Tensorflow

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.

Major Features and Improvements

* TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10,
Windows 7, and Windows Server 2016). Supported languages include Python (via
a pip package) and C++. CUDA 8.0 and cuDNN 5.1 are supported for GPU
acceleration. Known limitations include: It is not currently possible to
load a custom op library. The GCS and HDFS file systems are not currently
supported. The following ops are not currently implemented: Dequantize,
QuantizeAndDequantize, QuantizedAvgPool,
QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat,
QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool,
QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape,
QuantizeV2, RequantizationRange, and Requantize.
* Go: Experimental API in Go to create and execute graphs
(https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
* New checkpoint format becomes the default in `tf.train.Saver`. Old V1
checkpoints continue to be readable; controlled by the `write_version`
argument, `tf.train.Saver` now by default writes out in the new V2 format.
It significantly reduces the peak memory required and latency incurred
during restore.
* Added a new library for library of matrix-free (iterative) solvers for
linear equations, linear least-squares, eigenvalues and singular values in
tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
conjugate gradients and CGLS.
* Added gradients for `matrix_solve_ls` and `self_adjoint_eig`.
* Large cleanup to add second order gradient for ops with C++ gradients and
improve existing gradients such that most ops can now be differentiated
multiple times.
* Added a solver for ordinary differential equations,
`tf.contrib.integrate.odeint`.
* New contrib module for tensors with named axes, `tf.contrib.labeled_tensor`.
* Visualization of embeddings in TensorBoard.

Breaking Changes to the API

* `BusAdjacency` enum replaced with a protocol buffer `DeviceLocality`. PCI
bus indexing now starts from 1 instead of 0, and `bus_id==0` is used where
previously `BUS_ANY` was used.
* `Env::FileExists` and `FileSystem::FileExists` now return a
tensorflow::Status instead of a bool. Any callers to this function can be
converted to a bool by adding .ok() to the call.
* The C API type `TF_SessionWithGraph` has been renamed to `TF_Session`,
indicating its preferred use in language bindings for TensorFlow. What was
previously `TF_Session` has been renamed to `TF_DeprecatedSession`.
* Renamed `TF_Port` to `TF_Output` in the C API.
* Removes RegisterShape from public API. Use C++ shape function registration
instead. indexing now starts from 1 instead of 0, and `bus_id==0` is used
where previously `BUS_ANY` was used.
* Most RNN cells and RNN functions now use different variable scopes to be
consistent with layers (`tf.contrib.layers`). This means old checkpoints
written using this code will not load after this change without providing
`Saver` a list of variable renames. Examples of variable scope changes
include `RNN` -> `rnn` in `tf.nn.rnn`, `tf.nn.dynamic_rnn` and moving from
`Linear/Matrix` -> `weights` and `Linear/Bias` -> `biases` in most RNN
cells.
* Deprecated tf.select op. tf.where should be used instead.
* `SparseTensor.shape` has been renamed to `SparseTensor.dense_shape`. Same
for `SparseTensorValue.shape`.
* `Env::FileExists` and `FileSystem::FileExists` now return a
`tensorflow::Status` instead of a bool. Any callers to this function can be
converted to a bool by adding `.ok()` to the call.
* C API: Type `TF_SessionWithGraph` has been renamed to `TF_Session`,
indicating its preferred use in language bindings for TensorFlow. What was
previously `TF_Session` has been renamed to `TF_DeprecatedSession`.
* C API: Renamed `TF_Port` to `TF_Output`.
* C API: The caller retains ownership of `TF_Tensor` objects provided to
`TF_Run`, `TF_SessionRun`, `TF_SetAttrTensor` etc.
* Renamed `tf.image.per_image_whitening()` to
`tf.image.per_image_standardization()`
* Move Summary protobuf constructors to `tf.summary` submodule.
* Deprecate `histogram_summary`, `audio_summary`, `scalar_summary`,
`image_summary`, `merge_summary`, and `merge_all_summaries`.
* Combined `batch_*` and regular version of linear algebra and FFT ops. The
regular op now handles batches as well. All `batch_*` Python interfaces were
removed.
* `tf.all_variables`, `tf.VARIABLES` and `tf.initialize_all_variables` renamed
to `tf.global_variables`, `tf.GLOBAL_VARIABLES` and
`tf.global_variables_initializer` respectively.
* `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()`

Bug Fixes and Other Changes

* Use threadsafe version of `lgamma` function.
* Fix `tf.sqrt` handling of negative arguments.
* Fixed bug causing incorrect number of threads to be used for multi-threaded
benchmarks.
* Performance optimizations for `batch_matmul` on multi-core CPUs.
* Improve trace, `matrix_set_diag`, `matrix_diag_part` and their gradients to
work for rectangular matrices.
* Support for SVD of complex valued matrices.

Thanks to our Contributors

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

a7744hsc, Abhi Agg, admcrae, Adriano Carmezim, Aki Sukegawa, Alex Kendall,
Alexander Rosenberg Johansen, amcrae, Amlan Kar, Andre Simpelo, Andreas Eberle,
Andrew Hundt, Arnaud Lenglet, b0noI, Balachander Ramachandran, Ben Barsdell,
Ben Guidarelli, Benjamin Mularczyk, Burness Duan, c0g, Changming Sun, chanis,
Corey Wharton, Dan J, Daniel Trebbien, Darren Garvey, David Brailovsky, David
Jones, Di Zeng, DjangoPeng, Dr. Kashif Rasul, drag0, Fabrizio (Misto) Milo,
FabríCio Ceschin, fp, Ghedeon, guschmue, Gökçen Eraslan, Haosdent Huang,
Haroen Viaene, Harold Cooper, Henrik Holst, hoangmit, Ivan Ukhov, Javier
Dehesa, Jingtian Peng, Jithin Odattu, Joan Pastor, Johan Mathe, Johannes Mayer,
Jongwook Choi, Justus Schwabedal, Kai Wolf, Kamil Hryniewicz, Kamran Amini,
Karen Brems, Karl Lattimer, kborer, Ken Shirriff, Kevin Rose, Larissa Laich,
Laurent Mazare, Leonard Lee, Liang-Chi Hsieh, Liangliang He, Luke Iwanski, Marek
Kolodziej, Moustafa Alzantot, MrQianjinsi, nagachika, Neil Han, Nick Meehan,
Niels Ole Salscheider, Nikhil Mishra, nschuc, Ondrej Skopek, OndřEj Filip,
OscarDPan, Pablo Moyano, Przemyslaw Tredak, qitaishui, Quarazy, raix852,
Philipp Helo, Sam Abrahams, SriramRamesh, Till Hoffmann, Tushar Soni, tvn,
tyfkda, Uwe Schmidt, Victor Villas, Vit Stepanovs, Vladislav Gubarev,
wujingyue, Xuesong Yang, Yi Liu, Yilei Yang, youyou3, Yuan (Terry) Tang,
Yuming Wang, Zafar Takhirov, zhongyuk, Ziming Dong, guotong1988

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

* CUDA 8 support.
* cuDNN 5 support.
* HDFS Support.
* Adds Fused LSTM support via cuDNN 5 in `tensorflow/contrib/cudnn_rnn`.
* Improved support for NumPy style basic slicing including non-1 strides,
ellipses, newaxis, and negative indices. For example complicated expressions
like `foo[1, 2:4, tf.newaxis, ..., :-3:-1, :]` are now supported. In
addition we have preliminary (non-broadcasting) support for sliced
assignment to variables. In particular one can write
`var[1:3].assign([1,11,111])`.
* Deprecated `tf.op_scope` and `tf.variable_op_scope` in favor of a unified
`tf.name_scope` and `tf.variable_scope`. The new argument order of
`tf.variable_scope` is incompatible with previous versions.
* Introducing `core/util/tensor_bundle` module: a module to efficiently
serialize/deserialize tensors to disk. Will be used in TF's new checkpoint
format.
* Added tf.svd for computing the singular value decomposition (SVD) of dense
matrices or batches of matrices (CPU only).
* Added gradients for eigenvalues and eigenvectors computed using
`self_adjoint_eig` or `self_adjoint_eigvals`.
* Eliminated `batch_*` methods for most linear algebra and FFT ops and
promoted the non-batch version of the ops to handle batches of matrices.
* Tracing/timeline support for distributed runtime (no GPU profiler yet).
* C API gives access to inferred shapes with `TF_GraphGetTensorNumDims` and
`TF_GraphGetTensorShape`.
* Shape functions for core ops have moved to C++ via
`REGISTER_OP(...).SetShapeFn(...)`. Python shape inference RegisterShape
calls use the C++ shape functions with `common_shapes.call_cpp_shape_fn`. A
future release will remove `RegisterShape` from python.

Bug Fixes and Other Changes

* Documentation now includes operator overloads on Tensor and Variable.
* `tensorflow.__git_version__` now allows users to identify the version of the
code that TensorFlow was compiled with. We also have
`tensorflow.__git_compiler__` which identifies the compiler used to compile
TensorFlow's core.
* Improved multi-threaded performance of `batch_matmul`.
* LSTMCell, BasicLSTMCell, and MultiRNNCell constructors now default to
`state_is_tuple=True`. For a quick fix while transitioning to the new
default, simply pass the argument `state_is_tuple=False`.
* DeviceFactory's AddDevices and CreateDevices functions now return a Status
instead of void.
* Int32 elements of list(type) arguments are no longer placed in host memory
by default. If necessary, a list(type) argument to a kernel can be placed in
host memory using a HostMemory annotation.
* `uniform_unit_scaling_initializer()` no longer takes a `full_shape` arg,
instead relying on the partition info passed to the initializer function
when it's called.
* The NodeDef protocol message is now defined in its own file `node_def.proto`
`instead of graph.proto`.
* `ops.NoGradient` was renamed `ops.NotDifferentiable`. `ops.NoGradient` will
be removed soon.
* `dot.h` / DotGraph was removed (it was an early analysis tool prior to
TensorBoard, no longer that useful). It remains in history should someone
find the code useful.
* re2 / regexp.h was removed from being a public interface of TF. Should users
need regular expressions, they should depend on the RE2 library directly
rather than via TensorFlow.

Thanks to our Contributors

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

Abid K, afshinrahimi, AidanGG, Ajay Rao, Aki Sukegawa, Alex Rothberg,
Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Thomas, Appleholic,
Bastiaan Quast, Ben Dilday, Bofu Chen, Brandon Amos, Bryon Gloden, Cissp®,
chanis, Chenyang Liu, Corey Wharton, Daeyun Shin, Daniel Julius Lasiman, Daniel
Waterworth, Danijar Hafner, Darren Garvey, Denis Gorbachev, DjangoPeng,
Egor-Krivov, Elia Palme, Eric Platon, Fabrizio Milo, Gaetan Semet, Georg
Nebehay, Gu Wang, Gustav Larsson, haosdent, Harold Cooper, Hw-Zz, ichuang,
Igor Babuschkin, Igor Macedo Quintanilha, Ilya Edrenkin, ironhead, Jakub
Kolodziejczyk, Jennifer Guo, Jihun Choi, Jonas Rauber, Josh Bleecher Snyder,
jpangburn, Jules Gagnon-Marchand, Karen Brems, kborer, Kirill Bobyrev, Laurent
Mazare, Longqi Yang, Malith Yapa, Maniteja Nandana, Martin Englund, Matthias
Winkelmann, mecab, Mu-Ik Jeon, Nand Dalal, Niels Ole Salscheider, Nikhil
Mishra, Park Jiin, Pieter De Rijk, raix852, Ritwik Gupta, Sahil Sharma,
Sangheum Hwang, SergejsRk, Shinichiro Hamaji, Simon Denel, Steve,
suiyuan2009, Tiago Jorge, Tijmen Tieleman, tvn, tyfkda, Wang Yang, Wei-Ting
Kuo, Wenjian Huang, Yan Chen, YenChenLin, Yuan (Terry) Tang, Yuncheng Li,
Yunfeng Wang, Zack Polizzi, zhongzyd, Ziming Dong, perhapszzy

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