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Java example source code file (DM.java)

This example Java source code file (DM.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

atomiclong, cbow, illegalstateexception, indarray, inmemorylookuptable, list, override, sequence, sequencelearningalgorithm, util, vectorsconfiguration, vocabcache, weightlookuptable

The DM.java Java example source code

package org.deeplearning4j.models.embeddings.learning.impl.sequence;

import lombok.NonNull;
import org.deeplearning4j.models.embeddings.WeightLookupTable;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.deeplearning4j.models.embeddings.learning.ElementsLearningAlgorithm;
import org.deeplearning4j.models.embeddings.learning.SequenceLearningAlgorithm;
import org.deeplearning4j.models.embeddings.learning.impl.elements.CBOW;
import org.deeplearning4j.models.embeddings.loader.VectorsConfiguration;
import org.deeplearning4j.models.sequencevectors.interfaces.SequenceIterator;
import org.deeplearning4j.models.sequencevectors.sequence.Sequence;
import org.deeplearning4j.models.sequencevectors.sequence.SequenceElement;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.atomic.AtomicLong;

 * DM implementation for DeepLearning4j
 * @author raver119@gmail.com
public class DM<T extends SequenceElement> implements SequenceLearningAlgorithm {
    private VocabCache<T> vocabCache;
    private WeightLookupTable<T> lookupTable;
    private VectorsConfiguration configuration;

    protected static double MAX_EXP = 6;

    protected int window;
    protected boolean useAdaGrad;
    protected double negative;
    protected double sampling;

    protected double[] expTable;

    protected INDArray syn0, syn1, syn1Neg, table;

    private CBOW<T> cbow = new CBOW<>();

    public String getCodeName() {
        return "PV-DM";

    public void configure(@NonNull VocabCache<T> vocabCache,@NonNull WeightLookupTable lookupTable,@NonNull VectorsConfiguration configuration) {
        this.vocabCache = vocabCache;
        this.lookupTable = lookupTable;
        this.configuration = configuration;

        cbow.configure(vocabCache, lookupTable, configuration);

        this.window = configuration.getWindow();
        this.useAdaGrad = configuration.isUseAdaGrad();
        this.negative = configuration.getNegative();
        this.sampling = configuration.getSampling();

        this.syn0 = ((InMemoryLookupTable<T>) lookupTable).getSyn0();
        this.syn1 = ((InMemoryLookupTable<T>) lookupTable).getSyn1();
        this.syn1Neg = ((InMemoryLookupTable<T>) lookupTable).getSyn1Neg();
        this.expTable = ((InMemoryLookupTable<T>) lookupTable).getExpTable();
        this.table = ((InMemoryLookupTable<T>) lookupTable).getTable();

    public void pretrain(SequenceIterator<T> iterator) {
        // no-op

    public void learnSequence(Sequence<T> sequence, AtomicLong nextRandom, double learningRate) {
        Sequence<T> seq = cbow.applySubsampling(sequence, nextRandom);

        List<T> labels = new ArrayList<>();

        if (sequence.getSequenceLabel() == null) throw new IllegalStateException("Label is NULL");

        if(seq.isEmpty() || labels.isEmpty())

        for (int i = 0; i < seq.size(); i++) {
            nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
            dm(i, seq,  (int) nextRandom.get() % window ,nextRandom, learningRate);

    public void dm(int i, Sequence<T> sequence, int b, AtomicLong nextRandom, double alpha) {
        int end =  window * 2 + 1 - b;
        int cw = 0;
        INDArray neu1 = Nd4j.zeros(lookupTable.layerSize());

        T currentWord = sequence.getElementByIndex(i);

        for(int a = b; a < end; a++) {
            if(a != window) {
                int c = i - window + a;
                if(c >= 0 && c < sequence.size()) {
                    T lastWord = sequence.getElementByIndex(c);


        for (T label: sequence.getSequenceLabels()) {

        if (cw == 0)


        INDArray neu1e = cbow.iterateSample(currentWord, neu1, nextRandom, alpha);

        for (T label: sequence.getSequenceLabels()) {
            INDArray syn0row = syn0.getRow(label.getIndex());
            Nd4j.getBlasWrapper().level1().axpy(lookupTable.layerSize(), 1.0, neu1e, syn0row);

    public boolean isEarlyTerminationHit() {
        return false;

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