Release 1.4.0

Major Features And Improvements

• tf.keras is now part of the core TensorFlow API.
• tf.data is now part of
  the core TensorFlow API.
  • The API is now subject to backwards compatibility guarantees.
  • For a guide to migrating from the tf.contrib.data API, see the
    README.
  • Major new features include Dataset.from_generator() (for building an input
    pipeline from a Python generator), and the Dataset.apply() method for
    applying custom transformation functions.
  • Several custom transformation functions have been added, including
    tf.contrib.data.batch_and_drop_remainder() and
    tf.contrib.data.sloppy_interleave().
• Add train_and_evaluate for simple distributed Estimator training.
• Add tf.spectral.dct for computing the DCT-II.
• Add Mel-Frequency Cepstral Coefficient support to tf.contrib.signal
  (with GPU and gradient support).
• Add a self-check on import tensorflow for Windows DLL issues.
• Add NCHW support to tf.depth_to_space on GPU.
• TensorFlow Debugger (tfdbg):
  • Add eval command to allow evaluation of arbitrary Python/numpy expressions
    in tfdbg command-line interface. See
    Debugging TensorFlow Programs
    for more details.
  • Usability improvement: The frequently used tensor filter has_inf_or_nan is
    now added to Session wrappers and hooks by default. So there is no need
    for clients to call .add_tensor_filter(tf_debug.has_inf_or_nan) anymore.
• SinhArcsinh (scalar) distribution added to contrib.distributions.
• Make GANEstimator opensource.
• Estimator.export_savedmodel() now includes all valid serving signatures
  that can be constructed from the Serving Input Receiver and all available
  ExportOutputs. For instance, a classifier may provide regression- and
  prediction-flavored outputs, in addition to the classification-flavored one.
  Building signatures from these allows TF Serving to honor requests using the
  different APIs (Classify, Regress, and Predict). Furthermore,
  serving_input_receiver_fn() may now specify alternative subsets of nodes
  that may act as inputs. This allows, for instance, producing a prediction
  signature for a classifier that accepts raw Tensors instead of a serialized
  tf.Example.
• Add tf.contrib.bayesflow.hmc.
• Add tf.contrib.distributions.MixtureSameFamily.
• Make Dataset.shuffle() always reshuffles after each iteration by default.
• Add tf.contrib.bayesflow.metropolis_hastings.
• Add log_rate parameter to tf.contrib.distributions.Poisson.
• Extend tf.contrib.distributions.bijector API to handle some non-injective
  transforms.
• Java:
  • Generics (e.g., Tensor<Integer>) for improved type-safety
    (courtesy @andrewcmyers).
  • Support for multi-dimensional string tensors.
  • Support loading of custom operations (e.g. many in tf.contrib) on Linux
    and OS X
• All our prebuilt binaries have been built with CUDA 8 and cuDNN 6.
  We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.

Bug Fixes and Other Changes

• tf.nn.rnn_cell.DropoutWrapper is now more careful about dropping out LSTM
  states. Specifically, it no longer ever drops the c (memory) state of an
  LSTMStateTuple. The new behavior leads to proper dropout behavior
  for LSTMs and stacked LSTMs. This bug fix follows recommendations from
  published literature, but is a behavioral change. State dropout behavior
  may be customized via the new dropout_state_filter_visitor argument.
• Removed tf.contrib.training.python_input. The same behavior, in a more
  flexible and reproducible package, is available via the new
  tf.contrib.data.Dataset.from_generator method!
• Fix tf.contrib.distributions.Affine incorrectly computing log-det-jacobian.
• Fix tf.random_gamma incorrectly handling non-batch, scalar draws.
• Resolved a race condition in TensorForest TreePredictionsV4Op.
• Google Cloud Storage file system, Amazon S3 file system, and Hadoop file
  system support are now default build options.
• Custom op libraries must link against libtensorflow_framework.so
  (installed at tf.sysconfig.get_lib()).
• Change RunConfig default behavior to not set a random seed, making random
  behavior independently random on distributed workers. We expect this to
  generally improve training performance. Models that do rely on determinism
  should set a random seed explicitly.

Breaking Changes to the API

• The signature of the tf.contrib.data.rejection_resample() function has been
  changed. It now returns a function that can be used as an argument to
  Dataset.apply().
• Remove tf.contrib.data.Iterator.from_dataset() method. Use
  Dataset.make_initializable_iterator() instead.
• Remove seldom used and unnecessary tf.contrib.data.Iterator.dispose_op().
• Reorder some TFGAN loss functions in a non-backwards compatible way.

Known Issues

• In Python 3, Dataset.from_generator() does not support Unicode strings.
  You must convert any strings to bytes objects before yielding them from
  the generator.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh,
Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu,
Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman,
Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall,
Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss,
Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller,
Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey,
David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe,
Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia,
Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang,
Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon,
James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf,
Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth,
John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan,
Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle,
Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm,
lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley,
Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez,
Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes,
Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy,
Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki,
sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss,
Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman,
superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki,
Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey,
Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao,
Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞

We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.