new modules

deeplearning4j/pom.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
+   <modelVersion>4.0.0</modelVersion>
+   <artifactId>deeplearning4j</artifactId>
+   <packaging>jar</packaging>
+   <name>deeplearning4j</name>
+
+   <parent>
+      <groupId>cn.tuyucheng.taketoday</groupId>
+      <artifactId>taketoday-tutorial4j</artifactId>
+      <version>1.0.0</version>
+   </parent>
+
+   <dependencies>
+      <dependency>
+         <groupId>org.nd4j</groupId>
+         <artifactId>nd4j-api</artifactId>
+         <version>${dl4j.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.nd4j</groupId>
+         <artifactId>nd4j-native-platform</artifactId>
+         <version>${dl4j.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.deeplearning4j</groupId>
+         <artifactId>deeplearning4j-core</artifactId>
+         <version>${dl4j.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.deeplearning4j</groupId>
+         <artifactId>deeplearning4j-nn</artifactId>
+         <version>${dl4j.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.slf4j</groupId>
+         <artifactId>slf4j-api</artifactId>
+         <version>${org.slf4j.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.slf4j</groupId>
+         <artifactId>slf4j-log4j12</artifactId>
+         <version>${org.slf4j.version}</version>
+      </dependency>
+      <!-- https://mvnrepository.com/artifact/org.datavec/datavec-api -->
+      <dependency>
+         <groupId>org.datavec</groupId>
+         <artifactId>datavec-api</artifactId>
+         <version>${dl4j.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.apache.httpcomponents</groupId>
+         <artifactId>httpclient</artifactId>
+         <version>${httpclient.version}</version>
+      </dependency>
+      <dependency>
+         <groupId>org.projectlombok</groupId>
+         <artifactId>lombok</artifactId>
+         <version>${lombok.version}</version>
+         <scope>provided</scope>
+      </dependency>
+   </dependencies>
+
+   <properties>
+      <dl4j.version>0.9.1</dl4j.version> <!-- Latest non beta version -->
+      <httpclient.version>4.3.5</httpclient.version>
+   </properties>
+</project>

deeplearning4j/src/main/java/cn/tuyucheng/taketoday/deeplearning4j/IrisClassifier.java

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+package cn.tuyucheng.taketoday.deeplearning4j;
+
+import org.datavec.api.records.reader.RecordReader;
+import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
+import org.datavec.api.split.FileSplit;
+import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
+import org.deeplearning4j.eval.Evaluation;
+import org.deeplearning4j.nn.conf.BackpropType;
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.DataSet;
+import org.nd4j.linalg.dataset.SplitTestAndTrain;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
+import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
+import org.nd4j.linalg.io.ClassPathResource;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+import java.io.IOException;
+
+public class IrisClassifier {
+
+   private static final int CLASSES_COUNT = 3;
+   private static final int FEATURES_COUNT = 4;
+
+   public static void main(String[] args) throws IOException, InterruptedException {
+      DataSet allData;
+      try (RecordReader recordReader = new CSVRecordReader(0, ',')) {
+         recordReader.initialize(new FileSplit(new ClassPathResource("iris.txt").getFile()));
+
+         DataSetIterator iterator = new RecordReaderDataSetIterator(recordReader, 150, FEATURES_COUNT, CLASSES_COUNT);
+         allData = iterator.next();
+      }
+
+      allData.shuffle(42);
+
+      DataNormalization normalizer = new NormalizerStandardize();
+      normalizer.fit(allData);
+      normalizer.transform(allData);
+
+      SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.65);
+      DataSet trainingData = testAndTrain.getTrain();
+      DataSet testData = testAndTrain.getTest();
+
+      MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
+            .iterations(1000)
+            .activation(Activation.TANH)
+            .weightInit(WeightInit.XAVIER)
+            .regularization(true)
+            .learningRate(0.1).l2(0.0001)
+            .list()
+            .layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT).nOut(3)
+                  .build())
+            .layer(1, new DenseLayer.Builder().nIn(3).nOut(3)
+                  .build())
+            .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
+                  .activation(Activation.SOFTMAX)
+                  .nIn(3).nOut(CLASSES_COUNT).build())
+            .backpropType(BackpropType.Standard).pretrain(false)
+            .build();
+
+      MultiLayerNetwork model = new MultiLayerNetwork(configuration);
+      model.init();
+      model.fit(trainingData);
+
+      INDArray output = model.output(testData.getFeatures());
+
+      Evaluation eval = new Evaluation(CLASSES_COUNT);
+      eval.eval(testData.getLabels(), output);
+      System.out.println(eval.stats());
+   }
+}

deeplearning4j/src/main/java/cn/tuyucheng/taketoday/deeplearning4j/cnn/CifarDataSetService.java

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+package cn.tuyucheng.taketoday.deeplearning4j.cnn;
+
+import lombok.Getter;
+import org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator;
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+
+import java.util.List;
+
+@Getter
+class CifarDataSetService implements IDataSetService {
+
+   private final InputType inputType = InputType.convolutional(32, 32, 3);
+   private final int trainImagesNum = 512;
+   private final int testImagesNum = 128;
+   private final int trainBatch = 16;
+   private final int testBatch = 8;
+
+   private final CifarDataSetIterator trainIterator;
+
+   private final CifarDataSetIterator testIterator;
+
+   CifarDataSetService() {
+      trainIterator = new CifarDataSetIterator(trainBatch, trainImagesNum, true);
+      testIterator = new CifarDataSetIterator(testBatch, testImagesNum, false);
+   }
+
+   @Override
+   public DataSetIterator trainIterator() {
+      return trainIterator;
+   }
+
+   @Override
+   public DataSetIterator testIterator() {
+      return testIterator;
+   }
+
+   @Override
+   public InputType inputType() {
+      return inputType;
+   }
+
+   @Override
+   public List<String> labels() {
+      return trainIterator.getLabels();
+   }
+}

deeplearning4j/src/main/java/cn/tuyucheng/taketoday/deeplearning4j/cnn/CnnExample.java

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+package cn.tuyucheng.taketoday.deeplearning4j.cnn;
+
+import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.eval.Evaluation;
+
+@Slf4j
+class CnnExample {
+
+   public static void main(String... args) {
+      CnnModel network = new CnnModel(new CifarDataSetService(), new CnnModelProperties());
+
+      network.train();
+      Evaluation evaluation = network.evaluate();
+
+      LOGGER.info(evaluation.stats());
+   }
+}

deeplearning4j/src/main/java/cn/tuyucheng/taketoday/deeplearning4j/cnn/CnnModel.java

@@ -0,0 +1,118 @@
+package cn.tuyucheng.taketoday.deeplearning4j.cnn;
+
+import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.eval.Evaluation;
+import org.deeplearning4j.nn.api.OptimizationAlgorithm;
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+import java.util.stream.IntStream;
+
+@Slf4j
+class CnnModel {
+
+   private final IDataSetService dataSetService;
+
+   private final MultiLayerNetwork network;
+
+   private final CnnModelProperties properties;
+
+   CnnModel(IDataSetService dataSetService, CnnModelProperties properties) {
+      this.dataSetService = dataSetService;
+      this.properties = properties;
+
+      MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
+            .seed(1611)
+            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
+            .learningRate(properties.getLearningRate())
+            .regularization(true)
+            .updater(properties.getOptimizer())
+            .list()
+            .layer(0, conv5x5())
+            .layer(1, pooling2x2Stride2())
+            .layer(2, conv3x3Stride1Padding2())
+            .layer(3, pooling2x2Stride1())
+            .layer(4, conv3x3Stride1Padding1())
+            .layer(5, pooling2x2Stride1())
+            .layer(6, dense())
+            .pretrain(false)
+            .backprop(true)
+            .setInputType(dataSetService.inputType())
+            .build();
+
+      network = new MultiLayerNetwork(configuration);
+   }
+
+   void train() {
+      network.init();
+      int epochsNum = properties.getEpochsNum();
+      IntStream.range(1, epochsNum + 1).forEach(epoch -> {
+         LOGGER.info("Epoch {} / {}", epoch, epochsNum);
+         network.fit(dataSetService.trainIterator());
+      });
+   }
+
+   Evaluation evaluate() {
+      return network.evaluate(dataSetService.testIterator());
+   }
+
+   private ConvolutionLayer conv5x5() {
+      return new ConvolutionLayer.Builder(5, 5)
+            .nIn(3)
+            .nOut(16)
+            .stride(1, 1)
+            .padding(1, 1)
+            .weightInit(WeightInit.XAVIER_UNIFORM)
+            .activation(Activation.RELU)
+            .build();
+   }
+
+   private SubsamplingLayer pooling2x2Stride2() {
+      return new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
+            .kernelSize(2, 2)
+            .stride(2, 2)
+            .build();
+   }
+
+   private ConvolutionLayer conv3x3Stride1Padding2() {
+      return new ConvolutionLayer.Builder(3, 3)
+            .nOut(32)
+            .stride(1, 1)
+            .padding(2, 2)
+            .weightInit(WeightInit.XAVIER_UNIFORM)
+            .activation(Activation.RELU)
+            .build();
+   }
+
+   private SubsamplingLayer pooling2x2Stride1() {
+      return new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
+            .kernelSize(2, 2)
+            .stride(1, 1)
+            .build();
+   }
+
+   private ConvolutionLayer conv3x3Stride1Padding1() {
+      return new ConvolutionLayer.Builder(3, 3)
+            .nOut(64)
+            .stride(1, 1)
+            .padding(1, 1)
+            .weightInit(WeightInit.XAVIER_UNIFORM)
+            .activation(Activation.RELU)
+            .build();
+   }
+
+   private OutputLayer dense() {
+      return new OutputLayer.Builder(LossFunctions.LossFunction.MEAN_SQUARED_LOGARITHMIC_ERROR)
+            .activation(Activation.SOFTMAX)
+            .weightInit(WeightInit.XAVIER_UNIFORM)
+            .nOut(dataSetService.labels().size() - 1)
+            .build();
+   }
+}

deeplearning4j/src/main/java/cn/tuyucheng/taketoday/deeplearning4j/cnn/CnnModelProperties.java

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+package cn.tuyucheng.taketoday.deeplearning4j.cnn;
+
+import lombok.Value;
+import org.deeplearning4j.nn.conf.Updater;
+
+@Value
+class CnnModelProperties {
+   private final int epochsNum = 512;
+
+   private final double learningRate = 0.001;
+
+   private final Updater optimizer = Updater.ADAM;
+}

deeplearning4j/src/main/java/cn/tuyucheng/taketoday/deeplearning4j/cnn/IDataSetService.java

@@ -0,0 +1,16 @@
+package cn.tuyucheng.taketoday.deeplearning4j.cnn;
+
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+
+import java.util.List;
+
+interface IDataSetService {
+   DataSetIterator trainIterator();
+
+   DataSetIterator testIterator();
+
+   InputType inputType();
+
+   List<String> labels();
+}