Merge changes from github.

END_PUBLIC

---
Commit fe5338177 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 161727345

---
Commit c65f69119 authored by Eugene Brevdo<ebrevdo@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Factor out DenseUpdate ops into dense_update_functor build dep.

Also add support for complex types.

PiperOrigin-RevId: 161726749

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Commit 9a172989e authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Update ops-related pbtxt files.

PiperOrigin-RevId: 161726324

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Commit fd5530d6e authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
adding bazel-toolchains repo to workspace. This repo will be necessary for remote execution (specifically for cross OS compilation)

PiperOrigin-RevId: 161719899

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Commit 71c4ec8ed authored by Derek Murray<mrry@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add a mechanism for switching between multiple iterators by feeding a handle.

With this change, you can do the following:

1. Fetch a string handle for any iterator, by evaluating the result of
   `Iterator.string_handle()`.
2. Define an `Iterator` object based on a `tf.string` placeholder handle.
3. Feed the placeholder using an evaluated string handle to use a particular
   iterator in a particular step.

Concretely, this allows you to define two iterators for a training dataset and
a test dataset, and choose which one to use on a per-run basis:

```python
train_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
train_iterator_handle = sess.run(train_iterator.string_handle())

test_iterator = tf.contrib.data.Dataset(...).make_one_shot_iterator()
test_iterator_handle = sess.run(test_iterator.string_handle())

handle = tf.placeholder(tf.string, shape=[])
iterator = tf.contrib.data.Iterator.from_string_handle(
    handle, train_iterator.output_types)

next_element = iterator.get_next()
loss = f(next_element)

train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle})
test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle})
```
PiperOrigin-RevId: 161719836

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Commit 6d6dda807 authored by Kay Zhu<kayzhu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Fix an issue where plugin/Executor backend is used by default when TF
is built from source with XLA support. See Github issue #11122.

The priority of the executor backend is set to be higher than the default (50)
and CPUs (<100), and is therefore selected as the default when tf.device is not
explicitly specified.

PiperOrigin-RevId: 161717173

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Commit 6b28eb084 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Rename HloLocation to HloPosition, to avoid ambiguity with MemoryLocation.

PiperOrigin-RevId: 161716528

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Commit 8e7f57371 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Expose tf.contrib.nn.rank_sampled_softmax_loss.

PiperOrigin-RevId: 161716450

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Commit e424d209a authored by Peter Hawkins<phawkins@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[TF:XLA] Use a more numerically accurate formulation of ResourceApplyRMSProp.

PiperOrigin-RevId: 161706120

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Commit 45a58d378 authored by Skye Wanderman-Milne<skyewm@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Introduce Python-only extensions to the C API

Implements an incomplete version of Operation._add_control_input()
using a new extension to make sure the plumbing works.

This also adds header guards to c_api_internal.h, which were missing. For some reason the missing guards caused problems in the cmake build even though there doesn't appear to be any #include cycles.

PiperOrigin-RevId: 161705859

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Commit 4f5433634 authored by Jonathan Hseu<jhseu@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Rename TpuEstimator to TPUEstimator and TpuConfig to TPUConfig to follow PEP8
naming conventions.

PiperOrigin-RevId: 161704561

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Commit 38180d7bb authored by Yun Peng<pcloudy@google.com>
Committed by gunan<gunan@google.com>:
Disable nn_test on Windows (#11445)

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Commit e1de7a1b0 authored by Yun Peng<pcloudy@google.com>
Committed by gunan<gunan@google.com>:
Windows Bazel Build: Build TensorFlow with wrapper-less CROSSTOOL (#11454)

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Commit c9d03a568 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add tf.contrib.nn.rank_sampled_softmax_loss, a variant of tf.nn.sampled_softmax_loss that has been shown to improve rank loss. Paper: https://arxiv.org/abs/1707.03073

PiperOrigin-RevId: 161702455

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Commit 9aa0dcbf2 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Add shape check for MakeQuantileSummariesOp.

PiperOrigin-RevId: 161698801

---
Commit 9c4da4a24 authored by vhasanov<KyotoSunshine@users.noreply.github.com>
Committed by Frank Chen<frankchn@gmail.com>:
Deleted unnecessary repetition of the same text. (#11459)

The same text was repeated two times. I deleted the repetition.
---
Commit d1e3cadda authored by DimanNe<dimanne@gmail.com>
Committed by drpngx<drpngx@users.noreply.github.com>:
Fix linking options issued by bazel in oorder to make gradients register (#11449)

---
Commit 8605f7ab8 authored by Taehoon Lee<me@taehoonlee.com>
Committed by Frank Chen<frankchn@gmail.com>:
Fix typos (#11444)

---
Commit 7c1fe9068 authored by Karl Lessard<karllessard@users.noreply.github.com>
Committed by Frank Chen<frankchn@gmail.com>:
[Java] Add base classes and utilities for operation wrappers. (#11188)

* Add base classes and utilities for operation wrappers.

* Rename Input interface to Operand

* Introduce changes after code review

---
Commit 2195db6d8 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Remove unused flag: xla_hlo_graph_for_compute_constant

PiperOrigin-RevId: 161686867

---
Commit a72fc31bc authored by Martin Wicke<martin.wicke@gmail.com>
Committed by Martin Wicke<martin.wicke@gmail.com>:
Remove tabs. Unassign contrib/framework.
---
Commit 6e74bd65a authored by Martin Wicke<martin.wicke@gmail.com>
Committed by Martin Wicke<martin.wicke@gmail.com>:
Add CODEOWNERS

Added what we know about contrib mainly, and some well-separated components.
---
Commit de546d066 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BUILD cleanup in tensorflow/compiler/...

PiperOrigin-RevId: 161679855

---
Commit 576c7b1ec authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 161218103

PiperOrigin-RevId: 161868747

1 files changed, 0 insertions, 31 deletions

diff --git a/RELEASE.md b/RELEASE.md
index 0cd4eef5d6..33fe88cd54 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -65,37 +65,6 @@
   integration into apps. See
   https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md
   for more details.
-* RNNCells' variable names have been renamed for consistency with Keras layers.
-  Specifically, the previous variable names "weights" and "biases" have
-  been changed to "kernel" and "bias", respectively.
-  This may cause backward incompatibility with regard to your old
-  checkpoints containing such RNN cells, in which case you can use the tool
-  [checkpoint_convert script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py)
-  to convert the variable names in your old checkpoints.
-* Many of the RNN functions and classes that were in the `tf.nn` namespace
-  before the 1.0 release and which were moved to `tf.contrib.rnn` have now
-  been moved back to the core namespace.  This includes
-  `RNNCell`, `LSTMCell`, `GRUCell`, and a number of other cells.  These
-  now reside in `tf.nn.rnn_cell` (with aliases in `tf.contrib.rnn` for backwards
-  compatibility).  The original `tf.nn.rnn` function is now `tf.nn.static_rnn`,
-  and the bidirectional static and state saving static rnn functions are also
-  now back in the `tf.nn` namespace.
-
-  Notable exceptions are the `EmbeddingWrapper`, `InputProjectionWrapper` and
-  `OutputProjectionWrapper`,  which will slowly be moved to deprecation
-  in `tf.contrib.rnn`.  These are inefficient wrappers that should often
-  be replaced by calling `embedding_lookup` or `layers.dense` as pre- or post-
-  processing of the rnn.  For RNN decoding, this functionality has been replaced
-  with an alternative API in `tf.contrib.seq2seq`.
-* Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of
-  optimized deep learning primitives: In addition to matrix multiplication and
-  convolution, these building blocks include:
-  Direct batched convolution
-  Pooling: maximum, minimum, average
-  Normalization: LRN, batch normalization
-  Activation: rectified linear unit (ReLU)
-  Data manipulation: multi-dimensional transposition (conversion), split,
-  concat, sum and scale.
 
 ## Deprecations