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Basic_​Example_​Iris_​Dataset

Simple Example for Multiclass Classification with Keras

This workflow trains a fully connected feedforward neural network with 4-8-3 units per layers to classify iris flowers.


Define network strucuture and train the network Read and preprocess iris data Apply trained network and extract predictions hidden layer8 unitsReLUtransformclass to indexapply trained networkreading the iris datasetz-scoreRMSProp50 epochsapplynormaliz.functioncreate collection cell of indicesoutput layer3 unitssoftmax75% for training 25 % for testinginput layer 4 unitsextract predictionevaluate network performance Keras Dense Layer Rule Engine Keras NetworkExecutor Table Reader Normalizer Keras NetworkLearner Normalizer (Apply) Create CollectionColumn Keras Dense Layer Partitioning Keras Input Layer Rule Engine Scorer Define network strucuture and train the network Read and preprocess iris data Apply trained network and extract predictions hidden layer8 unitsReLUtransformclass to indexapply trained networkreading the iris datasetz-scoreRMSProp50 epochsapplynormaliz.functioncreate collection cell of indicesoutput layer3 unitssoftmax75% for training 25 % for testinginput layer 4 unitsextract predictionevaluate network performance Keras Dense Layer Rule Engine Keras NetworkExecutor Table Reader Normalizer Keras NetworkLearner Normalizer (Apply) Create CollectionColumn Keras Dense Layer Partitioning Keras Input Layer Rule Engine Scorer

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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: Emilio Silvestri
Created at: 2020-02-12
On NodePit since: 2020-12-17
Last update: 2024-05-01
Created with KNIME version: v4.3.0
Tags: Perceptronneural networksdeep learning

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