196 lines
9.9 KiB
Markdown
196 lines
9.9 KiB
Markdown
# Release 0.8.0
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## Major Features and Improvements
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* Added a distributed runtime using GRPC
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* Move skflow to `contrib/learn`
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* Better linear optimizer in `contrib/linear_optimizer`
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* Random forest implementation in `contrib/tensor_forest`
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* CTC loss and decoders in `contrib/ctc`
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* Basic support for `half` data type
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* Better support for loading user ops (see examples in `contrib/`)
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* Allow use of (non-blocking) Eigen threadpool with `TENSORFLOW_USE_EIGEN_THREADPOOL` define
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* Add an extension mechanism for adding network file system support
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* TensorBoard displays metadata stats (running time, memory usage and device used) and tensor shapes
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## Big Fixes and Other Changes
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* Utility for inspecting checkpoints
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* Basic tracing and timeline support
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* Allow building against cuDNN 5 (not incl. RNN/LSTM support)
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* Added instructions and binaries for ProtoBuf library with fast serialization and without 64MB limit
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* Added special functions
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* `bool`-strictness: Tensors have to be explictly compared to `None`
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* Shape strictness: all fed values must have a shape that is compatible with the tensor they are replacing
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* Exposed `tf.while_loop` (deprecated `control_flow_ops.While`)
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* run() now takes RunOptions and RunMetadata, which enable timing stats
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* Fixed lots of potential overflow problems in op kernels
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* Various performance improvements, especially for RNNs and convolutions
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* Many bugfixes
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* Nightly builds, tutorial tests, many test improvements
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* New examples: transfer learning and deepdream ipython notebook
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* Added tutorials, many documentation fixes.
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## Thanks to our Contributors
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This release contains contributions from many people at Google, as well as:
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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, 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.
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We are also grateful to all who filed issues or helped resolve them, asked and
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answered questions, and were part of inspiring discussions.
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# Release 0.7.1
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## Bug Fixes and Other Changes
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* Added gfile.Open and gfile.Copy, used by input_data.py.
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* Fixed Saver bug when MakeDirs tried to create empty directory.
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* GPU Pip wheels are built with cuda 7.5 and cudnn-v4, making them
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required for the binary releases. Lower versions of cuda/cudnn can
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be supported by installing from sources and setting the options
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during ./configure
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* Fix dataset encoding example for Python3 (@danijar)
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* Fix PIP installation by not packaging protobuf as part of wheel,
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require protobuf 3.0.0b2.
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* Fix Mac pip installation of numpy by requiring pip >= 1.10.1.
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* Improvements and fixes to Docker image.
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# Release 0.7.0
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## Major Features and Improvements
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* Allow using any installed Cuda >= 7.0 and cuDNN >= R2, and add support
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for cuDNN R4
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* Added a `contrib/` directory for unsupported or experimental features,
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including higher level `layers` module
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* Added an easy way to add and dynamically load user-defined ops
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* Built out a good suite of tests, things should break less!
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* Added `MetaGraphDef` which makes it easier to save graphs with metadata
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* Added assignments for "Deep Learning with TensorFlow" udacity course
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## Bug Fixes and Other Changes
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* Added a versioning framework for `GraphDef`s to ensure compatibility
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* Enforced Python 3 compatibility
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* Internal changes now show up as sensibly separated commits
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* Open-sourced the doc generator
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* Un-fork Eigen
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* Simplified the `BUILD` files and cleaned up C++ headers
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* TensorFlow can now be used as a submodule in another bazel build
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* New ops (e.g., `*fft`, `*_matrix_solve`)
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* Support for more data types in many ops
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* Performance improvements
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* Various bugfixes
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* Documentation fixes and improvements
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## Breaking Changes to the API
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* `AdjustContrast` kernel deprecated, new kernel `AdjustContrastv2` takes and
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outputs float only. `adjust_contrast` now takes all data types.
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* `adjust_brightness`'s `delta` argument is now always assumed to be in `[0,1]`
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(as is the norm for images in floating point formats), independent of the
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data type of the input image.
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* The image processing ops do not take `min` and `max` inputs any more, casting
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safety is handled by `saturate_cast`, which makes sure over- and underflows
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are handled before casting to data types with smaller ranges.
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* For C++ API users: `IsLegacyScalar` and `IsLegacyVector` are now gone from
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`TensorShapeUtils` since TensorFlow is scalar strict within Google (for
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example, the shape argument to `tf.reshape` can't be a scalar anymore). The
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open source release was already scalar strict, so outside Google `IsScalar`
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and `IsVector` are exact replacements.
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* The following files are being removed from `tensorflow/core/public/`:
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* `env.h` -> `../platform/env.h`
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* `status.h` -> `../lib/core/status.h`
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* `tensor.h` -> `../framework/tensor.h`
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* `tensor_shape.h` -> `../framework/tensor_shape.h`
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* `partial_tensor_shape.h` -> `../framework/partial_tensor_shape.h`
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* `tensorflow_server.h` deleted
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* For C++ API users: `TensorShape::ShortDebugString` has been renamed to
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`DebugString`, and the previous `DebugString` behavior is gone (it was
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needlessly verbose and produced a confusing empty string for scalars).
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* `GraphOptions.skip_common_subexpression_elimination` has been removed. All
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graph optimizer options are now specified via
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`GraphOptions.OptimizerOptions`.
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* `ASSERT_OK` / `EXPECT_OK` macros conflicted with external projects, so they
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were renamed `TF_ASSERT_OK`, `TF_EXPECT_OK`. The existing macros are
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currently maintained for short-term compatibility but will be removed.
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* The non-public `nn.rnn` and the various `nn.seq2seq` methods now return
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just the final state instead of the list of all states.
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* `tf.scatter_update` now no longer guarantees that lexicographically largest
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index be used for update when duplicate entries exist.
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* `tf.image.random_crop(image, [height, width])` is now
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`tf.random_crop(image, [height, width, depth])`, and `tf.random_crop` works
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for any rank (not just 3-D images). The C++ `RandomCrop` op has been replaced
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with pure Python.
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* Renamed `tf.test.GetTempDir` and `tf.test.IsBuiltWithCuda` to
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`tf.test.get_temp_dir` and `tf.test.is_built_with_cuda` for PEP-8
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compatibility.
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* `parse_example`'s interface has changed, the old interface is accessible in
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`legacy_parse_example` (same for related functions).
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* New `Variable`s are not added to the same collection several times even if
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a list with duplicates is passed to the constructor.
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* The Python API will now properly set the `list` member of `AttrValue` in
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constructed `GraphDef` messages for empty lists. The serialization of some
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graphs will change, but the change is both forwards and backwards compatible.
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It will break tests that compare a generated `GraphDef` to a golden serialized
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`GraphDef` (which is discouraged).
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## Thanks to our Contributors
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This release contains contributions from many people at Google, as well as:
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Akiomi Kamakura, Alex Vig, Alexander Rosenberg Johansen, Andre Cruz, Arun Ahuja,
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Bart Coppens, Bernardo Pires, Carl Vondrick, Cesar Salgado, Chen Yu,
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Christian Jauvin, Damien Aymeric, Dan Vanderkam, Denny Britz, Dongjoon Hyun,
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Eren Güven, Erik Erwitt, Fabrizio Milo, G. Hussain Chinoy, Jim Fleming,
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Joao Felipe Santos, Jonas Meinertz Hansen, Joshi Rekha, Julian Viereck,
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Keiji Ariyama, Kenton Lee, Krishna Sankar, Kristina Chodorow, Linchao Zhu,
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Lukas Krecan, Mark Borgerding, Mark Daoust, Moussa Taifi,
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Nathan Howell, Naveen Sundar Govindarajulu, Nick Sweeting, Niklas Riekenbrauck,
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Olivier Grisel, Patrick Christ, Povilas Liubauskas, Rainer Wasserfuhr,
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Romain Thouvenin, Sagan Bolliger, Sam Abrahams, Taehoon Kim, Timothy J Laurent,
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Vlad Zavidovych, Yangqing Jia, Yi-Lin Juang, Yuxin Wu, Zachary Lipton,
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Zero Chen, Alan Wu, @brchiu, @emmjaykay, @jalammar, @Mandar-Shinde,
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@nsipplswezey, @ninotoshi, @panmari, @prolearner and @rizzomichaelg.
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We are also grateful to all who filed issues or helped resolve them, asked and
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answered questions, and were part of inspiring discussions.
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# Release 0.6.0
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## Major Features and Improvements
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* Python 3.3+ support via changes to python codebase and ability
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to specify python version via ./configure.
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* Some improvements to GPU performance and memory usage:
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[convnet benchmarks](https://github.com/soumith/convnet-benchmarks/issues/66)
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roughly equivalent with native cudnn v2 performance. Improvements mostly due
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to moving to 32-bit indices, faster shuffling kernels. More improvements to
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come in later releases.
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## Bug Fixes
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* Lots of fixes to documentation and tutorials, many contributed
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by the public.
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* 271 closed issues on github issues.
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## Backwards-Incompatible Changes
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* `tf.nn.fixed_unigram_candidate_sampler` changed its default 'distortion'
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attribute from 0.0 to 1.0. This was a bug in the original release
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that is now fixed.
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# Release 0.5.0
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Initial release of TensorFlow.
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