java.lang.NoSuchFieldError: HALF Error with DL4J

Question

Upon executing the MNIST example in my own project I get the following errors:

    o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend
Exception in thread "main" java.lang.NoSuchFieldError: HALF
    at org.nd4j.linalg.factory.Nd4j.initWithBackend(Nd4j.java:5593)
    at org.nd4j.linalg.factory.Nd4j.initContext(Nd4j.java:5554)
    at org.nd4j.linalg.factory.Nd4j.<clinit>(Nd4j.java:189)
    at org.deeplearning4j.nn.conf.NeuralNetConfiguration$Builder.seed(NeuralNetConfiguration.java:624)
    at com.baus.visualagent.DigitTrainer.startTraining(DigitTrainer.java:47)
    at com.baus.visualagent.App.main(App.java:17)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at com.intellij.rt.execution.application.AppMain.main(AppMain.java:147)

It would be lovely if I could know what is causing this issue. Is it due to the POM file not being properly configured or something else?

The POM file contents are:

<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/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.baus.visualagent</groupId>
  <artifactId>Visual Agent</artifactId>
  <version>1.0-SNAPSHOT</version>
  <packaging>jar</packaging>

  <name>Visual Agent</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
      <nd4j.backend>nd4j-native-platform</nd4j.backend>
      <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
      <shadedClassifier>bin</shadedClassifier>
      <java.version>1.8</java.version>
      <nd4j.version>0.7.2</nd4j.version>
      <dl4j.version>0.7.2</dl4j.version>
      <datavec.version>0.7.2</datavec.version>
      <arbiter.version>0.7.2</arbiter.version>
      <rl4j.version>0.7.2</rl4j.version>
      <guava.version>19.0</guava.version>
      <logback.version>1.1.7</logback.version>
      <jfreechart.version>1.0.13</jfreechart.version>
      <jcommon.version>1.0.23</jcommon.version>
      <maven-shade-plugin.version>2.4.3</maven-shade-plugin.version>
      <exec-maven-plugin.version>1.4.0</exec-maven-plugin.version>
      <maven.minimum.version>3.3.1</maven.minimum.version>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>

      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>nd4j-native-platform</artifactId>
          <version>${nd4j.version}</version>
      </dependency>

      <dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>deeplearning4j-core</artifactId>
          <version>0.7.2</version>
      </dependency>

      <dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>deeplearning4j-nlp</artifactId>
          <version>0.7.2</version>
      </dependency>

      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>canova-nd4j-image</artifactId>
          <version>0.0.0.17</version>
      </dependency>

      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>canova-nd4j-codec</artifactId>
          <version>0.0.0.17</version>
      </dependency>

      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>nd4j-api</artifactId>
          <version>0.7.2</version>
      </dependency>

      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>nd4j-blas</artifactId>
          <version>unknown</version>
      </dependency>
      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>nd4j-x86</artifactId>
          <version>0.4-rc3.8</version>
      </dependency>
      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>nd4j-examples</artifactId>
          <version>0.0.3.5.4</version>
      </dependency>
      <dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>dl4j-examples</artifactId>
          <version>0.7-SNAPSHOT</version>
      </dependency>

      <dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>deeplearning4j-modelimport</artifactId>
          <version>0.7.2</version>
      </dependency>
      <dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>deeplearning4j-nn</artifactId>
          <version>0.7.2</version>
      </dependency>
      <dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>deeplearning4j-ui</artifactId>
          <version>0.6.0</version>
      </dependency>

  </dependencies>

    <build>
        <plugins>
            <plugin>
                <artifactId>maven-enforcer-plugin</artifactId>
                <executions>
                    <execution>
                        <id>enforce-default</id>
                        <goals>
                            <goal>enforce</goal>
                        </goals>
                        <configuration>
                            <rules>
                                <requireMavenVersion>
                                    <version>[${maven.minimum.version},)</version>
                                    <message>********** Minimum Maven Version is ${maven.minimum.version}. Please upgrade Maven before continuing (run "mvn --version" to check). **********</message>
                                </requireMavenVersion>
                            </rules>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

The Code consists of 2 classes:

1. The Main class called App
2. A neural network class called DigitTrainer

The code of App is as follows:

public class App 
{
    public static void main( String[] args )throws IOException
    {
        DigitTrainer t=new DigitTrainer();
        t.startTraining();
        t.startTesting();
    }
}

The code of DigitTrainer is as follows:

public class DigitTrainer {
    private int layers;
    private int rows;
    private int cols;
    private int out;
    private int batch;
    private int seed;
    private int epochs;
    private DataSetIterator test,train;
    private MultiLayerConfiguration config;
    private MultiLayerNetwork ann;

    public DigitTrainer() throws IOException {
        rows=28;
        cols=28;
        out=10;
        batch=128;
        seed=123;
        epochs=20;
        train=new MnistDataSetIterator(batch,true,seed);
        test=new MnistDataSetIterator(batch,false,seed);
    }
    public void startTraining(){
        config = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .iterations(1)
                .learningRate(0.006)
                .updater(Updater.NESTEROVS)
                .momentum(0.9)
                .regularization(true).l2(1e-4)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(rows*cols)
                        .nOut(1000)
                        .activation(Activation.IDENTITY)
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .layer(1, new DenseLayer.Builder()
                        .nIn(1000)
                        .nOut(out)
                        .activation(Activation.SIGMOID)
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .pretrain(false)
                .backprop(true)
                .build();
        ann=new MultiLayerNetwork(config);
        ann.init();
        ann.setListeners(new ScoreIterationListener(1));
        System.out.println("\n******Beginning Training******\n");
        for(int i=0;i<epochs;i++){
            ann.fit(train);
        }
        System.out.println("\n******Model Trained******\n");
    }
    public void startTesting(){
        System.out.println("\n******Starting Testing******\n");
        Evaluation e=new Evaluation(out);
        while(test.hasNext()){
            DataSet next = test.next();
            INDArray x=ann.output(next.getFeatureMatrix());
            e.eval(next.getLabels(),x);
        }
        System.out.println(e.stats());
    }
}

Answer 1

That exception occurs when you run with a different .class file than you used when you compiled. This can occur when a different version of a library is used at runtime than was used during compilation.

Answer 2

Solved. I had to reduce the dependencies to include only:

<dependency>
          <groupId>org.deeplearning4j</groupId>
          <artifactId>deeplearning4j-core</artifactId>
          <version>${dl4j.version}</version>
      </dependency>
      <dependency>
          <groupId>org.nd4j</groupId>
          <artifactId>nd4j-native-platform</artifactId>
          <version>${nd4j.version}</version>
      </dependency>
      <dependency>
          <groupId>org.datavec</groupId>
          <artifactId>datavec-api</artifactId>
          <version>${datavec.version}</version>
      </dependency>
  </dependencies>

Also in the configuration the layer 1 config should be:

.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nIn(1000)
                        .nOut(out)
                        .activation(Activation.SIGMOID)
                        .weightInit(WeightInit.XAVIER)
                        .build())