TensorFlow: Upstream latest commits to git.

Changes:
- Updates to Documentation, README.md, installation
  instructions, anchor links, etc.

- Adds Readme for embedding directory.

Base CL: 107308461

Author: Vijay Vasudevan

README.md

@@ -11,9 +11,21 @@ organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
 
+
+**Note: Currently we do not accept pull requests on github -- see
+[CONTRIBUTING.md](CONTRIBUTING.md) for information on how to contribute code
+changes to TensorFlow through
+[tensorflow.googlesource.com](https://tensorflow.googlesource.com/tensorflow)**
+
+**We use [github issues](https://github.com/tensorflow/tensorflow/issues) for
+tracking requests and bugs, but please see
+[Community](resources/index.md#community) for general questions and
+discussion.**
+
 # Download and Setup
 
-For detailed installation instructions, see
+To install TensorFlow using a binary package, see the instructions below.  For
+more detailed installation instructions, including installing from source, see
 [here](tensorflow/g3doc/get_started/os_setup.md).
 
 ## Binary Installation
@@ -32,7 +44,8 @@ Install TensorFlow:
 # For CPU-only version
 $ sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 
-# For GPU-enabled version
+# For GPU-enabled version.  See detailed install instructions
+# for GPU configuration information.
 $ sudo pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 ```
 

tensorflow/g3doc/api_docs/python/client.md

@@ -5,6 +5,7 @@
 ## Contents
 * [Session management](#AUTOGENERATED-session-management)
   * [class tf.Session](#Session)
+  * [class tf.InteractiveSession](#InteractiveSession)
   * [tf.get_default_session()](#get_default_session)
 * [Error classes](#AUTOGENERATED-error-classes)
   * [class tf.OpError](#OpError)
@@ -257,6 +258,78 @@ thread's function.
 
 
 
+- - -
+
+### class tf.InteractiveSession <div class="md-anchor" id="InteractiveSession">{#InteractiveSession}</div>
+
+A TensorFlow `Session` for use in interactive contexts, such as a shell.
+
+The only difference with a regular `Session` is that an `InteractiveSession`
+installs itself as the default session on construction.
+The methods [`Tensor.eval()`](framework.md#Tensor.eval) and
+[`Operation.run()`](framework.md#Operation.run) will use that session
+to run ops.
+
+This is convenient in interactive shells and [IPython
+notebooks](http://ipython.org), as it avoids having to pass an explicit
+`Session` object to run ops.
+
+For example:
+
+```python
+sess = tf.InteractiveSession()
+a = tf.constant(5.0)
+b = tf.constant(6.0)
+c = a * b
+# We can just use 'c.eval()' without passing 'sess'
+print c.eval()
+sess.close()
+```
+
+Note that a regular session installs itself as the default session when it
+is created in a `with` statement.  The common usage in non-interactive
+programs is to follow that pattern:
+
+```python
+a = tf.constant(5.0)
+b = tf.constant(6.0)
+c = a * b
+with tf.Session():
+  # We can also use 'c.eval()' here.
+  print c.eval()
+```
+
+- - -
+
+#### tf.InteractiveSession.__init__(target='', graph=None) {#InteractiveSession.__init__}
+
+Creates a new interactive TensorFlow session.
+
+If no `graph` argument is specified when constructing the session,
+the default graph will be launched in the session. If you are
+using more than one graph (created with `tf.Graph()` in the same
+process, you will have to use different sessions for each graph,
+but each graph can be used in multiple sessions. In this case, it
+is often clearer to pass the graph to be launched explicitly to
+the session constructor.
+
+##### Args:
+
+
+*  <b>target</b>: (Optional.) The execution engine to connect to.
+    Defaults to using an in-process engine. At present, no value
+    other than the empty string is supported.
+*  <b>graph</b>: (Optional.) The `Graph` to be launched (described above).
+
+
+- - -
+
+#### tf.InteractiveSession.close() {#InteractiveSession.close}
+
+Closes an `InteractiveSession`.
+
+
+
 
 - - -
 

tensorflow/g3doc/api_docs/python/index.md

@@ -299,6 +299,7 @@
   * [`DeadlineExceededError`](client.md#DeadlineExceededError)
   * [`FailedPreconditionError`](client.md#FailedPreconditionError)
   * [`get_default_session`](client.md#get_default_session)
+  * [`InteractiveSession`](client.md#InteractiveSession)
   * [`InternalError`](client.md#InternalError)
   * [`InvalidArgumentError`](client.md#InvalidArgumentError)
   * [`NotFoundError`](client.md#NotFoundError)

tensorflow/g3doc/get_started/index.md

@@ -54,10 +54,10 @@ suggest skimming blue, then red.
 
 <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px; display: flex; flex-direction: row">
  <a href="../tutorials/mnist/beginners/index.md">
-   <img style="flex-grow:1; flex-shrink:1;border: 1px solid black;" src="./blue_pill.png">
+   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="blue_pill.png">
  </a>
  <a href="../tutorials/mnist/pros/index.md">
-   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="./red_pill.png">
+   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="red_pill.png">
  </a>
 </div>
 <p style="font-size:10px;">Images licensed CC BY-SA 4.0; original by W. Carter</p>

tensorflow/g3doc/get_started/os_setup.md

@@ -88,35 +88,8 @@ ImportError: libcudart.so.7.0: cannot open shared object file: No such file or d
 you most likely need to set your `LD_LIBRARY_PATH` to point to the location of
 your CUDA libraries.
 
-### Train the MNIST neural net model
-
-```sh
-$ python tensorflow/models/image/mnist/convolutional.py
-Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
-Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
-Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
-Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
-Extracting data/train-images-idx3-ubyte.gz
-Extracting data/train-labels-idx1-ubyte.gz
-Extracting data/t10k-images-idx3-ubyte.gz
-Extracting data/t10k-labels-idx1-ubyte.gz
-can't determine number of CPU cores: assuming 4
-I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op
-parallelism threads: 3
-can't determine number of CPU cores: assuming 4
-I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op
-parallelism threads: 4
-Initialized!
-Epoch 0.00
-Minibatch loss: 12.054, learning rate: 0.010000
-Minibatch error: 90.6%
-Validation error: 84.6%
-...
-...
-
-```
-
-## Installing from sources {#source}
+<a name="source"></a>
+## Installing from sources
 
 ### Clone the TensorFlow repository
 
@@ -260,7 +233,8 @@ Notes : You need to install
 Follow installation instructions [here](http://docs.scipy.org/doc/numpy/user/install.html).
 
 
-### Create the pip package and install {#create-pip}
+<a name="create-pip"></a>
+### Create the pip package and install
 
 ```sh
 $ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package

tensorflow/g3doc/how_tos/adding_an_op/index.md

@@ -1,4 +1,4 @@
-# Adding a New Op to TensorFlow
+# Adding a New Op
 
 PREREQUISITES:
 
@@ -27,27 +27,28 @@ to:
 
 <!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! -->
 ## Contents
-* [Define the Op's interface](#define_interface)
+* [Define the Op's interface](#AUTOGENERATED-define-the-op-s-interface)
 * [Implement the kernel for the Op](#AUTOGENERATED-implement-the-kernel-for-the-op)
 * [Generate the client wrapper](#AUTOGENERATED-generate-the-client-wrapper)
   * [The Python Op wrapper](#AUTOGENERATED-the-python-op-wrapper)
   * [The C++ Op wrapper](#AUTOGENERATED-the-c---op-wrapper)
 * [Verify it works](#AUTOGENERATED-verify-it-works)
-* [Validation](#validation)
+* [Validation](#AUTOGENERATED-validation)
 * [Op registration](#AUTOGENERATED-op-registration)
   * [Attrs](#AUTOGENERATED-attrs)
   * [Attr types](#AUTOGENERATED-attr-types)
-  * [Polymorphism](#polymorphism)
+  * [Polymorphism](#AUTOGENERATED-polymorphism)
   * [Inputs and Outputs](#AUTOGENERATED-inputs-and-outputs)
   * [Backwards compatibility](#AUTOGENERATED-backwards-compatibility)
-* [GPU Support](#mult-archs)
+* [GPU Support](#AUTOGENERATED-gpu-support)
 * [Implement the gradient in Python](#AUTOGENERATED-implement-the-gradient-in-python)
 * [Implement a shape function in Python](#AUTOGENERATED-implement-a-shape-function-in-python)
 
 
 <!-- TOC-END This section was generated by neural network, THANKS FOR READING! -->
 
-## Define the Op's interface <div class="md-anchor" id="define_interface">{#define_interface}</div>
+<a name="define_interface"></a>
+## Define the Op's interface <div class="md-anchor" id="AUTOGENERATED-define-the-op-s-interface">{#AUTOGENERATED-define-the-op-s-interface}</div>
 
 You define the interface of an Op by registering it with the TensorFlow system.
 In the registration, you specify the name of your Op, its inputs (types and
@@ -210,7 +211,7 @@ Then run your test:
 $ bazel test tensorflow/python:zero_out_op_test
 ```
 
-## Validation <div class="md-anchor" id="validation">{#validation}</div>
+## Validation <div class="md-anchor" id="AUTOGENERATED-validation">{#AUTOGENERATED-validation}</div>
 
 The example above assumed that the Op applied to a tensor of any shape.  What
 if it only applied to vectors?  That means adding a check to the above OpKernel
@@ -445,8 +446,8 @@ REGISTER_OP("AttrDefaultExampleForAllTypes")
 Note in particular that the values of type `type` use [the `DT_*` names
 for the types](../../resources/dims_types.md#data-types).
 
-### Polymorphism <div class="md-anchor" id="polymorphism">{#polymorphism}</div>
-#### Type Polymorphism {#type-polymorphism}
+### Polymorphism <div class="md-anchor" id="AUTOGENERATED-polymorphism">{#AUTOGENERATED-polymorphism}</div>
+#### Type Polymorphism
 
 For ops that can take different types as input or produce different output
 types, you can specify [an attr](#attrs) in
@@ -466,7 +467,8 @@ REGISTER\_OP("ZeroOut")
 Your Op registration now specifies that the input's type must be `float`, or
 `int32`, and that its output will be the same type, since both have type `T`.
 
-> A note on naming:{#naming} Inputs, outputs, and attrs generally should be
+<a name="naming"></a>
+> A note on naming: Inputs, outputs, and attrs generally should be
 > given snake_case names.  The one exception is attrs that are used as the type
 > of an input or in the type of an input. Those attrs can be inferred when the
 > op is added to the graph and so don't appear in the op's function.  For
@@ -482,7 +484,7 @@ Your Op registration now specifies that the input's type must be `float`, or
 >     name: A name for the operation (optional).
 >
 >   Returns:
->     A `Tensor`. Has the same type as `x`.
+>     A `Tensor`. Has the same type as `to_zero`.
 >   """
 > ```
 >
@@ -674,7 +676,8 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
 #undef REGISTER_KERNEL
 ```
 
-#### List Inputs and Outputs {#list-input-output}
+<a name="list-input-output"></a>
+#### List Inputs and Outputs
 
 In addition to being able to accept or produce different types, ops can consume
 or produce a variable number of tensors.
@@ -894,7 +897,8 @@ There are several ways to preserve backwards-compatibility.
 If you cannot make your change to an operation backwards compatible, then
 create a new operation with a new name with the new semantics.
 
-## GPU Support <div class="md-anchor" id="mult-archs">{#mult-archs}</div>
+<a name="mult-archs"></a>
+## GPU Support <div class="md-anchor" id="AUTOGENERATED-gpu-support">{#AUTOGENERATED-gpu-support}</div>
 
 You can implement different OpKernels and register one for CPU and another for
 GPU, just like you can [register kernels for different types](#polymorphism).

tensorflow/g3doc/how_tos/graph_viz/index.md

@@ -1,4 +1,4 @@
-# TensorBoard: Visualizing Your Graph
+# TensorBoard: Graph Visualization
 
 TensorFlow computation graphs are powerful but complicated. The graph visualization can help you understand and debug them. Here's an example of the visualization at work.
 

tensorflow/g3doc/how_tos/index.md

@@ -18,7 +18,7 @@ example.
 [View Tutorial](../tutorials/mnist/tf/index.md)
 
 
-## TensorBoard: Visualizing Your Training
+## TensorBoard: Visualizing Learning
 
 TensorBoard is a useful tool for visualizing the training and evaluation of
 your model(s).  This tutorial describes how to build and run TensorBoard as well
@@ -28,7 +28,7 @@ TensorBoard uses for display.
 [View Tutorial](summaries_and_tensorboard/index.md)
 
 
-## TensorBoard: Visualizing Your Graph
+## TensorBoard: Graph Visualization
 
 This tutorial describes how to use the graph visualizer in TensorBoard to help
 you understand the dataflow graph and debug it.
@@ -60,15 +60,15 @@ compose in your graph, but here are the details of how to add you own custom Op.
 [View Tutorial](adding_an_op/index.md)
 
 
-## New Data Formats
+## Custom Data Readers
 
 If you have a sizable custom data set, you may want to consider extending
 TensorFlow to read your data directly in it's native format.  Here's how.
 
 [View Tutorial](new_data_formats/index.md)
 
 
-## Using One or More GPUs
+## Using GPUs
 
 This tutorial describes how to construct and execute models on GPU(s).
 

tensorflow/g3doc/how_tos/new_data_formats/index.md

@@ -1,4 +1,4 @@
-# Extending TF: Supporting new data formats
+# Custom Data Readers
 
 PREREQUISITES:
 

tensorflow/g3doc/how_tos/reading_data/index.md

@@ -10,13 +10,13 @@ There are three main methods of getting data into a TensorFlow program:
 
 <!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! -->
 ## Contents
-* [Feeding](#Feeding)
+* [Feeding](#AUTOGENERATED-feeding)
 * [Reading from files](#AUTOGENERATED-reading-from-files)
   * [Filenames, shuffling, and epoch limits](#AUTOGENERATED-filenames--shuffling--and-epoch-limits)
   * [File formats](#AUTOGENERATED-file-formats)
   * [Preprocessing](#AUTOGENERATED-preprocessing)
   * [Batching](#AUTOGENERATED-batching)
-  * [Creating threads to prefetch using `QueueRunner` objects](#QueueRunner)
+  * [Creating threads to prefetch using `QueueRunner` objects](#AUTOGENERATED-creating-threads-to-prefetch-using--queuerunner--objects)
   * [Filtering records or producing multiple examples per record](#AUTOGENERATED-filtering-records-or-producing-multiple-examples-per-record)
   * [Sparse input data](#AUTOGENERATED-sparse-input-data)
 * [Preloaded data](#AUTOGENERATED-preloaded-data)
@@ -25,7 +25,7 @@ There are three main methods of getting data into a TensorFlow program:
 
 <!-- TOC-END This section was generated by neural network, THANKS FOR READING! -->
 
-## Feeding <div class="md-anchor" id="Feeding">{#Feeding}</div>
+## Feeding <div class="md-anchor" id="AUTOGENERATED-feeding">{#AUTOGENERATED-feeding}</div>
 
 TensorFlow's feed mechanism lets you inject data into any Tensor in a
 computation graph. A python computation can thus feed data directly into the
@@ -267,7 +267,8 @@ summary to the graph that indicates how full the example queue is. If you have
 enough reading threads, that summary will stay above zero.  You can
 [view your summaries as training progresses using TensorBoard](../summaries_and_tensorboard/index.md).
 
-### Creating threads to prefetch using `QueueRunner` objects <div class="md-anchor" id="QueueRunner">{#QueueRunner}</div>
+<a name="QueueRunner"></a>
+### Creating threads to prefetch using `QueueRunner` objects <div class="md-anchor" id="AUTOGENERATED-creating-threads-to-prefetch-using--queuerunner--objects">{#AUTOGENERATED-creating-threads-to-prefetch-using--queuerunner--objects}</div>
 
 The short version: many of the `tf.train` functions listed above add
 [`QueueRunner`](../../api_docs/python/train.md#QueueRunner) objects to your