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:

The following guide is helpful when training a complex model over multiple days:

TensorFlow provides a debugger named tfdbg, which is documented in the following guide:

  • Debugging TensorFlow Programs, which walks you through the use of tfdbg within an application. It covers using tfdbg with both the low-level TensorFlow API and the Estimator 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:

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:

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

We conclude this section with a FAQ about TensorFlow programming: