The workflow downloads, uncompresses and preprocesses the orignal MNIST dataset from: http://yann.lecun.com/exdb/mnist/ 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 https://www.knime.org/blog/MNIST-DL4J-Intro 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: https://www.knime.org/blog/how-to-setup-the-python-extension
EXAMPLES Server: 04_Analytics/14_Deep_Learning/01_DL4J/14_DeepLearningTutorial_MNIST/01_Using_DeepLearning4J_to_classify_MNIST_Digits04_Analytics/14_Deep_Learning/01_DL4J/14_DeepLearningTutorial_MNIST/01_Using_DeepLearning4J_to_classify_MNIST_Digits*
Download a zip-archive
* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually