Icon

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.

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 Category To Number Create CollectionColumn Keras Input Layer Keras Convolution2D Layer Keras Flatten Layer Keras Dense Layer Keras Dense Layer Keras NetworkLearner Image Calculator Partitioning Keras NetworkExecutor Scorer Read Images Keras NetworkWriter Extract Prediction Keras Max Pooling2D 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 2,2 CSV Reader Cell Replacer Category To Number Create CollectionColumn Keras Input Layer Keras Convolution2D Layer Keras Flatten Layer Keras Dense Layer Keras Dense Layer Keras NetworkLearner Image Calculator Partitioning Keras NetworkExecutor Scorer Read Images Keras NetworkWriter Extract Prediction Keras Max Pooling2D Layer

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: kathrinmelcher
Created at: 2021-06-11
On NodePit since: 2021-08-31
Last update: 2024-04-27
Created with KNIME version: v4.4.0
Tags: CNNImage ClassificationDeep LearningKeras

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!