use of org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer in project deeplearning4j by deeplearning4j.
the class TestEarlyStopping method testBadTuning.
@Test
public void testBadTuning() {
//Test poor tuning (high LR): should terminate on MaxScoreIterationTerminationCondition
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(//Intentionally huge LR
5.0).weightInit(WeightInit.XAVIER).list().layer(0, new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.setListeners(new ScoreIterationListener(1));
DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(5000)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES), //Initial score is ~2.5
new MaxScoreIterationTerminationCondition(10)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).modelSaver(saver).build();
IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, net, irisIter);
EarlyStoppingResult result = trainer.fit();
assertTrue(result.getTotalEpochs() < 5);
assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition, result.getTerminationReason());
String expDetails = new MaxScoreIterationTerminationCondition(10).toString();
assertEquals(expDetails, result.getTerminationDetails());
assertEquals(0, result.getBestModelEpoch());
assertNotNull(result.getBestModel());
}
use of org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer in project deeplearning4j by deeplearning4j.
the class TestEarlyStopping method testTimeTermination.
@Test
public void testTimeTermination() {
//test termination after max time
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(1e-6).weightInit(WeightInit.XAVIER).list().layer(0, new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.setListeners(new ScoreIterationListener(1));
DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(10000)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS), //Initial score is ~2.5
new MaxScoreIterationTerminationCondition(7.5)).modelSaver(saver).build();
IEarlyStoppingTrainer<MultiLayerNetwork> trainer = new EarlyStoppingTrainer(esConf, net, irisIter);
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());
}
use of org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer in project deeplearning4j by deeplearning4j.
the class TestEarlyStopping method testMinImprovementNEpochsTermination.
@Test
public void testMinImprovementNEpochsTermination() {
//Idea: terminate training if score (test set loss) does not improve more than minImprovement for 5 consecutive epochs
//Simulate this by setting LR = 0.0
Random rng = new Random(123);
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(123).iterations(10).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.0).updater(Updater.NESTEROVS).momentum(0.9).list().layer(0, new DenseLayer.Builder().nIn(1).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).nIn(20).nOut(1).build()).pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.setListeners(new ScoreIterationListener(1));
int nSamples = 100;
//Generate the training data
INDArray x = Nd4j.linspace(-10, 10, nSamples).reshape(nSamples, 1);
INDArray y = Nd4j.getExecutioner().execAndReturn(new Sin(x.dup()));
DataSet allData = new DataSet(x, y);
List<DataSet> list = allData.asList();
Collections.shuffle(list, rng);
DataSetIterator training = new ListDataSetIterator(list, nSamples);
double minImprovement = 0.0009;
EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(1000), //Go on for max 5 epochs without any improvements that are greater than minImprovement
new ScoreImprovementEpochTerminationCondition(5, minImprovement)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculator(training, true)).modelSaver(saver).build();
IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, net, training);
EarlyStoppingResult result = trainer.fit();
assertEquals(6, result.getTotalEpochs());
assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
String expDetails = new ScoreImprovementEpochTerminationCondition(5, minImprovement).toString();
assertEquals(expDetails, result.getTerminationDetails());
}
use of org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer in project deeplearning4j by deeplearning4j.
the class TestEarlyStopping method testEarlyStoppingIris.
@Test
public void testEarlyStoppingIris() {
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).list().layer(0, new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build()).pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.setListeners(new ScoreIterationListener(1));
DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>().epochTerminationConditions(new MaxEpochsTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).modelSaver(saver).build();
IEarlyStoppingTrainer<MultiLayerNetwork> trainer = new EarlyStoppingTrainer(esConf, net, irisIter);
EarlyStoppingResult<MultiLayerNetwork> 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());
MultiLayerNetwork out = result.getBestModel();
assertNotNull(out);
//Check that best score actually matches (returned model vs. manually calculated score)
MultiLayerNetwork bestNetwork = result.getBestModel();
irisIter.reset();
double score = bestNetwork.score(irisIter.next());
assertEquals(result.getBestModelScore(), score, 1e-2);
}
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