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Java example source code file (SentenceBatch.java)

This example Java source code file (SentenceBatch.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, deprecated, exception, function, indarray, inmemorylookuptable, list, max_exp, sentencebatch, stop, unk, util, vocabword, word2vecchange, word2vecparam

The SentenceBatch.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 */

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

import org.apache.spark.api.java.function.Function;
import org.deeplearning4j.berkeley.Triple;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.deeplearning4j.models.word2vec.VocabWord;
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;

/**
 * @author Adam Gibson
 */
@Deprecated
public class SentenceBatch implements Function<Word2VecFuncCall,Word2VecChange> {

    private AtomicLong nextRandom = new AtomicLong(5);
//    private static Logger log = LoggerFactory.getLogger(SentenceBatch.class);


    @Override
    public Word2VecChange call(Word2VecFuncCall sentence) throws Exception {
        Word2VecParam param = sentence.getParam().getValue();
        List<Triple changed = new ArrayList<>();
        double alpha = Math.max(param.getMinAlpha(), param.getAlpha() *
                (1 - (1.0 * sentence.getWordsSeen() / (double) param.getTotalWords())));

        trainSentence(param, sentence.getSentence(), alpha, changed);
        return new Word2VecChange(changed, param);
    }


    /**
     * Train on a list of vocab words
     * @param sentence the list of vocab words to train on
     */
    public void trainSentence(Word2VecParam param, final List<VocabWord> sentence, double alpha,
                              List<Triple changed) {
        if (sentence != null && !sentence.isEmpty()) {
            for (int i = 0; i < sentence.size(); i++) {
                VocabWord vocabWord = sentence.get(i);
                if (vocabWord != null) {
                    if (vocabWord.getWord().endsWith("STOP")) {
                        nextRandom.set(nextRandom.get() * 25214903917L + 11);
                        skipGram(param, i, sentence, (int) nextRandom.get() % param.getWindow(), alpha, changed);
                    }
                }
            }
        }
    }


    /**
     * Train via skip gram
     * @param i the current word
     * @param sentence the sentence to train on
     * @param b
     * @param alpha the learning rate
     */
    public void skipGram(Word2VecParam param, int i, List<VocabWord> sentence, int b,
                         double alpha,List<Triple changed) {

        final VocabWord word = sentence.get(i);
        int window = param.getWindow();
        if (word != null && !sentence.isEmpty()) {
            int end = window * 2 + 1 - b;
            for (int a = b; a < end; a++) {
                if (a != window) {
                    int c = i - window + a;
                    if (c >= 0 && c < sentence.size()) {
                        VocabWord lastWord = sentence.get(c);
                        iterateSample(param, word, lastWord, alpha, changed);
                    }
                }
            }
        }
    }



    /**
     * Iterate on the given 2 vocab words
     *
     * @param w1 the first word to iterate on
     * @param w2 the second word to iterate on
     */
    public void iterateSample(Word2VecParam param, VocabWord w1, VocabWord w2, double alpha,
                              List<Triple changed) {
        if(w2 == null || w2.getIndex() < 0 || w1.getIndex() == w2.getIndex() || w1.getWord().equals("STOP")
                || w2.getWord().equals("STOP") || w1.getWord().equals("UNK") || w2.getWord().equals("UNK"))
            return;
        int vectorLength = param.getVectorLength();
        InMemoryLookupTable weights = param.getWeights();
        boolean useAdaGrad = param.isUseAdaGrad();
        double negative = param.getNegative();
        INDArray table = param.getTable();
        double[] expTable = param.getExpTable().getValue();
        double MAX_EXP = 6;
        int numWords = param.getNumWords();
        //current word vector
        INDArray l1 = weights.vector(w2.getWord());


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

        for(int i = 0; i < w1.getCodeLength(); i++) {
            int code = w1.getCodes().get(i);
            int point = w1.getPoints().get(i);

            INDArray syn1 = weights.getSyn1().slice(point);

            double dot = Nd4j.getBlasWrapper().level1().dot(syn1.length(),1.0,l1,syn1);

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

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

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


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


            changed.add(new Triple<>(point,w1.getIndex(), -1));

        }


        changed.add(new Triple<>(w1.getIndex(),w2.getIndex(),-1));
        //negative sampling
        if(negative > 0) {
            int target = w1.getIndex();
            int label;
            INDArray syn1Neg = weights.getSyn1Neg().slice(target);

            for (int d = 0; d < negative + 1; d++) {
                if (d == 0) {

                    label = 1;
                } else {
                    nextRandom.set(nextRandom.get() * 25214903917L + 11);
                    target = table.getInt((int) (nextRandom.get() >> 16) % table.length());
                    if (target == 0)
                        target = (int) nextRandom.get() % (numWords - 1) + 1;
                    if (target == w1.getIndex())
                        continue;
                    label = 0;
                }

                double f = Nd4j.getBlasWrapper().dot(l1, syn1Neg);
                double g;
                if (f > MAX_EXP)
                    g = useAdaGrad ? w1.getGradient(target, (label - 1), alpha) : (label - 1) *  alpha;
                else if (f < -MAX_EXP)
                    g = label * (useAdaGrad ?  w1.getGradient(target, alpha, alpha) : alpha);
                else
                    g = useAdaGrad ? w1.getGradient(target, label - expTable[(int)((f + MAX_EXP) * (expTable.length / MAX_EXP / 2))], alpha) : (label - expTable[(int)((f + MAX_EXP) * (expTable.length / MAX_EXP / 2))]) *   alpha;
                    Nd4j.getBlasWrapper().level1().axpy(l1.length(),g,neu1e,l1);

                Nd4j.getBlasWrapper().level1().axpy(l1.length(),g,syn1Neg,l1);

                changed.add(new Triple<>(-1,-1,label));

            }
        }


        Nd4j.getBlasWrapper().level1().axpy(l1.length(), 1.0f,neu1e,l1);


    }

}

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