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Java example source code file (TestEarlyStoppingSparkCompGraph.java)
This example Java source code file (TestEarlyStoppingSparkCompGraph.java) is included in the alvinalexander.com
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The TestEarlyStoppingSparkCompGraph.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;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
import org.deeplearning4j.earlystopping.EarlyStoppingResult;
import org.deeplearning4j.earlystopping.listener.EarlyStoppingListener;
import org.deeplearning4j.earlystopping.saver.InMemoryModelSaver;
import org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition;
import org.deeplearning4j.earlystopping.termination.MaxScoreIterationTerminationCondition;
import org.deeplearning4j.earlystopping.termination.MaxTimeIterationTerminationCondition;
import org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition;
import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.spark.api.TrainingMaster;
import org.deeplearning4j.spark.earlystopping.SparkEarlyStoppingGraphTrainer;
import org.deeplearning4j.spark.earlystopping.SparkLossCalculatorComputationGraph;
import org.deeplearning4j.spark.impl.graph.dataset.DataSetToMultiDataSetFn;
import org.deeplearning4j.spark.impl.paramavg.ParameterAveragingTrainingMaster;
import org.junit.Test;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.TimeUnit;
import static org.junit.Assert.*;
public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
@Test
public void testEarlyStoppingIris() {
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.SGD)
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0")
.pretrain(false).backprop(true)
.build();
ComputationGraph net = new ComputationGraph(conf);
net.setListeners(new ScoreIterationListener(1));
JavaRDD<DataSet> irisData = getIris();
EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(new MaxEpochsTerminationCondition(5))
.iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES))
.scoreCalculator(new SparkLossCalculatorComputationGraph(irisData.map(new DataSetToMultiDataSetFn()), true, sc.sc()))
.modelSaver(saver)
.build();
TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 10, 1, 0);
IEarlyStoppingTrainer<ComputationGraph> trainer = new SparkEarlyStoppingGraphTrainer(getContext().sc(), tm, esConf, net, irisData.map(new DataSetToMultiDataSetFn()));
EarlyStoppingResult<ComputationGraph> result = trainer.fit();
System.out.println(result);
assertEquals(5, result.getTotalEpochs());
assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
Map<Integer, Double> scoreVsIter = result.getScoreVsEpoch();
assertEquals(5, scoreVsIter.size());
String expDetails = esConf.getEpochTerminationConditions().get(0).toString();
assertEquals(expDetails, result.getTerminationDetails());
ComputationGraph out = result.getBestModel();
assertNotNull(out);
//Check that best score actually matches (returned model vs. manually calculated score)
ComputationGraph bestNetwork = result.getBestModel();
double score = bestNetwork.score(new IrisDataSetIterator(150, 150).next());
assertEquals(result.getBestModelScore(), score, 1e-3);
}
@Test
public void testBadTuning() {
//Test poor tuning (high LR): should terminate on MaxScoreIterationTerminationCondition
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.SGD).learningRate(2.0) //Intentionally huge LR
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0")
.pretrain(false).backprop(true)
.build();
ComputationGraph net = new ComputationGraph(conf);
net.setListeners(new ScoreIterationListener(1));
JavaRDD<DataSet> irisData = getIris();
EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(new MaxEpochsTerminationCondition(5000))
.iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES),
new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
.scoreCalculator(new SparkLossCalculatorComputationGraph(irisData.map(new DataSetToMultiDataSetFn()), true, sc.sc()))
.modelSaver(saver)
.build();
TrainingMaster tm = new ParameterAveragingTrainingMaster(true,numExecutors(),10,1,0);
IEarlyStoppingTrainer<ComputationGraph> trainer = new SparkEarlyStoppingGraphTrainer(getContext().sc(), tm, esConf, net, irisData.map(new DataSetToMultiDataSetFn()));
EarlyStoppingResult result = trainer.fit();
assertTrue(result.getTotalEpochs() < 5);
assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition, result.getTerminationReason());
String expDetails = new MaxScoreIterationTerminationCondition(7.5).toString();
assertEquals(expDetails, result.getTerminationDetails());
}
@Test
public void testTimeTermination() {
//test termination after max time
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.SGD).learningRate(1e-6)
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0")
.pretrain(false).backprop(true)
.build();
ComputationGraph net = new ComputationGraph(conf);
net.setListeners(new ScoreIterationListener(1));
JavaRDD<DataSet> irisData = getIris();
EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(new MaxEpochsTerminationCondition(10000))
.iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS),
new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
.scoreCalculator(new SparkLossCalculatorComputationGraph(irisData.map(new DataSetToMultiDataSetFn()), true, sc.sc()))
.modelSaver(saver)
.build();
TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 10, 1, 0);
IEarlyStoppingTrainer<ComputationGraph> trainer = new SparkEarlyStoppingGraphTrainer(getContext().sc(), tm, esConf, net, irisData.map(new DataSetToMultiDataSetFn()));
long startTime = System.currentTimeMillis();
EarlyStoppingResult result = trainer.fit();
long endTime = System.currentTimeMillis();
int durationSeconds = (int) (endTime - startTime) / 1000;
assertTrue(durationSeconds >= 3);
assertTrue(durationSeconds <= 9);
assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition, result.getTerminationReason());
String expDetails = new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS).toString();
assertEquals(expDetails, result.getTerminationDetails());
}
@Test
public void testNoImprovementNEpochsTermination() {
//Idea: terminate training if score (test set loss) does not improve for 5 consecutive epochs
//Simulate this by setting LR = 0.0
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(12345)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.SGD).learningRate(0.0)
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0")
.pretrain(false).backprop(true)
.build();
ComputationGraph net = new ComputationGraph(conf);
net.setListeners(new ScoreIterationListener(1));
JavaRDD<DataSet> irisData = getIris();
EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(new MaxEpochsTerminationCondition(100),
new ScoreImprovementEpochTerminationCondition(5))
.iterationTerminationConditions(new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
.scoreCalculator(new SparkLossCalculatorComputationGraph(irisData.map(new DataSetToMultiDataSetFn()), true, sc.sc()))
.modelSaver(saver)
.build();
TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 10, 1, 0);
IEarlyStoppingTrainer<ComputationGraph> trainer = new SparkEarlyStoppingGraphTrainer(getContext().sc(), tm, esConf, net, irisData.map(new DataSetToMultiDataSetFn()));
EarlyStoppingResult result = trainer.fit();
//Expect no score change due to 0 LR -> terminate after 6 total epochs
assertTrue(result.getTotalEpochs() < 12); //Normally expect 6 epochs exactly; get a little more than that here due to rounding + order of operations
assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
String expDetails = new ScoreImprovementEpochTerminationCondition(5).toString();
assertEquals(expDetails, result.getTerminationDetails());
}
@Test
public void testListeners() {
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.SGD)
.weightInit(WeightInit.XAVIER)
.graphBuilder()
.addInputs("in")
.addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
.setOutputs("0")
.pretrain(false).backprop(true)
.build();
ComputationGraph net = new ComputationGraph(conf);
net.setListeners(new ScoreIterationListener(1));
JavaRDD<DataSet> irisData = getIris();
EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(new MaxEpochsTerminationCondition(5))
.iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES))
.scoreCalculator(new SparkLossCalculatorComputationGraph(irisData.map(new DataSetToMultiDataSetFn()), true, sc.sc()))
.modelSaver(saver)
.build();
LoggingEarlyStoppingListener listener = new LoggingEarlyStoppingListener();
TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 10, 1, 0);
IEarlyStoppingTrainer<ComputationGraph> trainer = new SparkEarlyStoppingGraphTrainer(getContext().sc(), tm, esConf, net, irisData.map(new DataSetToMultiDataSetFn()));
trainer.setListener(listener);
trainer.fit();
assertEquals(1, listener.onStartCallCount);
assertEquals(5, listener.onEpochCallCount);
assertEquals(1, listener.onCompletionCallCount);
}
private static class LoggingEarlyStoppingListener implements EarlyStoppingListener<ComputationGraph> {
private static Logger log = LoggerFactory.getLogger(LoggingEarlyStoppingListener.class);
private int onStartCallCount = 0;
private int onEpochCallCount = 0;
private int onCompletionCallCount = 0;
@Override
public void onStart(EarlyStoppingConfiguration esConfig, ComputationGraph net) {
log.info("EarlyStopping: onStart called");
onStartCallCount++;
}
@Override
public void onEpoch(int epochNum, double score, EarlyStoppingConfiguration esConfig, ComputationGraph net) {
log.info("EarlyStopping: onEpoch called (epochNum={}, score={}}", epochNum, score);
onEpochCallCount++;
}
@Override
public void onCompletion(EarlyStoppingResult esResult) {
log.info("EorlyStopping: onCompletion called (result: {})", esResult);
onCompletionCallCount++;
}
}
private JavaRDD<DataSet> getIris() {
JavaSparkContext sc = getContext();
IrisDataSetIterator iter = new IrisDataSetIterator(1, 150);
List<DataSet> list = new ArrayList<>(150);
while (iter.hasNext()) list.add(iter.next());
return sc.parallelize(list);
}
}
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