: A list of (height, width) pairs of integers, containing
-      different kernel sizes to use.
-    input_channels: A list of (begin, end) pairs of integers, which describe
-      which channels in the input to use.
-    kernel_selection_weights: SelectionWeights object to use for choosing
-      among kernel sizes.
-    channel_selection_weights: SelectionWeights object to use for choosing
-      among which input channels to use.
-
-  Returns:
-    The multiplier.
-  """
-  kernel_weights = []
-  for kernel_i, (h, w) in enumerate(kernel_sizes):
-    top = (max_kernel_size[0] - h) // 2
-    bot = max_kernel_size[0] - h - top
-    left = (max_kernel_size[1] - w) // 2
-    right = max_kernel_size[1] - w - left
-    kernel_weight = tf.fill((h, w),
-                            kernel_selection_weights.normalized[kernel_i])
-    if top != 0 or bot != 0 or left != 0 or right != 0:
-      kernel_weight = tf.pad(kernel_weight, [[top, bot], [left, right]])
-    kernel_weights.append(kernel_weight)
-  kernel_weight = tf.add_n(kernel_weights)
-
-  channel_weights = []
-  min_channel = np.min(input_channels)
-  max_channel = np.max(input_channels)
-  for channel_i, (begin, end) in enumerate(input_channels):
-    channel_weight = tf.pad(
-        tf.fill((end - begin,),
-                channel_selection_weights.normalized[channel_i]),
-        [[begin - min_channel, max_channel - end]])
-    channel_weights.append(channel_weight)
-  channel_weight = tf.add_n(channel_weights)
-
-  multiplier = (tf.reshape(kernel_weight, max_kernel_size +
-                           (1, 1)) * tf.reshape(channel_weight, (1, 1, -1, 1)))
-  return multiplier
-
-
-def make_subseparable_kernel(kernel_size, input_channels, filters, separability,
-                             kernel_initializer, kernel_regularizer):
-  """Make a kernel to do subseparable convolution wiht  `tf.nn.conv2d`.
-
-  Args:
-    kernel_size: (height, width) tuple.
-    input_channels: Number of input channels.
-    filters: Number of output channels.
-    separability: Integer denoting separability.
-    kernel_initializer: Initializer to use for the kernel.
-    kernel_regularizer: Regularizer to use for the kernel.
-
-  Returns:
-    A 4D tensor.
-  """
-  if separability == 1:
-    # Non-separable convolution
-    return tf.get_variable(
-        "kernel",
-        kernel_size + (input_channels, filters),
-        initializer=kernel_initializer,
-        regularizer=kernel_regularizer)
-
-  elif separability == 0 or separability == -1:
-    # Separable convolution
-    # TODO(rshin): Check initialization is as expected, as these are not 4D.
-    depthwise_kernel = tf.get_variable(
-        "depthwise_kernel",
-        kernel_size + (input_channels,),
-        initializer=kernel_initializer,
-        regularizer=kernel_regularizer)
-
-    pointwise_kernel = tf.get_variable(
-        "pointwise_kernel", (input_channels, filters),
-        initializer=kernel_initializer,
-        regularizer=kernel_regularizer)
-
-    expanded_depthwise_kernel = tf.transpose(
-        tf.scatter_nd(
-            indices=tf.tile(
-                tf.expand_dims(tf.range(0, input_channels), axis=1), [1, 2]),
-            updates=tf.transpose(depthwise_kernel, (2, 0, 1)),
-            shape=(input_channels, input_channels) + kernel_size), (2, 3, 0, 1))
-
-    return tf.reshape(
-        tf.matmul(
-            tf.reshape(expanded_depthwise_kernel, (-1, input_channels)),
-            pointwise_kernel), kernel_size + (input_channels, filters))
-
-  elif separability >= 2:
-    assert filters % separability == 0, (filters, separability)
-    assert input_channels % separability == 0, (filters, separability)
-
-    raise NotImplementedError
-
-  elif separability <= -2:
-    separability *= -1
-    assert filters % separability == 0, (filters, separability)
-    assert input_channels % separability == 0, (filters, separability)
-
-    raise NotImplementedError
-
-
-def multi_subseparable_conv(inputs,
-                            filters,
-                            kernel_sizes,
-                            input_channels,
-                            separabilities,
-                            kernel_selection_weights=None,
-                            channel_selection_weights=None,
-                            separability_selection_weights=None,
-                            kernel_selection_weights_params=None,
-                            channel_selection_weights_params=None,
-                            separability_selection_weights_params=None,
-                            kernel_initializer=None,
-                            kernel_regularizer=None,
-                            scope=None):
-  """Simultaneously compute different kinds of convolutions on subsets of input.
-
-  Args:
-    inputs: 4D tensor containing the input, in NHWC format.
-    filters: Integer, number of output channels.
-    kernel_sizes: A list of (height, width) pairs of integers, containing
-      different kernel sizes to use.
-    input_channels: A list of (begin, end) pairs of integers, which describe
-      which channels in the input to use.
-    separabilities: An integer or a list, how separable are the convolutions.
-    kernel_selection_weights: SelectionWeights object to use for choosing
-      among kernel sizes.
-    channel_selection_weights: SelectionWeights object to use for choosing
-      among which input channels to use.
-    separability_selection_weights: SelectionWeights object to use for choosing
-      separability.
-    kernel_selection_weights_params: dict with up to three keys
-      - initializer
-      - regularizer
-      - inv_t
-    channel_selection_weights_params: dict with up to three keys
-      - initializer
-      - regularizer
-      - inv_t
-    separability_selection_weights_params: dict with up to three keys
-      - initializer
-      - regularizer
-      - inv_t
-    kernel_initializer: Initializer to use for kernels.
-    kernel_regularizer: Regularizer to use for kernels.
-    scope: the scope to use.
-
-  Returns:
-    Result of convolution.
-  """
-  kernel_selection_weights_params = kernel_selection_weights_params or {}
-  channel_selection_weights_params = channel_selection_weights_params or {}
-  if separability_selection_weights_params is None:
-    separability_selection_weights_params = {}
-
-  # Get input image size.
-  input_shape = inputs.get_shape().as_list()
-  assert len(input_shape) == 4
-  in_channels = input_shape[3]
-  assert in_channels is not None
-
-  max_kernel_size = tuple(np.max(kernel_sizes, axis=0))
-  max_num_channels = np.max(input_channels) - np.min(input_channels)
-
-  with tf.variable_scope(scope or "selection_weights"):
-    if kernel_selection_weights is None:
-      kernel_selection_weights = create_selection_weights(
-          "kernels",
-          "softmax", (len(kernel_sizes),),
-          names=["kernel_h{}_w{}".format(h, w) for h, w in kernel_sizes],
-          **kernel_selection_weights_params)
-
-    if channel_selection_weights is None:
-      channel_selection_weights = create_selection_weights(
-          "channels",
-          "softmax", (len(input_channels),),
-          names=["channels_{}_{}".format(c1, c2) for c1, c2 in input_channels],
-          **channel_selection_weights_params)
-
-    if separability_selection_weights is None:
-      separability_selection_weights = create_selection_weights(
-          "separability",
-          "softmax", (len(separabilities),),
-          names=["separability_{}".format(s) for s in separabilities],
-          **separability_selection_weights_params)
-
-  kernels = []
-  for separability in separabilities:
-    with tf.variable_scope("separablity_{}".format(separability)):
-      kernel = make_subseparable_kernel(max_kernel_size, max_num_channels,
-                                        filters, separability,
-                                        kernel_initializer, kernel_regularizer)
-
-      premultiplier = kernel_premultiplier(
-          max_kernel_size, kernel_sizes, input_channels,
-          kernel_selection_weights, channel_selection_weights)
-
-      kernels.append(kernel * premultiplier)
-
-  kernel = tf.add_n([
-      separability_selection_weights.normalized[i] * k
-      for i, k in enumerate(kernels)
-  ])
-
-  if np.min(input_channels) != 0 or np.max(input_channels) != in_channels:
-    inputs = inputs[:, :, :, np.min(input_channels):np.max(input_channels)]
-
-  return tf.nn.conv2d(
-      inputs,
-      filter=kernel,
-      strides=[1, 1, 1, 1],
-      padding="SAME",
-      data_format="NHWC",
-      name="conv2d")
-
-
-def conv_module(kw, kh, sep, div):
-
-  def convfn(x, hparams):
-    return common_layers.subseparable_conv(
-        x,
-        hparams.hidden_size // div, (kw, kh),
-        padding="SAME",
-        separability=sep,
-        name="conv_%d%d_sep%d_div%d" % (kw, kh, sep, div))
-
-  return convfn
-
-
-def multi_conv_module(kernel_sizes, seps):
-
-  def convfn(x, hparams):
-    return multi_subseparable_conv(x, hparams.hidden_size, kernel_sizes,
-                                   [(0, hparams.hidden_size)], seps)
-
-  return convfn
-
-
-def layernorm_module(x, hparams):
-  return common_layers.layer_norm(x, hparams.hidden_size, name="layer_norm")
-
-
-def noamnorm_module(x, hparams):
-  del hparams  # Unused.
-  return common_layers.noam_norm(x)
-
-
-def identity_module(x, hparams):
-  del hparams  # Unused.
-  return x
-
-
-def first_binary_module(x, y, hparams):
-  del y, hparams  # Unused.
-  return x
-
-
-def second_binary_module(x, y, hparams):
-  del x, hparams  # Unused.
-  return y
-
-
-def sum_binary_module(x, y, hparams):
-  del hparams  # Unused.
-  return x + y
-
-
-def shakeshake_binary_module(x, y, hparams):
-  del hparams  # Unused.
-  return common_layers.shakeshake2(x, y)
-
-
-def run_binary_modules(modules, cur1, cur2, hparams):
-  """Run binary modules."""
-  selection_weights = create_selection_weights(
-      "selection",
-      "softmax",
-      shape=[len(modules)],
-      inv_t=100.0 * common_layers.inverse_exp_decay(
-          hparams.anneal_until, min_value=0.01))
-  all_res = [modules[n](cur1, cur2, hparams) for n in xrange(len(modules))]
-  all_res = tf.concat([tf.expand_dims(r, axis=0) for r in all_res], axis=0)
-  res = all_res * tf.reshape(selection_weights.normalized, [-1, 1, 1, 1, 1])
-  return tf.reduce_sum(res, axis=0)
-
-
-def run_unary_modules_basic(modules, cur, hparams):
-  """Run unary modules."""
-  selection_weights = create_selection_weights(
-      "selection",
-      "softmax",
-      shape=[len(modules)],
-      inv_t=100.0 * common_layers.inverse_exp_decay(
-          hparams.anneal_until, min_value=0.01))
-  all_res = [modules[n](cur, hparams) for n in xrange(len(modules))]
-  all_res = tf.concat([tf.expand_dims(r, axis=0) for r in all_res], axis=0)
-  res = all_res * tf.reshape(selection_weights.normalized, [-1, 1, 1, 1, 1])
-  return tf.reduce_sum(res, axis=0)
-
-
-def run_unary_modules_sample(modules, cur, hparams, k):
-  """Run modules, sampling k."""
-  selection_weights = create_selection_weights(
-      "selection", ("softmax_topk", k),
-      shape=[len(modules)],
-      inv_t=100.0 * common_layers.inverse_exp_decay(
-          hparams.anneal_until, min_value=0.01))
-  all_res = [
-      tf.cond(
-          tf.less(selection_weights.normalized[n], 1e-6),
-          lambda: tf.zeros_like(cur),
-          lambda i=n: modules[i](cur, hparams)) for n in xrange(len(modules))
-  ]
-  all_res = tf.concat([tf.expand_dims(r, axis=0) for r in all_res], axis=0)
-  res = all_res * tf.reshape(selection_weights.normalized, [-1, 1, 1, 1, 1])
-  return tf.reduce_sum(res, axis=0)
-
-
-def run_unary_modules(modules, cur, hparams):
-  if len(modules) < 8:
-    return run_unary_modules_basic(modules, cur, hparams)
-  return run_unary_modules_sample(modules, cur, hparams, 4)
-
-
-def batch_deviation(x):
-  """Average deviation of the batch."""
-  x_mean = tf.reduce_mean(x, axis=[0], keep_dims=True)
-  x_variance = tf.reduce_mean(tf.square(x - x_mean), axis=[0], keep_dims=True)
-  return tf.reduce_mean(tf.sqrt(x_variance))
-
-
-@registry.register_model
-class BlueNet(t2t_model.T2TModel):
-
-  def body(self, features):
-    hparams = self._hparams
-    # TODO(rshin): Give identity_module lower weight by default.
-    multi_conv = multi_conv_module(
-        kernel_sizes=[(3, 3), (5, 5), (7, 7)], seps=[0, 1])
-    conv_modules = [multi_conv, identity_module]
-    activation_modules = [
-        identity_module, lambda x, _: tf.nn.relu(x), lambda x, _: tf.nn.elu(x),
-        lambda x, _: tf.tanh(x)
-    ]
-    norm_modules = [identity_module, layernorm_module, noamnorm_module]
-    binary_modules = [
-        first_binary_module, second_binary_module, sum_binary_module,
-        shakeshake_binary_module
-    ]
-    inputs = features["inputs"]
-
-    def run_unary(x, name):
-      """A single step of unary modules."""
-      x_shape = x.get_shape()
-      with tf.variable_scope(name):
-        with tf.variable_scope("norm"):
-          x = run_unary_modules(norm_modules, x, hparams)
-          x.set_shape(x_shape)
-        with tf.variable_scope("activation"):
-          x = run_unary_modules(activation_modules, x, hparams)
-          x.set_shape(x_shape)
-        with tf.variable_scope("conv"):
-          x = run_unary_modules(conv_modules, x, hparams)
-          x.set_shape(x_shape)
-      return tf.nn.dropout(x, 1.0 - hparams.dropout), batch_deviation(x)
-
-    cur1, cur2, cur3, extra_loss = inputs, inputs, inputs, 0.0
-    cur_shape = inputs.get_shape()
-    for i in xrange(hparams.num_hidden_layers):
-      with tf.variable_scope("layer_%d" % i):
-        cur1, loss1 = run_unary(cur1, "unary1")
-        cur2, loss2 = run_unary(cur2, "unary2")
-        cur3, loss3 = run_unary(cur2, "unary3")
-        extra_loss += (loss1 + loss2 + loss3) / float(hparams.num_hidden_layers)
-        with tf.variable_scope("binary1"):
-          next1 = run_binary_modules(binary_modules, cur1, cur2, hparams)
-          next1.set_shape(cur_shape)
-        with tf.variable_scope("binary2"):
-          next2 = run_binary_modules(binary_modules, cur1, cur3, hparams)
-          next2.set_shape(cur_shape)
-        with tf.variable_scope("binary3"):
-          next3 = run_binary_modules(binary_modules, cur2, cur3, hparams)
-          next3.set_shape(cur_shape)
-        cur1, cur2, cur3 = next1, next2, next3
-
-    anneal = common_layers.inverse_exp_decay(hparams.anneal_until)
-    extra_loss *= hparams.batch_deviation_loss_factor * anneal
-    return cur1, extra_loss
-
-
-@registry.register_hparams
-def bluenet_base():
-  """Set of hyperparameters."""
-  hparams = common_hparams.basic_params1()
-  hparams.batch_size = 4096
-  hparams.hidden_size = 256
-  hparams.dropout = 0.2
-  hparams.symbol_dropout = 0.5
-  hparams.label_smoothing = 0.1
-  hparams.clip_grad_norm = 2.0
-  hparams.num_hidden_layers = 8
-  hparams.kernel_height = 3
-  hparams.kernel_width = 3
-  hparams.learning_rate_decay_scheme = "exp10k"
-  hparams.learning_rate = 0.05
-  hparams.learning_rate_warmup_steps = 3000
-  hparams.initializer_gain = 1.0
-  hparams.weight_decay = 3.0
-  hparams.num_sampled_classes = 0
-  hparams.sampling_method = "argmax"
-  hparams.optimizer_adam_epsilon = 1e-6
-  hparams.optimizer_adam_beta1 = 0.85
-  hparams.optimizer_adam_beta2 = 0.997
-  hparams.add_hparam("anneal_until", 40000)
-  hparams.add_hparam("batch_deviation_loss_factor", 5.0)
-  return hparams
-
-
-@registry.register_hparams
-def bluenet_tiny():
-  hparams = bluenet_base()
-  hparams.batch_size = 1024
-  hparams.hidden_size = 128
-  hparams.num_hidden_layers = 4
-  hparams.learning_rate_decay_scheme = "none"
-  return hparams
diff --git a/tensor2tensor/models/bluenet_test.py b/tensor2tensor/models/bluenet_test.py
deleted file mode 100644
index 15f1f46e6..000000000
--- a/tensor2tensor/models/bluenet_test.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# coding=utf-8
-# Copyright 2017 The Tensor2Tensor Authors.
-#
-# 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.
-
-"""BlueNet tests."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-# Dependency imports
-
-import numpy as np
-
-from tensor2tensor.data_generators import problem_hparams
-from tensor2tensor.models import bluenet
-
-import tensorflow as tf
-
-
-class BlueNetTest(tf.test.TestCase):
-
-  def testBlueNet(self):
-    vocab_size = 9
-    x = np.random.random_integers(1, high=vocab_size - 1, size=(3, 5, 1, 1))
-    y = np.random.random_integers(1, high=vocab_size - 1, size=(3, 1, 1, 1))
-    hparams = bluenet.bluenet_tiny()
-    p_hparams = problem_hparams.test_problem_hparams(vocab_size, vocab_size)
-    with self.test_session() as session:
-      tf.train.get_or_create_global_step()
-      features = {
-          "inputs": tf.constant(x, dtype=tf.int32),
-          "targets": tf.constant(y, dtype=tf.int32),
-      }
-      model = bluenet.BlueNet(
-          hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
-      logits, _ = model(features)
-      session.run(tf.global_variables_initializer())
-      res = session.run(logits)
-    self.assertEqual(res.shape, (3, 5, 1, 1, vocab_size))
-
-
-if __name__ == "__main__":
-  tf.test.main()
diff --git a/tensor2tensor/models/bytenet.py b/tensor2tensor/models/bytenet.py
index ceefd54b5..d65580f1f 100644
--- a/tensor2tensor/models/bytenet.py
+++ b/tensor2tensor/models/bytenet.py
@@ -98,7 +98,7 @@ def bytenet_base():
   hparams.num_hidden_layers = 4
   hparams.kernel_height = 3
   hparams.kernel_width = 1
-  hparams.learning_rate_decay_scheme = "exp50k"
+  hparams.learning_rate_decay_scheme = "exp"
   hparams.learning_rate = 0.05
   hparams.learning_rate_warmup_steps = 3000
   hparams.initializer_gain = 1.0
diff --git a/tensor2tensor/models/cycle_gan.py b/tensor2tensor/models/cycle_gan.py
index 2867be214..0efeaeae5 100644
--- a/tensor2tensor/models/cycle_gan.py
+++ b/tensor2tensor/models/cycle_gan.py
@@ -29,38 +29,40 @@
 import tensorflow as tf
 
 
-
 def discriminator(x, compress, hparams, name, reuse=None):
   with tf.variable_scope(name, reuse=reuse):
-    x = tf.stop_gradient(2 * x) - x  # Reverse gradient. 
+    x = tf.stop_gradient(2 * x) - x  # Reverse gradient.
     if compress:
       x = transformer_vae.compress(x, None, False, hparams, "compress")
     else:
-      x = transformer_vae.residual_conv(x, 1, 3,hparams, "compress_rc")
+      x = transformer_vae.residual_conv(x, 1, 3, hparams, "compress_rc")
     y = tf.reduce_mean(x, axis=1)
     return tf.tanh(tf.layers.dense(y, 1, name="reduce"))
 
+
 def generator(x, hparams, name, reuse=False):
   with tf.variable_scope(name, reuse=reuse):
-    return transformer_vae.residual_conv(x, 1, 3, hparams,"generator")
+    return transformer_vae.residual_conv(x, 1, 3, hparams, "generator")
+
 
-        
-def loss(real_input, fake_input, compress, hparams, lsgan, name):
+def lossfn(real_input, fake_input, compress, hparams, lsgan, name):
   eps = 1e-12
   with tf.variable_scope(name):
     d1 = discriminator(real_input, compress, hparams, "discriminator")
-    d2 = discriminator(fake_input, compress, hparams, "discriminator", reuse=True)
+    d2 = discriminator(fake_input, compress, hparams, "discriminator",
+                       reuse=True)
     if lsgan:
-        dloss = tf.reduce_mean(tf.squared_difference(d1, 0.9)) + tf.reduce_mean(tf.square(d2)) 
-        gloss = tf.reduce_mean(tf.squared_difference(d2, 0.9))
-        loss = (dloss + gloss)/2
-    else: #cross_entropy 
-        dloss = -tf.reduce_mean(tf.log(d1 + eps)) - tf.reduce_mean(tf.log(1 - d2 + eps)) 
-        gloss = -tf.reduce_mean(tf.log(d2 + eps)) 
-        loss = (dloss + gloss)/2    
+      dloss = tf.reduce_mean(
+          tf.squared_difference(d1, 0.9)) + tf.reduce_mean(tf.square(d2))
+      gloss = tf.reduce_mean(tf.squared_difference(d2, 0.9))
+      loss = (dloss + gloss)/2
+    else:  # cross_entropy
+      dloss = -tf.reduce_mean(
+          tf.log(d1 + eps)) - tf.reduce_mean(tf.log(1 - d2 + eps))
+      gloss = -tf.reduce_mean(tf.log(d2 + eps))
+      loss = (dloss + gloss)/2
     return loss
 
-    
 
 def split_on_batch(x):
   batch_size = tf.shape(x)[0]
@@ -78,38 +80,37 @@ def cycle_gan_internal(inputs, targets, _, hparams):
     targets = common_layers.embedding(
         targets_orig, hparams.vocab_size, hparams.hidden_size,
         "embed", reuse=True)
-    
-    X, _ = split_on_batch(inputs)
-    _, Y = split_on_batch(targets)
 
-    X_unembeded, _ = split_on_batch(inputs_orig)
-    _, Y_unembeded = split_on_batch(targets_orig)
-    
+    x, _ = split_on_batch(inputs)
+    _, y = split_on_batch(targets)
 
     # Y --> X
-    Y_fake = generator(Y, hparams, 'Fy', reuse=False)    
-    YtoXloss = loss(X, Y_fake, True, hparams, True,  "YtoX")
-       
+    y_fake = generator(y, hparams, "Fy", reuse=False)
+    y_to_x_loss = lossfn(y, y_fake, True, hparams, True, "YtoX")
+
     # X --> Y
-    X_fake = generator(X, hparams, 'Gx', reuse=False)   
-    XtoYloss = loss(Y, X_fake, True, hparams, True,  "XtoY")        
-        
+    x_fake = generator(x, hparams, "Gx", reuse=False)
+    x_to_y_loss = lossfn(y, x_fake, True, hparams, True, "XtoY")
+
     # Cycle-Consistency
-    Y_fake_ = generator(Y_fake, hparams, 'Gx', reuse=True) 
-    X_fake_ = generator(X_fake, hparams, 'Fy', reuse=True) 
-    XtoXloss = hparams.cycle_loss_multiplier1 * tf.reduce_mean(tf.abs(X_fake_ - X))
-    YtoYloss = hparams.cycle_loss_multiplier2 * tf.reduce_mean(tf.abs(Y_fake_ - Y))
-    cycloss = XtoXloss + YtoYloss
-    
-
-    sample_generated = generator(inputs, hparams, 'Gx', reuse=True) 
-    sample_generated = tf.layers.dense(sample_generated, hparams.vocab_size, name="softmax", reuse=None)                
-    sample_generated = tf.stop_gradient(tf.expand_dims(sample_generated, axis=2))
-
-    losses = {"cycloss": cycloss,              
-              "YtoXloss": YtoXloss,
-              "XtoYloss": XtoYloss} 
-    
+    y_fake_ = generator(y_fake, hparams, "Gx", reuse=True)
+    x_fake_ = generator(x_fake, hparams, "Fy", reuse=True)
+    x_to_x_loss = hparams.cycle_loss_multiplier1 * tf.reduce_mean(
+        tf.abs(x_fake_ - x))
+    y_to_y_loss = hparams.cycle_loss_multiplier2 * tf.reduce_mean(
+        tf.abs(y_fake_ - y))
+    cycloss = x_to_x_loss + y_to_y_loss
+
+    sample_generated = generator(inputs, hparams, "Gx", reuse=True)
+    sample_generated = tf.layers.dense(
+        sample_generated, hparams.vocab_size, name="softmax", reuse=None)
+    sample_generated = tf.stop_gradient(
+        tf.expand_dims(sample_generated, axis=2))
+
+    losses = {"cycloss": cycloss,
+              "y_to_x_loss": y_to_x_loss,
+              "x_to_y_loss": x_to_y_loss}
+
     return sample_generated, losses
 
 
@@ -132,15 +133,8 @@ def cycle_gan_small():
   hparams.weight_decay = 3.0
   hparams.learning_rate = 0.05
   hparams.kl_warmup_steps = 5000
-  #hparams.hidden_size = 8
   hparams.learning_rate_warmup_steps = 3000
   hparams.add_hparam("vocab_size", 66)  # Vocabulary size, need to set here.
   hparams.add_hparam("cycle_loss_multiplier1", 10.0)
   hparams.add_hparam("cycle_loss_multiplier2", 10.0)
   return hparams
-
-# line 43 - 80 -82  are changed : residual network config 
-#line 42 is changed - compress function 
-
-
-
diff --git a/tensor2tensor/models/lstm.py b/tensor2tensor/models/lstm.py
index 8a0b5a41f..9348cdc2c 100644
--- a/tensor2tensor/models/lstm.py
+++ b/tensor2tensor/models/lstm.py
@@ -214,3 +214,18 @@ def lstm_luong_attention_multi():
   hparams = lstm_luong_attention()
   hparams.num_heads = 4
   return hparams
+
+
+@registry.register_hparams
+def lstm_asr_v1():
+  """Basic LSTM Params."""
+  hparams = lstm_bahdanau_attention()
+  hparams.num_hidden_layers = 2
+  hparams.hidden_size = 256
+  hparams.batch_size = 36
+  hparams.max_input_seq_length = 600000
+  hparams.max_target_seq_length = 350
+  hparams.max_length = hparams.max_input_seq_length
+  hparams.min_length_bucket = hparams.max_input_seq_length // 2
+  hparams.learning_rate = 0.05
+  return hparams
diff --git a/tensor2tensor/models/lstm_test.py b/tensor2tensor/models/lstm_test.py
index 863518fa1..f9e472af9 100644
--- a/tensor2tensor/models/lstm_test.py
+++ b/tensor2tensor/models/lstm_test.py
@@ -57,7 +57,7 @@ def testLSTMSeq2SeqAttention(self):
 
     p_hparams = problem_hparams.test_problem_hparams(vocab_size, vocab_size)
     x = tf.constant(x, dtype=tf.int32)
-    x._shape = tf.TensorShape([None, None, 1, 1])
+    x = tf.placeholder_with_default(x, shape=[None, None, 1, 1])
 
     with self.test_session() as session:
       features = {
diff --git a/tensor2tensor/models/multimodel_test.py b/tensor2tensor/models/multimodel_test.py
index 86f92ced6..6f490b280 100644
--- a/tensor2tensor/models/multimodel_test.py
+++ b/tensor2tensor/models/multimodel_test.py
@@ -23,7 +23,7 @@
 
 import numpy as np
 
-from tensor2tensor.data_generators import image  # pylint: disable=unused-import
+from tensor2tensor.data_generators import cifar  # pylint: disable=unused-import
 from tensor2tensor.models import multimodel
 from tensor2tensor.utils import registry
 
diff --git a/tensor2tensor/models/neural_gpu.py b/tensor2tensor/models/neural_gpu.py
index 681423190..fe1519344 100644
--- a/tensor2tensor/models/neural_gpu.py
+++ b/tensor2tensor/models/neural_gpu.py
@@ -111,7 +111,7 @@ def neural_gpu():
   hparams.num_hidden_layers = 1
   hparams.kernel_height = 3
   hparams.kernel_width = 1
-  hparams.learning_rate_decay_scheme = "exp50k"
+  hparams.learning_rate_decay_scheme = "exp"
   hparams.learning_rate = 0.02
   hparams.learning_rate_warmup_steps = 3000
   hparams.initializer_gain = 1.0
diff --git a/tensor2tensor/models/resnet.py b/tensor2tensor/models/resnet.py
index 5858c364b..44c88dc8b 100644
--- a/tensor2tensor/models/resnet.py
+++ b/tensor2tensor/models/resnet.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 
 """Resnets."""
-# Copied from cloud_tpu/models/resnet_garden and modified
+# Copied from cloud_tpu/models/resnet/resnet_model.py and modified
 
 from __future__ import absolute_import
 from __future__ import division
@@ -28,225 +28,479 @@
 
 import tensorflow as tf
 
-# TODO(rsepassi): make hparams
-_BATCH_NORM_DECAY = 0.997
-_BATCH_NORM_EPSILON = 1e-5
+BATCH_NORM_DECAY = 0.9
+BATCH_NORM_EPSILON = 1e-5
 
 
-def bottleneck_block(inputs, filters, is_training, projection_shortcut, strides,
-                     data_format):
-  """Bottleneck block variant for residual networks with BN before convolutions.
+def batch_norm_relu(inputs,
+                    is_training,
+                    relu=True,
+                    init_zero=False,
+                    data_format="channels_first"):
+  """Performs a batch normalization followed by a ReLU.
 
   Args:
-    inputs: A tensor of size [batch, channels, height, width].
-    filters: The number of filters for the first two convolutions. Note that the
-      third and final convolution will use 4 times as many filters.
-    is_training: A Boolean for whether the model is in training or inference
-      mode. Needed for batch normalization.
-    projection_shortcut: The function to use for projection shortcuts (typically
-      a 1x1 convolution when downsampling the input).
-    strides: The block's stride. If greater than 1, this block will ultimately
-      downsample the input.
-    data_format: channels_{first, last}
+    inputs: `Tensor` of shape `[batch, channels, ...]`.
+    is_training: `bool` for whether the model is training.
+    relu: `bool` if False, omits the ReLU operation.
+    init_zero: `bool` if True, initializes scale parameter of batch
+        normalization with 0 instead of 1 (default).
+    data_format: `str` either "channels_first" for `[batch, channels, height,
+        width]` or "channels_last for `[batch, height, width, channels]`.
 
   Returns:
-    The output tensor of the block.
+    A normalized `Tensor` with the same `data_format`.
   """
+  if init_zero:
+    gamma_initializer = tf.zeros_initializer()
+  else:
+    gamma_initializer = tf.ones_initializer()
+
+  if data_format == "channels_first":
+    axis = 1
+  else:
+    axis = 3
+
+  inputs = tf.layers.batch_normalization(
+      inputs=inputs,
+      axis=axis,
+      momentum=BATCH_NORM_DECAY,
+      epsilon=BATCH_NORM_EPSILON,
+      center=True,
+      scale=True,
+      training=is_training,
+      fused=True,
+      gamma_initializer=gamma_initializer)
+
+  if relu:
+    inputs = tf.nn.relu(inputs)
+  return inputs
+
+
+def fixed_padding(inputs, kernel_size, data_format="channels_first"):
+  """Pads the input along the spatial dimensions independently of input size.
+
+  Args:
+    inputs: `Tensor` of size `[batch, channels, height, width]` or
+        `[batch, height, width, channels]` depending on `data_format`.
+    kernel_size: `int` kernel size to be used for `conv2d` or max_pool2d`
+        operations. Should be a positive integer.
+    data_format: `str` either "channels_first" for `[batch, channels, height,
+        width]` or "channels_last for `[batch, height, width, channels]`.
+
+  Returns:
+    A padded `Tensor` of the same `data_format` with size either intact
+    (if `kernel_size == 1`) or padded (if `kernel_size > 1`).
+  """
+  pad_total = kernel_size - 1
+  pad_beg = pad_total // 2
+  pad_end = pad_total - pad_beg
+  if data_format == "channels_first":
+    padded_inputs = tf.pad(
+        inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
+  else:
+    padded_inputs = tf.pad(
+        inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
+
+  return padded_inputs
+
+
+def conv2d_fixed_padding(inputs,
+                         filters,
+                         kernel_size,
+                         strides,
+                         data_format="channels_first"):
+  """Strided 2-D convolution with explicit padding.
+
+  The padding is consistent and is based only on `kernel_size`, not on the
+  dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
+
+  Args:
+    inputs: `Tensor` of size `[batch, channels, height_in, width_in]`.
+    filters: `int` number of filters in the convolution.
+    kernel_size: `int` size of the kernel to be used in the convolution.
+    strides: `int` strides of the convolution.
+    data_format: `str` either "channels_first" for `[batch, channels, height,
+        width]` or "channels_last for `[batch, height, width, channels]`.
+
+  Returns:
+    A `Tensor` of shape `[batch, filters, height_out, width_out]`.
+  """
+  if strides > 1:
+    inputs = fixed_padding(inputs, kernel_size, data_format=data_format)
+
+  return tf.layers.conv2d(
+      inputs=inputs,
+      filters=filters,
+      kernel_size=kernel_size,
+      strides=strides,
+      padding=("SAME" if strides == 1 else "VALID"),
+      use_bias=False,
+      kernel_initializer=tf.variance_scaling_initializer(),
+      data_format=data_format)
+
+
+def residual_block(inputs,
+                   filters,
+                   is_training,
+                   projection_shortcut,
+                   strides,
+                   final_block,
+                   data_format="channels_first"):
+  """Standard building block for residual networks with BN before convolutions.
+
+  Args:
+    inputs: `Tensor` of size `[batch, channels, height, width]`.
+    filters: `int` number of filters for the first two convolutions. Note that
+        the third and final convolution will use 4 times as many filters.
+    is_training: `bool` for whether the model is in training.
+    projection_shortcut: `function` to use for projection shortcuts (typically
+        a 1x1 convolution to match the filter dimensions). If None, no
+        projection is used and the input is passed as unchanged through the
+        shortcut connection.
+    strides: `int` block stride. If greater than 1, this block will ultimately
+        downsample the input.
+    final_block: unused parameter to keep the same function signature as
+        `bottleneck_block`.
+    data_format: `str` either "channels_first" for `[batch, channels, height,
+        width]` or "channels_last for `[batch, height, width, channels]`.
+
+  Returns:
+    The output `Tensor` of the block.
+  """
+  del final_block
   shortcut = inputs
-  out = inputs
-  out = batch_norm_relu(out, is_training, data_format)
+  inputs = batch_norm_relu(inputs, is_training, data_format=data_format)
 
-  # The projection shortcut should come after the first batch norm and ReLU
-  # since it performs a 1x1 convolution.
   if projection_shortcut is not None:
-    shortcut = projection_shortcut(out)
-
-  do_bn_relus = [False, True, True]
-  kernel_sizes = [1, 3, 1]
-  layer_strides = [1, strides, 1]
-  filter_sizes = [filters, filters, 4 * filters]
-
-  for do_bn_relu, kernel_size, layer_stride, filter_size in zip(
-      do_bn_relus, kernel_sizes, layer_strides, filter_sizes):
-    if do_bn_relu:
-      out = batch_norm_relu(out, is_training, data_format)
-    out = conv2d_fixed_padding(
-        inputs=out,
-        filters=filter_size,
-        kernel_size=kernel_size,
-        strides=layer_stride,
-        data_format=data_format)
+    shortcut = projection_shortcut(inputs)
 
-  return out + shortcut
+  inputs = conv2d_fixed_padding(
+      inputs=inputs,
+      filters=filters,
+      kernel_size=3,
+      strides=strides,
+      data_format=data_format)
+
+  inputs = batch_norm_relu(inputs, is_training, data_format=data_format)
+  inputs = conv2d_fixed_padding(
+      inputs=inputs,
+      filters=filters,
+      kernel_size=3,
+      strides=1,
+      data_format=data_format)
+
+  return inputs + shortcut
 
 
-def batch_norm_relu(inputs, is_training, data_format):
-  """Performs a batch normalization followed by a ReLU."""
-  # We set fused=True for a significant performance boost.
-  out = tf.layers.batch_normalization(
+def bottleneck_block(inputs,
+                     filters,
+                     is_training,
+                     projection_shortcut,
+                     strides,
+                     final_block,
+                     data_format="channels_first"):
+  """Bottleneck block variant for residual networks with BN after convolutions.
+
+  Args:
+    inputs: `Tensor` of size `[batch, channels, height, width]`.
+    filters: `int` number of filters for the first two convolutions. Note that
+        the third and final convolution will use 4 times as many filters.
+    is_training: `bool` for whether the model is in training.
+    projection_shortcut: `function` to use for projection shortcuts (typically
+        a 1x1 convolution to match the filter dimensions). If None, no
+        projection is used and the input is passed as unchanged through the
+        shortcut connection.
+    strides: `int` block stride. If greater than 1, this block will ultimately
+        downsample the input.
+    final_block: `bool` set to True if it is this the final block in the group.
+        This is changes the behavior of batch normalization initialization for
+        the final batch norm in a block.
+    data_format: `str` either "channels_first" for `[batch, channels, height,
+        width]` or "channels_last for `[batch, height, width, channels]`.
+
+  Returns:
+    The output `Tensor` of the block.
+  """
+  # TODO(chrisying): this block is technically the post-activation resnet-v1
+  # bottlneck unit. Test with v2 (pre-activation) and replace if there is no
+  # difference for consistency.
+  shortcut = inputs
+  if projection_shortcut is not None:
+    shortcut = projection_shortcut(inputs)
+
+  inputs = conv2d_fixed_padding(
       inputs=inputs,
-      axis=1 if data_format == "channels_first" else 3,
-      momentum=_BATCH_NORM_DECAY,
-      epsilon=_BATCH_NORM_EPSILON,
-      center=True,
-      scale=True,
-      training=is_training,
-      fused=True)
-  out = tf.nn.relu(out)
-  return out
+      filters=filters,
+      kernel_size=1,
+      strides=1,
+      data_format=data_format)
+
+  inputs = batch_norm_relu(inputs, is_training, data_format=data_format)
+  inputs = conv2d_fixed_padding(
+      inputs=inputs,
+      filters=filters,
+      kernel_size=3,
+      strides=strides,
+      data_format=data_format)
+
+  inputs = batch_norm_relu(inputs, is_training, data_format=data_format)
+  inputs = conv2d_fixed_padding(
+      inputs=inputs,
+      filters=4 * filters,
+      kernel_size=1,
+      strides=1,
+      data_format=data_format)
+  inputs = batch_norm_relu(
+      inputs,
+      is_training,
+      relu=False,
+      init_zero=final_block,
+      data_format=data_format)
 
+  return tf.nn.relu(inputs + shortcut)
 
-def block_layer(inputs, filters, block_fn, blocks, strides, is_training,
-                data_format, name):
+
+def block_layer(inputs,
+                filters,
+                block_fn,
+                blocks,
+                strides,
+                is_training,
+                name,
+                data_format="channels_first"):
   """Creates one layer of blocks for the ResNet model.
 
   Args:
-    inputs: A tensor of size [batch, channels, height, width].
-    filters: The number of filters for the first convolution of the layer.
-    block_fn: The block to use within the model, either `building_block` or
-      `bottleneck_block`.
-    blocks: The number of blocks contained in the layer.
-    strides: The stride to use for the first convolution of the layer. If
-      greater than 1, this layer will ultimately downsample the input.
-    is_training: Either True or False, whether we are currently training the
-      model. Needed for batch norm.
-    data_format: channels_{first, last}
-    name: A string name for the tensor output of the block layer.
+    inputs: `Tensor` of size `[batch, channels, height, width]`.
+    filters: `int` number of filters for the first convolution of the layer.
+    block_fn: `function` for the block to use within the model
+    blocks: `int` number of blocks contained in the layer.
+    strides: `int` stride to use for the first convolution of the layer. If
+        greater than 1, this layer will downsample the input.
+    is_training: `bool` for whether the model is training.
+    name: `str`name for the Tensor output of the block layer.
+    data_format: `str` either "channels_first" for `[batch, channels, height,
+        width]` or "channels_last for `[batch, height, width, channels]`.
 
   Returns:
-    The output tensor of the block layer.
+    The output `Tensor` of the block layer.
   """
   # Bottleneck blocks end with 4x the number of filters as they start with
   filters_out = 4 * filters if block_fn is bottleneck_block else filters
 
   def projection_shortcut(inputs):
-    return conv2d_fixed_padding(
+    inputs = conv2d_fixed_padding(
         inputs=inputs,
         filters=filters_out,
         kernel_size=1,
         strides=strides,
         data_format=data_format)
+    return batch_norm_relu(
+        inputs, is_training, relu=False, data_format=data_format)
 
   # Only the first block per block_layer uses projection_shortcut and strides
   inputs = block_fn(inputs, filters, is_training, projection_shortcut, strides,
-                    data_format)
+                    False, data_format)
 
-  for _ in range(1, blocks):
-    inputs = block_fn(inputs, filters, is_training, None, 1, data_format)
+  for i in range(1, blocks):
+    inputs = block_fn(inputs, filters, is_training, None, 1, (i + 1 == blocks),
+                      data_format)
 
   return tf.identity(inputs, name)
 
 
-def fixed_padding(inputs, kernel_size, data_format):
-  """Pads the input along the spatial dimensions independently of input size.
+def resnet_v2(inputs,
+              block_fn,
+              layers,
+              data_format="channels_first",
+              is_training=False):
+  """Resnet model.
 
   Args:
-    inputs: A 4D tensor layed out according to data_format
-    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
-                 Should be a positive integer.
-    data_format: channels_{first, last}
+    inputs: `Tensor` images.
+    block_fn: `function` for the block to use within the model. Either
+        `residual_block` or `bottleneck_block`.
+    layers: list of 4 `int`s denoting the number of blocks to include in each
+      of the 4 block groups. Each group consists of blocks that take inputs of
+      the same resolution.
+    data_format: `str`, "channels_first" `[batch, channels, height,
+        width]` or "channels_last" `[batch, height, width, channels]`.
+    is_training: bool, build in training mode or not.
 
   Returns:
-    A tensor of size [batch, channels, height_out, width_out] with the
-      input either intact (if kernel_size == 1) or padded (if kernel_size > 1).
+    Pre-logit activations.
   """
-  pad_total = kernel_size - 1
-  pad_beg = pad_total // 2
-  pad_end = pad_total - pad_beg
-  spatial_pads = [[pad_beg, pad_end], [pad_beg, pad_end]]
-  if data_format == "channels_first":
-    pads = [[0, 0], [0, 0]] + spatial_pads
-  else:
-    assert data_format == "channels_last"
-    pads = [[0, 0]] + spatial_pads + [[0, 0]]
-  padded_inputs = tf.pad(inputs, pads)
-  return padded_inputs
-
+  inputs = conv2d_fixed_padding(
+      inputs=inputs,
+      filters=64,
+      kernel_size=7,
+      strides=2,
+      data_format=data_format)
+  inputs = tf.identity(inputs, "initial_conv")
+  inputs = batch_norm_relu(inputs, is_training, data_format=data_format)
 
-def conv2d_fixed_padding(**kwargs):
-  """conv2d with fixed_padding, based only on kernel_size."""
-  strides = kwargs["strides"]
-  if strides > 1:
-    kwargs["inputs"] = fixed_padding(kwargs["inputs"], kwargs["kernel_size"],
-                                     kwargs["data_format"])
-
-  defaults = {
-      "padding": ("SAME" if strides == 1 else "VALID"),
-      "use_bias": False,
-      "kernel_initializer": tf.variance_scaling_initializer(),
-  }
-  defaults.update(kwargs)
-
-  return tf.layers.conv2d(**defaults)
-
-
-def resnet50(inputs, hparams):
-  """Resnet50."""
-  is_training = hparams.mode == tf.estimator.ModeKeys.TRAIN
-  block_fn = bottleneck_block
-
-  out = inputs
-  data_format = "channels_first" if hparams.use_nchw else "channels_last"
-  if hparams.use_nchw:
-    # Convert from channels_last (NHWC) to channels_first (NCHW). This provides
-    # a large performance boost on GPU.
-    out = tf.transpose(inputs, [0, 3, 1, 2])
-
-  out = conv2d_fixed_padding(
-      inputs=out, filters=64, kernel_size=7, strides=2, data_format=data_format)
-  out = tf.identity(out, "initial_conv")
-  out = tf.layers.max_pooling2d(
-      inputs=out,
+  inputs = tf.layers.max_pooling2d(
+      inputs=inputs,
       pool_size=3,
       strides=2,
       padding="SAME",
       data_format=data_format)
-  out = tf.identity(out, "initial_max_pool")
-
-  for i, (num_filters, stride, block_size) in enumerate(
-      zip(hparams.num_filters, hparams.strides, hparams.layer_sizes)):
-    out = block_layer(
-        inputs=out,
-        filters=num_filters,
-        block_fn=block_fn,
-        blocks=block_size,
-        strides=stride,
-        is_training=is_training,
-        data_format=data_format,
-        name="block_layer_%d" % i)
-
-  out = batch_norm_relu(out, is_training, data_format)
-  out = tf.layers.average_pooling2d(
-      inputs=out,
+  inputs = tf.identity(inputs, "initial_max_pool")
+
+  inputs = block_layer(
+      inputs=inputs,
+      filters=64,
+      block_fn=block_fn,
+      blocks=layers[0],
+      strides=1,
+      is_training=is_training,
+      name="block_layer1",
+      data_format=data_format)
+  inputs = block_layer(
+      inputs=inputs,
+      filters=128,
+      block_fn=block_fn,
+      blocks=layers[1],
+      strides=2,
+      is_training=is_training,
+      name="block_layer2",
+      data_format=data_format)
+  inputs = block_layer(
+      inputs=inputs,
+      filters=256,
+      block_fn=block_fn,
+      blocks=layers[2],
+      strides=2,
+      is_training=is_training,
+      name="block_layer3",
+      data_format=data_format)
+  inputs = block_layer(
+      inputs=inputs,
+      filters=512,
+      block_fn=block_fn,
+      blocks=layers[3],
+      strides=2,
+      is_training=is_training,
+      name="block_layer4",
+      data_format=data_format)
+
+  inputs = tf.layers.average_pooling2d(
+      inputs=inputs,
       pool_size=7,
       strides=1,
       padding="VALID",
       data_format=data_format)
-  out = tf.identity(out, "final_avg_pool")
-
-  if hparams.use_nchw:
-    # Back to NHWC
-    out = tf.transpose(out, [0, 2, 3, 1])
-  return out
+  inputs = tf.identity(inputs, "final_avg_pool")
+  inputs = tf.reshape(inputs,
+                      [-1, 2048 if block_fn is bottleneck_block else 512])
+  return inputs
 
 
 @registry.register_model
-class Resnet50(t2t_model.T2TModel):
+class Resnet(t2t_model.T2TModel):
 
   def body(self, features):
-    return resnet50(features["inputs"], self.hparams)
+    hp = self.hparams
+    block_fns = {
+        "residual": residual_block,
+        "bottleneck": bottleneck_block,
+    }
+    assert hp.block_fn in block_fns
+
+    inputs = features["inputs"]
+
+    data_format = "channels_last"
+    if hp.use_nchw:
+      # Convert from channels_last (NHWC) to channels_first (NCHW). This
+      # provides a large performance boost on GPU.
+      inputs = tf.transpose(inputs, [0, 3, 1, 2])
+      data_format = "channels_first"
+
+    out = resnet_v2(
+        inputs,
+        block_fns[hp.block_fn],
+        hp.layer_sizes,
+        data_format,
+        is_training=hp.mode == tf.estimator.ModeKeys.TRAIN)
+
+    out = tf.expand_dims(out, 1)
+    out = tf.expand_dims(out, 1)
+    return out
 
 
-@registry.register_hparams
 def resnet_base():
   """Set of hyperparameters."""
+  # For imagenet on TPU:
+  # Set train_steps=120000
+  # Set eval_steps=48
+
+  # Base
   hparams = common_hparams.basic_params1()
+
+  # Model-specific parameters
   hparams.add_hparam("layer_sizes", [3, 4, 6, 3])
+  hparams.add_hparam("block_fn", "bottleneck")
   hparams.add_hparam("use_nchw", True)
-  hparams.add_hparam("num_filters", [64, 128, 256, 512])
-  hparams.add_hparam("strides", [1, 2, 2, 2])
 
-  # Can run with a batch size of 128 with Problem ImageImagenet224
+  # Variable init
+  hparams.initializer = "normal_unit_scaling"
+  hparams.initializer_gain = 2.
+
+  # Optimization
+  hparams.optimizer = "Momentum"
+  hparams.optimizer_momentum_momentum = 0.9
+  hparams.optimizer_momentum_nesterov = True
+  hparams.weight_decay = 1e-4
+  hparams.clip_grad_norm = 0.0
+  # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)
+  hparams.learning_rate = 0.4
+  hparams.learning_rate_decay_scheme = "cosine"
+  # For image_imagenet224, 120k training steps, which effectively makes this a
+  # cosine decay (i.e. no cycles).
+  hparams.learning_rate_cosine_cycle_steps = 120000
+
   hparams.batch_size = 128
-  hparams.tpu_batch_size_per_shard = 128
   return hparams
+
+
+@registry.register_hparams
+def resnet_50():
+  hp = resnet_base()
+  return hp
+
+
+@registry.register_hparams
+def resnet_18():
+  hp = resnet_base()
+  hp.block_fn = "residual"
+  hp.layer_sizes = [2, 2, 2, 2]
+  return hp
+
+
+@registry.register_hparams
+def resnet_34():
+  hp = resnet_base()
+  hp.block_fn = "residual"
+  return hp
+
+
+@registry.register_hparams
+def resnet_101():
+  hp = resnet_base()
+  hp.layer_sizes = [3, 4, 23, 3]
+  return hp
+
+
+@registry.register_hparams
+def resnet_152():
+  hp = resnet_base()
+  hp.layer_sizes = [3, 8, 36, 3]
+  return hp
+
+
+@registry.register_hparams
+def resnet_200():
+  hp = resnet_base()
+  hp.layer_sizes = [3, 24, 36, 3]
+  return hp
diff --git a/tensor2tensor/models/resnet_test.py b/tensor2tensor/models/resnet_test.py
index d911dcbd7..ddcb1627b 100644
--- a/tensor2tensor/models/resnet_test.py
+++ b/tensor2tensor/models/resnet_test.py
@@ -33,7 +33,6 @@
 def resnet_tiny_cpu():
   hparams = resnet.resnet_base()
   hparams.layer_sizes = [2, 2, 2, 2]
-  hparams.num_filters = [10, 20, 30, 40]
   hparams.use_nchw = False
   return hparams
 
@@ -55,14 +54,14 @@ def _testResnet(self, img_size, output_size):
           "inputs": tf.constant(x, dtype=tf.int32),
           "targets": tf.constant(y, dtype=tf.int32),
       }
-      model = resnet.Resnet50(hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
+      model = resnet.Resnet(hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
       logits, _ = model(features)
       session.run(tf.global_variables_initializer())
       res = session.run(logits)
     self.assertEqual(res.shape, (batch_size,) + output_size + (1, vocab_size))
 
   def testResnetLarge(self):
-    self._testResnet(img_size=299, output_size=(4, 4))
+    self._testResnet(img_size=224, output_size=(1, 1))
 
 
 if __name__ == "__main__":
diff --git a/tensor2tensor/models/revnet.py b/tensor2tensor/models/revnet.py
index 23e87d1b4..28b4cf681 100644
--- a/tensor2tensor/models/revnet.py
+++ b/tensor2tensor/models/revnet.py
@@ -277,7 +277,7 @@ def final_block(x1, x2, dim='2d', training=True, scope='final_block'):
 
     # Global average pooling
     net = tf.reduce_mean(y, CONFIG[dim]['reduction_dimensions'],
-                         name='final_pool', keep_dims=True)
+                         name='final_pool', keepdims=True)
 
     return net
 
@@ -353,17 +353,25 @@ def revnet_base():
   hparams.add_hparam('num_channels_init_block', 64)
   hparams.add_hparam('dim', '2d')
 
-  hparams.optimizer = 'Momentum'
-  hparams.learning_rate = 0.4
-
-  hparams.learning_rate_boundaries = [40000, 80000, 120000, 140000]
-  hparams.learning_rate_multiples = [0.1, 0.01, 0.001, 0.0002]
-  hparams.learning_rate_decay_scheme = 'piecewise'
+  # Variable init
+  hparams.initializer = 'normal_unit_scaling'
+  hparams.initializer_gain = 2.
 
+  # Optimization
+  hparams.optimizer = 'Momentum'
+  hparams.optimizer_momentum_momentum = 0.9
+  hparams.optimizer_momentum_nesterov = True
   hparams.weight_decay = 1e-4
+  hparams.clip_grad_norm = 0.0
+  # (base_lr=0.1) * (batch_size=128*8 (on TPU, or 8 GPUs)=1024) / (256.)
+  hparams.learning_rate = 0.4
+  hparams.learning_rate_decay_scheme = 'cosine'
+  # For image_imagenet224, 120k training steps, which effectively makes this a
+  # cosine decay (i.e. no cycles).
+  hparams.learning_rate_cosine_cycle_steps = 120000
 
   # Can run with a batch size of 128 with Problem ImageImagenet224
-  hparams.tpu_batch_size_per_shard = 128
+  hparams.batch_size = 128
   return hparams
 
 
@@ -381,7 +389,7 @@ def revnet_cifar_base():
   hparams.init_kernel_size = 3
   hparams.init_maxpool = False
   hparams.strides = [1, 2, 2]
-  hparams.tpu_batch_size_per_shard = 128
+  hparams.batch_size = 128
   hparams.weight_decay = 5e-3
 
   hparams.learning_rate = 0.1
diff --git a/tensor2tensor/models/shake_shake.py b/tensor2tensor/models/shake_shake.py
index d1745bff8..5e1680edb 100644
--- a/tensor2tensor/models/shake_shake.py
+++ b/tensor2tensor/models/shake_shake.py
@@ -21,8 +21,6 @@
 
 # Dependency imports
 
-from six.moves import xrange  # pylint: disable=redefined-builtin
-
 from tensor2tensor.layers import common_hparams
 from tensor2tensor.layers import common_layers
 from tensor2tensor.utils import registry
@@ -31,61 +29,100 @@
 import tensorflow as tf
 
 
-def shake_shake_block_branch(x, conv_filters, stride):
+def shake_shake_skip_connection(x, output_filters, stride, is_training):
+  """Adds a residual connection to the filter x for the shake-shake model."""
+  curr_filters = common_layers.shape_list(x)[-1]
+  if curr_filters == output_filters:
+    return x
+  stride_spec = [1, stride, stride, 1]
+  # Skip path 1.
+  path1 = tf.nn.avg_pool(x, [1, 1, 1, 1], stride_spec, "VALID")
+  path1 = tf.layers.conv2d(path1, int(output_filters / 2), (1, 1),
+                           padding="SAME", name="path1_conv")
+
+  # Skip path 2.
+  pad_arr = [[0, 0], [0, 1], [0, 1], [0, 0]]  # First pad with 0's then crop.
+  path2 = tf.pad(x, pad_arr)[:, 1:, 1:, :]
+  path2 = tf.nn.avg_pool(path2, [1, 1, 1, 1], stride_spec, "VALID")
+  path2 = tf.layers.conv2d(path2, int(output_filters / 2), (1, 1),
+                           padding="SAME", name="path2_conv")
+
+  # Concat and apply BN.
+  final_path = tf.concat(values=[path1, path2], axis=-1)
+  final_path = tf.layers.batch_normalization(
+      final_path, training=is_training, name="final_path_bn")
+  return final_path
+
+
+def shake_shake_branch(x, output_filters, stride, rand_forward, rand_backward,
+                       hparams):
+  """Building a 2 branching convnet."""
+  is_training = hparams.mode == tf.contrib.learn.ModeKeys.TRAIN
   x = tf.nn.relu(x)
-  x = tf.layers.conv2d(
-      x, conv_filters, (3, 3), strides=(stride, stride), padding="SAME")
-  x = tf.layers.batch_normalization(x)
+  x = tf.layers.conv2d(x, output_filters, (3, 3), strides=(stride, stride),
+                       padding="SAME", name="conv1")
+  x = tf.layers.batch_normalization(x, training=is_training, name="bn1")
   x = tf.nn.relu(x)
-  x = tf.layers.conv2d(x, conv_filters, (3, 3), strides=(1, 1), padding="SAME")
-  x = tf.layers.batch_normalization(x)
+  x = tf.layers.conv2d(x, output_filters, (3, 3), padding="SAME", name="conv2")
+  x = tf.layers.batch_normalization(x, training=is_training, name="bn2")
+  if is_training:
+    x = x * rand_backward + tf.stop_gradient(x * rand_forward -
+                                             x * rand_backward)
+  else:
+    x *= 1.0 / hparams.shake_shake_num_branches
   return x
 
 
-def downsampling_residual_branch(x, conv_filters):
-  x = tf.nn.relu(x)
-  x1 = tf.layers.average_pooling2d(x, pool_size=(1, 1), strides=(2, 2))
-  x1 = tf.layers.conv2d(x1, conv_filters / 2, (1, 1), padding="SAME")
-  x2 = tf.pad(x[:, 1:, 1:], [[0, 0], [0, 1], [0, 1], [0, 0]])
-  x2 = tf.layers.average_pooling2d(x2, pool_size=(1, 1), strides=(2, 2))
-  x2 = tf.layers.conv2d(x2, conv_filters / 2, (1, 1), padding="SAME")
-  return tf.concat([x1, x2], axis=3)
-
-
-def shake_shake_block(x, conv_filters, stride, hparams):
-  """A shake-shake block."""
-  with tf.variable_scope("branch_1"):
-    branch1 = shake_shake_block_branch(x, conv_filters, stride)
-  with tf.variable_scope("branch_2"):
-    branch2 = shake_shake_block_branch(x, conv_filters, stride)
-  if x.shape[-1] == conv_filters:
-    skip = tf.identity(x)
-  else:
-    skip = downsampling_residual_branch(x, conv_filters)
-
-  # TODO(rshin): Use different alpha for each image in batch.
-  if hparams.mode == tf.estimator.ModeKeys.TRAIN:
-    if hparams.shakeshake_type == "batch":
-      shaken = common_layers.shakeshake2(branch1, branch2)
-    elif hparams.shakeshake_type == "image":
-      shaken = common_layers.shakeshake2_indiv(branch1, branch2)
-    elif hparams.shakeshake_type == "equal":
-      shaken = common_layers.shakeshake2_py(branch1, branch2, equal=True)
-    else:
-      raise ValueError("Invalid shakeshake_type: {!r}".format(shaken))
+def shake_shake_block(x, output_filters, stride, hparams):
+  """Builds a full shake-shake sub layer."""
+  is_training = hparams.mode == tf.contrib.learn.ModeKeys.TRAIN
+  batch_size = common_layers.shape_list(x)[0]
+
+  # Generate random numbers for scaling the branches.
+  rand_forward = [
+      tf.random_uniform(
+          [batch_size, 1, 1, 1], minval=0, maxval=1, dtype=tf.float32)
+      for _ in range(hparams.shake_shake_num_branches)
+  ]
+  rand_backward = [
+      tf.random_uniform(
+          [batch_size, 1, 1, 1], minval=0, maxval=1, dtype=tf.float32)
+      for _ in range(hparams.shake_shake_num_branches)
+  ]
+  # Normalize so that all sum to 1.
+  total_forward = tf.add_n(rand_forward)
+  total_backward = tf.add_n(rand_backward)
+  rand_forward = [samp / total_forward for samp in rand_forward]
+  rand_backward = [samp / total_backward for samp in rand_backward]
+  zipped_rand = zip(rand_forward, rand_backward)
+
+  branches = []
+  for branch, (r_forward, r_backward) in enumerate(zipped_rand):
+    with tf.variable_scope("branch_{}".format(branch)):
+      b = shake_shake_branch(x, output_filters, stride, r_forward, r_backward,
+                             hparams)
+      b = tf.nn.dropout(b, 1.0 - hparams.layer_prepostprocess_dropout)
+      branches.append(b)
+  res = shake_shake_skip_connection(x, output_filters, stride, is_training)
+  if hparams.shake_shake_concat:
+    concat_values = [res] + branches
+    concat_output = tf.concat(values=concat_values, axis=-1)
+    concat_output = tf.nn.relu(concat_output)
+    concat_output = tf.layers.conv2d(
+        concat_output, output_filters, (1, 1), name="concat_1x1")
+    concat_output = tf.layers.batch_normalization(
+        concat_output, training=is_training, name="concat_bn")
+    return concat_output
   else:
-    shaken = common_layers.shakeshake2_py(branch1, branch2, equal=True)
-  shaken.set_shape(branch1.get_shape())
-
-  return skip + shaken
+    return res + tf.add_n(branches)
 
 
-def shake_shake_stage(x, num_blocks, conv_filters, initial_stride, hparams):
-  with tf.variable_scope("block_0"):
-    x = shake_shake_block(x, conv_filters, initial_stride, hparams)
-  for i in xrange(1, num_blocks):
-    with tf.variable_scope("block_{}".format(i)):
-      x = shake_shake_block(x, conv_filters, 1, hparams)
+def shake_shake_layer(x, output_filters, num_blocks, stride, hparams):
+  """Builds many sub layers into one full layer."""
+  for block_num in range(num_blocks):
+    curr_stride = stride if (block_num == 0) else 1
+    with tf.variable_scope("layer_{}".format(block_num)):
+      x = shake_shake_block(x, output_filters, curr_stride, hparams)
   return x
 
 
@@ -100,60 +137,57 @@ class ShakeShake(t2t_model.T2TModel):
 
   def body(self, features):
     hparams = self._hparams
+    is_training = hparams.mode == tf.contrib.learn.ModeKeys.TRAIN
     inputs = features["inputs"]
     assert (hparams.num_hidden_layers - 2) % 6 == 0
-    blocks_per_stage = (hparams.num_hidden_layers - 2) // 6
-
-    # For canonical Shake-Shake, the entry flow is a 3x3 convolution with 16
-    # filters then a batch norm. Instead we will rely on the one in
-    # SmallImageModality, which seems to instead use a layer norm.
+    assert hparams.hidden_size % 16 == 0
+    k = hparams.hidden_size // 16
+    n = (hparams.num_hidden_layers - 2) // 6
     x = inputs
-    with tf.variable_scope("shake_shake_stage_1"):
-      x = shake_shake_stage(x, blocks_per_stage, hparams.base_filters, 1,
-                            hparams)
-    with tf.variable_scope("shake_shake_stage_2"):
-      x = shake_shake_stage(x, blocks_per_stage, hparams.base_filters * 2, 2,
-                            hparams)
-    with tf.variable_scope("shake_shake_stage_3"):
-      x = shake_shake_stage(x, blocks_per_stage, hparams.base_filters * 4, 2,
-                            hparams)
-
-    # For canonical Shake-Shake, we should perform 8x8 average pooling and then
-    # have a fully-connected layer (which produces the logits for each class).
-    # Instead, we rely on the Xception exit flow in ClassLabelModality.
-    #
-    # Also, this model_fn does not return an extra_loss. However, TensorBoard
-    # reports an exponential moving average for extra_loss, where the initial
-    # value for the moving average may be a large number, so extra_loss will
-    # look large at the beginning of training.
+
+    x = tf.layers.conv2d(x, 16, (3, 3), padding="SAME", name="init_conv")
+    x = tf.layers.batch_normalization(x, training=is_training, name="init_bn")
+    with tf.variable_scope("L1"):
+      x = shake_shake_layer(x, 16 * k, n, 1, hparams)
+    with tf.variable_scope("L2"):
+      x = shake_shake_layer(x, 32 * k, n, 2, hparams)
+    with tf.variable_scope("L3"):
+      x = shake_shake_layer(x, 64 * k, n, 2, hparams)
+    x = tf.nn.relu(x)
+
+    # Global avg on [1, 2] (we're nhwc) and dense to num_classes done by top.
     return x
 
 
 @registry.register_hparams
-def shakeshake_cifar10():
-  """Parameters for CIFAR-10."""
-  tf.logging.warning("shakeshake_cifar10 hparams have not been verified to "
-                     "achieve good performance.")
+def shakeshake_small():
+  """Parameters for CIFAR-10. Gets to about 96% accuracy@700K steps, 1 GPU."""
   hparams = common_hparams.basic_params1()
   hparams.batch_size = 128
-  hparams.hidden_size = 16
+  hparams.hidden_size = 32
+  hparams.layer_prepostprocess_dropout = 0.0
   hparams.dropout = 0
   hparams.label_smoothing = 0.0
-  hparams.clip_grad_norm = 2.0
+  hparams.clip_grad_norm = 0.0  # No clipping for now, one can also try 2.0.
   hparams.num_hidden_layers = 26
-  hparams.kernel_height = -1  # Unused
-  hparams.kernel_width = -1  # Unused
   hparams.learning_rate_decay_scheme = "cosine"
   # Model should be run for 700000 steps with batch size 128 (~1800 epochs)
   hparams.learning_rate_cosine_cycle_steps = 700000
   hparams.learning_rate = 0.2
-  hparams.learning_rate_warmup_steps = 3000
+  hparams.learning_rate_warmup_steps = 100  # That's basically unused.
   hparams.initializer = "uniform_unit_scaling"
   hparams.initializer_gain = 1.0
-  # TODO(rshin): Adjust so that effective value becomes ~1e-4
-  hparams.weight_decay = 3.0
+  hparams.weight_decay = 1e-4
   hparams.optimizer = "Momentum"
   hparams.optimizer_momentum_momentum = 0.9
-  hparams.add_hparam("base_filters", 16)
-  hparams.add_hparam("shakeshake_type", "batch")
+  hparams.add_hparam("shake_shake_num_branches", 2)
+  hparams.add_hparam("shake_shake_concat", int(False))
+  return hparams
+
+
+@registry.register_hparams
+def shakeshake_big():
+  hparams = shakeshake_small()
+  hparams.layer_prepostprocess_dropout = 0.0
+  hparams.hidden_size = 96
   return hparams
diff --git a/tensor2tensor/models/slicenet.py b/tensor2tensor/models/slicenet.py
index a18676967..e77412513 100644
--- a/tensor2tensor/models/slicenet.py
+++ b/tensor2tensor/models/slicenet.py
@@ -322,7 +322,7 @@ def slicenet_params1():
   hparams.kernel_height = 3
   hparams.kernel_width = 1
   hparams.norm_type = "layer"
-  hparams.learning_rate_decay_scheme = "exp50k"
+  hparams.learning_rate_decay_scheme = "exp"
   hparams.learning_rate = 0.05
   hparams.learning_rate_warmup_steps = 3000
   hparams.initializer_gain = 1.0
diff --git a/tensor2tensor/models/slicenet_test.py b/tensor2tensor/models/slicenet_test.py
index 7efdf7a33..55bebf910 100644
--- a/tensor2tensor/models/slicenet_test.py
+++ b/tensor2tensor/models/slicenet_test.py
@@ -23,7 +23,7 @@
 
 import numpy as np
 
-from tensor2tensor.data_generators import image  # pylint: disable=unused-import
+from tensor2tensor.data_generators import cifar  # pylint: disable=unused-import
 from tensor2tensor.layers import modalities  # pylint: disable=unused-import
 from tensor2tensor.models import slicenet
 from tensor2tensor.utils import registry
diff --git a/tensor2tensor/models/super_lm.py b/tensor2tensor/models/super_lm.py
index d004087a6..079507d2a 100644
--- a/tensor2tensor/models/super_lm.py
+++ b/tensor2tensor/models/super_lm.py
@@ -20,6 +20,8 @@
 Each shard (device) has a similar structure with different weights.
 Occasional cross-replica-sum across shards.
 
+Example problem: languagemodel_lm1b8k_packed
+
 """
 
 from __future__ import absolute_import
@@ -43,13 +45,6 @@
 ModeKeys = tf.estimator.ModeKeys  # pylint: disable=invalid-name
 
 
-def _embedding(inputs, vocab_size, dense_size):
-  embedding_var = tf.get_variable("embedding", [vocab_size, dense_size])
-  emb_x = tf.gather(embedding_var, tf.to_int32(inputs))
-  emb_x *= dense_size ** 0.5
-  return emb_x
-
-
 @registry.register_model
 class SuperLM(t2t_model.T2TModel):
   """Attention net.  See file docstring."""
@@ -72,7 +67,10 @@ def body(self, features):
     shifted_targets = common_layers.shift_right_2d(targets)
     # Bypass the symbol modality and use a different embedding on each shard.
     decoder_input = mp(
-        _embedding, shifted_targets, vocab_size, hparams.hidden_size)
+        common_layers.embedding, shifted_targets, vocab_size,
+        hparams.hidden_size,
+        multiplier=hparams.hidden_size**0.5,
+        symbol_dropout_rate=hparams.symbol_dropout)
     decoder_self_attention_bias = mp(
         common_attention.attention_bias_lower_triangle,
         tf.shape(targets)[1])
@@ -147,7 +145,6 @@ def _super_stack(inputs,
     extra_loss: an optional scalar
   """
   layers = hparams.layers.strip(",").split(",")
-  ffn_hidden_sizes = [int(s) for s in hparams.ffn_hidden_sizes.split(",")]
   moe_hidden_sizes = [int(s) for s in hparams.moe_hidden_sizes.split(",")]
   if hparams.diet_experts:
     hsize, = moe_hidden_sizes
@@ -159,8 +156,8 @@ def _diet_expert(x):
         hparams.hidden_size, moe_hidden_sizes, hparams.hidden_size)
   # scaled_dot_product_attention_with_projections uses a 3d attention bias
   # (no heads), where multihead_attention uses 4d attention bias.
-  mix_size = int(hparams.mix_fraction * hparams.hidden_size)
   attention_bias_3d = mp(tf.squeeze, attention_bias, 1)
+  mix_size = int(hparams.mix_fraction * hparams.hidden_size)
   accumulator = inputs
   x = inputs
   extra_losses = []
@@ -203,17 +200,15 @@ def _split(t):
             x,
             None,
             attention_bias,  # bias
-            hparams.attention_key_channels or hparams.hidden_size,
-            hparams.attention_value_channels or hparams.hidden_size,
+            hparams.multihead_attention_key_channels or hparams.hidden_size,
+            hparams.multihead_attention_value_channels or hparams.hidden_size,
             hparams.hidden_size,
-            hparams.num_heads,
+            hparams.multihead_attention_num_heads,
             hparams.attention_dropout)
       elif layer_type == "ffn":
-        y = mp(
-            expert_utils.ffn_expert_fn(
-                hparams.hidden_size, ffn_hidden_sizes, hparams.hidden_size),
-            mp(expert_utils.flatten_all_but_last, x))
-        x = mp(expert_utils.reshape_like, y, x)
+        x = mp(
+            common_layers.dense_relu_dense, x,
+            hparams.filter_size, hparams.hidden_size)
       elif layer_type == "conv":
         # convolution
         x = mp(
@@ -252,18 +247,20 @@ def super_lm_base():
   hparams.moe_hidden_sizes = "512"
   hparams.batch_size = 16384
   hparams.max_length = 0
+  # All hyperparameters ending in "dropout" are automatically set to 0.0
+  # when not in training mode.
   hparams.layer_prepostprocess_dropout = 0.0
+  hparams.symbol_dropout = 0.1
+  hparams.add_hparam("attention_dropout", 0.0)
   hparams.label_smoothing = 0.0
   hparams.clip_grad_norm = 0.  # i.e. no gradient clipping
-  hparams.optimizer_adam_epsilon = 1e-9
+  hparams.optimizer = "Adafactor"
   hparams.learning_rate_decay_scheme = "noam"
   hparams.learning_rate = 0.1
   hparams.learning_rate_warmup_steps = 8000
   hparams.initializer_gain = 1.0
   hparams.initializer = "uniform_unit_scaling"
   hparams.weight_decay = 0.0
-  hparams.optimizer_adam_beta1 = 0.9
-  hparams.optimizer_adam_beta2 = 0.999
   hparams.shared_embedding_and_softmax_weights = False
   hparams.layer_preprocess_sequence = "n"
   hparams.layer_postprocess_sequence = "da"
@@ -271,15 +268,12 @@ def super_lm_base():
   hparams.no_data_parallelism = True
   # bypass the symbol modality so that we can use model parallelism.
   hparams.target_modality = "symbol:identity"
-  hparams.add_hparam("ffn_hidden_sizes", "512")  # Add new ones like this.
+  hparams.add_hparam("filter_size", 512)
   hparams.add_hparam("mix_fraction", 0.5)
   # attention-related flags
-  hparams.add_hparam("num_heads", 4)
-  hparams.add_hparam("attention_key_channels", 0)
-  hparams.add_hparam("attention_value_channels", 0)
-  # All hyperparameters ending in "dropout" are automatically set to 0.0
-  # when not in training mode.
-  hparams.add_hparam("attention_dropout", 0.0)
+  hparams.add_hparam("multihead_attention_num_heads", 4)
+  hparams.add_hparam("multihead_attention_key_channels", 0)
+  hparams.add_hparam("multihead_attention_value_channels", 0)
   hparams.add_hparam("pos", "timing")  # timing, none
   hparams.add_hparam(
       "layers", ("n,att,m,d,a," "n,ffn,m,d,a,") * 4 + "n,ffn,d")
@@ -304,7 +298,7 @@ def super_lm_big():
   """Big model."""
   hparams = super_lm_base()
   hparams.hidden_size = 1024
-  hparams.ffn_hidden_sizes = "2048"
+  hparams.filter_size = 2048
   return hparams
 
 
@@ -366,3 +360,46 @@ def super_lm_moe_4b_diet():
   hparams.moe_num_experts = 128
   hparams.diet_experts = True
   return hparams
+
+
+@registry.register_hparams
+def super_lm_tpu():
+  """Hyperparameters for data-parallel training on TPU.
+
+  This is not the intended usage - we would really like to use model-parallelism
+  with the model shards mapping to cores and cross_replica_sum used for
+  communication.  Currently, we replicate the entire model on each core.
+
+  Returns:
+    An hparams object.
+  """
+  hparams = super_lm_base()
+  hparams.batch_size = 4096
+  return hparams
+
+
+@registry.register_hparams
+def super_lm_big_tpu():
+  hparams = super_lm_big()
+  hparams.batch_size = 1024
+  return hparams
+
+
+@registry.register_hparams
+def super_lm_tpu_memtest():
+  """Crazy set of hyperparameters to test memory optimizations.
+
+  Quality will be very poor due to lack of attention layers.
+  853M parameters
+  This seems to run on TPU for languagemodel_lm1b8k_packed as of 2018-01-19.
+
+  Returns:
+    An hparams object.
+  """
+  hparams = super_lm_base()
+  hparams.num_model_shards = 1
+  hparams.layers = "ffn," * 8
+  hparams.hidden_size = 4096
+  hparams.filter_size = 12000
+  hparams.batch_size = 512
+  return hparams
diff --git a/tensor2tensor/models/transformer.py b/tensor2tensor/models/transformer.py
index 67a342e67..b241cc24a 100644
--- a/tensor2tensor/models/transformer.py
+++ b/tensor2tensor/models/transformer.py
@@ -37,7 +37,6 @@
 
 import tensorflow as tf
 
-from tensorflow.python.eager import context
 from tensorflow.python.util import nest
 
 
@@ -123,7 +122,8 @@ def decode(self,
         nonpadding=nonpadding,
         save_weights_to=self.attention_weights)
 
-    if hparams.use_tpu and hparams.mode == tf.estimator.ModeKeys.TRAIN:
+    if (common_layers.is_on_tpu() and
+        hparams.mode == tf.estimator.ModeKeys.TRAIN):
       # TPU does not react kindly to extra dimensions.
       # TODO(noam): remove this once TPU is more forgiving of extra dims.
       return decoder_output
@@ -172,16 +172,19 @@ def _greedy_infer(self, features, decode_length):
       decode_length: an integer.  How many additional timesteps to decode.
 
     Returns:
-       samples: [batch_size, input_length + decode_length]
-       logits: Not returned
-       losses: Not returned
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if beam_size == 1 or
+              [batch_size, top_beams, <= decode_length]
+          "scores": decoding log probs from the beam search,
+              None if using greedy decoding (beam_size=1)
+      }
 
     Raises:
       NotImplementedError: If there are multiple data shards.
     """
     with tf.variable_scope(self.name):
-      decoded_ids, _ = self._fast_decode(features, decode_length)
-      return decoded_ids, None, None
+      return self._fast_decode(features, decode_length)
 
   def _beam_decode(self, features, decode_length, beam_size, top_beams, alpha):
     """Beam search decoding.
@@ -195,12 +198,17 @@ def _beam_decode(self, features, decode_length, beam_size, top_beams, alpha):
         the preference for slonger translations.
 
     Returns:
-       samples: an integer `Tensor`. Top samples from the beam search
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if beam_size == 1 or
+              [batch_size, top_beams, <= decode_length]
+          "scores": decoding log probs from the beam search,
+              None if using greedy decoding (beam_size=1)
+      }
     """
     with tf.variable_scope(self.name):
-      decoded_ids, scores = self._fast_decode(features, decode_length,
-                                              beam_size, top_beams, alpha)
-      return {"outputs": decoded_ids, "scores": scores}
+      return self._fast_decode(
+          features, decode_length, beam_size, top_beams, alpha)
 
   def _fast_decode(self,
                    features,
@@ -222,7 +230,13 @@ def _fast_decode(self,
         the preference for slonger translations.
 
     Returns:
-       samples: an integer `Tensor`. Top samples from the beam search
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if beam_size == 1 or
+              [batch_size, top_beams, <= decode_length]
+          "scores": decoding log probs from the beam search,
+              None if using greedy decoding (beam_size=1)
+      }
 
     Raises:
       NotImplementedError: If there are multiple data shards.
@@ -358,9 +372,13 @@ def fast_decode(encoder_output,
     eos_id: End-of-sequence symbol in beam search.
 
   Returns:
-    Pair of tensors `(decoded_ids, scores)`, where `decoded_ids` is a 2-d or 3-d
-    (when doing beam search with top_beams > 1) tensor containing result of
-    decoding, and `scores` is the beam search scores.
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if top_beams == 1 or
+              [batch_size, top_beams, <= decode_length] otherwise
+          "scores": decoding log probs from the beam search,
+              None if using greedy decoding (beam_size=1)
+      }
   """
   batch_size = common_layers.shape_list(encoder_output)[0]
 
@@ -376,15 +394,6 @@ def fast_decode(encoder_output,
       for layer in range(num_layers)
   }
 
-  # Set 2nd dim to None since it's not invariant in the tf.while_loop
-  # Note: Tensor.set_shape() does not work here since it merges shape info.
-  # TODO(llion); Find a more robust solution.
-  # pylint: disable=protected-access
-  if not context.in_eager_mode():
-    for layer in cache:
-      cache[layer]["k"]._shape = tf.TensorShape([None, None, key_channels])
-      cache[layer]["v"]._shape = tf.TensorShape([None, None, value_channels])
-  # pylint: enable=protected-access
   cache["encoder_output"] = encoder_output
   cache["encoder_decoder_attention_bias"] = encoder_decoder_attention_bias
 
@@ -407,31 +416,36 @@ def fast_decode(encoder_output,
       decoded_ids = decoded_ids[:, :top_beams, 1:]
   else:  # Greedy
 
-    def inner_loop(i, next_id, decoded_ids, cache):
+    def inner_loop(i, finished, next_id, decoded_ids, cache):
       logits, cache = symbols_to_logits_fn(next_id, i, cache)
       temperature = (0.0 if hparams.sampling_method == "argmax" else
                      hparams.sampling_temp)
-      next_id = tf.expand_dims(
-          common_layers.sample_with_temperature(logits, temperature), axis=1)
+      next_id = common_layers.sample_with_temperature(logits, temperature)
+      finished |= tf.equal(next_id, eos_id)
+      next_id = tf.expand_dims(next_id, axis=1)
       decoded_ids = tf.concat([decoded_ids, next_id], axis=1)
-      return i + 1, next_id, decoded_ids, cache
+      return i + 1, finished, next_id, decoded_ids, cache
+
+    def is_not_finished(i, finished, *_):
+      return (i < decode_length) & tf.logical_not(tf.reduce_all(finished))
 
     decoded_ids = tf.zeros([batch_size, 0], dtype=tf.int64)
-    scores = None
+    finished = tf.constant(False, shape=[batch_size])
     next_id = tf.zeros([batch_size, 1], dtype=tf.int64)
-    _, _, decoded_ids, _ = tf.while_loop(
-        # TODO(llion): Early stopping.
-        lambda i, *_: tf.less(i, decode_length),
+    _, _, _, decoded_ids, _ = tf.while_loop(
+        is_not_finished,
         inner_loop,
-        [tf.constant(0), next_id, decoded_ids, cache],
+        [tf.constant(0), finished, next_id, decoded_ids, cache],
         shape_invariants=[
             tf.TensorShape([]),
+            tf.TensorShape([None]),
             tf.TensorShape([None, None]),
             tf.TensorShape([None, None]),
-            nest.map_structure(lambda t: tf.TensorShape(t.shape), cache),
+            nest.map_structure(beam_search.get_state_shape_invariants, cache),
         ])
+    scores = None
 
-  return decoded_ids, scores
+  return {"outputs": decoded_ids, "scores": scores}
 
 
 @registry.register_model
@@ -567,7 +581,8 @@ def transformer_encoder(encoder_input,
                         hparams,
                         name="encoder",
                         nonpadding=None,
-                        save_weights_to=None):
+                        save_weights_to=None,
+                        make_image_summary=True):
   """A stack of transformer layers.
 
   Args:
@@ -585,11 +600,15 @@ def transformer_encoder(encoder_input,
     save_weights_to: an optional dictionary to capture attention weights
       for vizualization; the weights tensor will be appended there under
       a string key created from the variable scope (including name).
+    make_image_summary: Whether to make an attention image summary.
 
   Returns:
     y: a Tensors
   """
   x = encoder_input
+  attention_dropout_broadcast_dims = (
+      common_layers.comma_separated_string_to_integer_list(
+          getattr(hparams, "attention_dropout_broadcast_dims", "")))
   with tf.variable_scope(name):
     if nonpadding is not None:
       padding = 1.0 - nonpadding
@@ -598,7 +617,7 @@ def transformer_encoder(encoder_input,
           encoder_self_attention_bias)
       nonpadding = 1.0 - padding
     pad_remover = None
-    if hparams.use_pad_remover:
+    if hparams.use_pad_remover and not common_layers.is_on_tpu():
       pad_remover = expert_utils.PadRemover(padding)
     for layer in xrange(hparams.num_encoder_layers or
                         hparams.num_hidden_layers):
@@ -615,7 +634,9 @@ def transformer_encoder(encoder_input,
               hparams.attention_dropout,
               attention_type=hparams.self_attention_type,
               save_weights_to=save_weights_to,
-              max_relative_position=hparams.max_relative_position)
+              max_relative_position=hparams.max_relative_position,
+              make_image_summary=make_image_summary,
+              dropout_broadcast_dims=attention_dropout_broadcast_dims)
           x = common_layers.layer_postprocess(x, y, hparams)
         with tf.variable_scope("ffn"):
           y = transformer_ffn_layer(
@@ -636,7 +657,8 @@ def transformer_decoder(decoder_input,
                         cache=None,
                         name="decoder",
                         nonpadding=None,
-                        save_weights_to=None):
+                        save_weights_to=None,
+                        make_image_summary=True):
   """A stack of transformer layers.
 
   Args:
@@ -658,11 +680,15 @@ def transformer_decoder(decoder_input,
     save_weights_to: an optional dictionary to capture attention weights
       for vizualization; the weights tensor will be appended there under
       a string key created from the variable scope (including name).
+    make_image_summary: Whether to make an attention image summary.
 
   Returns:
     y: a Tensors
   """
   x = decoder_input
+  attention_dropout_broadcast_dims = (
+      common_layers.comma_separated_string_to_integer_list(
+          getattr(hparams, "attention_dropout_broadcast_dims", "")))
   with tf.variable_scope(name):
     for layer in xrange(hparams.num_decoder_layers or
                         hparams.num_hidden_layers):
@@ -682,19 +708,25 @@ def transformer_decoder(decoder_input,
               attention_type=hparams.self_attention_type,
               save_weights_to=save_weights_to,
               max_relative_position=hparams.max_relative_position,
-              cache=layer_cache)
+              cache=layer_cache,
+              make_image_summary=make_image_summary,
+              dropout_broadcast_dims=attention_dropout_broadcast_dims)
           x = common_layers.layer_postprocess(x, y, hparams)
         if encoder_output is not None:
           with tf.variable_scope("encdec_attention"):
             # TODO(llion): Add caching.
             y = common_attention.multihead_attention(
-                common_layers.layer_preprocess(
-                    x, hparams), encoder_output, encoder_decoder_attention_bias,
+                common_layers.layer_preprocess(x, hparams),
+                encoder_output,
+                encoder_decoder_attention_bias,
                 hparams.attention_key_channels or hparams.hidden_size,
                 hparams.attention_value_channels or hparams.hidden_size,
-                hparams.hidden_size, hparams.num_heads,
+                hparams.hidden_size,
+                hparams.num_heads,
                 hparams.attention_dropout,
-                save_weights_to=save_weights_to)
+                save_weights_to=save_weights_to,
+                make_image_summary=make_image_summary,
+                dropout_broadcast_dims=attention_dropout_broadcast_dims)
             x = common_layers.layer_postprocess(x, y, hparams)
         with tf.variable_scope("ffn"):
           y = transformer_ffn_layer(
@@ -730,6 +762,9 @@ def transformer_ffn_layer(x,
     a Tensor of shape [batch_size, length, hparams.hidden_size]
   """
   ffn_layer = hparams.ffn_layer
+  relu_dropout_broadcast_dims = (
+      common_layers.comma_separated_string_to_integer_list(
+          getattr(hparams, "relu_dropout_broadcast_dims", "")))
   if ffn_layer == "conv_hidden_relu":
     # Backwards compatibility
     ffn_layer = "dense_relu_dense"
@@ -744,7 +779,8 @@ def transformer_ffn_layer(x,
         x,
         hparams.filter_size,
         hparams.hidden_size,
-        dropout=hparams.relu_dropout)
+        dropout=hparams.relu_dropout,
+        dropout_broadcast_dims=relu_dropout_broadcast_dims)
     if pad_remover:
       # Restore `conv_output` to the original shape of `x`, including padding.
       conv_output = tf.reshape(
@@ -803,6 +839,7 @@ def transformer_base_v1():
   hparams.label_smoothing = 0.1
   hparams.shared_embedding_and_softmax_weights = True
   hparams.symbol_modality_num_shards = 16
+
   # Add new ones like this.
   hparams.add_hparam("filter_size", 2048)
   # Layer-related flags. If zero, these fall back on hparams.num_hidden_layers.
@@ -818,7 +855,9 @@ def transformer_base_v1():
   # All hyperparameters ending in "dropout" are automatically set to 0.0
   # when not in training mode.
   hparams.add_hparam("attention_dropout", 0.0)
+  hparams.add_hparam("attention_dropout_broadcast_dims", "")
   hparams.add_hparam("relu_dropout", 0.0)
+  hparams.add_hparam("relu_dropout_broadcast_dims", "")
   hparams.add_hparam("pos", "timing")  # timing, none
   hparams.add_hparam("nbr_decoder_problems", 1)
   hparams.add_hparam("proximity_bias", False)
@@ -830,6 +869,7 @@ def transformer_base_v1():
 
 @registry.register_hparams
 def transformer_base_v2():
+  """Set of hyperparameters."""
   hparams = transformer_base_v1()
   hparams.layer_preprocess_sequence = "n"
   hparams.layer_postprocess_sequence = "da"
@@ -843,33 +883,10 @@ def transformer_base_v2():
 
 @registry.register_hparams
 def transformer_base():
-  return transformer_base_v2()
-
-
-@registry.register_hparams
-def transformer_n_da():
-  """Normalize on layer input, instead of after residual connection.
-
-  This version seems to cure failure-to-learn bugs - for example, with very
-  deep networks or hard-to-learn mappings.
-
-  Probably this should become the default.
-
-  Returns:
-    a hyperparameters.
-  """
-  hparams = transformer_base()
-  hparams.layer_preprocess_sequence = "n"
-  hparams.layer_postprocess_sequence = "da"
-  # This version seems to benefit from a higher learning rate.
-  hparams.learning_rate = 0.4
-  return hparams
-
-
-@registry.register_hparams
-def transformer_n_da_l10():
-  hparams = transformer_n_da()
-  hparams.num_hidden_layers = 10
+  # Update parameters here, then occasionally cut a versioned set, e.g.
+  # transformer_base_v2.
+  hparams = transformer_base_v2()
+  hparams.optimizer_adam_beta2 = 0.997
   return hparams
 
 
@@ -890,7 +907,6 @@ def transformer_big_single_gpu():
   hparams = transformer_big()
   hparams.layer_prepostprocess_dropout = 0.1
   hparams.learning_rate_warmup_steps = 16000
-  hparams.optimizer_adam_beta2 = 0.998
   return hparams
 
 
@@ -1205,29 +1221,58 @@ def transformer_relative_big():
   return hparams
 
 
+def update_hparams_for_tpu(hparams):
+  """Change hparams to be compatible with TPU training."""
+
+  # Adafactor uses less memory than Adam.
+  hparams.optimizer = "Adafactor"
+
+  # Avoid an expensive concat on TPU.
+  # >1 shards helps with faster parameter distribution on multi-GPU machines
+  hparams.symbol_modality_num_shards = 1
+
+  # Adaptive batch sizes and sequence lengths are not supported on TPU.
+  # Instead, every batch has the same sequence length and the same batch size.
+  # Longer sequences are dropped and shorter ones are padded.
+  #
+  # It is therefore suggested to use a problem where examples have been combined
+  # to a longer length, e.g. the "_packed" problems.
+  #
+  # For problems with variable sequence lengths, this parameter controls the
+  # maximum sequence length.  Shorter sequences are dropped and longer ones
+  # are padded.
+  #
+  # For problems with fixed sequence lengths - e.g. the "_packed" problems,
+  # this hyperparameter is ignored.
+  hparams.max_length = 64
+
+  # TPUs have less memory than GPUs, so decrease the batch size
+  hparams.batch_size = 2048
+
+  # Using noise broadcast in the dropout layers saves memory during training.
+  hparams.attention_dropout_broadcast_dims = "0,1"  # batch, heads
+  hparams.relu_dropout_broadcast_dims = "1"  # length
+  hparams.layer_prepostprocess_dropout_broadcast_dims = "1"  # length
+
+
 @registry.register_hparams
 def transformer_tpu():
   """HParams for Transformer model on TPU."""
   hparams = transformer_base()
   update_hparams_for_tpu(hparams)
-  hparams.tpu_batch_size_per_shard = 56
   return hparams
 
 
 @registry.register_hparams
 def transformer_packed_tpu():
-  """For packed problems, length 256, batch 14."""
-  hparams = transformer_base()
-  update_hparams_for_tpu(hparams)
-  hparams.tpu_batch_size_per_shard = 12
-  return hparams
+  """Deprecated alias for transformer_tpu()."""
+  return transformer_tpu()
 
 
 @registry.register_hparams
 def transformer_big_tpu():
   hparams = transformer_big()
   update_hparams_for_tpu(hparams)
-  hparams.tpu_batch_size_per_shard = 16
   return hparams
 
 
@@ -1266,7 +1311,7 @@ def transformer_tpu_range(rhp):
 def transformer_tpu_batch_range(rhp):
   hparams = transformer_tpu()
   common_hparams.fill_ranged_hparams_from_hparams(hparams, rhp)
-  rhp.set_discrete("tpu_batch_size_per_shard", [1, 2, 3, 4])
+  rhp.set_discrete("batch_size", [256, 512, 768, 1024])
 
 
 @registry.register_hparams
@@ -1281,23 +1326,6 @@ def transformer_small_tpu():
   return hparams
 
 
-def update_hparams_for_tpu(hparams):
-  """Change hparams to be compatible with TPU training."""
-  hparams.use_pad_remover = False  # where op not supported
-  hparams.optimizer = "TrueAdam"
-  hparams.learning_rate = 0.2
-  # Avoid an expensive concat on TPU
-  hparams.symbol_modality_num_shards = 1
-
-  # Inputs
-  # Each example in the batch will be of (padded) length hparams.max_length
-  # It is suggested to use a dataset that where examples have been combined
-  # to a longer length, e.g. the "_packed" datasets. If that's the case, reduce
-  # the tpu_batch_size_per_shard as necessary to fit in memory.
-  # For translate_ende_wmt32k_packed, transformer_packed_tpu is a good config.
-  hparams.max_length = 64
-
-
 @registry.register_hparams
 def transformer_clean():
   """No dropout, label smoothing, max_length."""
@@ -1318,6 +1346,13 @@ def transformer_clean_big():
   return hparams
 
 
+@registry.register_hparams
+def transformer_clean_big_tpu():
+  hparams = transformer_clean_big()
+  update_hparams_for_tpu(hparams)
+  return hparams
+
+
 @registry.register_hparams
 def transformer_tpu_with_conv():
   """Cut down on the number of heads, and use convs instead."""
@@ -1328,12 +1363,21 @@ def transformer_tpu_with_conv():
 
 
 @registry.register_hparams
-def transformer_tpu_base_language_model():
-  """Hparams for training languagemodel_lm1b8k on tpu."""
+def transformer_lm_tpu_0():
+  """Hparams for training languagemodel_lm1b8k on tpu.  92M Params."""
   hparams = transformer_clean_big()
   update_hparams_for_tpu(hparams)
-  hparams.tpu_batch_size_per_shard = 16
   hparams.num_heads = 4   # heads are expensive on tpu
-  hparams.learning_rate_warmup_steps = 1000
+  hparams.batch_size = 4096
   hparams.shared_embedding_and_softmax_weights = False
+  hparams.layer_prepostprocess_dropout = 0.1
+  return hparams
+
+
+@registry.register_hparams
+def transformer_lm_tpu_1():
+  """Hparams for training languagemodel_lm1b8k on tpu.  335M Params."""
+  hparams = transformer_lm_tpu_0()
+  hparams.hidden_size = 2048
+  hparams.filter_size = 8192
   return hparams
diff --git a/tensor2tensor/models/transformer_moe.py b/tensor2tensor/models/transformer_moe.py
index 202c4c9f3..efa67bf27 100644
--- a/tensor2tensor/models/transformer_moe.py
+++ b/tensor2tensor/models/transformer_moe.py
@@ -300,6 +300,74 @@ def transformer_moe_8k():
   return hparams
 
 
+@registry.register_hparams
+def transformer_moe_8k_lm():
+  """Language modeling params.
+
+  Will have the following architecture by default:
+  * No encoder.
+  * Decoder architecture:
+    * Layer 0: a - sepm  (masked self-attention/masked separable convolutions)
+    * Layer 1: a - sepm
+    * Layer 2: a - moe  (mixture of expert layers in the middle)
+    * Layer 3: a - sepm
+    * Layer 4: a - sepm
+
+  Returns:
+    hparams
+  """
+  hparams = transformer_moe_8k()
+
+  # Use masked versions of local attention and separable convolution
+  hparams.default_ff = "sepm"
+
+  # hparams.layer_types contains the network architecture:
+  # Start with '#' for decoder only architecture
+  hparams.layer_types = "#a/a/a-moe/a/a"  # 5 full attention layers with 1 moe
+  # For long sequences, if running out of memory, it's possible to use the
+  # one of those two optimized versions instead:
+  #  * Memory efficient multihead attention (slow):
+  # hparams.layer_types = "#mem/mem/mem-moe/mem/mem"
+  #  * Alternate between local/compressed attention layers (faster):
+  # hparams.layer_types = "#locm/red/locm-moe/red/locm"
+
+  return hparams
+
+
+@registry.register_hparams
+def transformer_moe_2k():
+  """Base transformers model with moe.
+
+  Will have the following architecture:
+  * No encoder.
+    * Layer 0: a - sep  (self-attention - unmasked separable convolutions)
+    * Layer 1: a - sep
+    * Layer 2: a - sep
+    * Layer 3: a - sep
+    * Layer 4: a - sep
+  * Decoder architecture:
+    * Layer 0: a - a - sepm  (self-attention - enco/deco-attention - masked sep)
+    * Layer 1: a - a - sepm
+    * Layer 2: a - a - moe  (mixture of expert layers in the middle)
+    * Layer 3: a - a - sepm
+    * Layer 4: a - a - sepm
+
+  Returns:
+    hparams
+  """
+  hparams = transformer_moe_8k()
+  hparams.batch_size = 2048
+
+  hparams.default_ff = "sep"
+
+  # hparams.layer_types contains the network architecture:
+  encoder_archi = "a/a/a/a/a"
+  decoder_archi = "a-sepm/a-sepm/a-moe/a-sepm/a-sepm"
+  hparams.layer_types = "{}#{}".format(encoder_archi, decoder_archi)
+
+  return hparams
+
+
 @registry.register_hparams
 def transformer_moe_12k():
   """Hyper parameters specifics for long sequence generation."""
@@ -317,6 +385,7 @@ def transformer_moe_prepend_8k():
   hparams.prepend_mode = "prepend_inputs_masked_attention"
   hparams.eval_drop_long_sequences = False
   hparams.max_input_seq_length = 7500,
-  hparams.layer_types = "loc/red/loc-moe/red/loc"
+  hparams.default_ff = "sepm"
+  hparams.layer_types = "locm/red/locm-moe/red/locm"
   hparams.moe_num_experts = 256
   return hparams
diff --git a/tensor2tensor/models/transformer_sketch.py b/tensor2tensor/models/transformer_sketch.py
index 7ef78bc59..913243f00 100644
--- a/tensor2tensor/models/transformer_sketch.py
+++ b/tensor2tensor/models/transformer_sketch.py
@@ -27,7 +27,6 @@
 from tensor2tensor.models import transformer
 from tensor2tensor.models import transformer_vae
 from tensor2tensor.models.transformer import transformer_base
-from tensor2tensor.models.transformer import transformer_n_da
 from tensor2tensor.models.transformer import transformer_small
 from tensor2tensor.utils import registry
 
@@ -58,7 +57,7 @@ def encode(self, inputs, target_space, hparams):
 @registry.register_hparams
 def transformer_sketch():
   """Basic transformer_sketch hparams."""
-  hparams = transformer_n_da()
+  hparams = transformer_base()
   hparams.batch_size = 2048
   hparams.max_length = 784
   hparams.clip_grad_norm = 5.
@@ -148,7 +147,7 @@ def transformer_sketch_ranged(rhp):
                       ["uniform", "orthogonal", "uniform_unit_scaling"])
   rhp.set_float("initializer_gain", 0.5, 3.5)
   rhp.set_categorical("learning_rate_decay_scheme",
-                      ["none", "sqrt", "noam", "exp10k"])
+                      ["none", "sqrt", "noam", "exp"])
   rhp.set_float("optimizer_adam_epsilon", 1e-7, 1e-2, scale=rhp.LOG_SCALE)
   rhp.set_float("optimizer_adam_beta1", 0.8, 0.9)
   rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
diff --git a/tensor2tensor/models/transformer_test.py b/tensor2tensor/models/transformer_test.py
index d4502e585..f67476006 100644
--- a/tensor2tensor/models/transformer_test.py
+++ b/tensor2tensor/models/transformer_test.py
@@ -95,10 +95,11 @@ def testGreedyVsFast(self):
     model.set_mode(tf.estimator.ModeKeys.PREDICT)
 
     with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-      greedy_result, _, _ = model._slow_greedy_infer(features, decode_length)
+      greedy_result = model._slow_greedy_infer(
+          features, decode_length)["outputs"]
       greedy_result = tf.squeeze(greedy_result, axis=[2, 3])
 
-      fast_result, _, _ = model._greedy_infer(features, decode_length)
+      fast_result = model._greedy_infer(features, decode_length)["outputs"]
 
     with self.test_session():
       greedy_res = greedy_result.eval()
diff --git a/tensor2tensor/models/transformer_vae.py b/tensor2tensor/models/transformer_vae.py
index ec87f6a86..ac9a66b77 100644
--- a/tensor2tensor/models/transformer_vae.py
+++ b/tensor2tensor/models/transformer_vae.py
@@ -18,13 +18,20 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
+import math
+
 # Dependency imports
+
 from six.moves import xrange  # pylint: disable=redefined-builtin
+
+from tensor2tensor.layers import common_attention
 from tensor2tensor.layers import common_layers
 from tensor2tensor.models import transformer
 from tensor2tensor.utils import expert_utils
 from tensor2tensor.utils import registry
 from tensor2tensor.utils import t2t_model
+
 import tensorflow as tf
 from tensorflow.python.training import moving_averages
 
@@ -49,6 +56,22 @@ def residual_conv(x, repeat, k, hparams, name, reuse=None):
     return x
 
 
+def attend(x, source, hparams, name):
+  with tf.variable_scope(name):
+    x = tf.squeeze(x, axis=2)
+    if len(source.get_shape()) > 3:
+      source = tf.squeeze(source, axis=2)
+    source = common_attention.add_timing_signal_1d(source)
+    y = common_attention.multihead_attention(
+        common_layers.layer_preprocess(x, hparams), source, None,
+        hparams.attention_key_channels or hparams.hidden_size,
+        hparams.attention_value_channels or hparams.hidden_size,
+        hparams.hidden_size, hparams.num_heads,
+        hparams.attention_dropout)
+    res = common_layers.layer_postprocess(x, y, hparams)
+    return tf.expand_dims(res, axis=2)
+
+
 def decompress_step(source, hparams, first_relu, is_2d, name):
   """Decompression function."""
   with tf.variable_scope(name):
@@ -67,15 +90,15 @@ def top_k_softmax(x, k):
   """Calculate softmax(x), select top-k and rescale to sum to 1."""
   x = tf.nn.softmax(x)
   top_x, _ = tf.nn.top_k(x, k=k+1)
-  min_top = tf.reduce_min(top_x, axis=-1, keep_dims=True)
+  min_top = tf.reduce_min(top_x, axis=-1, keepdims=True)
   x = tf.nn.relu((x - min_top) + 1e-12)
-  x /= tf.reduce_sum(x, axis=-1, keep_dims=True)
+  x /= tf.reduce_sum(x, axis=-1, keepdims=True)
   return x, tf.reduce_max(top_x, axis=-1)
 
 
 def top_k_experts(x, k, hparams):
   x_shape = common_layers.shape_list(x)
-  x_flat = tf.reshape(x, [-1, x.get_shape().as_list()[-1]])
+  x_flat = tf.reshape(x, [-1, common_layers.shape_list(x)[-1]])
   is_training = hparams.mode == tf.contrib.learn.ModeKeys.TRAIN
   gates, load = expert_utils.noisy_top_k_gating(
       x_flat, hparams.v_size, is_training, k)
@@ -117,7 +140,7 @@ def dae(x, hparams, name):
     maxvhot = tf.stop_gradient(tf.one_hot(maxvec, hparams.v_size))
     # Add losses that prevent too few being used.
     distrib = tf.reshape(logsm, [-1, hparams.v_size]) * maxvhot
-    d_mean = tf.reduce_mean(distrib, axis=[0], keep_dims=True)
+    d_mean = tf.reduce_mean(distrib, axis=[0], keepdims=True)
     d_variance = tf.reduce_mean(tf.square(distrib - d_mean), axis=[0])
     d_dev = - tf.reduce_mean(d_variance)
     ret = s
@@ -140,14 +163,51 @@ def vae(x, z_size, name):
     return z, kl_loss, mu, log_sigma
 
 
+def project_hidden(x, hparams):
+  """Project encoder hidden state into block_dim using projection tensors.
+
+  Args:
+    x: Encoder hidden state of shape [-1, hidden_size]
+    hparams: Hparams
+
+  Returns:
+    Projected states of shape [-1, num_blocks, block_dim].
+  """
+  x = tf.reshape(x, shape=[1, -1, hparams.hidden_size])
+  x_tiled = tf.reshape(
+      tf.tile(x, multiples=[hparams.num_blocks, 1, 1]),
+      shape=[hparams.num_blocks, -1, hparams.hidden_size])
+  x_projected = tf.matmul(x_tiled, hparams.projection_tensors)
+  x_projected = tf.transpose(x_projected, perm=[1, 0, 2])
+  return x_projected
+
+
+def slice_hidden(x, hparams):
+  """Slice encoder hidden state into block_dim.
+
+  Args:
+    x: Encoder hidden state of shape [-1, hidden_size]
+    hparams: Hparams
+
+  Returns:
+    Sliced states of shape [-1, num_blocks, block_dim].
+  """
+  assert hparams.num_blocks * hparams.block_dim == hparams.hidden_size
+  x_sliced = tf.reshape(x, shape=[-1, hparams.num_blocks, hparams.block_dim])
+  return x_sliced
+
+
 def nearest(x, means, hparams):
   """Find the nearest means to elements in x."""
-  x_flat = tf.reshape(x, [-1, hparams.hidden_size])
-  x_norm_sq = tf.reduce_sum(x_flat ** 2, axis=-1, keep_dims=True)
-  means_norm_sq = tf.reduce_sum(means ** 2, axis=-1, keep_dims=True)
-  dist = (
-      x_norm_sq + tf.transpose(means_norm_sq) -
-      2 * tf.matmul(x_flat, means, transpose_b=True))
+  x_reshaped = hparams.reshape_fn(x, hparams)
+  x_norm_sq = tf.reduce_sum(tf.square(x_reshaped), axis=-1, keepdims=True)
+  means_norm_sq = tf.reduce_sum(tf.square(means), axis=-1, keepdims=True)
+  scalar_prod = tf.matmul(
+      tf.transpose(x_reshaped, perm=[1, 0, 2]),
+      tf.transpose(means, perm=[0, 2, 1]))
+  scalar_prod = tf.transpose(scalar_prod, perm=[1, 0, 2])
+  dist = x_norm_sq + tf.transpose(
+      means_norm_sq, perm=[2, 0, 1]) - 2 * scalar_prod
   if hparams.random_top_k > 1:
     _, top_k_idx = tf.nn.top_k(-dist, k=hparams.random_top_k)
     nearest_idx = tf.gather(
@@ -157,37 +217,46 @@ def nearest(x, means, hparams):
         axis=-1)
   else:
     nearest_idx = tf.argmax(-dist, axis=-1)
-  nearest_hot = tf.one_hot(nearest_idx, hparams.v_size)
+  nearest_hot = tf.one_hot(nearest_idx, hparams.block_v_size)
   shape = common_layers.shape_list(x)
-  shape[-1] = hparams.v_size
+  shape[-1] = hparams.num_blocks
+  shape.append(hparams.block_v_size)
   nearest_hot = tf.reshape(nearest_hot, shape=shape)
   return tf.stop_gradient(nearest_hot)
 
 
 def kmeans(x, means, hparams):
+  """Compute the nearest neighbors and the loss for training the embeddings."""
   x_means_hot = nearest(x, means, hparams)
-  x_means = tf.gather(means, tf.argmax(x_means_hot, axis=-1))
-  q_loss = tf.reduce_mean((tf.stop_gradient(x) - x_means)**2)
-  e_loss = tf.reduce_mean((x - tf.stop_gradient(x_means))**2)
+  x_means_hot_flat = tf.reshape(x_means_hot,
+                                [-1, hparams.num_blocks, hparams.block_v_size])
+  x_means = tf.matmul(tf.transpose(x_means_hot_flat, perm=[1, 0, 2]), means)
+  x_means = tf.transpose(x_means, [1, 0, 2])
+  x_reshaped = hparams.reshape_fn(x, hparams)
+  q_loss = tf.reduce_mean(tf.square((tf.stop_gradient(x_reshaped) - x_means)))
+  e_loss = tf.reduce_mean(tf.square(x_reshaped - tf.stop_gradient(x_means)))
   return x_means_hot, x_means, q_loss, e_loss
 
 
-def bit_to_int(x_bit, nbits):
+def bit_to_int(x_bit, nbits, base=2):
   """Turn x_bit representing numbers bitwise (lower-endian) to int tensor."""
   x_l = tf.stop_gradient(tf.reshape(x_bit, [-1, nbits]))
   x_labels = []
   for i in range(nbits):
-    x_labels.append(x_l[:, i] * 2**i)
+    x_labels.append(x_l[:, i] * tf.to_int32(base)**tf.to_int32(i))
   res = sum(x_labels)
   return tf.to_int32(tf.reshape(res, common_layers.shape_list(x_bit)[:-1]))
 
 
-def int_to_bit(x_int, nbits):
+def int_to_bit(x_int, nbits, base=2):
   """Turn x_int representing numbers into a bitwise (lower-endian) tensor."""
   x_l = tf.expand_dims(x_int, axis=-1)
   x_labels = []
   for i in range(nbits):
-    x_labels.append(tf.floormod(tf.floordiv(x_l, 2**i), 2))
+    x_labels.append(
+        tf.floormod(
+            tf.floordiv(tf.to_int32(x_l),
+                        tf.to_int32(base)**i), tf.to_int32(base)))
   res = tf.concat(x_labels, axis=-1)
   return tf.to_float(res)
 
@@ -218,12 +287,25 @@ def embed(x):
         hot = tf.one_hot(x, hparams.v_size)
         h1 = tf.layers.dense(hot, hparams.hidden_size, name="dae_dense")
       elif hparams.bottleneck_kind == "vq-vae":
-        if hparams.ema:
-          means_embed = ema_means
-        else:
-          means_embed = means
-
-        h1 = tf.gather(means_embed, x)
+        means_embed = means
+        shape_x = common_layers.shape_list(x)
+        x_flat = tf.reshape(x, [-1, 1])
+        c = int_to_bit(x_flat, nbits=int(math.log(hparams.v_size, 2)), base=2)
+        shape = common_layers.shape_list(c)
+        new_shape = shape
+        new_shape[-1] = hparams.num_blocks
+        new_shape.append(int(math.log(hparams.v_size, 2) // hparams.num_blocks))
+        c = tf.to_int32(tf.reshape(c, shape=new_shape))
+        c = bit_to_int(
+            c,
+            nbits=int(math.log(hparams.v_size, 2) // hparams.num_blocks),
+            base=2)
+        h1 = tf.gather(tf.transpose(means_embed, [1, 0, 2]), c)
+        h1 = tf.stack(
+            [h1[:, :, i, i, :] for i in range(hparams.num_blocks)], axis=-2)
+        new_shape = shape_x
+        new_shape.append(hparams.hidden_size)
+        h1 = tf.reshape(h1, new_shape)
       elif hparams.bottleneck_kind == "rounding":
         h1 = x
 
@@ -269,22 +351,45 @@ def embed(x):
       h1 = tf.layers.dense(hot, hparams.hidden_size, name="dae_dense")
     if hparams.bottleneck_kind == "vq-vae":
       x_means_hot, x_means, q_loss, e_loss = kmeans(x, means, hparams)
-      c = tf.argmax(x_means_hot, axis=-1)
+
+      # Get the discrete latent represenation
+      x_means_idx = tf.argmax(x_means_hot, axis=-1)
+      # Get the binary representation
+      x_means_bits = int_to_bit(
+          x_means_idx,
+          nbits=int(math.log(hparams.v_size, 2) // hparams.num_blocks),
+          base=2)
+      shape = common_layers.shape_list(x_means_bits)
+      new_shape = shape[:-1]
+      new_shape[-1] = int(math.log(hparams.v_size, 2))
+      x_means_bits = tf.reshape(x_means_bits, shape=new_shape)
+      c = bit_to_int(
+          tf.to_int32(x_means_bits),
+          nbits=int(math.log(hparams.v_size, 2)),
+          base=2)
 
       # Update the ema variables
       if hparams.ema:
         tf.logging.info("Using EMA with beta = {}".format(hparams.beta))
-        x_means_hot_flat = tf.reshape(x_means_hot, shape=[-1, hparams.v_size])
         updated_ema_count = moving_averages.assign_moving_average(
             ema_count,
-            tf.reduce_sum(x_means_hot_flat, axis=0),
+            tf.reduce_sum(
+                tf.reshape(
+                    x_means_hot,
+                    shape=[-1, hparams.num_blocks, hparams.block_v_size]),
+                axis=0),
             hparams.decay,
             zero_debias=False)
-        x_flat = tf.reshape(x, [-1, hparams.hidden_size])
-        dw = tf.matmul(x_means_hot_flat, x_flat, transpose_a=True)
+
+        x_means_hot_flat = tf.reshape(
+            x_means_hot, shape=[-1, hparams.num_blocks, hparams.block_v_size])
+        x_reshaped = hparams.reshape_fn(x, hparams)
+        dw = tf.matmul(
+            tf.transpose(x_means_hot_flat, perm=[1, 2, 0]),
+            tf.transpose(x_reshaped, perm=[1, 0, 2]))
         updated_ema_means = moving_averages.assign_moving_average(
             ema_means, dw, hparams.decay, zero_debias=False)
-        n = tf.reduce_sum(updated_ema_count)
+        n = tf.reduce_sum(updated_ema_count, axis=-1, keepdims=True)
         updated_ema_count = ((updated_ema_count + hparams.epsilon) /
                              (n + hparams.v_size * hparams.epsilon) * n)
         updated_ema_means /= tf.expand_dims(updated_ema_count, axis=-1)
@@ -296,7 +401,11 @@ def embed(x):
       else:
         l = q_loss + hparams.beta * e_loss
 
-      h1 = tf.stop_gradient(x_means) + x - tf.stop_gradient(x)
+      x_reshaped = hparams.reshape_fn(x, hparams)
+      shape = common_layers.shape_list(x)
+      x_means = tf.reshape(x_means, shape)
+      x_reshaped = tf.reshape(x_reshaped, shape)
+      h1 = x_reshaped + tf.stop_gradient(x_means - x_reshaped)
 
     if hparams.bottleneck_kind == "rounding":
       h = tf.layers.dense(x, 1, name="vcc")
@@ -316,15 +425,20 @@ def embed(x):
     return res, c, l, embed
 
 
-def compress(x, is_2d, hparams, name):
+def compress(x, c, is_2d, hparams, name):
   """Compress."""
   with tf.variable_scope(name):
     # Run compression by strided convs.
     cur = x
     k1 = (3, 3) if is_2d else (3, 1)
-    cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
     k2 = (2, 2) if is_2d else (2, 1)
+    cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams, "rc")
+    if c is not None and hparams.do_attend_compress:
+      cur = attend(cur, c, hparams, "compress_attend")
     for i in xrange(hparams.num_compress_steps):
+      if hparams.do_residual_compress:
+        cur = residual_conv(cur, hparams.num_compress_steps, k1, hparams,
+                            "rc_%d" % i)
       cur = common_layers.conv_block(
           cur, hparams.hidden_size, [((1, 1), k2)],
           strides=k2, name="compress_%d" % i)
@@ -377,12 +491,49 @@ def multinomial_sample(x, vocab_size, temperature):
   return tf.to_int32(reshaped_samples)
 
 
+def ae_latent_softmax(latents_pred, latents_discrete, hparams):
+  """Latent prediction and loss."""
+  vocab_size = hparams.v_size
+  if hparams.bottleneck_kind == "semhash":
+    vocab_size = 2**hparams.z_size
+  if hparams.num_blocks < 2:
+    latents_logits = tf.layers.dense(latents_pred, vocab_size,
+                                     name="extra_logits")
+    loss = None
+    if latents_discrete is not None:
+      loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
+          labels=latents_discrete, logits=latents_logits)
+    sample = multinomial_sample(
+        latents_logits, vocab_size, hparams.sampling_temp)
+    return sample, loss
+
+  # Multi-block case.
+  vocab_bits = int(math.log(vocab_size, 2))
+  assert vocab_size == 2**vocab_bits
+  assert vocab_bits % hparams.num_blocks == 0
+  block_vocab_size = 2**(vocab_bits // hparams.num_blocks)
+  latents_logits = [tf.layers.dense(latents_pred, block_vocab_size,
+                                    name="extra_logits_%d" % i)
+                    for i in xrange(hparams.num_blocks)]
+  loss = None
+  if latents_discrete is not None:
+    losses = []
+    for i in xrange(hparams.num_blocks):
+      d = tf.floormod(tf.floordiv(latents_discrete,
+                                  block_vocab_size**i), block_vocab_size)
+      losses.append(tf.nn.sparse_softmax_cross_entropy_with_logits(
+          labels=d, logits=latents_logits[i]))
+    loss = sum(losses)
+  samples = [multinomial_sample(l, block_vocab_size, hparams.sampling_temp)
+             for l in latents_logits]
+  sample = sum([s * block_vocab_size**i for i, s in enumerate(samples)])
+  return sample, loss
+
+
 def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
   """Sample from the latent space in the autoencoder."""
   latents_pred = decode_transformer(inputs, ed, latents_dense, hparams, "extra")
-  latents_pred = tf.layers.dense(latents_pred, 2**16, name="extra_logits")
-  latents_discrete = multinomial_sample(
-      latents_pred, 2**16, hparams.sampling_temp)
+  latents_discrete, _ = ae_latent_softmax(latents_pred, None, hparams)
 
   def next_bit(latents_discrete, i):
     latents_discrete_prev = latents_discrete
@@ -390,9 +541,7 @@ def next_bit(latents_discrete, i):
       latents_dense = embed(latents_discrete)
       latents_pred = decode_transformer(
           inputs, ed, latents_dense, hparams, "extra")
-      latents_pred = tf.layers.dense(latents_pred, 2**16, name="extra_logits")
-      latents_discrete = multinomial_sample(
-          latents_pred, 2**16, hparams.sampling_temp)
+      latents_discrete, _ = ae_latent_softmax(latents_pred, None, hparams)
       return tf.concat([latents_discrete_prev[:, :(i+1), :],
                         latents_discrete[:, (i+1):, :]], axis=1)
 
@@ -434,14 +583,14 @@ def ae_transformer_internal(inputs,
     targets, _ = common_layers.pad_to_same_length(
         targets, max_targets_len_from_inputs,
         final_length_divisible_by=2**hparams.num_compress_steps)
-    targets_c = compress(targets, False, hparams, "compress")
+    targets_c = compress(targets, inputs, False, hparams, "compress")
     if hparams.mode != tf.estimator.ModeKeys.PREDICT:
       # Compress and bottleneck.
       latents_dense, latents_discrete, extra_loss, _ = bottleneck(
           targets_c, hparams, 2 * 2048, "vc", means, ema_count, ema_means)
       if _DO_SUMMARIES:
         tf.summary.histogram("b0", tf.reshape(latents_discrete[:, 0, :], [-1]))
-      pc = common_layers.inverse_exp_decay(hparams.startup_steps) * 0.95
+      pc = common_layers.inverse_exp_decay(hparams.startup_steps)
       pc = pc if hparams.mode == tf.estimator.ModeKeys.TRAIN else 1.0
       cond = tf.less(tf.random_uniform([batch_size]), pc)
       latents_dense = tf.where(cond, latents_dense, targets_c)
@@ -452,11 +601,10 @@ def ae_transformer_internal(inputs,
         latents_pred = decode_transformer(
             tf.stop_gradient(inputs), tf.stop_gradient(ed),
             tf.stop_gradient(latents_dense), hparams, "extra")
-        latents_pred = tf.layers.dense(latents_pred, 2**16, name="extra_logits")
-        losses["latent_pred"] = tf.nn.sparse_softmax_cross_entropy_with_logits(
-            labels=latents_discrete, logits=latents_pred)
+        _, latent_pred_loss = ae_latent_softmax(
+            latents_pred, latents_discrete, hparams)
         losses["latent_pred"] = tf.reduce_mean(
-            losses["latent_pred"] * 0.5 * tf.to_float(cond))
+            latent_pred_loss * 0.5 * tf.to_float(cond))
       else:
         inputs_c = decode_transformer(inputs, ed, targets_c, hparams, "dec_c")
         losses["latent_pred"] = tf.reduce_mean((inputs_c - targets_c)**2) * 20
@@ -494,10 +642,11 @@ def bn_inputs():
 
     # Masking.
     if hparams.do_mask:
-      masking = common_layers.inverse_lin_decay(100000)
-      masking *= common_layers.inverse_exp_decay(25000)  # Not much at start.
+      masking = common_layers.inverse_lin_decay(hparams.mask_startup_steps)
+      masking *= common_layers.inverse_exp_decay(
+          hparams.mask_startup_steps // 4)  # Not much at start.
       if not hparams.do_refine:
-        masking -= tf.random_uniform([]) * 0.3
+        masking -= tf.random_uniform([]) * hparams.unmasked_percentage
       masking = tf.minimum(tf.maximum(masking, 0.0), 1.0)
       if hparams.mode == tf.estimator.ModeKeys.PREDICT:
         masking = predict_mask
@@ -507,6 +656,8 @@ def bn_inputs():
       for i in xrange(hparams.num_compress_steps):
         j = hparams.num_compress_steps - i - 1
         d = residual_conv(d, 1, (3, 1), hparams, "decompress_rc_%d" % j)
+        if hparams.do_attend_decompress:
+          d = attend(d, inputs, hparams, "decompress_attend_%d" % j)
         d = decompress_step(d, hparams, i > 0, False, "decompress_%d" % j)
       targets = mask * targets + (1.0 - mask) * d
     targets = tf.concat([tf.reverse(latents_dense, [1]), targets], axis=1)
@@ -516,19 +667,17 @@ def bn_inputs():
     res = res[:, common_layers.shape_list(latents_dense)[1]:, :, :]
     if hparams.do_mask and hparams.do_refine:
       def refine_res():
-        return residual_conv(res, 1, (5, 1), hparams, "refine")
+        # return residual_conv(res, 1, (5, 1), hparams, "refine")
+        r, _ = encode(tf.squeeze(res, axis=[2]),
+                      target_space, hparams, "refine_enc")
+        return tf.expand_dims(r, axis=2)
       masked_batches = tf.reduce_sum(mask, axis=[1, 2, 3])
       all_masked = tf.less(masked_batches, 0.1)
       res = tf.where(all_masked, refine_res(), res)
-    # We'll start training only the extra model of latents after 400K steps.
-    # Before we train only this, we decrease lr for other weights.
-    latent_time = tf.less(300000, tf.to_int32(tf.train.get_global_step()))
-    decreased_lr = common_layers.inverse_lin_decay(400000)
+    # We'll start training the extra model of latents after mask_startup_steps.
+    latent_time = tf.less(hparams.mask_startup_steps,
+                          tf.to_int32(tf.train.get_global_step()))
     losses["latent_pred"] *= tf.to_float(latent_time)
-    losses["extra"] *= 1.0 - tf.to_float(latent_time)
-    decreased_lr_res = tf.stop_gradient(decreased_lr * res)
-    decreased_lr_res += (1.0 - decreased_lr) * res
-    res = tf.cond(latent_time, lambda: decreased_lr_res, lambda: res)
   return res, losses, cache
 
 
@@ -545,15 +694,47 @@ def __init__(self, *args, **kwargs):
     self.ema_count = None
     self.ema_means = None
     if self._hparams.bottleneck_kind == "vq-vae":
+      # Check that num_blocks exactly divides hidden_size and v_size
+      assert self._hparams.hidden_size % self._hparams.num_blocks == 0
+      assert self._hparams.v_size % self._hparams.num_blocks == 0
+
+      self._hparams.block_dim = int(
+          self._hparams.hidden_size // self._hparams.num_blocks)
+      self._hparams.block_v_size = 2**(
+          math.log(self._hparams.v_size, 2) / self._hparams.num_blocks)
+      self._hparams.block_v_size = int(self._hparams.block_v_size)
+
+      if self._hparams.reshape_method == "project":
+        tf.logging.info("Using random projections for hierarchical vq-vae")
+        tf.logging.info("Trainable projections = {}".format(
+            self._hparams.trainable_projections))
+        self._hparams.projection_tensors = tf.get_variable(
+            name="projection",
+            shape=[
+                self._hparams.num_blocks, self._hparams.hidden_size,
+                self._hparams.block_dim
+            ],
+            initializer=tf.random_normal_initializer(),
+            trainable=self._hparams.trainable_projections)
+        self._hparams.reshape_fn = project_hidden
+      elif self._hparams.reshape_method == "slice":
+        tf.logging.info("Using slices for hierarchical vq-vae")
+        self._hparams.reshape_fn = slice_hidden
+      else:
+        raise ValueError("Unknown reshape method")
+
       self.means = tf.get_variable(
           name="means",
-          shape=[self._hparams.v_size, self._hparams.hidden_size],
-          initializer=tf.random_normal_initializer())
+          shape=[
+              self._hparams.num_blocks, self._hparams.block_v_size,
+              self._hparams.block_dim
+          ],
+          initializer=tf.uniform_unit_scaling_initializer())
 
       # Create the shadow variables if we are using EMA
       if self._hparams.ema:
         self.ema_count = tf.get_variable(
-            "ema_count", [self._hparams.v_size],
+            "ema_count", [self._hparams.num_blocks, self._hparams.block_v_size],
             initializer=tf.constant_initializer(0))
         with tf.colocate_with(self.means):
           self.ema_means = tf.get_variable(
@@ -602,7 +783,7 @@ def infer(self, features=None, decode_length=50, beam_size=1, top_beams=1,
     """Produce predictions from the model."""
     if not self._hparams.do_mask:
       return super(TransformerAE, self).infer(
-          features, decode_length, beam_size, top_beams, alpha)
+          features, decode_length, beam_size, top_beams, alpha)["outputs"]
     if not features:
       features = {}
     inputs_old = None
@@ -649,20 +830,30 @@ def transformer_ae_small():
   hparams.hidden_size = 384
   hparams.filter_size = 2048
   hparams.label_smoothing = 0.0
+  hparams.optimizer = "Adafactor"
   hparams.add_hparam("z_size", 16)
   hparams.add_hparam("noise_dev", 0.0)
   hparams.add_hparam("d_mix", 0.5)
   # Bottleneck kinds supported: dense, vae, semhash, gumbel-softmax, vq-vae.
   hparams.add_hparam("bottleneck_kind", "semhash")
+  hparams.add_hparam("num_blocks", 1)
+  # Reshape method for hierarchical vq-vae: slice, project
+  hparams.add_hparam("reshape_method", "slice")
+  hparams.add_hparam("trainable_projections", False)
+  hparams.add_hparam("unmasked_percentage", 0.3)
   hparams.add_hparam("do_ae", True)
   hparams.add_hparam("do_mask", True)
   hparams.add_hparam("do_refine", False)
+  hparams.add_hparam("do_attend_compress", False)
+  hparams.add_hparam("do_attend_decompress", True)
+  hparams.add_hparam("do_residual_compress", False)
   hparams.add_hparam("drop_inputs", False)
   hparams.add_hparam("v_size", 1024*64)
   hparams.add_hparam("max_context_length", 64)
   hparams.add_hparam("num_compress_steps", 3)
   hparams.add_hparam("kl_steps", 35000)
   hparams.add_hparam("startup_steps", 10000)
+  hparams.add_hparam("mask_startup_steps", 50000)
   hparams.add_hparam("kmeans_lr_factor", 0.002)
   hparams.add_hparam("z_dropout", 0.1)
   hparams.add_hparam("is_2d", 0)
diff --git a/tensor2tensor/models/vanilla_gan.py b/tensor2tensor/models/vanilla_gan.py
index a6196c491..459753b95 100644
--- a/tensor2tensor/models/vanilla_gan.py
+++ b/tensor2tensor/models/vanilla_gan.py
@@ -89,6 +89,8 @@ def vanilla_gan_internal(inputs, hparams, train):
     losses = {}
     losses["discriminator"] = d_loss
     losses["generator"] = g_loss
+    # Include a dummy training loss to skip self.top and self.loss
+    losses["training"] = tf.constant(0., dtype=tf.float32)
 
     z_sampled = tf.random_uniform(shape=[1, hparams.random_sample_size],
                                   minval=-1, maxval=1, name="z")
@@ -146,9 +148,6 @@ def vanilla_gan():
 
   hparams = common_hparams.basic_params1()
 
-  hparams.input_modalities = "inputs:image:zero_loss"
-  hparams.target_modality = "image:zero_loss"
-
   hparams.batch_size = 32
   hparams.label_smoothing = 0.0
   hparams.add_hparam("startup_steps", 10000)
diff --git a/tensor2tensor/models/xception.py b/tensor2tensor/models/xception.py
index 9e2174161..401694580 100644
--- a/tensor2tensor/models/xception.py
+++ b/tensor2tensor/models/xception.py
@@ -95,9 +95,7 @@ def xnet_resblock(x, filters, res_relu, name):
             force2d=True,
             name="res_conv0")
 
-    inputs = common_layers.standardize_images(inputs)
-    # TODO(lukaszkaiser): summaries here don't work in multi-problem case yet.
-    # tf.summary.image("inputs", inputs, max_outputs=2)
+    tf.summary.image("inputs", inputs, max_outputs=2)
     x = common_layers.conv_block(
         inputs,
         32, [((1, 1), (3, 3))],
@@ -155,7 +153,7 @@ def xception_base():
   hparams.num_hidden_layers = 8
   hparams.kernel_height = 3
   hparams.kernel_width = 3
-  hparams.learning_rate_decay_scheme = "exp50k"
+  hparams.learning_rate_decay_scheme = "exp"
   hparams.learning_rate = 0.05
   hparams.learning_rate_warmup_steps = 3000
   hparams.initializer_gain = 1.0
@@ -181,9 +179,7 @@ def xception_tiny():
 @registry.register_hparams
 def xception_tiny_tpu():
   hparams = xception_base()
-  hparams.tpu_batch_size_per_shard = 2
-  # The base exp50k scheme uses a cond which fails to compile on TPU
-  hparams.learning_rate_decay_scheme = "noam"
+  hparams.batch_size = 2
   hparams.num_hidden_layers = 2
   hparams.hidden_size = 128
   hparams.optimizer = "TrueAdam"
diff --git a/tensor2tensor/notebooks/hello_t2t.ipynb b/tensor2tensor/notebooks/hello_t2t.ipynb
index 27ce78bb5..cc9f66a02 100644
--- a/tensor2tensor/notebooks/hello_t2t.ipynb
+++ b/tensor2tensor/notebooks/hello_t2t.ipynb
@@ -60,8 +60,7 @@
       },
       "source": [
         "# Install deps\n",
-        "# We're using some new features from tensorflow so we install 1.5.0rc0\n",
-        "!pip install -q 'tensor2tensor==1.4.1' 'tensorflow==1.5.0rc0'"
+        "!pip install -q tensor2tensor"
       ],
       "cell_type": "code",
       "execution_count": 0,
@@ -190,7 +189,7 @@
               " 'audio_timit_characters_tune',\n",
               " 'audio_timit_tokens8k_test',\n",
               " 'audio_timit_tokens8k_tune',\n",
-              " 'image_celeba_tune',\n",
+              " 'image_celeba',\n",
               " 'image_cifar10',\n",
               " 'image_cifar10_plain',\n",
               " 'image_cifar10_plain8',\n",
@@ -699,7 +698,7 @@
         "def translate(inputs):\n",
         "  encoded_inputs = encode(inputs)\n",
         "  with tfe.restore_variables_on_create(ckpt_path):\n",
-        "    model_output = translate_model.infer(encoded_inputs)\n",
+        "    model_output = translate_model.infer(encoded_inputs)[\"outputs\"]\n",
         "  return decode(model_output)\n",
         "\n",
         "inputs = \"The animal didn't cross the street because it was too tired\"\n",
diff --git a/tensor2tensor/serving/README.md b/tensor2tensor/serving/README.md
index aadffa9f8..2081553cc 100644
--- a/tensor2tensor/serving/README.md
+++ b/tensor2tensor/serving/README.md
@@ -3,8 +3,7 @@
 Tensor2Tensor and the TensorFlow ecosystem make it easy to serve a model once
 trained.
 
-**Note**: The following requires recent features in TensorFlow as so if you get
-import errors or the like, try installing `tensorflow==1.5.0rc0`.
+**Note**: Requires TF 1.5+.
 
 ## 1. Export for Serving
 
diff --git a/tensor2tensor/test_data/transformer_test_ckpt/flags.txt b/tensor2tensor/test_data/transformer_test_ckpt/flags.txt
index 26988922c..2587e3e2d 100644
--- a/tensor2tensor/test_data/transformer_test_ckpt/flags.txt
+++ b/tensor2tensor/test_data/transformer_test_ckpt/flags.txt
@@ -1,7 +1,7 @@
 --eval_steps=1
 --hparams_range=
 --t2t_usr_dir=
---experimental_optimize_placement=False
+--enable_graph_rewriter=False
 --sync=False
 --eval_run_autoregressive=False
 --eval_use_test_set=False
@@ -46,4 +46,4 @@
 --locally_shard_to_cpu=False
 --worker_job=/job:localhost
 --model=transformer
---parsing_path=
\ No newline at end of file
+--parsing_path=
diff --git a/tensor2tensor/test_data/transformer_test_ckpt/hparams.json b/tensor2tensor/test_data/transformer_test_ckpt/hparams.json
index 196d736ba..b07ac9486 100644
--- a/tensor2tensor/test_data/transformer_test_ckpt/hparams.json
+++ b/tensor2tensor/test_data/transformer_test_ckpt/hparams.json
@@ -1 +1 @@
-{"daisy_chain_variables": true, "optimizer_adam_beta1": 0.9, "scheduled_sampling_prob": 0.0, "num_hidden_layers": 2, "moe_loss_coef": 0.01, "max_target_seq_length": 0, "clip_grad_norm": 0.0, "pos": "timing", "scheduled_sampling_gold_mixin_prob": 0.5, "initializer": "uniform_unit_scaling", "grad_noise_scale": 0.0, "optimizer_momentum_momentum": 0.9, "nbr_decoder_problems": 1, "attention_key_channels": 0, "tpu_batch_size_per_shard": 24, "eval_drop_long_sequences": false, "learning_rate_cosine_cycle_steps": 250000, "prepend_mode": "none", "weight_decay": 0.0, "symbol_modality_skip_top": false, "weight_noise": 0.0, "target_modality": "default", "use_tpu": false, "attention_dropout": 0.1, "parameter_attention_value_channels": 0, "factored_logits": false, "relu_dropout": 0.1, "no_data_parallelism": false, "layer_preprocess_sequence": "n", "sampling_method": "argmax", "learning_rate": 0.2, "num_heads": 2, "max_length": 256, "summarize_grads": false, "attention_value_channels": 0, "num_encoder_layers": 0, "label_smoothing": 0.1, "use_fixed_batch_size": false, "optimizer": "Adam", "moe_k": 2, "self_attention_type": "dot_product", "learning_rate_decay_scheme": "noam", "sampling_temp": 1.0, "kernel_height": 3, "use_pad_remover": true, "batch_size": 4096, "problem_choice": "adaptive", "max_relative_position": 0, "force_full_predict": false, "min_length_bucket": 8, "layer_prepostprocess_dropout": 0.1, "eval_run_autoregressive": false, "shared_embedding_and_softmax_weights": true, "symbol_modality_num_shards": 16, "dropout": 0.2, "compress_steps": 0, "parameter_attention_key_channels": 0, "length_bucket_step": 1.1, "kernel_width": 1, "hidden_size": 16, "num_decoder_layers": 0, "input_modalities": "default", "filter_size": 8, "optimizer_adam_beta2": 0.98, "scheduled_sampling_warmup_steps": 50000, "norm_type": "layer", "min_length": 0, "moe_num_experts": 64, "multiply_embedding_mode": "sqrt_depth", "max_input_seq_length": 0, "learning_rate_warmup_steps": 8000, "proximity_bias": false, "ffn_layer": "dense_relu_dense", "initializer_gain": 1.0, "layer_postprocess_sequence": "da", "moe_hidden_sizes": "2048", "optimizer_adam_epsilon": 1e-09, "norm_epsilon": 1e-06}
\ No newline at end of file
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diff --git a/tensor2tensor/test_data/vocab.ende.32768 b/tensor2tensor/test_data/vocab.ende.32768
new file mode 100644
index 000000000..8036581fe
--- /dev/null
+++ b/tensor2tensor/test_data/vocab.ende.32768
@@ -0,0 +1,33708 @@
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+'<EOS>'
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+'._'
+'the_'
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+'Nun_'
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+'Mitteilung_'
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+'Instrument_'
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+'Beschreibung_'
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+'Amt_'
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+'Air_'
+'AIDS_'
+'9'
+'5'
+'1997_'
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+'01_'
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+'е'
+'á_'
+'Über_'
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+'weltweite_'
+'vertreten_'
+'verloren_'
+'unseres_'
+'unlikely_'
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+'tischen_'
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+'supporting_'
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+'stress_'
+'strengthen_'
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+'standing_'
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+'saving_'
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+'politisch_'
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+'gave_'
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+'früheren_'
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+'democracies_'
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+'cable_'
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+'behaupten_'
+'behandeln_'
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+'bathrooms_'
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+'angesprochen_'
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+'amenities_'
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+'adequate_'
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+'Wirtschaftspolitik_'
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+'Weltkrieg_'
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+'Ratspräsidentschaft_'
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+'Palestinians_'
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+'Mittelmeer_'
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+'Mad'
+'MS_'
+'MP'
+'Licht_'
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+'Kr'
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+'Kilometer_'
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+'Kap'
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+'Insbesondere_'
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+'Ihres_'
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+'Hauptbahnhof_'
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+'Firma_'
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+'ups_'
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+'unzählige_'
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+'unwahrscheinlich_'
+'untersucht_'
+'unterm'
+'unpro'
+'unmittelbaren_'
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+'unknown_'
+'univers'
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+'ungssystem'
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+'understands_'
+'underscore'
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+'tum'
+'tub'
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+'treatments_'
+'traten_'
+'transmission_'
+'transit'
+'transformed_'
+'transc'
+'transatlantischen_'
+'transactions_'
+'transaction_'
+'trains_'
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+'tou'
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+'tod'
+'tive'
+'tischer_'
+'tire'
+'tir'
+'tieren_'
+'tiefe_'
+'tib'
+'throw'
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+'technische'
+'techni'
+'tc'
+'tasty_'
+'tar_'
+'tankers_'
+'talent_'
+'tak'
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+'tage_'
+'tage'
+'systematically_'
+'symbolic_'
+'sym'
+'sy_'
+'suspension_'
+'surpluses_'
+'surg'
+'supplement'
+'suff'
+'sudah_'
+'subway_'
+'substan'
+'stück_'
+'stö'
+'stuff_'
+'stuf'
+'studie'
+'struktur_'
+'stro'
+'strikte'
+'strikes_'
+'stretch'
+'streng_'
+'straight_'
+'straf'
+'stoffe_'
+'stle'
+'stieg_'
+'stick_'
+'stet_'
+'stes_'
+'steam_'
+'steadily_'
+'stea'
+'statt'
+'statisti'
+'stamm'
+'stakes_'
+'stabile_'
+'ssen'
+'sprachliche'
+'sprachen_'
+'spell'
+'speed'
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+'spectacular_'
+'specify_'
+'sound'
+'soul_'
+'sorgfältige'
+'sorgfältig_'
+'sophisticated_'
+'solved_'
+'sogenannten_'
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+'smart_'
+'slowly_'
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+'shut_'
+'shortcomings_'
+'ship'
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+'sexuelle_'
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+'secondly_'
+'seba'
+'scrutin'
+'screening_'
+'scrap'
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+'schwäch'
+'schwedischen_'
+'scholar'
+'schließt_'
+'sches_'
+'schauen_'
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+'saubere'
+'satisfied_'
+'sak'
+'safeguard_'
+'rückg'
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+'räge_'
+'rui'
+'ruh'
+'ruch_'
+'rter'
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+'romantic_'
+'roman'
+'roa'
+'rnähr'
+'rna'
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+'risiko_'
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+'richtet_'
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+'restoration_'
+'ress'
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+'residents_'
+'reside'
+'reproduc'
+'repr'
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+'renminbi_'
+'removing_'
+'remind'
+'remark'
+'relocat'
+'reinforce_'
+'reiche_'
+'register_'
+'regierung_'
+'regelmäßig_'
+'refusal_'
+'redistribution_'
+'recon'
+'recht'
+'rechnen_'
+'receives_'
+'reben_'
+'realistische'
+'realisier'
+'realised_'
+'reader_'
+'rator'
+'ratifiziert_'
+'ratification_'
+'rapide_'
+'ranks_'
+'rank'
+'rall'
+'rahmen'
+'ract'
+'quoten_'
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+'quir'
+'quie'
+'ques_'
+'quanti'
+'qualifizierte_'
+'qualifications_'
+'pushed_'
+'pup'
+'präsentier'
+'prozesses_'
+'prove'
+'protektionistische'
+'prosperous_'
+'proof_'
+'prol'
+'projekt_'
+'programmen_'
+'professionelle'
+'prob'
+'prize_'
+'privilege_'
+'printing_'
+'preventive_'
+'prevail'
+'prestigious_'
+'preserved_'
+'presentation_'
+'prescri'
+'premature_'
+'pragmatic_'
+'potenziellen_'
+'poss'
+'poses_'
+'pollut'
+'pole'
+'polar_'
+'po_'
+'plural'
+'pill'
+'pier'
+'philosophy_'
+'phas'
+'pflege'
+'pfl'
+'pfel'
+'pfe_'
+'petit'
+'pes_'
+'persönliche'
+'persuade_'
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+'persist'
+'perpet'
+'permi'
+'periode_'
+'pere'
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+'pensions_'
+'penda'
+'pemba'
+'pel'
+'peacekeeping_'
+'patient_'
+'passes_'
+'partitions_'
+'parlamentarische_'
+'parity_'
+'paren'
+'papers_'
+'panel_'
+'pana'
+'painting_'
+'own'
+'owe_'
+'overw'
+'oversight_'
+'overe'
+'outs_'
+'outs'
+'outl'
+'oti'
+'ote_'
+'oste'
+'osen_'
+'osc'
+'ori'
+'operates_'
+'opens_'
+'openly_'
+'opa'
+'onym'
+'onat'
+'onal_'
+'ome_'
+'ologie_'
+'ologi'
+'oil'
+'ofi'
+'offensichtliche'
+'odie'
+'ock_'
+'ochen_'
+'och_'
+'occupation'
+'oca'
+'obst'
+'observation_'
+'obli'
+'ober'
+'nzi'
+'nutrition_'
+'ntw'
+'nsu'
+'nous_'
+'nost'
+'nos'
+'nomin'
+'noi'
+'nnt_'
+'nner'
+'nne_'
+'nlage'
+'nko'
+'niu'
+'nity_'
+'nio'
+'nik_'
+'nightlife_'
+'nien_'
+'niedrigere'
+'niedrige_'
+'ngst'
+'ngn'
+'nglich_'
+'ngel'
+'neuro'
+'neuesten_'
+'nett'
+'ness'
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+'ners_'
+'neglig'
+'neglect_'
+'negativ_'
+'necessity_'
+'ndete'
+'nden'
+'naval_'
+'namens_'
+'nah_'
+'nada_'
+'männ'
+'mutige'
+'musik_'
+'multilateralen_'
+'mpin'
+'mpf'
+'mp_'
+'movies_'
+'mov'
+'mounted_'
+'mortgage_'
+'monument'
+'moment'
+'modules_'
+'moderni'
+'modern'
+'mixture_'
+'mitge'
+'missi'
+'mism'
+'misc'
+'mir'
+'minist'
+'mining_'
+'minimi'
+'mina'
+'militärisch'
+'militant'
+'meta'
+'mengu'
+'mengak'
+'menga'
+'membu'
+'med'
+'mechanismen_'
+'measurement_'
+'matche'
+'master'
+'massiv_'
+'massage_'
+'marine_'
+'marginal_'
+'mapp'
+'mali'
+'makroökonomische'
+'mak'
+'mai'
+'mah'
+'magne'
+'magazine_'
+'ländliche_'
+'lve'
+'lv'
+'lungs'
+'lue'
+'luc'
+'lua'
+'lu_'
+'loses_'
+'logo_'
+'loc'
+'lobb'
+'lize'
+'liza'
+'litik'
+'liti'
+'literar'
+'listening_'
+'liste'
+'list'
+'limiting_'
+'liebe_'
+'licherweise_'
+'license_'
+'lib'
+'lf'
+'leute_'
+'letting_'
+'lending_'
+'lem_'
+'leitung_'
+'leitete_'
+'leitet_'
+'leis'
+'legislat'
+'leere'
+'lect'
+'laute'
+'lati'
+'latein'
+'lager_'
+'künstlich'
+'käm'
+'kta'
+'kritisch'
+'kris'
+'kre'
+'kopieren_'
+'kontrollierte'
+'kontrolle_'
+'konkret_'
+'konflikt'
+'komplette_'
+'kommunistische_'
+'kolle'
+'kenne_'
+'kebijakan_'
+'karten_'
+'kampf'
+'kam'
+'kali'
+'kale'
+'kai'
+'jüngere'
+'jährliche_'
+'judgment_'
+'judge_'
+'judge'
+'jegliche'
+'jed'
+'jas'
+'itze'
+'itution_'
+'ities_'
+'istisch'
+'ister'
+'issen_'
+'irt'
+'irgendwann_'
+'iranischen_'
+'ira_'
+'ip_'
+'iot'
+'io'
+'invade'
+'interpreti'
+'interprete'
+'interim_'
+'interessanten_'
+'intellektuelle'
+'integrierten_'
+'integrieren_'
+'integral'
+'institutionelle_'
+'installer_'
+'insch'
+'inner_'
+'inj'
+'inie'
+'inglich'
+'ingen'
+'infringement_'
+'informati'
+'influential_'
+'ineffective_'
+'industrielle_'
+'induce'
+'incredibly_'
+'inan'
+'imposing_'
+'immer'
+'ily_'
+'illo'
+'ildung_'
+'ihrerseits_'
+'ignoriert_'
+'ignorieren_'
+'ift'
+'iete'
+'iere_'
+'iere'
+'ielen_'
+'iehen_'
+'iegel'
+'idor'
+'identical_'
+'idealer_'
+'icul'
+'ichte'
+'ices_'
+'ican'
+'ias_'
+'hü'
+'hôtel_'
+'hunting_'
+'hunderte'
+'hrung_'
+'hre'
+'hosted_'
+'hospitality_'
+'hone'
+'holen_'
+'hol_'
+'hochwertige_'
+'hne_'
+'hme'
+'hm_'
+'hle'
+'historisch_'
+'his'
+'hinzugefügt_'
+'hint'
+'hil'
+'hike'
+'hierzu_'
+'hic'
+'heutzutage_'
+'hersteller_'
+'heri'
+'here'
+'herausragende'
+'height_'
+'hearts_'
+'health'
+'heading_'
+'hbo'
+'haushalt'
+'has'
+'harg'
+'hard'
+'handled_'
+'handed_'
+'ham'
+'hall'
+'halb'
+'hafte_'
+'had'
+'habit'
+'gutem_'
+'gten_'
+'greift_'
+'grasp_'
+'good'
+'gni'
+'gn_'
+'globale'
+'gleicher_'
+'git'
+'gewünschten_'
+'gespe'
+'geru'
+'gericht_'
+'gerei'
+'gerechter'
+'gerechten_'
+'geprägt_'
+'gepflegt'
+'geographical_'
+'gent'
+'gena'
+'gemäßigte'
+'gelo'
+'geklärt_'
+'geist'
+'gehandelt_'
+'gehabt_'
+'gegens'
+'geblieben_'
+'gebeten_'
+'geber'
+'gation_'
+'ganis'
+'gangen_'
+'fäll'
+'fä'
+'fut'
+'fus'
+'fung_'
+'friedliche_'
+'freundlicher_'
+'freu'
+'freez'
+'fossile'
+'formula_'
+'formats_'
+'formally_'
+'forever_'
+'fores'
+'foods_'
+'fond'
+'fließen_'
+'fl'
+'finnische'
+'findings_'
+'fig'
+'fiel_'
+'fi_'
+'ffs'
+'ffen_'
+'fet'
+'fern'
+'fer_'
+'fel_'
+'feind'
+'fehler'
+'federa'
+'favourite_'
+'favour'
+'fassen_'
+'fascinating_'
+'fantas'
+'fals'
+'fairen_'
+'faire_'
+'fahrzeuge_'
+'facility_'
+'eßen_'
+'extremism_'
+'externen_'
+'externe_'
+'express'
+'explicit_'
+'expertise_'
+'experimental_'
+'exhibition_'
+'executi'
+'executed_'
+'excluded_'
+'exclude'
+'ewe'
+'evol'
+'evo'
+'eventuelle'
+'eve'
+'etzt_'
+'eter_'
+'ete'
+'este_'
+'esan'
+'erweiterten_'
+'erwe'
+'ervi'
+'ertrag'
+'erstklassige'
+'erson'
+'erschienen'
+'errichtete'
+'errichten_'
+'erra'
+'erp'
+'ernste_'
+'erneuer'
+'ermutigen_'
+'erla'
+'erka'
+'erin'
+'erheben_'
+'ergibt_'
+'erfü'
+'erarbeiten_'
+'entscheide'
+'entl'
+'ensu'
+'enm'
+'eng'
+'ened_'
+'enco'
+'emphasis'
+'emm'
+'els'
+'ellt_'
+'elit'
+'eliminated_'
+'elf_'
+'einse'
+'einsch'
+'eins'
+'einmalige_'
+'einig'
+'eingereicht_'
+'eingeg'
+'eingeb'
+'einf'
+'einbr'
+'einbezogen_'
+'eigentlichen_'
+'eigentliche_'
+'eich'
+'egel'
+'effektiver_'
+'ee'
+'ecological_'
+'echn'
+'eche'
+'ebu'
+'eat'
+'earn'
+'durchd'
+'dunkle'
+'duc'
+'drittens_'
+'dre'
+'draw'
+'drag_'
+'dos'
+'donat'
+'dominate_'
+'dlich'
+'dle_'
+'disturb'
+'distribut'
+'distr'
+'diss'
+'disrupti'
+'disi'
+'discretion_'
+'disappear'
+'dip'
+'dina'
+'digitalen_'
+'digitale_'
+'dig_'
+'differently_'
+'dier'
+'dich_'
+'dge_'
+'dge'
+'devastating_'
+'dete'
+'destabili'
+'desp'
+'design'
+'dero'
+'derjenigen_'
+'derive_'
+'dera'
+'deposit_'
+'deport'
+'deployment_'
+'deployed_'
+'denjenigen_'
+'demonstrators_'
+'demographic_'
+'delle_'
+'delicate_'
+'defending_'
+'defect'
+'defeat'
+'declined_'
+'declare_'
+'declar'
+'decken_'
+'dad_'
+'dac'
+'cus'
+'cultures_'
+'cts_'
+'crystal'
+'cry_'
+'courage_'
+'coordinat'
+'convey'
+'convert'
+'conv'
+'controller_'
+'contaminated_'
+'consistently_'
+'cong'
+'conf_'
+'comput'
+'compulsory_'
+'complaints_'
+'competent_'
+'competences_'
+'compatibility_'
+'command'
+'column'
+'colo'
+'collected_'
+'clothing_'
+'clim'
+'clicking_'
+'clarification_'
+'citizenship_'
+'cil'
+'cigarette'
+'cien'
+'cial_'
+'chung_'
+'chte_'
+'chs_'
+'chronische'
+'chro'
+'christliche'
+'chlich'
+'chlag'
+'chir'
+'chic'
+'chemische_'
+'chee'
+'chart'
+'chairman_'
+'chafts'
+'ces'
+'cent_'
+'cen'
+'cement_'
+'cea'
+'carrier_'
+'cap'
+'camp'
+'cali'
+'burned_'
+'bung_'
+'buch'
+'bt'
+'brother_'
+'broker'
+'breiter_'
+'breast_'
+'bran'
+'brachten_'
+'boy_'
+'boom'
+'booked_'
+'blow_'
+'blis'
+'blick_'
+'bless'
+'blame_'
+'bili'
+'bike_'
+'bien_'
+'bewährte'
+'beweist_'
+'bewaffneten_'
+'bevorzugte'
+'betragen_'
+'betrachte_'
+'bet_'
+'beständig'
+'bestre'
+'beschrieben_'
+'beschleunigt'
+'besagt_'
+'berl'
+'berichtet_'
+'bereitgestellt_'
+'bereich'
+'berechtigt_'
+'beneficial_'
+'benefi'
+'benannt'
+'benachrichtigt_'
+'bemerkt_'
+'beliebtes'
+'bekämpfung_'
+'behörden_'
+'befürwortet_'
+'befreien_'
+'befehl'
+'befasst_'
+'beeinflusst_'
+'bedingungen_'
+'bede'
+'bedanken_'
+'beck_'
+'beberapa_'
+'bbi'
+'baut_'
+'bathing_'
+'bath'
+'basically_'
+'banyak_'
+'bankr'
+'bahan_'
+'azi'
+'aya_'
+'automatic_'
+'auto_'
+'authorise'
+'auszusch'
+'auswärtige_'
+'ausschl'
+'ausreichende_'
+'ausreichen_'
+'ausgi'
+'ausgel'
+'aufweisen_'
+'aufste'
+'aufrecht_'
+'aufges'
+'auffordern_'
+'auber'
+'attribute'
+'attending_'
+'attacking_'
+'attached_'
+'attach'
+'ats_'
+'atis'
+'atic_'
+'aster'
+'aste'
+'assessments_'
+'arts_'
+'armo'
+'ark'
+'aries_'
+'ards_'
+'archa'
+'arbeiter_'
+'approval_'
+'approaching_'
+'appointment_'
+'ape'
+'anzus'
+'anzug'
+'anxiety_'
+'anu'
+'anticipated_'
+'anschließend_'
+'anschl'
+'ansa'
+'anr'
+'annually_'
+'annten_'
+'anle'
+'anhalten'
+'angka'
+'anger'
+'angenehme'
+'anfangen_'
+'ando'
+'andern'
+'ande'
+'ances_'
+'analyze'
+'analyst'
+'amtierende'
+'ame'
+'ambitions_'
+'amazing_'
+'albeit_'
+'alarm'
+'ala_'
+'akzept'
+'aktionen_'
+'airlines_'
+'ahren_'
+'ahr'
+'ahl_'
+'agte'
+'aging_'
+'affi'
+'advocate_'
+'advice_'
+'adidas_'
+'adhere'
+'adapti'
+'adapted_'
+'adapt'
+'acy_'
+'actively_'
+'act'
+'acr'
+'acquired_'
+'acknowledg'
+'achts'
+'accura'
+'accountable_'
+'accountability_'
+'accomplished_'
+'accidents_'
+'accident_'
+'academic_'
+'ac'
+'abzulehnen_'
+'absolute'
+'ablehnen_'
+'abh'
+'abges'
+'abe'
+'abba'
+'abb'
+']] | _'
+'Zwischenzeit_'
+'Zwei_'
+'Zustellbetten_'
+'Zusammenfassung_'
+'Zusagen_'
+'Zugriff'
+'Zuge'
+'Zimmerbeschreibung_'
+'Zertifikat'
+'Zeitungen_'
+'Zeitplan_'
+'Zauber'
+'Zahlreiche_'
+'Young_'
+'Xi'
+'XML_'
+'Wur'
+'Wu'
+'Wohnungs'
+'Wissenschaftlern_'
+'Wirtschaftss'
+'Wirtschaftsr'
+'Wirtschaftsm'
+'Wirtschaftskrise_'
+'Wirtschaftsa'
+'Wirtschaftlich'
+'Winter'
+'Wilhelm_'
+'Wiederg'
+'Wichtig_'
+'Who'
+'Werte'
+'Weltraum'
+'Wellness'
+'Well_'
+'Weiteren_'
+'Wechselkurs'
+'Wachstumspakt'
+'WO'
+'WM'
+'Voyager_'
+'Votum_'
+'Vorsorge'
+'Vorgänger'
+'Vordergrund_'
+'Vorbereitungen_'
+'Visionen_'
+'Vin'
+'Village_'
+'Vil'
+'Viertens_'
+'Vid'
+'Verwe'
+'Vertretung_'
+'Versorgungs'
+'Versicherungen_'
+'Versi'
+'Verr'
+'Verordnungen_'
+'Vern'
+'Veri'
+'Verheugen_'
+'Vergnügen_'
+'Vereinigung_'
+'Verbrauch_'
+'Verantwortungs'
+'Ven'
+'VP_'
+'Ut'
+'Untersuchungs'
+'Unterricht'
+'Unterdrückung_'
+'Unsere'
+'Unruhen_'
+'Unr'
+'Unm'
+'Unlike_'
+'Ungl'
+'Una'
+'Umstrukturierung_'
+'UNESCO_'
+'Trä'
+'Tower_'
+'Touristen_'
+'Tourismus'
+'Tos'
+'Tools_'
+'Tom'
+'Together_'
+'Tisch_'
+'Tickets_'
+'Throughout_'
+'Through_'
+'Thro'
+'Thi'
+'Theater_'
+'Terror_'
+'Tempora'
+'Tel_'
+'Tech_'
+'Tea'
+'Taxi'
+'Taten_'
+'Tap'
+'Tao'
+'Tak'
+'Tag'
+'Tabelle_'
+'TU'
+'TC'
+'Sü'
+'Szen'
+'Swoboda_'
+'Sun_'
+'Suche'
+'Subject_'
+'Stü'
+'Sturm_'
+'Stri'
+'Streben_'
+'Stiftung_'
+'Steuerung_'
+'Sternen'
+'Staatsverschuldung_'
+'Staatsschulden_'
+'Staatss'
+'Spiels'
+'Spezie'
+'Speci'
+'Spani'
+'Spaltung_'
+'Sp'
+'Sometimes_'
+'Solid'
+'Solche_'
+'Solarium_'
+'Solar'
+'Soci'
+'Sm'
+'Slow'
+'Six_'
+'Sisko_'
+'Single_'
+'Singapur_'
+'Similar_'
+'Signale_'
+'Shops_'
+'Server'
+'Series_'
+'Sende'
+'Senate_'
+'Senat'
+'Semi'
+'Scien'
+'Schöne'
+'Schweizer_'
+'Schre'
+'Schlu'
+'Scha'
+'Say'
+'Saturday_'
+'SanDisk_'
+'Sam'
+'Salzburg_'
+'ST_'
+'STO'
+'STE'
+'ST'
+'Rüstungs'
+'Rule_'
+'Rohstoff'
+'Rock'
+'Robert'
+'River'
+'Rica_'
+'Republikaner_'
+'Repo'
+'Rental_'
+'Renn'
+'Rem'
+'Rekord'
+'Registrierung_'
+'Regions_'
+'Reduc'
+'Rede'
+'Recent_'
+'Rea'
+'RS_'
+'RI_'
+'RC_'
+'Quo'
+'Quest'
+'QE_'
+'Putins_'
+'Publik'
+'Prävention_'
+'Program_'
+'Political_'
+'Polit'
+'Poi'
+'Plu'
+'Platz'
+'Picard_'
+'Photo'
+'Philip'
+'Phasen_'
+'Pflege_'
+'Petr'
+'Pet'
+'Pes'
+'Personen'
+'Perl'
+'Pent'
+'Peer_'
+'Patt'
+'Patent'
+'Patch'
+'Parlament'
+'Parks_'
+'Parag'
+'Palästina_'
+'Palacio_'
+'Paar_'
+'PXI_'
+'PM'
+'PH'
+'PDF_'
+'Ozean'
+'Ot'
+'Oslo_'
+'Original_'
+'Ordner_'
+'Ora'
+'Operation_'
+'Online'
+'Ombudsman_'
+'Olympus_'
+'Og'
+'Offizier'
+'Obamas_'
+'ODER_'
+'Nue'
+'Nova'
+'Normal'
+'Nob'
+'Nik'
+'Niger'
+'Nichts_'
+'Neue_'
+'Netzwerke'
+'Neigung'
+'Nehmen_'
+'Nan'
+'Nahrungsmittel_'
+'Nachrichten'
+'Nachfolger_'
+'Nachf'
+'NP'
+'NH'
+'NF'
+'Mü'
+'Möglich'
+'Männern_'
+'Mund'
+'Multi_'
+'Mountain'
+'Motion_'
+'Mord_'
+'Monats_'
+'Moldova_'
+'Moham'
+'Modul_'
+'Mode'
+'Mod'
+'Mittag'
+'Mitgliedsländer_'
+'Mitentscheidung'
+'Michel_'
+'Messen'
+'Messe_'
+'Mes'
+'Mem'
+'Meinungsumfragen_'
+'Medi'
+'Mea'
+'McC'
+'Mazedonien_'
+'Maus'
+'Materialien_'
+'Marco_'
+'Map'
+'Manu'
+'Manche_'
+'Male'
+'Make_'
+'Maha'
+'MH'
+'ME_'
+'MED'
+'Lösungs'
+'Länge_'
+'Län'
+'Luxus_'
+'Lula_'
+'Lui'
+'Luftverschmutzung_'
+'Luftraum'
+'Low_'
+'Louis_'
+'Look_'
+'Logo_'
+'Loc'
+'Lobby_'
+'Lis'
+'Linke_'
+'Lif'
+'Leuten_'
+'Lesung_'
+'Leone_'
+'Lektion_'
+'Leiden'
+'Leid_'
+'Leader'
+'Lauf'
+'Last'
+'Lands'
+'Lah'
+'LT'
+'LD'
+'Körper'
+'König'
+'Kurd'
+'Kreis'
+'Kre'
+'Kontinents_'
+'Kontin'
+'Konsumenten_'
+'Konst'
+'Konflikts_'
+'Konferenz'
+'Kom'
+'Kolonial'
+'Koll'
+'Kohle'
+'Know_'
+'Know'
+'Klin'
+'Keynesian_'
+'Kel'
+'Kein_'
+'Kau'
+'Kath'
+'Kapitalismus_'
+'Kanal'
+'Kam'
+'Kaffee'
+'Kabel_'
+'KON'
+'Juli'
+'Jugendlichen_'
+'Jud'
+'Jews_'
+'Jakob'
+'Ist'
+'Islamist_'
+'Ir'
+'Investment_'
+'Intera'
+'Instanz_'
+'Instan'
+'Installations'
+'Inhalte_'
+'Inflations'
+'Industriep'
+'Impl'
+'Impf'
+'Immunität_'
+'Imagin'
+'Illusion'
+'Il'
+'IV_'
+'ISIS_'
+'IN_'
+'ICEcat_'
+'IA_'
+'Höhen'
+'Häufig'
+'Häfen_'
+'Hyatt_'
+'Hop'
+'Hollywood_'
+'Holland_'
+'Hohen_'
+'Hisbollah_'
+'Hinweise_'
+'Hinter'
+'Hindernis_'
+'Hil'
+'High'
+'Herz_'
+'Herrsch'
+'Herbst_'
+'Her_'
+'Hen'
+'Heinrich_'
+'Hat_'
+'Hard'
+'Hannover_'
+'HI_'
+'Guinea_'
+'Guatemala_'
+'Grupp'
+'Greens_'
+'Greeks_'
+'Glücklicherweise_'
+'Gibt_'
+'Gewinner_'
+'Gewinn_'
+'Gewalt'
+'Gesetzes'
+'Gerichten_'
+'Generalsekretär_'
+'Genau_'
+'Gemeinw'
+'Gemeinschaftsp'
+'Gemein'
+'Geiste'
+'Gegenwärtig_'
+'Gegenwart_'
+'Gee'
+'Gaz'
+'Gaulle_'
+'Ganze_'
+'Gall'
+'G20_'
+'Förder'
+'Föderation_'
+'Fußball'
+'Funktionsweise_'
+'Fundamental_'
+'Frie'
+'Freundschaft_'
+'Freitag_'
+'Freihandelsabkommen_'
+'Freihandels'
+'Franklin_'
+'Format'
+'Folgendes_'
+'Folge'
+'Flug_'
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+'Find'
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+'Fehler'
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+'Fam'
+'Fakten_'
+'Fahrer_'
+'Face'
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+'Explosi'
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+'Eva'
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+'Estonia_'
+'Esc'
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+'Erkenntnis_'
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+'Erbe_'
+'Entschlossenheit_'
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+'Entlastung_'
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+'End_'
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+'Emp'
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+'Einw'
+'Einladung_'
+'Einigkeit_'
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+'Ehre_'
+'Ehr'
+'Ecuador_'
+'Ebenen_'
+'EWG_'
+'ENT_'
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+'Dusche_'
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+'Dur'
+'Drei_'
+'Dre'
+'Donnerstag_'
+'Dona'
+'Don'
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+'Differenzen_'
+'Differenz_'
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+'Defense_'
+'Deck'
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+'Darau'
+'DP'
+'Cup_'
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+'Buchung_'
+'Brut'
+'Browser_'
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+'Boutique_'
+'Bot'
+'Boh'
+'Binnenmarktes_'
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+'Beschwerden_'
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+'sections_'
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+'secara_'
+'script'
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+'schwächen_'
+'schwi'
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+'restraint_'
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+'republic'
+'representing_'
+'renovated_'
+'reno'
+'remuneration_'
+'remit'
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+'reminded_'
+'reliability_'
+'relative'
+'relationships_'
+'relat'
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+'reit'
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+'reichlich'
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+'refle'
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+'recourse_'
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+'reconcile'
+'reckung'
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+'reale_'
+'readily_'
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+'rachte'
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+'psychische'
+'prüf'
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+'produkte_'
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+'preparing_'
+'premium_'
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+'predictable_'
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+'poker_'
+'pointing_'
+'pocket'
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+'pharmaceutical_'
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+'permission_'
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+'orientiert_'
+'organisieren_'
+'ordnungsgemäße'
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+'ord_'
+'orbit'
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+'nutzbar_'
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+'newspapers_'
+'newsletter_'
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+'masa_'
+'market'
+'marken'
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+'maps_'
+'manufacturer_'
+'manipulation_'
+'manifest'
+'mangelnde_'
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+'malig'
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+'maintain'
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+'luxuriöse_'
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+'lette_'
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+'lebenslange'
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+'lar_'
+'langjährigen_'
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+'lamp'
+'label_'
+'kürz'
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+'kühne'
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+'kirche_'
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+'kingdom_'
+'kilometers_'
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+'kg_'
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+'interna'
+'intern'
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+'interact'
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+'integrierte_'
+'integrate_'
+'insurgents_'
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+'insti'
+'installi'
+'inspections_'
+'insecurity_'
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+'industri'
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+'iPod_'
+'iPhone_'
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+'grundlegend_'
+'grouping'
+'grobe'
+'grip'
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+'grenzt_'
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+'gefr'
+'gebühren_'
+'gebra'
+'gebaut_'
+'gases_'
+'gard'
+'garage_'
+'gangs_'
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+'fühl'
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+'funktion_'
+'funk'
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+'frühere_'
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+'freedoms_'
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+'daten'
+'dateien_'
+'databases_'
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+'cow'
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+'circum'
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+'chä'
+'chsen'
+'chose_'
+'chnungen_'
+'chil'
+'chief'
+'chest'
+'chemicals_'
+'checks_'
+'chauen'
+'chaften_'
+'ceremon'
+'cer_'
+'cease_'
+'caution_'
+'catastrophe_'
+'casinos_'
+'cari'
+'care'
+'capt'
+'capitals_'
+'capitali'
+'cano'
+'cac'
+'cabin'
+'bürgerliche'
+'butt'
+'bust'
+'burn_'
+'bureaucrats_'
+'bull'
+'broke_'
+'brochure'
+'brilliant_'
+'bread_'
+'branche_'
+'bour'
+'borrowing_'
+'bombing'
+'bombe'
+'bolster'
+'blocked_'
+'block'
+'bl'
+'biot'
+'billions_'
+'bezug_'
+'bewusste'
+'bewegung_'
+'bewa'
+'bevorzug'
+'beu'
+'betriebe_'
+'betrieb_'
+'betrachtete'
+'betra'
+'besucht_'
+'bestä'
+'bestimmter_'
+'besticht_'
+'beso'
+'beschaff'
+'berufen_'
+'berp'
+'bereiten_'
+'bereiche_'
+'berei'
+'bemerkenswerte'
+'beliebige'
+'belie'
+'bekannter'
+'beizu'
+'beis'
+'behold_'
+'beh'
+'begleiten_'
+'beginne'
+'begehen_'
+'began'
+'befr'
+'beeinträchtigen_'
+'bedeutendsten_'
+'beauftragt'
+'bears_'
+'beanspruch'
+'bble'
+'bay_'
+'basierte_'
+'bargain'
+'baltischen_'
+'balcony_'
+'balancing_'
+'bak'
+'bach'
+'ax_'
+'avo'
+'außer'
+'außenpolitische'
+'automo'
+'automatische'
+'auszur'
+'auszuführen_'
+'auswe'
+'auslösen_'
+'ausgewählt_'
+'ausgenutzt_'
+'auft'
+'aufrechterhalten_'
+'aufl'
+'aufkommen_'
+'aufget'
+'aufgebaut_'
+'auen_'
+'audit'
+'attracti'
+'ats'
+'ata'
+'astr'
+'assignment'
+'asser'
+'asia'
+'ase'
+'ars'
+'arrive_'
+'arre'
+'arme'
+'arit'
+'arises_'
+'arische'
+'aren_'
+'architekt'
+'architect'
+'arche'
+'arb'
+'appro'
+'appar'
+'apologi'
+'anzuh'
+'anz'
+'anyway_'
+'any'
+'anxi'
+'anwesend_'
+'antwortete_'
+'antworten_'
+'anten_'
+'anteil_'
+'ante'
+'anstreben_'
+'ansteigen_'
+'ansprechen_'
+'anschließen_'
+'ansch'
+'annu'
+'announce_'
+'anno'
+'anme'
+'anlässlich_'
+'anl'
+'ani_'
+'angenehm_'
+'angemessenen_'
+'angelegt_'
+'angegriffen_'
+'angebot_'
+'angebliche'
+'angan_'
+'anes'
+'anen'
+'anderweitig_'
+'anden_'
+'andauern'
+'anbelangt_'
+'analyse_'
+'amplif'
+'amme'
+'ami_'
+'amended_'
+'amen_'
+'amb'
+'alter'
+'alte'
+'allocat'
+'allo'
+'alliances_'
+'alleged_'
+'alleg'
+'alla'
+'alit'
+'alike_'
+'alig'
+'alie'
+'alia'
+'alg'
+'ain'
+'agog'
+'agent_'
+'aft'
+'afrikanische'
+'affirm'
+'advertisement'
+'adventur'
+'admit'
+'activat'
+'aco'
+'achung_'
+'accr'
+'accord_'
+'accompanying_'
+'accommodati'
+'ac_'
+'abw'
+'absurd_'
+'abse'
+'abschließen'
+'abs'
+'able'
+'abl'
+'abhängt_'
+'abhängig'
+'abgele'
+'abgehalten_'
+'abgeben_'
+'aat'
+'`_'
+']]'''
+'] ._'
+'Zypern_'
+'Zwangs'
+'Zunahme_'
+'Zulassung_'
+'Zukunfts'
+'Zivilisten_'
+'Zie'
+'Zh'
+'Zentrala'
+'Zeita'
+'Zealand_'
+'Zahlungs'
+'Zahlung_'
+'YO'
+'Xi_'
+'XL'
+'Würden_'
+'Wünsche_'
+'Wäre'
+'Wurzel'
+'Wri'
+'Works'
+'Wood'
+'Wolf_'
+'Wol'
+'Wohl_'
+'Wis'
+'Wirtschaftsl'
+'Wind'
+'Willi'
+'Wies'
+'Wiederaufbau_'
+'Wider'
+'WiFi_'
+'Wetter_'
+'Wett'
+'Werk'
+'Werden_'
+'Weltb'
+'Weiterhin_'
+'Weis'
+'Wechselkurse_'
+'Wechsel_'
+'Wars'
+'Wand'
+'Walt'
+'Wai'
+'Wah'
+'Wach'
+'WA'
+'Vorw'
+'Vorteil'
+'Vorr'
+'Vorfeld_'
+'Volle'
+'Vogel'
+'Vit'
+'Visual_'
+'Visit'
+'Ville'
+'Villa'
+'Viel'
+'Vet'
+'Verzögerungen_'
+'Verzeichnis_'
+'Verwa'
+'Vertrauens'
+'Versicherungs'
+'Versand'
+'Versa'
+'Vermögens'
+'Vermittlungs'
+'Verlängerung_'
+'Vergleich'
+'Vergessen'
+'Verfasser'
+'Vereinfachung_'
+'Verda'
+'Veranstaltungsräume_'
+'Vegas_'
+'Varia'
+'Van'
+'Vale'
+'VAT_'
+'VA'
+'Users_'
+'Ursprung_'
+'Urheber'
+'Unterschied'
+'Untere'
+'Unsicherheit_'
+'Uns'
+'Universum'
+'Unge'
+'Unbe'
+'Unabhängig_'
+'Un_'
+'Umstände_'
+'Uganda'
+'Ub'
+'UL'
+'UC'
+'Tür_'
+'Tätigkeiten_'
+'Twin'
+'Tul'
+'Tuesday_'
+'Treiber_'
+'Treib'
+'Travel_'
+'Translat'
+'Track'
+'Total_'
+'Tor_'
+'Top_'
+'Tochter'
+'Tob'
+'Tir'
+'Tibe'
+'Theater'
+'Text'
+'Teufel'
+'Territori'
+'Terminal_'
+'Techno'
+'Tarif'
+'Tanz'
+'TT'
+'TS'
+'TEC'
+'Südafrika_'
+'Szenario_'
+'Synchron'
+'Suppo'
+'Suiten_'
+'Stütz'
+'Studie'
+'Strände_'
+'Strahlung_'
+'Straftat'
+'Store_'
+'Steuersystem'
+'Statistik_'
+'Stand'
+'Stalin_'
+'Stagnation_'
+'Spri'
+'Sprech'
+'Spre'
+'Spl'
+'Spiegel'
+'Sozialversicherung'
+'Sozialdemokrati'
+'Source_'
+'Sou'
+'Sonnens'
+'Solo'
+'Solana_'
+'Software'
+'Soft_'
+'Socialist_'
+'Sno'
+'Sn'
+'Small_'
+'Sla'
+'Sky_'
+'SilverFast_'
+'Sigma'
+'Sicherung_'
+'Sicherheitsfragen_'
+'Sh'
+'Seve'
+'Settings_'
+'Sem'
+'Sek'
+'Seen_'
+'Seele_'
+'Script'
+'Schwan'
+'Schulungs'
+'Schuld_'
+'Schuh'
+'Schriftsteller_'
+'Schriften_'
+'Schreib'
+'Schluss'
+'Schiffs'
+'Schic'
+'Schach'
+'Satellite'
+'Santiago_'
+'Sanc'
+'Sammlung_'
+'Sale'
+'SW'
+'SV_'
+'STA'
+'SS'
+'SP_'
+'SLR_'
+'SL'
+'SH'
+'SD_'
+'SCO'
+'SCH'
+'Rö'
+'Routine'
+'Route'
+'Round_'
+'Roten_'
+'Rosa'
+'Rod'
+'Richtlinien'
+'Rich'
+'Rezept'
+'Revolution'
+'Revision_'
+'Reserven_'
+'Res'
+'Repr'
+'Renminbi_'
+'Religio'
+'Relax_'
+'Reisende'
+'Reisen_'
+'Reinigung_'
+'Reichen_'
+'Regulierungs'
+'Registr'
+'Redner_'
+'Record'
+'Rechtsg'
+'Rechtsetzung'
+'Ratsvorsitzes_'
+'Rate'
+'Randlage_'
+'Rande_'
+'Ramblas_'
+'Rahmenprogramm'
+'RSS_'
+'RGB_'
+'RA_'
+'Quer'
+'Quark'
+'Pun'
+'Prüf'
+'Präsidenten'
+'Präsenz_'
+'Prozent'
+'Protest'
+'Protection_'
+'Pros'
+'Promo'
+'Progress'
+'Programmen_'
+'Prof_'
+'Product_'
+'Process_'
+'Prob'
+'Priv'
+'Princes'
+'Prince'
+'Premi'
+'Prad'
+'Power'
+'Pou'
+'Portal'
+'Polizei'
+'Poli'
+'Plätze_'
+'Pil'
+'Pie'
+'Physi'
+'Photos'
+'Phone'
+'Philosoph'
+'Philadelphia_'
+'Petersburg_'
+'Peru_'
+'Pensions'
+'Pel'
+'Paul'
+'Partners'
+'Parliamentary_'
+'Parlamentswahlen_'
+'Parkplätze_'
+'Parameter_'
+'Papier_'
+'Pakistani_'
+'Ox'
+'Outs'
+'Others_'
+'Ost_'
+'Os_'
+'Ori'
+'Options'
+'Optimismus_'
+'Olive'
+'Oli'
+'Offensi'
+'Objektiv'
+'Oberfläche_'
+'OLAF_'
+'OL'
+'OG'
+'Nü'
+'Nuevo_'
+'Nothing_'
+'Notenbanken_'
+'Norm'
+'Nordic_'
+'Nin'
+'Nieder'
+'News'
+'Nepal'
+'Nelson_'
+'Neg'
+'Near_'
+'Nap'
+'Nahrungsmittel'
+'Nachdruck_'
+'NS_'
+'NRO_'
+'NOT'
+'NE_'
+'NB'
+'Müll'
+'Mä'
+'Mut'
+'Music_'
+'Mus'
+'Monte_'
+'Mitter'
+'Mittelschicht_'
+'Mitarbeitern_'
+'Ministeri'
+'Mine'
+'Milo'
+'Mille'
+'Militar'
+'Milan_'
+'Migrations'
+'Miet'
+'Mexik'
+'Metall'
+'Meta'
+'Meinungsäußerung_'
+'Meines_'
+'Meilen'
+'Mehrere_'
+'McCa'
+'Maz'
+'Mauer_'
+'Master_'
+'Marken'
+'Marke_'
+'Mang'
+'Malta_'
+'MC'
+'M5'
+'Lösch'
+'Luxemburg_'
+'Lux'
+'Lunch'
+'Luggage_'
+'Lud'
+'Lot'
+'Lor'
+'Lohn_'
+'Lohn'
+'Lock'
+'Loca'
+'Lizenz_'
+'Literatur_'
+'Linien_'
+'Line_'
+'Lind'
+'Liikanen_'
+'Lich'
+'Liberalen_'
+'Leu'
+'Letzte'
+'Leonardo_'
+'Leistungsbilanz'
+'Leica_'
+'Leh'
+'Lega'
+'Lebensst'
+'Lebensqualität_'
+'Learn_'
+'Las'
+'Lar'
+'Landes'
+'Lan'
+'Lama'
+'Lai'
+'Lager_'
+'Laden_'
+'Kürz'
+'Königs'
+'Kämpfe_'
+'Kurse'
+'Kub'
+'Kräften_'
+'Kroatien_'
+'Kro'
+'Kreditgeber_'
+'Krankenh'
+'Kraftstoff'
+'Korr'
+'Kopie'
+'Konservativen_'
+'Kompon'
+'Komplettpreise_'
+'Kollege_'
+'Kohäsions'
+'Kohlenstoff'
+'Kohle_'
+'Knoten_'
+'Kn'
+'Klon'
+'Kleid'
+'Kis'
+'Kinders'
+'Ket'
+'Kenya_'
+'Kennzeichnung'
+'Kathedrale_'
+'Kaffee_'
+'Jose'
+'Jon'
+'Jobs_'
+'Jin'
+'Jh'
+'Jas'
+'Jam'
+'Jagd'
+'Jacuzzi_'
+'JA'
+'Italia'
+'Inzwischen_'
+'Investi'
+'Inve'
+'Intelligen'
+'Intellektuelle'
+'Integrität_'
+'Inte'
+'Instabilität_'
+'Insp'
+'Insofern_'
+'Innere'
+'Inland'
+'Impfung'
+'Imperial'
+'Imm'
+'Ice'
+'INI_'
+'ICA'
+'Höhepunkt_'
+'Händen_'
+'Hyper'
+'Hurri'
+'Hungers'
+'How'
+'Hos'
+'Homo'
+'Holocaust_'
+'Holl'
+'Hold'
+'Hoffnungen_'
+'Hof'
+'Hindernisse_'
+'Hilton_'
+'Hillary_'
+'Hierzu_'
+'Hier'
+'Hes'
+'Heran'
+'Hence_'
+'Help_'
+'Helm'
+'Heimatl'
+'Heb'
+'Heads_'
+'Haushalts_'
+'Hauptb'
+'Hast'
+'Hans_'
+'Hall'
+'HP_'
+'Gü'
+'Göteborg_'
+'Gute_'
+'Gui'
+'Greenspan_'
+'Greater_'
+'Grad'
+'Gr'
+'Gordon_'
+'God'
+'Goals_'
+'Gn'
+'Glaubens'
+'Gipfels_'
+'Gewährleistung_'
+'Gesellschafts'
+'Gesan'
+'Gerät_'
+'Gere'
+'Gerade_'
+'Gepäck'
+'Geno'
+'Genf'
+'Gelegenheiten_'
+'Gelds'
+'Gegen_'
+'Gefähr'
+'Gebühr_'
+'Game'
+'Gam'
+'Gad'
+'Gabriel'
+'Gab'
+'GR'
+'Fünfte'
+'Fünf'
+'Further_'
+'Fuji'
+'Fris'
+'Friedensprozess_'
+'Freib'
+'Freedom_'
+'Four_'
+'Fotokopiereinrichtungen_'
+'Fond'
+'Fokus_'
+'Flughafen'
+'Fluggesellschaften_'
+'Flexi'
+'Fleisch'
+'Fitness_'
+'Fischereipolitik_'
+'Finanzsystems_'
+'Finanzminister_'
+'Finanzau'
+'Filter'
+'Fertig'
+'Fernseher_'
+'Fehlen_'
+'Features_'
+'Fabriken_'
+'FR'
+'FDI_'
+'FA_'
+'FARC_'
+'Extrem'
+'Expo'
+'Exi'
+'Excel_'
+'Evolution_'
+'Events_'
+'Event_'
+'Europäisches_'
+'Eth'
+'Ess'
+'Erö'
+'Erweiterungs'
+'Ersch'
+'Erre'
+'Eropa_'
+'Ern'
+'Ermittlung'
+'Erkrankung'
+'Ericsson_'
+'Erfüllung_'
+'Erfolgs'
+'Erdöl'
+'Episode'
+'Entwicklungsst'
+'Entwicklungsa'
+'Enth'
+'Enron_'
+'Enl'
+'Enk'
+'Eng'
+'Elektronik'
+'Elektro'
+'Eisen'
+'Einzelh'
+'Einkaufss'
+'Einhei'
+'Einh'
+'Eing'
+'Einfuhr'
+'Einf'
+'Einerseits_'
+'Eine'
+'Eigenschaft_'
+'Eigenheim'
+'Edin'
+'Eden_'
+'Eck'
+'Easy_'
+'ESS'
+'ENT'
+'Dörfer'
+'Dutzend_'
+'Drittstaaten_'
+'Drei'
+'Draghi_'
+'Dow_'
+'Dou'
+'Dominion_'
+'Dominica'
+'Doc'
+'Diktator'
+'Digi'
+'Dienstes_'
+'Dienste'
+'Did'
+'Dick'
+'Dez'
+'Dev'
+'Dest'
+'Denk'
+'Demand_'
+'Defi'
+'Days_'
+'Damals_'
+'Dach'
+'DR_'
+'DR'
+'DPJ_'
+'DB'
+'DAM'
+'Crystal_'
+'Croatia_'
+'Cri'
+'Court'
+'Could_'
+'Corr'
+'Conver'
+'Contact_'
+'Consult'
+'Consequently_'
+'Conc'
+'Comple'
+'Commerce_'
+'Commander_'
+'Coll'
+'Coffee_'
+'Chirac_'
+'Chip'
+'Chechnya_'
+'Chat_'
+'Charlie_'
+'Champs_'
+'Chai'
+'Centr'
+'Cell'
+'Cath'
+'Carr'
+'Carmen_'
+'Cari'
+'CL'
+'Büro_'
+'Büchern_'
+'Bäume_'
+'By'
+'Business'
+'Bundesrepublik_'
+'Bundeskanzlerin_'
+'Building_'
+'Buck'
+'Brüder_'
+'Bruttoinlandsprodukt'
+'Brown_'
+'Bridge_'
+'Brandenburg'
+'Bour'
+'Borde'
+'Bonus_'
+'Bonn_'
+'Bomb'
+'Bog'
+'Bob'
+'Blut_'
+'Blockade_'
+'Bilde'
+'Bibliotheken_'
+'Bezeichnung_'
+'Bewe'
+'Bevor_'
+'Between_'
+'Betriebssystem_'
+'Betrachtung_'
+'Besuche'
+'Bestätigung_'
+'Bestände_'
+'Bestellung_'
+'Bestand'
+'Besser'
+'Beruf'
+'Berechnung_'
+'Bere'
+'Benutzern'
+'Benach'
+'Bem'
+'Bell_'
+'Beli'
+'Belgian_'
+'Bekenntnis_'
+'Bekannt'
+'Beg'
+'Befürchtungen_'
+'Befür'
+'Beau'
+'Bearbeit'
+'Bath_'
+'Baj'
+'Bahrain_'
+'Bac'
+'BSE_'
+'BERLIN_'
+'BD'
+'B6_'
+'Award'
+'Automobilindustrie_'
+'Auswahl'
+'Austritt_'
+'Australian_'
+'Austin_'
+'Ausstellung_'
+'Aussichten_'
+'Ausschluss_'
+'Aussagen_'
+'Aussage_'
+'Ausrüstung_'
+'Ausbr'
+'Ausblick_'
+'Ausbildungs'
+'Ausarbeitung_'
+'Aufwertung_'
+'Aufl'
+'Aufg'
+'Aufb'
+'Atlantik'
+'Ate'
+'Astro'
+'Arzneimittel_'
+'Arth'
+'Arr'
+'Arms'
+'Armenia_'
+'Armeni'
+'Arme'
+'Archiv'
+'Arbeitsplatz_'
+'Arbeitnehmern_'
+'Arbeitnehmer'
+'Applikation'
+'Appl'
+'Appart'
+'Appar'
+'Apart_'
+'Apa'
+'Any'
+'Anschlag_'
+'Ansch'
+'Anruf'
+'Anreiz_'
+'Anr'
+'Anpassungs'
+'Annäherung_'
+'Anne'
+'Anmeld'
+'Angabe_'
+'Anf'
+'Anderson_'
+'Anbieter_'
+'Analysen_'
+'Anal'
+'Amerikanern_'
+'Alon'
+'Algeria_'
+'Alge'
+'Alf'
+'Alc'
+'Albu'
+'Aktienm'
+'Aero'
+'Admi'
+'Acro'
+'Acqu'
+'Acht_'
+'Access'
+'Abänderung'
+'Abw'
+'Absicherung_'
+'Abschwung'
+'Absa'
+'Abkommens_'
+'Abbas_'
+'ASE'
+'AL_'
+'ALDE_'
+'AKVIS_'
+'89_'
+'82_'
+'240_'
+'215'
+'1982_'
+'1973_'
+'1948_'
+'1900_'
+'18th_'
+'177'
+'175'
+'1701_'
+'160'
+'128_'
+'/-_'
+'. ({{_'
+'*_'
+'**'
+')]'
+''']]'
+'%) _'
+'">'
+' �_'
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+'komplizierten_'
+'komplexen_'
+'kompet'
+'kompakt'
+'komfortabel_'
+'kolonial'
+'koll'
+'kok_'
+'knappe'
+'kluge'
+'klinische'
+'klich'
+'kleines_'
+'klassische'
+'klassi'
+'klapp'
+'kl'
+'kka'
+'kische'
+'kinder'
+'kilometre'
+'kid'
+'kf'
+'kett'
+'kerja_'
+'kehr_'
+'kehr'
+'kamera'
+'kal'
+'jö'
+'justification_'
+'juristische'
+'jung'
+'jun'
+'journalist_'
+'journal'
+'jou'
+'join'
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+'jeni'
+'jeglicher_'
+'jan'
+'izi'
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+'ium'
+'ité_'
+'itz'
+'itts'
+'itis'
+'ita_'
+'istic_'
+'isti'
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+'issuing_'
+'issi'
+'ison_'
+'isolated_'
+'isolat'
+'islamistische'
+'islamische_'
+'isi_'
+'irrev'
+'irr'
+'irgendeiner_'
+'ires_'
+'irc'
+'iplin'
+'ios_'
+'ioni'
+'iona'
+'inviting_'
+'investasi_'
+'invasion_'
+'intuitive'
+'intra'
+'intolera'
+'interview_'
+'interventions_'
+'interv'
+'interrupt'
+'internationally_'
+'interior_'
+'interg'
+'intensive'
+'intensiv_'
+'intelligente_'
+'inte'
+'intak'
+'inta'
+'institutionalis'
+'instances_'
+'inst'
+'inspiri'
+'inspectors_'
+'insofern_'
+'insist'
+'inse'
+'inquir'
+'innehat'
+'inne'
+'injection'
+'iniert'
+'inhibit'
+'inhaltlich'
+'infra'
+'inflict'
+'infi'
+'infectious_'
+'infe'
+'inex'
+'inevitably_'
+'iness_'
+'ineffiziente'
+'industriellen_'
+'industrialis'
+'indu'
+'indische_'
+'indirect_'
+'indication_'
+'incur'
+'incorrect'
+'incorporate_'
+'inclusive_'
+'incl'
+'incapable_'
+'inca'
+'inat'
+'inander_'
+'inability_'
+'impl'
+'imperial'
+'impedi'
+'impede'
+'impair'
+'immigrant_'
+'imba'
+'ilte'
+'illnesses_'
+'ilde'
+'iken_'
+'ignor'
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+'idio'
+'identification_'
+'identifi'
+'ideale'
+'ick'
+'icial'
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+'ibi'
+'iba'
+'hübsch'
+'höf'
+'höchste'
+'häus'
+'häufiger_'
+'hängen_'
+'hw'
+'hus'
+'hurr'
+'hunt'
+'hts_'
+'htm'
+'hten_'
+'hrung'
+'hospitals_'
+'hos'
+'hors'
+'hop'
+'homo'
+'homeland_'
+'holt'
+'hohes_'
+'hochrangige'
+'hnte'
+'hmig'
+'hmen_'
+'hija'
+'highway_'
+'hierfür_'
+'herz'
+'hervorrufen_'
+'hervorragende'
+'herkömmlichen_'
+'herausf'
+'hende_'
+'hemm'
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+'headlines_'
+'header'
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+'hb'
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+'hause'
+'haus'
+'hate_'
+'hast'
+'harmonise_'
+'harm'
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+'harass'
+'hanya_'
+'handlungen_'
+'halb_'
+'haft'
+'haf'
+'habt_'
+'günstige'
+'gängig'
+'gym'
+'gura'
+'gul'
+'guitar'
+'guide'
+'guidance_'
+'guest'
+'guardian_'
+'gste'
+'grüne'
+'gründet_'
+'größerem_'
+'größe'
+'grösste'
+'großartige_'
+'grou'
+'grie'
+'gravierende'
+'grausam'
+'gog'
+'god'
+'gnet'
+'gma'
+'glücklich_'
+'globalis'
+'glichen_'
+'girls_'
+'gion'
+'gin_'
+'gin'
+'gie_'
+'giant_'
+'geäußerten_'
+'gewöhnlich'
+'gewi'
+'gewec'
+'gewe'
+'gewalt'
+'getau'
+'gesti'
+'gesteckt_'
+'gestaltete'
+'geson'
+'gesenkt_'
+'gesellschaftlichen_'
+'geschäft_'
+'geschwindigkeit_'
+'geschm'
+'gerufen_'
+'geriet'
+'geri'
+'gerechtfertigt_'
+'gerechte_'
+'geprägten_'
+'geopolitische'
+'geopolitical_'
+'genom'
+'gener'
+'genen_'
+'gende'
+'gence_'
+'gemessen_'
+'gemeins'
+'geldpolitische'
+'geld'
+'gehe_'
+'geha'
+'gegr'
+'gegner'
+'gegenseitige_'
+'gefü'
+'gefo'
+'gefangen_'
+'gef'
+'geeignete_'
+'geehrt'
+'gedenkt_'
+'gebiete_'
+'geber_'
+'gebe'
+'gea'
+'gather_'
+'gastro'
+'gasse_'
+'gaps_'
+'gall'
+'gage'
+'füllen_'
+'förderung_'
+'fähigen_'
+'fungier'
+'fung'
+'fundamentally_'
+'func'
+'ftig'
+'fter_'
+'fst'
+'früh'
+'fruit'
+'fronts_'
+'frontier'
+'fristig'
+'frisch_'
+'freut_'
+'freundlichen_'
+'frequent'
+'fragile_'
+'founder_'
+'foun'
+'fortzu'
+'fortune_'
+'fortgeschrittene'
+'formulierte_'
+'formali'
+'formale'
+'forg'
+'foresee'
+'forecast_'
+'forder'
+'forci'
+'font'
+'folgte'
+'fold_'
+'fn'
+'flü'
+'flugzeuge'
+'flowing_'
+'flower'
+'flexibler'
+'flexible'
+'fled'
+'flavo'
+'flag'
+'fizi'
+'fix'
+'fisch'
+'finger'
+'fiction_'
+'fica'
+'ffl'
+'ffene'
+'ffekt'
+'feststellt'
+'feste_'
+'feiern_'
+'fehlenden_'
+'fec'
+'featured_'
+'feasible_'
+'fea'
+'favorite_'
+'favored_'
+'fau'
+'fasc'
+'farbe'
+'fantastische'
+'fangen_'
+'faire'
+'fahrts'
+'fahrer'
+'fahren'
+'facilitating_'
+'facilitat'
+'fache_'
+'fache'
+'fabric_'
+'fab'
+'exzellenten_'
+'extensi'
+'exquisite_'
+'exposure_'
+'exploration_'
+'exploiting_'
+'experiencing_'
+'exklusiv'
+'existierende'
+'exert_'
+'exercise'
+'exempl'
+'exclu'
+'exchange'
+'excellence_'
+'even'
+'euro'
+'euer'
+'ette_'
+'eting'
+'eth_'
+'eternal_'
+'etabliert'
+'etablieren_'
+'esu'
+'esto'
+'estig'
+'esta_'
+'esses_'
+'eso'
+'erwecken_'
+'erupt'
+'erungen_'
+'erta'
+'erstens_'
+'erstatte'
+'erschl'
+'errors_'
+'errichtet_'
+'erreich'
+'erpr'
+'ernen'
+'ermä'
+'erma'
+'erläutern_'
+'erleichter'
+'erklär'
+'erit'
+'eris'
+'eries_'
+'erholsame'
+'ergi'
+'erge_'
+'ergab'
+'erfe'
+'ereich_'
+'erbe_'
+'erbaut_'
+'erbar'
+'eran'
+'equip'
+'equat'
+'epidemic_'
+'epidemi'
+'envi'
+'entziehen_'
+'entworfen_'
+'enttäuscht'
+'entspr'
+'entschied_'
+'entscheidung'
+'entity_'
+'entgegens'
+'entgegenge'
+'entfernten_'
+'entfallen_'
+'entail'
+'ensured_'
+'enorm_'
+'enli'
+'enh'
+'engineer'
+'energisch'
+'ener'
+'endanger'
+'enact'
+'employee_'
+'empfäng'
+'empfunden'
+'empf'
+'emotions_'
+'emble'
+'ellung_'
+'elles_'
+'electr'
+'ekte'
+'ej'
+'eitig_'
+'eir'
+'einzust'
+'einzuräumen_'
+'einzurichten_'
+'einzul'
+'einzuf'
+'einseitig'
+'einräumen_'
+'eingerichteten_'
+'eingeh'
+'einführen_'
+'einfü'
+'einbezieh'
+'einbar'
+'einb'
+'eilt_'
+'eilen_'
+'eigne'
+'eigenständige'
+'eigens'
+'eigenem_'
+'eid'
+'eichn'
+'eho'
+'ehl'
+'eful_'
+'efi'
+'effizientere'
+'effiziente_'
+'effizient_'
+'effizien'
+'effekt_'
+'editor'
+'ede_'
+'ecu'
+'eck'
+'echo'
+'ebung_'
+'ebnen_'
+'dü'
+'durchsetzen_'
+'durchschnittliche_'
+'durchschnittlich_'
+'durchgesetzt_'
+'duration_'
+'duk'
+'dst'
+'dry'
+'drucken'
+'dron'
+'drinking_'
+'drift'
+'dreh'
+'drastisch_'
+'drastic_'
+'drasti'
+'drag'
+'dou'
+'dorf_'
+'dop'
+'dog_'
+'document'
+'divisions_'
+'dividing_'
+'divide_'
+'divi'
+'diversen_'
+'div'
+'dition'
+'distress_'
+'distort'
+'dist'
+'dispose'
+'dismissed_'
+'disg'
+'discre'
+'discourse_'
+'discourage'
+'dische'
+'disappointed_'
+'disabilit'
+'diper'
+'dioxide_'
+'dik'
+'dih'
+'digung'
+'digkeit'
+'diesbezügliche_'
+'dib'
+'diak'
+'dg'
+'devote_'
+'devi'
+'deutlichen_'
+'detriment_'
+'deten'
+'det_'
+'destru'
+'despair_'
+'designation_'
+'desde_'
+'derived_'
+'dere'
+'derartiger_'
+'depressed_'
+'deposits_'
+'deploy'
+'dense'
+'denounce'
+'demonstrieren_'
+'demokratisch_'
+'demograph'
+'democrat'
+'dementsprechend_'
+'dell_'
+'delightful_'
+'delete_'
+'degrees_'
+'ded'
+'decor'
+'declines_'
+'debe'
+'debated_'
+'dde'
+'dba'
+'dauerhafte'
+'dare'
+'dad'
+'dacht'
+'cycles_'
+'curtail'
+'cultivat'
+'culminat'
+'cue'
+'ctur'
+'ction'
+'crush'
+'crude_'
+'critici'
+'cript'
+'crash_'
+'cras'
+'craft_'
+'craft'
+'cr'
+'cozy_'
+'couple'
+'coup_'
+'cotton_'
+'cosmetic'
+'correspond_'
+'corps'
+'copie'
+'convince_'
+'convicted_'
+'convict'
+'conversation_'
+'controversial_'
+'contagion_'
+'contacts_'
+'consult_'
+'constitutes_'
+'constituenc'
+'constitu'
+'consist'
+'conquer_'
+'connecting_'
+'coni'
+'confo'
+'confined_'
+'configure_'
+'confe'
+'conf'
+'conciliation_'
+'concentrated_'
+'compre'
+'compr'
+'compliment_'
+'complaint_'
+'complain_'
+'comparative_'
+'common'
+'commodities_'
+'commission'
+'commercial'
+'comer'
+'come'
+'colonial_'
+'collectively_'
+'collections_'
+'cola'
+'cock'
+'coat'
+'coastal_'
+'clu'
+'closure_'
+'clinical_'
+'clin'
+'cli'
+'ckig'
+'cket_'
+'cke'
+'cis'
+'cinema'
+'chtigen_'
+'chsel'
+'child'
+'checke'
+'chas'
+'charisma'
+'charakter_'
+'characteristics_'
+'characteristic_'
+'change'
+'chair'
+'cere'
+'censorship_'
+'ced'
+'cce'
+'cca'
+'cave'
+'cautious_'
+'cau'
+'casual'
+'casino_'
+'carries_'
+'capture_'
+'captur'
+'capability_'
+'cap_'
+'cant'
+'cans_'
+'cana'
+'came'
+'calculat'
+'cafe'
+'caci'
+'bürokratische'
+'bösartige'
+'byl'
+'but'
+'burning_'
+'burn'
+'bureaucracy_'
+'bum'
+'bullet'
+'builds_'
+'buffer'
+'brü'
+'browsing_'
+'brothers_'
+'brake'
+'boxe'
+'bottle'
+'borrowers_'
+'borne_'
+'bora'
+'bookings_'
+'bombard'
+'boli'
+'boil'
+'bn'
+'bloße_'
+'bloß'
+'blocking_'
+'blockiert_'
+'bloc'
+'bliche'
+'blase_'
+'blam'
+'birds_'
+'billig_'
+'bilität'
+'bid_'
+'bias'
+'bia'
+'bezi'
+'bezeichnen_'
+'bewert'
+'beverages_'
+'beunruhig'
+'bett_'
+'bett'
+'betrü'
+'beträchtliche'
+'betrieb'
+'betreiber'
+'bete'
+'bestrafen_'
+'bestehender_'
+'bestanden_'
+'besitze'
+'beside'
+'besetzte'
+'beset'
+'beschw'
+'beschreiben_'
+'beschrei'
+'berühmte_'
+'beru'
+'bereitstellen_'
+'bereitet_'
+'benötigten_'
+'benötigte_'
+'benen'
+'benchmark'
+'benachteiligt'
+'benachbarten_'
+'bemerken_'
+'belong'
+'beliefs_'
+'beke'
+'bekannteste'
+'beit'
+'beides_'
+'behinder'
+'begünstig'
+'begriffen_'
+'begrenzten_'
+'begleitet_'
+'begeben_'
+'bege'
+'begangen_'
+'beförder'
+'befriedigen_'
+'befri'
+'befrei'
+'befa'
+'bedeutete_'
+'bedding_'
+'bedauerlich_'
+'bearbeiten_'
+'bb'
+'bat_'
+'bases_'
+'base'
+'barri'
+'banner'
+'banned_'
+'bang_'
+'bailout_'
+'bai_'
+'ays_'
+'aya'
+'aw_'
+'avoiding_'
+'aviation_'
+'avel'
+'außergewöhnlichen_'
+'automati'
+'author'
+'auszuw'
+'auszul'
+'auszuarbeiten_'
+'ausstatt'
+'aussi'
+'ausser'
+'auss'
+'ausreichende'
+'ausr'
+'ausp'
+'ausn'
+'ausgest'
+'ausges'
+'ausgehend_'
+'ausgedehnt'
+'ausfallen_'
+'ausbr'
+'aum_'
+'ault_'
+'auli'
+'aufzuh'
+'aufrufen_'
+'aufri'
+'aufregende'
+'aufgez'
+'aufgel'
+'aufgegriffen_'
+'aufgef'
+'auferleg'
+'aufbe'
+'auer_'
+'audit_'
+'aucht'
+'ature_'
+'aturan_'
+'attr'
+'atta'
+'atra'
+'atori'
+'atm'
+'atla'
+'ativ'
+'ata_'
+'asy'
+'astu'
+'ast_'
+'assumption_'
+'assum'
+'assessing_'
+'assess_'
+'asserti'
+'assembly_'
+'assembl'
+'asse_'
+'aspir'
+'asks_'
+'asa'
+'artige'
+'artif'
+'arsen'
+'arrangement_'
+'arranged_'
+'arm_'
+'arität'
+'arist'
+'arising_'
+'arde'
+'archives_'
+'arbeitende'
+'arabische_'
+'aqua'
+'apt'
+'approv'
+'appoint'
+'apart'
+'anzust'
+'anzukurbeln_'
+'anzugehen_'
+'anzuerkennen_'
+'anzi'
+'antis'
+'antiqu'
+'antic'
+'anta'
+'anstieg'
+'anstehende'
+'anspruchsvolle'
+'ansi'
+'anny'
+'announcement'
+'anni'
+'anna'
+'ann'
+'ankomm'
+'anische_'
+'angry_'
+'angre'
+'angestrebte'
+'angesprochene'
+'anges'
+'angene'
+'angelegte'
+'angekündigte'
+'angekündigt_'
+'angehör'
+'angehen_'
+'angegeben_'
+'angebot'
+'angeblich_'
+'anfä'
+'anen_'
+'ands'
+'anat'
+'analysier'
+'analyses_'
+'amin'
+'amer'
+'ambiance_'
+'aman'
+'alu'
+'altogether_'
+'alternat'
+'allu'
+'alls'
+'algo'
+'aler_'
+'alem'
+'akzeptabel_'
+'aktivist'
+'aktiviert_'
+'aktiven_'
+'aktiv'
+'aktion'
+'ais'
+'aim'
+'ahlung_'
+'ahlen_'
+'ahe'
+'ags_'
+'agon'
+'aggressive'
+'aggre'
+'after'
+'afraid_'
+'afi'
+'afford'
+'afflict'
+'advocating_'
+'advocates_'
+'adventure_'
+'adu'
+'ads_'
+'admitted_'
+'administrat'
+'adjust'
+'adj'
+'add'
+'acute_'
+'actress_'
+'acquisition_'
+'acle'
+'acke'
+'aches_'
+'ache_'
+'ace_'
+'accumulati'
+'accounted_'
+'accessories_'
+'accesse'
+'abzust'
+'abzule'
+'abuses_'
+'abstain_'
+'abstain'
+'absor'
+'abschl'
+'aboard_'
+'ablen'
+'ablauf'
+'abkommens_'
+'abgez'
+'abgegeben_'
+'abandoning_'
+'Zwischen'
+'Zwi'
+'Zweig'
+'Zut'
+'Zuschauer'
+'Zuf'
+'Zucker_'
+'Zit'
+'Zell'
+'Zeitschrift_'
+'Zeitr'
+'Zeilen'
+'Zehn_'
+'Zar'
+'Yuk'
+'Yen_'
+'Yemen_'
+'Yam'
+'Xa'
+'XVI'
+'XLS'
+'Wüsten'
+'Wür'
+'Wälder_'
+'Wy'
+'Worf_'
+'Word'
+'Woods_'
+'Wissens_'
+'Wissens'
+'Wirtschaftswachstums_'
+'Winters'
+'Winds'
+'Will'
+'Wiener_'
+'Widersprüche_'
+'Wide'
+'Whenever_'
+'Wettbewerbe'
+'Wertpapiere_'
+'Wertpapier'
+'Werkzeug_'
+'Werkst'
+'Werde_'
+'Wenige'
+'Weltwirtschafts'
+'Wellnessbereich_'
+'Weiterentwicklung_'
+'Weihnachten_'
+'Weich'
+'Wed'
+'Weber_'
+'Wave'
+'Watt'
+'Wasch'
+'Warnung'
+'Wandels_'
+'Wan'
+'Wahrnehmung_'
+'Wahlkampf'
+'Wag'
+'Wachstumss'
+'WP'
+'Völkerrecht'
+'Vs_'
+'Vorredner'
+'Vorre'
+'Vorrang_'
+'Vorherrschaft_'
+'Voraus_'
+'Voraus'
+'Volume'
+'Vitorino_'
+'Visu'
+'Visa_'
+'Vis'
+'Vinc'
+'Victoria_'
+'Via_'
+'Verwirklichung_'
+'Vertrieb'
+'Vertreter'
+'Vertrags_'
+'Vertrages_'
+'Verteidigungsminister'
+'Vermögen_'
+'Verletz'
+'Verlagerung_'
+'Verkehrsnetz'
+'Verkehrsa'
+'Verkaufs'
+'Verhältnis'
+'Vereinig'
+'Verbraucherschutz'
+'Verbrauchern_'
+'Verantwortlichkeit'
+'Vario'
+'VIC'
+'VE'
+'VD'
+'Ura'
+'Updates_'
+'Unterscheidung_'
+'Unters'
+'Unternehmer'
+'Unterfangen_'
+'Unst'
+'Universal_'
+'Unionsbürger'
+'Unfälle'
+'Underground_'
+'Unde'
+'Umst'
+'Umsatz_'
+'Umbr'
+'Ultimate'
+'Ul'
+'Uf'
+'USE_'
+'UP'
+'UNM'
+'Türen_'
+'Tät'
+'Twe'
+'Turni'
+'Turm'
+'Turi'
+'Tunnel'
+'Tud'
+'Tsi'
+'Tschech'
+'Truppe'
+'Troi'
+'Tric'
+'Tradi'
+'Tr'
+'Toyota_'
+'Ton_'
+'Tomo'
+'Tom_'
+'Toleranz_'
+'Tode_'
+'Tod'
+'Thor'
+'Thom'
+'Thirdly_'
+'Thinking_'
+'Theor'
+'Theatre_'
+'Thal'
+'Th'
+'Terrace_'
+'Terra'
+'Tenn'
+'Tendenz_'
+'Ten_'
+'Temp'
+'Tell'
+'Tehran_'
+'Technologie'
+'Tay'
+'Tausend'
+'Tatsachen_'
+'Task_'
+'Take'
+'Table'
+'Tabak'
+'TP'
+'TOS_'
+'TION'
+'Süde'
+'Südamerika'
+'Säule'
+'Sydney_'
+'Superma'
+'Sum'
+'Sud'
+'Subve'
+'Substanz'
+'Subsidiarität_'
+'Stück_'
+'Stuttgart_'
+'Stufe_'
+'Studierende'
+'Student'
+'Stress_'
+'Stock_'
+'Sto'
+'Stil'
+'Stig'
+'Stift'
+'Sti'
+'Steuererhöhungen_'
+'Stereo'
+'Steigen'
+'Stay'
+'Statut'
+'Statistiken_'
+'Station'
+'Starts'
+'Standort'
+'Stamm'
+'Stal'
+'Stabilitäts'
+'Staatsb'
+'Staatsa'
+'Sri_'
+'Sponsor'
+'Spenden'
+'Spekulation'
+'Speed_'
+'Spaziergang_'
+'Sozials'
+'Sozialpartner'
+'Souvenir'
+'Sonic_'
+'Songs'
+'Somit_'
+'Solutions_'
+'Sobald_'
+'Slowakei_'
+'Slideshows_'
+'Sk'
+'Sina'
+'Simpl'
+'Silver_'
+'Silv'
+'Sil'
+'Siedlungen_'
+'Sichtweise_'
+'Sich'
+'Shopping_'
+'Sharon_'
+'Sex_'
+'Seuche'
+'Session_'
+'Serikat_'
+'Seri'
+'Sensor'
+'Selbstver'
+'Selbstbe'
+'Sekunden_'
+'Sekt'
+'Seitens'
+'Segel'
+'Seg'
+'Schü'
+'Schönheit_'
+'Schä'
+'Schwerpunkt'
+'Schulb'
+'Schra'
+'Schmidt_'
+'Schlacht_'
+'Schiffe_'
+'Schichten_'
+'Schengen_'
+'Schauspieler_'
+'Scandinavia'
+'Save_'
+'Sav'
+'Sat_'
+'Sanierung_'
+'Samu'
+'Samstag_'
+'Same'
+'Saharan_'
+'Sah'
+'Sag'
+'Safe_'
+'Sac'
+'Sab'
+'Saatgut_'
+'SOL'
+'SC_'
+'Rückzug_'
+'Rücken_'
+'Roth'
+'Rollen'
+'Ring'
+'Rig'
+'Ries'
+'Richtig'
+'Rice_'
+'Ria'
+'Review_'
+'Reu'
+'Result'
+'Ressourcen'
+'Residenz_'
+'Residence_'
+'Reparatur'
+'Rennen_'
+'Renditen_'
+'Rek'
+'Reit'
+'Reinh'
+'Reihenfolge_'
+'Reife'
+'Reichtum_'
+'Reichs'
+'Reich'
+'Regulierungsbehörden_'
+'Regen'
+'Reformp'
+'Refle'
+'Referen'
+'Redebeitr'
+'Recovery_'
+'Rechtsvorschrift_'
+'Rechtsgrundlage_'
+'Rechnungshof'
+'Rechner_'
+'Rechn'
+'Recently_'
+'Read_'
+'Read'
+'Raumfahrt'
+'Rauch'
+'Ras'
+'Rang'
+'Radisson_'
+'RS'
+'RP'
+'REACH_'
+'RC'
+'Quin'
+'Quart'
+'Qi'
+'Pä'
+'Pyr'
+'Putsch'
+'Ps'
+'Präsidentschafts'
+'Provid'
+'Protokolls_'
+'Prost'
+'Promi'
+'Produktp'
+'Produktivitäts'
+'Prinz'
+'Print'
+'Primär'
+'Prima'
+'Price_'
+'Pres'
+'Prag_'
+'Posten_'
+'Portfolio'
+'Populis'
+'Polizist'
+'Polizeia'
+'Poettering_'
+'Poe'
+'Plugin'
+'PlayStation_'
+'Plattform_'
+'Pir'
+'Pipe'
+'Philippines_'
+'Phil'
+'Pfe'
+'Persönlichkeiten_'
+'Persian_'
+'Pec'
+'Pazifik'
+'Passag'
+'Partition'
+'Part_'
+'Part'
+'Parlamente_'
+'Parking_'
+'Palästinensern_'
+'Paketen_'
+'Paa'
+'PNR_'
+'PCs_'
+'PA_'
+'PAR'
+'Otto'
+'Osteuropa_'
+'Ostasien_'
+'Oscar_'
+'Ort'
+'Oro'
+'Orange_'
+'Oppositions'
+'Operationen_'
+'Olympischen_'
+'Olympi'
+'Office'
+'Ocean_'
+'Obs'
+'Obl'
+'Oberflächen'
+'OSZE_'
+'OM'
+'Nähr'
+'Nous_'
+'Nixon_'
+'Nicaragua_'
+'Nic'
+'Neus'
+'Netz'
+'Netanyahu_'
+'Nes'
+'Nenn'
+'Navigation_'
+'Nau'
+'Natural_'
+'Nationalen_'
+'Namens'
+'Nahrung_'
+'Nag'
+'Nad'
+'Nachweis_'
+'Nachk'
+'NU'
+'NPT_'
+'NN_'
+'NL_'
+'NL'
+'NET_'
+'NAFTA_'
+'Mühl'
+'Mächte'
+'Must'
+'Motors'
+'Motor_'
+'Moro'
+'Morgan_'
+'Morg'
+'Monti_'
+'Mont_'
+'Mone'
+'Monday_'
+'Moderni'
+'Mittelmeerraum_'
+'Mittela'
+'Mitgliedsländern_'
+'Mis'
+'Minister'
+'Mind'
+'Migu'
+'Mexican_'
+'Meth'
+'Mercosur_'
+'Menschenrechten_'
+'Meldung'
+'Mehrheit'
+'Meeting_'
+'Medikament'
+'Mayo'
+'Maximum_'
+'Materi'
+'Masse_'
+'Maschine_'
+'Marktk'
+'Marken_'
+'Marine'
+'Marin'
+'Mandrake'
+'Mandela_'
+'Mandel'
+'Mand'
+'Manchester_'
+'Main'
+'Maes'
+'MU'
+'MT'
+'MIT_'
+'MIL'
+'Lyon_'
+'Lun'
+'Luftverkehr_'
+'Los'
+'Londoner_'
+'Liv'
+'Little_'
+'Lithuania_'
+'Liquiditäts'
+'Linz_'
+'Linken_'
+'Line'
+'Limit_'
+'Lig'
+'Licht'
+'Libert'
+'Liberia_'
+'Liberal_'
+'Level_'
+'Lev'
+'Les'
+'Leiter_'
+'Leib'
+'Legislat'
+'Legi'
+'Lebensmittelsicherheit_'
+'Lebensmitteln_'
+'Leb'
+'Lay'
+'Lauf_'
+'Large_'
+'Lanzarote_'
+'Lane_'
+'Landschaft_'
+'Lad'
+'Labora'
+'Labor'
+'LL_'
+'LCD_'
+'LAN_'
+'Kurs'
+'Kura'
+'Kuch'
+'Kreis_'
+'Kredit_'
+'Kosm'
+'Kopf'
+'Kooperations'
+'Konzert'
+'Konzentration_'
+'Kontakte_'
+'Konsultation_'
+'Konsolidierung'
+'Konse'
+'Kongress'
+'Konflikt'
+'Konfiguration'
+'Kompl'
+'Kommen'
+'Kommando_'
+'Kommando'
+'Komit'
+'Kohä'
+'Kofi_'
+'Koch'
+'Kob'
+'Knopf'
+'Klä'
+'Klick'
+'Klarheit_'
+'Klang'
+'Kirchen'
+'Kirch'
+'Ki_'
+'Khamenei_'
+'Kernkraft'
+'Kennedy_'
+'Kay'
+'Kasse'
+'Kartell'
+'Karibik_'
+'Kapitel_'
+'Kapitalm'
+'Kapazität_'
+'Kanten_'
+'Kampa'
+'Kaiser_'
+'KT_'
+'KT'
+'KP'
+'KOM_'
+'Jungen_'
+'Jugoslawien_'
+'Jugendliche_'
+'Jugend_'
+'Journal_'
+'José_'
+'Joint_'
+'Joe'
+'Jia'
+'Jeff'
+'Jedoch_'
+'Jede'
+'Jardin_'
+'Jane'
+'Jahrzehnts_'
+'Jahr'
+'Isle'
+'Islamist'
+'Investmentbank'
+'Investitionsbank_'
+'Interpret'
+'Internetseite'
+'Interinstitution'
+'Integrationsp'
+'Instrument'
+'Instituts_'
+'Institution'
+'Insi'
+'Innovat'
+'Innenhof_'
+'Innen_'
+'Ingenieure_'
+'Informationsschalter_'
+'Infolgedessen_'
+'Indians_'
+'Index'
+'Independen'
+'Impulse_'
+'Importe'
+'Immigration_'
+'Ig'
+'Ideal_'
+'Ib'
+'IST'
+'ION'
+'IDE'
+'ICC_'
+'Händler_'
+'Hyde_'
+'Hung'
+'Hotelsafe_'
+'Horn'
+'Horde_'
+'Hochschule'
+'Hochschul'
+'History_'
+'Hintergr'
+'Him'
+'Heu'
+'Herkunft_'
+'Herausgeber_'
+'Heim'
+'Heat'
+'Haushaltsp'
+'Haushaltsl'
+'Hat'
+'Harbor_'
+'Handt'
+'Handlungen_'
+'Handelspartner_'
+'Handelsb'
+'Handb'
+'Halle_'
+'Halle'
+'Halbinsel_'
+'Haiti_'
+'Hag'
+'Hafen'
+'Hab'
+'HN'
+'HE'
+'Gy'
+'Gul_'
+'Grünen_'
+'Gründer_'
+'Grundst'
+'Grill'
+'Graz'
+'Gras'
+'Gran'
+'Grafik'
+'Governor_'
+'Gouverneur'
+'Go_'
+'Gleichw'
+'Gleichheit_'
+'Gleiche'
+'Gift_'
+'Gewissen_'
+'Gett'
+'Get'
+'Gesundheitsp'
+'Gesprächen_'
+'Gespräch'
+'Gesetzgeb'
+'Geschäftsle'
+'Geschäftsb'
+'Geschäfts_'
+'Gerhard_'
+'Gent'
+'Geni'
+'Geneva_'
+'Genehmigung_'
+'Gene_'
+'Gemüse'
+'Gemeinschaftsm'
+'Gemeinde_'
+'Geltung'
+'Geis'
+'Geheimdienst'
+'Gegenzug_'
+'Gefühle_'
+'Gefä'
+'Gefangenen_'
+'Geduld_'
+'Gebäuden_'
+'Gebäude'
+'Geburt_'
+'Gazastreifen_'
+'Garten'
+'Garni'
+'Gare_'
+'GNOME_'
+'GM_'
+'GEN'
+'GC'
+'GAP_'
+'G8_'
+'Führungskräfte'
+'Födera'
+'Future_'
+'Futtermittel'
+'Fusion'
+'Furcht_'
+'Funktion'
+'Fuku'
+'Fuerte'
+'Frühling_'
+'Frühjahr_'
+'Friedrich'
+'Friedman_'
+'Friedensnobelpreis'
+'Fremden'
+'Freie_'
+'Fran'
+'Frageb'
+'Fracht'
+'Former_'
+'Forge'
+'Foot'
+'Fon'
+'Following_'
+'Flüchtlingen_'
+'Flächen_'
+'Fläche'
+'Flugzeug_'
+'Fluggäste_'
+'Flugg'
+'Flotte'
+'Florenz_'
+'Fli'
+'Fisher'
+'Fine_'
+'Finanzr'
+'Finanzinstitute_'
+'Finanzielle_'
+'Finanz_'
+'Files_'
+'Fift'
+'Few_'
+'Fests'
+'Festplatten_'
+'Festland'
+'Ferien_'
+'Fels'
+'Felder'
+'Feind'
+'Fei'
+'Fea'
+'Fav'
+'Fasc'
+'Fantas'
+'Fall'
+'Fahrzeug_'
+'Fachw'
+'FU'
+'FPGA_'
+'FP'
+'FOR_'
+'FF_'
+'Extremisten_'
+'External_'
+'Ex_'
+'Evo'
+'Ev'
+'Euros'
+'Euch_'
+'Eti'
+'Etage_'
+'Erwerb_'
+'Erw'
+'Ersten_'
+'Ersatz_'
+'Ero'
+'Erneuerung_'
+'Eri'
+'Erforder'
+'Erdoğan_'
+'Erdbeben'
+'Erbe'
+'Entwicklungsb'
+'Entschädigung'
+'Entschuldigung'
+'Entschließungen_'
+'Enhance'
+'Engl'
+'Energieb'
+'Energiea'
+'Endes_'
+'Employment_'
+'Empfang_'
+'Electric'
+'Eisb'
+'Eis'
+'Einzig'
+'Einver'
+'Eintritt'
+'Einsparungen_'
+'Einschränkungen_'
+'Eins'
+'Einkaufs'
+'Einheiten_'
+'Eingreif'
+'Einfa'
+'Eigentumsrechte'
+'Eigenkapital_'
+'Eiffel_'
+'Eif'
+'Ef'
+'Economi'
+'Ecke_'
+'Ec'
+'Dänemark_'
+'Dut'
+'Durban_'
+'Drug'
+'Drohungen_'
+'Dritt'
+'Dringlichkeit_'
+'Dresden_'
+'Drama'
+'Download'
+'Double_'
+'Doll'
+'Dokument'
+'Document'
+'Division_'
+'Dist'
+'Diskurs'
+'Disco'
+'Direktinvestitionen_'
+'Diplom'
+'Dies'
+'Dialog'
+'Dha'
+'Devisen'
+'Denkens_'
+'Deng'
+'Demokratischen_'
+'Deli'
+'Delhi_'
+'Deg'
+'Defizite_'
+'Decision_'
+'Datum_'
+'Datenschutz'
+'Dat'
+'Danke_'
+'Dan_'
+'Dali'
+'DT'
+'DNA_'
+'Cz'
+'Currently_'
+'Curren'
+'Cubase_'
+'Cs_'
+'Crown'
+'Cross_'
+'Crisis_'
+'Criminal_'
+'Cove'
+'Cost'
+'Corporate_'
+'Corn'
+'Cori'
+'Copyright_'
+'Convenient'
+'Contra'
+'Continu'
+'Connect'
+'Competiti'
+'Columbia_'
+'Color_'
+'Colla'
+'Cocktail'
+'Client'
+'Clear'
+'Claudi'
+'Clar'
+'Civi'
+'Choose_'
+'Chemie'
+'Chef'
+'Check'
+'Charakter'
+'Channel_'
+'Chame'
+'Certain'
+'Catholic_'
+'Cathedral_'
+'Castel'
+'Cash_'
+'Case_'
+'Casa_'
+'Casa'
+'Carolyn_'
+'Carne'
+'Cara'
+'Capital_'
+'Cance'
+'Cala'
+'Cafés_'
+'CS_'
+'CSS_'
+'COM'
+'COD_'
+'CNS_'
+'CN'
+'CHI'
+'CAS'
+'Burk'
+'Bundesregierung_'
+'Bui'
+'Buche'
+'Brutto'
+'Brun'
+'Bruch'
+'Brothers_'
+'Brot'
+'Broadway_'
+'Bring'
+'Brid'
+'Brea'
+'Brazilian_'
+'Bou'
+'Boris_'
+'Bombe'
+'Bolivien_'
+'Blume'
+'Blizzard_'
+'Blitz'
+'Bisc'
+'Bir'
+'Biokraftstoffe'
+'Bildungss'
+'Bib'
+'Bh'
+'Bezug'
+'Bezirk'
+'Bevölkerungen_'
+'Betriebe'
+'Betre'
+'Betrag_'
+'Bestrebungen_'
+'Beste_'
+'Besonders_'
+'Beseitigung_'
+'Beschränkungen_'
+'Bergen_'
+'Berechtigung'
+'Berater_'
+'Berat'
+'Benzin'
+'Benutzer'
+'Benutz'
+'Bemühen_'
+'Belle'
+'Bell'
+'Beleidigung'
+'Beitrittsverhandlungen_'
+'Behinderte'
+'Behauptung_'
+'Begriffe_'
+'Begriff'
+'Begleiter'
+'Begin'
+'Befehl'
+'Bedauerlicherweise_'
+'Bed_'
+'Beam'
+'Bavaria_'
+'Baust'
+'Battle'
+'Basi'
+'Bashir_'
+'Bart'
+'Barrier'
+'Barnier_'
+'Barcode_'
+'Barcelon'
+'Barc'
+'Barbara_'
+'Banglades'
+'Bang'
+'Ban_'
+'Balkan'
+'Baker'
+'Bahnh'
+'BT'
+'BES'
+'BA_'
+'Außenministeri'
+'Autorit'
+'Autonom'
+'Ausw'
+'Ausse'
+'Ausmaße'
+'Auslös'
+'August'
+'Augenblick_'
+'Auftritt'
+'Auftrags'
+'Aufsichtsrat'
+'Aufsichts'
+'Aufschwung_'
+'Aufruf'
+'Aufpreis_'
+'Auflösung_'
+'Audio'
+'Ath'
+'Astrium_'
+'Asp'
+'Argumentation_'
+'Arbeitsweise_'
+'Arbeitsk'
+'Arbeitsbedingungen_'
+'Arbeits_'
+'App'
+'Anwe'
+'Antonio_'
+'Anton'
+'Anth'
+'Anreisedatum_'
+'Ano'
+'Anhang_'
+'Angriffs'
+'Angestellte'
+'Angehörige'
+'Ang'
+'Andrew_'
+'Andreas'
+'Anb'
+'Alten'
+'Alt_'
+'Alpha_'
+'Alltag_'
+'Allgemein'
+'Allen_'
+'Alco'
+'Alan_'
+'Akteur_'
+'Akku'
+'Aix_'
+'Ahn'
+'Ahmadinejad_'
+'Agr'
+'Again_'
+'Afrikan'
+'Affi'
+'Aff'
+'Adv'
+'Admiral_'
+'Adi'
+'Add_'
+'Add'
+'Activ'
+'Achse_'
+'Academy_'
+'Abstand_'
+'Abend'
+'Abbildung'
+'ATT'
+'ASPs_'
+'API'
+'AN_'
+'AKP_'
+'A4'
+'='
+'94'
+'91_'
+'89'
+'86'
+'77'
+'75'
+'74_'
+'73'
+'61'
+'58'
+'55'
+'500'
+'48'
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+'escalati'
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+'erörtern_'
+'eröffnete'
+'erzähl'
+'erzeugte'
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+'erweiterte_'
+'erweise_'
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+'erschaffen_'
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+'ernsthaften_'
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+'erneuerbaren_'
+'erneuerbare_'
+'ermu'
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+'erhobene'
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+'ergebnisse'
+'ergeb'
+'erfreu'
+'erfolgreicher_'
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+'entirety_'
+'enthusiasm_'
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+'enp'
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+'engaging_'
+'engagieren_'
+'enforced_'
+'energi_'
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+'endi'
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+'endemi'
+'ency'
+'encryption_'
+'encouragement_'
+'encountered_'
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+'encompass'
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+'empfiehlt_'
+'empfehle'
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+'elevat'
+'elemente'
+'elem'
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+'elegan'
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+'eks'
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+'eisung'
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+'einzutreten_'
+'einzuh'
+'einzugehen_'
+'einzub'
+'einzigartige_'
+'einzigartig'
+'einz'
+'einw'
+'eintritt_'
+'einstimmig_'
+'einsti'
+'einstellungen_'
+'einste'
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+'einrichten_'
+'einkommens'
+'einhergehen'
+'einher'
+'einheimischen_'
+'eingest'
+'eingesp'
+'eingeschl'
+'eingeräumt_'
+'eingeleitet_'
+'eingeladen_'
+'eingehe'
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+'eindruck'
+'eindeutige'
+'einander'
+'eih'
+'eigen_'
+'eigen'
+'eift'
+'eien_'
+'eichnet_'
+'ehrung'
+'ehrgeizige_'
+'ehrgeizige'
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+'ehmen_'
+'ehens'
+'ehe_'
+'egg_'
+'efor'
+'eff'
+'ef_'
+'editing_'
+'edit_'
+'eding'
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+'ection_'
+'eci'
+'echse'
+'ebenen_'
+'dämm'
+'dynamische'
+'dyna'
+'dwindl'
+'dust_'
+'durchschnitt'
+'dumping_'
+'dumm'
+'dum'
+'dua_'
+'drohende'
+'droh'
+'dritter_'
+'dring'
+'dres'
+'dramatische_'
+'dox'
+'downs_'
+'downloading_'
+'downloade'
+'dose_'
+'dore'
+'doppelt_'
+'doppel'
+'dong'
+'dominiert_'
+'dominated_'
+'domains_'
+'dom'
+'dogs_'
+'dog'
+'documentation_'
+'dn'
+'dk'
+'diving_'
+'diversi'
+'divergen'
+'disziplin'
+'distributions_'
+'distract'
+'distinct_'
+'disso'
+'disre'
+'dispo'
+'displa'
+'dispe'
+'dispa'
+'diskriminierung'
+'discriminat'
+'discovering_'
+'discount_'
+'disclose_'
+'disciplin'
+'disappointing_'
+'disagreement'
+'disadvantage'
+'dire_'
+'diplomatische'
+'dip_'
+'dioxid'
+'dio'
+'diminish'
+'dimensions_'
+'dili'
+'dilemma_'
+'dikti'
+'digt_'
+'dif'
+'diet_'
+'dies'
+'dienste'
+'dick'
+'dichte'
+'diagnosti'
+'dez'
+'devo'
+'devis'
+'developer'
+'devaluation'
+'deutscher_'
+'deutsch'
+'deut'
+'determining_'
+'determin'
+'deter_'
+'deter'
+'detection_'
+'detai'
+'destinations_'
+'designated_'
+'deserv'
+'ders'
+'derogations_'
+'derartig'
+'depression_'
+'depreciation_'
+'depreciat'
+'depre'
+'depos'
+'dependency_'
+'depan_'
+'denomination'
+'dende'
+'demonstration_'
+'demonstr'
+'delte'
+'deliberate'
+'delegati'
+'delays_'
+'deko'
+'dek'
+'deinem_'
+'deflation'
+'definitive'
+'definitions_'
+'definierte'
+'definieren_'
+'defer'
+'defensive_'
+'defa'
+'deeper_'
+'deepen'
+'decreas'
+'decoupling_'
+'deciding_'
+'decides_'
+'debattiert'
+'deal'
+'dauernd'
+'date'
+'dargelegt'
+'dankbar_'
+'dana_'
+'damaligen_'
+'damalige_'
+'damaging_'
+'dahin'
+'cynic'
+'customize'
+'custom_'
+'curr'
+'cure'
+'cula'
+'ctable_'
+'crown'
+'crossing'
+'cron'
+'criticise'
+'criterion_'
+'criminali'
+'cove'
+'coupon'
+'coun'
+'couldn_'
+'cott'
+'corresponds_'
+'correlation'
+'corrections_'
+'correction_'
+'correct'
+'corr'
+'corpora'
+'cornerstone'
+'corn'
+'cooperative_'
+'cooked_'
+'coo'
+'convers'
+'contribu'
+'contradiction'
+'contra'
+'continuously_'
+'continuous_'
+'continents_'
+'contest_'
+'contemp'
+'cont'
+'consume'
+'consultations_'
+'constructi'
+'constitution'
+'conso'
+'consent_'
+'connectivity_'
+'conjunction_'
+'congress_'
+'congratulations_'
+'confort_'
+'conform'
+'conflict'
+'configured_'
+'confess'
+'conductors_'
+'conditione'
+'concessions_'
+'concert_'
+'concert'
+'composit'
+'composer'
+'complied_'
+'complementary_'
+'complement_'
+'comple'
+'complacen'
+'compensat'
+'compelling_'
+'compe'
+'comparable_'
+'communa'
+'commu'
+'commend'
+'comf'
+'comb'
+'colorful_'
+'color'
+'colony_'
+'collecti'
+'collect'
+'collapsed_'
+'coi'
+'coe'
+'cob'
+'coastline'
+'coach_'
+'cluster_'
+'clus'
+'cloth'
+'clos'
+'climb_'
+'clearer_'
+'clean'
+'clamp'
+'cking_'
+'cited_'
+'cisi'
+'circles_'
+'circ'
+'cipa'
+'cion_'
+'cil_'
+'cic'
+'chw'
+'chuld'
+'chu'
+'chtung'
+'chtigt'
+'chter_'
+'chtbar'
+'chste'
+'chst'
+'chor'
+'choi'
+'chk'
+'chips_'
+'ching'
+'chi_'
+'chei'
+'cheat'
+'chat_'
+'chat'
+'chase'
+'charit'
+'charakteristisch'
+'chapter'
+'challenge'
+'chairs_'
+'cerca'
+'centrali'
+'center'
+'cens'
+'celebration_'
+'cease'
+'cci'
+'categori'
+'cated_'
+'cate_'
+'catalyst_'
+'carri'
+'card'
+'capa'
+'cancell'
+'camps_'
+'cals_'
+'call'
+'calculation'
+'calculated_'
+'caf'
+'cabl'
+'bürge'
+'busi'
+'bureaucratic_'
+'bureaucra'
+'burdens_'
+'bundle'
+'bukan'
+'bud'
+'buck'
+'buchs'
+'brücke'
+'bräu'
+'broaden'
+'broad'
+'bridge'
+'brid'
+'breites_'
+'breakthrough_'
+'breaking_'
+'breach_'
+'brat'
+'brand'
+'branche'
+'brai'
+'brac'
+'boutique_'
+'bout'
+'borrow_'
+'bone_'
+'bond'
+'bombs_'
+'bold'
+'boh'
+'board'
+'blä'
+'blutigen_'
+'blutig'
+'blue'
+'bloods'
+'blo_'
+'blier'
+'bliebe'
+'blicken'
+'blick'
+'blen_'
+'bkommen_'
+'bj'
+'bits_'
+'biss'
+'bish'
+'bip'
+'biologischen_'
+'biologis'
+'biolog'
+'bination'
+'billigen_'
+'bilie'
+'bile'
+'bilaterale'
+'bila'
+'bicycle'
+'bezogene'
+'beziehungsweise_'
+'bezeichnete'
+'bewä'
+'bewussten_'
+'bewirkt_'
+'bewerten_'
+'beweg'
+'bew'
+'bevorstehende'
+'bev'
+'beurteilen_'
+'beträchtliche_'
+'beträ'
+'betrug_'
+'betreffend_'
+'betray'
+'besuchten_'
+'bestätig'
+'bestraft_'
+'bestmögliche'
+'bestimmungen_'
+'bester_'
+'bestehe'
+'besseres_'
+'besonderem_'
+'besitz_'
+'besiege'
+'besichtig'
+'beseitig'
+'bese'
+'beschädigt_'
+'beschränkung'
+'beschränkte'
+'beschl'
+'berühr'
+'berühmteste'
+'berüc'
+'beruh'
+'berufliche_'
+'berufliche'
+'bern_'
+'berkembang_'
+'berk'
+'berichterstatt'
+'berichten_'
+'bericht'
+'bereitzustellen_'
+'bereite'
+'berda'
+'berb'
+'bequeme'
+'beobacht'
+'bent'
+'bemerkenswerte_'
+'beme'
+'bem'
+'belonging'
+'believ'
+'beliebige_'
+'belgischen_'
+'beleb'
+'belastet_'
+'belast'
+'bekämpft'
+'bekräftigt_'
+'bekräftig'
+'bekanntlich_'
+'beiträgt_'
+'beitreten_'
+'beiten_'
+'beit_'
+'beispiellose'
+'beispielhaft'
+'bein'
+'behält_'
+'behindertenfreundliche_'
+'behi'
+'beherrsch'
+'beher'
+'behandlung'
+'begrenzte_'
+'begreifen_'
+'begeistert'
+'befolgt_'
+'befand'
+'beer_'
+'beeindruckende'
+'beein'
+'bedien'
+'bedenke'
+'bedenk'
+'beautifully_'
+'beating'
+'beat_'
+'beamte'
+'beam_'
+'beam'
+'beachtet_'
+'beabsichtigt_'
+'bd'
+'baute'
+'baue'
+'basket'
+'basierende'
+'barometer'
+'bares_'
+'barem_'
+'bao_'
+'bankruptcy_'
+'banken_'
+'bands_'
+'ballot_'
+'balances_'
+'baik_'
+'backward_'
+'backd'
+'awful_'
+'awaken'
+'await_'
+'aven'
+'avant'
+'autoritären_'
+'autor'
+'automated_'
+'autob'
+'authorize'
+'authentic'
+'ausüben_'
+'auszugleichen_'
+'auszud'
+'auszubauen_'
+'ausspr'
+'ausse'
+'ausschließ'
+'ausmachen_'
+'auslös'
+'ausgezeichnet_'
+'ausgez'
+'ausgewählten_'
+'ausgewogenen_'
+'ausgestellt'
+'ausgeprägte'
+'ausgelegt_'
+'ausgehende'
+'ausgeh'
+'ausgebildete'
+'ausg'
+'ausführlich_'
+'ausf'
+'ausd'
+'ausbe'
+'augment'
+'aufzuz'
+'aufwa'
+'auftragten_'
+'aufstrebende'
+'aufle'
+'aufhören_'
+'aufgestellt_'
+'aufen_'
+'aufeinander'
+'aue'
+'audiovisual_'
+'auchen_'
+'atur'
+'attraktiv_'
+'attitudes_'
+'attent'
+'attacked_'
+'ato'
+'atively_'
+'ations'
+'ational_'
+'ation'
+'atie'
+'atemberaubende'
+'asur'
+'astronom'
+'astonish'
+'asti'
+'aster_'
+'assung_'
+'assiste'
+'assist'
+'asses'
+'ass'
+'asil'
+'ash_'
+'asch'
+'asc'
+'aru'
+'artner'
+'articulate'
+'arte_'
+'arrives_'
+'arri'
+'arkt_'
+'ark_'
+'aring_'
+'ardo'
+'architectural_'
+'arbe'
+'approximat'
+'appreciation_'
+'applicant_'
+'appease'
+'appear'
+'appeal'
+'appalling_'
+'app_'
+'aper'
+'anzusehen_'
+'anze'
+'anza'
+'antwort'
+'antra'
+'antly_'
+'antin'
+'antik'
+'antibiotics_'
+'antara_'
+'answered_'
+'anstr'
+'anstelle_'
+'ansta'
+'anspruch'
+'anspr'
+'anor'
+'announc'
+'anniversary_'
+'annex'
+'ankurbel'
+'ankung'
+'animation'
+'anima'
+'ania'
+'anhe'
+'anhaltende_'
+'anh'
+'angu'
+'angle_'
+'angez'
+'angewendet_'
+'angewa'
+'angeschlossen_'
+'angeh'
+'angeführt'
+'angebotenen_'
+'anfällig_'
+'aneinander_'
+'anderung'
+'andern_'
+'andards_'
+'anbieter'
+'anbet'
+'analog'
+'ams'
+'amount'
+'amerikanische'
+'aly'
+'aluminum_'
+'altige'
+'alti'
+'alor'
+'allocation_'
+'allgemeiner'
+'allge'
+'aller'
+'allen'
+'alkohol'
+'aliz'
+'alische'
+'algorithms_'
+'ald_'
+'alas'
+'alarming'
+'alarmier'
+'aktuelle'
+'akibat'
+'akh'
+'aket'
+'aken_'
+'ais_'
+'ains'
+'aining_'
+'ahrt_'
+'agu'
+'ags'
+'agree'
+'agne'
+'agn'
+'aggressi'
+'aggravat'
+'agentur'
+'afts'
+'aften_'
+'affiliate'
+'affe'
+'advoca'
+'advisor'
+'advisers_'
+'advanc'
+'adr'
+'adore'
+'ador'
+'admi'
+'adjusted_'
+'additi'
+'adding_'
+'acu'
+'activate_'
+'acquire_'
+'acion'
+'acies_'
+'achse'
+'achievable_'
+'achi'
+'accumulate'
+'accordingly_'
+'accord'
+'accomplish_'
+'accompany_'
+'accom'
+'accidental'
+'access'
+'abzuwe'
+'abz'
+'abweichen'
+'abus'
+'abstract'
+'absti'
+'abschließend_'
+'abra'
+'abolish'
+'ables_'
+'ablehn'
+'abit'
+'abgeschnitten_'
+'abger'
+'abgeb'
+'aber'
+']]), '
+'].'
+'Zö'
+'Zweit'
+'Zwangsv'
+'Zuwanderer'
+'Zusätzlich'
+'Zurich_'
+'Zun'
+'Zuerst_'
+'Zube'
+'Zor'
+'Zoom'
+'Zoo_'
+'Zionis'
+'Zielsetzung'
+'Ziels'
+'Zentrali'
+'Zel'
+'Zehn'
+'Yugoslav_'
+'YouTube_'
+'Yi'
+'Yel'
+'Yacht'
+'Xinjiang_'
+'Xin'
+'Xe'
+'Wörter_'
+'Wört'
+'Wählern_'
+'Worse_'
+'Working_'
+'Worker'
+'Wonder'
+'Wollen_'
+'Woll'
+'Wochenende_'
+'Wirtschaftsw'
+'Wirtschaftse'
+'Wirkung'
+'Willkommen_'
+'Williams_'
+'Wild_'
+'Wil'
+'Wiederbelebung_'
+'Whatever_'
+'Wette_'
+'Westjordanland_'
+'Werke'
+'Wen_'
+'Weltmarkt'
+'Weltkriegs_'
+'Weltgesundheitsorganisation_'
+'Wellen'
+'Welle_'
+'Weißbuch_'
+'Weiterver'
+'Wehr'
+'Week'
+'Wasserstoff'
+'Wassers'
+'Want_'
+'Wales_'
+'Wahrscheinlich'
+'Waf'
+'WWII_'
+'WE_'
+'Vulkan'
+'Vr'
+'Vorwand_'
+'Vortr'
+'Vorsicht_'
+'Vormittag_'
+'Vorm'
+'Vorhan'
+'Vorg'
+'Vorb'
+'Vollst'
+'Voice_'
+'Vita'
+'Visa'
+'Vila'
+'Vig'
+'Vietnam'
+'Vie'
+'Victor'
+'Veto'
+'Verwundbarkeit'
+'Verwend'
+'Verv'
+'Verurteilung_'
+'Vertriebs'
+'Vertreibung'
+'Vertiefung_'
+'Versuchen_'
+'Versu'
+'Verstöße'
+'Verstärkung_'
+'Verständ'
+'Verschärf'
+'Verschuldung_'
+'Verschmutzung_'
+'Versch'
+'Versagen_'
+'Vermögenswerten_'
+'Vermittlung_'
+'Verlierern_'
+'Verknüpfung'
+'Verkehrsanbindung_'
+'Veridian_'
+'Verhältnisse_'
+'Verhinderung_'
+'Verhaftung_'
+'Verfechter_'
+'Verfassungsvertrag_'
+'Verdienst'
+'Verbrennung'
+'Verbreche'
+'Verbindungs'
+'Verarbeitung'
+'Verabschiedung_'
+'Vend'
+'Vat'
+'Vas'
+'Vall'
+'Vali'
+'Valent'
+'VIP_'
+'VII'
+'VC_'
+'Uruguay_'
+'Ursprungs'
+'Ursache_'
+'Urb'
+'Unzufriedenheit_'
+'Unz'
+'Unw'
+'Unver'
+'Untu'
+'Unternehmenss'
+'Untergang'
+'Unterg'
+'Unterb'
+'Unless_'
+'Ungleichgewichte_'
+'Ungeachtet_'
+'Unemployment_'
+'Unein'
+'Unabhängig'
+'Umweltbe'
+'Umweltausschuss_'
+'Umwelta'
+'Umsch'
+'Ultra_'
+'Ult'
+'Ukrain'
+'Uhr'
+'Ufer_'
+'Ud'
+'UNI'
+'UNHCR_'
+'UND_'
+'UA'
+'Tür'
+'Täuschung'
+'Ty'
+'Tube'
+'Tschechische'
+'Tsa'
+'Trü'
+'Trump'
+'Truc'
+'Trop'
+'Trock'
+'Trip'
+'Trinkwasser'
+'Trin'
+'Tribu'
+'Trial'
+'Trennung_'
+'Traum'
+'Transparen'
+'Tran'
+'Tram'
+'Trainings'
+'Training_'
+'Train_'
+'Trail'
+'Touristen'
+'Toulouse_'
+'Touch'
+'Toten_'
+'Tol'
+'Titel'
+'Titan'
+'Tit'
+'Tip'
+'Tin'
+'Tierschutz'
+'Tiefe'
+'Tie'
+'Tibetan'
+'Tia'
+'Thyssen'
+'Threa'
+'Thr'
+'Tho'
+'Therma'
+'Thema'
+'Textes_'
+'Termine_'
+'Tennis_'
+'Templates_'
+'Telekommunikations'
+'Techniken_'
+'Tauche'
+'Tastatur'
+'Tank'
+'Tang'
+'Tamp'
+'Tam'
+'Tai'
+'Tahrir_'
+'Tabellen_'
+'TTIP_'
+'TS_'
+'TRA'
+'TG'
+'TFT'
+'Sünden_'
+'Südwest'
+'Sá'
+'Synth'
+'Symptom'
+'Symp'
+'Symbol'
+'Swe'
+'Sustainable_'
+'Surf'
+'Supp'
+'Superior_'
+'Super_'
+'Sunni_'
+'Subsidiarit'
+'Stö'
+'Sty'
+'Student_'
+'Stro'
+'Stress'
+'Strecke_'
+'Strategi'
+'Strat'
+'Strassen_'
+'Stran'
+'Strafgericht'
+'Strafe'
+'Stol'
+'Stoffen_'
+'Stipendi'
+'Still'
+'Stick'
+'Stich'
+'Steve_'
+'Steuersenkungen_'
+'Stern_'
+'Sterb'
+'Steph'
+'Step'
+'Stellvertreter_'
+'Stellungnahmen_'
+'Stellenwert_'
+'Steinberg_'
+'Steel'
+'Statisti'
+'Stadtteil_'
+'Stadium_'
+'Stable_'
+'Stabilisierung'
+'Staatsanw'
+'Staat'
+'Später_'
+'Spyware_'
+'Spy'
+'Spr'
+'Sporta'
+'Spion'
+'Speak'
+'Spannung'
+'Spanische'
+'Spaniens_'
+'South'
+'Sonntag_'
+'Song'
+'Sonders'
+'Sonderbe'
+'Somet'
+'Solution_'
+'Solidarity_'
+'Sofi'
+'Socialists_'
+'Smart'
+'Slovakia'
+'Sli'
+'Skript'
+'Ski_'
+'Skepti'
+'Ske'
+'Sixt'
+'Sitzung'
+'Sis'
+'Singh'
+'Simply_'
+'Silva_'
+'Silicon'
+'Signal'
+'Sig'
+'Sicherheitsst'
+'Sicherheitsrates_'
+'Sicherheitskräfte_'
+'Sich_'
+'Show'
+'Short_'
+'Sheraton_'
+'Sham'
+'Shaf'
+'Shadow'
+'Sev'
+'Setz'
+'Sensibili'
+'Sender_'
+'Seminar_'
+'Select'
+'Segment'
+'Securit'
+'Screen'
+'Scottish_'
+'Schätz'
+'Schäd'
+'Schwung_'
+'Schwimm'
+'Schwelle'
+'Schwed'
+'Schwarzen_'
+'Schutzmaßnahmen_'
+'Schutze'
+'Schur'
+'Schuldner_'
+'Schuldenerlass_'
+'Schottland_'
+'Schnittstelle_'
+'Schn'
+'Schmu'
+'Schmit'
+'Schmi'
+'Schlussfolgerung_'
+'Schließung_'
+'Schim'
+'Schiffen_'
+'Schar'
+'Schan'
+'Schall'
+'Scar'
+'Scanne'
+'Scal'
+'Saudis_'
+'Satelliten_'
+'Sant_'
+'Sample'
+'Sammel'
+'Sal'
+'Saison'
+'Saf'
+'Sacr'
+'Sachver'
+'Sachs_'
+'SY'
+'SV'
+'SSL'
+'SSE'
+'SR'
+'SN'
+'SK'
+'SITE_'
+'SIM'
+'SDR'
+'SARS_'
+'SAP_'
+'Rückz'
+'Rückschlag_'
+'Rückg'
+'Röm'
+'Räume'
+'Russische'
+'Rus'
+'Rumsfeld_'
+'Rov'
+'Route_'
+'Rotarian'
+'Rota'
+'Romulan'
+'Romans_'
+'Romano'
+'Rol'
+'Rog'
+'Roc'
+'Road'
+'Rival'
+'Risk'
+'Rindfleisch'
+'Rin'
+'Rif'
+'Ride'
+'Richtungen_'
+'Rh'
+'Revolutionary_'
+'Revol'
+'Resultate_'
+'Restaura'
+'Resources_'
+'Resol'
+'Reso'
+'Repu'
+'Representative'
+'Rentner'
+'Renov'
+'Reli'
+'Release'
+'Relati'
+'Rein'
+'Reichweite_'
+'Reich_'
+'Regulierungen_'
+'Regulat'
+'Regionalpolitik_'
+'Regierungsvertreter'
+'Regierungsführung_'
+'Regierungse'
+'Regent'
+'Regens'
+'Regel'
+'Rega'
+'Referenz'
+'Redner'
+'Recon'
+'Rechtsp'
+'Rechtsakt'
+'Rechtfertigung_'
+'Recht'
+'Rechenschaft_'
+'Rebellen'
+'Realis'
+'Reakt'
+'Rav'
+'Raus'
+'Ratspräsident'
+'Rassismus_'
+'Rasse'
+'Range'
+'Ramada'
+'Rak'
+'Rai'
+'Rag'
+'Radi'
+'Rabatt'
+'RU'
+'ROM'
+'RL'
+'Quit'
+'Quartet'
+'Quartal'
+'Quality_'
+'Qualifi'
+'Quadra'
+'QU'
+'Py'
+'Purvis_'
+'Pur'
+'Puffer'
+'Präventi'
+'Prämi'
+'Prozessor_'
+'Provokation'
+'Provi'
+'Protest_'
+'Protektionismus_'
+'Prophet'
+'Prope'
+'Prog'
+'Profite_'
+'Production'
+'Produ'
+'Probe'
+'Privatsphäre_'
+'Privathaus'
+'Price'
+'Preventi'
+'Prestige_'
+'Prese'
+'Preiss'
+'Portug'
+'Porti'
+'Popular'
+'Poor_'
+'Poly'
+'Politis'
+'Polic'
+'Points_'
+'Plug_'
+'Ple'
+'Platte'
+'Platform_'
+'Plasma'
+'Pix'
+'Pitt'
+'Pira'
+'Picture'
+'PiS_'
+'Photo_'
+'Pflege'
+'Pferde'
+'Pfei'
+'Petitions_'
+'Petitions'
+'Petition'
+'Pesti'
+'Peru'
+'Persönlichkeit'
+'Peripherie_'
+'Period'
+'Pere'
+'Pentax_'
+'Pensionen_'
+'Penis'
+'Peninsula_'
+'Pear'
+'Pav'
+'Pauls'
+'Patente_'
+'Passi'
+'Passagiere_'
+'Partition_'
+'Partie'
+'Parlamentsabgeordnete'
+'Pare'
+'Panzer'
+'Panasonic_'
+'Palästin'
+'Palais_'
+'Paket'
+'Pai'
+'Pack'
+'PV_'
+'PU'
+'PT'
+'POS'
+'PM_'
+'PE_'
+'Oz'
+'Outdoor_'
+'Organismen_'
+'Optimierung'
+'Omniture_'
+'Om'
+'Okt'
+'Ohren_'
+'Oft'
+'Offs'
+'Official_'
+'Occ'
+'Obst'
+'Obr'
+'Objekte_'
+'Objekt_'
+'Objekt'
+'Obers'
+'OR'
+'ONE_'
+'OE'
+'Nächte'
+'Ny'
+'Nuclear_'
+'Novo'
+'Nov'
+'Nots'
+'Noti'
+'Noten'
+'Norwege'
+'Norway_'
+'Norw'
+'Normalerweise_'
+'Norde'
+'Nordamerika'
+'Nikotin'
+'Nieders'
+'Nicholas_'
+'Ng'
+'Neuigkeiten_'
+'Neug'
+'Neue'
+'Neube'
+'Neub'
+'Neuan'
+'Netze'
+'Network'
+'Nephi_'
+'Need_'
+'Need'
+'Nazis_'
+'Navy_'
+'Navig'
+'Natursch'
+'Native_'
+'Nationalstaaten_'
+'Nationalist'
+'Nationale_'
+'Namun_'
+'Nai'
+'Nahverkehr'
+'Nachteil_'
+'Nachbarschafts'
+'Münzen_'
+'Möchte'
+'Mé'
+'Mär'
+'Mängel_'
+'Muss'
+'Musk'
+'Musiker_'
+'Musi'
+'Muni'
+'Mun'
+'Mum'
+'Movi'
+'Movement_'
+'Mosc'
+'Montp'
+'Montenegro_'
+'Montage'
+'Monaco_'
+'Modells_'
+'Mobili'
+'Mits'
+'Mitgliedstaat'
+'Mitgefühl_'
+'Mischung_'
+'Ministr'
+'Minimum_'
+'Minderheiten'
+'Million'
+'Millennium'
+'Militär_'
+'Miles_'
+'Mig'
+'Messung_'
+'Merkmale'
+'Mercur'
+'Menschenrechtsverletzungen_'
+'Meinungsverschiedenheiten_'
+'Mehrwert_'
+'Mehr'
+'Megapixel'
+'Meg'
+'Meer'
+'Medit'
+'Medikamenten_'
+'Medikamente_'
+'Medicine'
+'Maur'
+'Matth'
+'Matte'
+'Mathematik_'
+'Mathe'
+'Massenvernichtungswaffen_'
+'Massen_'
+'Massagen_'
+'Massage'
+'Massachusetts_'
+'Maschinen'
+'Mary'
+'Marshall_'
+'Marra'
+'Maritime_'
+'Marie'
+'Margaret_'
+'Marco'
+'Marbella_'
+'Mao'
+'Mant'
+'Mano'
+'Mannschaft_'
+'Mann'
+'Mandat'
+'Malay'
+'Mahm'
+'Magne'
+'Magn'
+'Maf'
+'Madr'
+'Made'
+'Maci'
+'Machthaber_'
+'Machi'
+'Macedonia_'
+'Maca'
+'MID'
+'MDGs_'
+'MAR'
+'MADRID_'
+'Luftfahrt'
+'Luca'
+'Lub'
+'Lore'
+'Looking_'
+'Loo'
+'Lond'
+'Lodge_'
+'Lob'
+'Ll'
+'Lith'
+'Lita'
+'List'
+'Liquidität_'
+'Lip'
+'Lion'
+'Linu'
+'Limited_'
+'Lieferungen_'
+'Lieferant'
+'Lieblings'
+'Liebe'
+'Lichte_'
+'License'
+'Letztere_'
+'Letter'
+'Lek'
+'Leistungsfähigkeit_'
+'Leipzig_'
+'Legitimation_'
+'Legislative_'
+'Lef'
+'Led'
+'Lebensbedingungen_'
+'Learning_'
+'Laur'
+'Laufwe'
+'Laser_'
+'Laos_'
+'Lanka_'
+'Langstrecken'
+'Landschaft'
+'Lande_'
+'Lance'
+'Lamanites_'
+'Lack'
+'LS'
+'LP'
+'LOS_'
+'LICH'
+'LES'
+'LDP_'
+'Kürzungen_'
+'Künstler'
+'Könnte_'
+'Kurzu'
+'Kurve'
+'Kun'
+'Kumari_'
+'Krugman_'
+'Krone'
+'Krist'
+'Krim'
+'Kriegsverbreche'
+'Kreditk'
+'Kreativität_'
+'Krat'
+'Krank'
+'Kraftfahr'
+'Korrekt'
+'Koordination_'
+'Kooperationsabkommen'
+'Konzern'
+'Konzepte_'
+'Konve'
+'Kontroverse'
+'Kontrast_'
+'Konstruktion'
+'Konkur'
+'Kondition'
+'Kompromisse_'
+'Kompe'
+'Kommunistische'
+'Kommissare_'
+'Kommentare_'
+'Kode'
+'Kne'
+'Klimasch'
+'Klassen_'
+'Klassen'
+'Klagen'
+'Kissinger'
+'Kind'
+'Kha'
+'Kenne'
+'Keep_'
+'Kaz'
+'Katzen'
+'Kategorien_'
+'Kaste'
+'Kasach'
+'Karten'
+'Karr'
+'Karl_'
+'Kare'
+'Kapitalst'
+'Kapell'
+'Kanzle'
+'Kandidatenländer'
+'Kamera'
+'Kambodscha_'
+'Kalt'
+'Kalifornien'
+'Kalif'
+'Kad'
+'Kabine'
+'KB_'
+'Jörg_'
+'Jup'
+'Juncker_'
+'Julian_'
+'Jugendherberge_'
+'Jos'
+'Johannes_'
+'Jiang_'
+'Jel'
+'Jazz'
+'Jarzembowski_'
+'Jame'
+'JPEG_'
+'JP'
+'Iss'
+'Isolation_'
+'Islamischen_'
+'Iron'
+'Iri'
+'Ira'
+'Ion'
+'Inva'
+'Intr'
+'Interview'
+'Interv'
+'Intern'
+'Interi'
+'Interess'
+'Inten'
+'Integrat'
+'Inspektor'
+'Insel'
+'Inn'
+'Inkrafttreten_'
+'Ink'
+'Ini'
+'Informations_'
+'Industrien'
+'Indikatoren_'
+'Indikat'
+'Indian'
+'Inde'
+'Inc'
+'Implementi'
+'Imperi'
+'Ih'
+'If'
+'Identitäten_'
+'Iber'
+'IX'
+'ISS'
+'INS'
+'IND'
+'INC'
+'Hürde'
+'Höh'
+'Häusern_'
+'Häuser_'
+'Hut_'
+'Hut'
+'Hus'
+'Humanit'
+'Hug'
+'Hu_'
+'Hoste'
+'Hospi'
+'Honor'
+'Home'
+'Hoheit'
+'Hohe'
+'Hochzeit'
+'Hob'
+'Hirsch'
+'Hip'
+'Hinterl'
+'Hinsichtlich_'
+'Himm'
+'Highlight'
+'Hierbei_'
+'Herzego'
+'Herunter'
+'Herstell'
+'Herkunftsl'
+'Herberge_'
+'Hell'
+'Held'
+'Hektar_'
+'Heimat_'
+'Heid'
+'Hegemonie_'
+'Heer'
+'Heating_'
+'Head_'
+'Head'
+'Haza'
+'Haw'
+'Havel_'
+'Haushaltsk'
+'Haushalten_'
+'Hauptziel'
+'Hauptg'
+'Harry_'
+'Hardliner'
+'Handy_'
+'Hands'
+'Handelsd'
+'Halbjahr_'
+'Haft_'
+'Had_'
+'Habr'
+'Gut_'
+'Gus'
+'Gul'
+'Guid'
+'Guer'
+'Guantánamo_'
+'Gründungs'
+'Grö'
+'Grä'
+'Grundwerte_'
+'Grundv'
+'Grundge'
+'Gru'
+'Green'
+'Grap'
+'Grant_'
+'Grand'
+'Granada_'
+'Gran_'
+'Gou'
+'Got'
+'Goe'
+'Glückw'
+'Globalis'
+'Globale_'
+'Glaube_'
+'Glacier_'
+'Gl'
+'Giu'
+'Gitarren'
+'Gir'
+'Ghana_'
+'Gewässer'
+'Gewi'
+'Gewebe'
+'Getreide'
+'Gesundheitswesen_'
+'Gesundheitsschutz'
+'Gestern_'
+'Gestalt_'
+'Gesta'
+'Gesichtspunkt'
+'Gesetzen'
+'Geschäftsreise'
+'Geschäftsführ'
+'Geschmack'
+'Geschichts'
+'Geschenk_'
+'Gescheh'
+'Geräten_'
+'Georgian_'
+'Gemeinschaftsrecht'
+'Gemeinsame_'
+'Gemeinsam_'
+'Gegebenheiten_'
+'Gefangene'
+'Gefa'
+'Gedanke'
+'Ged'
+'Gebühren_'
+'Gebietskörperschaften_'
+'Gaul'
+'Gau'
+'Gastl'
+'Garc'
+'Ganzes_'
+'Gandhi_'
+'Galic'
+'Galax'
+'Gai'
+'GT'
+'GMO_'
+'GM'
+'GIMP_'
+'GF'
+'GBP_'
+'Fürsten'
+'Fürs'
+'Führungsp'
+'Führungen_'
+'Führer'
+'Fäl'
+'Fäh'
+'Fuss_'
+'Funktionieren_'
+'Funktionalität_'
+'Full_'
+'Fue'
+'Früher'
+'Frucht'
+'Fronte'
+'Frist_'
+'Friedh'
+'Freilassung_'
+'FreeBSD_'
+'Frattini_'
+'Frassoni_'
+'Franz_'
+'Franz'
+'Fox_'
+'Fourth_'
+'Fotograf'
+'Fortschritts'
+'Forst'
+'Formulierung_'
+'Forme'
+'Forex_'
+'Folter_'
+'Folgende'
+'Flüge_'
+'Flü'
+'Flugver'
+'Flucht'
+'Flach'
+'Fl'
+'Fitnessraum_'
+'Fitnesscenter_'
+'Fitness'
+'Fiskalp'
+'Fischf'
+'Finn'
+'Finanzt'
+'Finanzministeri'
+'Finanzma'
+'Finanzinstitut'
+'Finanziellen_'
+'Finanzi'
+'Finanzhilfe'
+'Finanzdienstleistungen_'
+'Filip'
+'Figu'
+'Field'
+'Fie'
+'Feu'
+'Ferrer'
+'Ferr'
+'Fernsehs'
+'Fen'
+'Feli'
+'Feinds'
+'Fehlern_'
+'Fehle'
+'Faz'
+'Fax'
+'Fanati'
+'Fan'
+'Fail'
+'Fahnen'
+'Fah'
+'FOR'
+'FI_'
+'FC'
+'FBI_'
+'Extremismus_'
+'Exporte'
+'Expansion'
+'Exce'
+'Exa'
+'Everyone_'
+'Eurosta'
+'Europäer'
+'Europe'
+'Europarat'
+'Eure'
+'Eto'
+'Ethiopia_'
+'Es'
+'Erzeug'
+'Erwägung_'
+'Erwe'
+'Ersparnisse_'
+'Ernährungs'
+'Erlös'
+'Erlebnis_'
+'Erla'
+'Erkenntnisse_'
+'Erkenn'
+'Erinnerungen_'
+'Erika_'
+'Eric'
+'Erhalt_'
+'Erfordernissen_'
+'Erfa'
+'Erf'
+'Ereignissen_'
+'Ereignis_'
+'Erb'
+'Era'
+'Equally_'
+'Equal'
+'Epidemi'
+'Entwicklungszusammenarbeit_'
+'Entwicklungshilfe_'
+'Entspannen_'
+'Entschl'
+'Entscheidungsfindung_'
+'Entscheide'
+'Entlassung'
+'Entfernung_'
+'Entdeckung_'
+'Entdecke'
+'Engine'
+'Engel'
+'Energieversorgung_'
+'Energieverbrauch'
+'Energietr'
+'Empfehlung_'
+'Emm'
+'Emb'
+'Eman'
+'Elys'
+'Eliten_'
+'Eli'
+'Element'
+'Elektrizität'
+'Elect'
+'Elb'
+'Einwohnern_'
+'Eintrag_'
+'Einstei'
+'Einnahme'
+'Einla'
+'Einig'
+'Einheitswährung_'
+'Eingriff'
+'Eingang_'
+'Eindr'
+'Eight'
+'Eigentums'
+'Ehren'
+'Effe'
+'Edward'
+'Economists_'
+'Ecol'
+'Echt'
+'Eben'
+'East'
+'Earl'
+'EV'
+'EQ'
+'EOS_'
+'ENE'
+'EME'
+'EFSF_'
+'EF'
+'EEC_'
+'EE'
+'EA_'
+'Durchschnitt_'
+'Durchs'
+'Duke'
+'Duc'
+'Dua'
+'Dry_'
+'Drogenh'
+'Drac'
+'Dosi'
+'Domain'
+'Dollars_'
+'Dokumenten_'
+'Dokt'
+'Dod'
+'Doctor_'
+'Divi'
+'Disziplin'
+'Distri'
+'Disney_'
+'Discover'
+'Direktor_'
+'Direkt_'
+'Diamant'
+'Devisenwechsel_'
+'Device'
+'Develope'
+'Dess'
+'Demonstrationen_'
+'Demonstration_'
+'Dei'
+'Defizit_'
+'Definitionen_'
+'Deep'
+'Deco'
+'Deborah_'
+'Deal_'
+'Davon_'
+'Dav'
+'Datenbank'
+'Darstell'
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+'textiles_'
+'textile_'
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+'terroristische_'
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+'template'
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+'telephones_'
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+'teilnehmer'
+'technologie_'
+'technologie'
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+'profile'
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+'produz'
+'produktiver'
+'produktiven_'
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+'processed_'
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+'probe'
+'probability_'
+'prizes_'
+'privatization'
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+'photographer'
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+'gerichteten_'
+'gerichte'
+'gericht'
+'geplante'
+'geometri'
+'geologi'
+'geographic'
+'geografisch'
+'genügt_'
+'gentur'
+'generosity_'
+'generali'
+'genauen_'
+'gemischte'
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+'gebieten_'
+'geben'
+'gay_'
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+'gathered_'
+'gastronomy_'
+'garten_'
+'garde_'
+'gant_'
+'game'
+'gamb'
+'galow'
+'galleries_'
+'gale'
+'gab'
+'fürs'
+'fürchtet'
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+'führ'
+'fühle'
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+'fälsch'
+'fusion_'
+'funktions'
+'funktional'
+'funktion'
+'fundamentalism_'
+'fulfilled_'
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+'fug'
+'fueling_'
+'fting_'
+'ftige'
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+'frustration'
+'fruitful_'
+'fruchtbare'
+'front'
+'froh_'
+'frighten'
+'frequen'
+'fremden'
+'freiz'
+'freiwillige'
+'frameworks_'
+'frames_'
+'fram'
+'fraglich'
+'foto'
+'fosil_'
+'fortunately_'
+'fortunate_'
+'fortschrittliche'
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+'entschä'
+'entschl'
+'ents'
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+'entrepreneurial_'
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+'entko'
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+'engst'
+'engines_'
+'engere_'
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+'engag'
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+'endig'
+'endg'
+'encr'
+'encounter'
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+'empör'
+'empt'
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+'empfinden'
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+'emitte'
+'emit_'
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+'embodie'
+'emancipation_'
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+'elektronische_'
+'elektroni'
+'eleg'
+'electorate_'
+'elderly_'
+'ela_'
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+'einziger_'
+'einzigartiger_'
+'einzelstaatliche_'
+'einzelstaatliche'
+'einsetzt_'
+'einschließ'
+'einnimmt_'
+'einn'
+'einm'
+'einleite'
+'einlage'
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+'einig_'
+'einheitlich'
+'einheit_'
+'einheimische_'
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+'eingreif'
+'eingesetzte'
+'eingereichten_'
+'einger'
+'eingehende'
+'eingefü'
+'eingebrachte'
+'einführt'
+'einfließen'
+'einfache'
+'eindr'
+'eilnehmer'
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+'ehm'
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+'egr'
+'egio'
+'efficiently_'
+'effi'
+'effektive'
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+'eed'
+'educat'
+'edoni'
+'edly_'
+'editorial_'
+'editi'
+'edingungen_'
+'ediate_'
+'ectiv'
+'ect_'
+'ecosystem_'
+'economic'
+'ecommerce_'
+'eckung'
+'echter_'
+'echnologi'
+'echni'
+'ece'
+'ec_'
+'ebi'
+'earthquake_'
+'eare'
+'eagle'
+'ead'
+'eab'
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+'düstere_'
+'dürft'
+'dünne'
+'dün'
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+'dè'
+'dänische'
+'dämpf'
+'dynamisch_'
+'dyn'
+'dying_'
+'durchgeführten_'
+'durchführ'
+'durable_'
+'dura'
+'duplicate'
+'dule'
+'dubio'
+'dual_'
+'dsch'
+'drückt'
+'drücke'
+'drängt_'
+'drink'
+'dringender_'
+'drige'
+'dress_'
+'dreim'
+'dreie'
+'drea'
+'dran_'
+'dramatische'
+'drama_'
+'drai'
+'drafts'
+'dozens_'
+'dozen_'
+'downs'
+'downfall_'
+'douche'
+'doubl'
+'dort'
+'doppelten_'
+'door'
+'doom'
+'dominieren_'
+'dominance_'
+'dom_'
+'dokument_'
+'doct'
+'dock'
+'doc'
+'dne'
+'dlin'
+'divide'
+'divert'
+'diversification_'
+'diversif'
+'diver'
+'dity'
+'distributi'
+'distinguish'
+'distinctive_'
+'distin'
+'distanzier'
+'distan'
+'dissi'
+'disqualif'
+'disproportionate'
+'disposition'
+'displaced_'
+'dispens'
+'disparities_'
+'disorders_'
+'disor'
+'diskriminier'
+'diskreditier'
+'disintegrati'
+'discover'
+'discontent'
+'discl'
+'discard'
+'disar'
+'disappoint'
+'disappeared_'
+'disadvantage_'
+'disabl'
+'dirty_'
+'diri'
+'direkter_'
+'directors_'
+'dir'
+'diplomati'
+'diplom'
+'dingung'
+'dinar'
+'dimensionale'
+'dimension'
+'dilakukan_'
+'diktat'
+'dika'
+'digte'
+'diffus'
+'differentiated_'
+'differ_'
+'diesjährigen_'
+'diesel_'
+'diesbezüglich_'
+'diente_'
+'dienstleistung'
+'dictator_'
+'dictator'
+'dicta'
+'dich'
+'dice'
+'dicat'
+'dibandingkan_'
+'dial_'
+'diagnostizier'
+'diagnosis_'
+'dezentral'
+'devise_'
+'develop'
+'devastat'
+'deva'
+'dev_'
+'deutschs'
+'deutliche'
+'deutig'
+'deterrence_'
+'determina'
+'detect_'
+'detaine'
+'destroying_'
+'destotrotz_'
+'destiny_'
+'desti'
+'destabilisieren'
+'dest'
+'desse'
+'desperate_'
+'desk'
+'designi'
+'designe'
+'desert_'
+'desert'
+'descript'
+'describing_'
+'desc'
+'derte'
+'derse'
+'derin'
+'deput'
+'depri'
+'depressive'
+'deplor'
+'depict'
+'depe'
+'dent_'
+'denkbar'
+'demselben_'
+'demonstrati'
+'demoli'
+'demokratische'
+'demografische'
+'democratiz'
+'democratically_'
+'demis'
+'demics_'
+'demi_'
+'demagogue'
+'dell'
+'delight'
+'delic'
+'deleveraging_'
+'deleted_'
+'delet'
+'deleg'
+'delay'
+'dei'
+'deg'
+'defizite'
+'definite_'
+'definierten_'
+'deficien'
+'defenses_'
+'dedicati'
+'decree'
+'decoration_'
+'decisively_'
+'decen'
+'dece'
+'deca'
+'debu'
+'debat'
+'deba'
+'dealers'
+'deaktiviert'
+'deadlock_'
+'daya_'
+'day'
+'davon'
+'daughter_'
+'dauern_'
+'dauerhaft_'
+'dauer_'
+'datei_'
+'dat_'
+'darl'
+'dark'
+'darge'
+'dank'
+'dangerously_'
+'dane'
+'dance'
+'dairy_'
+'dai'
+'dage'
+'dae'
+'dachte_'
+'custom'
+'curi'
+'cup_'
+'culos'
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+'culati'
+'cui'
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+'ctor_'
+'cto'
+'cteri'
+'cruel_'
+'crop_'
+'crop'
+'critics_'
+'criticise_'
+'cription'
+'cred'
+'creativity_'
+'crea'
+'cram'
+'crafted_'
+'covert'
+'coverage_'
+'courte'
+'court'
+'courag'
+'counting_'
+'counterpart'
+'cosmo'
+'corrupt'
+'correspond'
+'coordinate_'
+'cool'
+'cooking_'
+'cookies_'
+'convinc'
+'conviction_'
+'convey_'
+'controvers'
+'contro'
+'contributes_'
+'contraction'
+'continuity_'
+'continual'
+'continental_'
+'conti'
+'contempt_'
+'contemplate'
+'container'
+'consum'
+'consultant'
+'consolidate'
+'console'
+'consig'
+'conservat'
+'conscien'
+'congratulat'
+'confirms_'
+'confine'
+'conducti'
+'condo'
+'conditi'
+'conceptual'
+'concepts_'
+'concentrating_'
+'conceive'
+'conceivable_'
+'compromises_'
+'comprise_'
+'composition_'
+'compos'
+'complimentary_'
+'competitors_'
+'compet'
+'communicati'
+'commission_'
+'commi'
+'commerce'
+'commentator'
+'comitology_'
+'combination'
+'combin'
+'colleg'
+'collateral_'
+'collaps'
+'colla'
+'coins_'
+'cof'
+'coexist'
+'codec'
+'cod_'
+'cock_'
+'clou'
+'cloning_'
+'cliente'
+'clear'
+'classification_'
+'classif'
+'clash_'
+'clarifi'
+'ckung_'
+'ckten_'
+'ckr'
+'ckne'
+'ckier'
+'civiliz'
+'civilisation'
+'civ'
+'circulation_'
+'circula'
+'circuit_'
+'cip'
+'ciona'
+'ciation'
+'ciar'
+'chwor'
+'chwi'
+'chwe'
+'chut'
+'chuss'
+'chur'
+'chunk'
+'chuh'
+'chtliche'
+'chtet_'
+'chtern'
+'chslung'
+'chrono'
+'chronis'
+'chronic_'
+'christ'
+'chriften_'
+'chreib'
+'chooses_'
+'choc'
+'chnik'
+'chni'
+'chn'
+'chme'
+'chläge_'
+'chli'
+'chke'
+'chisc'
+'chip_'
+'chinesischer_'
+'chine'
+'china_'
+'chier'
+'chicken_'
+'chess'
+'cherung'
+'chere'
+'chemis'
+'chem_'
+'charta_'
+'characteris'
+'chara'
+'chanis'
+'champions_'
+'chambres_'
+'chamber'
+'cham'
+'chain'
+'chaff'
+'ceu'
+'cet_'
+'certification'
+'certificates_'
+'certificate_'
+'cents_'
+'centrist_'
+'centraliz'
+'cent'
+'cen_'
+'cema'
+'celebrati'
+'celebrat'
+'cele'
+'ceiling_'
+'cattle_'
+'cations_'
+'cater'
+'catching_'
+'catas'
+'carte_'
+'caro'
+'cardi'
+'cape'
+'capacities_'
+'cane_'
+'campaign'
+'calme_'
+'calibration'
+'cache_'
+'bürger_'
+'bünd'
+'bücher'
+'bü'
+'byte_'
+'byp'
+'buyers_'
+'bustl'
+'burst'
+'burgh'
+'burg'
+'bure'
+'bundes'
+'build'
+'bug'
+'buf'
+'buen'
+'bten'
+'bte_'
+'bsc'
+'brut'
+'brus'
+'bruch'
+'browse'
+'brow'
+'britischer_'
+'bringen'
+'brig'
+'brew'
+'brethren_'
+'breiteren_'
+'brechen'
+'brav'
+'brasilianische_'
+'bras'
+'brands_'
+'bourne_'
+'boundary_'
+'boss'
+'bos'
+'borrowed_'
+'bonus_'
+'bomb'
+'bodi'
+'boden_'
+'bnis'
+'blü'
+'blur'
+'blood'
+'blogs_'
+'blogg'
+'blockier'
+'blin'
+'bled_'
+'blaue'
+'blatt_'
+'blasen'
+'bisc'
+'birthday_'
+'birth'
+'bird_'
+'biom'
+'biological_'
+'biog'
+'biofuel'
+'bindung_'
+'binary_'
+'bilitati'
+'bilis'
+'bildete'
+'bilder_'
+'bilanz_'
+'bil_'
+'bike'
+'biete_'
+'bid'
+'bibliot'
+'bias_'
+'bia_'
+'bezwe'
+'bezog'
+'bezieh'
+'bewunder'
+'bewohne'
+'bewirt'
+'bewi'
+'bewerbe'
+'bewege'
+'bewe'
+'bewahr'
+'bewaffnet'
+'beur'
+'beunruhigende'
+'betriebs'
+'betriebene'
+'betr'
+'betonte'
+'beteiligten_'
+'beteiligen_'
+'beta'
+'bestände_'
+'bestr'
+'besto'
+'bestimmtes_'
+'besti'
+'bestens_'
+'bestellt'
+'bestellen_'
+'besser'
+'besorgniserregende'
+'besie'
+'besetzt_'
+'beschriebenen_'
+'beschrieb_'
+'beschr'
+'beschlossene'
+'beschloss_'
+'bescheiden'
+'besche'
+'besa'
+'berühren_'
+'beruhte_'
+'beruhigen'
+'berke'
+'berichtete_'
+'berh'
+'bereits'
+'bereichen_'
+'berater'
+'berada_'
+'beobachtet_'
+'benign_'
+'bende'
+'benar_'
+'bemüh'
+'belt_'
+'belt'
+'belongs_'
+'belohn'
+'belo'
+'beln_'
+'beliebt_'
+'belegen_'
+'beleben'
+'bekunde'
+'bekomm'
+'beklagen_'
+'bekl'
+'beinhalten_'
+'beider_'
+'beherrscht_'
+'behave'
+'behauptete_'
+'beharrt'
+'begrüß'
+'begrenzter'
+'begrenzen_'
+'beginn'
+'begi'
+'begegnet'
+'befürworten_'
+'befürworte_'
+'befürchten_'
+'befürchte'
+'befu'
+'befo'
+'befindlichen_'
+'befindliche'
+'beeindruckt'
+'beeindruckenden_'
+'bedingt_'
+'bedeutsam'
+'bedeutender'
+'bedeutend_'
+'bedauere_'
+'bedank'
+'beauf'
+'bearbeitung'
+'bearbeitet'
+'bear'
+'beant'
+'beans'
+'beachtliche'
+'beachte'
+'beabsichtig'
+'bbe'
+'battles_'
+'baths_'
+'basierte'
+'basel'
+'baru'
+'barre'
+'barr'
+'baro'
+'barbe'
+'barbari'
+'bant'
+'bans_'
+'banker'
+'bani'
+'bana'
+'bakar_'
+'baj'
+'baggage_'
+'baden_'
+'bacteria'
+'backup_'
+'backpack'
+'backlash_'
+'až_'
+'año'
+'axis_'
+'ax'
+'awards_'
+'await'
+'awa_'
+'avoid'
+'außergewöhnliche_'
+'außerge'
+'außenst'
+'autoritäre'
+'automatisier'
+'automatischen_'
+'authorita'
+'authorisation_'
+'authentic_'
+'ausüb'
+'auszusp'
+'auszugeben_'
+'auswirkt_'
+'ausweiten'
+'ausweis'
+'auswa'
+'austausch'
+'aust'
+'aussp'
+'aussichten_'
+'aussa'
+'ausrüstung'
+'ausrichte'
+'ausreicht_'
+'auslöste'
+'ausländischer_'
+'ausle'
+'ausla'
+'ausgewählte_'
+'ausgewogen_'
+'ausgewertet_'
+'ausgetragen_'
+'ausgerichteten_'
+'ausgenommen_'
+'ausgeglichene'
+'ausgegli'
+'ausgefü'
+'ausgebe'
+'ausgearbeitet'
+'ausgab'
+'ausfäll'
+'auseinandersetz'
+'ausdrückliche'
+'ausdrück'
+'ausar'
+'aur'
+'augen'
+'aufzut'
+'aufzust'
+'aufzub'
+'aufwer'
+'auftrag'
+'aufsch'
+'aufs_'
+'aufr'
+'aufnahme_'
+'aufkommende'
+'aufk'
+'aufhalten'
+'aufgreif'
+'aufgeworfen'
+'aufgewe'
+'aufgetr'
+'aufgeben_'
+'aufeinander_'
+'auern_'
+'audi'
+'auction'
+'auch'
+'auben'
+'atu'
+'attraktiver_'
+'attraktiv'
+'attracted_'
+'attacker'
+'atrocities_'
+'aton'
+'atom'
+'atmospher'
+'ative'
+'atische'
+'atisch_'
+'ationsa'
+'athlet'
+'ater_'
+'aten'
+'atel'
+'atastrophe'
+'assured_'
+'assur'
+'assu'
+'associate'
+'assert_'
+'asse'
+'assault'
+'assassination'
+'asis'
+'asin'
+'asce'
+'artung'
+'artist'
+'artis'
+'artikel_'
+'artig'
+'arriving_'
+'arrange'
+'arra'
+'arom'
+'armies_'
+'arl'
+'arke'
+'aris_'
+'arien_'
+'arians_'
+'argument'
+'arguabl'
+'arf_'
+'arena_'
+'ardin'
+'archäologische'
+'archiv'
+'architektonische'
+'archipelago'
+'archi'
+'arbeitung_'
+'arbeitslos_'
+'arbeitete'
+'arabisch'
+'appropriati'
+'appropriate'
+'appreciat'
+'applicants_'
+'applaud'
+'appelliere_'
+'apli'
+'ao_'
+'anzub'
+'anzeig'
+'anybody_'
+'anwendungen_'
+'antwortlich'
+'antrags_'
+'antivirus_'
+'antim'
+'antiken_'
+'anticipate_'
+'antibioti'
+'anter'
+'antag'
+'anstrebt'
+'ansieht_'
+'anschließende'
+'anschaue'
+'anpassen_'
+'annähernd_'
+'anktionen_'
+'ankl'
+'anken_'
+'ank_'
+'anj'
+'angriffe'
+'angig'
+'angga'
+'angetrieben'
+'angesp'
+'angese'
+'angesch'
+'angesammelt'
+'angenomme'
+'angenehme_'
+'angemessener'
+'angeme'
+'angehoben_'
+'anga'
+'anfü'
+'anfängt_'
+'anforderung'
+'anesische'
+'anerkenn'
+'aner_'
+'andin'
+'andi'
+'andeu'
+'anchor'
+'ance'
+'analyti'
+'analysing_'
+'analy'
+'analog_'
+'anak_'
+'ample_'
+'amid'
+'amending_'
+'ambiguous_'
+'ambient'
+'ambience_'
+'amba'
+'altr'
+'altet_'
+'alternative'
+'altern'
+'alm'
+'allo_'
+'allmählich'
+'alljährlich'
+'alli'
+'alleine_'
+'allegedly_'
+'alk'
+'alive_'
+'alität'
+'alition'
+'alisieren_'
+'alin'
+'align_'
+'align'
+'alienation_'
+'alien_'
+'alan_'
+'aktualisiert'
+'aktu'
+'aktivitäten_'
+'aktive'
+'aktion_'
+'akti'
+'airspace_'
+'aire'
+'aikan'
+'aid'
+'ahlung'
+'agung_'
+'agt_'
+'agrees_'
+'agierende'
+'aggebe'
+'agendas_'
+'agein'
+'agar'
+'aftermath_'
+'afor'
+'afghanischen_'
+'afghanische_'
+'affen_'
+'affair_'
+'aerospace_'
+'advertise'
+'adversaries_'
+'adver'
+'admissi'
+'admira'
+'administrator_'
+'administrativ'
+'administer'
+'admin_'
+'adjusting_'
+'adic'
+'ader'
+'adan'
+'activis'
+'activ'
+'acquis_'
+'acquiring_'
+'acqui'
+'achtung'
+'achte'
+'acht'
+'ached_'
+'accueil_'
+'accomodati'
+'accommodate_'
+'acco'
+'accessibility_'
+'accede'
+'abzuw'
+'abzus'
+'abzielen_'
+'abwechs'
+'abundan'
+'abstra'
+'absorbi'
+'absorb_'
+'absol'
+'absi'
+'absent_'
+'abschrecken'
+'abort'
+'abolition_'
+'abolished_'
+'abnehmen'
+'abilität'
+'abilities_'
+'abili'
+'abide_'
+'abhalten_'
+'abgeschl'
+'abgescha'
+'abgesch'
+'aben'
+'abduction'
+'aban'
+'aat_'
+']]. _'
+']] '
+'[ _'
+'['
+'Zürich_'
+'Zü'
+'Zyp'
+'Zyklus_'
+'Zyklen_'
+'Zwe'
+'Zw'
+'Zuwa'
+'Zuw'
+'Zuverlässigkeit'
+'Zutaten_'
+'Zuständ'
+'Zusch'
+'Zusammenschluss_'
+'Zusammenhänge'
+'Zusammen_'
+'Zurückh'
+'Zula'
+'Zuhöre'
+'Zuhause_'
+'Zugeh'
+'Zufriedenheit_'
+'Zuflu'
+'Zivilisation_'
+'Zinssatz'
+'Zin'
+'Zimmerman'
+'Zielvorgabe'
+'Zeug'
+'Zentren_'
+'Zensur'
+'Zem'
+'Zellen'
+'Zeitungs'
+'Zeits'
+'Zeitlinie_'
+'Zeitge'
+'Zahlungsv'
+'Zahlungsaus'
+'Zah'
+'Yor'
+'Yale_'
+'YA'
+'XWB_'
+'XT'
+'XII'
+'XF'
+'X1'
+'Wünsch'
+'Wäldern_'
+'Wähler'
+'Wähl'
+'Wut'
+'Wunde'
+'Works_'
+'Wolfs'
+'Wolfgang_'
+'Wolfensohn_'
+'Wohnungen_'
+'Wohnung_'
+'Wohnb'
+'Wohlstands_'
+'Wohlergehen'
+'Wohlbefinden_'
+'Wochen'
+'Wirtschaftsn'
+'Wirtschaftsg'
+'Wirtschaftsbe'
+'Wirts'
+'Wirks'
+'Wire'
+'Wirbelst'
+'Wins'
+'Wing'
+'Winde'
+'Wind_'
+'Willens'
+'Wille_'
+'Wilders_'
+'Wiedervereinigung'
+'Wiederholung_'
+'Wiederh'
+'Wiederaufnahme_'
+'Wieder_'
+'Widget'
+'Wichtiger'
+'Wic'
+'Whirlpool_'
+'Whereas_'
+'Whe'
+'Wetter'
+'Wettbewerbsvor'
+'Wettbewerbsver'
+'Wettbewerbspolitik_'
+'Westeuropa_'
+'Wesentliche'
+'Wertschöpfung'
+'Werts'
+'Wert'
+'Weltr'
+'Weltor'
+'Weltme'
+'Weltkultur'
+'Weltkrieges_'
+'Welthandels'
+'Weltg'
+'Weltbevölkerung_'
+'Weiße_'
+'Weish'
+'Weine_'
+'Weile'
+'Weigerung_'
+'Wednesday_'
+'Wechsel'
+'Webserver'
+'Way'
+'Wasserkraft'
+'Washing'
+'Wanderung'
+'Wallström_'
+'Waldbrände'
+'Wald_'
+'Wake'
+'Wahler'
+'Wahlbe'
+'Wagen_'
+'Waffenstillstand_'
+'Wachstumsraten_'
+'Wachstumsrate_'
+'WT'
+'WS_'
+'WM_'
+'WEI'
+'WAV_'
+'Völkern_'
+'Völkermord'
+'Vö'
+'Vé'
+'Vä'
+'Vá'
+'Vulcan_'
+'Vul'
+'Vot'
+'Vos'
+'Vorstands'
+'Vorsta'
+'Vorst'
+'Vorsitzende'
+'Vorliebe'
+'Vorla'
+'Vorhersage'
+'Vorgang'
+'Vorgabe'
+'Vorfall_'
+'Vorder'
+'Volvo_'
+'Vollm'
+'Volkswagen_'
+'Volkspartei_'
+'Volksabstimmung_'
+'Vladimir_'
+'Vizepräsident_'
+'Vize'
+'Vitamin'
+'Virginia'
+'Viol'
+'Vik'
+'Viewer_'
+'View'
+'Viertel'
+'Vielf'
+'Via'
+'Verzögerung_'
+'Verzweiflung'
+'Verzug'
+'Verwer'
+'Verweise'
+'Verw'
+'Verträgen_'
+'Vertrauens_'
+'Vertragsver'
+'Verteil'
+'Verteidiger'
+'Versäum'
+'Verste'
+'Versp'
+'Verschwörung'
+'Verschwi'
+'Verschwendung_'
+'Verschuld'
+'Verschlechterung'
+'Verschiedene_'
+'Verschieb'
+'Vers'
+'Verpa'
+'Verp'
+'Vero'
+'Vernichtung_'
+'Verne'
+'Vermeidung_'
+'Verlaufe_'
+'Verlangsamung_'
+'Verkäufe'
+'Verkehrst'
+'Verkehrss'
+'Verhandlung_'
+'Vergleichss'
+'Vergew'
+'Verfassungsentwurf_'
+'Verfall'
+'Vere'
+'Verbü'
+'Verbr'
+'Verbe'
+'Verantwortlichen_'
+'Veranstaltungs'
+'Veran'
+'Venus_'
+'Vent'
+'Venezia'
+'Vec'
+'Vaters_'
+'Variablen_'
+'VS'
+'VIN'
+'VA_'
+'Uti'
+'Urteile'
+'Ursprünge_'
+'Urlaube'
+'Uribe_'
+'Urheberrechts'
+'Urbanis'
+'Upgrade_'
+'Unterwa'
+'Untert'
+'Unterschrift'
+'Untersch'
+'Unternehmensf'
+'Unterl'
+'Unterhaltung'
+'Unterha'
+'Unterbrechung'
+'Untera'
+'Unsinn_'
+'Unrecht_'
+'Unix_'
+'Universitäts'
+'Universal'
+'Unit_'
+'Unglücklicherweise_'
+'Unglück_'
+'Ungleichgewicht'
+'Ungere'
+'Une'
+'Umweltver'
+'Umweltschutz'
+'Umwelts'
+'Umweltpr'
+'Umweltpolitik_'
+'Umverteilung'
+'Umstellung_'
+'Umfragen_'
+'UV_'
+'URL'
+'UNICEF_'
+'Türke'
+'Tyrol'
+'Tyran'
+'Tyr'
+'Type_'
+'Tusk'
+'Turnier_'
+'Tunis'
+'Tunes_'
+'Tsunami_'
+'Tschad_'
+'Träger_'
+'Truste'
+'Trojan'
+'Trichet_'
+'Trend'
+'Tren'
+'Treibhausgasemissionen_'
+'Treibhausgas'
+'Treasury_'
+'Travel'
+'Trau'
+'Trap'
+'Transit_'
+'Transform'
+'Transaktionen_'
+'Trans_'
+'Trainer_'
+'Tow'
+'Tours_'
+'Tourist'
+'Tool'
+'Too'
+'Tole'
+'Tisch'
+'Timor'
+'Timo'
+'Til'
+'Tiger'
+'Tiere'
+'Tiera'
+'Tic'
+'Think'
+'Thing'
+'Therme'
+'Therap'
+'Ther'
+'Theorien_'
+'Theodore_'
+'Thatcher_'
+'Texte'
+'Terrorismusbekämpfung_'
+'Terroranschl'
+'Territorium_'
+'Terr'
+'Terms_'
+'Tendenzen_'
+'Temperatur_'
+'Temperatur'
+'Televis'
+'Telekommunikation_'
+'Teilung'
+'Teilnehmer'
+'Technologies_'
+'Technis'
+'Technical_'
+'Tea_'
+'Taylor_'
+'Tax_'
+'Tax'
+'Tatatabot_'
+'Taste_'
+'Tasche'
+'Target_'
+'Tale'
+'Tagung'
+'TZ'
+'TW'
+'TU_'
+'TRO'
+'TPP_'
+'TEN'
+'TA_'
+'T2'
+'Südtirol_'
+'Südoste'
+'Südostasien'
+'Süd_'
+'Säug'
+'Sän'
+'São_'
+'Sz'
+'Syriza_'
+'Syndrom'
+'Swim'
+'Sustain'
+'Surve'
+'Surely_'
+'Suprem'
+'Sup'
+'Sunnis_'
+'Sunn'
+'Summers_'
+'Summer_'
+'Summen_'
+'Summ'
+'Suf'
+'Suda'
+'Substantiv'
+'Subsi'
+'Subscri'
+'Subs'
+'Subm'
+'Subjekt'
+'Stücke_'
+'Störun'
+'Stärken_'
+'Stuf'
+'Studi'
+'Ström'
+'Stric'
+'Strau'
+'Strategic_'
+'Strasse'
+'Strafv'
+'Stornierung'
+'Stop'
+'Stone'
+'Stock'
+'Stimmen'
+'Stillstand_'
+'Stie'
+'Steven'
+'Steuerh'
+'Steuerer'
+'Steuereinnahmen_'
+'Stern'
+'Stehende_'
+'Stefan_'
+'Stea'
+'Statute_'
+'Standpunkten_'
+'Standpunkte_'
+'Stammzellen'
+'Stahl_'
+'Stadtk'
+'Stadi'
+'Stabilitätspakt_'
+'Staatsp'
+'Staatsbürgerschaft_'
+'Sprung'
+'Sprin'
+'Split'
+'Spitzenpo'
+'Spit'
+'Spirit'
+'Spezifikation'
+'Spezialist'
+'Spend'
+'Spen'
+'Spektrum'
+'Speise'
+'Speicherka'
+'Spaß_'
+'Spazier'
+'Sparpolitik_'
+'Sparmaßnahmen_'
+'Sparen'
+'Spam'
+'Spalte'
+'Sozialp'
+'Sozialisten_'
+'Sozialismus_'
+'Sozialdemokraten_'
+'Sowjet'
+'Sonnenunterg'
+'Sonderg'
+'Sommers'
+'Somalia_'
+'Soft'
+'Society_'
+'Snowboard'
+'Slu'
+'Slovenia_'
+'Sle'
+'Skl'
+'Skandal'
+'Sitzungsperiode_'
+'Sitz'
+'Simon_'
+'Silber'
+'Siena_'
+'Siedl'
+'Sieben'
+'Side'
+'Sicherheitsbe'
+'Sicherheit'
+'Shut'
+'Shows'
+'Shop'
+'Shinzo'
+'Shell_'
+'Shel'
+'Sharia_'
+'Sex'
+'Seven_'
+'Ses'
+'Serv'
+'Senkaku_'
+'Senior'
+'Senegal_'
+'Sendung_'
+'Semit'
+'Self'
+'Selbstz'
+'Selbstvertrauen_'
+'Selbstmord'
+'Seiten'
+'Seit'
+'Sehen'
+'Seeverkehr'
+'Sechste'
+'Sech'
+'Seattle_'
+'Seas'
+'Screening'
+'Schüt'
+'Schönheit'
+'Schön'
+'Schö'
+'Schwächung'
+'Schwächen_'
+'Schwinde'
+'Schwimmbad_'
+'Schwie'
+'Schwester_'
+'Schwellenmärkte'
+'Schweigen_'
+'Schwe'
+'Schwarze'
+'Schwachstelle'
+'Schutzge'
+'Schuss'
+'Schuman'
+'Schulter'
+'Schuldenlast'
+'Schuhputzmaschine_'
+'Schröder'
+'Schritten_'
+'Schrei'
+'School'
+'Schock_'
+'Schock'
+'Schnitt'
+'Schloss'
+'Schlo'
+'Schlecht'
+'Schlacht'
+'Schla'
+'Schil'
+'Schiene_'
+'Schied'
+'Schein'
+'Schauspiele'
+'Schatten'
+'Schaff'
+'Schaf'
+'Schadens'
+'Schad'
+'Saš'
+'Savo'
+'Savi'
+'Sauberkeit_'
+'Sauber'
+'Sard'
+'Saraj'
+'Sarah_'
+'Santo'
+'Sandstr'
+'Sand_'
+'Samo'
+'Sammlungen_'
+'Samb'
+'Salva'
+'Sali'
+'Sak'
+'Saint'
+'Sahara_'
+'Sachverhalte'
+'Saal_'
+'SSI'
+'SPE'
+'SOEs_'
+'SMEs_'
+'SL_'
+'SIS_'
+'SING'
+'SDL_'
+'SB'
+'Rücküberweisung'
+'Rücktritt_'
+'Rückst'
+'Rückhalt_'
+'Rückführung_'
+'Ryan_'
+'Ry'
+'Rural_'
+'Run_'
+'Run'
+'Rum'
+'Ruine'
+'Ruhes'
+'Rue'
+'Roy'
+'Row'
+'Rotterdam_'
+'Rotarier'
+'Rost'
+'Ros'
+'Root'
+'Roo'
+'Ron_'
+'Ron'
+'Rohstoffpreise'
+'Rohstoffe_'
+'Robot'
+'Robin'
+'Risikobe'
+'Rio'
+'Rim'
+'Richter'
+'Rib'
+'Rhythm'
+'Rho'
+'Rhin'
+'Reze'
+'Revi'
+'Reve'
+'Respons'
+'Residen'
+'Reservierung'
+'Reservation'
+'Republikan'
+'Repräsentanten_'
+'Repräsentant'
+'Repression'
+'Renzi_'
+'Renten_'
+'Rente_'
+'Religions'
+'Relevanz_'
+'Relation'
+'Reisez'
+'Reisever'
+'Reisetipp_'
+'Reis'
+'Reha'
+'Register'
+'Regierungspo'
+'Regarding_'
+'Refu'
+'Reden_'
+'Reco'
+'Rechtssysteme'
+'Rechtssystem'
+'Rechtsst'
+'Rechtssicherheit_'
+'Rechtsr'
+'Rechtschreib'
+'Rechtsbe'
+'Rechtsausschuss'
+'Rechtsanw'
+'Recherche'
+'Rechenschaftspflicht_'
+'Recep'
+'Rebellion_'
+'Realit'
+'Realisierung_'
+'Reading'
+'Reac'
+'Raums_'
+'Raume'
+'Rauchen_'
+'Ration'
+'Rating_'
+'Ratifi'
+'Rathaus'
+'Rapid_'
+'Rangliste'
+'Ral'
+'Raketen_'
+'Rail_'
+'Raci'
+'RT_'
+'RK'
+'RANT'
+'RAM_'
+'RAC'
+'Quoten_'
+'Quellcode_'
+'Quar'
+'Quanti'
+'Quant'
+'Qualifikationen_'
+'Qual'
+'Quadrat'
+'Qu'
+'Qatar_'
+'Qaddafi_'
+'Qa'
+'QUI'
+'Pv'
+'Push'
+'Pump'
+'Pull'
+'Pub'
+'Psycho'
+'Psych'
+'Präzisi'
+'Präsidentschaftswahlen_'
+'Präsentation'
+'Prämien_'
+'Präf'
+'Provinz'
+'Provider_'
+'Prototyp'
+'Proto'
+'Proteine_'
+'Protein'
+'Protect'
+'Propo'
+'Propaganda_'
+'Promenade_'
+'Programmier'
+'Profit_'
+'Profil_'
+'Professional_'
+'Produkts'
+'Produktionsst'
+'Produktionspr'
+'Product'
+'Proc'
+'Problemati'
+'Privileg_'
+'Privats'
+'Privatis'
+'Prior_'
+'Priester'
+'Prev'
+'Prepa'
+'Premierminister'
+'Preisstabilität_'
+'Preises_'
+'Prakti'
+'Prag'
+'PowerP'
+'Potter'
+'Potsdam'
+'Poten'
+'Postgre'
+'Postdienst'
+'Posse'
+'Position'
+'Portugies'
+'Portale'
+'Porta'
+'Population_'
+'Pon'
+'Politicians_'
+'Pola'
+'Plo'
+'Plenar'
+'Play_'
+'Plata_'
+'Plast'
+'Planet_'
+'Pizza'
+'Pino'
+'Pilote'
+'Pilot'
+'Pier'
+'Pick'
+'Picc'
+'Pic'
+'Photocopying_'
+'Phoeni'
+'Philippi'
+'Pharma'
+'Pflanz'
+'Pfl'
+'Pfeiler_'
+'Pfad_'
+'Peters'
+'Perspective_'
+'Persi'
+'Perm'
+'Perfe'
+'Pension_'
+'Pennsylvania_'
+'Pend'
+'Pemb'
+'Pedro'
+'Peak'
+'Pax_'
+'Paus'
+'Pati'
+'Patent_'
+'Passw'
+'Passport_'
+'Passei'
+'Passe'
+'Pass_'
+'Partner'
+'Partic'
+'Parti'
+'Parm'
+'Parlamentarier_'
+'Parkplatz_'
+'Parke'
+'Paris'
+'Parc'
+'Paramet'
+'Parallelen_'
+'Parallel_'
+'Paradox'
+'Paolo_'
+'Pani'
+'Pand'
+'Pan_'
+'Palästinensischen_'
+'Pakets_'
+'Paint'
+'Packag'
+'PROGR'
+'POL'
+'PN'
+'PLAYER'
+'PIC'
+'PAS'
+'Oxford_'
+'Ow'
+'Outlook_'
+'Ostse'
+'Oster'
+'Osborne_'
+'Ortho'
+'Ortega'
+'Organisat'
+'Organen_'
+'Ordn'
+'Order'
+'Orden_'
+'Optimi'
+'Opportuni'
+'Opfern_'
+'Operation'
+'Omni'
+'Offi'
+'Oe'
+'Obersten_'
+'Oberste_'
+'Oberh'
+'Obergrenze_'
+'ORT'
+'OM_'
+'OFI'
+'Nöt'
+'Nö'
+'Nutzungsbedingungen_'
+'Nutze'
+'Num'
+'Now'
+'Nove'
+'Notwendig'
+'Notfall'
+'North'
+'Normali'
+'Nordi'
+'Nomin'
+'Nomad_'
+'Nobody_'
+'Nis'
+'Nina_'
+'Niko'
+'Night_'
+'Night'
+'Niederla'
+'Niedergang_'
+'Nico'
+'Nick'
+'Nichte'
+'Newsletter_'
+'Nevada_'
+'Nev'
+'Neustart'
+'Neuschwanstein_'
+'Neuf'
+'Neues_'
+'Neuen'
+'Neuausrichtung_'
+'Nerv'
+'Ner'
+'Neo'
+'Neighbo'
+'Nego'
+'Neb'
+'Neapel_'
+'Nazi'
+'Naturwissenschaft'
+'Naturpark'
+'Naturk'
+'Natural'
+'Nationalst'
+'Nationalpark_'
+'Nationalpar'
+'Namib'
+'Nam'
+'Nak'
+'Nahrungs'
+'Nahost_'
+'Nahe'
+'Nachw'
+'Nachmittag_'
+'Nachhaltige'
+'Nachbarschaftspolitik_'
+'Nachbarschaft_'
+'Nachbarländer'
+'Nachbar'
+'NY'
+'NIC'
+'NG_'
+'NGO_'
+'NEC'
+'ND_'
+'NDE'
+'Mütter_'
+'Mün'
+'Mönch'
+'Möglicherweise_'
+'Mythos_'
+'MySpace_'
+'Mutter'
+'Muslim'
+'Musical'
+'Museums_'
+'Museen_'
+'Muse'
+'Murdoch_'
+'Mull'
+'Mozart'
+'Moz'
+'Movielearn_'
+'Movie_'
+'Mosle'
+'Moses_'
+'Mosambik_'
+'Mord'
+'Mora'
+'Montreal_'
+'Monterrey_'
+'Montai'
+'Montag'
+'Monster_'
+'Mons'
+'Molda'
+'Modi_'
+'Moderne_'
+'Moder'
+'Model'
+'Moda'
+'Mobiltelefon_'
+'Mobilität_'
+'Mittleren_'
+'Mittelwe'
+'Mitteleurop'
+'Mitteil'
+'Mitleid'
+'Mitbe'
+'Mitarbeiter'
+'Mist'
+'Missverständnis'
+'Misstrauen_'
+'Mission'
+'Missb'
+'Mira'
+'Mir'
+'Mins'
+'Ministerrat_'
+'Ministerpräsidenten_'
+'Min_'
+'Millions'
+'Millia'
+'Militära'
+'Milchprodukt'
+'Milch'
+'Mikrof'
+'Mike_'
+'Metropoli'
+'Metalle'
+'Metal'
+'Messa'
+'Mercedes_'
+'Menü'
+'Menschenrechtsko'
+'Mena'
+'Memory_'
+'Memori'
+'Meli'
+'Meister'
+'Mein'
+'Mehrzahl_'
+'Mehrwertsteuer'
+'Meeresf'
+'Meere'
+'Medina'
+'Mechani'
+'McK'
+'McG'
+'Maßstäbe'
+'Maxi'
+'Max_'
+'Mauri'
+'Mauer'
+'Massenm'
+'Massaker'
+'Marí'
+'Marta_'
+'Mars_'
+'Marr'
+'Marqu'
+'Marktzug'
+'Marktt'
+'Market'
+'Mare'
+'Mara'
+'Malware_'
+'Mali_'
+'Maje'
+'Mainstream'
+'Main_'
+'Mailand_'
+'Mahlzeiten_'
+'Magst_'
+'Magazin_'
+'Magazin'
+'Machtver'
+'Machts'
+'MX'
+'MT_'
+'MS'
+'MP4_'
+'MOV_'
+'MOS'
+'MIN'
+'MIDI_'
+'MG'
+'MENT'
+'MB'
+'MAT'
+'MAN_'
+'MAN'
+'M4'
+'Lüg'
+'Lücke_'
+'Lä'
+'Luz'
+'Lup'
+'Luk'
+'Lufthansa_'
+'Ludwig_'
+'Lucas_'
+'Loyali'
+'Louvre'
+'Lohns'
+'Logi'
+'Lloyd'
+'Livi'
+'Liverpool_'
+'Liter'
+'Listen_'
+'Lima_'
+'Liese'
+'Lieferu'
+'Lied'
+'Libye'
+'Libyan'
+'Leser_'
+'Lesen_'
+'Leo'
+'Leno'
+'Leiche'
+'Lehrer'
+'Lehman_'
+'Legend'
+'Leg'
+'Lebensstandard_'
+'Lebense'
+'Lebanese_'
+'Leave_'
+'Laz'
+'Lava'
+'Laufzeit_'
+'Laufen'
+'Latvia'
+'Latin'
+'Lass'
+'Larry_'
+'Lannoye_'
+'Langzeit'
+'Landwirtschafts'
+'Landw'
+'Lampe'
+'Lamaniten_'
+'Lager'
+'Lagen'
+'Lady'
+'Label_'
+'Lab_'
+'LR'
+'LC_'
+'Küsten_'
+'Künst'
+'Kün'
+'Kühlschra'
+'Kü'
+'Körperschaft'
+'Königreichs_'
+'Köln'
+'Käufer'
+'Käuf'
+'Kyr'
+'Kyi'
+'Kuwait_'
+'Kurze'
+'Kuro'
+'Kurdish_'
+'Kunde_'
+'Kulissen_'
+'Kuli'
+'Kriterium_'
+'Kriminelle'
+'Krieg'
+'Krem'
+'Kreditvergabe_'
+'Kreditv'
+'Kreditnehmer'
+'Kreditkarten'
+'Kreditb'
+'Kreditaufnahme_'
+'Krebs_'
+'Kreaturen'
+'Kreat'
+'Kraftwerk'
+'Kow'
+'Kosov'
+'Korruptions'
+'Kori'
+'Koreans_'
+'Koran'
+'Kora'
+'Kopiere'
+'Kopfs'
+'Koordin'
+'Konzerne'
+'Konvertier'
+'Konvers'
+'Kontextmenü'
+'Konte'
+'Konsultationen_'
+'Konserv'
+'Konferenzr'
+'Konferenze'
+'Kompetenz_'
+'Kommuni'
+'Kommissionsvorschlag_'
+'Kommissionsmitglied'
+'Kommissar'
+'Kommentar_'
+'Kolumbien_'
+'Kolonie'
+'Kollekti'
+'Koizumi_'
+'Kohlendioxid_'
+'Koh'
+'Kofinanzierung'
+'Knowledge_'
+'Kno'
+'Kni'
+'Klu'
+'Klingon'
+'Kleine'
+'Klein_'
+'Klausel'
+'Klassifi'
+'Klage_'
+'Kinnock_'
+'King'
+'Kindes'
+'Kinderar'
+'Kin'
+'Khomeini_'
+'Kho'
+'Khan'
+'Khal'
+'Keys'
+'Kernel'
+'Kenn'
+'Kaukasus_'
+'Katastrophenschutz'
+'Katalo'
+'Kaschmir'
+'Karzai_'
+'Kapitalmärkte_'
+'Kapitalflu'
+'Kanäle_'
+'Kana'
+'Kammer'
+'Kamin'
+'Kama'
+'Kalk'
+'Kaliningrad'
+'Kale'
+'Kade'
+'Kaczyński_'
+'Kabel'
+'KW'
+'KU'
+'KMU_'
+'KLM_'
+'KING'
+'KEI'
+'KB'
+'Jury'
+'Juris'
+'Jugendlicher_'
+'Jugendherberg'
+'Juden_'
+'Journalist'
+'Jord'
+'Jong_'
+'Jonas'
+'Johnson'
+'Johanne'
+'Jog'
+'Joan'
+'Jim_'
+'Jet'
+'Jes'
+'Jem_'
+'Javi'
+'Jaro'
+'Jar'
+'Japaner_'
+'Jan'
+'Jahrtausend'
+'Jahrhunderte'
+'Jahresz'
+'Jag'
+'Jacob_'
+'JRE_'
+'JE'
+'Ivan'
+'Iv'
+'Ite'
+'Italiens_'
+'Italien'
+'Issu'
+'Iraker_'
+'Investor'
+'Internetverbindung_'
+'Interneta'
+'International'
+'Intensität'
+'Int'
+'Insur'
+'Insti'
+'Inse'
+'Innovation'
+'Innenpoliti'
+'Initiati'
+'Inhalts'
+'Inhaber_'
+'Infrastructure_'
+'Infos_'
+'Informationss'
+'Informationsa'
+'Informati'
+'Inflationsr'
+'Infineon'
+'Infektions'
+'Infekt'
+'Infe'
+'Industriesta'
+'Industrielle'
+'Industrial_'
+'Industri'
+'Indoor_'
+'Indo'
+'Indiz'
+'Individual'
+'Indic'
+'Increased_'
+'Inco'
+'Inclu'
+'Inci'
+'Improvi'
+'Impres'
+'Impfstoffe_'
+'Immerhin_'
+'Immer_'
+'Ign'
+'Ideologie_'
+'Ideolog'
+'Ideally_'
+'Ideale'
+'Ideal'
+'Icon'
+'Ibn_'
+'IR_'
+'INE'
+'INCLUD'
+'ILA'
+'IFA'
+'IES_'
+'IEN'
+'ICT'
+'Hügel_'
+'Hör'
+'Höl'
+'Höf'
+'Hôtel_'
+'Händler'
+'Hypo'
+'Hyg'
+'Hydr'
+'Hybri'
+'Hv'
+'Hungarian_'
+'Hunderttausende'
+'Hunderte'
+'Hund'
+'Hul'
+'Hubschrauber_'
+'Hua'
+'House'
+'Hotelzimmer_'
+'Hot_'
+'Horizonte'
+'Horizont'
+'Hond'
+'Hom'
+'Holy_'
+'Hoff'
+'Hochwasser'
+'Hochgeschwindigkeits'
+'His'
+'Hinr'
+'Hindus_'
+'Himmel_'
+'Hilfsmittel_'
+'Hilfe'
+'Highway'
+'Hig'
+'Heut'
+'Het_'
+'Herzog'
+'Herv'
+'Herrscher_'
+'Herman'
+'Herangehensweise_'
+'Heilige'
+'Hed'
+'Hebr'
+'Header'
+'Hay'
+'Hava'
+'Haustür'
+'Haushaltspolitik_'
+'Haushaltsplan'
+'Haushaltsmittel'
+'Haushaltskon'
+'Haushaltsausschusses_'
+'Haushaltsaus'
+'Hauptt'
+'Hauptstr'
+'Hauptp'
+'Hauptau'
+'Hatoyama_'
+'Hass_'
+'Harris_'
+'Harr'
+'Harm'
+'Hariri'
+'Hara'
+'Happ'
+'Hanse'
+'Handl'
+'Handelsa'
+'Hande'
+'Hamm'
+'Halte'
+'Hack'
+'Haben'
+'Haag'
+'HR'
+'HOT'
+'HER'
+'HEN'
+'HC_'
+'HAVEN_'
+'H1'
+'Gültigkeit_'
+'Gül'
+'Göttin'
+'Gän'
+'Gutes_'
+'Gunsten_'
+'Gues'
+'Guardi'
+'Guard'
+'Größen_'
+'Größ'
+'Grundzüge'
+'Grundwasser'
+'Grundsätzlich'
+'Grundsätzen_'
+'Grundsatze'
+'Grunds'
+'Grundrechte'
+'Grundl'
+'Großka'
+'Große'
+'Grou'
+'Griechen'
+'Grey'
+'Grenzwert'
+'Gremien_'
+'Gregori'
+'Grego'
+'Gramm'
+'Graci'
+'Gourmet_'
+'Gothic_'
+'Goth'
+'Gos'
+'Gore_'
+'Good'
+'Golfplätze'
+'Golds'
+'Gob'
+'Glück'
+'Gloucester'
+'Glied'
+'Glen'
+'Gleichg'
+'Gleichbehandlung_'
+'Gle'
+'Girl'
+'Gio'
+'Gibraltar_'
+'Gia'
+'Gewinne'
+'Gewin'
+'Gewerkschaft'
+'Gewerbe'
+'Gewalttaten_'
+'Getränke'
+'Getränk_'
+'Gesundheitsz'
+'Gesundheitsv'
+'Gesundheitssystem'
+'Gesundheitsm'
+'Geste'
+'Gesicht'
+'Gesetz'
+'Geschäftsv'
+'Geschäftsbereich'
+'Geschäften_'
+'Geschw'
+'Geschlecht'
+'Geschirr'
+'Geschichten_'
+'Gesamth'
+'Gesamtbe'
+'Geräte'
+'Germani'
+'Germ'
+'Gerichtsh'
+'Georgi'
+'George'
+'Geogra'
+'Genießen_'
+'Generalsekret'
+'Genauigkeit'
+'Genau'
+'Gen_'
+'Gemäß'
+'Gemeinschaftsin'
+'Gelände_'
+'Gele'
+'Geldes_'
+'Gei'
+'Gehälter'
+'Gehirn_'
+'Gehirn'
+'Geheimnis'
+'Gehei'
+'Gehalt_'
+'Gegenden_'
+'Gefüh'
+'Gefängnisse'
+'Gefahren'
+'Gedächtnis_'
+'Gedicht'
+'Geburtstag'
+'Geburten'
+'Gebot'
+'Gate_'
+'Gastge'
+'Garden'
+'Garan'
+'Ganz'
+'Gamm'
+'Gaming_'
+'Gallery_'
+'Galerie_'
+'Gale'
+'Gala'
+'GW_'
+'GUI'
+'GUE_'
+'GP_'
+'GO_'
+'GMT_'
+'GMOs_'
+'GL'
+'GE_'
+'Fülle'
+'Fä'
+'Fut'
+'Fundament'
+'Frühstücks'
+'Frustration_'
+'Frontier'
+'Frist'
+'Freud'
+'Frequenz'
+'Fremdenverkehr_'
+'Fremdenfeindlichkeit_'
+'Freizeita'
+'Freilich'
+'Freigabe_'
+'Freedoms_'
+'Fred_'
+'Fred'
+'François_'
+'Franco'
+'Fragment'
+'Fragestunde_'
+'Frage'
+'Founde'
+'Fotografie_'
+'Fortschr'
+'Fort_'
+'Forschungss'
+'Forschungsergebnisse_'
+'Forschungsa'
+'Formular'
+'Formel_'
+'Forest_'
+'Fore'
+'Football_'
+'Fonta'
+'Folglich_'
+'Focus'
+'Flut_'
+'Flusse'
+'Flus'
+'Flie'
+'Flemi'
+'Flasche'
+'Flam'
+'Flagg'
+'Fixed_'
+'Fit'
+'Fischereiabkommen_'
+'Fische_'
+'Firmware_'
+'Firm'
+'Firew'
+'Finger_'
+'Finanzwesen_'
+'Finanzst'
+'Finanzp'
+'Finanzmittel_'
+'Finanzmi'
+'Finanzinstrument'
+'Finanzb'
+'Fina'
+'Filme_'
+'Filme'
+'Fig'
+'Fic'
+'Feuerwe'
+'Feststellung'
+'Festiv'
+'Feste'
+'Fertigkeit'
+'Ferna'
+'Ferienhäuser_'
+'Fed'
+'Fatah_'
+'Fans_'
+'Familienzimmer_'
+'Falle'
+'Faktum_'
+'Fahrzeug'
+'Fahrt_'
+'Fahrplan_'
+'Fahren_'
+'Fact'
+'Faci'
+'Fachk'
+'Fabrik'
+'Fab'
+'FRE'
+'FAQ'
+'Extreme_'
+'Extras_'
+'Exten'
+'Exporteure'
+'Experts_'
+'Experte'
+'Experimente'
+'Exper'
+'Exo'
+'Exist'
+'Exhibit'
+'Exekutiv'
+'Except'
+'Ew'
+'Evidence_'
+'Everest_'
+'Even'
+'Evangeli'
+'Eurojust_'
+'Eurocopter_'
+'Euroc'
+'Eurobonds_'
+'EuroM'
+'Eurasi'
+'Eur'
+'Euph'
+'Establishment_'
+'Essential_'
+'Esse'
+'Especially_'
+'Erziehung_'
+'Erzeugung'
+'Erzeugnisse_'
+'Erwähnung_'
+'Erwägungen_'
+'Erweiterungen_'
+'Erwachsenen_'
+'Erwach'
+'Erträge_'
+'Ertr'
+'Erstelle'
+'Erste_'
+'Erstau'
+'Ersparnissen_'
+'Erscheinungsbild_'
+'Ersatz'
+'Err'
+'Ernähr'
+'Ernst_'
+'Erleichterung_'
+'Erleb'
+'Erheb'
+'Erha'
+'Erh'
+'Ergänz'
+'Erg'
+'Erfolgsgeschichte'
+'Erfolg'
+'Erbr'
+'Eras'
+'Equip'
+'Equ'
+'Entwicklungsziele'
+'Entwicklungs_'
+'Entstehung'
+'Entspann'
+'Entscheidungsprozess'
+'Entführung'
+'Entf'
+'Enter'
+'Enr'
+'Englischen_'
+'Englische'
+'Energietechnologie'
+'Energier'
+'Energiepolitik_'
+'Endp'
+'Endl'
+'Ende'
+'Employ'
+'Empfang'
+'Emotion'
+'Emirate'
+'Embr'
+'Email_'
+'Elysées_'
+'Ell'
+'Elizabeth_'
+'Elis'
+'Elend_'
+'Elemente'
+'Eleganz_'
+'Elefanten'
+'Elde'
+'Eisenbahnver'
+'Einzelpersonen_'
+'Einwi'
+'Einwanderungspolitik_'
+'Einwanderungs'
+'Eint'
+'Einsp'
+'Einse'
+'Einrei'
+'Einmischung'
+'Einmarsch'
+'Einm'
+'Einkommens_'
+'Einkaufszentr'
+'Einflüsse'
+'Einfach_'
+'Eindämmung'
+'Einblick_'
+'Einbindung_'
+'Eid'
+'Editor_'
+'Edit_'
+'Echtzeit_'
+'Early_'
+'EUR'
+'ESM'
+'ENI'
+'EMAS_'
+'Düsseldorf_'
+'Dür'
+'Dü'
+'Dynast'
+'Dynamik'
+'Dutzende_'
+'Dus'
+'Durchschnitts'
+'Durchführ'
+'Durchb'
+'Duomo_'
+'Duff_'
+'Dub'
+'Ds_'
+'Drücke'
+'Drum'
+'Drucker'
+'Droh'
+'Drittl'
+'Dritte'
+'Dringlichkeits'
+'Dri'
+'Dream'
+'Drago'
+'Dr'
+'Doyle_'
+'Downloads'
+'Down'
+'Dornik_'
+'Dorn_'
+'Dorf'
+'Doppelzimmer_'
+'Doo'
+'Dominikan'
+'Dolomit'
+'Dolmetsch'
+'Dokumentation_'
+'Divers'
+'DivX_'
+'Div'
+'Distributoren_'
+'Disku'
+'Diskriminierung'
+'Directory_'
+'Director'
+'Directi'
+'Diplomaten_'
+'Diktatur_'
+'Different_'
+'Dienststelle'
+'Dienstleistungssektor'
+'Dienstleist'
+'Diensta'
+'Dienst'
+'Dictionary_'
+'Dichte'
+'Dich'
+'Dialogs_'
+'Diabetes_'
+'Deutsche'
+'Deut'
+'Detail_'
+'Designs'
+'Designer_'
+'Desi'
+'Deregul'
+'Derartige_'
+'Denkweise'
+'Denis'
+'Demokratisierung_'
+'Demogra'
+'Demagog'
+'Delu'
+'Delta_'
+'Delo'
+'Delegation'
+'Dele'
+'Dela'
+'Deine'
+'Deckmantel_'
+'Death_'
+'Daw'
+'Datenschutz_'
+'Datenbl'
+'Date'
+'Dasselbe_'
+'Daseins'
+'Das'
+'Darwi'
+'Darlehen'
+'Darauf_'
+'Dara'
+'Dampfb'
+'Damas'
+'Dalma'
+'Dai'
+'Dafürhalten_'
+'DVDs_'
+'DK'
+'DJ_'
+'DI_'
+'DIC'
+'DES'
+'DEN_'
+'DC'
+'DAX_'
+'Cycl'
+'Cyber'
+'Curt'
+'Cul'
+'Cott'
+'Cord'
+'Copy'
+'Cop'
+'Cooper'
+'Cool'
+'Controller_'
+'Conti'
+'Constant'
+'Conservati'
+'Congress'
+'Confedera'
+'Condo'
+'Conditions_'
+'Conci'
+'Concern'
+'Computern_'
+'Compr'
+'Compani'
+'Communication'
+'Commen'
+'Comi'
+'Comfort'
+'Combi'
+'Colomb'
+'Collect'
+'Cohe'
+'Coelho_'
+'Coch'
+'Cob'
+'Clu'
+'Close_'
+'Clip'
+'Clif'
+'Cleverl'
+'Cleaning_'
+'Clean_'
+'Classic'
+'Clas'
+'Clark'
+'Circle_'
+'Circ'
+'Cind'
+'Chrom'
+'Christus_'
+'Christopher_'
+'Christo'
+'Christie_'
+'Christiani'
+'Christen_'
+'Christdemokraten_'
+'Chr_'
+'Chev'
+'Cherno'
+'Chechen'
+'Charle'
+'Chapel_'
+'Chap'
+'Champ'
+'Ces'
+'Center'
+'Cav'
+'Caucas'
+'Castil'
+'Cassi'
+'Casio'
+'Casino'
+'Cash'
+'Carrie_'
+'Carp'
+'Carlo'
+'Carl_'
+'Caribbean_'
+'Care'
+'Cardi'
+'Capt'
+'Canc'
+'Canari'
+'Canal'
+'Canadian_'
+'Campingpl'
+'Camera'
+'Cambodia_'
+'Calendar_'
+'Cairo'
+'Caesar'
+'COS'
+'CONT'
+'CHF_'
+'CHE'
+'CGI_'
+'CET_'
+'Bürgerkrieg_'
+'Bürgerkrieg'
+'Bürgerbe'
+'Bündnis_'
+'Bünd'
+'Bücher_'
+'Böge_'
+'Buy'
+'Busse'
+'Busc'
+'Bundestag_'
+'Bundesstaaten_'
+'Bundesp'
+'Bum'
+'Buddh'
+'Buchungs'
+'Buchst'
+'Buchführung'
+'Bucher_'
+'Bry'
+'Brunnen_'
+'Bruc'
+'Brow'
+'Brooklyn_'
+'Bronze'
+'Broc'
+'Britis'
+'Brigade'
+'Brian_'
+'Brew'
+'Bretton_'
+'Bret'
+'Brenner'
+'Bremen_'
+'Breitband'
+'Brei'
+'Bree'
+'Brav'
+'Braun'
+'Branc'
+'Box'
+'Boul'
+'Botschafter'
+'Boston'
+'Bosnien_'
+'Bosnia_'
+'Boots'
+'Boot'
+'Bond'
+'Bombardier'
+'Boeing'
+'Boe'
+'Bodensch'
+'Bode'
+'Boar'
+'Blog'
+'Blingee_'
+'Blick'
+'Blaž_'
+'Birma'
+'Biot'
+'Biog'
+'Binnenm'
+'Bindungen_'
+'Bin_'
+'Billig'
+'Billi'
+'Bildern_'
+'Bhutan_'
+'Bezirks'
+'Bey'
+'Bewä'
+'Bewu'
+'Bewertungs'
+'Bewert'
+'Bewerber'
+'Better_'
+'Betrü'
+'Beträge'
+'Betreuer'
+'Betracht'
+'Besuchern_'
+'Bestra'
+'Besten'
+'Bestell'
+'Besonderheiten_'
+'Besetzung_'
+'Beschwerde_'
+'Beschlussfassung'
+'Beschlusse'
+'Beschleunigung'
+'Beschaff'
+'Berufsbildung'
+'Berufe'
+'Bert'
+'Berl'
+'Berichterstattung'
+'Berb'
+'Beobachtungsst'
+'Benz_'
+'Belohnung_'
+'Beliebt'
+'Belgrade_'
+'Belastung_'
+'Belast'
+'Belarus'
+'Beitrittsl'
+'Beitrittskandidaten_'
+'Beis'
+'Being_'
+'Beine'
+'Bein'
+'Beihilfe'
+'Behau'
+'Behandlungs'
+'Begrenzung_'
+'Bege'
+'Beförderungs'
+'Befu'
+'Befr'
+'Bedürf'
+'Bedienung_'
+'Bede'
+'Bec'
+'Beauf'
+'Beatri'
+'Beachten_'
+'Bayer'
+'Bavarian_'
+'Baute'
+'Baustein'
+'Baum_'
+'Batt'
+'Bath'
+'Basket'
+'Basis'
+'Base'
+'Barry_'
+'Barro'
+'Baro'
+'Bargeld'
+'Barbe'
+'Bann'
+'Banker'
+'Bankensystem'
+'Bande'
+'Banc'
+'Ballo'
+'Bald_'
+'Bajor_'
+'Bahnstation_'
+'Bah'
+'Baghdad'
+'Baden_'
+'Bachelo'
+'Bach_'
+'Babys'
+'BOJ_'
+'BN'
+'BJ'
+'BF'
+'BERKELEY_'
+'Azu'
+'Ax'
+'Aw'
+'Avatar'
+'Außenbe'
+'Autoren_'
+'Autonomiebehörde_'
+'Autobahn_'
+'Ausübung'
+'Auswärtige'
+'Ausweg_'
+'Auswe'
+'Austausch'
+'Aust'
+'Ausspr'
+'Ausschü'
+'Ausschuß_'
+'Ausscheiden'
+'Ausscha'
+'Auskunft'
+'Ausgrenzung_'
+'Ausgew'
+'Ausgehend_'
+'Ausgabe'
+'Ausflu'
+'Ausd'
+'Aurora_'
+'Aug'
+'Aufträge_'
+'Auft'
+'Aufstände'
+'Aufstand_'
+'Aufse'
+'Aufge'
+'Auffü'
+'Auffassungen_'
+'Attraktionen_'
+'Attent'
+'Atta'
+'Atlantis'
+'Atla'
+'Asylsuchende'
+'Asylant'
+'Assist'
+'Asset'
+'Asians_'
+'Arzt'
+'Aru'
+'Articles_'
+'Arsenal_'
+'Ars'
+'Arou'
+'Armenian_'
+'Ark'
+'Argumen'
+'Argentine'
+'Argen'
+'Arena'
+'Are'
+'Arbeitszeit_'
+'Arbeitsver'
+'Arbeitsrecht'
+'Arbeitspro'
+'Arbeitsp'
+'Arbeitsgruppe_'
+'Arbeitsg'
+'Arbeitsbe'
+'Arbeit'
+'Apr'
+'Appro'
+'Anzeigen'
+'Anzeige'
+'Anz'
+'Anyone_'
+'Anwesenheit_'
+'Anwendungsbereich_'
+'Anwender_'
+'Anwa'
+'Antrags'
+'Anteils'
+'Anteile_'
+'Ansä'
+'Ansprüchen_'
+'Ansprech'
+'Anson'
+'Anschrift'
+'Anschließend_'
+'Ansatzes_'
+'Anreise'
+'Anregungen_'
+'Anpassungen_'
+'Annex'
+'Annahmen_'
+'Anmerkungen_'
+'Anmerkung_'
+'Anleihe'
+'Ankunft_'
+'Anku'
+'Anklage_'
+'Ani'
+'Anhörung_'
+'Angr'
+'Angeles_'
+'Angel'
+'Angebots'
+'Angeb'
+'Ane'
+'Andria_'
+'Andorra'
+'Anden'
+'Andalusien'
+'Andalusia'
+'Anda'
+'Anbau_'
+'Anato'
+'Analys'
+'Amu'
+'Amo'
+'Ambitionen_'
+'Amazon_'
+'Ama'
+'Aly'
+'Alum'
+'Altern'
+'Alta_'
+'Alr'
+'Alps_'
+'Alpi'
+'Alltags'
+'Alkohol'
+'Algor'
+'Algerie'
+'Algar'
+'Alegr'
+'Alber'
+'Albanian'
+'Alb'
+'Aktuell'
+'Aktualisierung_'
+'Aktivisten_'
+'Aktionär'
+'Aktionsprogramm_'
+'Akk'
+'Aki'
+'Aka'
+'Airp'
+'Airconditioning_'
+'Aid_'
+'Agrars'
+'Agenturen_'
+'Agent_'
+'Afri'
+'Advanced_'
+'Administrat'
+'Ade'
+'Addis_'
+'Ada'
+'Active_'
+'Acid'
+'Acht'
+'Achsen'
+'Accord'
+'Abzug_'
+'Abwicklung_'
+'Abweichung'
+'Abtreibung'
+'Abtei'
+'Abstimmungs'
+'Absti'
+'Absolvent'
+'Abso'
+'Absen'
+'Abschl'
+'Abl'
+'Abhängig'
+'Abh'
+'Abgeordnete'
+'Abfälle_'
+'Abdullah'
+'Abbe'
+'Aa'
+'ATM'
+'AR_'
+'AP_'
+'AO'
+'AMS'
+'AMR_'
+'ALE_'
+'AD_'
+'ADE'
+'ACI'
+'ACCE'
+'ABS'
+'A2'
+'=_'
+';&'
+'90er_'
+'83'
+'750'
+'70er_'
+'681'
+'67'
+'63'
+'5th_'
+'520'
+'52'
+'4th_'
+'45'
+'43'
+'3G_'
+'3G'
+'39'
+'370'
+'37'
+'270_'
+'220_'
+'21s'
+'202'
+'201'
+'199'
+'1972_'
+'1961_'
+'1960s_'
+'1960'
+'1959_'
+'1950er_'
+'1946_'
+'1939_'
+'1936_'
+'1907_'
+'171'
+'170'
+'16th_'
+'145_'
+'142'
+'127_'
+'124_'
+'121_'
+'117'
+'116'
+'105_'
+'102'
+'101'
+'0er_'
+'07'
+'020'
+'007'
+'/+_'
+'/ '
+'......'
+'....'
+'.'_'
+'->'
+',..._'
+',- '
+',,_'
+', (_'
+', $_'
+'++'
+'* '
+'):_'
+'), '
+')) ('
+'))'
+'() ._'
+'': _'
+'')'
+'''.'
+''''_'
+'%\\'
+'$ '
+'">- _'
+'"...'
+'". _'
+'" ._'
+'" -'
+'!! _'
+'!! !'
+' …'
+' ”'
+' ’_'
+' ­'
+' £_'
+' [...]'
+' = {_'
+' = '
+' ;'
+' -> _'
+' ***'
+' ). _'
+' (“_'
+' (.'
+' (''
+' &#_'
+' !!'
+'−'
+'ي'
+'ט'
+'ג'
+'ь'
+'щ'
+'ц'
+'σ'
+'ş'
+'œ'
+'ě'
+'ę'
+'ā'
+'õ'
+'ñ'
+'¿'
+'º'
+'~'
+'$'
+'™'
+'†'
+'–'
+'ن'
+'ل'
+'ف'
+'ر'
+'ר'
+'נ'
+'Ж'
+'Д'
+'υ'
+'ν'
+'λ'
+'ś'
+'ń'
+'ù'
+'ì'
+'Ñ'
+'É'
+'Ã'
+'Á'
+'§'
+'–'
+'&'
+'ー'
+'‚'
+'م'
+'ק'
+'ד'
+'Я'
+'П'
+'О'
+'Л'
+'Е'
+'А'
+'π'
+'κ'
+'θ'
+'β'
+'ū'
+'Ś'
+'ō'
+'ć'
+'æ'
+'Ê'
+'Â'
+'¼'
+'·'
+'¶'
+'´'
+'¥'
+'`'
+'@'
+'#'
+''
+'년'
+'語'
+'简'
+'本'
+'日'
+'文'
+'年'
+'中'
+'•'
+'ṳ'
+'ศ'
+'พ'
+'ा'
+'र'
+'ى'
+'ه'
+'ص'
+'ت'
+'ب'
+'פ'
+'ס'
+'ן'
+'ו'
+'ֿ'
+'В'
+'ω'
+'χ'
+'δ'
+'Ω'
+'̤'
+'ư'
+'ů'
+'ř'
+'ľ'
+'ė'
+'ĕ'
+'ą'
+'û'
+'À'
+'½'
+'¹'
+'­'
+'¤'
+'¡'
+'’'
+'\'
+'：'
+''
+'fi'
+'黵'
+'黃'
+'鰀'
+'鋘'
+'鋓'
+'遝'
+'蒸'
+'致'
+'美'
+'网'
+'紙'
+'熨'
+'斗'
+'応'
+'女'
+'味'
+'友'
+'信'
+'介'
+'丨'
+'一'
+'ャ'
+'バ'
+'チ'
+'ジ'
+'カ'
+'ん'
+'ら'
+'め'
+'●'
+'▼'
+'→'
+'※'
+'ớ'
+'ọ'
+'ị'
+'ẽ'
+'ẻ'
+'ấ'
+'ी'
+'ि'
+'य'
+'ब'
+'त'
+'छ'
+'आ'
+'ِ'
+'ك'
+'غ'
+'ع'
+'د'
+'ج'
+'إ'
+'،'
+'צ'
+'ל'
+'ה'
+'Қ'
+'Ғ'
+'Э'
+'Ш'
+'Ц'
+'Х'
+'Р'
+'М'
+'φ'
+'ζ'
+'γ'
+'Χ'
+'Τ'
+'Ι'
+'Ε'
+'̯'
+'̆'
+'ː'
+'ˈ'
+'ɾ'
+'ɛ'
+'ɐ'
+'ſ'
+'ű'
+'ŭ'
+'ő'
+'Ő'
+'ŏ'
+'ň'
+'İ'
+'ī'
+'đ'
+'Đ'
+'ă'
+'ý'
+'ã'
+'à'
+'Ô'
+'Ó'
+'È'
+'Å'
+'¾'
+'µ'
+'³'
+'°'
+'¬'
+'¢'
+''
+'™'
+'—'
+'“'
+''
+'^'
+'—'
+'²'
+'£'
+'<'
diff --git a/tensor2tensor/utils/beam_search.py b/tensor2tensor/utils/beam_search.py
index b42503cbf..3841b5953 100644
--- a/tensor2tensor/utils/beam_search.py
+++ b/tensor2tensor/utils/beam_search.py
@@ -81,8 +81,16 @@ def _expand_to_beam_size(tensor, beam_size):
   return tf.tile(tensor, tile_dims)
 
 
+def get_state_shape_invariants(tensor):
+  """Returns the shape of the tensor but sets middle dims to None."""
+  shape = tensor.shape.as_list()
+  for i in range(1, len(shape) - 1):
+    shape[i] = None
+  return tf.TensorShape(shape)
+
+
 def log_prob_from_logits(logits):
-  return logits - tf.reduce_logsumexp(logits, axis=2, keep_dims=True)
+  return logits - tf.reduce_logsumexp(logits, axis=2, keepdims=True)
 
 
 def compute_batch_indices(batch_size, beam_size):
@@ -200,6 +208,10 @@ def beam_search(symbols_to_logits_fn,
   capturing observed from these operations, tensors, clients can make
   assumptions about which step is being recorded.
 
+  WARNING: Assumes 2nd dimension of tensors in `states` and not invariant, this
+  means that the shape of the 2nd dimension of these tensors will not be
+  available (i.e. set to None) inside symbols_to_logits_fn.
+
   Args:
     symbols_to_logits_fn: Interface to the model, to provide logits.
         Shoud take [batch_size, decoded_ids] and return [batch_size, vocab_size]
@@ -513,8 +525,7 @@ def _is_finished(i, unused_alive_seq, alive_log_probs, unused_finished_seq,
            tf.TensorShape([None, None, None]),
            finished_scores.get_shape(),
            finished_flags.get_shape(),
-           nest.map_structure(
-               lambda tensor: tf.TensorShape(tensor.shape), states),
+           nest.map_structure(get_state_shape_invariants, states),
        ],
        parallel_iterations=1,
        back_prop=False)
diff --git a/tensor2tensor/utils/beam_search_test.py b/tensor2tensor/utils/beam_search_test.py
index ec911f051..13a4e64c5 100644
--- a/tensor2tensor/utils/beam_search_test.py
+++ b/tensor2tensor/utils/beam_search_test.py
@@ -303,7 +303,8 @@ def symbols_to_logits(ids, _, states):
     states = {
         "state": tf.zeros((batch_size, 1)),
     }
-    states["state"]._shape = tf.TensorShape((None, 1))
+    states["state"] = tf.placeholder_with_default(
+        states["state"], shape=(None, 1))
 
     final_ids, _ = beam_search.beam_search(
         symbols_to_logits,
@@ -352,7 +353,8 @@ def symbols_to_logits(ids, _, states):
     states = {
         "state": tf.zeros((batch_size, 1)),
     }
-    states["state"]._shape = tf.TensorShape((None, 1))
+    states["state"] = tf.placeholder_with_default(
+        states["state"], shape=(None, 1))
 
     final_ids, _ = beam_search.beam_search(
         symbols_to_logits,
diff --git a/tensor2tensor/utils/bleu_hook.py b/tensor2tensor/utils/bleu_hook.py
index 9a7985045..3974244ec 100644
--- a/tensor2tensor/utils/bleu_hook.py
+++ b/tensor2tensor/utils/bleu_hook.py
@@ -24,7 +24,6 @@
 import re
 import sys
 import time
-import glob
 import unicodedata
 
 # Dependency imports
@@ -159,8 +158,10 @@ def property_chars(self, prefix):
     return "".join(six.unichr(x) for x in range(sys.maxunicode)
                    if unicodedata.category(six.unichr(x)).startswith(prefix))
 
+
 uregex = UnicodeRegex()
 
+
 def bleu_tokenize(string):
   r"""Tokenize a string following the official BLEU implementation.
 
@@ -206,24 +207,38 @@ def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
 
 StepFile = collections.namedtuple("StepFile", "filename mtime ctime steps")
 
+
 def _try_twice_tf_glob(pattern):
-  """tf.gfile.Glob may crash with
+  """Glob twice, first time possibly catching `NotFoundError`.
+
+  tf.gfile.Glob may crash with
+
+  ```
   tensorflow.python.framework.errors_impl.NotFoundError:
   xy/model.ckpt-1130761_temp_9cb4cb0b0f5f4382b5ea947aadfb7a40;
   No such file or directory
+  ```
 
-  Standard glob.glob does not have this bug, but does not hangle gs://...
-  So let's use tf.gfile.Glob twice to handle most concurrency problems.
+  Standard glob.glob does not have this bug, but does not handle multiple
+  filesystems (e.g. `gs://`), so we call tf.gfile.Glob, the first time possibly
+  catching the `NotFoundError`.
+
+  Args:
+    pattern: str, glob pattern.
+
+  Returns:
+    list<str> matching filepaths.
   """
   try:
     return tf.gfile.Glob(pattern)
   except tf.errors.NotFoundError:
     return tf.gfile.Glob(pattern)
 
+
 def _read_stepfiles_list(path_prefix, path_suffix=".index", min_steps=0):
   """Return list of StepFiles sorted by step from files at path_prefix."""
   stepfiles = []
-  for filename in _try_twice_tf_glob(path_prefix + '*-[0-9]*' + path_suffix):
+  for filename in _try_twice_tf_glob(path_prefix + "*-[0-9]*" + path_suffix):
     basename = filename[:-len(path_suffix)] if len(path_suffix) else filename
     try:
       steps = int(basename.rsplit("-")[-1])
diff --git a/tensor2tensor/utils/cloud.py b/tensor2tensor/utils/cloud.py
new file mode 100644
index 000000000..937c6ee46
--- /dev/null
+++ b/tensor2tensor/utils/cloud.py
@@ -0,0 +1,382 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""Launch on GCP."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib
+import json
+import multiprocessing.pool as mp
+import os
+import random
+import signal
+import socket
+import subprocess as sp
+import time
+
+from six.moves import input  # pylint: disable=redefined-builtin
+import tensorflow as tf
+
+TPU_IP = "10.240.%d.2"
+TPU_PORT = 8470
+TPU_PROFILE_PORT = 8466
+TB_PORT = 6006
+
+# TODO(rsepassi):
+# --cloud_zone
+# --cloud_project
+
+
+class CloudState(object):
+  """Manage state across multiple trainer runs."""
+
+  def __init__(self):
+    self._tmp_dir = os.path.expanduser("~/.t2t/cloud_state")
+    tf.gfile.MakeDirs(self._tmp_dir)
+
+  def cleanup(self, current_vm_name=None, current_tpu_name=None):
+    process_pids = os.listdir(self._tmp_dir)
+    for pid in process_pids:
+      try:
+        # Check if trainer pid is still running
+        os.kill(int(pid), 0)
+      except OSError:
+        # Trainer died ungracefully
+        pid_file = os.path.join(self._tmp_dir, pid)
+        with tf.gfile.Open(pid_file) as f:
+          info = json.loads(f.read())
+
+        # Kill possibly zombie tunnel process
+        try:
+          os.kill(info["tunnel_pid"], signal.SIGTERM)
+        except OSError:
+          pass
+
+        # Delete VM and TPU if requested
+        del_vm = False
+        del_tpu = False
+        if info["delete_on_done"]:
+          if (info["vm_name"] != current_vm_name and
+              info["vm_name"] in zip(*list_vm_names_and_ips())[0]):
+            print("Old VM %s found. Delete?" % info["vm_name"])
+            if confirm():
+              del_vm = True
+          if (info["tpu_name"] != current_tpu_name and
+              info["tpu_name"] in zip(*list_tpu_names_and_ips())[0]):
+            print("Old TPU %s found. Delete?" % info["tpu_name"])
+            if confirm():
+              del_tpu = True
+
+        results = []
+        pool = mp.Pool(2)
+        if del_vm:
+          results.append(pool.apply_async(delete_vm, (info["vm_name"],)))
+        if del_tpu:
+          results.append(pool.apply_async(delete_tpu, (info["tpu_name"],)))
+        _ = [res.get() for res in results]
+
+        # Remove the now cleaned up state file
+        tf.gfile.Remove(pid_file)
+
+  def delete_current(self):
+    pid_file = os.path.join(self._tmp_dir, str(os.getpid()))
+    if tf.gfile.Exists(pid_file):
+      tf.gfile.Remove(pid_file)
+
+  def add_current(self, tunnel_pid, vm_name, tpu_name, delete_on_done):
+    state = {
+        "tunnel_pid": tunnel_pid,
+        "vm_name": vm_name,
+        "tpu_name": tpu_name,
+        "delete_on_done": delete_on_done,
+    }
+
+    with tf.gfile.Open(os.path.join(self._tmp_dir, str(os.getpid())), "w") as f:
+      f.write(json.dumps(state))
+
+
+@contextlib.contextmanager
+def cloud_tpu(vm_name, tpu_name, delete_on_done=False):
+  """Gets or creates a VM and TPU instance, and forwards ports.
+
+  Args:
+    vm_name: str, name of VM.
+    tpu_name: str, name of TPU instance.
+    delete_on_done: bool, whether to delete the instances when done.
+
+  Yields:
+    master: str, grpc master pointing to the TPU instance.
+  """
+  state = CloudState()
+  # Read state from previous processes and possibly cleanup
+  state.cleanup(current_vm_name=vm_name, current_tpu_name=tpu_name)
+
+  done_str = "" if delete_on_done else "NOT "
+  print("Will %sdelete VM and TPU instance on done." % done_str)
+  assert confirm()
+  _, tpu_ip = create_vm_tpu_pair(vm_name, tpu_name)
+  with tpu_tunnel(vm_name, tpu_ip) as (local_ports, tunnel_pid):
+    master = "grpc://localhost:%d" % local_ports["tpu"]
+
+    state.add_current(tunnel_pid, vm_name, tpu_name, delete_on_done)
+
+    yield master
+
+  if delete_on_done:
+    pool = mp.Pool(2)
+    vm_res = pool.apply_async(delete_vm, (vm_name,))
+    tpu_res = pool.apply_async(delete_tpu, (tpu_name,))
+    vm_res.get()
+    tpu_res.get()
+
+  # Cleanup state from this process
+  state.delete_current()
+
+
+class Gcloud(object):
+  """gcloud command strings."""
+  # Note these can be modified by set_versions
+  VM_VERSION = "tf-1-5"
+  TPU_VERSION = "1.5"
+
+  @classmethod
+  def set_versions(cls, vm, tpu):
+    cls.VM_VERSION = vm
+    cls.TPU_VERSION = tpu
+
+  @classmethod
+  def create_vm(cls):
+    create_vm_str = """
+    gcloud compute instances create {name} \
+      --machine-type=n1-standard-8 \
+      --image-family=%s \
+      --image-project=ml-images \
+      --scopes=https://www.googleapis.com/auth/cloud-platform
+    """ % cls.VM_VERSION
+    return create_vm_str
+
+  DELETE_VM = "gcloud compute instances delete {name} --quiet"
+
+  @classmethod
+  def create_tpu(cls):
+    create_tpu_str = """
+    gcloud alpha compute tpus create \
+      {name} \
+      --range={tpu_ip}/29 \
+      --version=%s
+    """ % cls.TPU_VERSION
+    return create_tpu_str
+
+  DELETE_TPU = "gcloud alpha compute tpus delete {name} --quiet"
+
+  LIST_TPU = "gcloud alpha compute tpus list"
+  LIST_VM = "gcloud compute instances list"
+
+  SSH_LOCAL_PORT_FORWARD = "-L {local_port}:{host}:{remote_port}"
+  SSH_TUNNEL = """
+  gcloud compute ssh {name} -- -N
+  """
+
+
+@contextlib.contextmanager
+def shell_background(cmd_, **kwargs):
+  """Run process in background, join on exit."""
+  args = format_cmd(cmd_, **kwargs)
+  process = sp.Popen(args)
+  try:
+    yield process
+  finally:
+    if process.poll() is None:
+      process.terminate()
+      time.sleep(1)
+    if process.poll() is None:
+      process.kill()
+      time.sleep(1)
+    if process.poll() is None:
+      raise ValueError(
+          "Cannot kill process %d - please kill manually" % process.pid)
+    time.sleep(1)
+
+
+def shell_output(cmd_, **kwargs):
+  return sp.check_output(format_cmd(cmd_, **kwargs))
+
+
+def shell_run(cmd_, **kwargs):
+  return sp.check_call(format_cmd(cmd_, **kwargs))
+
+
+def format_cmd(cmd_, **kwargs):
+  return cmd_.format(**kwargs).strip().split()
+
+
+def create_vm(vm_name):
+  out = shell_output(Gcloud.create_vm(), name=vm_name)
+  return out.split("\n")[1:-1][0].split()[4]
+
+
+def list_tpu_names_and_ips():
+  list_out = shell_output(Gcloud.LIST_TPU)
+  lines = [l.split() for l in list_out.split("\n")[1:-1]]
+  names_and_ips = [(l[0].strip(), l[3].strip().split(":")[0]) for l in lines]
+  return names_and_ips
+
+
+def list_vm_names_and_ips():
+  list_out = shell_output(Gcloud.LIST_VM)
+  lines = [l.split() for l in list_out.split("\n")[1:-1]]
+  names_and_ips = [(l[0].strip(), l[4].strip()) for l in lines]
+  return names_and_ips
+
+
+def unique_tpu_ip(tpu_names_and_ips):
+  inuse = [el[1].split(".")[2] for el in tpu_names_and_ips]
+  selection = random.choice(list(set(range(256)) - set(inuse)))
+  return TPU_IP % selection
+
+
+def delete_tpu(tpu_name):
+  shell_run(Gcloud.DELETE_TPU, name=tpu_name)
+
+
+def delete_vm(vm_name):
+  shell_run(Gcloud.DELETE_VM, name=vm_name)
+
+
+def create_tpu(tpu_name, tpu_names_and_ips=None):
+  tpu_names_and_ips = tpu_names_and_ips or list_tpu_names_and_ips()
+  tpu_ip = unique_tpu_ip(tpu_names_and_ips)
+
+  rounded_tpu_ip = tpu_ip
+  if rounded_tpu_ip.endswith("2"):
+    rounded_tpu_ip = rounded_tpu_ip[:-1] + "0"
+
+  shell_run(Gcloud.create_tpu(), name=tpu_name, tpu_ip=rounded_tpu_ip)
+  return tpu_ip
+
+
+@contextlib.contextmanager
+def tpu_tunnel(vm_name, tpu_ip):
+  """Forward TPU and TPU profiling ports."""
+  local_ports = {
+      "tpu": get_open_port(),
+      "tpu_profile": get_open_port(),
+  }
+
+  tpu = format_cmd(
+      Gcloud.SSH_LOCAL_PORT_FORWARD,
+      local_port=local_ports["tpu"],
+      host=tpu_ip,
+      remote_port=TPU_PORT)
+  tpu_profile = format_cmd(
+      Gcloud.SSH_LOCAL_PORT_FORWARD,
+      local_port=local_ports["tpu_profile"],
+      host=tpu_ip,
+      remote_port=TPU_PROFILE_PORT)
+
+  args = format_cmd(Gcloud.SSH_TUNNEL, name=vm_name) + tpu + tpu_profile
+  # Launch process running in background
+  with shell_background(" ".join(args)) as tunnel_process:
+    time.sleep(1)
+    if tunnel_process.poll() is not None:
+      raise ValueError("SSH failed")
+    tf.logging.info("Set up port fowarding. Local ports: %s", local_ports)
+    yield local_ports, tunnel_process.pid
+
+
+def create_vm_tpu_pair(vm_name, tpu_name, reuse_if_exists=True):
+  """Create a VM and paired TPU instance.
+
+  Args:
+    vm_name: str, name for VM.
+    tpu_name: str, name for TPU instance.
+    reuse_if_exists: bool, if True, this will act as a get or create. If False
+      and vm_name or tpu_name already exists, will error.
+
+  Returns:
+    tuple: (vm_ip, tpu_ip)
+
+  Raises:
+    ValueError: if instance exists but reuse_if_exists=False.
+  """
+  vm_info = list_vm_names_and_ips()
+  tpu_info = list_tpu_names_and_ips()
+
+  vm_names = zip(*vm_info)[0]
+  tpu_names = zip(*tpu_info)[0]
+
+  make_vm = False
+  vm_ip = None
+  if vm_name in vm_names:
+    if not reuse_if_exists:
+      raise ValueError(
+          "VM %s already exists and reuse_if_exists=False" % vm_name)
+    tf.logging.info("VM %s already exists, reusing.", vm_name)
+    vm_ip = vm_info[vm_names.index(vm_name)][1]
+  else:
+    print("Creating VM %s" % vm_name)
+    assert confirm()
+    make_vm = True
+
+  make_tpu = False
+  tpu_ip = None
+  if tpu_name in tpu_names:
+    if not reuse_if_exists:
+      raise ValueError(
+          "TPU instance %s already exists and reuse_if_exists=False" % tpu_name)
+    tf.logging.info("TPU %s already exists, reusing.", tpu_name)
+    tpu_ip = tpu_info[tpu_names.index(tpu_name)][1]
+  else:
+    print("Creating TPU instance %s" % tpu_name)
+    assert confirm()
+    make_tpu = True
+
+  # Create VM and TPU in parallel
+  pool = mp.Pool(2)
+  vm_res = None
+  tpu_res = None
+  if make_vm:
+    vm_res = pool.apply_async(create_vm, (vm_name,))
+  if make_tpu:
+    tpu_res = pool.apply_async(create_tpu, (tpu_name, tpu_info))
+  if vm_res is not None:
+    vm_ip = vm_res.get()
+  if tpu_res is not None:
+    tpu_ip = tpu_res.get()
+
+  tf.logging.info("VM (Name, IP): %s, %s", vm_name, vm_ip)
+  tf.logging.info("TPU (Name, IP): %s, %s", tpu_name, tpu_ip)
+  tf.logging.info(
+      "To delete the VM, run: %s", Gcloud.DELETE_VM.format(name=vm_name))
+  tf.logging.info(
+      "To delete the TPU instance, run: %s",
+      Gcloud.DELETE_TPU.format(name=tpu_name))
+  return vm_ip, tpu_ip
+
+
+def get_open_port():
+  s = socket.socket()
+  s.bind(("", 0))
+  s.listen(1)
+  port = s.getsockname()[1]
+  s.close()
+  return port
+
+
+def confirm():
+  out = input("Confirm (Y/n)? > ")
+  return out == "Y"
diff --git a/tensor2tensor/utils/decoding.py b/tensor2tensor/utils/decoding.py
index 9f3f5a131..8133eeca4 100644
--- a/tensor2tensor/utils/decoding.py
+++ b/tensor2tensor/utils/decoding.py
@@ -29,6 +29,7 @@
 from six.moves import input  # pylint: disable=redefined-builtin
 
 from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.data_generators.problem import problem_hparams_to_features
 import tensorflow as tf
 
 FLAGS = tf.flags.FLAGS
@@ -68,6 +69,7 @@ def log_decode_results(inputs,
                        identity_output=False):
   """Log inference results."""
   is_image = "image" in problem_name
+  decoded_inputs = None
   if is_image and save_images:
     save_path = os.path.join(model_dir, "%s_prediction_%d.jpg" %
                              (problem_name, prediction_idx))
@@ -81,6 +83,7 @@ def log_decode_results(inputs,
     tf.logging.info("Inference results INPUT: %s" % decoded_inputs)
 
   decoded_targets = None
+  decoded_outputs = None
   if identity_output:
     decoded_outputs = "".join(map(str, outputs.flatten()))
     if targets is not None:
@@ -93,7 +96,7 @@ def log_decode_results(inputs,
   tf.logging.info("Inference results OUTPUT: %s" % decoded_outputs)
   if targets is not None:
     tf.logging.info("Inference results TARGET: %s" % decoded_targets)
-  return decoded_outputs, decoded_targets
+  return decoded_inputs, decoded_outputs, decoded_targets
 
 
 def decode_from_dataset(estimator,
@@ -138,9 +141,12 @@ def decode_from_dataset(estimator,
       parts = output_filepath.split(".")
       parts[-1] = "targets"
       target_filepath = ".".join(parts)
+      parts[-1] = "inputs"
+      input_filepath = ".".join(parts)
 
       output_file = tf.gfile.Open(output_filepath, "w")
       target_file = tf.gfile.Open(target_filepath, "w")
+      input_file = tf.gfile.Open(input_filepath, "w")
 
     problem_hparams = hparams.problems[problem_idx]
     # Inputs vocabulary is set to targets if there are no inputs in the problem,
@@ -196,13 +202,14 @@ def decode_from_dataset(estimator,
 
       # Write out predictions if decode_to_file passed
       if decode_to_file:
-        for i, (decoded_output, decoded_target) in enumerate(decoded_outputs):
+        for i, (d_input, d_output, d_target) in enumerate(decoded_outputs):
           beam_score_str = ""
           if decode_hp.write_beam_scores:
             beam_score_str = "\t%.2f" % decoded_scores[i]
           output_file.write(
-              str(decoded_output) + beam_score_str + decode_hp.delimiter)
-          target_file.write(str(decoded_target) + decode_hp.delimiter)
+              str(d_output) + beam_score_str + decode_hp.delimiter)
+          target_file.write(str(d_target) + decode_hp.delimiter)
+          input_file.write(str(d_input) + decode_hp.delimiter)
 
       if (decode_hp.num_samples >= 0 and
           num_predictions >= decode_hp.num_samples):
@@ -211,6 +218,7 @@ def decode_from_dataset(estimator,
     if decode_to_file:
       output_file.close()
       target_file.close()
+      input_file.close()
 
     tf.logging.info("Completed inference on %d samples." % num_predictions)  # pylint: disable=undefined-loop-variable
 
@@ -261,9 +269,9 @@ def input_fn():
       for k, beam in enumerate(output_beams):
         tf.logging.info("BEAM %d:" % k)
         score = scores and scores[k]
-        decoded_outputs, _ = log_decode_results(result["inputs"], beam,
-                                                problem_name, None,
-                                                inputs_vocab, targets_vocab)
+        _, decoded_outputs, _ = log_decode_results(result["inputs"], beam,
+                                                   problem_name, None,
+                                                   inputs_vocab, targets_vocab)
         beam_decodes.append(decoded_outputs)
         if decode_hp.write_beam_scores:
           beam_scores.append(score)
@@ -274,9 +282,9 @@ def input_fn():
       else:
         decodes.append("\t".join(beam_decodes))
     else:
-      decoded_outputs, _ = log_decode_results(result["inputs"],
-                                              result["outputs"], problem_name,
-                                              None, inputs_vocab, targets_vocab)
+      _, decoded_outputs, _ = log_decode_results(
+          result["inputs"], result["outputs"], problem_name,
+          None, inputs_vocab, targets_vocab)
       decodes.append(decoded_outputs)
 
   # Reversing the decoded inputs and outputs because they were reversed in
@@ -472,23 +480,26 @@ def _interactive_input_fn(hparams):
         x = [num_samples, decode_length, len(input_ids)] + input_ids
         assert len(x) < const_array_size
         x += [0] * (const_array_size - len(x))
-        yield {
+        features = {
             "inputs": np.array(x).astype(np.int32),
         }
       elif input_type == "image":
         input_path = input_string
         img = vocabulary.encode(input_path)
-        yield {
+        features = {
             "inputs": img.astype(np.int32),
         }
       elif input_type == "label":
         input_ids = [int(input_string)]
         x = [num_samples, decode_length, len(input_ids)] + input_ids
-        yield {
+        features = {
             "inputs": np.array(x).astype(np.int32),
         }
       else:
         raise Exception("Unsupported input type.")
+      for k, v in six.iteritems(problem_hparams_to_features(p_hparams)):
+        features[k] = np.array(v).astype(np.int32)
+      yield features
 
 
 def show_and_save_image(img, save_path):
diff --git a/tensor2tensor/utils/diet.py b/tensor2tensor/utils/diet.py
index 7ecfba693..19702338b 100644
--- a/tensor2tensor/utils/diet.py
+++ b/tensor2tensor/utils/diet.py
@@ -193,10 +193,10 @@ def update_variable(self, var, grad_var):
     beta2_pow = tf.pow(params.beta2, global_step)
     if params.factored_second_moment_accumulator and len(var.shape) == 2:
       vr_update = tf.assign(slots["adam_vr"], slots["adam_vr"] * params.beta2 +
-                            tf.reduce_mean(grad_squared, 1, keep_dims=True) *
+                            tf.reduce_mean(grad_squared, 1, keepdims=True) *
                             (1.0 - params.beta2))
       vc_update = tf.assign(slots["adam_vc"], slots["adam_vc"] * params.beta2 +
-                            tf.reduce_mean(grad_squared, 0, keep_dims=True) *
+                            tf.reduce_mean(grad_squared, 0, keepdims=True) *
                             (1.0 - params.beta2))
       with tf.control_dependencies([vr_update, vc_update]):
         vr = tf.sqrt(slots["adam_vr"] / (1.0 - beta2_pow)) + params.epsilon
diff --git a/tensor2tensor/utils/flags.py b/tensor2tensor/utils/flags.py
index 23da5ed3c..f5d28e9f3 100644
--- a/tensor2tensor/utils/flags.py
+++ b/tensor2tensor/utils/flags.py
@@ -74,21 +74,22 @@
                   "Whether to use the '-test' data for EVAL (and PREDICT).")
 flags.DEFINE_integer("keep_checkpoint_max", 20,
                      "How many recent checkpoints to keep.")
-flags.DEFINE_bool("experimental_optimize_placement", False,
-                  "Optimize ops placement with experimental session options.")
+flags.DEFINE_bool("enable_graph_rewriter", False,
+                  "Enable graph optimizations that are not on by default.")
 flags.DEFINE_integer("keep_checkpoint_every_n_hours", 10000,
                      "Number of hours between each checkpoint to be saved. "
                      "The default value 10,000 hours effectively disables it.")
 flags.DEFINE_integer("save_checkpoints_secs", 0,
                      "Save checkpoints every this many seconds. "
                      "Default=0 means save checkpoints each x steps where x "
-                     "depends on iterations_per_loop and local_eval_frequency.")
+                     "is max(iterations_per_loop, local_eval_frequency).")
 flags.DEFINE_bool("log_device_placement", False,
                   "Whether to log device placement.")
 
 # Distributed training flags
-flags.DEFINE_integer("local_eval_frequency", 2000,
-                     "Run evaluation every this steps during local training.")
+flags.DEFINE_integer("local_eval_frequency", 1000,
+                     "Save checkpoints and run evaluation every N steps during "
+                     "local training.")
 flags.DEFINE_bool("locally_shard_to_cpu", False,
                   "Use CPU as a sharding device running locally. This allows "
                   "to test sharded model construction on a machine with 1 GPU.")
diff --git a/tensor2tensor/utils/optimize.py b/tensor2tensor/utils/optimize.py
index c0cbf5677..a497d56bd 100644
--- a/tensor2tensor/utils/optimize.py
+++ b/tensor2tensor/utils/optimize.py
@@ -33,19 +33,27 @@ def optimize(loss, learning_rate, hparams, use_tpu=False):
   """Minimize loss."""
   loss = weight_decay_and_noise(loss, hparams, learning_rate)
   loss = tf.identity(loss, name="total_loss")
-  log_variable_sizes()
+  log_variable_sizes(verbose=hparams.summarize_vars)
   diet_vars = [
       v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
   ]
-  log_variable_sizes(diet_vars, "Diet Variables")
-  opt = ConditionalOptimizer(hparams.optimizer, learning_rate, hparams)
+  log_variable_sizes(
+      diet_vars, "Diet Variables", verbose=hparams.summarize_vars)
+  opt = ConditionalOptimizer(hparams.optimizer, learning_rate, hparams, use_tpu)
   if use_tpu:
     opt = tf.contrib.tpu.CrossShardOptimizer(opt)
 
   tf.summary.scalar("learning_rate", learning_rate)
-  opt_summaries = ["loss"]
+  opt_summaries = ["loss", "global_gradient_norm"]
   if hparams.summarize_grads:
-    opt_summaries.extend(["gradients", "gradient_norm", "global_gradient_norm"])
+    tf.logging.info("Summarizing gradients")
+    opt_summaries.extend(["gradients", "gradient_norm"])
+
+  if hparams.clip_grad_norm:
+    tf.logging.info("Clipping gradients, norm: %0.5f", hparams.clip_grad_norm)
+  if hparams.grad_noise_scale:
+    tf.logging.info("Adding noise to gradients, noise scale: %0.5f",
+                    hparams.grad_noise_scale)
 
   train_op = tf.contrib.layers.optimize_loss(
       name="training",
@@ -63,7 +71,13 @@ def optimize(loss, learning_rate, hparams, use_tpu=False):
 class ConditionalOptimizer(tf.train.Optimizer):
   """Conditional optimizer."""
 
-  def __init__(self, optimizer_name, lr, hparams):
+  def __init__(self, optimizer_name, lr, hparams, use_tpu=False):
+    if optimizer_name == "Adam" and use_tpu:
+      # LazyAdamOptimizer does not work on TPU
+      optimizer_name = "TrueAdam"
+
+    tf.logging.info("Using optimizer %s", optimizer_name)
+
     if optimizer_name == "Adam":
       # We change the default epsilon for Adam and re-scale lr.
       # Using LazyAdam as it's much faster for large vocabulary embeddings.
@@ -74,9 +88,10 @@ def __init__(self, optimizer_name, lr, hparams):
           epsilon=hparams.optimizer_adam_epsilon)
     elif optimizer_name == "Momentum":
       self._opt = tf.train.MomentumOptimizer(
-          lr, momentum=hparams.optimizer_momentum_momentum)
+          lr,
+          momentum=hparams.optimizer_momentum_momentum,
+          use_nesterov=hparams.optimizer_momentum_nesterov)
     elif optimizer_name == "YellowFin":
-      tf.logging.info("Init YellowFin Optimizer.")
       self._opt = yellowfin.YellowFinOptimizer(
           learning_rate=lr, momentum=hparams.optimizer_momentum_momentum)
     elif optimizer_name == "TrueAdam":
@@ -86,8 +101,7 @@ def __init__(self, optimizer_name, lr, hparams):
           beta2=hparams.optimizer_adam_beta2,
           epsilon=hparams.optimizer_adam_epsilon)
     elif optimizer_name == "Adafactor":
-      self._opt = AdafactorOptimizer(
-          lr / 500.0, epsilon=hparams.optimizer_adam_epsilon)
+      self._opt = AdafactorOptimizer(lr / 500.0)
     else:
       self._opt = tf.contrib.layers.OPTIMIZER_CLS_NAMES[optimizer_name](lr)
 
@@ -131,66 +145,83 @@ def piecewise_learning_rate(step, boundaries, values):
       step, boundaries, values, name="piecewise_lr")
 
 
-def learning_rate_decay(hparams, num_worker_replicas=1):
-  """Inverse-decay learning rate until warmup_steps, then decay."""
-  if hparams.learning_rate_decay_scheme == "piecewise":
-    return piecewise_learning_rate(tf.train.get_or_create_global_step(),
+def learning_rate_decay(hparams, warmup_steps=0):
+  """Learning rate decay multiplier."""
+  scheme = hparams.learning_rate_decay_scheme
+  warmup_steps = tf.to_float(warmup_steps)
+  global_step = tf.to_float(tf.train.get_or_create_global_step())
+
+  if not scheme or scheme == "none":
+    return tf.constant(1.)
+
+  tf.logging.info("Applying learning rate decay: %s.", scheme)
+
+  if scheme == "exp":
+    decay_steps = hparams.learning_rate_decay_steps
+    p = (global_step - warmup_steps) / decay_steps
+    if hparams.learning_rate_decay_staircase:
+      p = tf.floor(p)
+    return tf.pow(hparams.learning_rate_decay_rate, p)
+
+  if scheme == "piecewise":
+    return piecewise_learning_rate(global_step,
                                    hparams.learning_rate_boundaries,
                                    hparams.learning_rate_multiples)
 
-  warmup_steps = tf.to_float(
-      hparams.learning_rate_warmup_steps * num_worker_replicas)
-  num_train_steps = hparams.train_steps
-  step = tf.to_float(tf.train.get_or_create_global_step())
-
-  if hparams.learning_rate_decay_scheme == "noam":
+  if scheme == "noam":
     return 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
-        (step + 1) * warmup_steps**-1.5, (step + 1)**-0.5)
-  elif hparams.learning_rate_decay_scheme == "exp100k":
-    return 0.94**(step // 100000)
-  elif hparams.learning_rate_decay_scheme == "cosine":
+        (global_step + 1) * warmup_steps**-1.5, (global_step + 1)**-0.5)
+
+  if scheme == "cosine":
     cycle_steps = hparams.learning_rate_cosine_cycle_steps
-    return 0.5 * (1 + tf.cos(np.pi * (step % cycle_steps) / cycle_steps))
-  elif hparams.learning_rate_decay_scheme == "cyclelinear10x":
+    cycle_position = global_step % (2 * cycle_steps)
+    cycle_position = cycle_steps - tf.abs(cycle_steps - cycle_position)
+    return 0.5 * (1 + tf.cos(np.pi * cycle_position / cycle_steps))
+
+  if scheme == "cyclelinear10x":
     # Cycle the rate linearly by 10x every warmup_steps, up and down.
-    cycle_steps = hparams.learning_rate_warmup_steps
-    cycle_position = step % (2 * cycle_steps)
+    cycle_steps = warmup_steps
+    cycle_position = global_step % (2 * cycle_steps)
     cycle_position = tf.to_float(  # Normalize to the interval [-1, 1].
         cycle_position - cycle_steps) / float(cycle_steps)
     cycle_position = 1.0 - tf.abs(cycle_position)  # 0 to 1 and back to 0.
     return (cycle_position + 0.1) * 3.0  # 10x difference each cycle (0.3-3).
 
-  inv_base = tf.exp(tf.log(0.01) / warmup_steps)
-  inv_decay = inv_base**(warmup_steps - step)
-  if hparams.learning_rate_decay_scheme == "sqrt":
-    decay = _sqrt_decay(step - warmup_steps)
-  elif hparams.learning_rate_decay_scheme == "exp":
-    total_steps = num_train_steps - warmup_steps
-    assert num_train_steps > hparams.learning_rate_warmup_steps
-    assert hparams.learning_rate_minimum is not None, "Must specify final LR"
-    total_steps = num_train_steps - hparams.learning_rate_warmup_steps
-    decay_needed = hparams.learning_rate_minimum / hparams.learning_rate
-    decay_rate = decay_needed**(1.0 / total_steps)
-    tf.logging.info("Decay rate: %f.  LR %f -> %f", decay_rate,
-                    hparams.learning_rate, hparams.learning_rate_minimum)
-    decay = _exp_decay_after(step, decay_rate,
-                             hparams.learning_rate_warmup_steps)
-    return decay
-  elif hparams.learning_rate_decay_scheme == "exp10k":
-    decay = _exp_decay_after(step - warmup_steps, 0.9995,
-                             num_train_steps - warmup_steps - 10000)
-  elif hparams.learning_rate_decay_scheme == "exp50k":
-    decay = _exp_decay_after(step - warmup_steps, 0.99995,
-                             num_train_steps - warmup_steps - 50000)
-  elif hparams.learning_rate_decay_scheme == "exp500k":
-    decay = _exp_decay_after(step - warmup_steps, 0.9999955,
-                             num_train_steps - warmup_steps - 500000)
-  elif hparams.learning_rate_decay_scheme == "none":
-    decay = tf.constant(1.0)
+  if scheme == "sqrt":
+    return _sqrt_decay(global_step - warmup_steps)
+
+  raise ValueError("Unrecognized learning rate decay scheme: %s" %
+                   hparams.learning_rate_decay_scheme)
+
+
+def learning_rate_warmup(warmup_steps, warmup_schedule="exp"):
+  """Learning rate warmup multiplier."""
+  if not warmup_steps:
+    return tf.constant(1.)
+
+  tf.logging.info("Applying %s learning rate warmup for %d steps",
+                  warmup_schedule, warmup_steps)
+
+  warmup_steps = tf.to_float(warmup_steps)
+  global_step = tf.to_float(tf.train.get_or_create_global_step())
+
+  if warmup_schedule == "exp":
+    return tf.exp(tf.log(0.01) / warmup_steps)**(warmup_steps - global_step)
   else:
-    raise ValueError("Unrecognized learning rate decay scheme: %s" %
-                     hparams.learning_rate_decay_scheme)
-  return tf.where(step < warmup_steps, inv_decay, decay)
+    assert warmup_schedule == "linear"
+    start = tf.constant(0.35)
+    return ((tf.constant(1.) - start) / warmup_steps) * global_step + start
+
+
+def learning_rate_decay_with_warmup(hparams, num_worker_replicas=1):
+  """Learning rate decay rate with warmup based on hparams."""
+  warmup_steps = hparams.learning_rate_warmup_steps * num_worker_replicas
+  warmup = learning_rate_warmup(warmup_steps)
+
+  decay = learning_rate_decay(hparams, warmup_steps)
+
+  global_step = tf.train.get_or_create_global_step()
+  return tf.where(global_step < warmup_steps, warmup, decay)
 
 
 def weight_decay_and_noise(loss, hparams, learning_rate, var_list=None):
@@ -198,7 +229,7 @@ def weight_decay_and_noise(loss, hparams, learning_rate, var_list=None):
   if var_list is None:
     var_list = tf.trainable_variables()
 
-  decay_vars = [v for v in var_list if len(v.shape.as_list()) > 1]
+  decay_vars = [v for v in var_list]
   noise_vars = [v for v in var_list if "/body/" in v.name]
 
   weight_decay_loss = weight_decay(hparams.weight_decay, decay_vars)
@@ -218,6 +249,9 @@ def weight_noise(noise_rate, learning_rate, var_list):
   if not noise_rate:
     return [tf.no_op()]
 
+  tf.logging.info("Applying weight noise scaled by learning rate, "
+                  "noise_rate: %0.5f", noise_rate)
+
   noise_ops = []
 
   for v in var_list:
@@ -231,16 +265,19 @@ def weight_noise(noise_rate, learning_rate, var_list):
   return noise_ops
 
 
-def weight_decay(decay_rate, var_list):
+def weight_decay(decay_rate, var_list, skip_biases=True):
   """Apply weight decay to vars in var_list."""
   if not decay_rate:
     return 0.
 
+  tf.logging.info("Applying weight decay, decay_rate: %0.5f", decay_rate)
+
   weight_decays = []
   for v in var_list:
-    # Weight decay
-    is_bias = len(v.shape.as_list()) <= 1
-    if not is_bias:
+    # Weight decay.
+    # This is a heuristic way to detect biases that works for main tf.layers.
+    is_bias = len(v.shape.as_list()) == 1 and v.name.endswith("bias:0")
+    if not (skip_biases and is_bias):
       with tf.device(v.device):
         v_loss = tf.nn.l2_loss(v)
       weight_decays.append(v_loss)
@@ -248,12 +285,13 @@ def weight_decay(decay_rate, var_list):
   return tf.add_n(weight_decays) * decay_rate
 
 
-def log_variable_sizes(var_list=None, tag=None):
+def log_variable_sizes(var_list=None, tag=None, verbose=False):
   """Log the sizes and shapes of variables, and the total size.
 
   Args:
     var_list: a list of variables; defaults to trainable_variables
     tag: a string; defaults to "Trainable Variables"
+    verbose: bool, if True, log every weight; otherwise, log total size only.
   """
   if var_list is None:
     var_list = tf.trainable_variables()
@@ -268,15 +306,20 @@ def log_variable_sizes(var_list=None, tag=None):
   for v_name in sorted(list(name_to_var)):
     v = name_to_var[v_name]
     v_size = int(np.prod(np.array(v.shape.as_list())))
-    tf.logging.info("Weight    %s\tshape    %s\tsize    %d",
-                    v.name[:-2].ljust(80),
-                    str(v.shape).ljust(20), v_size)
+    if verbose:
+      tf.logging.info("Weight    %s\tshape    %s\tsize    %d",
+                      v.name[:-2].ljust(80),
+                      str(v.shape).ljust(20), v_size)
     total_size += v_size
   tf.logging.info("%s Total size: %d", tag, total_size)
 
 
 def get_variable_initializer(hparams):
   """Get variable initializer from hparams."""
+  if not hparams.initializer:
+    return None
+
+  tf.logging.info("Using variable initializer: %s", hparams.initializer)
   if hparams.initializer == "orthogonal":
     return tf.orthogonal_initializer(gain=hparams.initializer_gain)
   elif hparams.initializer == "uniform":
@@ -295,61 +338,97 @@ def get_variable_initializer(hparams):
 class AdafactorOptimizer(tf.train.Optimizer):
   """Optimizer that implements the Adafactor algorithm.
 
-  Adafactor is similar to Adam, but seeks to reduce the memory
-  requirements due to the moment estimates.  The auxiliary memory
-  requirements for an `AxB` weight matrix are `A+B` for Adafactor,
-  versus `2AB` for Adam.
-
-  Adam is described in [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
-  ([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
-
-  The differences are as follows:
-
-  1. No momentum - this removes the first-moment estimate.
-  2. For an AxB weight matrix, instead of keeping a full AxB second-moment
-     estimate matrix, Adafactor keeps only the row and column means of that
-     estimate matrix, and estimate the full second-moment estimate matrix
-     from on the fly, based on the means.
-  3. Adafactor uses a variable decay rate for the second-moment estaimtes -
-     faster decay at the start of training and slower decay later. This
-     elimnates the awkwardness in Adam related to having biased moment
-     estimates at the start of training.
-
-  For non-2d variables:
-    We initialize
-    ```
-    t <- 0
-    v <- zeros(shape(var))
-    ```
-
-    The update rule is as follows:
-    ```
-    t <- t + 1
-    decay_horizon = min(t, t * relative_decay_horizon + absolute_decay_horizon)
-    decay_rate = 1 - 1 / decay_horizon
-    v <- decay_rate * v + (1 - decay_rate) * grad^2
-    var <- var - lr * grad / (sqrt(v) + epsilon)
-    ```
-
-  For 2d variables:
-    We initialize
-    ```
-    t <- 0
+  Adafactor is similar to RMSProp (ADAM, etc.), but takes advantage of the
+  structure of weight matrices to use less memory and to be more resilient to
+  sudden large gradients.
+
+  The RMSProp algorithm works on each component independently as follows:
+    w -= grad * learning_rate / sqrt(estimated_mean_square_grad)
+
+    learning_rate is the desired update magnitude, and
+    estimated_mean_square_grad is computed by exponential smoothing of the
+    square of the gradient.
+
+  Adafactor addresses two shortcomings of RMSProp:
+
+  1. In RMSProp (ADAM, etc), maintaining estimated_mean_square_grad requires
+     memory equal to the number of parameters.  This can be an impediment to
+     training large models on GPU/TPU systems with limited memory.
+
+     Adafactor uses less memory.
+     For an AxB weight matrix, instead of keeping a full AxB
+     estimated_mean_square_grad matrix, Adafactor keeps only
+     exponentially-smoothed row and column means, and bases its estimates on
+     those means.   Thus the memory requirements drop from `2AB` to `A+B`.
+
+  2. Depending on the decay rate of the exponential smoothing, we run into one
+     of two problems.
+
+     If the decay rate is high (short memory), we see the problem described
+     here - worse final quality:
+       On the Convergence of Adam and Beyond
+       https://openreview.net/forum?id=ryQu7f-RZ
+
+     If the decay rate is low (long memory), then the estimate does not adjust
+     rapidly to suddenly large gradients, and the model diverges.
+     Suddenly large gradients (which we will call anomalies), may happen either
+     due to weird training data, or because the model has just learned something
+     important and can now rush to exploit it.  Momentum (as in ADAM) can help
+     prevent divergence, but it also requires more memory.  Gradient clipping
+     can also help prevent divergence, but it is irritating in that setting
+     the right threshold depends on the knowing the scale of the gradients.
+
+     Adafactor uses a relatively long memory (setting the decay rate to
+     step_num^-0.8), but detects and corrects for anomalies.   An anomaly
+     is detected if the mean-square gradient for the current step
+     (across the entire weight matrix) is much greater than the historical
+     average.  When this occurs, we increase estimated_mean_square_grad
+     for the current step for all weights in the matrix.  Note: it is important
+     to detect anomalies based on entire matrices, rather than individual
+     weights, since any individual weight may legitimately have a pattern
+     of many small gradients and occasional very large ones.
+
+  HYPERPARAMETERS:
+    learning_rate: desired magnitude of variable updates.  a scalar - can be a
+      constant, but more likely should have a warmup and then decay
+      proportionally to rsqrt(step_num)
+    epsilon: 1e-20 - a small floating point value to avoid division by zero.
+    horizon_exponent: 0.8 - a value between 0 and 1 - The effective decay
+      horizon of the second-moment estimator is step_num^horizon_exponent.
+    anomaly_threshold: 2.0 - a value greater than 1.  Suppress anomalies
+      where the mean-square-gradients for a step exceed the long-term average
+      by at least this factor.
+
+  ALGORITHM:
+
+  We initialize
+  ```
+  t <- 0
+  if var is 2-dimensional:
     v_r <- zeros([num_rows])
     v_c <- zeros([num_cols])
-    ```
-
-    The update rule is as follows:
-    ```
-    t <- t + 1
-    decay_horizon = min(t, t * relative_decay_horizon + absolute_decay_horizon)
-    decay_rate = 1 - 1 / decay_horizon
-    v_r <- decay_rate * v_r + (1 - decay_rate) * reduce_mean(grad^2, 1)
-    v_c <- decay_rate * v_c + (1 - decay_rate) * reduce_mean(grad^2, 0)
-    approx_v = expand_dims(v_r, 1) * expand_dims(v_c, 0) / reduce_mean(v_c)
-    var <- var - lr * grad / (sqrt(approx_v) + epsilon)
-    ```
-
+  else:
+    v <- zeros(shape(var))
+  ```
+
+  The update rule is as follows:
+  ```
+  t <- t + 1
+  decay_rate = 1 - t ^ (-horizon_exponent)
+  grad_squared = tf.square(grad) + epsilon
+  if var is 2-dimensional:
+    v_r <- decay_rate * v_r + (1 - decay_rate) * reduce_mean(grad_squared, 1)
+    v_c <- decay_rate * v_c + (1 - decay_rate) * reduce_mean(grad_squared, 0)
+    anomaly_factor = max(1.0,
+      reduce_mean(grad_squared) / reduce_mean(v_r) / anomaly_threshold)
+    est_v = anomaly_factor * outer_prod(v_r, v_c) / reduce_mean(v_r)
+  else:
+    v <- decay_rate * v + (1 - decay_rate) * grad_squared
+    anomaly_factor = max(1.0,
+      reduce_mean(grad_squared) / reduce_mean(v) / anomaly_threshold)
+    est_v = v * anomaly_factor
+  var <- var - lr * grad / sqrt(est_v)
+  ```
   TODO(noam): write a paper.
   TODO(noam): we should also apply the 2d logic to the two final dimensions.
     of >2d convolutional kernels.
@@ -357,9 +436,9 @@ class AdafactorOptimizer(tf.train.Optimizer):
 
   def __init__(self,
                learning_rate=0.001,
-               epsilon=1e-8,
-               relative_decay_horizon=0.2,
-               absolute_decay_horizon=100.0,
+               epsilon=1e-20,
+               horizon_exponent=0.8,
+               anomaly_threshold=2.0,
                use_locking=False,
                name="Adafactor"):
     """Construct a new Adafactor optimizer.
@@ -369,27 +448,17 @@ def __init__(self,
     Args:
       learning_rate: A Tensor or a floating point value.  The learning rate.
       epsilon: A small constant for numerical stability.
-      relative_decay_horizon: a floating point value <= 1
-      absolute_decay_horizon: a floating point value (representing a step count)
+      horizon_exponent: a floating point value between 0 and 1
+      anomaly_threshold: a floating point value >= 1.0
       use_locking: If True use locks for update operations.
       name: Optional name for the operations created when applying gradients.
         Defaults to "AdafactorOptimizer".
     """
     super(AdafactorOptimizer, self).__init__(use_locking, name)
     self._lr = learning_rate
-    self._relative_decay_horizon = relative_decay_horizon
-    self._absolute_decay_horizon = absolute_decay_horizon
     self._epsilon = epsilon
-
-  def _prepare(self):
-    global_step = tf.to_float(tf.train.get_or_create_global_step()) + 1.0
-    decay_horizon = tf.minimum(global_step,
-                               global_step * self._relative_decay_horizon +
-                               self._absolute_decay_horizon)
-    self._mixing_rate = 1.0 / decay_horizon
-    self._decay_rate = 1.0 - self._mixing_rate
-    self._epsilon = tf.to_float(self._epsilon)
-    self._lr = tf.to_float(self._lr)
+    self._horizon_exponent = horizon_exponent
+    self._anomaly_threshold = anomaly_threshold
 
   def _should_use_factored_second_moment_estimate(self, shape):
     """Should we use a factored second moment estimator.
@@ -418,30 +487,78 @@ def _apply_dense(self, grad, var):
     return self._resource_apply_dense(grad, var)
 
   def _resource_apply_dense(self, grad, var):
+    grad_squared = tf.square(grad) + self._epsilon
+    grad_squared_mean = tf.reduce_mean(grad_squared)
+    lr = tf.to_float(self._lr)
+    global_step = tf.to_float(tf.train.get_or_create_global_step()) + 1.0
+    # HACK: Make lr and global_step dependent on grad.
+    # This confounds the XLA rewriter and keeps it from fusing computations
+    # across different variables.  This fusion is a bad for HBM usage, since
+    # it causes the gradients to persist in memory.
+    lr += grad_squared_mean * 1e-30
+    global_step += grad_squared_mean * 1e-30
+    # END HACK
+    mixing_rate = tf.pow(global_step, -self._horizon_exponent)
+    decay_rate = 1.0 - mixing_rate
     shape = var.get_shape().as_list()
-    grad_squared = tf.square(grad)
     updates = []
     if self._should_use_factored_second_moment_estimate(shape):
+      grad_squared_row_mean = tf.reduce_mean(grad_squared, 1)
+      grad_squared_col_mean = tf.reduce_mean(grad_squared, 0)
       vr = self.get_slot(var, "vr")
-      new_vr = (self._decay_rate * vr +
-                self._mixing_rate * tf.reduce_mean(grad_squared, 1))
+      new_vr = (decay_rate * vr + mixing_rate * grad_squared_row_mean)
       vc = self.get_slot(var, "vc")
-      new_vc = (self._decay_rate * vc +
-                self._mixing_rate * tf.reduce_mean(grad_squared, 0))
+      new_vc = (decay_rate * vc + mixing_rate * grad_squared_col_mean)
       vr_update = tf.assign(vr, new_vr, use_locking=self._use_locking)
       vc_update = tf.assign(vc, new_vc, use_locking=self._use_locking)
       updates = [vr_update, vc_update]
-      vr = tf.sqrt(new_vr) + self._epsilon
-      vc = tf.sqrt(new_vc) + self._epsilon
-      vc /= tf.reduce_mean(vc)
-      denom = tf.expand_dims(vr, 1) * tf.expand_dims(vc, 0)
+      long_term_mean = tf.reduce_mean(new_vr)
+      anomaly_factor = self._anomaly_factor(grad_squared_mean, long_term_mean)
+      # This is the computation we should do.
+      # est_v = (tf.expand_dims(new_vr, 1) * tf.expand_dims(new_vc, 0)
+      #          * anomaly_factor / long_term_mean)
+      # subtrahend = grad * lr / tf.sqrt(est_v)
+      # Instead we do the following, which is mathematically equivalent.
+      r_factor = lr * tf.rsqrt(new_vr * anomaly_factor / long_term_mean)
+      c_factor = tf.rsqrt(new_vc)
+      subtrahend = (
+          grad * tf.expand_dims(r_factor, 1) * tf.expand_dims(c_factor, 0))
     else:
       v = self.get_slot(var, "v")
-      new_v = (self._decay_rate * v + self._mixing_rate * grad_squared)
+      new_v = decay_rate * v + mixing_rate * grad_squared
       v_update = tf.assign(v, new_v, use_locking=self._use_locking)
       updates = [v_update]
-      denom = tf.sqrt(new_v) + self._epsilon
-    subtrahend = self._lr * grad / denom
+      long_term_mean = tf.reduce_mean(new_v)
+      anomaly_factor = self._anomaly_factor(grad_squared_mean, long_term_mean)
+      # This is the computation we should do.
+      # est_v = (new_v * anomaly_factor)
+      # subtrahend = grad * lr / tf.sqrt(est_v)
+      # Instead we do the following, which is mathematically equivalent.
+      subtrahend = grad * (lr / tf.sqrt(anomaly_factor)) * tf.rsqrt(new_v)
     var_update = tf.assign_sub(var, subtrahend, use_locking=self._use_locking)
     updates = [var_update] + updates
     return tf.group(*updates)
+
+  def _anomaly_factor(self, grad_squared_mean, long_term_mean):
+    """Multiplier for second-moment estimator, due to short-term anomalies.
+
+    A step may have gradients with magnitudes much larger than the long-term
+    average.  This can cause the model to diverge.  In these cases, we want to
+    temoporarily increase the second-moment estimators to reflect that these
+    steps are anomalous.
+
+    It is important to make these calculations on whole weight matrices, rather
+    than on individual parameters, since we want to allow individual parameters
+    to have occasional large updates.
+
+    Args:
+      grad_squared_mean: A scalar.  The mean square gradient on the varaible
+         for the current step.
+      long_term_mean: A scalar.  The mean of the long-term second-moment
+         estimator.
+    Returns:
+      a scalar that should be multiplied into the second-moment-estimator for
+      this step.
+    """
+    ratio = grad_squared_mean / long_term_mean
+    return tf.maximum(1.0, ratio / self._anomaly_threshold)
diff --git a/tensor2tensor/utils/t2t_model.py b/tensor2tensor/utils/t2t_model.py
index 8e5a76d67..0623a975e 100644
--- a/tensor2tensor/utils/t2t_model.py
+++ b/tensor2tensor/utils/t2t_model.py
@@ -29,6 +29,7 @@
 import six
 
 from tensor2tensor.data_generators import text_encoder
+from tensor2tensor.data_generators.problem import problem_hparams_to_features
 from tensor2tensor.layers import common_layers
 from tensor2tensor.utils import beam_search
 from tensor2tensor.utils import decoding
@@ -107,8 +108,8 @@ def __init__(self,
     self._original_hparams = hparams
     self.set_mode(mode)
 
-    self._decode_hparams = copy.copy(
-        decode_hparams or decoding.decode_hparams())
+    self._decode_hparams = copy.copy(decode_hparams or
+                                     decoding.decode_hparams())
     self._data_parallelism = data_parallelism or eu.Parallelism([""])
     self._num_datashards = self._data_parallelism.n
     self._ps_devices = self._data_parallelism.ps_devices
@@ -149,7 +150,6 @@ def model_fn_sharded(self, sharded_features):
     dp = self._data_parallelism
     summarize_features(sharded_features, num_shards=dp.n)
     datashard_to_features = self._to_features_per_datashard(sharded_features)
-
     if self.use_body_sharded:
       # MoE models override body_sharded
       transformed_features = dp(self.bottom, datashard_to_features)
@@ -157,8 +157,8 @@ def model_fn_sharded(self, sharded_features):
           self._to_single_features_dict(transformed_features))
       body_out, losses = self._normalize_body_output(body_out)
       if "training" in losses:
-        # If body has returned the training loss, the body outputs are
-        # considered the logits and no further work is done.
+        tf.logging.info("Skipping T2TModel top and loss because training loss "
+                        "returned from body")
         sharded_logits = body_out
       else:
         sharded_logits = dp(self.top, body_out, datashard_to_features)
@@ -189,10 +189,13 @@ def model_fn(self, features):
     transformed_features = self.bottom(features)
 
     with tf.variable_scope("body"):
+      tf.logging.info("Building model body")
       body_out = self.body(transformed_features)
     output, losses = self._normalize_body_output(body_out)
 
     if "training" in losses:
+      tf.logging.info("Skipping T2TModel top and loss because training loss "
+                      "returned from body")
       logits = output
     else:
       logits = self.top(output, features)
@@ -213,12 +216,16 @@ def bottom(self, features):
         self._problem_hparams.input_modality):
       do_reuse = input_modality.name in all_previous_modalities
       with tf.variable_scope(input_modality.name, reuse=do_reuse):
+        tf.logging.info("Transforming feature '%s' with %s.bottom", key,
+                        input_modality.name)
         transformed_features[key] = input_modality.bottom(features[key])
       all_previous_modalities.append(input_modality.name)
 
     # Transform the targets (for autoregressive models)
     target_modality = self._problem_hparams.target_modality
     with tf.variable_scope(target_modality.name):
+      tf.logging.info("Transforming 'targets' with %s.targets_bottom",
+                      target_modality.name)
       transformed_features["targets"] = target_modality.targets_bottom(
           features["targets"])
 
@@ -256,6 +263,8 @@ def top(self, body_output, features):
 
     target_modality = self._problem_hparams.target_modality
     with tf.variable_scope(target_modality.name):
+      tf.logging.info("Transforming body output with %s.top",
+                      target_modality.name)
       last_only = (
           target_modality.top_is_pointwise and
           self.hparams.mode == tf.estimator.ModeKeys.PREDICT and
@@ -285,20 +294,32 @@ def loss(self, logits, features):
 
   def optimize(self, loss, num_async_replicas=1):
     """Return a training op minimizing loss."""
-    use_tpu = self.hparams.use_tpu
-    lr = self.hparams.learning_rate * optimize.learning_rate_decay(self.hparams)
+    tf.logging.info("Base learning rate: %f", self.hparams.learning_rate)
+    lr = self.hparams.learning_rate
+    decay_rate = optimize.learning_rate_decay_with_warmup(self.hparams)
+    lr *= decay_rate
+    if self.hparams.learning_rate_minimum:
+      lr_min = float(self.hparams.learning_rate_minimum)
+      tf.logging.info("Applying learning rate minimum: %f", lr_min)
+      lr = tf.max(lr, tf.to_float(lr_min))
+    if num_async_replicas > 1:
+      tf.logging.info("Dividing learning rate by num_async_replicas: %d",
+                      num_async_replicas)
     lr /= math.sqrt(float(num_async_replicas))
-    train_op = optimize.optimize(loss, lr, self.hparams, use_tpu=use_tpu)
+    train_op = optimize.optimize(
+        loss, lr, self.hparams, use_tpu=common_layers.is_on_tpu())
     return train_op
 
   def set_mode(self, mode):
     """Set hparams with the given mode."""
+    tf.logging.info("Setting T2TModel mode to '%s'", mode)
     hparams = copy.copy(self._original_hparams)
     hparams.add_hparam("mode", mode)
     # When not in training mode, set all forms of dropout to zero.
     if mode != tf.estimator.ModeKeys.TRAIN:
       for key in hparams.values():
         if key.endswith("dropout"):
+          tf.logging.info("Setting hparams.%s to 0.0", key)
           setattr(hparams, key, 0.0)
     self._hparams = hparams
 
@@ -352,27 +373,15 @@ def eval_autoregressive(self, features=None, decode_length=50):
       losses: a dictionary: {loss-name (string): floating point `Scalar`}.
           Contains a single key "training".
     """
-    _, logits, losses = self._slow_greedy_infer(
-        features, decode_length=decode_length)
-    return logits, losses
+    results = self._slow_greedy_infer(features, decode_length=decode_length)
+    return results["logits"], results["losses"]
 
   def _fill_problem_hparams_features(self, features):
-    if features is None:
-      return
-
-    input_space_id, target_space_id = 0, 0
-    if self._problem_hparams:
-      input_space_id = self._problem_hparams.input_space_id
-      target_space_id = self._problem_hparams.target_space_id
-
-    if "problem_choice" not in features:
-      features["problem_choice"] = tf.constant(0, name="problem_choice")
-    if "input_space_id" not in features:
-      features["input_space_id"] = tf.constant(
-          input_space_id, name="input_space_id")
-    if "target_space_id" not in features:
-      features["target_space_id"] = tf.constant(
-          target_space_id, name="target_space_id")
+    if features is not None:
+      for k, v in six.iteritems(
+          problem_hparams_to_features(self._problem_hparams)):
+        if k not in features:
+          features[k] = tf.constant(v, name=k)
 
   def infer(self,
             features=None,
@@ -393,7 +402,17 @@ def infer(self,
         the preference for slonger translations.
 
     Returns:
-       samples: an integer `Tensor`.
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if beam_size == 1 or
+              [batch_size, top_beams, <= decode_length]
+          "scores": decoding log probs from the beam search,
+              None if using greedy decoding (beam_size=1)
+      }
+      if slow greedy decoding is used then the dict will also contain {
+          "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
+          "losses": a dictionary: {loss-name (string): floating point `Scalar`
+      }
     """
     with self._eager_var_store.as_default():
       # TODO(rsepassi): Make decoding work with real-valued model outputs
@@ -411,12 +430,13 @@ def infer(self,
           beam_size = 1  # No use to run beam-search for a single class.
       if beam_size == 1:
         tf.logging.info("Greedy Decoding")
-        samples, _, _ = self._greedy_infer(features, decode_length)
+        results = self._greedy_infer(features, decode_length)
       else:
         tf.logging.info("Beam Decoding with beam size %d" % beam_size)
-        samples = self._beam_decode(
+        results = self._beam_decode(
             features, decode_length, beam_size, top_beams, alpha)
-      return samples
+
+      return results
 
   def _beam_decode(self, features, decode_length, beam_size, top_beams, alpha):
     """Beam search decoding.
@@ -515,15 +535,13 @@ def symbols_to_logits_fn(ids):
       features["inputs"] = inputs_old
 
     # Return `top_beams` decodings (also remove initial id from the beam search)
-    return_scores = True  # TODO(lukaszkaiser): make it work multi-problem.
+    # TODO(lukaszkaiser): make it work multi-problem.
     if top_beams == 1:
-      if return_scores:
-        return {"outputs": ids[:, 0, 1:], "scores": scores}
-      return ids[:, 0, 1:]
+      samples = ids[:, 0, 1:]
     else:
-      if return_scores:
-        return {"outputs": ids[:, :top_beams, 1:], "scores": scores}
-      return ids[:, :top_beams, 1:]
+      samples = ids[:, :top_beams, 1]
+
+    return {"outputs": samples, "scores": scores}
 
   def _greedy_infer(self, features, decode_length):
     """A greedy inference method.
@@ -535,9 +553,14 @@ def _greedy_infer(self, features, decode_length):
       decode_length: an integer.  How many additional timesteps to decode.
 
     Returns:
-       samples: an integer `Tensor`.
-       logits: `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
-       losses: a dictionary: {loss-name (string): floating point `Scalar`}
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if beam_size == 1 or
+              [batch_size, top_beams, <= decode_length]
+          "scores": None
+          "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
+          "losses": a dictionary: {loss-name (string): floating point `Scalar`}
+      }
     """
     return self._slow_greedy_infer(features, decode_length)
 
@@ -551,9 +574,14 @@ def _slow_greedy_infer(self, features, decode_length):
       decode_length: an integer.  How many additional timesteps to decode.
 
     Returns:
-       samples: an integer `Tensor`.
-       logits: `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
-       losses: a dictionary: {loss-name (string): floating point `Scalar`}
+      A dict of decoding results {
+          "outputs": integer `Tensor` of decoded ids of shape
+              [batch_size, <= decode_length] if beam_size == 1 or
+              [batch_size, top_beams, <= decode_length]
+          "scores": None
+          "logits": `Tensor` of shape [batch_size, time, 1, 1, vocab_size].
+          "losses": a dictionary: {loss-name (string): floating point `Scalar`}
+      }
     """
     if not features:
       features = {}
@@ -673,7 +701,12 @@ def fn_not_eos():
           features["partial_targets"])[1]
       result = tf.slice(result, [0, partial_target_length, 0, 0],
                         [-1, -1, -1, -1])
-    return result, logits, losses
+    return {
+        "outputs": result,
+        "scores": None,
+        "logits": logits,
+        "losses": losses,
+    }
 
   def sample(self, features):
     """Run the model and extract samples.
@@ -714,9 +747,10 @@ def _shard_features(self, features):  # pylint: disable=missing-docstring
         v_shape = [1]
       if v_shape == [1]:
         v = tf.tile(v, [self._num_datashards])
-      sharded_features[k] = self._data_parallelism(
-          tf.identity,
-          tf.split(v, self._num_datashards, 0))
+      sharded_features[k] = self._data_parallelism(tf.identity,
+                                                   tf.split(
+                                                       v, self._num_datashards,
+                                                       0))
     return sharded_features
 
   def _to_features_per_datashard(self, features):
@@ -782,14 +816,16 @@ def estimator_model_fn(cls,
     """
     _create_dummy_vars()
     hparams = copy.deepcopy(hparams)
-    hparams.use_tpu = use_tpu
 
     # Instantiate model
     data_parallelism = None
     if not use_tpu and config:
       data_parallelism = config.data_parallelism
-    model = cls(hparams, mode, data_parallelism=data_parallelism,
-                decode_hparams=decode_hparams)
+    model = cls(
+        hparams,
+        mode,
+        data_parallelism=data_parallelism,
+        decode_hparams=decode_hparams)
 
     # PREDICT mode
     if mode == tf.estimator.ModeKeys.PREDICT:
@@ -827,18 +863,16 @@ def estimator_model_fn(cls,
 
     # TRAIN mode
     assert mode == tf.estimator.ModeKeys.TRAIN
-    num_async_replicas = (
-        1 if (use_tpu or not config)
-        else config.t2t_device_info["num_async_replicas"])
-    return model.estimator_spec_train(loss,
-                                      num_async_replicas=num_async_replicas)
+    num_async_replicas = (1 if (use_tpu or not config) else
+                          config.t2t_device_info["num_async_replicas"])
+    return model.estimator_spec_train(
+        loss, num_async_replicas=num_async_replicas)
 
   def estimator_spec_train(self, loss, num_async_replicas=1):
     """Construct EstimatorSpec for TRAIN mode."""
-    use_tpu = self.hparams.use_tpu
     train_op = self.optimize(loss, num_async_replicas=num_async_replicas)
 
-    if use_tpu:
+    if common_layers.is_on_tpu():
       _remove_summaries()  # summaries not currently working on TPU
       return tf.contrib.tpu.TPUEstimatorSpec(
           tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
@@ -846,25 +880,21 @@ def estimator_spec_train(self, loss, num_async_replicas=1):
       return tf.estimator.EstimatorSpec(
           tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op)
 
-  def estimator_spec_eval(self,
-                          features,
-                          logits,
-                          labels,
-                          loss):
+  def estimator_spec_eval(self, features, logits, labels, loss):
     """Construct EstimatorSpec for EVAL mode."""
     hparams = self.hparams
-    use_tpu = hparams.use_tpu
 
     if not hasattr(hparams, "problem_instances"):
       raise NotImplementedError(_no_problem_err("estimator_spec_eval"))
 
     problem = hparams.problem_instances[0]
-    if use_tpu:
+    if common_layers.is_on_tpu():
       eval_metrics_fn = _create_tpu_eval_metrics_fn(problem, hparams)
       _remove_summaries()
       return tf.contrib.tpu.TPUEstimatorSpec(
           tf.estimator.ModeKeys.EVAL,
-          eval_metrics=(eval_metrics_fn, [logits, labels]), loss=loss)
+          eval_metrics=(eval_metrics_fn, [logits, labels]),
+          loss=loss)
     else:
       eval_metrics_fns = metrics.create_evaluation_metrics([problem], hparams)
       eval_metrics = {}
@@ -883,20 +913,20 @@ def estimator_spec_predict(self, features):
     infer_out = self.infer(
         features,
         beam_size=decode_hparams.beam_size,
-        top_beams=(
-            decode_hparams.beam_size if decode_hparams.return_beams else 1),
+        top_beams=(decode_hparams.beam_size
+                   if decode_hparams.return_beams else 1),
         alpha=decode_hparams.alpha,
         decode_length=decode_hparams.extra_length)
     if isinstance(infer_out, dict):
-      # Beam searching
       outputs = infer_out["outputs"]
       scores = infer_out["scores"]
     else:
       outputs = infer_out
       scores = None
 
-    batched_problem_choice = (features["problem_choice"] * tf.ones(
-        (common_layers.shape_list(features["inputs"])[0],), dtype=tf.int32))
+    batched_problem_choice = (
+        features["problem_choice"] * tf.ones(
+            (common_layers.shape_list(features["inputs"])[0],), dtype=tf.int32))
     predictions = {
         "outputs": outputs,
         "scores": scores,
@@ -1017,13 +1047,6 @@ def _remove_summaries():
   assert not g.get_collection(key)
 
 
-def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
-  """Clips gradients by global norm."""
-  gradients, variables = zip(*grads_and_vars)
-  clipped_gradients, _ = tf.clip_by_global_norm(gradients, clip_gradients)
-  return list(zip(clipped_gradients, variables))
-
-
 def _del_dict_nones(d):
   for k in list(d.keys()):
     if d[k] is None:
diff --git a/tensor2tensor/utils/trainer_lib.py b/tensor2tensor/utils/trainer_lib.py
index abf01d428..039b06e68 100644
--- a/tensor2tensor/utils/trainer_lib.py
+++ b/tensor2tensor/utils/trainer_lib.py
@@ -47,10 +47,7 @@ def create_session_config(log_device_placement=False,
   else:
     if enable_graph_rewriter:
       rewrite_options = rewriter_config_pb2.RewriterConfig()
-      rewrite_options.optimizers.append("pruning")
-      rewrite_options.optimizers.append("constfold")
-      rewrite_options.optimizers.append("arithmetic")
-      rewrite_options.optimizers.append("layout")
+      rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.ON
       graph_options = tf.GraphOptions(rewrite_options=rewrite_options)
     else:
       graph_options = tf.GraphOptions(
@@ -87,7 +84,7 @@ def create_run_config(master="",
                       num_shards=8,
                       log_device_placement=False,
                       save_checkpoints_steps=1000,
-                      save_checkpoints_secs=0,
+                      save_checkpoints_secs=None,
                       keep_checkpoint_max=20,
                       keep_checkpoint_every_n_hours=10000,
                       num_gpus=1,
@@ -114,19 +111,19 @@ def create_run_config(master="",
       enable_graph_rewriter=enable_graph_rewriter,
       gpu_mem_fraction=gpu_mem_fraction,
       use_tpu=use_tpu)
-  session_config = tf.ConfigProto(
-      allow_soft_placement=True, log_device_placement=log_device_placement)
   run_config_args = {
       "master": master,
       "model_dir": model_dir,
       "session_config": session_config,
       "save_summary_steps": 100,
       "save_checkpoints_steps": save_checkpoints_steps,
-      "save_checkpoints_secs": save_checkpoints_secs,
       "keep_checkpoint_max": keep_checkpoint_max,
       "keep_checkpoint_every_n_hours": keep_checkpoint_every_n_hours,
       "tf_random_seed": random_seed,
   }
+  if save_checkpoints_secs:
+    del run_config_args["save_checkpoints_steps"]
+    run_config_args["save_checkpoints_secs"] = save_checkpoints_secs
   run_config_cls = tf.contrib.learn.RunConfig
 
   # If using TPU, use TPU RunConfig, add TPUConfig, and add additional args
@@ -135,7 +132,7 @@ def create_run_config(master="",
     tpu_config = tf.contrib.tpu.TPUConfig(
         iterations_per_loop=iterations_per_loop,
         num_shards=num_shards,
-        per_host_input_for_training=(num_shards <= 8),
+        per_host_input_for_training=True,
         initial_infeed_sleep_secs=tpu_infeed_sleep_secs)
     run_config_args["tpu_config"] = tpu_config
 
@@ -175,8 +172,9 @@ def create_estimator(model_name,
       model_name, hparams, decode_hparams=decode_hparams, use_tpu=use_tpu)
 
   if use_tpu:
-    batch_size = hparams.tpu_batch_size_per_shard
-    batch_size *= run_config.tpu_config.num_shards
+    problem = hparams.problem_instances[0]
+    batch_size = (problem.tpu_batch_size_per_shard(hparams) *
+                  run_config.tpu_config.num_shards)
     return tf.contrib.tpu.TPUEstimator(
         model_fn=model_fn,
         model_dir=run_config.model_dir,
@@ -203,16 +201,19 @@ def create_hooks(use_tfdbg=False, use_dbgprofile=False, dbgprofile_kwargs=None,
   if use_dbgprofile:
     # Recorded traces can be visualized with chrome://tracing/
     # The memory/tensor lifetime is also profiled
+    tf.logging.info("Using ProfilerHook")
     defaults = dict(save_steps=10, show_dataflow=True, show_memory=True)
     defaults.update(dbgprofile_kwargs)
     train_monitors.append(tf.contrib.hooks.ProfilerHook(**defaults))
 
   if use_validation_monitor:
+    tf.logging.info("Using ValidationMonitor")
     train_monitors.append(
         tf.contrib.learn.monitors.ValidationMonitor(
             hooks=eval_hooks, **validation_monitor_kwargs))
 
   if use_early_stopping:
+    tf.logging.info("Using EarlyStoppingHook")
     hook = metrics_hook.EarlyStoppingHook(**early_stopping_kwargs)
     # Adding to both training and eval so that eval aborts as well
     train_monitors.append(hook)
@@ -243,6 +244,7 @@ def create_experiment(run_config,
   # HParams
   hparams.add_hparam("data_dir", data_dir)
   hparams.add_hparam("train_steps", train_steps)
+  hparams.add_hparam("eval_steps", eval_steps)
   add_problem_hparams(hparams, problem_name)
 
   # Estimator
@@ -286,10 +288,13 @@ def create_experiment(run_config,
         every_n_steps=min_eval_frequency)
 
     # In-process eval (and possible early stopping)
-    local_schedules = ["train_and_evaluate", "continuous_train_and_eval"]
+    if schedule == "continuous_train_and_eval" and min_eval_frequency:
+      tf.logging.warn("ValidationMonitor only works with "
+                      "--schedule=train_and_evaluate")
     use_validation_monitor = (
-        schedule in local_schedules and min_eval_frequency)
+        schedule == "train_and_evaluate" and min_eval_frequency)
     # Distributed early stopping
+    local_schedules = ["train_and_evaluate", "continuous_train_and_eval"]
     use_early_stopping = (
         schedule not in local_schedules and eval_early_stopping_steps)
     train_monitors, eval_hooks = create_hooks(
diff --git a/tensor2tensor/visualization/TransformerVisualization.ipynb b/tensor2tensor/visualization/TransformerVisualization.ipynb
index bec758327..91ae49ea1 100644
--- a/tensor2tensor/visualization/TransformerVisualization.ipynb
+++ b/tensor2tensor/visualization/TransformerVisualization.ipynb
@@ -15,33 +15,25 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from __future__ import absolute_import\n",
-    "from __future__ import division\n",
-    "from __future__ import print_function\n",
-    "\n",
-    "import json\n",
+    "import os\n",
     "\n",
     "import tensorflow as tf\n",
-    "import numpy as np\n",
     "\n",
-    "from tensor2tensor.utils import t2t_model\n",
-    "from tensor2tensor.utils import decoding\n",
-    "from tensor2tensor.utils import devices\n",
+    "from tensor2tensor import problems\n",
+    "from tensor2tensor.bin import t2t_decoder  # To register the hparams set\n",
+    "from tensor2tensor.utils import registry\n",
     "from tensor2tensor.utils import trainer_lib\n",
-    "from tensor2tensor.visualization import attention\n"
+    "from tensor2tensor.visualization import attention\n",
+    "from tensor2tensor.visualization import visualization"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -73,376 +65,118 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Data"
+    "## HParams"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "import os\n",
     "# PUT THE MODEL YOU WANT TO LOAD HERE!\n",
-    "\n",
-    "PROBLEM = 'translate_ende_wmt32k'\n",
-    "MODEL = 'transformer'\n",
-    "HPARAMS = 'transformer_base_single_gpu'\n",
-    "\n",
-    "DATA_DIR=os.path.expanduser('~/t2t_data')\n",
-    "TRAIN_DIR=os.path.expanduser('~/t2t_train/%s/%s-%s' % (PROBLEM, MODEL, HPARAMS))\n",
-    "print(TRAIN_DIR)\n",
-    "\n",
-    "FLAGS = tf.flags.FLAGS\n",
-    "FLAGS.problems = PROBLEM\n",
-    "FLAGS.hparams_set = HPARAMS\n",
-    "FLAGS.data_dir = DATA_DIR\n",
-    "FLAGS.model = MODEL\n",
-    "\n",
-    "FLAGS.schedule = 'train_and_evaluate'"
+    "CHECKPOINT = os.path.expanduser('~/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:datashard_devices: ['gpu:0']\n",
-      "INFO:tensorflow:caching_devices: None\n",
-      "INFO:tensorflow:batching_scheme = {'min_length': 0, 'window_size': 720, 'shuffle_queue_size': 270, 'boundaries': [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 33, 36, 39, 42, 46, 50, 55, 60, 66, 72, 79, 86, 94, 103, 113, 124, 136, 149, 163, 179, 196, 215, 236], 'max_length': 1000000000, 'batch_sizes': [240, 180, 180, 180, 144, 144, 144, 120, 120, 120, 90, 90, 90, 90, 80, 72, 72, 60, 60, 48, 48, 48, 40, 40, 36, 30, 30, 24, 24, 20, 20, 18, 18, 16, 15, 12, 12, 10, 10, 9, 8, 8]}\n",
-      "INFO:tensorflow:Updated batching_scheme = {'min_length': 0, 'window_size': 720, 'shuffle_queue_size': 270, 'boundaries': [], 'max_length': 1000000000, 'batch_sizes': [1]}\n",
-      "INFO:tensorflow:Reading data files from /usr/local/google/home/llion/t2t_data/translate_ende_wmt32k-dev*\n"
-     ]
-    }
-   ],
-   "source": [
-    "hparams = trainer_lib.create_hparams(FLAGS.hparams_set, data_dir=FLAGS.data_dir, problem_name=PROBLEM)\n",
-    "hparams.use_fixed_batch_size = True\n",
-    "hparams.batch_size = 1\n",
-    "\n",
-    "# SET EXTRA HYPER PARAMS HERE!\n",
-    "#hparams.null_slot = True\n",
-    "\n",
-    "mode = tf.estimator.ModeKeys.EVAL\n",
-    "\n",
-    "problem = hparams.problem_instances[0]\n",
-    "inputs, target = problem.input_fn(\n",
-    "      mode=mode,\n",
-    "      hparams=hparams,\n",
-    "      data_dir=DATA_DIR)\n",
-    "\n",
-    "features = inputs\n",
-    "features['targets'] = target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "def encode(string):\n",
-    "    subtokenizer = hparams.problems[0].vocabulary['inputs']\n",
-    "    return [subtokenizer.encode(string) + [1] + [0]]\n",
-    "\n",
-    "def decode(ids):\n",
-    "    return hparams.problems[0].vocabulary['targets'].decode(np.squeeze(ids))\n",
-    "\n",
-    "def to_tokens(ids):\n",
-    "    ids = np.squeeze(ids)\n",
-    "    subtokenizer = hparams.problems[0].vocabulary['targets']\n",
-    "    tokens = []\n",
-    "    for _id in ids:\n",
-    "        if _id == 0:\n",
-    "            tokens.append('<PAD>')\n",
-    "        elif _id == 1:\n",
-    "            tokens.append('<EOS>')\n",
-    "        else:\n",
-    "            tokens.append(subtokenizer._subtoken_id_to_subtoken_string(_id))\n",
-    "    return tokens"
+    "# HParams\n",
+    "problem_name = 'translate_ende_wmt32k'\n",
+    "data_dir = os.path.expanduser('~/t2t_data/')\n",
+    "model_name = \"transformer\"\n",
+    "hparams_set = \"transformer_base_single_gpu\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:datashard_devices: ['gpu:0']\n",
-      "INFO:tensorflow:caching_devices: None\n",
-      "INFO:tensorflow:Doing model_fn_body took 1.881 sec.\n",
-      "INFO:tensorflow:This model_fn took 2.023 sec.\n"
-     ]
-    }
-   ],
-   "source": [
-    "decode_hparams = decoding.decode_hparams(FLAGS.decode_hparams)\n",
-    "model_fn = t2t_model.T2TModel.make_estimator_model_fn(\n",
-    "    MODEL,\n",
-    "    hparams,\n",
-    "    decode_hparams=decode_hparams)\n",
-    "est_spec = model_fn(features, target, mode)"
+    "## Visualization"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:datashard_devices: ['gpu:0']\n",
-      "INFO:tensorflow:caching_devices: None\n",
-      "INFO:tensorflow:Beam Decoding with beam size 4\n",
-      "INFO:tensorflow:Doing model_fn_body took 1.393 sec.\n",
-      "INFO:tensorflow:This model_fn took 1.504 sec.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with tf.variable_scope(tf.get_variable_scope(), reuse=True):\n",
-    "    beam_out = model_fn(features, target, tf.contrib.learn.ModeKeys.INFER)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 5,
    "metadata": {},
-   "source": [
-    "# Session"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from /usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu/model.ckpt-1\n",
-      "INFO:tensorflow:Starting standard services.\n",
-      "INFO:tensorflow:Starting queue runners.\n",
-      "INFO:tensorflow:Saving checkpoint to path /usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu/model.ckpt\n"
+      "INFO:tensorflow:Setting T2TModel mode to 'eval'\n",
+      "INFO:tensorflow:Setting hparams.layer_prepostprocess_dropout to 0.0\n",
+      "INFO:tensorflow:Setting hparams.symbol_dropout to 0.0\n",
+      "INFO:tensorflow:Setting hparams.attention_dropout to 0.0\n",
+      "INFO:tensorflow:Setting hparams.dropout to 0.0\n",
+      "INFO:tensorflow:Setting hparams.relu_dropout to 0.0\n",
+      "INFO:tensorflow:Using variable initializer: uniform_unit_scaling\n",
+      "INFO:tensorflow:Transforming feature 'inputs' with symbol_modality_33708_512.bottom\n",
+      "INFO:tensorflow:Transforming 'targets' with symbol_modality_33708_512.targets_bottom\n",
+      "INFO:tensorflow:Building model body\n",
+      "WARNING:tensorflow:From /tmp/t2t/tensor2tensor/layers/common_layers.py:512: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n",
+      "INFO:tensorflow:Transforming body output with symbol_modality_33708_512.top\n",
+      "WARNING:tensorflow:From /tmp/t2t/tensor2tensor/layers/common_layers.py:1707: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "\n",
+      "Future major versions of TensorFlow will allow gradients to flow\n",
+      "into the labels input on backprop by default.\n",
+      "\n",
+      "See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
+      "\n",
+      "INFO:tensorflow:Greedy Decoding\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "sv = tf.train.Supervisor(\n",
-    "    logdir=TRAIN_DIR,\n",
-    "    global_step=tf.Variable(0, dtype=tf.int64, trainable=False, name='global_step'))\n",
-    "sess = sv.PrepareSession(config=tf.ConfigProto(allow_soft_placement=True))\n",
-    "sv.StartQueueRunners(\n",
-    "    sess,\n",
-    "    tf.get_default_graph().get_collection(tf.GraphKeys.QUEUE_RUNNERS))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualization"
+    "visualizer = visualization.AttentionVisualizer(hparams_set, model_name, data_dir, problem_name, beam_size=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Get the attention tensors from the graph.\n",
-    "# This need to be done using the training graph since the inference uses a tf.while_loop\n",
-    "# and you cant fetch tensors from inside a while_loop.\n",
-    "\n",
-    "enc_atts = []\n",
-    "dec_atts = []\n",
-    "encdec_atts = []\n",
-    "\n",
-    "for i in range(hparams.num_hidden_layers):\n",
-    "    enc_att = tf.get_default_graph().get_operation_by_name(\n",
-    "        \"body/model/parallel_0/body/encoder/layer_%i/self_attention/multihead_attention/dot_product_attention/attention_weights\" % i).values()[0]\n",
-    "    dec_att = tf.get_default_graph().get_operation_by_name(\n",
-    "        \"body/model/parallel_0/body/decoder/layer_%i/self_attention/multihead_attention/dot_product_attention/attention_weights\" % i).values()[0]\n",
-    "    encdec_att = tf.get_default_graph().get_operation_by_name(\n",
-    "        \"body/model/parallel_0/body/decoder/layer_%i/encdec_attention/multihead_attention/dot_product_attention/attention_weights\" % i).values()[0]\n",
-    "\n",
-    "    enc_atts.append(enc_att)\n",
-    "    dec_atts.append(dec_att)\n",
-    "    encdec_atts.append(encdec_att)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 6,
    "metadata": {},
-   "source": [
-    "## Test translation from the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "scrolled": false
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:global_step/sec: 0\n",
-      "Input:     For example, during the 2008 general election in Florida, 33% of early voters were African-Americans, who accounted however for only 13% of voters in the State.\n",
-      "Gold:      Beispielsweise waren bei den allgemeinen Wahlen 2008 in Florida 33% der Wähler, die im Voraus gewählt haben, Afro-Amerikaner, obwohl sie nur 13% der Wähler des Bundesstaates ausmachen.\n",
-      "Gold out:  So waren 33 den allgemeinen Wahlen im in der a 33 % der Frühjungdie nur Land die wurden, die ro- Amerikaner, die sie nur 13 % der Wähler im Staates staats betra.\n",
-      "INFO:tensorflow:Recording summary at step 250000.\n"
+      "INFO:tensorflow:Create CheckpointSaverHook.\n",
+      "INFO:tensorflow:Restoring parameters from /usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu/model.ckpt-1\n"
      ]
     }
    ],
    "source": [
-    "inp, out, logits = sess.run([inputs['inputs'], target, est_spec.predictions['predictions']])\n",
+    "tf.Variable(0, dtype=tf.int64, trainable=False, name='global_step')\n",
     "\n",
-    "print(\"Input:    \", decode(inp[0]))\n",
-    "print(\"Gold:     \", decode(out[0]))\n",
-    "logits = np.squeeze(logits[0])\n",
-    "tokens = np.argmax(logits, axis=1)\n",
-    "print(\"Gold out: \", decode(tokens))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Visualize Custom Sentence"
+    "sess = tf.train.MonitoredTrainingSession(\n",
+    "    checkpoint_dir=CHECKPOINT,\n",
+    "    save_summaries_secs=0,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "eng = \"I have three dogs.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ich habe drei Hunde.\n"
+      "INFO:tensorflow:Saving checkpoints for 1 into /usr/local/google/home/llion/t2t_train/translate_ende_wmt32k/transformer-transformer_base_single_gpu/model.ckpt.\n"
      ]
     }
    ],
    "source": [
-    "inp_ids = encode(eng)\n",
-    "beam_decode = sess.run(beam_out.predictions['outputs'], {\n",
-    "    inputs['inputs']: np.expand_dims(np.expand_dims(inp_ids, axis=2), axis=3),\n",
-    "})\n",
-    "trans = decode(beam_decode[0])\n",
-    "print(trans)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "output_ids = beam_decode\n",
-    "\n",
-    "# Get attentions\n",
-    "np_enc_atts, np_dec_atts, np_encdec_atts = sess.run([enc_atts, dec_atts, encdec_atts], {\n",
-    "    inputs['inputs']: np.expand_dims(np.expand_dims(inp_ids, axis=2), axis=3),\n",
-    "    target: np.expand_dims(np.expand_dims(output_ids, axis=2), axis=3),\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
-       "    return false;\n",
-       "}"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%javascript\n",
-    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
-    "    return false;\n",
-    "}"
+    "input_sentence = \"I have two dogs.\"\n",
+    "output_string, inp_text, out_text, att_mats = visualizer.get_vis_data_from_string(sess, input_sentence)\n",
+    "print(output_string)"
    ]
   },
   {
@@ -462,22 +196,16 @@
     "  - Double clicking a color will hide all other colors, double clicking on a color when it’s the only head showing will show all the heads again.\n",
     "- You can hover over a word to see the individual attention weights for just that position.\n",
     "  - Hovering over the words on the left will show what that position attended to.\n",
-    "  - Hovering over the words on the right will show what positions attended to it.\n"
+    "  - Hovering over the words on the right will show what positions attended to it."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "inp_text = to_tokens(inp_ids)\n",
-    "out_text = to_tokens(output_ids)\n",
-    "\n",
-    "attention.show(inp_text, out_text, np_enc_atts, np_dec_atts, np_encdec_atts)"
+    "attention.show(inp_text, out_text, *att_mats)"
    ]
   }
  ],
diff --git a/tensor2tensor/visualization/visualization.py b/tensor2tensor/visualization/visualization.py
new file mode 100644
index 000000000..e92ca518c
--- /dev/null
+++ b/tensor2tensor/visualization/visualization.py
@@ -0,0 +1,202 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""Shared code for visualizing transformer attentions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import numpy as np
+
+# To register the hparams set
+from tensor2tensor import models  # pylint: disable=unused-import
+from tensor2tensor import problems
+from tensor2tensor.utils import registry
+from tensor2tensor.utils import trainer_lib
+
+import tensorflow as tf
+
+EOS_ID = 1
+
+
+class AttentionVisualizer(object):
+  """Helper object for creating Attention visualizations."""
+
+  def __init__(
+      self, hparams_set, model_name, data_dir, problem_name, beam_size=1):
+    inputs, targets, samples, att_mats = build_model(
+        hparams_set, model_name, data_dir, problem_name, beam_size=beam_size)
+
+    # Fetch the problem
+    ende_problem = problems.problem(problem_name)
+    encoders = ende_problem.feature_encoders(data_dir)
+
+    self.inputs = inputs
+    self.targets = targets
+    self.att_mats = att_mats
+    self.samples = samples
+    self.encoders = encoders
+
+  def encode(self, input_str):
+    """Input str to features dict, ready for inference."""
+    inputs = self.encoders['inputs'].encode(input_str) + [EOS_ID]
+    batch_inputs = np.reshape(inputs, [1, -1, 1, 1])  # Make it 3D.
+    return batch_inputs
+
+  def decode(self, integers):
+    """List of ints to str."""
+    integers = list(np.squeeze(integers))
+    return self.encoders['inputs'].decode(integers)
+
+  def decode_list(self, integers):
+    """List of ints to list of str."""
+    integers = list(np.squeeze(integers))
+    return self.encoders['inputs'].decode_list(integers)
+
+  def get_vis_data_from_string(self, sess, input_string):
+    """Constructs the data needed for visualizing attentions.
+
+    Args:
+      sess: A tf.Session object.
+      input_string: The input setence to be translated and visulized.
+
+    Returns:
+      Tuple of (
+          output_string: The translated sentence.
+          input_list: Tokenized input sentence.
+          output_list: Tokenized translation.
+          att_mats: Tuple of attention matrices; (
+              enc_atts: Encoder self attention weights.
+                A list of `num_layers` numpy arrays of size
+                (batch_size, num_heads, inp_len, inp_len)
+              dec_atts: Decoder self attention weights.
+                A list of `num_layers` numpy arrays of size
+                (batch_size, num_heads, out_len, out_len)
+              encdec_atts: Encoder-Decoder attention weights.
+                A list of `num_layers` numpy arrays of size
+                (batch_size, num_heads, out_len, inp_len)
+          )
+    """
+    encoded_inputs = self.encode(input_string)
+
+    # Run inference graph to get the translation.
+    out = sess.run(self.samples, {
+        self.inputs: encoded_inputs,
+    })
+
+    # Run the decoded translation through the training graph to get the
+    # attention tensors.
+    att_mats = sess.run(self.att_mats, {
+        self.inputs: encoded_inputs,
+        self.targets: np.reshape(out, [1, -1, 1, 1]),
+    })
+
+    output_string = self.decode(out)
+    input_list = self.decode_list(encoded_inputs)
+    output_list = self.decode_list(out)
+
+    return output_string, input_list, output_list, att_mats
+
+
+def build_model(hparams_set, model_name, data_dir, problem_name, beam_size=1):
+  """Build the graph required to featch the attention weights.
+
+  Args:
+    hparams_set: HParams set to build the model with.
+    model_name: Name of model.
+    data_dir: Path to directory contatining training data.
+    problem_name: Name of problem.
+    beam_size: (Optional) Number of beams to use when decoding a traslation.
+        If set to 1 (default) then greedy decoding is used.
+
+  Returns:
+    Tuple of (
+        inputs: Input placeholder to feed in ids to be translated.
+        targets: Targets placeholder to feed to translation when fetching
+            attention weights.
+        samples: Tensor representing the ids of the translation.
+        att_mats: Tensors representing the attention weights.
+    )
+  """
+  hparams = trainer_lib.create_hparams(
+      hparams_set, data_dir=data_dir, problem_name=problem_name)
+  translate_model = registry.model(model_name)(
+      hparams, tf.estimator.ModeKeys.EVAL)
+
+  inputs = tf.placeholder(tf.int32, shape=(1, None, 1, 1), name='inputs')
+  targets = tf.placeholder(tf.int32, shape=(1, None, 1, 1), name='targets')
+  translate_model({
+      'inputs': inputs,
+      'targets': targets,
+  })
+
+  # Must be called after building the training graph, so that the dict will
+  # have been filled with the attention tensors. BUT before creating the
+  # interence graph otherwise the dict will be filled with tensors from
+  # inside a tf.while_loop from decoding and are marked unfetchable.
+  att_mats = get_att_mats(translate_model)
+
+  with tf.variable_scope(tf.get_variable_scope(), reuse=True):
+    samples = translate_model.infer({
+        'inputs': inputs,
+    }, beam_size=beam_size)['outputs']
+
+  return inputs, targets, samples, att_mats
+
+
+def get_att_mats(translate_model):
+  """Get's the tensors representing the attentions from a build model.
+
+  The attentions are stored in a dict on the Transformer object while building
+  the graph.
+
+  Args:
+    translate_model: Transformer object to fetch the attention weights from.
+
+  Returns:
+  Tuple of attention matrices; (
+      enc_atts: Encoder self attention weights.
+        A list of `num_layers` numpy arrays of size
+        (batch_size, num_heads, inp_len, inp_len)
+      dec_atts: Decoder self attetnion weights.
+        A list of `num_layers` numpy arrays of size
+        (batch_size, num_heads, out_len, out_len)
+      encdec_atts: Encoder-Decoder attention weights.
+        A list of `num_layers` numpy arrays of size
+        (batch_size, num_heads, out_len, inp_len)
+  )
+  """
+  enc_atts = []
+  dec_atts = []
+  encdec_atts = []
+
+  prefix = 'transformer/body/'
+  postfix = '/multihead_attention/dot_product_attention'
+
+  for i in range(translate_model.hparams.num_hidden_layers):
+    enc_att = translate_model.attention_weights[
+        '%sencoder/layer_%i/self_attention%s' % (prefix, i, postfix)]
+    dec_att = translate_model.attention_weights[
+        '%sdecoder/layer_%i/self_attention%s' % (prefix, i, postfix)]
+    encdec_att = translate_model.attention_weights[
+        '%sdecoder/layer_%i/encdec_attention%s' % (prefix, i, postfix)]
+    enc_atts.append(enc_att)
+    dec_atts.append(dec_att)
+    encdec_atts.append(encdec_att)
+
+  return enc_atts, dec_atts, encdec_atts
diff --git a/tensor2tensor/visualization/visualization_test.py b/tensor2tensor/visualization/visualization_test.py
new file mode 100644
index 000000000..b4d6b96f6
--- /dev/null
+++ b/tensor2tensor/visualization/visualization_test.py
@@ -0,0 +1,109 @@
+# coding=utf-8
+# Copyright 2017 The Tensor2Tensor Authors.
+#
+# 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.
+
+"""Tests for visualization library.
+
+    IF ANY OF THESE TESTS BREAK PLEASE UPDATE THE CODE IN THE VIZ NOTEBOOK
+******************************************************************************
+
+Any fixes you have to make to this test or visualization.py to fix this test
+might have to be reflected in the visualization notebook, for example if the
+name of the hparams_set changes.
+
+If you need help testing the changes please contact llion@.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from tensor2tensor.utils import trainer_lib
+from tensor2tensor.visualization import visualization
+import tensorflow as tf
+
+
+def get_data_dir():
+  pkg, _ = os.path.split(__file__)
+  pkg, _ = os.path.split(pkg)
+  return os.path.join(pkg, 'test_data')
+
+
+problem_name = 'translate_ende_wmt32k'
+model_name = 'transformer'
+hparams_set = 'transformer_base_single_gpu'
+
+
+class VisualizationTest(tf.test.TestCase):
+
+  def setUp(self):
+    super(VisualizationTest, self).setUp()
+    self.data_dir = get_data_dir()
+
+  def test_build_model_greedy(self):
+    inputs, targets, outputs, _ = visualization.build_model(
+        hparams_set, model_name, self.data_dir, problem_name, beam_size=1)
+
+    self.assertAllEqual((1, None, 1, 1), inputs.shape.as_list())
+    self.assertAllEqual((1, None, 1, 1), targets.shape.as_list())
+    self.assertAllEqual((None, None), outputs.shape.as_list())
+
+  def test_build_model_beam(self):
+    inputs, targets, outputs, _ = visualization.build_model(
+        hparams_set, model_name, self.data_dir, problem_name, beam_size=8)
+
+    self.assertAllEqual((1, None, 1, 1), inputs.shape.as_list())
+    self.assertAllEqual((1, None, 1, 1), targets.shape.as_list())
+    self.assertAllEqual((None, None), outputs.shape.as_list())
+
+  def test_get_vis_data_from_string(self):
+    visualizer = visualization.AttentionVisualizer(
+        hparams_set, model_name, self.data_dir, problem_name, beam_size=8)
+
+    input_sentence = 'I have two dogs.'
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      _, inp_text, out_text, att_mats = (
+          visualizer.get_vis_data_from_string(sess, input_sentence))
+
+    self.assertAllEqual(
+        [u'I_', u'have_', u'two_', u'dogs_', u'._', u'<EOS>'], inp_text)
+
+    hparams = trainer_lib.create_hparams(
+        hparams_set, data_dir=self.data_dir, problem_name=problem_name)
+
+    enc_atts, dec_atts, encdec_atts = att_mats
+
+    self.assertAllEqual(hparams.num_hidden_layers, len(enc_atts))
+
+    enc_atts = enc_atts[0]
+    dec_atts = dec_atts[0]
+    encdec_atts = encdec_atts[0]
+
+    batch_size = 1
+    num_heads = hparams.num_heads
+    inp_len = len(inp_text)
+    out_len = len(out_text)
+
+    self.assertAllEqual(
+        (batch_size, num_heads, inp_len, inp_len), enc_atts.shape)
+    self.assertAllEqual(
+        (batch_size, num_heads, out_len, out_len), dec_atts.shape)
+    self.assertAllEqual(
+        (batch_size, num_heads, out_len, inp_len), encdec_atts.shape)
+
+if __name__ == '__main__':
+  tf.test.main()