TensorFlow v0.12.0 RC0

Release 0.12.0

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:
  DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter,
  DepthwiseConv2dNativeBackpropInput, Dequantize, Digamma, Erf, Erfc, Igamma,
  Igammac, Lgamma, Polygamma, 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 intead 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_variable_initializers respectively.

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.

TensorFlow v0.11.0

Merge pull request #5503 from yifeif/r0.11

Reformat markdown.

TensorFlow v0.11.0 RC2

Bug fixes and minor changes.

TensorFlow v0.11.0 RC1

Major Features and Improvements

• CUDA 8.0 support.

TensorFlow v0.11.0 RC0

Major Features and Improvements

• 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]).
• 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.

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, @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.

TensorFlow v0.10.0

Bug fixes for rc0

TensorFlow v0.10.0 RC0

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

Big 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, 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.

TensorFlow 0.9.0

Bug fixes on top of RC0.

TensorFlow v0.9.0 RC0

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 for many ops
• Higher level functionality in contrib.{layers,losses,metrics,learn}
• More features to Tensorboard
• Improved support for string embedding and sparse features
• TensorBoard now has an Audio Dashboard, with associated audio summaries.

Big 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, 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 (Terry) 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.

TensorFlow v0.8.0

Bugfixes on top of RC0.