Programmer's Guide

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: Creation, Initialization, Saving, and Loading, which details the mechanics of TensorFlow Variables.
• Tensor Ranks, Shapes, and Types, which explains Tensor rank (the number of dimensions), shape (the size of each dimension), and datatypes.
• Sharing Variables, which explains how to share and manage large sets of variables when building complex models.
• Threading and Queues, which explains TensorFlow's rich queuing system.
• 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: 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:

• 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-
• 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:

• Exporting and Importing a MetaGraph.

SavedModel is the universal serialization format for Tensorflow models. TensorFlow provides SavedModel CLI (command-line interface) as a tool to inspect and execute a MetaGraph in a SavedModel. The detailed usages and examples are documented in the following guide:

• SavedModel CLI (Command-Line Interface).

To learn about the TensorFlow versioning scheme, consult the following two guides:

• TensorFlow Version Semantics, which explains TensorFlow's versioning nomenclature and compatibility rules.
• 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:

• Frequently Asked Questions