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How do I remove boilerplate code with TensorFlow-Slim’s meta-operator?

Learn how to use TensorFlow-Slim’s meta-operators to build deep learning models with a substantially reduced amount of code.

By Marvin Bertin June 2, 2017 • 1 minute read

Screenshot from "How do I remove boilerplate code with TensorFlow-Slim's meta-operator?" Screenshot from "How do I remove boilerplate code with TensorFlow-Slim's meta-operator?" (source: O'Reilly)

Reduce neural network complexity with TensorFlow-Slim’s meta-operators. Data scientist, Marvin Bertin, demonstrates how to build readable and maintainable deep learning models using TF-Slim. You’ll learn how the wrapper functions and high level layers added to the core TensorFlow library allow you to eliminate the boilerplate code that plagues many deep learning algorithms.



Learn more about TensorFlow-Slim (TF-Slim), and how it allows you to build and train complex deep learning models in an easy, intuitive way.

Post topics: Intelligence matters: Artificial intelligence and algorithms