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Java example source code file (ScoreFlatMapFunction.java)

This example Java source code file (ScoreFlatMapFunction.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

arraylist, broadcast, dataset, exception, indarray, iterable, logger, multilayernetwork, network, override, scoreflatmapfunction, scoring, string, util

The ScoreFlatMapFunction.java Java example source code

package org.deeplearning4j.spark.impl.multilayer.scoring;

import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.broadcast.Broadcast;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;

public class ScoreFlatMapFunction implements FlatMapFunction<Iterator {

    private String json;
    private Broadcast<INDArray> params;
    private static Logger log = LoggerFactory.getLogger(ScoreFlatMapFunction.class);

    public ScoreFlatMapFunction(String json, Broadcast<INDArray> params){
        this.json = json;
        this.params = params;
    }

    @Override
    public Iterable<Double> call(Iterator dataSetIterator) throws Exception {
        if(!dataSetIterator.hasNext()) {
            return Collections.singletonList(0.0);
        }
        List<DataSet> collect = new ArrayList<>();
        while(dataSetIterator.hasNext()) {
            collect.add(dataSetIterator.next());
        }

        DataSet data = DataSet.merge(collect,false);
        if(log.isDebugEnabled()) {
            log.debug("Scoring {} examples with data {}",data.numExamples(), data.labelCounts());
        }

        MultiLayerNetwork network = new MultiLayerNetwork(MultiLayerConfiguration.fromJson(json));
        network.init();
        INDArray val = params.value();  //.value() object will be shared by all executors on each machine -> OK, as params are not modified by score function
        if(val.length() != network.numParams(false))
            throw new IllegalStateException("Network did not have same number of parameters as the broadcasted set parameters");
        network.setParameters(val);

        double score = network.score(data,false);
        if(network.conf().isMiniBatch()) score *= data.getFeatureMatrix().size(0);
        return Collections.singletonList(score);
    }
}

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