Examples with DataSetLossCalculator org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator used on opensource projects

Example 1 with DataSetLossCalculator

use of org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testListeners.

@Test
public void testListeners() {
    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();
    LoggingEarlyStoppingListener listener = new LoggingEarlyStoppingListener();
    IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, net, irisIter, listener);
    trainer.fit();
    assertEquals(1, listener.onStartCallCount);
    assertEquals(5, listener.onEpochCallCount);
    assertEquals(1, listener.onCompletionCallCount);
}

Example 2 with DataSetLossCalculator

use of org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testEarlyStoppingEveryNEpoch.

@Test
public void testEarlyStoppingEveryNEpoch() {
    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)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).evaluateEveryNEpochs(2).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());
}

Example 3 with DataSetLossCalculator

use of org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testNoImprovementNEpochsTermination.

@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);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(0.0).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(100), new ScoreImprovementEpochTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS), //Initial score is ~2.5
    new MaxScoreIterationTerminationCondition(7.5)).scoreCalculator(new DataSetLossCalculator(irisIter, true)).modelSaver(saver).build();
    IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, net, irisIter);
    EarlyStoppingResult result = trainer.fit();
    //Expect no score change due to 0 LR -> terminate after 6 total epochs
    assertEquals(6, result.getTotalEpochs());
    assertEquals(0, result.getBestModelEpoch());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
    String expDetails = new ScoreImprovementEpochTerminationCondition(5).toString();
    assertEquals(expDetails, result.getTerminationDetails());
}

Example 4 with DataSetLossCalculator

use of org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testEarlyStoppingGetBestModel.

@Test
public void testEarlyStoppingGetBestModel() {
    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);
    MultipleEpochsIterator mIter = new MultipleEpochsIterator(10, irisIter);
    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, mIter);
    EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
    System.out.println(result);
    MultiLayerNetwork mln = result.getBestModel();
    assertEquals(net.getnLayers(), mln.getnLayers());
    assertEquals(net.conf().getNumIterations(), mln.conf().getNumIterations());
    assertEquals(net.conf().getOptimizationAlgo(), mln.conf().getOptimizationAlgo());
    assertEquals(net.conf().getLayer().getActivationFn().toString(), mln.conf().getLayer().getActivationFn().toString());
    assertEquals(net.conf().getLayer().getUpdater(), mln.conf().getLayer().getUpdater());
}

Example 5 with DataSetLossCalculator

use of org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator in project deeplearning4j by deeplearning4j.

the class TestEarlyStopping method testEarlyStoppingIrisMultiEpoch.

@Test
public void testEarlyStoppingIrisMultiEpoch() {
    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);
    MultipleEpochsIterator mIter = new MultipleEpochsIterator(10, irisIter);
    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, mIter);
    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);
}