message: Grammar fixes for Programmers guide FAQ
diff: @@ -72,7 +72,7 @@ tensors in the execution of a step.
  If `t` is a @{tf.Tensor} object,
  @{tf.Tensor.eval} is shorthand for
  -@{tf.Session.run} (where `sess` is the
  +@{tf.Session.run}, where `sess` is the
  current @{tf.get_default_session}. The
  two following snippets of code are equivalent:
  @@ -101,9 +101,8 @@ sessions, it may be more straightforward to make explicit calls to
  Sessions can own resources, such as
  @{tf.Variable},
  @{tf.QueueBase}, and
  -@{tf.ReaderBase}; and these resources can use
  -a significant amount of memory. These resources (and the associated memory) are
  -released when the session is closed, by calling
  +@{tf.ReaderBase}. These resources can sometimes use
  +a significant amount of memory, and can be released when the session is closed by calling
  @{tf.Session.close}.
  The intermediate tensors that are created as part of a call to
  @@ -210,8 +209,8 @@ a new tensor with a different dynamic shape.
  #### How do I build a graph that works with variable batch sizes?
  -It is often useful to build a graph that works with variable batch sizes, for
  -example so that the same code can be used for (mini-)batch training, and
  +It is often useful to build a graph that works with variable batch sizes
  +so that the same code can be used for (mini-)batch training, and
  single-instance inference. The resulting graph can be
  @{tf.Graph.as_graph_def$saved as a protocol buffer}
  and
  @@ -260,7 +259,7 @@ See the how-to documentation for
  There are three main options for dealing with data in a custom format.
  The easiest option is to write parsing code in Python that transforms the data
  -into a numpy array. Then use @{tf.data.Dataset.from_tensor_slices} to
  +into a numpy array. Then, use @{tf.data.Dataset.from_tensor_slices} to
  create an input pipeline from the in-memory data.
  If your data doesn't fit in memory, try doing the parsing in the Dataset
  @@ -274,7 +273,7 @@ If your data is not easily parsable with the built-in TensorFlow operations,
  consider converting it, offline, to a format that is easily parsable, such
  as @{tf.python_io.TFRecordWriter$`TFRecord`} format.
  -The more efficient method to customize the parsing behavior is to
  +The most efficient method to customize the parsing behavior is to
  @{$adding_an_op$add a new op written in C++} that parses your
  data format. The @{$new_data_formats$guide to handling new data formats} has
  more information about the steps for doing this.
  