Classifying handwritten digits using KNIME, DL4J and a LeNet variant

The workflow downloads, uncompresses and preprocesses the orignal MNIST dataset from: The two "Normalize Images" wrapped metanodes use the KNIME Streaming functionality to convert the input files into KNIME image cells that can be used by the DL4J Learner and Predictor. The "LeNet" metanode (taken from the Node Repository) is a variant of the originally described LeNet convolutional neural network. The images and the DL4J model is then used by the Learner to train a model (saved using the DL4J Model Writer), which is then applied to the test set, which is finally scored. This Tutorial is described in the blog post Required Installations: Tools: KNIME Analytics Platform 3.3.1 (or greater) on your machine OR KNIME Cloud Analytics Platform on Azure Cloud OR KNIME Cloud Analytics Platform on AWS Cloud; Python 2.7.x configured for use with KNIME Analytics Platform:

This is a companion discussion topic for the original entry at