/*-
*
* * 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.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.nn.api.Model;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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.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<T> {
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<>(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();
}
}
}