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Java example source code file (BaseEarlyStoppingTrainer.java)

This example Java source code file (BaseEarlyStoppingTrainer.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

baseearlystoppingtrainer, earlystoppinglistener, earlystoppingresult, epochterminationcondition, error, hit, ioexception, iterationterminationcondition, logger, multidatasetiterator, override, runtimeexception, score, util

The BaseEarlyStoppingTrainer.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.earlystopping.trainer;

import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
import org.deeplearning4j.earlystopping.EarlyStoppingResult;
import org.deeplearning4j.earlystopping.listener.EarlyStoppingListener;
import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
import org.deeplearning4j.earlystopping.termination.EpochTerminationCondition;
import org.deeplearning4j.earlystopping.termination.IterationTerminationCondition;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.BaseOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;

import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.IOException;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

/**Base/abstract class for conducting early stopping training locally (single machine).<br>
 * Can be used to train a {@link MultiLayerNetwork} or a {@link ComputationGraph} via early stopping
 * @author Alex Black
 */
public abstract class BaseEarlyStoppingTrainer<T extends Model> implements IEarlyStoppingTrainer {

    private static Logger log = LoggerFactory.getLogger(BaseEarlyStoppingTrainer.class);

    protected T model;

    protected final EarlyStoppingConfiguration<T> esConfig;
    private final DataSetIterator train;
    private final MultiDataSetIterator trainMulti;
    private final Iterator<?> iterator;
    private EarlyStoppingListener<T> listener;

    private double bestModelScore = Double.MAX_VALUE;
    private int bestModelEpoch = -1;

    protected BaseEarlyStoppingTrainer(EarlyStoppingConfiguration<T> earlyStoppingConfiguration, T model, DataSetIterator train,
                                       MultiDataSetIterator trainMulti, EarlyStoppingListener<T> listener) {
        this.esConfig = earlyStoppingConfiguration;
        this.model = model;
        this.train = train;
        this.trainMulti = trainMulti;
        this.iterator = (train != null ? train : trainMulti);
        this.listener = listener;
    }

    protected abstract void fit(DataSet ds);

    protected abstract void fit(MultiDataSet mds);

    @Override
    public EarlyStoppingResult<T> fit() {
        log.info("Starting early stopping training");
        if (esConfig.getScoreCalculator() == null)
            log.warn("No score calculator provided for early stopping. Score will be reported as 0.0 to epoch termination conditions");

        //Initialize termination conditions:
        if (esConfig.getIterationTerminationConditions() != null) {
            for (IterationTerminationCondition c : esConfig.getIterationTerminationConditions()) {
                c.initialize();
            }
        }
        if (esConfig.getEpochTerminationConditions() != null) {
            for (EpochTerminationCondition c : esConfig.getEpochTerminationConditions()) {
                c.initialize();
            }
        }

        if (listener != null) listener.onStart(esConfig, model);

        Map<Integer, Double> scoreVsEpoch = new LinkedHashMap<>();

        int epochCount = 0;
        while (true) {
            reset();
            double lastScore;
            boolean terminate = false;
            IterationTerminationCondition terminationReason = null;
            int iterCount = 0;
            while (iterator.hasNext()) {
                try {
                    if (train != null) {
                        fit((DataSet) iterator.next());
                    } else
                        fit(trainMulti.next());
                } catch (Exception e) {
                    log.warn("Early stopping training terminated due to exception at epoch {}, iteration {}",
                            epochCount, iterCount, e);
                    //Load best model to return
                    T bestModel;
                    try {
                        bestModel = esConfig.getModelSaver().getBestModel();
                    } catch (IOException e2) {
                        throw new RuntimeException(e2);
                    }
                    return new EarlyStoppingResult<T>(
                            EarlyStoppingResult.TerminationReason.Error,
                            e.toString(),
                            scoreVsEpoch,
                            bestModelEpoch,
                            bestModelScore,
                            epochCount,
                            bestModel);
                }

                //Check per-iteration termination conditions
                lastScore = model.score();
                for (IterationTerminationCondition c : esConfig.getIterationTerminationConditions()) {
                    if (c.terminate(lastScore)) {
                        terminate = true;
                        terminationReason = c;
                        break;
                    }
                }
                if (terminate) break;

                iterCount++;
            }
            if (terminate) {
                //Handle termination condition:
                log.info("Hit per iteration epoch termination condition at epoch {}, iteration {}. Reason: {}",
                        epochCount, iterCount, terminationReason);

                if (esConfig.isSaveLastModel()) {
                    //Save last model:
                    try {
                        esConfig.getModelSaver().saveLatestModel(model, 0.0);
                    } catch (IOException e) {
                        throw new RuntimeException("Error saving most recent model", e);
                    }
                }

                T bestModel;
                try {
                    bestModel = esConfig.getModelSaver().getBestModel();
                } catch (IOException e2) {
                    throw new RuntimeException(e2);
                }


                EarlyStoppingResult<T> result = new EarlyStoppingResult<>(
                        EarlyStoppingResult.TerminationReason.IterationTerminationCondition,
                        terminationReason.toString(),
                        scoreVsEpoch,
                        bestModelEpoch,
                        bestModelScore,
                        epochCount,
                        bestModel);
                if (listener != null) listener.onCompletion(result);
                return result;
            }

            log.info("Completed training epoch {}", epochCount);


            if ((epochCount == 0 && esConfig.getEvaluateEveryNEpochs() == 1) || epochCount % esConfig.getEvaluateEveryNEpochs() == 0) {
                //Calculate score at this epoch:
                ScoreCalculator sc = esConfig.getScoreCalculator();
                double score = (sc == null ? 0.0 : esConfig.getScoreCalculator().calculateScore(model));
                scoreVsEpoch.put(epochCount - 1, score);

                if (sc != null && score < bestModelScore) {
                    //Save best model:
                    if (bestModelEpoch == -1) {
                        //First calculated/reported score
                        log.info("Score at epoch {}: {}", epochCount, score);
                    } else {
                        log.info("New best model: score = {}, epoch = {} (previous: score = {}, epoch = {})",
                                score, epochCount, bestModelScore, bestModelEpoch);
                    }
                    bestModelScore = score;
                    bestModelEpoch = epochCount;

                    try {
                        esConfig.getModelSaver().saveBestModel(model, score);
                    } catch (IOException e) {
                        throw new RuntimeException("Error saving best model", e);
                    }
                }

                if (esConfig.isSaveLastModel()) {
                    //Save last model:
                    try {
                        esConfig.getModelSaver().saveLatestModel(model, score);
                    } catch (IOException e) {
                        throw new RuntimeException("Error saving most recent model", e);
                    }
                }

                if (listener != null) listener.onEpoch(epochCount, score, esConfig, model);

                //Check per-epoch termination conditions:
                boolean epochTerminate = false;
                EpochTerminationCondition termReason = null;
                for (EpochTerminationCondition c : esConfig.getEpochTerminationConditions()) {
                    if (c.terminate(epochCount, score)) {
                        epochTerminate = true;
                        termReason = c;
                        break;
                    }
                }
                if (epochTerminate) {
                    log.info("Hit epoch termination condition at epoch {}. Details: {}", epochCount, termReason.toString());
                    T bestModel;
                    try {
                        bestModel = esConfig.getModelSaver().getBestModel();
                    } catch (IOException e2) {
                        throw new RuntimeException(e2);
                    }
                    EarlyStoppingResult<T> result = new EarlyStoppingResult<>(
                            EarlyStoppingResult.TerminationReason.EpochTerminationCondition,
                            termReason.toString(),
                            scoreVsEpoch,
                            bestModelEpoch,
                            bestModelScore,
                            epochCount + 1,
                            bestModel);
                    if (listener != null) listener.onCompletion(result);

                    return result;
                }
            }
            epochCount++;

        }
    }

    @Override
    public void setListener(EarlyStoppingListener<T> listener) {
        this.listener = listener;
    }

    protected void reset() {
        if (train != null) train.reset();
        if (trainMulti != null) trainMulti.reset();
    }


}

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