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Java example source code file (FirstIterationFunction.java)

This example Java source code file (FirstIterationFunction.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, broadcast, firstiterationfunction, hashmap, indarray, iterator, list, long, map, negativeholder, object, tuple2, util, vocabcache, vocabword

The FirstIterationFunction.java Java example source code

package org.deeplearning4j.spark.models.embeddings.word2vec;

import lombok.NonNull;
import org.apache.commons.lang3.tuple.Pair;
import org.apache.commons.math3.util.FastMath;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.broadcast.Broadcast;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.FloatBuffer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.heartbeat.reports.Environment;
import org.nd4j.linalg.heartbeat.utils.EnvironmentUtils;
import org.nd4j.linalg.ops.transforms.Transforms;
import scala.Tuple2;

import java.util.*;
import java.util.Map.Entry;
import java.util.concurrent.atomic.AtomicLong;

/**
 * @author jeffreytang
 * @author raver119@gmail.com
 */
public class FirstIterationFunction
        implements FlatMapFunction< Iterator>, Entry > {

    private int ithIteration = 1;
    private int vectorLength;
    private boolean useAdaGrad;
    private int batchSize = 0;
    private double negative;
    private int window;
    private double alpha;
    private double minAlpha;
    private long totalWordCount;
    private long seed;
    private int maxExp;
    private double[] expTable;
    private int iterations;
    private Map<VocabWord, INDArray> indexSyn0VecMap;
    private Map<Integer, INDArray> pointSyn1VecMap;
    private AtomicLong nextRandom = new AtomicLong(5);

    private volatile VocabCache<VocabWord> vocab;
    private volatile NegativeHolder negativeHolder;
    private AtomicLong cid = new AtomicLong(0);
    private AtomicLong aff = new AtomicLong(0);





    public FirstIterationFunction(Broadcast<Map word2vecVarMapBroadcast,
                                  Broadcast<double[]> expTableBroadcast, Broadcast> vocabCacheBroadcast) {

        Map<String, Object> word2vecVarMap = word2vecVarMapBroadcast.getValue();
        this.expTable = expTableBroadcast.getValue();
        this.vectorLength = (int) word2vecVarMap.get("vectorLength");
        this.useAdaGrad = (boolean) word2vecVarMap.get("useAdaGrad");
        this.negative = (double) word2vecVarMap.get("negative");
        this.window = (int) word2vecVarMap.get("window");
        this.alpha = (double) word2vecVarMap.get("alpha");
        this.minAlpha = (double) word2vecVarMap.get("minAlpha");
        this.totalWordCount = (long) word2vecVarMap.get("totalWordCount");
        this.seed = (long) word2vecVarMap.get("seed");
        this.maxExp = (int) word2vecVarMap.get("maxExp");
        this.iterations = (int) word2vecVarMap.get("iterations");
        this.batchSize = (int) word2vecVarMap.get("batchSize");
        this.indexSyn0VecMap = new HashMap<>();
        this.pointSyn1VecMap = new HashMap<>();
        this.vocab = vocabCacheBroadcast.getValue();

        if (this.vocab == null) throw new RuntimeException("VocabCache is null");

        if (negative > 0) {
            negativeHolder = NegativeHolder.getInstance();
            negativeHolder.initHolder(vocab, expTable, this.vectorLength);
        }
    }



    @Override
    public Iterable<Entry call(Iterator, Long>> pairIter) {
        while (pairIter.hasNext()) {
            List<Pair> batch = new ArrayList<>();
            while (pairIter.hasNext() && batch.size() < batchSize) {
                Tuple2<List pair = pairIter.next();
                List<VocabWord> vocabWordsList = pair._1();
                Long sentenceCumSumCount = pair._2();
                batch.add(Pair.of(vocabWordsList, sentenceCumSumCount));
            }

            for (int i = 0; i < iterations; i++) {
                //System.out.println("Training sentence: " + vocabWordsList);
                for (Pair<List pair: batch) {
                    List<VocabWord> vocabWordsList = pair.getKey();
                    Long sentenceCumSumCount = pair.getValue();
                    double currentSentenceAlpha = Math.max(minAlpha,
                            alpha - (alpha - minAlpha) * (sentenceCumSumCount / (double) totalWordCount));
                    trainSentence(vocabWordsList, currentSentenceAlpha);
                }
            }
        }
        return indexSyn0VecMap.entrySet();
    }


    public void trainSentence(List<VocabWord> vocabWordsList, double currentSentenceAlpha) {

        if (vocabWordsList != null && !vocabWordsList.isEmpty()) {
            for (int ithWordInSentence = 0; ithWordInSentence < vocabWordsList.size(); ithWordInSentence++) {
                // Random value ranging from 0 to window size
                nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
                int b = (int) (long) this.nextRandom.get() % window;
                VocabWord currentWord = vocabWordsList.get(ithWordInSentence);
                if (currentWord != null) {
                    skipGram(ithWordInSentence, vocabWordsList, b, currentSentenceAlpha);
                }
            }
        }
    }

    public void skipGram(int ithWordInSentence, List<VocabWord> vocabWordsList, int b, double currentSentenceAlpha) {

        VocabWord currentWord = vocabWordsList.get(ithWordInSentence);
        if (currentWord != null && !vocabWordsList.isEmpty()) {
            int end = window * 2 + 1 - b;
            for (int a = b; a < end; a++) {
                if (a != window) {
                    int c = ithWordInSentence - window + a;
                    if (c >= 0 && c < vocabWordsList.size()) {
                        VocabWord lastWord = vocabWordsList.get(c);
                        iterateSample(currentWord, lastWord, currentSentenceAlpha);
                    }
                }
            }
        }
    }

    public void iterateSample(VocabWord w1, VocabWord w2, double currentSentenceAlpha) {


        if (w1 == null || w2 == null || w2.getIndex() < 0 || w2.getIndex() == w1.getIndex())
            return;
        final int currentWordIndex = w2.getIndex();

        // error for current word and context
        INDArray neu1e = Nd4j.create(vectorLength);

        // First iteration Syn0 is random numbers
        INDArray l1 = null;
        if (indexSyn0VecMap.containsKey(vocab.elementAtIndex(currentWordIndex))) {
            l1 = indexSyn0VecMap.get(vocab.elementAtIndex(currentWordIndex));
        } else {
            l1 = getRandomSyn0Vec(vectorLength, (long) currentWordIndex);
        }

        //
        for (int i = 0; i < w1.getCodeLength(); i++) {
            int code = w1.getCodes().get(i);
            int point = w1.getPoints().get(i);
            if(point < 0)
                throw new IllegalStateException("Illegal point " + point);
            // Point to
            INDArray syn1;
            if (pointSyn1VecMap.containsKey(point)) {
                syn1 = pointSyn1VecMap.get(point);
            } else {
                syn1 = Nd4j.zeros(1, vectorLength); // 1 row of vector length of zeros
                pointSyn1VecMap.put(point, syn1);
            }

            // Dot product of Syn0 and Syn1 vecs
            double dot = Nd4j.getBlasWrapper().level1().dot(vectorLength, 1.0, l1, syn1);

            if (dot < -maxExp || dot >= maxExp)
                continue;

            int idx = (int) ((dot + maxExp) * ((double) expTable.length / maxExp / 2.0));

            if (idx > expTable.length) continue;

            //score
            double f = expTable[idx];
            //gradient
            double g = (1 - code - f) * (useAdaGrad ? w1.getGradient(i, currentSentenceAlpha, currentSentenceAlpha) : currentSentenceAlpha);


            Nd4j.getBlasWrapper().level1().axpy(vectorLength, g, syn1, neu1e);
            Nd4j.getBlasWrapper().level1().axpy(vectorLength, g, l1, syn1);
        }

        int target = w1.getIndex();
        int label;
        //negative sampling
        if(negative > 0)
            for (int d = 0; d < negative + 1; d++) {
                if (d == 0)
                    label = 1;
                else {
                    nextRandom.set(Math.abs(nextRandom.get() * 25214903917L + 11));
                    int idx = Math.abs((int) (nextRandom.get() >> 16) % negativeHolder.getTable().length());

                    target = negativeHolder.getTable().getInt(idx);
                    if (target <= 0)
                        target = (int) nextRandom.get() % (vocab.numWords() - 1) + 1;

                    if (target == w1.getIndex())
                        continue;
                    label = 0;
                }

                if(target >= negativeHolder.getSyn1Neg().rows() || target < 0)
                    continue;

                double f = Nd4j.getBlasWrapper().dot(l1,negativeHolder.getSyn1Neg().slice(target));
                double g;
                if (f > maxExp)
                    g = useAdaGrad ? w1.getGradient(target, (label - 1), alpha) : (label - 1) *  alpha;
                else if (f < -maxExp)
                    g = label * (useAdaGrad ?  w1.getGradient(target, alpha, alpha) : alpha);
                else {
                    int idx = (int) ((f + maxExp) * (expTable.length / maxExp / 2));
                    if (idx >= expTable.length)
                        continue;

                    g = useAdaGrad ? w1.getGradient(target, label - expTable[idx], alpha) : (label - expTable[idx]) * alpha;
                }

                    Nd4j.getBlasWrapper().level1().axpy(vectorLength, g, negativeHolder.getSyn1Neg().slice(target),neu1e);

                    Nd4j.getBlasWrapper().level1().axpy(vectorLength, g, l1,negativeHolder.getSyn1Neg().slice(target));
            }


        // Updated the Syn0 vector based on gradient. Syn0 is not random anymore.
        Nd4j.getBlasWrapper().level1().axpy(vectorLength, 1.0f, neu1e, l1);

        VocabWord word = vocab.elementAtIndex(currentWordIndex);
        indexSyn0VecMap.put(word, l1);
    }

    private INDArray getRandomSyn0Vec(int vectorLength, long lseed) {
        /*
            we use wordIndex as part of seed here, to guarantee that during word syn0 initialization on dwo distinct nodes, initial weights will be the same for the same word
         */
        return Nd4j.rand(lseed * seed, new int[]{1 ,vectorLength}).subi(0.5).divi(vectorLength);
    }
}

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