Tensorflow

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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.

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.

* added DeterministicRandomTestTool to migration_utils.py. This is useful when
you are migrating from TF 1.x to TF2 and need to make sure your computation
is still happening correctly along the way. See the
[validating correctness migration guide](https://www.tensorflow.org/guide/migrate/validate_correctness)
for more info.

0.5.0

Initial release of TensorFlow.

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0.1.4

release notes.

Major Features and Improvements

* Added canned estimators to Tensorflow library. List of added estimators:
* `DNNClassifier`
* `DNNRegressor`
* `LinearClassifier`
* `LinearRegressor`
* `DNNLinearCombinedClassifier`
* `DNNLinearCombinedRegressor`.
* All our prebuilt binaries have been built with cuDNN 6. We anticipate
releasing TensorFlow 1.4 with cuDNN 7.
* `import tensorflow` now goes much faster.
* Adds a file cache to the GCS filesystem with configurable max staleness for
file contents. This permits caching of file contents across close/open
boundaries.
* Added an axis parameter to `tf.gather`.
* Added a `constant_values` keyword argument to `tf.pad`.
* Adds `Dataset.interleave` transformation.
* Add `ConcatenateDataset` to concatenate two datasets.
* Added Mobilenet support to TensorFlow for Poets training script.
* Adds a block cache to the GCS filesystem with configurable block size and
count.
* SinhArcSinh bijector added.
* Added `Dataset.list_files` API.
* Introduces new operations and Python bindings for the Cloud TPU.
* Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
* Introduces base implementations of ClusterResolvers.
* Unify memory representations of TensorShape and PartialTensorShape. As a
consequence, tensors now have a maximum of 254 dimensions, not 255.
* Changed references to LIBXSMM to use version 1.8.1.
* TensorFlow Debugger (tfdbg):
* Display summaries of numeric tensor values with the `-s` flag to command
`print_tensor` or `pt`.
* Display feed values with the `print_feed` or `pf` command and clickable
links in the curses UI.
* Runtime profiler at the op level and the Python source line level with
the `run -p` command.
* Initial release of the statistical distribution library `tf.distributions`.
* GPU kernels and speed improvements for unary `tf.where` and `tf.nn.top_k`.
* Monotonic Attention wrappers added to `tf.contrib.seq2seq`.
* Added `tf.contrib.signal`, a library for signal processing primitives.
* Added `tf.contrib.resampler`, containing CPU and GPU ops for differentiable
resampling of images.

Breaking Changes to the API

* `tf.RewriterConfig` was removed from the Python API after being available in

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