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Java example source code file (GloveWeightLookupTable.java)

This example Java source code file (GloveWeightLookupTable.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

adagrad, builder, deprecated, gloveweightlookuptable, hashmap, illegalargumentexception, indarray, inmemorylookuptable, lineiterator, override, random, sequenceelement, string, utf-8, util

The GloveWeightLookupTable.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.models.glove;



import org.apache.commons.io.IOUtils;
import org.apache.commons.io.LineIterator;
import org.deeplearning4j.models.sequencevectors.sequence.SequenceElement;
import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.Word2Vec;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.rng.Random;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.AdaGrad;


import java.io.IOException;
import java.io.InputStream;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.atomic.AtomicLong;

/**
 * Glove lookup table
 *
 * @author Adam Gibson
 */
// Deprecated due to logic being pulled off WeightLookupTable classes into LearningAlgorithm interfaces for better code.
@Deprecated
public class GloveWeightLookupTable<T extends SequenceElement> extends InMemoryLookupTable {


    private AdaGrad weightAdaGrad;
    private AdaGrad biasAdaGrad;
    private INDArray bias;
    //also known as alpha
    private double xMax = 0.75;
    private double maxCount = 100;


    public GloveWeightLookupTable(VocabCache<T> vocab, int vectorLength, boolean useAdaGrad, double lr, Random gen, double negative, double xMax,double maxCount) {
        super(vocab, vectorLength, useAdaGrad, lr, gen, negative);
        this.xMax = xMax;
        this.maxCount = maxCount;
    }

    @Override
    public void resetWeights(boolean reset) {
        if(rng == null)
            this.rng = Nd4j.getRandom();

        //note the +2 which is the unk vocab word and the bias
        if(syn0 == null || syn0 != null && reset) {
            syn0 = Nd4j.rand(new int[]{vocab.numWords() + 1, vectorLength}, rng).subi(0.5).divi((double) vectorLength);
            INDArray randUnk = Nd4j.rand(1,vectorLength,rng).subi(0.5).divi(vectorLength);
            putVector(Word2Vec.DEFAULT_UNK, randUnk);
        }
        if(weightAdaGrad == null || weightAdaGrad != null && reset) {
            weightAdaGrad = new AdaGrad(new int[]{vocab.numWords() + 1, vectorLength}, lr.get());
        }


        //right after unknown
        if(bias == null || bias != null && reset)
            bias = Nd4j.create(syn0.rows());

        if(biasAdaGrad == null || biasAdaGrad != null && reset) {
            biasAdaGrad = new AdaGrad(bias.shape(), lr.get());
        }


    }

    /**
     * Reset the weights of the cache
     */
    @Override
    public void resetWeights() {
        resetWeights(true);

    }

    /**
     * glove iteration
     * @param w1 the first word
     * @param w2 the second word
     * @param score the weight learned for the particular co occurrences
     */
    public   double iterateSample(T w1, T w2,double score) {
        INDArray w1Vector = syn0.slice(w1.getIndex());
        INDArray w2Vector = syn0.slice(w2.getIndex());
        //prediction: input + bias
        if(w1.getIndex() < 0 || w1.getIndex() >= syn0.rows())
            throw new IllegalArgumentException("Illegal index for word " + w1.getLabel());
        if(w2.getIndex() < 0 || w2.getIndex() >= syn0.rows())
            throw new IllegalArgumentException("Illegal index for word " + w2.getLabel());


        //w1 * w2 + bias
        double prediction = Nd4j.getBlasWrapper().dot(w1Vector,w2Vector);
        prediction +=  bias.getDouble(w1.getIndex()) + bias.getDouble(w2.getIndex());

        double weight = Math.pow(Math.min(1.0,(score / maxCount)),xMax);

        double fDiff = score > xMax ? prediction :  weight * (prediction - Math.log(score));
        if(Double.isNaN(fDiff))
            fDiff = Nd4j.EPS_THRESHOLD;
        //amount of change
        double gradient =  fDiff;

        //note the update step here: the gradient is
        //the gradient of the OPPOSITE word
        //for adagrad we will use the index of the word passed in
        //for the gradient calculation we will use the context vector
        update(w1,w1Vector,w2Vector,gradient);
        update(w2,w2Vector,w1Vector,gradient);
        return fDiff;



    }


    private void update(T w1,INDArray wordVector,INDArray contextVector,double gradient) {
        //gradient for word vectors
        INDArray grad1 =  contextVector.mul(gradient);
        INDArray update = weightAdaGrad.getGradient(grad1,w1.getIndex(),syn0.shape());

        //update vector
        wordVector.subi(update);

        double w1Bias = bias.getDouble(w1.getIndex());
        double biasGradient = biasAdaGrad.getGradient(gradient,w1.getIndex(),bias.shape());
        double update2 = w1Bias - biasGradient;
        bias.putScalar(w1.getIndex(),update2);
    }

    public AdaGrad getWeightAdaGrad() {
        return weightAdaGrad;
    }


    public AdaGrad getBiasAdaGrad() {
        return biasAdaGrad;
    }



    /**
     * Load a glove model from an input stream.
     * The format is:
     * word num1 num2....
     * @param is the input stream to read from for the weights
     * @param vocab the vocab for the lookuptable
     * @return the loaded model
     * @throws java.io.IOException if one occurs
     */
    public static GloveWeightLookupTable load(InputStream is,VocabCache<? extends SequenceElement> vocab) throws IOException {
        LineIterator iter = IOUtils.lineIterator(is, "UTF-8");
        GloveWeightLookupTable glove = null;
        Map<String,float[]> wordVectors = new HashMap<>();
        while(iter.hasNext()) {
            String line = iter.nextLine().trim();
            if(line.isEmpty())
                continue;
            String[] split = line.split(" ");
            String word = split[0];
            if(glove == null)
                glove = new GloveWeightLookupTable.Builder()
                        .cache(vocab).vectorLength(split.length - 1)
                        .build();



            if(word.isEmpty())
                continue;
            float[] read = read(split,glove.layerSize());
            if(read.length < 1)
                continue;


            wordVectors.put(word,read);



        }

        glove.setSyn0(weights(glove,wordVectors,vocab));
        glove.resetWeights(false);


        iter.close();


        return glove;

    }

    private static INDArray weights(GloveWeightLookupTable glove,Map<String,float[]> data,VocabCache vocab) {
        INDArray ret = Nd4j.create(data.size(),glove.layerSize());
        for(String key : data.keySet()) {
            INDArray row = Nd4j.create(Nd4j.createBuffer(data.get(key)));
            if(row.length() != glove.layerSize())
                continue;
            if(vocab.indexOf(key) >= data.size())
                continue;
            if(vocab.indexOf(key) < 0)
                continue;
            ret.putRow(vocab.indexOf(key), row);
        }
        return ret;
    }


    private static float[] read(String[] split,int length) {
        float[] ret = new float[length];
        for(int i = 1; i < split.length; i++) {
            ret[i - 1] = Float.parseFloat(split[i]);
        }
        return ret;
    }


    @Override
    public void iterateSample(T w1, T w2, AtomicLong nextRandom, double alpha) {
        throw new UnsupportedOperationException();

    }

    public double getxMax() {
        return xMax;
    }

    public void setxMax(double xMax) {
        this.xMax = xMax;
    }

    public double getMaxCount() {
        return maxCount;
    }

    public void setMaxCount(double maxCount) {
        this.maxCount = maxCount;
    }

    public INDArray getBias() {
        return bias;
    }

    public void setBias(INDArray bias) {
        this.bias = bias;
    }

    public static class Builder<T extends SequenceElement> extends  InMemoryLookupTable.Builder {
        private double xMax = 0.75;
        private double maxCount = 100;


        public Builder<T> maxCount(double maxCount) {
            this.maxCount = maxCount;
            return this;
        }


        public Builder<T> xMax(double xMax) {
            this.xMax = xMax;
            return this;
        }

        @Override
        public Builder<T> cache(VocabCache vocab) {
            super.cache(vocab);
            return this;
        }

        @Override
        public Builder<T> negative(double negative) {
            super.negative(negative);
            return this;
        }

        @Override
        public Builder<T> vectorLength(int vectorLength) {
            super.vectorLength(vectorLength);
            return this;
        }

        @Override
        public Builder<T> useAdaGrad(boolean useAdaGrad) {
            super.useAdaGrad(useAdaGrad);
            return this;
        }

        @Override
        public Builder<T> lr(double lr) {
            super.lr(lr);
            return this;
        }

        @Override
        public Builder<T> gen(Random gen) {
            super.gen(gen);
            return this;
        }

        @Override
        public Builder<T> seed(long seed) {
            super.seed(seed);
            return this;
        }

        public GloveWeightLookupTable<T> build() {
            return new GloveWeightLookupTable<T>(vocabCache,vectorLength,useAdaGrad,lr,gen,negative,xMax,maxCount);
        }
    }

}

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