Tensorflow

Major Features and Improvements

* Added support for C++ shape inference
* Added graph-construction C API
* Major revision to the graph-construction C++ API
* Support makefile build for iOS
* Added Mac GPU support
* Full version of TF-Slim available as `tf.contrib.slim`
* Added k-Means clustering and WALS matrix factorization

Bug Fixes and Other Changes

* Allow gradient computation for scalar values.
* Performance improvements for gRPC
* Improved support for fp16
* New high-level ops in tf.contrib. {layers,metrics}
* New features for TensorBoard, such as shape display, exponential smoothing
* Faster and more stable Google Cloud Storage (GCS) filesystem support
* Support for zlib compression and decompression for TFRecordReader and
TFRecordWriter
* Support for reading (animated) GIFs
* Improved support for SparseTensor
* Added support for more probability distributions (Dirichlet, Beta,
Bernoulli, etc.)
* Added Python interfaces to reset resource containers.
* Many bugfixes and performance improvements
* Many documentation fixes

Thanks to our Contributors

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

Alex Rothberg, Andrew Royer, Austin Marshall, BlackCoal, Bob Adolf, Brian
Diesel, Charles-Emmanuel Dias, chemelnucfin, Chris Lesniewski, Daeyun Shin,
Daniel Rodriguez, Danijar Hafner, Darcy Liu, Kristinn R. Thórisson, Daniel
Castro, Dmitry Savintsev, Kashif Rasul, Dylan Paiton, Emmanuel T. Odeke, Ernest
Grzybowski, Gavin Sherry, Gideon Dresdner, Gregory King, Harold Cooper,
heinzbeinz, Henry Saputra, Huarong Huo, Huazuo Gao, Igor Babuschkin, Igor
Macedo Quintanilha, Ivan Ukhov, James Fysh, Jan Wilken Dörrie, Jihun Choi,
Johnny Lim, Jonathan Raiman, Justin Francis, lilac, Li Yi, Marc Khoury, Marco
Marchesi, Max Melnick, Micael Carvalho, mikowals, Mostafa Gazar, Nico Galoppo,
Nishant Agrawal, Petr Janda, Yuncheng Li, raix852, Robert Rose,
Robin-des-Bois, Rohit Girdhar, Sam Abrahams, satok16, Sergey Kishchenko, Sharkd
Tu, shotat, Siddharth Agrawal, Simon Denel, sono-bfio, SunYeop Lee, Thijs
Vogels, tobegit3hub, Undo1, Wang Yang, Wenjian Huang, Yaroslav Bulatov, Yuan
Tang, Yunfeng Wang, Ziming Dong

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

Major Features and Improvements

* Python 3.5 support and binaries
* Added iOS support
* Added support for processing on GPUs on MacOS
* Added makefile for better cross-platform build support (C API only)
* fp16 support and improved complex128 support for many ops
* Higher level functionality in contrib. {layers,losses,metrics,learn}
* More features to Tensorboard
* Improved support for string embedding and sparse features
* The RNN api is finally "official" (see, e.g., `tf.nn.dynamic_rnn`,
`tf.nn.rnn`, and the classes in `tf.nn.rnn_cell`).
* TensorBoard now has an Audio Dashboard, with associated audio summaries.

Bug Fixes and Other Changes

* Turned on CuDNN Autotune.
* Added support for using third-party Python optimization algorithms
(contrib.opt).
* Google Cloud Storage filesystem support.
* HDF5 support
* Add support for 3d convolutions and pooling.
* Update gRPC release to 0.14.
* Eigen version upgrade.
* Switch to eigen thread pool
* `tf.nn.moments()` now accepts a `shift` argument. Shifting by a good
estimate of the mean improves numerical stability. Also changes the behavior
of the `shift` argument to `tf.nn.sufficient_statistics()`.
* Performance improvements
* Many bugfixes
* Many documentation fixes
* TensorBoard fixes: graphs with only one data point, Nan values, reload
button and auto-reload, tooltips in scalar charts, run filtering, stable
colors
* Tensorboard graph visualizer now supports run metadata. Clicking on nodes
while viewing a stats for a particular run will show runtime statistics,
such as memory or compute usage. Unused nodes will be faded out.

Thanks to our Contributors

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

Aaron Schumacher, Aidan Dang, Akihiko ITOH, Aki Sukegawa, Arbit Chen, Aziz Alto,
Danijar Hafner, Erik Erwitt, Fabrizio Milo, Felix Maximilian Möller, Henry
Saputra, Sung Kim, Igor Babuschkin, Jan Zikes, Jeremy Barnes, Jesper Steen
Møller, Johannes Mayer, Justin Harris, Kashif Rasul, Kevin Robinson, Loo Rong
Jie, Lucas Moura, Łukasz Bieniasz-Krzywiec, Mario Cho, Maxim Grechkin, Michael
Heilman, Mostafa Rahmani, Mourad Mourafiq, ninotoshi, Orion Reblitz-Richardson,
Yuncheng Li, raoqiyu, Robert DiPietro, Sam Abrahams, Sebastian Raschka,
Siddharth Agrawal, snakecharmer1024, Stephen Roller, Sung Kim, SunYeop Lee,
Thijs Vogels, Till Hoffmann, Victor Melo, Ville Kallioniemi, Waleed Abdulla,
Wenjian Huang, Yaroslav Bulatov, Yeison Rodriguez, Yuan Tang, Yuxin Wu,
zhongzyd, Ziming Dong, Zohar Jackson

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

Major Features and Improvements

* Added a distributed runtime using GRPC
* Move skflow to `contrib/learn`
* Better linear optimizer in `contrib/linear_optimizer`
* Random forest implementation in `contrib/tensor_forest`
* CTC loss and decoders in `contrib/ctc`
* Basic support for `half` data type
* Better support for loading user ops (see examples in `contrib/`)
* Allow use of (non-blocking) Eigen threadpool with
`TENSORFLOW_USE_EIGEN_THREADPOOL` define
* Add an extension mechanism for adding network file system support
* TensorBoard displays metadata stats (running time, memory usage and device
used) and tensor shapes

Bug Fixes and Other Changes

* Utility for inspecting checkpoints
* Basic tracing and timeline support
* Allow building against cuDNN 5 (not incl. RNN/LSTM support)
* Added instructions and binaries for ProtoBuf library with fast serialization
and without 64MB limit
* Added special functions
* `bool`-strictness: Tensors have to be explicitly compared to `None`
* Shape strictness: all fed values must have a shape that is compatible with
the tensor they are replacing
* Exposed `tf.while_loop` (deprecated `control_flow_ops.While`)
* run() now takes RunOptions and RunMetadata, which enable timing stats
* Fixed lots of potential overflow problems in op kernels
* Various performance improvements, especially for RNNs and convolutions
* Many bugfixes
* Nightly builds, tutorial tests, many test improvements
* New examples: transfer learning and deepdream ipython notebook
* Added tutorials, many documentation fixes.

Thanks to our Contributors

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

Abhinav Upadhyay, Aggelos Avgerinos, Alan Wu, Alexander G. de G. Matthews,
Aleksandr Yahnev, amchercashin, Andy Kitchen, Aurelien Geron, Awni Hannun,
BanditCat, Bas Veeling, Cameron Chen, cg31, Cheng-Lung Sung, Christopher
Bonnett, Dan Becker, Dan Van Boxel, Daniel Golden, Danijar Hafner, Danny
Goodman, Dave Decker, David Dao, David Kretch, Dongjoon Hyun, Dustin Dorroh,
e-lin, Eurico Doirado, Erik Erwitt, Fabrizio Milo, gaohuazuo, Iblis Lin, Igor
Babuschkin, Isaac Hodes, Isaac Turner, Iván Vallés, J Yegerlehner, Jack Zhang,
James Wexler, Jan Zikes, Jay Young, Jeff Hodges, jmtatsch, Johnny Lim, Jonas
Meinertz Hansen, Kanit Wongsuphasawat, Kashif Rasul, Ken Shirriff, Kenneth
Mitchner, Kenta Yonekura, Konrad Magnusson, Konstantin Lopuhin, lahwran,
lekaha, liyongsea, Lucas Adams, makseq, Mandeep Singh, manipopopo, Mark
Amery, Memo Akten, Michael Heilman, Michael Peteuil, Nathan Daly, Nicolas
Fauchereau, ninotoshi, Olav Nymoen, panmari, papelita1234, Pedro Lopes,
Pranav Sailesh Mani, RJ Ryan, Rob Culliton, Robert DiPietro, ronrest, Sam
Abrahams, Sarath Shekkizhar, Scott Graham, Sebastian Raschka, Sung Kim, Surya
Bhupatiraju, Syed Ahmed, Till Hoffmann, timsl, urimend, vesnica, Vlad Frolov,
Vlad Zagorodniy, Wei-Ting Kuo, Wenjian Huang, William Dmitri Breaden Madden,
Wladimir Schmidt, Yuan Tang, Yuwen Yan, Yuxin Wu, Yuya Kusakabe, zhongzyd,
znah.

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

Bug Fixes and Other Changes

* Added gfile.Open and gfile.Copy, used by input_data.py.
* Fixed Saver bug when MakeDirs tried to create empty directory.
* GPU Pip wheels are built with cuda 7.5 and cudnn-v4, making them required
for the binary releases. Lower versions of cuda/cudnn can be supported by
installing from sources and setting the options during ./configure
* Fix dataset encoding example for Python3 (danijar)
* Fix PIP installation by not packaging protobuf as part of wheel, require
protobuf 3.0.0b2.
* Fix Mac pip installation of numpy by requiring pip >= 1.10.1.
* Improvements and fixes to Docker image.

Major Features and Improvements

* Allow using any installed Cuda >= 7.0 and cuDNN >= R2, and add support for
cuDNN R4
* Added a `contrib/` directory for unsupported or experimental features,
including higher level `layers` module
* Added an easy way to add and dynamically load user-defined ops
* Built out a good suite of tests, things should break less!
* Added `MetaGraphDef` which makes it easier to save graphs with metadata
* Added assignments for "Deep Learning with TensorFlow" udacity course

Bug Fixes and Other Changes

* Added a versioning framework for `GraphDef`s to ensure compatibility
* Enforced Python 3 compatibility
* Internal changes now show up as sensibly separated commits
* Open-sourced the doc generator
* Un-fork Eigen
* Simplified the `BUILD` files and cleaned up C++ headers
* TensorFlow can now be used as a submodule in another bazel build
* New ops (e.g., `*fft`, `*_matrix_solve`)
* Support for more data types in many ops
* Performance improvements
* Various bugfixes
* Documentation fixes and improvements

Breaking Changes to the API

* `AdjustContrast` kernel deprecated, new kernel `AdjustContrastv2` takes and
outputs float only. `adjust_contrast` now takes all data types.
* `adjust_brightness`'s `delta` argument is now always assumed to be in
`[0,1]` (as is the norm for images in floating point formats), independent
of the data type of the input image.
* The image processing ops do not take `min` and `max` inputs any more,
casting safety is handled by `saturate_cast`, which makes sure over- and
underflows are handled before casting to data types with smaller ranges.
* For C++ API users: `IsLegacyScalar` and `IsLegacyVector` are now gone from
`TensorShapeUtils` since TensorFlow is scalar strict within Google (for
example, the shape argument to `tf.reshape` can't be a scalar anymore). The
open source release was already scalar strict, so outside Google `IsScalar`
and `IsVector` are exact replacements.
* The following files are being removed from `tensorflow/core/public/`:
* `env.h` -> `../platform/env.h`
* `status.h` -> `../lib/core/status.h`
* `tensor.h` -> `../framework/tensor.h`
* `tensor_shape.h` -> `../framework/tensor_shape.h`
* `partial_tensor_shape.h` -> `../framework/partial_tensor_shape.h`
* `tensorflow_server.h` deleted
* For C++ API users: `TensorShape::ShortDebugString` has been renamed to
`DebugString`, and the previous `DebugString` behavior is gone (it was
needlessly verbose and produced a confusing empty string for scalars).
* `GraphOptions.skip_common_subexpression_elimination` has been removed. All
graph optimizer options are now specified via
`GraphOptions.OptimizerOptions`.
* `ASSERT_OK` / `EXPECT_OK` macros conflicted with external projects, so they
were renamed `TF_ASSERT_OK`, `TF_EXPECT_OK`. The existing macros are
currently maintained for short-term compatibility but will be removed.
* The non-public `nn.rnn` and the various `nn.seq2seq` methods now return just
the final state instead of the list of all states.
* `tf.scatter_update` now no longer guarantees that lexicographically largest
index be used for update when duplicate entries exist.
* `tf.image.random_crop(image, [height, width])` is now `tf.random_crop(image,
[height, width, depth])`, and `tf.random_crop` works for any rank (not just
3-D images). The C++ `RandomCrop` op has been replaced with pure Python.
* Renamed `tf.test.GetTempDir` and `tf.test.IsBuiltWithCuda` to
`tf.test.get_temp_dir` and `tf.test.is_built_with_cuda` for PEP-8
compatibility.
* `parse_example`'s interface has changed, the old interface is accessible in
`legacy_parse_example` (same for related functions).
* New `Variable`s are not added to the same collection several times even if a
list with duplicates is passed to the constructor.
* The Python API will now properly set the `list` member of `AttrValue` in
constructed `GraphDef` messages for empty lists. The serialization of some
graphs will change, but the change is both forwards and backwards
compatible. It will break tests that compare a generated `GraphDef` to a
golden serialized `GraphDef` (which is discouraged).

Thanks to our Contributors

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

Akiomi Kamakura, Alex Vig, Alexander Rosenberg Johansen, Andre Cruz, Arun Ahuja,
Bart Coppens, Bernardo Pires, Carl Vondrick, Cesar Salgado, Chen Yu, Christian
Jauvin, Damien Aymeric, Dan Vanderkam, Denny Britz, Dongjoon Hyun, Eren Güven,
Erik Erwitt, Fabrizio Milo, G. Hussain Chinoy, Jim Fleming, Joao Felipe Santos,
Jonas Meinertz Hansen, Joshi Rekha, Julian Viereck, Keiji Ariyama, Kenton Lee,
Krishna Sankar, Kristina Chodorow, Linchao Zhu, Lukas Krecan, Mark Borgerding,
Mark Daoust, Moussa Taifi, Nathan Howell, Naveen Sundar Govindarajulu, Nick
Sweeting, Niklas Riekenbrauck, Olivier Grisel, Patrick Christ, Povilas
Liubauskas, Rainer Wasserfuhr, Romain Thouvenin, Sagan Bolliger, Sam Abrahams,
Taehoon Kim, Timothy J Laurent, Vlad Zavidovych, Yangqing Jia, Yi-Lin Juang,
Yuxin Wu, Zachary Lipton, Zero Chen, Alan Wu, brchiu, emmjaykay, jalammar,
Mandar-Shinde, nsipplswezey, ninotoshi, panmari, prolearner and
rizzomichaelg.

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

Major Features and Improvements

* Python 3.3+ support via changes to python codebase and ability to specify
python version via ./configure.

* Some improvements to GPU performance and memory usage:
[convnet benchmarks](https://github.com/soumith/convnet-benchmarks/issues/66)
roughly equivalent with native cudnn v2 performance. Improvements mostly due
to moving to 32-bit indices, faster shuffling kernels. More improvements to
come in later releases.

Bug Fixes

* Lots of fixes to documentation and tutorials, many contributed by the
public.

* 271 closed issues on github issues.

Backwards-Incompatible Changes

* `tf.nn.fixed_unigram_candidate_sampler` changed its default 'distortion'
attribute from 0.0 to 1.0. This was a bug in the original release that is
now fixed.

* added DeterministicRandomTestTool to migration_utils.py. This is useful when
you are migrating from TF 1.x to TF2 and need to make sure your computation
is still happening correctly along the way. See the
[validating correctness migration guide](https://www.tensorflow.org/guide/migrate/validate_correctness)
for more info.