Multilayerperceptron example added with seaborndataset #51
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Samyssmile
requested changes
Oct 17, 2023
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We have already gradle wrapper. Delete this file.
| * Knn - K nearest neighbors | ||
| * Dataset: Seaborn Penguins | ||
| */ | ||
| public class MultilayerPerceptronExample2 { |
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Rename this to MultilayerPerceptronSeabornExample
Also Rename the old MultilayerPerceptronExample class to MultilayerPerceptronIrisExample
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| NetworkConfiguration networkConfiguration = new NetworkConfiguration(seabornProvider.getTrainFeatures()[0].length, List.of(32, 6), 3, 0.01, 1000, ActivationFunction.LEAKY_RELU, ActivationFunction.SOFTMAX, LossFunction.CATEGORICAL_CROSS_ENTROPY, Initialization.XAVIER, Initialization.XAVIER); | ||
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| MultilayerPerceptron multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels); |
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We have unified Interface for all classifiers. Replace datatype here with
Classifier multilayerPerceptron = ...
| import de.edux.functions.activation.ActivationFunction; | ||
| import de.edux.functions.initialization.Initialization; | ||
| import de.edux.functions.loss.LossFunction; | ||
| import de.edux.ml.knn.KnnClassifier; |
| NetworkConfiguration networkConfiguration = new NetworkConfiguration(seabornProvider.getTrainFeatures()[0].length, List.of(32, 6), 3, 0.01, 1000, ActivationFunction.LEAKY_RELU, ActivationFunction.SOFTMAX, LossFunction.CATEGORICAL_CROSS_ENTROPY, Initialization.XAVIER, Initialization.XAVIER); | ||
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| MultilayerPerceptron multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels); | ||
| multilayerPerceptron.train(seabornProvider.getTrainFeatures(), labels); |
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You defined already double[][] features = seabornProvider.getTrainFeatures();
Replace your seabornProvider.getTrainFeatures() call here, with variable features
| import java.util.List; | ||
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| /** | ||
| * Knn - K nearest neighbors |
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Remove this copy pasted part. Wrong JavaDoc here.
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