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 two guides:
tfdbgwithin an application written in the low-level TensorFlow API.
tfdbgwithin the Estimators API.
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
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:
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.