- 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 argumentstate_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.
- 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
- 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
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.
- 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 intf.nn.rnn_cell). - TensorBoard now has an Audio Dashboard, with associated audio summaries.
- 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 ashiftargument. Shifting by a good estimate of the mean improves numerical stability. Also changes the behavior of theshiftargument totf.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.
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.
- Added a distributed runtime using GRPC
- Move skflow to
contrib/learn - Better linear optimizer in
contrib/linear_optimizer - Random forest implementation in
contrib/tensor_forest - CTC loss and decoders in
contrib/ctc - Basic support for
halfdata type - Better support for loading user ops (see examples in
contrib/) - Allow use of (non-blocking) Eigen threadpool with
TENSORFLOW_USE_EIGEN_THREADPOOLdefine - Add an extension mechanism for adding network file system support
- TensorBoard displays metadata stats (running time, memory usage and device used) and tensor shapes
- Utility for inspecting checkpoints
- Basic tracing and timeline support
- Allow building against cuDNN 5 (not incl. RNN/LSTM support)
- Added instructions and binaries for ProtoBuf library with fast serialization and without 64MB limit
- Added special functions
bool-strictness: Tensors have to be explictly compared toNone- Shape strictness: all fed values must have a shape that is compatible with the tensor they are replacing
- Exposed
tf.while_loop(deprecatedcontrol_flow_ops.While) - run() now takes RunOptions and RunMetadata, which enable timing stats
- Fixed lots of potential overflow problems in op kernels
- Various performance improvements, especially for RNNs and convolutions
- Many bugfixes
- Nightly builds, tutorial tests, many test improvements
- New examples: transfer learning and deepdream ipython notebook
- Added tutorials, many documentation fixes.
This release contains contributions from many people at Google, as well as:
Abhinav Upadhyay, Aggelos Avgerinos, Alan Wu, Alexander G. de G. Matthews, Aleksandr Yahnev, @amchercashin, Andy Kitchen, Aurelien Geron, Awni Hannun, @BanditCat, Bas Veeling, Cameron Chen, @cg31, Cheng-Lung Sung, Christopher Bonnett, Dan Becker, Dan Van Boxel, Daniel Golden, Danijar Hafner, Danny Goodman, Dave Decker, David Dao, David Kretch, Dongjoon Hyun, Dustin Dorroh, @e-lin, Eurico Doirado, Erik Erwitt, Fabrizio Milo, @gaohuazuo, Iblis Lin, Igor Babuschkin, Isaac Hodes, Isaac Turner, Iván Vallés, J Yegerlehner, Jack Zhang, James Wexler, Jan Zikes, Jay Young, Jeff Hodges, @jmtatsch, Johnny Lim, Jonas Meinertz Hansen, Kanit Wongsuphasawat, Kashif Rasul, Ken Shirriff, Kenneth Mitchner, Kenta Yonekura, Konrad Magnusson, Konstantin Lopuhin, @lahwran, @lekaha, @liyongsea, Lucas Adams, @makseq, Mandeep Singh, @manipopopo, Mark Amery, Memo Akten, Michael Heilman, Michael Peteuil, Nathan Daly, Nicolas Fauchereau, @ninotoshi, Olav Nymoen, @panmari, @papelita1234, Pedro Lopes, Pranav Sailesh Mani, RJ Ryan, Rob Culliton, Robert DiPietro, @ronrest, Sam Abrahams, Sarath Shekkizhar, Scott Graham, Sebastian Raschka, Sung Kim, Surya Bhupatiraju, Syed Ahmed, Till Hoffmann, @timsl, @urimend, @vesnica, Vlad Frolov, Vlad Zagorodniy, Wei-Ting Kuo, Wenjian Huang, William Dmitri Breaden Madden, Wladimir Schmidt, Yuan Tang, Yuwen Yan, Yuxin Wu, Yuya Kusakabe, @zhongzyd, @znah.
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
- Added gfile.Open and gfile.Copy, used by input_data.py.
- Fixed Saver bug when MakeDirs tried to create empty directory.
- GPU Pip wheels are built with cuda 7.5 and cudnn-v4, making them required for the binary releases. Lower versions of cuda/cudnn can be supported by installing from sources and setting the options during ./configure
- Fix dataset encoding example for Python3 (@danijar)
- Fix PIP installation by not packaging protobuf as part of wheel, require protobuf 3.0.0b2.
- Fix Mac pip installation of numpy by requiring pip >= 1.10.1.
- Improvements and fixes to Docker image.
- Allow using any installed Cuda >= 7.0 and cuDNN >= R2, and add support for cuDNN R4
- Added a
contrib/directory for unsupported or experimental features, including higher levellayersmodule - Added an easy way to add and dynamically load user-defined ops
- Built out a good suite of tests, things should break less!
- Added
MetaGraphDefwhich makes it easier to save graphs with metadata - Added assignments for "Deep Learning with TensorFlow" udacity course
- Added a versioning framework for
GraphDefs to ensure compatibility - Enforced Python 3 compatibility
- Internal changes now show up as sensibly separated commits
- Open-sourced the doc generator
- Un-fork Eigen
- Simplified the
BUILDfiles and cleaned up C++ headers - TensorFlow can now be used as a submodule in another bazel build
- New ops (e.g.,
*fft,*_matrix_solve) - Support for more data types in many ops
- Performance improvements
- Various bugfixes
- Documentation fixes and improvements
AdjustContrastkernel deprecated, new kernelAdjustContrastv2takes and outputs float only.adjust_contrastnow takes all data types.adjust_brightness'sdeltaargument is now always assumed to be in[0,1](as is the norm for images in floating point formats), independent of the data type of the input image.- The image processing ops do not take
minandmaxinputs any more, casting safety is handled bysaturate_cast, which makes sure over- and underflows are handled before casting to data types with smaller ranges. - For C++ API users:
IsLegacyScalarandIsLegacyVectorare now gone fromTensorShapeUtilssince TensorFlow is scalar strict within Google (for example, the shape argument totf.reshapecan't be a scalar anymore). The open source release was already scalar strict, so outside GoogleIsScalarandIsVectorare exact replacements. - The following files are being removed from
tensorflow/core/public/:env.h->../platform/env.hstatus.h->../lib/core/status.htensor.h->../framework/tensor.htensor_shape.h->../framework/tensor_shape.hpartial_tensor_shape.h->../framework/partial_tensor_shape.htensorflow_server.hdeleted
- For C++ API users:
TensorShape::ShortDebugStringhas been renamed toDebugString, and the previousDebugStringbehavior is gone (it was needlessly verbose and produced a confusing empty string for scalars). GraphOptions.skip_common_subexpression_eliminationhas been removed. All graph optimizer options are now specified viaGraphOptions.OptimizerOptions.ASSERT_OK/EXPECT_OKmacros conflicted with external projects, so they were renamedTF_ASSERT_OK,TF_EXPECT_OK. The existing macros are currently maintained for short-term compatibility but will be removed.- The non-public
nn.rnnand the variousnn.seq2seqmethods now return just the final state instead of the list of all states. tf.scatter_updatenow no longer guarantees that lexicographically largest index be used for update when duplicate entries exist.tf.image.random_crop(image, [height, width])is nowtf.random_crop(image, [height, width, depth]), andtf.random_cropworks for any rank (not just 3-D images). The C++RandomCropop has been replaced with pure Python.- Renamed
tf.test.GetTempDirandtf.test.IsBuiltWithCudatotf.test.get_temp_dirandtf.test.is_built_with_cudafor PEP-8 compatibility. parse_example's interface has changed, the old interface is accessible inlegacy_parse_example(same for related functions).- New
Variables are not added to the same collection several times even if a list with duplicates is passed to the constructor. - The Python API will now properly set the
listmember ofAttrValuein constructedGraphDefmessages for empty lists. The serialization of some graphs will change, but the change is both forwards and backwards compatible. It will break tests that compare a generatedGraphDefto a golden serializedGraphDef(which is discouraged).
This release contains contributions from many people at Google, as well as:
Akiomi Kamakura, Alex Vig, Alexander Rosenberg Johansen, Andre Cruz, Arun Ahuja, Bart Coppens, Bernardo Pires, Carl Vondrick, Cesar Salgado, Chen Yu, Christian Jauvin, Damien Aymeric, Dan Vanderkam, Denny Britz, Dongjoon Hyun, Eren Güven, Erik Erwitt, Fabrizio Milo, G. Hussain Chinoy, Jim Fleming, Joao Felipe Santos, Jonas Meinertz Hansen, Joshi Rekha, Julian Viereck, Keiji Ariyama, Kenton Lee, Krishna Sankar, Kristina Chodorow, Linchao Zhu, Lukas Krecan, Mark Borgerding, Mark Daoust, Moussa Taifi, Nathan Howell, Naveen Sundar Govindarajulu, Nick Sweeting, Niklas Riekenbrauck, Olivier Grisel, Patrick Christ, Povilas Liubauskas, Rainer Wasserfuhr, Romain Thouvenin, Sagan Bolliger, Sam Abrahams, Taehoon Kim, Timothy J Laurent, Vlad Zavidovych, Yangqing Jia, Yi-Lin Juang, Yuxin Wu, Zachary Lipton, Zero Chen, Alan Wu, @brchiu, @emmjaykay, @jalammar, @Mandar-Shinde, @nsipplswezey, @ninotoshi, @panmari, @prolearner and @rizzomichaelg.
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
-
Python 3.3+ support via changes to python codebase and ability to specify python version via ./configure.
-
Some improvements to GPU performance and memory usage: convnet benchmarks roughly equivalent with native cudnn v2 performance. Improvements mostly due to moving to 32-bit indices, faster shuffling kernels. More improvements to come in later releases.
-
Lots of fixes to documentation and tutorials, many contributed by the public.
-
271 closed issues on github issues.
tf.nn.fixed_unigram_candidate_samplerchanged its default 'distortion' attribute from 0.0 to 1.0. This was a bug in the original release that is now fixed.
Initial release of TensorFlow.