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# Programmer's Guide
The documents in this unit dive into the details of writing TensorFlow
code. This section begins with the following guides, each of which
explain a particular aspect of TensorFlow:
* @{$variables$Variables: Creation, Initialization, Saving, and Loading},
which details the mechanics of TensorFlow Variables.
* @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
rank (the number of dimensions), shape (the size of each dimension),
and datatypes.
* @{$variable_scope$Sharing Variables}, which explains how to share and
manage large sets of variables when building complex models.
* @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
rich queuing system.
* @{$reading_data$Reading Data}, which documents three different mechanisms
for getting data into a TensorFlow program.
The following guide is helpful when training a complex model over multiple
days:
* @{$supervisor$Supervisor: Training Helper for Days-Long Trainings}, which
explains how to gracefully handle system crashes during a lengthy training
session.
TensorFlow provides a debugger named `tfdbg`, which is documented in the
following two guides:
* @{$debugger$TensorFlow Debugger (tfdbg) Command-Line-Interface Tutorial: MNIST},
which walks you through the use of `tfdbg` within an application written
in the low-level TensorFlow API.
* @{$tfdbg-tflearn$How to Use TensorFlow Debugger (tfdbg) with tf.contrib.learn},
which demonstrates how to use `tfdbg` within the Estimators API.
A `MetaGraph` consists of both a computational graph and its associated
metadata. A `MetaGraph` contains the information required to continue
training, perform evaluation, or run inference on a previously
trained graph. The following guide details `MetaGraph` objects:
* @{$meta_graph$Exporting and Importing a MetaGraph}.
To learn about the TensorFlow versioning scheme, consult the following two
guides:
* @{$version_semantics$TensorFlow Version Semantics}, which explains
TensorFlow's versioning nomenclature and compatibility rules.
* @{$data_versions$TensorFlow Data Versioning: GraphDefs and Checkpoints},
which explains how TensorFlow adds versioning information to computational
graphs and checkpoints in order to support compatibility across versions.
We conclude this section with a FAQ about TensorFlow programming:
* @{$faq$Frequently Asked Questions}
reading_data.md
threading_and_queues.md
index.md
variables.md
dims_types.md
variable_scope.md
version_semantics.md
data_versions.md
threading_and_queues.md
reading_data.md
supervisor.md
debugger.md
tfdbg-tflearn.md
meta_graph.md
version_semantics.md
data_versions.md
faq.md
dims_types.md
variables.md
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