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Train an FFNN for Classification

This workflow shows how to train an FFNN for multionomial classification of the iris dataset.

Reading and preprocessing the data Assembling the neural network Training the neuralnetwork Evaluating the trained neural network Saving the trained neural network Train a Feed Forward Neural Network (FFNN) for Multionomial ClassificationThis workflow shows how to train an FFNN for multionomial classification of the iris dataset. Training thenetwork100 epochsBatch size 5Adam optimizerApply the trained network70% - training15 % - validation15 % - testingHidden layer8 unitsReLUOutput layer3 unitSoftmaxMin-Max[0,1]Input layer 4 unitsExtractpredictionIrisdatasetEvaluateSave the networkApply one hotencoding to thetarget column Keras NetworkLearner Normalizer (Apply) Keras NetworkExecutor Partitioning Keras Dense Layer Keras Dense Layer Normalizer Keras Input Layer Rule Engine Table Reader Scorer Partitioning Normalizer (Apply) Keras NetworkWriter One to Many Reading and preprocessing the data Assembling the neural network Training the neuralnetwork Evaluating the trained neural network Saving the trained neural network Train a Feed Forward Neural Network (FFNN) for Multionomial ClassificationThis workflow shows how to train an FFNN for multionomial classification of the iris dataset. Training thenetwork100 epochsBatch size 5Adam optimizerApply the trained network70% - training15 % - validation15 % - testingHidden layer8 unitsReLUOutput layer3 unitSoftmaxMin-Max[0,1]Input layer 4 unitsExtractpredictionIrisdatasetEvaluateSave the networkApply one hotencoding to thetarget column Keras NetworkLearner Normalizer (Apply) Keras NetworkExecutor Partitioning Keras Dense Layer Keras Dense Layer Normalizer Keras Input Layer Rule Engine Table Reader Scorer Partitioning Normalizer (Apply) Keras NetworkWriter One to Many

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