Tensorflow

Bug Fixes and Other Changes

* Updating markdown version required to >= 2.6.8.
* Support tensors as dropout rates again, by removing the min(max(..))

is still available, just not as `tf.RewriterConfig`. Instead add an explicit
import.
* Breaking change to `tf.contrib.data.Dataset` APIs that expect a nested
structure. Lists are now converted to `tf.Tensor` implicitly. You may need
to change uses of lists to tuples in existing code. In addition, dicts are
now supported as a nested structure.

Changes to contrib APIs

* Adds tf.contrib.nn.rank_sampled_softmax_loss, a sampled-softmax variant that
can improve rank loss.
* `tf.contrib.metrics`.{streaming_covariance,streaming_pearson_correlation}
modified to return nan when they have seen less or equal to 1 unit of
weight.
* Adds time series models to contrib. See contrib/timeseries/README.md for
details.
* Adds FULLY_CONNECTED Op to tensorflow/lite/schema.fbs

Known Issues

* Tensorflow_gpu compilation fails with Bazel 0.5.3.

Bug Fixes and Other Changes

* Fixes `strides` and `begin` dtype mismatch when slicing using int64 Tensor
index in python.
* Improved convolution padding documentation.
* Add a tag constant, gpu, to present graph with GPU support.
* `saved_model.utils` now support SparseTensors transparently.
* A more efficient implementation of non-max suppression.
* Add support for the shrinkage-type L2 to FtrlOptimizer in addition to the
online L2 it already supports.
* Fix negative variance in moments calculation.
* Expand UniqueOp Benchmark Tests to cover more collision cases.
* Improves stability of GCS filesystem on Mac.
* Add time estimation to HloCostAnalysis.
* Fixed the bug in Estimator that params in constructor was not a deepcopy of
the user provided one. This bugs inadvertently enabled user to mutate the
params after the creation of Estimator, leading to potentially undefined
behavior.
* Added None check for save_path in `saver.restore`.
* Register devices under their legacy names in device_mgr to ease the
transition to clusterspec-propagated configurations.
* VectorExponential added to distributions.
* Add a bitwise module with bitwise_and, bitwise_or, bitwise_xor, and invert
functions.
* Add fixed-grid ODE integration routines.
* Allow passing bounds to ScipyOptimizerInterface.
* Correctness fixes for fft_length parameter to `tf.spectral.rfft` &
`tf.spectral.irfft`.
* Exported model signatures using the 'predict' method will no longer have
their input and output keys silently ignored and rewritten to 'inputs' and
'outputs'. If a model was exported with different names before 1.2, and is
now served with tensorflow/serving, it will accept requests using 'inputs'
and 'outputs'. Starting at 1.2, such a model will accept the keys specified
during export. Therefore, inference requests using 'inputs' and 'outputs'
may start to fail. To fix this, either update any inference clients to send
requests with the actual input and output keys used by the trainer code, or
conversely, update the trainer code to name the input and output Tensors
'inputs' and 'outputs', respectively. Signatures using the 'classify' and
'regress' methods are not affected by this change; they will continue to
standardize their input and output keys as before.
* Add in-memory caching to the Dataset API.
* Set default end_of_sequence variable in datasets iterators to false.
* [Performance] Increase performance of `tf.layers.conv2d` when setting
use_bias=True by 2x by using nn.bias_add.
* Update iOS examples to use CocoaPods, and moved to tensorflow/examples/ios.
* Adds a family= attribute in `tf.summary` ops to allow controlling the tab
name used in Tensorboard for organizing summaries.
* When GPU is configured, do not require --config=cuda, instead, automatically
build for GPU if this is requested in the configure script.
* Fix incorrect sampling of small probabilities in CPU/GPU multinomial.
* Add a list_devices() API on sessions to list devices within a cluster.
Additionally, this change augment the ListDevices master API to support
specifying a session.
* Allow uses of over-parameterized separable convolution.
* TensorForest multi-regression bug fix.
* Framework now supports armv7, cocoapods.org now displays correct page.
* Script to create iOS framework for CocoaPods.
* Android releases of TensorFlow are now pushed to jcenter for easier
integration into apps. See
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/android/inference_interface/README.md
for more details.
* TensorFlow Debugger (tfdbg):
* Fixed a bug that prevented tfdbg from functioning with multi-GPU setups.
* Fixed a bug that prevented tfdbg from working with
`tf.Session.make_callable`.

Thanks to our Contributors

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

4F2E4A2E, Adriano Carmezim, Adrià Arrufat, Alan Yee, Alex Lattas, Alex Rothberg,
Alexandr Baranezky, Ali Siddiqui, Andreas Solleder, Andrei Costinescu, Andrew
Hundt, Androbin, Andy Kernahan, Anish Shah, Anthony Platanios, Arvinds-Ds, b1rd,
Baptiste Arnaud, Ben Mabey, Benedikt Linse, Beomsu Kim, Bo Wang, Boyuan Deng,
Brett Koonce, Bruno Rosa, Carl Thomé, Changming Sun, Chase Roberts, Chirag
Bhatia, Chris Antaki, Chris Hoyean Song, Chris Tava, Christos Nikolaou, Croath
Liu, cxx, Czxck001, Daniel Ylitalo, Danny Goodman, Darren Garvey, David
Brailovsky, David Norman, DavidNorman, davidpham87, ddurham2, Dhruv, DimanNe,
Drew Hintz, Dustin Tran, Earthson Lu, ethiraj, Fabian Winnen, Fei Sun, Freedom"
Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Gautam, Guenther Schmuelling, Gyu-Ho
Lee, Hauke Brammer, horance, Humanity123, J Alammar, Jayeol Chun, Jeroen BéDorf,
Jianfei Wang, jiefangxuanyan, Jing Jun Yin, Joan Puigcerver, Joel Hestness,
Johannes Mayer, John Lawson, Johnson145, Jon Malmaud, Jonathan
Alvarez-Gutierrez, Juang, Yi-Lin, Julian Viereck, Kaarthik Sivashanmugam, Karl
Lessard, karlkubx.ca, Kevin Carbone, Kevin Van Der Burgt, Kongsea, ksellesk,
lanhin, Lef Ioannidis, Liangliang He, Louis Tiao, Luke Iwanski, LáSzló Csomor,
magixsno, Mahmoud Abuzaina, Marcel Hlopko, Mark Neumann, Maxwell Paul Brickner,
mdfaijul, MichaëL Defferrard, Michał JastrzęBski, Michele Colombo, Mike Brodie,
Mosnoi Ion, mouradmourafiq, myPrecious, Nayana Thorat, Neeraj Kashyap, Nelson
Liu, Niranjan Hasabnis, Olivier Moindrot, orome, Pankaj Gupta, Paul Van Eck,
peeyush18, Peng Yu, Pierre, preciousdp11, qjivy, Raingo, raoqiyu, ribx, Richard
S. Imaoka, Rishabh Patel, Robert Walecki, Rockford Wei, Ryan Kung, Sahil Dua,
Sandip Giri, Sayed Hadi Hashemi, sgt101, Shitian Ni, Shuolongbj, Siim PõDer,
Simon Perkins, sj6077, SOLARIS, Spotlight0xff, Steffen Eberbach, Stephen Fox,
superryanguo, Sven Mayer, Tapan Prakash, Tiago Morais Morgado, Till Hoffmann, Tj
Rana, Vadim Markovtsev, vhasanov, Wei Wu, windead, Yan (Asta) Li, Yan Chen, Yann
Henon, Yi Wang, Yong Tang, yorkie, Yuan (Terry) Tang, Yuxin Wu, zhengjiajin,
zhongzyd, 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.

Major Features and Improvements

* Python 3.6 support on Windows.
* Added `tf.layers.conv3d_transpose` layer for spatio temporal deconvolution.
* Added `tf.Session.make_callable()`, which provides a lower overhead means of
running a similar step multiple times.
* Added libverbs-based RDMA support to contrib (courtesy junshi15 from
Yahoo).
* Bring `tf.feature_column.*` into the API. Non-deprecated functionality from
`tf.contrib.layers.*` is moved to `tf.feature_column.*` with cosmetic
changes.
* `RNNCell` objects now subclass `tf.layers.Layer`. The strictness described
in the TensorFlow 1.1 release is gone: The first time an RNNCell is used, it
caches its scope. All future uses of the RNNCell will reuse variables from
that same scope. This is a breaking change from the behavior of RNNCells in
TensorFlow versions <= 1.0.1. TensorFlow 1.1 had checks in place to ensure
old code works correctly with the new semantics; this version allows more
flexible uses of RNNCell but can lead to subtle errors if using code meant
for TensorFlow <= 1.0.1. For example, writing: `MultiRNNCell([lstm] * 5)`
will now build a 5-layer LSTM stack where each layer shares the **same**
parameters. To get 5 layers each with their own parameters, write:
`MultiRNNCell([LSTMCell(...) for _ in range(5)])`. If at all unsure, first
test your code with TF 1.1; ensure it raises no errors, and then upgrade to
TF 1.2.
* RNNCells' variable names have been renamed for consistency with Keras
layers. Specifically, the previous variable names "weights" and "biases"
have been changed to "kernel" and "bias", respectively. This may cause
backward incompatibility with regard to your old checkpoints containing such
RNN cells, in which case you can use the tool
[checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
to convert the variable names in your old checkpoints.
* Many of the RNN functions and classes that were in the `tf.nn` namespace
before the 1.0 release and which were moved to `tf.contrib.rnn` have now
been moved back to the core namespace. This includes `RNNCell`, `LSTMCell`,
`GRUCell`, and a number of other cells. These now reside in `tf.nn.rnn_cell`
(with aliases in `tf.contrib.rnn` for backwards compatibility). The original
`tf.nn.rnn` function is now `tf.nn.static_rnn`, and the bidirectional static
and state saving static rnn functions are also now back in the `tf.nn`
namespace.

Notable exceptions are the `EmbeddingWrapper`, `InputProjectionWrapper` and
`OutputProjectionWrapper`, which will slowly be moved to deprecation in
`tf.contrib.rnn`. These are inefficient wrappers that should often be
replaced by calling `embedding_lookup` or `layers.dense` as pre- or post-
processing of the rnn. For RNN decoding, this functionality has been
replaced with an alternative API in `tf.contrib.seq2seq`.

* Intel MKL Integration
(https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture).
Intel developed a number of optimized deep learning primitives: In addition
to matrix multiplication and convolution, these building blocks include:
Direct batched convolution Pooling: maximum, minimum, average Normalization:
LRN, batch normalization Activation: rectified linear unit (ReLU) Data
manipulation: multi-dimensional transposition (conversion), split, concat,
sum and scale.

* TensorForest Estimator now supports SavedModel export for serving.

* Support client-provided ClusterSpec's and propagate them to all workers to
enable the creation of dynamic TensorFlow clusters.

* TensorFlow C library now available for Windows.

* We released a new open-source version of TensorBoard.

* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/guide/saved_model_cli)
tool available to inspect and execute MetaGraph in SavedModel

* Android releases of TensorFlow are now pushed to jcenter for easier
integration into apps. See
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/android/inference_interface/README.md
for more details.

Deprecations

* TensorFlow 1.2 may be the last time we build with cuDNN 5.1. Starting with
TensorFlow 1.3, we will try to build all our prebuilt binaries with cuDNN
6.0. While we will try to keep our source code compatible with cuDNN 5.1, it
will be best effort.

Breaking Changes to the API

* `org.tensorflow.contrib.android.TensorFlowInferenceInterface` now throws
exceptions where possible and has simplified method signatures.

Changes to contrib APIs

* Added `tf.contrib.util.create_example`.
* Added bilinear interpolation to `tf.contrib.image`.
* Add `tf.contrib.stateless` for random ops with custom seed control.
* MultivariateNormalFullCovariance added to contrib/distributions/
* tensorflow/contrib/rnn undergoes RNN cell variable renaming for consistency
with Keras layers. Specifically, the previous variable names "weights" and
"biases" are changed to "kernel" and "bias", respectively. This may cause
backward incompatibility with regard to your old checkpoints containing such
RNN cells, in which case you can use the
[checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
to convert the variable names in your old checkpoints.
* Added `tf.contrib.kernel_methods` module with Ops and estimators for primal
(explicit) kernel methods in TensorFlow.

Bug Fixes and Other Changes

* In python, `Operation.get_attr` on type attributes returns the Python DType
version of the type to match expected get_attr documentation rather than the
protobuf enum.
* tensorflow/contrib/rnn undergoes RNN cell variable renaming for consistency
with Keras layers. Specifically, the previous variable names "weights" and
"biases" are changed to "kernel" and "bias", respectively.
* Changed MIN_SDK version to 8.0 when building iOS libraries.
* Fixed LIBXSMM integration.
* Make decode_jpeg/decode_png/decode_gif handle all formats, since users
frequently try to decode an image as the wrong type.
* Improve implicit broadcasting lowering.
* Improving stability of GCS/BigQuery clients by a faster retrying of stale
transmissions.
* Remove OpKernelConstruction::op_def() as part of minimizing proto
dependencies.
* VectorLaplaceDiag distribution added.
* Android demo no longer requires libtensorflow_demo.so to run
(libtensorflow_inference.so still required)
* Added `categorical_column_with_vocabulary_file`.
* Introduce ops for batching/unbatching tensors across Session::Run() calls.
* Add tf.log_sigmoid(x) = tf.log(tf.sigmoid(x)) = -tf.nn.softplus(-x).
* Changed hooks lists to immutable tuples, and now allow any iterable for the
associated arguments.
* Introduce TFDecorator.
* Added an Mfcc op for speech feature generation.
* Improved DirectSession::Run() overhead and error checking. Feeding a value
of the wrong type will now synchronously raise an INVALID_ARGUMENT error
instead of asynchronously raising an INTERNAL error. Code that depends on
the (undefined) behavior when feeding a tensor of the wrong type may need to
be updated.
* Added unreduced NONE, and reduced MEAN options for losses. Removed
"WEIGHTED_" prefix from other Reduction constants.
* assertAllClose now handles dicts.
* Added Gmock matcher for HloInstructions.
* Add var name to errors on variable restore.
* Added an AudioSpectrogram op for audio feature generation.
* Added `reduction` arg to losses.
* `tf.placeholder` can represent scalar shapes and partially known.
* Remove estimator_spec(mode) argument.
* Added an AudioSpectrogram op for audio feature generation.
* TensorBoard disables all runs by default if there are more than 40 runs.
* Removed old doc generator code.
* GCS file system integration now supports domain buckets, e.g
gs://bucket.domain.com/path.
* Add `tf.summary.text` for outputting text to TensorBoard.
* The "run" command of tfdbg's command-line interface now supports filtering
of tensors by node name, op type and tensor dtype.
* `tf.string_to_number` now supports int64 and float64 outputs.

Thanks to our Contributors

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

4F2E4A2E, Aaron Schumacher, Abhi Agg, admcrae, Adriano Carmezim, Adrià Arrufat,
agramesh1, Akimitsu Seo, Alan Mosca, Alex Egg, Alex Rothberg, Alexander
Heinecke, Alexander Matyasko, Alexandr Baranezky, Alexandre Caulier, Ali
Siddiqui, Anand Venkat, Andrew Hundt, Androbin, Anmol Sharma, Arie, Arno Leist,
Arron Cao, AuréLien Geron, Bairen Yi, Beomsu Kim, Carl Thomé, cfperez, Changming
Sun, Corey Wharton, critiqjo, Dalei Li, Daniel Rasmussen, Daniel Trebbien, DaríO
Hereñú, David Eng, David Norman, David Y. Zhang, Davy Song, ddurham2, Deepak
Subburam, Dmytro Kyrychuk, Dominic Rossi, Dominik SchlöSser, Dustin Tran,
Eduardo Pinho, Egil Martinsson, Elliot Saba, Eric Bigelow, Erik Smistad, Evan
Klitzke, Fabrizio Milo, Falcon Dai, Fei Gao, FloopCZ, Fung Lam, Gautam,
GBLin5566, Greg Peatfield, Gu Wang, Guenther Schmuelling, Hans Pabst, Harun
Gunaydin, Huaizheng, Ido Shamay, Ikaro Silva, Ilya Edrenkin, Immexxx, James
Mishra, Jamie Cooke, Jay Young, Jayaram Bobba, Jianfei Wang, jinghua2, Joey
Meyer, John Maidens, Jonghoon Jin, Julian Villella, Jun Kim, Jun Shi, Junwei
Pan, jyegerlehner, Karan Desai, Karel Van De Plassche, Kb Sriram,
KhabarlakKonstantin, Koan-Sin Tan, krivard, Kwotsin, Leandro Gracia Gil, Li
Chen, Liangliang He, Louie Helm, lspvic, Luiz Henrique Soares, LáSzló Csomor,
Mark Wong, Mathew Wicks, Matthew Rahtz, Maxwell Paul Brickner, Michael Hofmann,
Miguel Flores Ruiz De Eguino, MikeTam1021, Mortada Mehyar, Mycosynth, Namnamseo,
Nate Harada, Neven Miculinic, Nghia Tran, Nick Lyu, Niranjan Hasabnis, Nishidha,
Oleksii Kuchaiev, Oyesh Mann Singh, Panmari, Patrick, Paul Van Eck, Piyush
Chaudhary, Quim Llimona, Raingo, Richard Davies, Ruben Vereecken, Sahit
Chintalapudi, Sam Abrahams, Santiago Castro, Scott Sievert, Sean O'Keefe,
Sebastian Schlecht, Shane, Shubhankar Deshpande, Spencer Schaber, Sunyeop Lee,
t13m, td2014, Thomas H. P. Andersen, Toby Petty, Umang Mehta, Vadim Markovtsev,
Valentin Iovene, Vincent Zhao, Vit Stepanovs, Vivek Rane, Vu Pham,
wannabesrevenge, weipingpku, wuhaixutab, wydwww, Xiang Gao, Xiaolin Lin,
xiaoyaozhuzi, Yaroslav Bulatov, Yi Liu, Yoshihiro Sugi, Yuan (Terry) Tang,
Yuming Wang, Yuxin Wu, Zader Zheng, Zhaojun Zhang, zhengjiajin, ZhipengShen,
Ziming Dong, zjj2wry

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

support. Going forward, we will stop testing on Mac GPU systems. We continue
to welcome patches that maintain Mac GPU support, and we will try to keep
the Mac GPU build working.

Changes to contrib APIs

* The behavior of RNNCells is now stricter due to the transition towards
making RNNCells act more like Keras layers.
* If an RNNCell is used twice in two different variable scopes, an error
is raised describing how to avoid this behavior.
* If an RNNCell is used in a variable scope with existing conflicting
variables, an error is raised showing that the RNNCell must be
constructed with argument `reuse=True`.
* Deprecated contrib/distributions `pmf`, `pdf`, `log_pmf`, `log_pdf`.
* Moved `bayesflow.special_math` to distributions.
* `tf.contrib.tensor_forest.python.tensor_forest.RandomForestDeviceAssigner`
removed.
* Changed some MVN classes and parameters:
* `tf.contrib.distributions.MultivariateNormalFull` replaced by
`tf.contrib.distributions.MultivariateNormalTriL`.
* `tf.contrib.distributions.MultivariateNormalCholesky` replaced by
`tf.contrib.distributions.MultivariateNormalTriL`
* `tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev`
replaced by
`tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale`
* `tf.contrib.distributions.MultivariateNormalDiag` arguments changed from
`mu`, `diag_stddev` to `log`, `scale_diag`.
* `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT` removed.
* `tf.contrib.distributions.MultivariateNormalDiagPlusLowRank` added.

Bug Fixes and Other Changes

* Java: Support for loading models exported using the SavedModel API (courtesy
EronWright).
* Go: Added support for incremental graph execution.
* Fix a bug in the WALS solver when single-threaded.
* Added support for integer sparse feature values in
`tf.contrib.layers.sparse_column_with_keys`.
* Fixed `tf.set_random_seed(0)` to be deterministic for all ops.
* Stability improvements for the GCS file system support.
* Improved TensorForest performance.
* Added support for multiple filename globs in `tf.matching_files`.
* `LogMessage` now includes a timestamp as beginning of a message.
* Added MultiBox person detector example standalone binary.
* Android demo: Makefile build functionality added to build.gradle to fully
support building TensorFlow demo in Android on Windows.
* Android demo: read MultiBox priors from txt file rather than protobuf.
* Added colocation constraints to `StagingArea`.
* `sparse_matmul_op` reenabled for Android builds.
* Restrict weights rank to be the same as the broadcast target, to avoid
ambiguity on broadcast rules.
* Upgraded libxsmm to 1.7.1 and applied other changes for performance and
memory usage.
* Fixed bfloat16 integration of LIBXSMM sparse mat-mul.
* Improved performance and reduce memory usage by allowing ops to forward
input buffers to output buffers and perform computations in-place.
* Improved the performance of CPU assignment for strings.
* Speed up matrix * vector multiplication and matrix * matrix with unknown
shapes.
* C API: Graph imports now support input remapping, control dependencies, and
returning imported nodes (see `TF_GraphImportGraphDefWithReturnOutputs()`)
* Multiple C++ API updates.
* Multiple TensorBoard updates including:
* Users can now view image summaries at various sampled steps (instead of
just the last step).
* Bugs involving switching runs as well as the image dashboard are fixed.
* Removed data download links from TensorBoard.
* TensorBoard uses a relative data directory, for easier embedding.
* TensorBoard automatically ignores outliers for domain calculation, and
formats proportional values consistently.
* Multiple tfdbg bug fixes:
* Fixed Windows compatibility issues.
* Command history now persists across runs.
* Bug fix in graph validation related to `tf.while_loops`.
* Java Maven fixes for bugs with Windows installation.
* Backport fixes and improvements from external keras.
* Keras config file handling fix.

Thanks to our Contributors

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

A. Besir Kurtulmus, Adal Chiriliuc, akash, Alec-Desouza, Alex Rothberg, Alex
Sergeev, Alexander Heinecke, Allen Guo, Andreas Madsen, Ankesh Anand, Anton
Loss, Aravind, Arie, Ashutosh Das, AuréLien Geron, Bairen Yi, bakunyo, Ben
Visser, Brady Zhou, Calpa Liu, Changming Sun, Chih Cheng Liang, Christopher
Berner, Clark Zinzow, Conchylicultor, Dan Ellis, Dan J, Dan Jarvis, Daniel
Ylitalo, Darren Garvey, David Norman, David Truong, DavidNorman, Dimitar
Pavlov, Dmitry Persiyanov, Eddie, elirex, Erfan Noury, Eron Wright, Evgeny
Mazovetskiy, Fabrizio (Misto) Milo, fanlu, Fisher Coder, Florian Courtial,
Franck Dernoncourt, Gagan Goel, Gao, Xiang, Gautam, Gefu Tang, guilherme,
guschmue, Hannah Provenza, Hans Pabst, hartb, Hsiao Yi, Huazuo Gao, Igor
ChorążEwicz, Ivan Smirnov, Jakub Kolodziejczyk, Jason Gavris, Jason Morton, Jay
Young, Jayaram Bobba, Jeremy Sawruk, Jiaming Liu, Jihun Choi, jiqiu, Joan
Thibault, John C F, Jojy George Varghese, Jon Malmaud, Julian Berman, Julian
Niedermeier, Junpeng Lao, Kai Sasaki, Kankroc, Karl Lessard, Kyle Bostelmann,
Lezcano, Li Yi, Luo Yun, lurker, Mahmoud-Abuzaina, Mandeep Singh, Marek
Kolodziej, Mark Szepieniec, Martial Hue, Medhat Omr, Memo Akten, Michael Gharbi,
MichaëL Defferrard, Milan Straka, MircoT, mlucool, Muammar Ibn Faisal, Nayana
Thorat, nghiattran, Nicholas Connor, Nikolaas Steenbergen, Niraj Patel,
Niranjan Hasabnis, Panmari, Pavel Bulanov, Philip Pries Henningsen, Philipp
Jund, polonez, Prayag Verma, Rahul Kavi, Raphael Gontijo Lopes, rasbt, Raven
Iqqe, Reid Pryzant, Richard Shin, Rizwan Asif, Russell Kaplan, Ryo Asakura,
RüDiger Busche, Saisai Shao, Sam Abrahams, sanosay, Sean Papay, seaotterman,
selay01, Shaurya Sharma, Sriram Narayanamoorthy, Stefano Probst, taknevski,
tbonza, teldridge11, Tim Anglade, Tomas Reimers, Tomer Gafner, Valentin
Iovene, Vamsi Sripathi, Viktor Malyi, Vit Stepanovs, Vivek Rane, Vlad Firoiu,
wangg12, will, Xiaoyu Tao, Yaroslav Bulatov, Yi Liu, Yuan (Terry) Tang,
Yufeng, Yuming Wang, Yuxin Wu, Zafar Takhirov, 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.

Bug Fixes and Other Changes

* Change GraphConstructor to not increase the version when importing, but
instead take the min of all versions.
* Google Cloud Storage fixes.
* Removed `tf.core` and `tf.python` modules from the API. These were never
intended to be exposed. Please use the same objects through top-level `tf`
module instead.

Major Features and Improvements

* XLA (experimental): initial release of
[XLA](https://www.tensorflow.org/versions/master/experimental/xla/), a
domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs.
* TensorFlow Debugger (tfdbg): command-line interface and API.
* New python 3 docker images added.
* Made pip packages pypi compliant. TensorFlow can now be installed by `pip
install tensorflow` command.
* Several python API calls have been changed to resemble NumPy more closely.
* Android: person detection + tracking demo implementing Scalable Object
Detection using Deep Neural Networks.
* New (experimental)
[Java API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java).
* Add new Android image stylization demo based on "A Learned Representation
For Artistic Style", and add YOLO object detector support.

Breaking Changes to the API

To help you upgrade your existing TensorFlow Python code to match the API
changes below, we have prepared a
[conversion script](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/compatibility).
* TensorFlow/models have been moved to a separate github repository. * Division
and modulus operators (/, //, %) now match Python (flooring) semantics. This
applies to `tf.div` and `tf.mod` as well. To obtain forced integer truncation
based behaviors you can use `tf.truncatediv` and `tf.truncatemod`. *
`tf.divide()` is now the recommended division function. `tf.div()` will remain,
but its semantics do not respond to Python 3 or `from future` mechanisms. *
tf.reverse() now takes indices of axes to be reversed. E.g. `tf.reverse(a,
[True, False, True])` must now be written as `tf.reverse(a, [0, 2])`.
`tf.reverse_v2()` will remain until 1.0 final. * `tf.mul`, `tf.sub` and `tf.neg`
are deprecated in favor of `tf.multiply`, `tf.subtract` and `tf.negative`. *
`tf.pack` and `tf.unpack` are deprecated in favor of `tf.stack` and
`tf.unstack`. * `TensorArray.pack` and `TensorArray.unpack` are getting
deprecated in favor of `TensorArray.stack` and `TensorArray.unstack`. * The
following Python functions have had their arguments changed to use `axis` when
referring to specific dimensions. We have kept the old keyword arguments for
compatibility currently, but we will be removing them well before the final 1.0.
* `tf.argmax`: `dimension` becomes `axis` * `tf.argmin`: `dimension` becomes
`axis` * `tf.count_nonzero`: `reduction_indices` becomes `axis` *
`tf.expand_dims`: `dim` becomes `axis` * `tf.reduce_all`: `reduction_indices`
becomes `axis` * `tf.reduce_any`: `reduction_indices` becomes `axis` *
`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:
`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`
becomes `axis` * `tf.reverse_sequence`: `batch_dim` becomes `batch_axis`,
`seq_dim` becomes `seq_axis` * `tf.sparse_concat`: `concat_dim` becomes `axis` *
`tf.sparse_reduce_sum`: `reduction_axes` becomes `axis` *
`tf.sparse_reduce_sum_sparse`: `reduction_axes` becomes `axis` *
`tf.sparse_split`: `split_dim` becomes `axis` * `tf.listdiff` has been renamed
to `tf.setdiff1d` to match NumPy naming. * `tf.inv` has been renamed to be
`tf.reciprocal` (component-wise reciprocal) to avoid confusion with `np.inv`
which is matrix inversion * tf.round now uses banker's rounding (round to even)
semantics to match NumPy. * `tf.split` now takes arguments in a reversed order
and with different keywords. In particular, we now match NumPy order as
`tf.split(value, num_or_size_splits, axis)`. * `tf.sparse_split` now takes
arguments in reversed order and with different keywords. In particular we now
match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have
temporarily made `tf.sparse_split` require keyword arguments. * `tf.concat` now
takes arguments in reversed order and with different keywords. In particular we
now match NumPy order as `tf.concat(values, axis, name)`. *
`tf.image.decode_jpeg` by default uses the faster DCT method, sacrificing a
little fidelity for improved speed. One can revert to the old behavior by
specifying the attribute `dct_method='INTEGER_ACCURATE'`. * `tf.complex_abs` has
been removed from the Python interface. `tf.abs` supports complex tensors and
should be used instead. * In the C++ API (in tensorflow/cc), Input, Output, etc.
have moved from the tensorflow::ops namespace to tensorflow. *
Template.`var_scope` property renamed to `.variable_scope` *
SyncReplicasOptimizer is removed and SyncReplicasOptimizerV2 renamed to
SyncReplicasOptimizer. * `tf.zeros_initializer()` and `tf.ones_initializer()`
now return a callable that must be called with initializer arguments, in your
code replace `tf.zeros_initializer` with `tf.zeros_initializer()`. *
`SparseTensor.shape` has been renamed to `SparseTensor.dense_shape`. Same for
`SparseTensorValue.shape`. * Replace tf.scalar_summary, tf.histogram_summary,
tf.audio_summary, tf.image_summary with tf.summary.scalar, tf.summary.histogram,
tf.summary.audio, tf.summary.image, respectively. The new summary ops take name
rather than tag as their first argument, meaning summary ops now respect
TensorFlow name scopes. * Replace tf.train.SummaryWriter and
tf.train.SummaryWriterCache with tf.summary.FileWriter and
tf.summary.FileWriterCache. * Removes RegisterShape from public API. Use C++
shape function registration instead. * Deprecated `_ref` dtypes from the python
API. * In the C++ API (in tensorflow/cc), Input, Output, etc. have moved from
the tensorflow::ops namespace to tensorflow. * Change arg order for
`{softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits` to be (labels,
predictions), and force use of named args. * tf.nn.rnn_cell.* and most functions
in tf.nn.rnn.* (with the exception of dynamic_rnn and raw_rnn) are temporarily
in tf.contrib.rnn. They will be moved back into core for TF 1.2. *
`tf.nn.sampled_softmax_loss` and `tf.nn.nce_loss` have both changed their API
such that you need to switch the `inputs, labels` to `labels, inputs`
parameters. * The shape keyword argument of the `SparseTensor` constructor
changes its name to `dense_shape` between Tensorflow 0.12 and Tensorflow 1.0.

Bug Fixes and Other Changes

* Numerous C++ API updates.
* New op: `parallel_stack`.
* Introducing common tf io compression options constants for
RecordReader/RecordWriter.
* Add `sparse_column_with_vocabulary_file`, to specify a feature column that
transform string features to IDs, where the mapping is defined by a
vocabulary file.
* Added `index_to_string_table` which returns a lookup table that maps indices
to strings.
* Add `string_to_index_table`, which returns a lookup table that matches
strings to indices.
* Add a `ParallelForWithWorkerId` function.
* Add `string_to_index_table`, which returns a lookup table that matches
strings to indices.
* Support restore session from checkpoint files in v2 in
`contrib/session_bundle`.
* Added a tf.contrib.image.rotate function for arbitrary angles.
* Added `tf.contrib.framework.filter_variables` as a convenience function to
filter lists of variables based on regular expressions.
* `make_template()` takes an optional `custom_getter_ param`.
* Added comment about how existing directories are handled by
`recursive_create_dir`.
* Added an op for QR factorizations.
* Divides and mods in Python API now use flooring (Python) semantics.
* Android: pre-built libs are now built nightly.
* Android: cmake/gradle build for TensorFlow Inference library under
`contrib/android/cmake`
* Android: Much more robust Session initialization code.
* Android: TF stats now exposed directly in demo and log when debug mode is
active
* Android: new/better README.md documentation
* saved_model is available as `tf.saved_model`.
* Empty op is now stateful.
* Improve speed of scatter_update on the cpu for ASSIGN operations.
* Change `reduce_join` to treat `reduction_indices` in the same way as other
`reduce_` ops.
* Move `TensorForestEstimator` to `contrib/tensor_forest`.
* Enable compiler optimizations by default and allow configuration in
configure.
* `tf.divide` now honors the name field.
* Make metrics weight broadcasting more strict.
* Add new queue-like `StagingArea` and new ops: `stage` and `unstage`.
* Enable inplace update ops for strings on CPU. Speed up string concat.

Thanks to our Contributors

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

Aaron Hu, Abhishek Aggarwal, Adam Michael, Adriano Carmezim, AfirSraftGarrier,
Alexander Novikov, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Hundt,
Anish Shah, Anton Loss, b0noI, BoyuanJiang, Carl Thomé, Chad Kennedy, Comic
Chang, Connor Braa, Daniel N. Lang, Daniel Trebbien, danielgordon10, Darcy Liu,
Darren Garvey, Dmitri Lapin, Eron Wright, Evan Cofer, Fabrizio Milo, Finbarr
Timbers, Franck Dernoncourt, Garrett Smith, guschmue, Hao Wei, Henrik Holst,
Huazuo Gao, Ian, Issac, Jacob Israel, Jangsoo Park, Jin Kim, Jingtian Peng,
John Pope, Kye Bostelmann, Liangliang He, Ling Zhang, Luheng He, Luke Iwanski,
lvli, Michael Basilyan, Mihir Patel, Mikalai Drabovich, Morten Just, newge,
Nick Butlin, Nishant Shukla, Pengfei Ni, Przemyslaw Tredak, rasbt, Ronny,
Rudolf Rosa, RustingSword, Sam Abrahams, Sam Putnam, SeongAhJo, Shi Jiaxin,
skavulya, Steffen MüLler, TheUSER123, tiriplicamihai, vhasanov, Victor
Costan, Vit Stepanovs, Wangda Tan, Wenjian Huang, Xingdong Zuo, Yaroslav
Bulatov, Yota Toyama, Yuan (Terry) Tang, Yuxin Wu

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