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CNN_​For_​Image_​Classification_​(Fashion_​MNIST)_​Simple

CNN for Image Classification of the MNIST Fashion Dataset

This workflow trains a Convolution Neural Network (CNN) to classify the images of the MNIST Fashion Dataset into ten different classes.

URL: KNIME Image Processing - Deep Learning Extension https://kni.me/e/JMlIEafbwxOPn652
URL: Dataset https://peltarion.com/knowledge-center/documentation/datasets-view/datasets-used-in-tutorials/fashion-mnist-dataset

Define network architecture Read and preprocss fashion MNIST dataset Train and apply network Evaluate and save network Note: This workflow uses only a subset of the Fashion MNIST dataset. You can download the full dataset here: https://peltarion.com/knowledge-center/documentation/datasets-view/datasets-used-in-tutorials/fashion-mnist-dataset Important: To execute this workflow you need to install KNIME Image Processing - Deep Learning Extension: https://kni.me/e/JMlIEafbwxOPn652 Read labelsAdd classlabelMap category to indexCreate collectioncell with label indexInput shape: 28,28,1Kernel 3,3Filter 64Units: 100Activation ReLUUnits 10Activation: SoftmaxEpochs: 20Batch Size: 32Normalizeimages70% training30% testingEvalutate the modelSave thetrained modelin a .h5 fileClass with highest probabilitySize 2,2 CSV Reader Cell Replacer(deprecated) Category to Number Create CollectionColumn MISSING KerasInput Layer MISSING KerasConvolution 2D Layer MISSING KerasFlatten Layer MISSING KerasDense Layer MISSING KerasDense Layer MISSING KerasNetwork Learner MISSING ImageCalculator Partitioning MISSING KerasNetwork Executor Scorer Read Images MISSING KerasNetwork Writer Extract Prediction MISSING Keras MaxPooling 2D Layer Define network architecture Read and preprocss fashion MNIST dataset Train and apply network Evaluate and save network Note: This workflow uses only a subset of the Fashion MNIST dataset. You can download the full dataset here: https://peltarion.com/knowledge-center/documentation/datasets-view/datasets-used-in-tutorials/fashion-mnist-dataset Important: To execute this workflow you need to install KNIME Image Processing - Deep Learning Extension: https://kni.me/e/JMlIEafbwxOPn652 Read labelsAdd classlabelMap category to indexCreate collectioncell with label indexInput shape: 28,28,1Kernel 3,3Filter 64Units: 100Activation ReLUUnits 10Activation: SoftmaxEpochs: 20Batch Size: 32Normalizeimages70% training30% testingEvalutate the modelSave thetrained modelin a .h5 fileClass with highest probabilitySize 2,2CSV Reader Cell Replacer(deprecated) Category to Number Create CollectionColumn MISSING KerasInput Layer MISSING KerasConvolution 2D Layer MISSING KerasFlatten Layer MISSING KerasDense Layer MISSING KerasDense Layer MISSING KerasNetwork Learner MISSING ImageCalculator Partitioning MISSING KerasNetwork Executor Scorer Read Images MISSING KerasNetwork Writer Extract Prediction MISSING Keras MaxPooling 2D Layer

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

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Created by: kathrinmelcher
Created at: 2021-06-11
On NodePit since: 2024-08-07
Last update: 2024-09-16
Created with KNIME version: v5.3.0
Tags: CNNImage ClassificationDeep LearningKeras

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