Merge changes from github.

END_PUBLIC

Note: this CL will break builds.  cl/159887762 to follow to fix all the breakages.

---
Commit 2336cdf7f authored by Maxwell Paul Brickner<mbrickn@users.noreply.github.com>
Committed by gunan<gunan@google.com>:
Updated link to use HTTPS (#10998)

Howdy!

I just updated a link to use https instead of http.

Thanks!
---
Commit ad0892df1 authored by Luke Iwanski<luke@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] Fixes run_metadata_test for SYCL

 This test is designed to test CUDA specific behavior

---
Commit 6b37a0725 authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Update comments
---
Commit 1699d904a authored by John Lawson<john@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] Fixes CUDA specific test run on SYCL (#56)

The testBadParentValuesOnGPU should only be run on CUDA devices, as the
test checks for particular CUDA behaviour. We don't actually provide a
SYCL kernel for GatherTree and so it's not a problem that the tests
don't target SYCL.
---
Commit 3c1946230 authored by myPrecious<Moriadry@users.noreply.github.com>
Committed by Shanqing Cai<cais@google.com>:
Java API to get the size of specified input list of operations. (#10865)

* Java API to get the size of specified input list of operations

* remove unnecessary explain to avoid bring a new term to users.

---
Commit e911c7480 authored by Luke Iwanski<luke@codeplay.com>
Committed by Luke Iwanski<luke@codeplay.com>:
[OpenCL] REGISTER -> REGISTER6

---
Commit fbf6c4cec authored by superryanguo<superryanguo@gmail.com>
Committed by superryanguo<superryanguo@gmail.com>:
Simplify the Quickstart section with the weblink is better

---
Commit 72e2918cc authored by Taehoon Lee<taehoonlee@snu.ac.kr>
Committed by Taehoon Lee<taehoonlee@snu.ac.kr>:
Fix typos

---
Commit 90c4406b7 authored by Rishabh Patel<patelrishabh@users.noreply.github.com>
Committed by GitHub<noreply@github.com>:
Correct the learning rate as per the code snippet
---
Commit 03da61134 authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Update ir_array.cc
---
Commit 2df6cd3ac authored by Todd Wang<toddwang@gmail.com>
Committed by GitHub<noreply@github.com>:
Another try
---
Commit af0cbace1 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Transpose to go through Eigen (#10321)

---
Commit fc7361081 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Registers RGBToHSV and HSVToRGB (#91) (#10848)

* [OpenCL] Added RGBToHSV and HSVToRGB

* Aligning '\'
---
Commit 832894ef8 authored by Luke Iwanski<luke@codeplay.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
[OpenCL] Registers AdjustContrastv2 (#10949)

* [OpenCL] Registers AdjustContrastv2 (#93)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL (#96)

* [OpenCL] Extended adjust_contrast_op_benchmark_test for OpenCL

* simplified to #ifndef

* Changed to "#if GOOGLE_CUDA"

* Update adjust_contrast_op_benchmark_test.cc

* Added comments

---
Commit cb4c2f8d1 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Make TransferBufferToInFeed not virual so it compiles.

---
Commit e89f04d80 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix calling Literal member functions.

---
Commit 15a8df724 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix mac build
clone from meheff's change:
[XLA] Change return type of DeviceAssignment::Deserialize to fix build
breakage on mac.
The mac build had the following error:

error: incomplete type 'xla::DeviceAssignment' used in type trait
expression

This was due to a static method returning a StatusOr<DeviceAssignment>
inside of the definition of DeviceAssignment.

---
Commit a54d43fa4 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Replace LiteralUtil to Literal in compiler/plugin/executor

---
Commit 88a6bb80c authored by Guenther Schmuelling<guschmue@microsoft.com>
Committed by Guenther Schmuelling<guschmue@microsoft.com>:
expand inline for debug builds to limit number of symbols

---
Commit 62fb49d31 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Fix visibility error for contrib/remote_fused_graph/pylib/BUILD.

---
Commit 4c75252f2 authored by Mark Neumann<markn@allenai.org>
Committed by Mark Neumann<markn@allenai.org>:
fix initial test values to avoid numerical instability

---
Commit b58d98353 authored by sj6077<epik03sj@gmail.com>
Committed by Benoit Steiner<benoitsteiner@users.noreply.github.com>:
Fixes of AutoParallel bug (#10368)

* Fix the bug that auto_parallel could replicate variable snapshot name

* Use NodeName in grappler:utils instead of substr, convert variables->variable_def of grappler item

* remove variable_def from grappler item, exclude snapshot nodes from dont_replicate_nodes in auto_parallel

---
Commit a286b7db8 authored by Yifei Feng<yifeif@google.com>
Committed by Yifei Feng<yifeif@google.com>:
Make debug_test slice integer.

---
Commit 97fcfdfa6 authored by Toby Boyd<tobyboyd@google.com>
Committed by GitHub<noreply@github.com>:
Fixed path to seq2seq.py and minor formatting
---
Commit 63c1befb8 authored by Anish Shah<shah.anish07@gmail.com>
Committed by Anish Shah<shah.anish07@gmail.com>:
Improve docs for tf.nn.depthwise_conv2d_native

---
Commit 8d42202b2 authored by Yong Tang<yong.tang.github@outlook.com>
Committed by Yong Tang<yong.tang.github@outlook.com>:
Fix mismatched delete in mkl_tfconv_op.cc

This fix fixes mismatched new[]-delete in mkl_tfconv_op.cc

(the file went through clang-format so there are some additional
changes)

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

---
Commit 26301bd55 authored by Danny Goodman<goodman.danny@gmail.com>
Committed by Danny Goodman<goodman.danny@gmail.com>:
fix error format

---
Commit b3f33ad46 authored by Yao Zhang<yaozhang@google.com>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Make changes to prepare for the fused option of batch norm to be set to None (None means using fused batch norm if possible).

PiperOrigin-RevId: 159649743

---
Commit a4a469832 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
[XLA] Add tests for select ops and while loops that produce tuples that contain predicates.

PiperOrigin-RevId: 159645900

---
Commit 980d3f2be authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Use C API to implement Operation.name property

This name property is used in many existing tests including those that
already run with C API enabled (math_ops_test, framework_ops_test,
session_test, session_partial_run_test, math_ops_test_gpu, etc).

PiperOrigin-RevId: 159645767

---
Commit 26239c706 authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
Previously we didn't have an implementation of BatchNormInference and BatchNormTraining, which gives a linker error if anyone ever tries to call that. A dummy implementation is friendlier than a linker error.

PiperOrigin-RevId: 159645612

---
Commit f671c5caa authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 159570549

PiperOrigin-RevId: 160182040

18 files changed, 88 insertions, 72 deletions

diff --git a/tensorflow/docs_src/api_guides/python/contrib.losses.md b/tensorflow/docs_src/api_guides/python/contrib.losses.md
index 8c289dd556..30123e367f 100644
--- a/tensorflow/docs_src/api_guides/python/contrib.losses.md
+++ b/tensorflow/docs_src/api_guides/python/contrib.losses.md
@@ -13,8 +13,8 @@ of samples in the batch and `d1` ... `dN` are the remaining dimensions.
 It is common, when training with multiple loss functions, to adjust the relative
 strengths of individual losses. This is performed by rescaling the losses via
 a `weight` parameter passed to the loss functions. For example, if we were
-training with both log_loss and mean_square_error, and we wished that the
-log_loss penalty be twice as severe as the mean_square_error, we would
+training with both log_loss and mean_squared_error, and we wished that the
+log_loss penalty be twice as severe as the mean_squared_error, we would
 implement this as:
 
 ```python
@@ -22,7 +22,7 @@ implement this as:
   tf.contrib.losses.log(predictions, labels, weight=2.0)
 
   # Uses default weight of 1.0
-  tf.contrib.losses.mean_square_error(predictions, labels)
+  tf.contrib.losses.mean_squared_error(predictions, labels)
 
   # All the losses are collected into the `GraphKeys.LOSSES` collection.
   losses = tf.get_collection(tf.GraphKeys.LOSSES)
@@ -74,7 +74,7 @@ these predictions.
   predictions = MyModelPredictions(images)
 
   weight = tf.cast(tf.greater(depths, 0), tf.float32)
-  loss  = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss  = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 Note that when using weights for the losses, the final average is computed
@@ -100,7 +100,7 @@ weighted average over the individual prediction errors:
 
   weight = MyComplicatedWeightingFunction(labels)
   weight = tf.div(weight, tf.size(weight))
-  loss = tf.contrib.losses.mean_square_error(predictions, depths, weight)
+  loss = tf.contrib.losses.mean_squared_error(predictions, depths, weight)
 ```
 
 @{tf.contrib.losses.absolute_difference}
diff --git a/tensorflow/docs_src/api_guides/python/math_ops.md b/tensorflow/docs_src/api_guides/python/math_ops.md
index b3c12a61dd..3d9f203297 100644
--- a/tensorflow/docs_src/api_guides/python/math_ops.md
+++ b/tensorflow/docs_src/api_guides/python/math_ops.md
@@ -59,6 +59,8 @@ mathematical functions to your graph.
 *   @{tf.acos}
 *   @{tf.asin}
 *   @{tf.atan}
+*   @{tf.cosh}
+*   @{tf.sinh}
 *   @{tf.lgamma}
 *   @{tf.digamma}
 *   @{tf.erf}
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md
index 77b8e2dd2e..d1c9cd696c 100644
--- a/tensorflow/docs_src/get_started/get_started.md
+++ b/tensorflow/docs_src/get_started/get_started.md
@@ -135,8 +135,9 @@ adder_node = a + b  # + provides a shortcut for tf.add(a, b)
 
 The preceding three lines are a bit like a function or a lambda in which we
 define two input parameters (a and b) and then an operation on them. We can
-evaluate this graph with multiple inputs by using the feed_dict parameter to
-specify Tensors that provide concrete values to these placeholders:
+evaluate this graph with multiple inputs by using the feed_dict argument to
+the [run method](https://www.tensorflow.org/api_docs/python/tf/Session#run)
+to feed concrete values to the placeholders:
 
 ```python
 print(sess.run(adder_node, {a: 3, b:4.5}))
diff --git a/tensorflow/docs_src/get_started/mnist/beginners.md b/tensorflow/docs_src/get_started/mnist/beginners.md
index 624d916474..175de2be76 100644
--- a/tensorflow/docs_src/get_started/mnist/beginners.md
+++ b/tensorflow/docs_src/get_started/mnist/beginners.md
@@ -367,7 +367,7 @@ train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
 
 In this case, we ask TensorFlow to minimize `cross_entropy` using the
 [gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent)
-with a learning rate of 0.5. Gradient descent is a simple procedure, where
+with a learning rate of 0.05. Gradient descent is a simple procedure, where
 TensorFlow simply shifts each variable a little bit in the direction that
 reduces the cost. But TensorFlow also provides
 @{$python/train#Optimizers$many other optimization algorithms}:
diff --git a/tensorflow/docs_src/get_started/mnist/mechanics.md b/tensorflow/docs_src/get_started/mnist/mechanics.md
index 48d9a395f2..27fae45b5b 100644
--- a/tensorflow/docs_src/get_started/mnist/mechanics.md
+++ b/tensorflow/docs_src/get_started/mnist/mechanics.md
@@ -82,7 +82,7 @@ After creating placeholders for the data, the graph is built from the
 `mnist.py` file according to a 3-stage pattern: `inference()`, `loss()`, and
 `training()`.
 
-1.  `inference()` - Builds the graph as far as is required for running
+1.  `inference()` - Builds the graph as far as required for running
 the network forward to make predictions.
 1.  `loss()` - Adds to the inference graph the ops required to generate
 loss.
diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md
index 91189f199d..81aa6e3f76 100644
--- a/tensorflow/docs_src/install/install_c.md
+++ b/tensorflow/docs_src/install/install_c.md
@@ -35,7 +35,7 @@ enable TensorFlow for C:
          OS="linux" # Change to "darwin" for Mac OS
          TARGET_DIRECTORY="/usr/local"
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md
index c9b8dffadb..3f9096b822 100644
--- a/tensorflow/docs_src/install/install_go.md
+++ b/tensorflow/docs_src/install/install_go.md
@@ -35,7 +35,7 @@ steps to install this library and enable TensorFlow for Go:
          TF_TYPE="cpu" # Change to "gpu" for GPU support
          TARGET_DIRECTORY='/usr/local'
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.2.0.tar.gz" |
          sudo tar -C $TARGET_DIRECTORY -xz
 
      The `tar` command extracts the TensorFlow C library into the `lib`
diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md
index 612c4c94f2..40ed9e1826 100644
--- a/tensorflow/docs_src/install/install_java.md
+++ b/tensorflow/docs_src/install/install_java.md
@@ -34,7 +34,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs:
 <dependency>
   <groupId>org.tensorflow</groupId>
   <artifactId>tensorflow</artifactId>
-  <version>1.2.0-rc2</version>
+  <version>1.2.0</version>
 </dependency>
 ```
 
@@ -63,7 +63,7 @@ As an example, these steps will create a Maven project that uses TensorFlow:
                <dependency>
                  <groupId>org.tensorflow</groupId>
                  <artifactId>tensorflow</artifactId>
-                 <version>1.2.0-rc2</version>
+                 <version>1.2.0</version>
                </dependency>
              </dependencies>
          </project>
@@ -122,7 +122,7 @@ refer to the simpler instructions above instead.
 Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
 
   2. Decide whether you will run TensorFlow for Java on CPU(s) only or with
@@ -141,7 +141,7 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
          OS=$(uname -s | tr '[:upper:]' '[:lower:]')
          mkdir -p ./jni
          curl -L \
-           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0-rc2.tar.gz" |
+           "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.2.0.tar.gz" |
            tar -xz -C ./jni
 
 ### Install on Windows
@@ -149,10 +149,10 @@ Take the following steps to install TensorFlow for Java on Linux or Mac OS:
 Take the following steps to install TensorFlow for Java on Windows:
 
   1. Download
-     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0-rc2.jar),
+     [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.2.0.jar),
      which is the TensorFlow Java Archive (JAR).
   2. Download the following Java Native Interface (JNI) file appropriate for
-     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0-rc2.zip).
+     [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.2.0.zip).
   3. Extract this .zip file.
 
 
@@ -200,7 +200,7 @@ must be part of your `classpath`. For example, you can include the
 downloaded `.jar` in your `classpath` by using the `-cp` compilation flag
 as follows:
 
-<pre><b>javac -cp libtensorflow-1.2.0-rc2.jar HelloTF.java</b></pre>
+<pre><b>javac -cp libtensorflow-1.2.0.jar HelloTF.java</b></pre>
 
 
 ### Running
@@ -214,11 +214,11 @@ two files are available to the JVM:
 For example, the following command line executes the `HelloTF` program on Linux
 and Mac OS X:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar:. -Djava.library.path=./jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar:. -Djava.library.path=./jni HelloTF</b></pre>
 
 And the following command line executes the `HelloTF` program on Windows:
 
-<pre><b>java -cp libtensorflow-1.2.0-rc2.jar;. -Djava.library.path=jni HelloTF</b></pre>
+<pre><b>java -cp libtensorflow-1.2.0.jar;. -Djava.library.path=jni HelloTF</b></pre>
 
 If the program prints <tt>Hello from <i>version</i></tt>, you've successfully
 installed TensorFlow for Java and are ready to use the API.  If the program
diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 8ce4acda13..99f27d7b85 100644
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -171,8 +171,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      issue the following command to install TensorFlow in the active
      virtualenv environment:
 
-     <pre>(tensorflow)$ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     <pre>(tensorflow)$ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common_installation_problems).
@@ -276,8 +276,8 @@ take the following steps:
      the following command:
 
      <pre>
-     $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b>
+     $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b>
      </pre>
 
      If this step fails, see
@@ -464,7 +464,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      <pre>
      (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -632,14 +632,14 @@ This section documents the relevant values for Linux installations.
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -651,14 +651,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
 </pre>
 
 Note that GPU support requires the NVIDIA hardware and software described in
@@ -670,14 +670,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
 </pre>
 
 
@@ -689,14 +689,14 @@ Note that GPU support requires the NVIDIA hardware and software described in
 CPU only:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
 GPU support:
 
 <pre>
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md
index f85ecefb83..8ff0fb872f 100644
--- a/tensorflow/docs_src/install/install_mac.md
+++ b/tensorflow/docs_src/install/install_mac.md
@@ -108,8 +108,8 @@ Take the following steps to install TensorFlow with Virtualenv:
      Python 2.7, the command to install
      TensorFlow in the active Virtualenv is as follows:
 
-     <pre> $ <b>pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     <pre> $ <b>pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 If you encounter installation problems, see
 [Common Installation Problems](#common-installation-problems).
@@ -229,8 +229,8 @@ take the following steps:
      you are installing TensorFlow for Mac OS and Python 2.7
      issue the following command:
 
-     <pre> $ <b>sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b> </pre>
+     <pre> $ <b>sudo pip install --upgrade \
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b> </pre>
 
      If the preceding command fails, see
      [installation problems](#common-installation-problems).
@@ -339,7 +339,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      TensorFlow for Python 2.7:
 
      <pre> (tensorflow)$ <b>pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl</b></pre>
+     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl</b></pre>
 
 
 <a name="ValidateYourInstallation"></a>
@@ -512,7 +512,7 @@ This section documents the relevant values for Mac OS installations.
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py2-none-any.whl
 </pre>
 
 
@@ -520,7 +520,7 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py2-none-a
 
 
 <pre>
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0rc2-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl
 </pre>
 
 
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md
index c455492387..a082c3ce78 100644
--- a/tensorflow/docs_src/install/install_sources.md
+++ b/tensorflow/docs_src/install/install_sources.md
@@ -342,10 +342,10 @@ Invoke `pip install` to install that pip package.
 The filename of the `.whl` file depends on your platform.
 For example, the following command will install the pip package
 
-for TensorFlow 1.2.0rc2 on Linux:
+for TensorFlow 1.2.0 on Linux:
 
 <pre>
-$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0rc2-py2-none-any.whl</b>
+$ <b>sudo pip install /tmp/tensorflow_pkg/tensorflow-1.2.0-py2-none-any.whl</b>
 </pre>
 
 ## Validate your installation
diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md
index 42820660ee..8282afaab4 100644
--- a/tensorflow/docs_src/install/install_windows.md
+++ b/tensorflow/docs_src/install/install_windows.md
@@ -38,9 +38,10 @@ installed on your system:
     [NVIDIA documentation](https://developer.nvidia.com/cuda-gpus) for a
     list of supported GPU cards.
 
-If you have an earlier version of the preceding packages, please
-upgrade to the specified versions.
-
+If you have a different version of one of the preceding packages, please
+change to the specified versions.  In particular, the cuDNN version
+must match exactly: TensorFlow will not load if it cannot find `cuDNN64_5.dll`.
+To use a different version of cuDNN, you must build from source.
 
 ## Determine how to install TensorFlow
 
@@ -73,7 +74,7 @@ Use that package at your own risk.
 If the following version of Python is not installed on your machine,
 install it now:
 
-  * [Python 3.5.x from python.org](https://www.python.org/downloads/release/python-352/)
+  * [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
 
 TensorFlow only supports version 3.5.x of Python on Windows.
 Note that Python 3.5.x comes with the pip3 package manager, which is the
@@ -114,12 +115,12 @@ Take the following steps to install TensorFlow in an Anaconda environment:
      environment. To install the CPU-only version of TensorFlow, enter the
      following command:
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
      To install the GPU version of TensorFlow, enter the following command
      (on a single line):
 
-     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0rc2-cp35-cp35m-win_amd64.whl</b> </pre>
+     <pre>(tensorflow)C:\> <b>pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-win_amd64.whl</b> </pre>
 
 ## Validate your installation
 
diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md
index 07c5d3087f..a5508ac23e 100644
--- a/tensorflow/docs_src/performance/performance_guide.md
+++ b/tensorflow/docs_src/performance/performance_guide.md
@@ -52,7 +52,8 @@ bazel build -c opt --copt=-march="broadwell" --config=cuda //tensorflow/tools/pi
     (pascal): 6.2, Titan X (maxwell): 5.2, and K80: 3.7.
 *   Install the latest CUDA platform and cuDNN libraries.
 *   Make sure to use a version of gcc that supports all of the optimizations of
-    the target CPU. The recommended minimum gcc version is 4.8.3.
+    the target CPU. The recommended minimum gcc version is 4.8.3.  On OS X upgrade
+    to the latest Xcode version and use the version of clang that comes with Xcode.
 *   TensorFlow checks on startup whether it has been compiled with the
     optimizations available on the CPU. If the optimizations are not included,
     TensorFlow will emit warnings, e.g. AVX, AVX2, and FMA instructions not
@@ -122,6 +123,11 @@ format.
 The best practice is to build models that work with both `NCHW` and `NHWC` as it
 is common to train using `NCHW` on GPU, and then do inference with NHWC on CPU.
 
+There are edge cases where `NCHW` can be slower on GPU than `NHWC`. One
+[case](https://github.com/tensorflow/tensorflow/issues/7551#issuecomment-280421351)
+is using non-fused batch norm on WRN-16-4 without dropout. In that case using
+fused batch norm, which is also recommended, is the optimal solution.
+
 The very brief history of these two formats is that TensorFlow started by using
 `NHWC` because it was a little faster on CPUs. Then the TensorFlow team
 discovered that `NCHW` performs better when using the NVIDIA cuDNN library.  The
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md
index 4667b4cad7..a37748d0c9 100644
--- a/tensorflow/docs_src/performance/quantization.md
+++ b/tensorflow/docs_src/performance/quantization.md
@@ -91,11 +91,14 @@ eight-bit computations:
 ```sh
 curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -o /tmp/inceptionv3.tgz
 tar xzf /tmp/inceptionv3.tgz -C /tmp/
-bazel build tensorflow/tools/quantization:quantize_graph
-bazel-bin/tensorflow/tools/quantization/quantize_graph \
-  --input=/tmp/classify_image_graph_def.pb \
-  --output_node_names="softmax" --output=/tmp/quantized_graph.pb \
-  --mode=eightbit
+bazel build tensorflow/tools/graph_transforms:transform_graph
+bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
+  --in_graph=/tmp/classify_image_graph_def.pb \
+  --outputs="softmax" --out_graph=/tmp/quantized_graph.pb \
+  --transforms='add_default_attributes strip_unused_nodes(type=float, shape="1,299,299,3")
+    remove_nodes(op=Identity, op=CheckNumerics) fold_constants(ignore_errors=true)
+    fold_batch_norms fold_old_batch_norms quantize_weights quantize_nodes
+    strip_unused_nodes sort_by_execution_order'
 ```
 
 This will produce a new model that runs the same operations as the original, but
diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md
index 6ffe3e8b03..dd2ca8d524 100644
--- a/tensorflow/docs_src/tutorials/seq2seq.md
+++ b/tensorflow/docs_src/tutorials/seq2seq.md
@@ -8,7 +8,10 @@ some input and generate a meaningful response? For example, could we train
 a neural network to translate from English to French? It turns out that
 the answer is *yes*.
 
-This tutorial will show you how to build and train such a system end-to-end. Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub. You can then start by running the translate program:
+This tutorial will show you how to build and train such a system end-to-end.
+Clone the [TensorFlow main repo](https://github.com/tensorflow/tensorflow) and
+the [TensorFlow models repo](https://github.com/tensorflow/models) from GitHub.
+You can then start by running the translate program:
 
 ```
 cd models/tutorials/rnn/translate
@@ -25,7 +28,7 @@ This tutorial references the following files.
 
 File | What's in it?
 --- | ---
-`tensorflow/tensorflow/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
+`tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py` | Library for building sequence-to-sequence models.
 `models/tutorials/rnn/translate/seq2seq_model.py` | Neural translation sequence-to-sequence model.
 `models/tutorials/rnn/translate/data_utils.py` | Helper functions for preparing translation data.
 `models/tutorials/rnn/translate/translate.py` | Binary that trains and runs the translation model.
@@ -148,9 +151,9 @@ have similar interfaces, so we will not describe them in detail. We will use
 ## Neural translation model
 
 While the core of the sequence-to-sequence model is constructed by
-the functions in `tensorflow/tensorflow/python/ops/seq2seq.py`, there are still a few tricks
-that are worth mentioning that are used in our translation model in
-`models/tutorials/rnn/translate/seq2seq_model.py`.
+the functions in `tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py`,
+there are still a few tricks that are worth mentioning that are used in our
+translation model in `models/tutorials/rnn/translate/seq2seq_model.py`.
 
 ### Sampled softmax and output projection
 
diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md
index c2621026c7..24c866eee5 100644
--- a/tensorflow/docs_src/tutorials/wide.md
+++ b/tensorflow/docs_src/tutorials/wide.md
@@ -1,6 +1,6 @@
 # TensorFlow Linear Model Tutorial
 
-In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary
+In this tutorial, we will use the tf.contrib.learn API in TensorFlow to solve a binary
 classification problem: Given census data about a person such as age, gender,
 education and occupation (the features), we will try to predict whether or not
 the person earns more than 50,000 dollars a year (the target label). We will
@@ -16,7 +16,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
@@ -69,8 +69,8 @@ COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
            "marital_status", "occupation", "relationship", "race", "gender",
            "capital_gain", "capital_loss", "hours_per_week", "native_country",
            "income_bracket"]
-df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
-df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1)
+df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True)
+df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1)
 ```
 
 Since the task is a binary classification problem, we'll construct a label
@@ -136,9 +136,9 @@ Here's a list of columns available in the Census Income dataset:
 
 ## Converting Data into Tensors
 
-When building a TF.Learn model, the input data is specified by means of an Input
+When building a tf.contrib.learn model, the input data is specified by means of an Input
 Builder function. This builder function will not be called until it is later
-passed to TF.Learn methods such as `fit` and `evaluate`. The purpose of this
+passed to tf.contrib.learn methods such as `fit` and `evaluate`. The purpose of this
 function is to construct the input data, which is represented in the form of
 @{tf.Tensor}s
 or
@@ -211,7 +211,7 @@ to predict the target label.
 ### Base Categorical Feature Columns
 
 To define a feature column for a categorical feature, we can create a
-`SparseColumn` using the TF.Learn API. If you know the set of all possible
+`SparseColumn` using the tf.contrib.learn API. If you know the set of all possible
 feature values of a column and there are only a few of them, you can use
 `sparse_column_with_keys`. Each key in the list will get assigned an
 auto-incremental ID starting from 0. For example, for the `gender` column we can
@@ -361,7 +361,7 @@ in `model_dir`.
 ## Training and Evaluating Our Model
 
 After adding all the features to the model, now let's look at how to actually
-train the model. Training a model is just a one-liner using the TF.Learn API:
+train the model. Training a model is just a one-liner using the tf.contrib.learn API:
 
 ```python
 m.fit(input_fn=train_input_fn, steps=200)
@@ -467,4 +467,4 @@ value would be high.
 
 If you're interested in learning more, check out our @{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to combine
 the strengths of linear models and deep neural networks by jointly training them
-using the TF.Learn API.
+using the tf.contrib.learn API.
diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/wide_and_deep.md
index 77c905fd51..0978005d6c 100644
--- a/tensorflow/docs_src/tutorials/wide_and_deep.md
+++ b/tensorflow/docs_src/tutorials/wide_and_deep.md
@@ -9,7 +9,7 @@ great for training deep neural networks too, and you might be thinking which one
 you should choose—Well, why not both? Would it be possible to combine the
 strengths of both in one model?
 
-In this tutorial, we'll introduce how to use the TF.Learn API to jointly train a
+In this tutorial, we'll introduce how to use the tf.contrib.learn API to jointly train a
 wide linear model and a deep feed-forward neural network. This approach combines
 the strengths of memorization and generalization. It's useful for generic
 large-scale regression and classification problems with sparse input features
@@ -23,7 +23,7 @@ The figure above shows a comparison of a wide model (logistic regression with
 sparse features and transformations), a deep model (feed-forward neural network
 with an embedding layer and several hidden layers), and a Wide & Deep model
 (joint training of both). At a high level, there are only 3 steps to configure a
-wide, deep, or Wide & Deep model using the TF.Learn API:
+wide, deep, or Wide & Deep model using the tf.contrib.learn API:
 
 1.  Select features for the wide part: Choose the sparse base columns and
     crossed columns you want to use.
@@ -42,7 +42,7 @@ To try the code for this tutorial:
 
 2.  Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
-3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
+3.  Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
 
     a. Get `pip`:
 
diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/word2vec.md
index dfb21334f8..8e7c19035e 100644
--- a/tensorflow/docs_src/tutorials/word2vec.md
+++ b/tensorflow/docs_src/tutorials/word2vec.md
@@ -351,7 +351,7 @@ to evaluate embeddings is to directly use them to predict syntactic and semantic
 relationships like `king is to queen as father is to ?`. This is called
 *analogical reasoning* and the task was introduced by
 [Mikolov and colleagues
-](http://msr-waypoint.com/en-us/um/people/gzweig/Pubs/NAACL2013Regularities.pdf).
+](http://www.anthology.aclweb.org/N/N13/N13-1090.pdf).
 Download the dataset for this task from
 [download.tensorflow.org](http://download.tensorflow.org/data/questions-words.txt).