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Java example source code file (ScoreExamplesFunction.java)

This example Java source code file (ScoreExamplesFunction.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, computationgraph, doubleflatmapfunction, illegalstateexception, indarray, iterable, list, logger, multidataset, network, override, scored, scoreexamplesfunction, util

The ScoreExamplesFunction.java Java example source code

/*
 *
 *  * Copyright 2016 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.impl.graph.scoring;

import org.apache.spark.api.java.function.DoubleFlatMapFunction;
import org.apache.spark.broadcast.Broadcast;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

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

/**Function to score examples individually. Note that scoring is batched for computational efficiency.<br>
 * This is essentially a Spark implementation of the {@link ComputationGraph#scoreExamples(MultiDataSet, boolean)} method<br>
 * <b>Note: This method returns a score for each example, but the association between examples and scores is lost. In
 * cases where we need to know the score for particular examples, use {@link ScoreExamplesWithKeyFunction}
 * @author Alex Black
 * @see ScoreExamplesWithKeyFunction
 */
public class ScoreExamplesFunction implements DoubleFlatMapFunction<Iterator {
    protected static final Logger log = LoggerFactory.getLogger(ScoreExamplesFunction.class);

    private final Broadcast<INDArray> params;
    private final Broadcast<String> jsonConfig;
    private final boolean addRegularization;
    private final int batchSize;

    public ScoreExamplesFunction(Broadcast<INDArray> params, Broadcast jsonConfig, boolean addRegularizationTerms,
                                 int batchSize){
        this.params = params;
        this.jsonConfig = jsonConfig;
        this.addRegularization = addRegularizationTerms;
        this.batchSize = batchSize;
    }


    @Override
    public Iterable<Double> call(Iterator iterator) throws Exception {
        if (!iterator.hasNext()) {
            return Collections.emptyList();
        }

        ComputationGraph network = new ComputationGraph(ComputationGraphConfiguration.fromJson(jsonConfig.getValue()));
        network.init();
        INDArray val = params.value();
        if (val.length() != network.numParams(false))
            throw new IllegalStateException("Network did not have same number of parameters as the broadcast set parameters");
        network.setParams(val);

        List<Double> ret = new ArrayList<>();

        List<MultiDataSet> collect = new ArrayList<>(batchSize);
        int totalCount = 0;
        while (iterator.hasNext()) {
            collect.clear();
            int nExamples = 0;
            while (iterator.hasNext() && nExamples < batchSize) {
                MultiDataSet ds = iterator.next();
                int n = ds.getFeatures(0).size(0);
                collect.add(ds);
                nExamples += n;
            }
            totalCount += nExamples;


            MultiDataSet data = org.nd4j.linalg.dataset.MultiDataSet.merge(collect);


            INDArray scores = network.scoreExamples(data,addRegularization);
            double[] doubleScores = scores.data().asDouble();

            for (double doubleScore : doubleScores) {
                ret.add(doubleScore);
            }
        }

        if (log.isDebugEnabled()) {
            log.debug("Scored {} examples ", totalCount);
        }

        return ret;
    }
}

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