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Small grammar fixes in the programmers guide FAQ. #19170

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May 9, 2018
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Small grammar fixes in the programmers guide FAQ. #19170

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Merge pull request #2 from tensorflow/master
ctiijima May 8, 2018
2f96e8c
Grammar fixes for Programmers guide FAQ
ctiijima May 9, 2018
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17 changes: 8 additions & 9 deletions tensorflow/docs_src/programmers_guide/faq.md
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Expand Up @@ -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:

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -137,7 +136,7 @@ TensorFlow also has a
to help build support for more client languages. We invite contributions of new
language bindings.

Bindings for various other languages (such as [C#](https://github.com/migueldeicaza/TensorFlowSharp), [Julia](https://github.com/malmaud/TensorFlow.jl), [Ruby](https://github.com/somaticio/tensorflow.rb) and [Scala](https://github.com/eaplatanios/tensorflow_scala)) created and supported by the opensource community build on top of the C API supported by the TensorFlow maintainers.
Bindings for various other languages (such as [C#](https://github.com/migueldeicaza/TensorFlowSharp), [Julia](https://github.com/malmaud/TensorFlow.jl), [Ruby](https://github.com/somaticio/tensorflow.rb) and [Scala](https://github.com/eaplatanios/tensorflow_scala)) created and supported by the open source community build on top of the C API supported by the TensorFlow maintainers.

#### Does TensorFlow make use of all the devices (GPUs and CPUs) available on my machine?

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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.
Expand Down