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Changes Since Last Release

Features & Improvements

  • TensorBoard now has an Audio Dashboard, with associated audio summaries.
  • TensorBoard now has a reload button, and supports auto-reloading
  • TensorBoard scalar charts now show tooltips with more information
  • TensorBoard now supports run filtering
  • TensorBoard has color changes: the same run always gets the same hue
  • 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.

Bug Fixes and Other Changes

  • TensorBoard now displays graphs with only one data point
  • TensorBoard now visually displays NaN values
  • 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().

Release 0.8.0

Major Features and Improvements

  • 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 half data type
  • Better support for loading user ops (see examples in contrib/)
  • Allow use of (non-blocking) Eigen threadpool with TENSORFLOW_USE_EIGEN_THREADPOOL define
  • Add an extension mechanism for adding network file system support
  • TensorBoard displays metadata stats (running time, memory usage and device used) and tensor shapes

Big Fixes and Other Changes

  • 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 to None
  • Shape strictness: all fed values must have a shape that is compatible with the tensor they are replacing
  • Exposed tf.while_loop (deprecated control_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.

Thanks to our Contributors

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, 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.

Release 0.7.1

Bug Fixes and Other Changes

  • 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.

Release 0.7.0

Major Features and Improvements

  • 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 level layers module
  • Added an easy way to add and dynamically load user-defined ops
  • Built out a good suite of tests, things should break less!
  • Added MetaGraphDef which makes it easier to save graphs with metadata
  • Added assignments for "Deep Learning with TensorFlow" udacity course

Bug Fixes and Other Changes

  • 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 BUILD files 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

Breaking Changes to the API

  • AdjustContrast kernel deprecated, new kernel AdjustContrastv2 takes and outputs float only. adjust_contrast now takes all data types.
  • adjust_brightness's delta argument 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 min and max inputs any more, casting safety is handled by saturate_cast, which makes sure over- and underflows are handled before casting to data types with smaller ranges.
  • For C++ API users: IsLegacyScalar and IsLegacyVector are now gone from TensorShapeUtils since TensorFlow is scalar strict within Google (for example, the shape argument to tf.reshape can't be a scalar anymore). The open source release was already scalar strict, so outside Google IsScalar and IsVector are exact replacements.
  • The following files are being removed from tensorflow/core/public/:
    • env.h -> ../platform/env.h
    • status.h -> ../lib/core/status.h
    • tensor.h -> ../framework/tensor.h
    • tensor_shape.h -> ../framework/tensor_shape.h
    • partial_tensor_shape.h -> ../framework/partial_tensor_shape.h
    • tensorflow_server.h deleted
  • For C++ API users: TensorShape::ShortDebugString has been renamed to DebugString, and the previous DebugString behavior is gone (it was needlessly verbose and produced a confusing empty string for scalars).
  • GraphOptions.skip_common_subexpression_elimination has been removed. All graph optimizer options are now specified via GraphOptions.OptimizerOptions.
  • ASSERT_OK / EXPECT_OK macros conflicted with external projects, so they were renamed TF_ASSERT_OK, TF_EXPECT_OK. The existing macros are currently maintained for short-term compatibility but will be removed.
  • The non-public nn.rnn and the various nn.seq2seq methods now return just the final state instead of the list of all states.
  • tf.scatter_update now no longer guarantees that lexicographically largest index be used for update when duplicate entries exist.
  • tf.image.random_crop(image, [height, width]) is now tf.random_crop(image, [height, width, depth]), and tf.random_crop works for any rank (not just 3-D images). The C++ RandomCrop op has been replaced with pure Python.
  • Renamed tf.test.GetTempDir and tf.test.IsBuiltWithCuda to tf.test.get_temp_dir and tf.test.is_built_with_cuda for PEP-8 compatibility.
  • parse_example's interface has changed, the old interface is accessible in legacy_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 list member of AttrValue in constructed GraphDef messages 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 generated GraphDef to a golden serialized GraphDef (which is discouraged).

Thanks to our Contributors

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.

Release 0.6.0

Major Features and Improvements

  • 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.

Bug Fixes

  • Lots of fixes to documentation and tutorials, many contributed by the public.

  • 271 closed issues on github issues.

Backwards-Incompatible Changes

  • tf.nn.fixed_unigram_candidate_sampler changed its default 'distortion' attribute from 0.0 to 1.0. This was a bug in the original release that is now fixed.

Release 0.5.0

Initial release of TensorFlow.