Evaluate the Performance Of Deep Learning Models in Keras

Last Updated on August 27, 2020

Keras is an easy to use and powerful Python library for deep learning.

There are a lot of decisions to make when designing and configuring your deep learning models. Most of these decisions must be resolved empirically through trial and error and evaluating them on real data.

As such, it is critically important to have a robust way to evaluate the performance of your neural networks and deep learning models.

In this post you will discover a few ways that you can use to evaluate model performance using Keras.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

• Update Oct/2016: Updated examples for Keras 1.1.0 and scikit-learn v0.18.
• Update Mar/2017: Updated example for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
• Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.