From 76e8a4ec287c11d5b1286244d1821994640dbecf Mon Sep 17 00:00:00 2001 From: ctiijima Date: Wed, 9 May 2018 09:50:48 -0700 Subject: [PATCH] Grammar fixes for Programmers guide FAQ (#19170) --- tensorflow/docs_src/programmers_guide/faq.md | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/programmers_guide/faq.md index 51c1a1e..b6291a9 100644 --- a/tensorflow/docs_src/programmers_guide/faq.md +++ b/tensorflow/docs_src/programmers_guide/faq.md @@ -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 @@ -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? @@ -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. -- 2.7.4