# 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 `GraphDef`s 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 `Variable`s 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](https://github.com/soumith/convnet-benchmarks/issues/66)
  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.
