Major Features And Improvements
* `tf.keras` is now part of the core TensorFlow API.
* [`tf.data`](http://tensorflow.org/guide/datasets) 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](https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/contrib/data/README.md).
* 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](https://www.tensorflow.org/guide/debugger)
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 TF-GAN 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, karlkubx.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.