Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras

Last Updated on August 27, 2020

A popular demonstration of the capability of deep learning techniques is object recognition in image data.

The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition.

In this post you will discover how to develop a deep learning model to achieve near state of the art performance on the MNIST handwritten digit recognition task in Python using the Keras deep learning library.

After completing this tutorial, you will know:

• How to load the MNIST dataset in Keras.
• How to develop and evaluate a baseline neural network model for the MNIST problem.
• How to implement and evaluate a simple Convolutional Neural Network for MNIST.
• How to implement a close to state-of-the-art deep learning model for MNIST.

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, TensorFlow 0.10.0 and scikit-learn v0.18.
• Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
• Update Sep/2019: Updated for Keras 2.2.5 API.

Note, for an extended version of this tutorial see: