How to Use Metrics for Deep Learning with Keras in Python
Last Updated on August 27, 2020
The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models.
In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if you want to keep track of a performance measure that better captures the skill of your model during training.
In this tutorial, you will discover how to use the built-in metrics and how to define and use your own metrics when training deep learning models in Keras.
After completing this tutorial, you will know:
- How Keras metrics work and how you can use them when training your models.
- How to use regression and classification metrics in Keras with worked examples.
- How to define and use your own custom metric in Keras with a worked example.
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- Update Jan/2020: Updated API for Keras 2.3 and TensorFlow 2.0.