[Docs] Fix a few incorrect names and missing links in queues HOWTO.

Change: 146718366

1 files changed, 20 insertions, 17 deletions

diff --git a/tensorflow/g3doc/how_tos/threading_and_queues/index.md b/tensorflow/g3doc/how_tos/threading_and_queues/index.md
index 639ad116c9..3b2c8adcbc 100644
--- a/tensorflow/g3doc/how_tos/threading_and_queues/index.md
+++ b/tensorflow/g3doc/how_tos/threading_and_queues/index.md
@@ -28,10 +28,12 @@ creating these operations.
 
 Now that you have a bit of a feel for queues, let's dive into the details...
 
-## Queue use overview
+## Queue usage overview
 
-Queues, such as `FIFOQueue` and `RandomShuffleQueue`, are important TensorFlow
-objects for computing tensors asynchronously in a graph.
+Queues, such as [`FIFOQueue`](../../api_docs/python/io_ops.md#FIFOQueue)
+and [`RandomShuffleQueue`](../../api_docs/python/io_ops.md#RandomShuffleQueue),
+are important TensorFlow objects for computing tensors asynchronously in a
+graph.
 
 For example, a typical input architecture is to use a `RandomShuffleQueue` to
 prepare inputs for training a model:
@@ -51,8 +53,8 @@ threads must be able to stop together, exceptions must be caught and
 reported, and queues must be properly closed when stopping.
 
 TensorFlow provides two classes to help:
-[tf.Coordinator](../../api_docs/python/train.md#Coordinator) and
-[tf.QueueRunner](../../api_docs/python/train.md#QueueRunner). These two classes
+[`tf.train.Coordinator`](../../api_docs/python/train.md#Coordinator) and
+[`tf.train.QueueRunner`](../../api_docs/python/train.md#QueueRunner). These two classes
 are designed to be used together. The `Coordinator` class helps multiple threads
 stop together and report exceptions to a program that waits for them to stop.
 The `QueueRunner` class is used to create a number of threads cooperating to
@@ -60,13 +62,13 @@ enqueue tensors in the same queue.
 
 ## Coordinator
 
-The Coordinator class helps multiple threads stop together.
+The `Coordinator` class helps multiple threads stop together.
 
 Its key methods are:
 
-* `should_stop()`: returns True if the threads should stop.
-* `request_stop(<exception>)`: requests that threads should stop.
-* `join(<list of threads>)`: waits until the specified threads have stopped.
+* [`should_stop()`](../../api_docs/python/train.md#Coordinator.should_stop): returns True if the threads should stop.
+* [`request_stop(exception)`](../../api_docs/python/train.md#Coordinator.request_stop): requests that threads should stop.
+* [`join(thread_list)`](../../api_docs/python/train.md#Coordinator.join): waits until the specified threads have stopped.
 
 You first create a `Coordinator` object, and then create a number of threads
 that use the coordinator.  The threads typically run loops that stop when
@@ -85,20 +87,21 @@ def MyLoop(coord):
     if ...some condition...:
       coord.request_stop()
 
-# Main code: create a coordinator.
-coord = Coordinator()
+# Main thread: create a coordinator.
+coord = tf.train.Coordinator()
 
 # Create 10 threads that run 'MyLoop()'
 threads = [threading.Thread(target=MyLoop, args=(coord,)) for i in xrange(10)]
 
 # Start the threads and wait for all of them to stop.
-for t in threads: t.start()
+for t in threads:
+  t.start()
 coord.join(threads)
 ```
 
 Obviously, the coordinator can manage threads doing very different things.
 They don't have to be all the same as in the example above.  The coordinator
-also has support to capture and report exceptions.  See the [Coordinator class](../../api_docs/python/train.md#Coordinator) documentation for more details.
+also has support to capture and report exceptions.  See the [`tf.train.Coordinator`](../../api_docs/python/train.md#Coordinator) documentation for more details.
 
 ## QueueRunner
 
@@ -109,7 +112,7 @@ queue if an exception is reported to the coordinator.
 
 You can use a queue runner to implement the architecture described above.
 
-First build a graph that uses a `Queue` for input examples.  Add ops that
+First build a graph that uses a TensorFlow queue (e.g. a `tf.RandomShuffleQueue`) for input examples.  Add ops that
 process examples and enqueue them in the queue.  Add training ops that start by
 dequeueing from the queue.
 
@@ -152,8 +155,8 @@ coord.join(enqueue_threads)
 ## Handling exceptions
 
 Threads started by queue runners do more than just run the enqueue ops.  They
-also catch and handle exceptions generated by queues, including
-`OutOfRangeError` which is used to report that a queue was closed.
+also catch and handle exceptions generated by queues, including the
+`tf.errors.OutOfRangeError` exception, which is used to report that a queue was closed.
 
 A training program that uses a coordinator must similarly catch and report
 exceptions in its main loop.
@@ -170,7 +173,7 @@ except Exception, e:
     # Report exceptions to the coordinator.
     coord.request_stop(e)
 finally:
-    # Terminate as usual.  It is innocuous to request stop twice.
+    # Terminate as usual. It is safe to call `coord.request_stop()` twice.
     coord.request_stop()
     coord.join(threads)
 ```