Applied Deep Learning with Keras by Matthew Moocarme Mahla Abdolahnejad and Ritesh Bhagwat

Author:Matthew Moocarme, Mahla Abdolahnejad, and Ritesh Bhagwat
Language: eng
Format: epub
Publisher: Packt Publishing Pvt. Ltd.
Published: 2020-07-28T00:00:00+00:00

Hyperparameter Tuning with scikit-learn

Hyperparameter tuning is a very important technique for improving the performance of deep learning models. In Chapter 4, Evaluating Your Model with Cross-Validation Using Keras Wrappers, you learned about using a Keras wrapper with scikit-learn, which allows for Keras models to be used in a scikit-learn workflow. As a result, different general machine learning and data analysis tools and methods that are available in scikit-learn can be applied to Keras deep learning models. Among those methods are scikit-learn hyperparameter optimizers.

In the previous chapter, you learned how to perform hyperparameter tuning by writing user-defined functions to loop over possible values for each hyperparameter. In this section, you will learn how to perform it in a much easier way by using the various hyperparameter optimization methods that are available in scikit-learn. You will also get to practice applying those methods by completing an activity involving a real-life dataset.

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