TensorFlow: more features, performance improvements, and doc fixes.

Changes:
- Add Split/Concat() methods to TensorUtil (meant for convenience, not
  speed) by Chris.

- Changes to linear algebra ops interface by Rasmus

- Tests for tensorboard by Daniel

- Fix bug in histogram calculation by Cassandra

- Added tool for backwards compatibility of OpDefs.  Tool
  Checks in history of opdefs and their changes, checks for
  backwards-incompatible changes.  All done by @josh11b

- Fix some protobuf example proto docs by Oliver

- Add derivative of MatrixDeterminant by @yaroslavvb

- Add a priority queue queue by @ebrevdo

- Doc and typo fixes by Aurelien and @dave-andersen

- Speed improvements to ConvBackwardFilter by @andydavis

- Improve speed of Alexnet on TitanX by @zheng-xq

- Add some host memory annotations to some GPU kernels by Yuan.

- Add support for doubles in histogram summary by @jmchen-g

Base CL: 108158338

1 files changed, 18 insertions, 34 deletions

diff --git a/tensorflow/g3doc/how_tos/reading_data/index.md b/tensorflow/g3doc/how_tos/reading_data/index.md
index 64209b8bd0..089ee4e34d 100644
--- a/tensorflow/g3doc/how_tos/reading_data/index.md
+++ b/tensorflow/g3doc/how_tos/reading_data/index.md
@@ -1,4 +1,4 @@
-# Reading data <a class="md-anchor" id="AUTOGENERATED-reading-data"></a>
+# Reading data
 
 There are three main methods of getting data into a TensorFlow program:
 
@@ -8,25 +8,9 @@ There are three main methods of getting data into a TensorFlow program:
 *   Preloaded data: a constant or variable in the TensorFlow graph holds
     all the data (for small data sets).
 
-<!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! -->
-## Contents
-### [Reading data](#AUTOGENERATED-reading-data)
-* [Feeding](#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)
-  * [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)
-* [Multiple input pipelines](#AUTOGENERATED-multiple-input-pipelines)
+[TOC]
 
-
-<!-- TOC-END This section was generated by neural network, THANKS FOR READING! -->
-
-## Feeding <a class="md-anchor" id="Feeding"></a>
+## Feeding {#Feeding}
 
 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
@@ -54,7 +38,7 @@ in
 [`tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py),
 and is described in the [MNIST tutorial](../../tutorials/mnist/tf/index.md).
 
-## Reading from files <a class="md-anchor" id="AUTOGENERATED-reading-from-files"></a>
+## Reading from files
 
 A typical pipeline for reading records from files has the following stages:
 
@@ -67,7 +51,7 @@ A typical pipeline for reading records from files has the following stages:
 7.  *Optional* preprocessing
 8.  Example queue
 
-### Filenames, shuffling, and epoch limits <a class="md-anchor" id="AUTOGENERATED-filenames--shuffling--and-epoch-limits"></a>
+### Filenames, shuffling, and epoch limits
 
 For the list of filenames, use either a constant string Tensor (like
 `["file0", "file1"]` or `[("file%d" % i) for i in range(2)]`) or the
@@ -89,7 +73,7 @@ The queue runner works in a thread separate from the reader that pulls
 filenames from the queue, so the shuffling and enqueuing process does not
 block the reader.
 
-### File formats <a class="md-anchor" id="AUTOGENERATED-file-formats"></a>
+### File formats
 
 Select the reader that matches your input file format and pass the filename
 queue to the reader's read method.  The read method outputs a key identifying
@@ -97,7 +81,7 @@ the file and record (useful for debugging if you have some weird records), and
 a scalar string value. Use one (or more) of the decoder and conversion ops to
 decode this string into the tensors that make up an example.
 
-#### CSV files <a class="md-anchor" id="AUTOGENERATED-csv-files"></a>
+#### CSV files
 
 To read text files in [comma-separated value (CSV)
 format](https://tools.ietf.org/html/rfc4180), use a
@@ -139,7 +123,7 @@ You must call `tf.train.start_queue_runners` to populate the queue before
 you call `run` or `eval` to execute the `read`. Otherwise `read` will
 block while it waits for filenames from the queue.
 
-#### Fixed length records <a class="md-anchor" id="AUTOGENERATED-fixed-length-records"></a>
+#### Fixed length records
 
 To read binary files in which each record is a fixed number of bytes, use
 [`tf.FixedLengthRecordReader`](../../api_docs/python/io_ops.md#FixedLengthRecordReader)
@@ -155,7 +139,7 @@ needed. For CIFAR-10, you can see how to do the reading and decoding in
 and described in
 [this tutorial](../../tutorials/deep_cnn/index.md#prepare-the-data).
 
-#### Standard TensorFlow format <a class="md-anchor" id="AUTOGENERATED-standard-tensorflow-format"></a>
+#### Standard TensorFlow format
 
 Another approach is to convert whatever data you have into a supported format.
 This approach makes it easier to mix and match data sets and network
@@ -181,7 +165,7 @@ found in
 [`tensorflow/g3doc/how_tos/reading_data/fully_connected_reader.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/g3doc/how_tos/reading_data/fully_connected_reader.py),
 which you can compare with the `fully_connected_feed` version.
 
-### Preprocessing <a class="md-anchor" id="AUTOGENERATED-preprocessing"></a>
+### Preprocessing
 
 You can then do any preprocessing of these examples you want. This would be any
 processing that doesn't depend on trainable parameters. Examples include
@@ -190,7 +174,7 @@ etc.  See
 [`tensorflow/models/image/cifar10/cifar10.py`](https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/models/image/cifar10/cifar10.py)
 for an example.
 
-### Batching <a class="md-anchor" id="AUTOGENERATED-batching"></a>
+### Batching
 
 At the end of the pipeline we use another queue to batch together examples for
 training, evaluation, or inference.  For this we use a queue that randomizes the
@@ -268,7 +252,7 @@ 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](../../how_tos/summaries_and_tensorboard/index.md).
 
-### Creating threads to prefetch using `QueueRunner` objects <a class="md-anchor" id="QueueRunner"></a>
+### Creating threads to prefetch using `QueueRunner` objects {#QueueRunner}
 
 The short version: many of the `tf.train` functions listed above add
 [`QueueRunner`](../../api_docs/python/train.md#QueueRunner) objects to your
@@ -312,7 +296,7 @@ coord.join(threads)
 sess.close()
 ```
 
-#### Aside: What is happening here? <a class="md-anchor" id="AUTOGENERATED-aside--what-is-happening-here-"></a>
+#### Aside: What is happening here?
 
 First we create the graph. It will have a few pipeline stages that are
 connected by queues. The first stage will generate filenames to read and enqueue
@@ -357,7 +341,7 @@ exception).
 For more about threading, queues, QueueRunners, and Coordinators
 [see here](../../how_tos/threading_and_queues/index.md).
 
-#### Aside: How clean shut-down when limiting epochs works <a class="md-anchor" id="AUTOGENERATED-aside--how-clean-shut-down-when-limiting-epochs-works"></a>
+#### Aside: How clean shut-down when limiting epochs works
 
 Imagine you have a model that has set a limit on the number of epochs to train
 on.  That means that the thread generating filenames will only run that many
@@ -400,7 +384,7 @@ errors and exiting.  Once all the training threads are done,
 [`tf.train.Coordinator.join`](../../api_docs/python/train.md#Coordinator.join)
 will return and you can exit cleanly.
 
-### Filtering records or producing multiple examples per record <a class="md-anchor" id="AUTOGENERATED-filtering-records-or-producing-multiple-examples-per-record"></a>
+### Filtering records or producing multiple examples per record
 
 Instead of examples with shapes `[x, y, z]`, you will produce a batch of
 examples with shape `[batch, x, y, z]`.  The batch size can be 0 if you want to
@@ -409,14 +393,14 @@ are producing multiple examples per record.  Then simply set `enqueue_many=True`
 when calling one of the batching functions (such as `shuffle_batch` or
 `shuffle_batch_join`).
 
-### Sparse input data <a class="md-anchor" id="AUTOGENERATED-sparse-input-data"></a>
+### Sparse input data
 
 SparseTensors don't play well with queues. If you use SparseTensors you have
 to decode the string records using
 [`tf.parse_example`](../../api_docs/python/io_ops.md#parse_example) **after**
 batching (instead of using `tf.parse_single_example` before batching).
 
-## Preloaded data <a class="md-anchor" id="AUTOGENERATED-preloaded-data"></a>
+## Preloaded data
 
 This is only used for small data sets that can be loaded entirely in memory.
 There are two approaches:
@@ -475,7 +459,7 @@ An MNIST example that preloads the data using constants can be found in
 You can compare these with the `fully_connected_feed` and
 `fully_connected_reader` versions above.
 
-## Multiple input pipelines <a class="md-anchor" id="AUTOGENERATED-multiple-input-pipelines"></a>
+## Multiple input pipelines
 
 Commonly you will want to train on one dataset and evaluate (or "eval") on
 another.  One way to do this is to actually have two separate processes: