Java IEarlyStoppingTrainer類代碼示例

本文整理匯總了Java中org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer類的典型用法代碼示例。如果您正苦於以下問題：Java IEarlyStoppingTrainer類的具體用法？Java IEarlyStoppingTrainer怎麽用？Java IEarlyStoppingTrainer使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。

IEarlyStoppingTrainer類屬於org.deeplearning4j.earlystopping.trainer包，在下文中一共展示了IEarlyStoppingTrainer類的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: testNoImprovementNEpochsTermination

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(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),
                                                    new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
                                    .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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:39，代碼來源:TestEarlyStopping.java

示例2: testEarlyStoppingEveryNEpoch

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@Test
public void testEarlyStoppingEveryNEpoch() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd(0.01)).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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:28，代碼來源:TestEarlyStopping.java

示例3: testBadTuning

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(5.0)) //Intentionally huge LR
                    .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),
                                                    new MaxScoreIterationTerminationCondition(10)) //Initial score is ~2.5
                                    .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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:39，代碼來源:TestEarlyStopping.java

示例4: testTimeTermination

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(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),
                                                    new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
                                    //.scoreCalculator(new DataSetLossCalculator(irisIter, true))   //No score calculator in this test (don't need score)
                                    .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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:41，代碼來源:TestEarlyStopping.java

示例5: testEarlyStoppingGetBestModel

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@Test
public void testEarlyStoppingGetBestModel() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd(0.001)).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().getOptimizationAlgo(), mln.conf().getOptimizationAlgo());
    BaseLayer bl = (BaseLayer) net.conf().getLayer();
    assertEquals(bl.getActivationFn().toString(), ((BaseLayer) mln.conf().getLayer()).getActivationFn().toString());
    assertEquals(bl.getIUpdater(), ((BaseLayer) mln.conf().getLayer()).getIUpdater());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:37，代碼來源:TestEarlyStopping.java

示例6: testListeners

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@Test
public void testListeners() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd(0.001)).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);
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:32，代碼來源:TestEarlyStopping.java

示例7: testBadTuning

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(5.0)) //Intentionally huge LR
                    .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
                    .addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                    .setOutputs("0").pretrain(false).backprop(true).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.setListeners(new ScoreIterationListener(1));

    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
                    .epochTerminationConditions(new MaxEpochsTerminationCondition(5000))
                    .iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES),
                                    new MaxScoreIterationTerminationCondition(10)) //Initial score is ~2.5
                    .scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();

    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:36，代碼來源:TestEarlyStoppingCompGraph.java

示例8: testTimeTermination

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(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));

    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);

    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
                    .epochTerminationConditions(new MaxEpochsTerminationCondition(10000))
                    .iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS),
                                    new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
                    //.scoreCalculator(new DataSetLossCalculator(irisIter, true))   //No score calculator in this test (don't need score)
                    .modelSaver(saver).build();

    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:40，代碼來源:TestEarlyStoppingCompGraph.java

示例9: testNoImprovementNEpochsTermination

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(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));

    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);

    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
                    .epochTerminationConditions(new MaxEpochsTerminationCondition(100),
                                    new ScoreImprovementEpochTerminationCondition(5))
                    .iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS),
                                    new MaxScoreIterationTerminationCondition(7.5)) //Initial score is ~2.5
                    .scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();

    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:37，代碼來源:TestEarlyStoppingCompGraph.java

示例10: testListeners

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@Test
public void testListeners() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd(0.001)).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));

    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
                    .epochTerminationConditions(new MaxEpochsTerminationCondition(5))
                    .iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES))
                    .scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();

    LoggingEarlyStoppingListener listener = new LoggingEarlyStoppingListener();

    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter, listener);

    trainer.fit();

    assertEquals(1, listener.onStartCallCount);
    assertEquals(5, listener.onEpochCallCount);
    assertEquals(1, listener.onCompletionCallCount);
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:29，代碼來源:TestEarlyStoppingCompGraph.java

示例11: testEarlyStoppingEveryNEpoch

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@Test
public void testEarlyStoppingEveryNEpoch() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new 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(50, 600);
    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 EarlyStoppingParallelTrainer<>(esConf, net, irisIter, null, 2, 6, 1);

    EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
    System.out.println(result);

    assertEquals(5, result.getTotalEpochs());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:29，代碼來源:TestParallelEarlyStopping.java

示例12: testBadTuning

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(1.0)) //Intentionally huge LR
                    .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(10, 150);
    EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf =
                    new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>()
                                    .epochTerminationConditions(new MaxEpochsTerminationCondition(5000))
                                    .iterationTerminationConditions(
                                                    new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES),
                                                    new MaxScoreIterationTerminationCondition(10)) //Initial score is ~2.5
                                    .scoreCalculator(new DataSetLossCalculator(irisIter, true)).modelSaver(saver)
                                    .build();

    IEarlyStoppingTrainer<MultiLayerNetwork> trainer =
                    new EarlyStoppingParallelTrainer<>(esConf, net, irisIter, null, 2, 2, 1);
    EarlyStoppingResult result = trainer.fit();

    assertTrue(result.getTotalEpochs() < 5);
    assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition,
                    result.getTerminationReason());
    String expDetails = new MaxScoreIterationTerminationCondition(10).toString();
    assertEquals(expDetails, result.getTerminationDetails());

    assertTrue(result.getBestModelEpoch() <= 0);
    assertNotNull(result.getBestModel());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:40，代碼來源:TestParallelEarlyStopping.java

示例13: testParallelStatsListenerCompatibility

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@Test
@Ignore //To be run manually
public void testParallelStatsListenerCompatibility() throws Exception {
    UIServer uiServer = UIServer.getInstance();

    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd()).weightInit(WeightInit.XAVIER).list()
                    .layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build())
                    .layer(1, new OutputLayer.Builder().nIn(3).nOut(3)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                    .pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);

    // it's important that the UI can report results from parallel training
    // there's potential for StatsListener to fail if certain properties aren't set in the model
    StatsStorage statsStorage = new InMemoryStatsStorage();
    net.setListeners(new StatsListener(statsStorage));
    uiServer.attach(statsStorage);

    DataSetIterator irisIter = new IrisDataSetIterator(50, 500);
    EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf =
                    new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>()
                                    .epochTerminationConditions(new MaxEpochsTerminationCondition(500))
                                    .scoreCalculator(new DataSetLossCalculator(irisIter, true))
                                    .evaluateEveryNEpochs(2).modelSaver(saver).build();

    IEarlyStoppingTrainer<MultiLayerNetwork> trainer =
                    new EarlyStoppingParallelTrainer<>(esConf, net, irisIter, null, 3, 6, 2);

    EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
    System.out.println(result);

    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:37，代碼來源:TestParallelEarlyStoppingUI.java

示例14: testBadTuning

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(2.0)) //Intentionally huge LR
                    .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
                    .addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.IDENTITY)
                                    .lossFunction(LossFunctions.LossFunction.MSE).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<ComputationGraph>()
                    .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(), 1, 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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:38，代碼來源:TestEarlyStoppingSparkCompGraph.java

示例15: testNoImprovementNEpochsTermination

import org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer; //導入依賴的package包/類
@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)
                    .updater(new Sgd(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<ComputationGraph>()
                    .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(), 1, 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());
}

開發者ID:deeplearning4j，項目名稱:deeplearning4j，代碼行數:40，代碼來源:TestEarlyStoppingSparkCompGraph.java

注：本文中的org.deeplearning4j.earlystopping.trainer.IEarlyStoppingTrainer類示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。