BAEL-1208: A guide to deeplearning4j (#2717)
This commit is contained in:
Sergey Petunin
Grzegorz Piwowarek
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491649a866
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a48a401dc2
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### Sample deeplearning4j Project
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This is a sample project for the [deeplearning4j](https://deeplearning4j.org) library.
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### Relevant Articles:
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- [A Guide to deeplearning4j](http://www.baeldung.com/a-guide-to-deeplearning4j/)
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<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>com.baeldung.deeplearning4j</groupId>
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<artifactId>deeplearning4j</artifactId>
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<packaging>jar</packaging>
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<version>1.0-SNAPSHOT</version>
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<name>deeplearning4j</name>
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<properties>
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<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
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<maven.compiler.source>1.8</maven.compiler.source>
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<maven.compiler.target>1.8</maven.compiler.target>
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<dl4j.version>0.9.1</dl4j.version>
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</properties>
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<dependencies>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native-platform</artifactId>
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<version>${dl4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.deeplearning4j</groupId>
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<artifactId>deeplearning4j-core</artifactId>
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<version>${dl4j.version}</version>
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</dependency>
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</dependencies>
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</project>
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package com.baeldung.deeplearning4j;
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import org.datavec.api.records.reader.RecordReader;
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import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
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import org.datavec.api.split.FileSplit;
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import org.datavec.api.util.ClassPathResource;
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import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.SplitTestAndTrain;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
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import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import java.io.IOException;
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public class IrisClassifier {
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private static final int CLASSES_COUNT = 3;
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private static final int FEATURES_COUNT = 4;
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public static void main(String[] args) throws IOException, InterruptedException {
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DataSet allData;
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try (RecordReader recordReader = new CSVRecordReader(0, ',')) {
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recordReader.initialize(new FileSplit(new ClassPathResource("iris.txt").getFile()));
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DataSetIterator iterator = new RecordReaderDataSetIterator(recordReader, 150, FEATURES_COUNT, CLASSES_COUNT);
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allData = iterator.next();
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}
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allData.shuffle(42);
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DataNormalization normalizer = new NormalizerStandardize();
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normalizer.fit(allData);
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normalizer.transform(allData);
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SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.65);
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DataSet trainingData = testAndTrain.getTrain();
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DataSet testData = testAndTrain.getTest();
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MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
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.iterations(1000)
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.activation(Activation.TANH)
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.weightInit(WeightInit.XAVIER)
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.learningRate(0.1)
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.regularization(true).l2(0.0001)
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.list()
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.layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT).nOut(3)
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.build())
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.layer(1, new DenseLayer.Builder().nIn(3).nOut(3)
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.build())
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.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.activation(Activation.SOFTMAX)
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.nIn(3).nOut(CLASSES_COUNT).build())
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.backprop(true).pretrain(false)
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.build();
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MultiLayerNetwork model = new MultiLayerNetwork(configuration);
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model.init();
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model.fit(trainingData);
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INDArray output = model.output(testData.getFeatureMatrix());
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Evaluation eval = new Evaluation(CLASSES_COUNT);
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eval.eval(testData.getLabels(), output);
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System.out.println(eval.stats());
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}
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}
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@ -0,0 +1,150 @@
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5.1,3.5,1.4,0.2,0
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4.9,3.0,1.4,0.2,0
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4.7,3.2,1.3,0.2,0
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4.6,3.1,1.5,0.2,0
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5.0,3.6,1.4,0.2,0
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5.4,3.9,1.7,0.4,0
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4.6,3.4,1.4,0.3,0
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5.0,3.4,1.5,0.2,0
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4.4,2.9,1.4,0.2,0
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4.9,3.1,1.5,0.1,0
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5.4,3.7,1.5,0.2,0
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4.8,3.4,1.6,0.2,0
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4.8,3.0,1.4,0.1,0
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4.3,3.0,1.1,0.1,0
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5.8,4.0,1.2,0.2,0
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5.7,4.4,1.5,0.4,0
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5.4,3.9,1.3,0.4,0
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5.1,3.5,1.4,0.3,0
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5.7,3.8,1.7,0.3,0
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5.1,3.8,1.5,0.3,0
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5.4,3.4,1.7,0.2,0
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5.1,3.7,1.5,0.4,0
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4.6,3.6,1.0,0.2,0
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5.1,3.3,1.7,0.5,0
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4.8,3.4,1.9,0.2,0
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5.0,3.0,1.6,0.2,0
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5.0,3.4,1.6,0.4,0
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5.2,3.5,1.5,0.2,0
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5.2,3.4,1.4,0.2,0
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4.7,3.2,1.6,0.2,0
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4.8,3.1,1.6,0.2,0
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5.4,3.4,1.5,0.4,0
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5.2,4.1,1.5,0.1,0
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5.5,4.2,1.4,0.2,0
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4.9,3.1,1.5,0.1,0
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5.0,3.2,1.2,0.2,0
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5.5,3.5,1.3,0.2,0
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4.9,3.1,1.5,0.1,0
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4.4,3.0,1.3,0.2,0
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5.1,3.4,1.5,0.2,0
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5.0,3.5,1.3,0.3,0
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4.5,2.3,1.3,0.3,0
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4.4,3.2,1.3,0.2,0
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5.0,3.5,1.6,0.6,0
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5.1,3.8,1.9,0.4,0
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4.8,3.0,1.4,0.3,0
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5.1,3.8,1.6,0.2,0
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4.6,3.2,1.4,0.2,0
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5.3,3.7,1.5,0.2,0
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5.0,3.3,1.4,0.2,0
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7.0,3.2,4.7,1.4,1
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6.4,3.2,4.5,1.5,1
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6.9,3.1,4.9,1.5,1
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5.5,2.3,4.0,1.3,1
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6.5,2.8,4.6,1.5,1
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5.7,2.8,4.5,1.3,1
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6.3,3.3,4.7,1.6,1
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4.9,2.4,3.3,1.0,1
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6.6,2.9,4.6,1.3,1
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5.2,2.7,3.9,1.4,1
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5.0,2.0,3.5,1.0,1
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5.9,3.0,4.2,1.5,1
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6.0,2.2,4.0,1.0,1
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6.1,2.9,4.7,1.4,1
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5.6,2.9,3.6,1.3,1
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6.7,3.1,4.4,1.4,1
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5.6,3.0,4.5,1.5,1
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5.8,2.7,4.1,1.0,1
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6.2,2.2,4.5,1.5,1
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5.6,2.5,3.9,1.1,1
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5.9,3.2,4.8,1.8,1
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6.1,2.8,4.0,1.3,1
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6.3,2.5,4.9,1.5,1
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6.1,2.8,4.7,1.2,1
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6.4,2.9,4.3,1.3,1
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6.6,3.0,4.4,1.4,1
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6.8,2.8,4.8,1.4,1
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6.7,3.0,5.0,1.7,1
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6.0,2.9,4.5,1.5,1
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5.7,2.6,3.5,1.0,1
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5.5,2.4,3.8,1.1,1
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5.5,2.4,3.7,1.0,1
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5.8,2.7,3.9,1.2,1
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6.0,2.7,5.1,1.6,1
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5.4,3.0,4.5,1.5,1
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6.0,3.4,4.5,1.6,1
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6.7,3.1,4.7,1.5,1
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6.3,2.3,4.4,1.3,1
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5.6,3.0,4.1,1.3,1
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5.5,2.5,4.0,1.3,1
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5.5,2.6,4.4,1.2,1
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6.1,3.0,4.6,1.4,1
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5.8,2.6,4.0,1.2,1
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5.0,2.3,3.3,1.0,1
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5.6,2.7,4.2,1.3,1
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5.7,3.0,4.2,1.2,1
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5.7,2.9,4.2,1.3,1
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6.2,2.9,4.3,1.3,1
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5.1,2.5,3.0,1.1,1
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5.7,2.8,4.1,1.3,1
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6.3,3.3,6.0,2.5,2
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5.8,2.7,5.1,1.9,2
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7.1,3.0,5.9,2.1,2
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6.3,2.9,5.6,1.8,2
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6.5,3.0,5.8,2.2,2
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7.6,3.0,6.6,2.1,2
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4.9,2.5,4.5,1.7,2
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7.3,2.9,6.3,1.8,2
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6.7,2.5,5.8,1.8,2
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7.2,3.6,6.1,2.5,2
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6.5,3.2,5.1,2.0,2
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6.4,2.7,5.3,1.9,2
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6.8,3.0,5.5,2.1,2
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5.7,2.5,5.0,2.0,2
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5.8,2.8,5.1,2.4,2
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6.4,3.2,5.3,2.3,2
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6.5,3.0,5.5,1.8,2
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7.7,3.8,6.7,2.2,2
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7.7,2.6,6.9,2.3,2
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6.0,2.2,5.0,1.5,2
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6.9,3.2,5.7,2.3,2
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5.6,2.8,4.9,2.0,2
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7.7,2.8,6.7,2.0,2
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6.3,2.7,4.9,1.8,2
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6.7,3.3,5.7,2.1,2
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7.2,3.2,6.0,1.8,2
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6.2,2.8,4.8,1.8,2
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6.1,3.0,4.9,1.8,2
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6.4,2.8,5.6,2.1,2
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7.2,3.0,5.8,1.6,2
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7.4,2.8,6.1,1.9,2
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7.9,3.8,6.4,2.0,2
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6.4,2.8,5.6,2.2,2
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6.3,2.8,5.1,1.5,2
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6.1,2.6,5.6,1.4,2
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7.7,3.0,6.1,2.3,2
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6.3,3.4,5.6,2.4,2
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6.4,3.1,5.5,1.8,2
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6.0,3.0,4.8,1.8,2
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6.9,3.1,5.4,2.1,2
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6.7,3.1,5.6,2.4,2
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6.9,3.1,5.1,2.3,2
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5.8,2.7,5.1,1.9,2
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6.8,3.2,5.9,2.3,2
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6.7,3.3,5.7,2.5,2
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6.7,3.0,5.2,2.3,2
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6.3,2.5,5.0,1.9,2
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6.5,3.0,5.2,2.0,2
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6.2,3.4,5.4,2.3,2
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5.9,3.0,5.1,1.8,2
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