Example usage for org.deeplearning4j.models.embeddings.wordvectors WordVectorsImpl setLookupTable

Introduction

In this page you can find the example usage for org.deeplearning4j.models.embeddings.wordvectors WordVectorsImpl setLookupTable.

Prototype

public void setLookupTable(@NonNull WeightLookupTable lookupTable) 

Source Link

Usage

From source file:de.mpii.docsimilarity.mr.utils.io.WordVectorSerializer.java

License:Apache License

/**
 * Load word vectors for the given vocab and table
 *
 * @param table//from   w  w  w .j  a va 2s  .  co m
 *            the weights to use
 * @param vocab
 *            the vocab to use
 * @return wordvectors based on the given parameters
 */
public static WordVectors fromTableAndVocab(WeightLookupTable table, VocabCache vocab) {
    WordVectorsImpl vectors = new WordVectorsImpl();
    vectors.setLookupTable(table);
    vectors.setVocab(vocab);
    return vectors;
}

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From source file:de.mpii.docsimilarity.mr.utils.io.WordVectorSerializer.java

License:Apache License

/**
 * Load word vectors from the given pair
 *
 * @param pair/*  w  w  w .j a va 2s.  co  m*/
 *            the given pair
 * @return a read only word vectors impl based on the given lookup table and vocab
 */
public static WordVectors fromPair(Pair<InMemoryLookupTable, VocabCache> pair) {
    WordVectorsImpl vectors = new WordVectorsImpl();
    vectors.setLookupTable(pair.getFirst());
    vectors.setVocab(pair.getSecond());
    return vectors;
}

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From source file:edu.umd.umiacs.clip.tools.scor.WordVectorUtils.java

License:Apache License

public static WordVectors loadTxt(File vectorsFile, boolean... normalize) {
    AbstractCache cache = new AbstractCache<>();
    INDArray arrays[] = lines(vectorsFile.toPath()).map(line -> line.split(" "))
            .filter(fields -> fields.length > 2).map(split -> {
                VocabWord word = new VocabWord(1.0, split[0]);
                word.setIndex(cache.numWords());
                cache.addToken(word);//from   w  ww .  j  av a  2s . c  o m
                cache.addWordToIndex(word.getIndex(), split[0]);
                float[] vector = new float[split.length - 1];
                range(1, split.length).parallel().forEach(i -> vector[i - 1] = parseFloat(split[i]));
                return Nd4j.create(vector);
            }).toArray(size -> new INDArray[size]);

    INDArray syn = Nd4j.vstack(arrays);

    InMemoryLookupTable lookupTable = new InMemoryLookupTable.Builder().vectorLength(arrays[0].columns())
            .useAdaGrad(false).cache(cache).useHierarchicSoftmax(false).build();
    Nd4j.clearNans(syn);
    if (normalize.length > 0 && normalize[0]) {
        syn.diviColumnVector(syn.norm2(1));
    }

    lookupTable.setSyn0(syn);

    WordVectorsImpl vectors = new WordVectorsImpl();
    vectors.setLookupTable(lookupTable);
    vectors.setVocab(cache);
    return vectors;
}

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