1.48 Programmer's Guide

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2023-12-01

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}