Multi-Class Classification Tutorial with the Keras Deep Learning Library

Last Updated on August 27, 2020

Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow.

In this tutorial, you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems.

After completing this step-by-step tutorial, you will know:

• How to load data from CSV and make it available to Keras.
• How to prepare multi-class classification data for modeling with neural networks.
• How to evaluate Keras neural network models with scikit-learn.

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 for Keras 1.1.0 and scikit-learn v0.18.
• Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
• Update Jun/2017: Updated to use softmax activation in output layer, larger hidden layer, default weight initialization.
• Update Aug/2019: Added complete working example for convenience, removed random seed.
• Update Sep/2019: Updated for Keras 2.2.5 API.