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01_​Using_​DeepLearning4J_​to_​classify_​MNIST_​Digits

Classifying handwritten digits using KNIME, DL4J and a LeNet variant

The workflow downloads, uncompresses and preprocesses the original MNIST dataset. The two "Normalize Images" components 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 workflow classifys handwritten digits using KNIME, DL4J and a LeNet variant Important! Install KNIME Image Processing - Deeplearning4J Integration (64bit only) extension before executing the workflow The single layer Neural Network architecture The five layer Neural Network (LeNet) architecture Double-click tosee the networkarchitectureInspect the trainingset imagesTrain the LeNetnetwork model(training will take some time)Make the predictionGenerate theconfusion matrixMake the predictionGenerate theconfusion matrixTrain a network modelSave the trained modelfor reuse LeNet DL4J ModelInitializer Image Viewer DL4J Feedforward Learner(Classification) DL4J Feedforward Predictor(Classification) Scorer DL4J Feedforward Predictor(Classification) Scorer Dense Layer DL4J Feedforward Learner(Classification) DL4J Model Writer Normalizeimages (test) Normalizeimages (train) Download datasetand convert to CSV Missing Value Normalizeimages (test) Normalizeimages (train) Download datasetand convert to CSV This workflow classifys handwritten digits using KNIME, DL4J and a LeNet variant Important! Install KNIME Image Processing - Deeplearning4J Integration (64bit only) extension before executing the workflow The single layer Neural Network architecture The five layer Neural Network (LeNet) architecture Double-click tosee the networkarchitectureInspect the trainingset imagesTrain the LeNetnetwork model(training will take some time)Make the predictionGenerate theconfusion matrixMake the predictionGenerate theconfusion matrixTrain a network modelSave the trained modelfor reuseLeNet DL4J ModelInitializer Image Viewer DL4J Feedforward Learner(Classification) DL4J Feedforward Predictor(Classification) Scorer DL4J Feedforward Predictor(Classification) Scorer Dense Layer DL4J Feedforward Learner(Classification) DL4J Model Writer Normalizeimages (test) Normalizeimages (train) Download datasetand convert to CSV Missing Value Normalizeimages (test) Normalizeimages (train) Download datasetand convert to CSV

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: jon
Created at: 2017-02-01
On NodePit since: 2021-05-12
Last update: 2024-12-17
Created with KNIME version: v4.1.0
Tags: deep learningGPUimage classificationdigit recognitionLe Net

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