Nvidia-tensorflow

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.

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.