Preprocessing Layers#

Keras Preprocessing Layers are a set of Keras layers aimed at making preprocessing data fit more naturally into model development workflows. They can handle a wide range of input, including structured data, images, and text and can be combined directly with Keras models and exported as part of a Keras SavedModel.


Keras input processing pipelines can also be used as independent preprocessing code in non-Keras workflows

With Keras preprocessing layers, you can build and export models that are truly end-to-end:

  • models that accept raw images or raw structured data as input

  • models that handle feature normalization or feature value indexing on their own.

To learn more about Keras Preprocessing layers, review the videos in the following sections.

Quick introduction#

Check out this quick introduction to machine learning data processing as well as its challenges.

Learn how to easily prepare your data using the new Keras Preprocessing Layers API – in particular, how to do asynchronous preprocessing as part of your data pipeline, and how to export an end-to-end model that embeds its own preprocessing logic:

End-to-End example#

Google Software Engineer Matthew Watson highlights Keras Preprocessing Layers’ ability to streamline model development workflows. Follow along as he builds an end-to-end model showing what you can do with these layers.