5 Step Life-Cycle for Neural Network Models in Keras

Last Updated on August 27, 2020

Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle.

In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model.

After reading this post you will know:

• How to define, compile, fit and evaluate a deep learning neural network in Keras.
• How to select standard defaults for regression and classification predictive modeling problems.
• How to tie it all together to develop and run your first Multilayer Perceptron network in 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 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.