Java example source code file (BaseEarlyStoppingTrainer.java)

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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();
    }


}