Java Code Examples for org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner#execute()

@Test(timeout = 20000L)
public void testAllInvalidConfigCG() throws Exception {
    //Invalid config - basically check that this actually terminates

    File f = temp.newFolder();
    ComputationGraphSpace mls = new ComputationGraphSpace.Builder()
            .addInputs("in")
            .layer("0", new DenseLayerSpace.Builder().nIn(4).nOut(new FixedValue<>(0))    //INVALID: nOut of 0
                    .activation(Activation.TANH)
                    .build(), "in")
            .layer("1", new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "0")
            .setOutputs("1")
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration);
    runner.execute();
}
 

@Test
public void GeneticSearchCandidateGenerator_getCandidate_ShouldGenerateCandidates() throws Exception {

    ScoreFunction scoreFunction = new BraninFunction.BraninScoreFunction();

    //Define configuration:
    CandidateGenerator candidateGenerator =
                    new GeneticSearchCandidateGenerator.Builder(new BraninFunction.BraninSpace(), scoreFunction)
                                    .build();

    TestTerminationCondition testTerminationCondition = new TestTerminationCondition();
    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator).scoreFunction(scoreFunction)
                    .terminationConditions(new MaxCandidatesCondition(50), testTerminationCondition).build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator());

    runner.addListeners(new LoggingStatusListener());
    runner.execute();

    Assert.assertFalse(testTerminationCondition.hasAFailedCandidate);
}
 

@Test
    public void test() throws Exception {
        Map<String, Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, new HashMap<>());

        //Define configuration:
        CandidateGenerator candidateGenerator = new RandomSearchGenerator(new BraninFunction.BraninSpace(), commands);
        OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                        .candidateGenerator(candidateGenerator).scoreFunction(new BraninFunction.BraninScoreFunction())
                        .terminationConditions(new MaxCandidatesCondition(50)).build();

        IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator());

        runner.addListeners(new LoggingStatusListener());
        runner.execute();


//        System.out.println("----- Complete -----");
    }
 

@Test
public void GeneticSearchCandidateGenerator_getCandidate_GeneticExceptionShouldMarkCandidateAsFailed() {

    ScoreFunction scoreFunction = new BraninFunction.BraninScoreFunction();

    //Define configuration:
    CandidateGenerator candidateGenerator =
                    new GeneticSearchCandidateGenerator.Builder(new BraninFunction.BraninSpace(), scoreFunction)
                                    .selectionOperator(new TestSelectionOperator()).build();

    TestTerminationCondition testTerminationCondition = new TestTerminationCondition();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator).scoreFunction(scoreFunction)
                    .terminationConditions(testTerminationCondition).build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator());

    runner.addListeners(new LoggingStatusListener());
    runner.execute();

    Assert.assertTrue(testTerminationCondition.hasAFailedCandidate);
}
 

@Test(timeout = 20000L)
public void testAllInvalidDataConfigMismatchCG() throws Exception {
    //Valid config - but mismatched with provided data

    File f = temp.newFolder();
    ComputationGraphSpace mls = new ComputationGraphSpace.Builder()
            .addInputs("in")
            .layer("0", new DenseLayerSpace.Builder().nIn(4).nOut(10)
                    .activation(Activation.TANH).build(), "in")
            .addLayer("1", new OutputLayerSpace.Builder().nIn(10).nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "0")
            .setOutputs("1")
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(
                    new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());
    runner.execute();
}
 

@Test(timeout = 20000L)
public void testAllInvalidDataConfigMismatch() throws Exception {
    //Valid config - but mismatched with provided data

    File f = temp.newFolder();
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
            .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(10)    //INVALID: nOut of 0
                    .activation(Activation.TANH)
                    .build())
            .addLayer(new OutputLayerSpace.Builder().nIn(10).nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(
                    new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration);
    runner.execute();
}
 

@Test(timeout = 20000L)
public void testAllInvalidConfig() throws Exception {
    //Invalid config - basically check that this actually terminates

    File f = temp.newFolder();
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
            .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(new FixedValue<>(0))    //INVALID: nOut of 0
                            .activation(Activation.TANH)
                            .build())
            .addLayer(new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
            .build();

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
            .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
            .terminationConditions(
                    new MaxCandidatesCondition(5))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration);
    runner.execute();
}
 

@Test
public void testInputTypeBasic() throws Exception {

    ParameterSpace<Integer> layerSizeHyperparam = new IntegerParameterSpace(20, 60);

    MultiLayerSpace hyperparameterSpace = new MultiLayerSpace.Builder().l2(0.0001)
                    .weightInit(WeightInit.XAVIER).updater(new Nesterovs())
                    .addLayer(new ConvolutionLayerSpace.Builder().kernelSize(5, 5).nIn(1).stride(1, 1)
                                    .nOut(layerSizeHyperparam).activation(Activation.IDENTITY).build())
                    .addLayer(new SubsamplingLayerSpace.Builder().poolingType(SubsamplingLayer.PoolingType.MAX)
                                    .kernelSize(2, 2).stride(2, 2).build())
                    .addLayer(new ConvolutionLayerSpace.Builder().kernelSize(5, 5)
                                    //Note that nIn need not be specified in later layers
                                    .stride(1, 1).nOut(50).activation(Activation.IDENTITY).build())
                    .addLayer(new SubsamplingLayerSpace.Builder().poolingType(SubsamplingLayer.PoolingType.MAX)
                                    .kernelSize(2, 2).stride(2, 2).build())
                    .addLayer(new DenseLayerSpace.Builder().activation(Activation.RELU).nOut(500).build())
                    .addLayer(new OutputLayerSpace.Builder()
                                    .lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10)
                                    .activation(Activation.SOFTMAX).build())
                    .setInputType(InputType.convolutionalFlat(28, 28, 1)).build();


    DataProvider dataProvider = new TestDataSetProvider();

    File f = testDir.newFolder();
    if (f.exists())
        f.delete();
    f.mkdir();
    ResultSaver modelSaver = new FileModelSaver(f.getAbsolutePath());

    ScoreFunction scoreFunction = new TestSetAccuracyScoreFunction();

    int maxCandidates = 4;
    TerminationCondition[] terminationConditions;
    terminationConditions = new TerminationCondition[] {new MaxCandidatesCondition(maxCandidates)};

    //Given these configuration options, let's put them all together:
    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(new RandomSearchGenerator(hyperparameterSpace, null))
                    .dataProvider(dataProvider).modelSaver(modelSaver).scoreFunction(scoreFunction)
                    .terminationConditions(terminationConditions).build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());
    runner.execute();

    assertEquals(maxCandidates, runner.getResults().size());
}
 

public static void main(String[] args) throws Exception {
    EarlyStoppingConfiguration<MultiLayerNetwork> esConf =
                    new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>()
                                    .epochTerminationConditions(new MaxEpochsTerminationCondition(3))
                                    .iterationTerminationConditions(
                                                    new MaxTimeIterationTerminationCondition(5, TimeUnit.MINUTES),
                                                    new MaxScoreIterationTerminationCondition(4.6) //Random score: -log_e(0.1) ~= 2.3
                                    ).scoreCalculator(new DataSetLossCalculator(new MnistDataSetIterator(64, 2000, false, false, true, 123), true)).modelSaver(new InMemoryModelSaver()).build();

    //Define: network config (hyperparameter space)
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
                    .l2(new ContinuousParameterSpace(0.0001, 0.05))
                    .addLayer(
                                    new ConvolutionLayerSpace.Builder().nIn(1)
                                                    .nOut(new IntegerParameterSpace(5, 30))
                                                    .kernelSize(new DiscreteParameterSpace<>(new int[] {3, 3},
                                                                    new int[] {4, 4}, new int[] {5, 5}))
                                                    .stride(new DiscreteParameterSpace<>(new int[] {1, 1},
                                                                    new int[] {2, 2}))
                                                    .activation(new DiscreteParameterSpace<>(Activation.RELU,
                                                                    Activation.SOFTPLUS, Activation.LEAKYRELU))
                                                    .build(),
                                    new IntegerParameterSpace(1, 2)) //1-2 identical layers
                    .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(new IntegerParameterSpace(2, 10))
                                    .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
                                    .build(), new IntegerParameterSpace(0, 1)) //0 to 1 layers
                    .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                    .earlyStoppingConfiguration(esConf).build();
    Map<String, Object> commands = new HashMap<>();
    commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());

    //Define configuration:
    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands);
    DataProvider dataProvider = new MnistDataSetProvider();


    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterMNISTSmall\\").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    if (!f.exists())
        throw new RuntimeException();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator)
                    .dataProvider(dataProvider)
                    .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(new TestSetLossScoreFunction(true))
                    .terminationConditions(new MaxTimeCondition(120, TimeUnit.MINUTES),
                                    new MaxCandidatesCondition(100))
                    .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());

    //        ArbiterUIServer server = ArbiterUIServer.getInstance();
    //        runner.addListeners(new UIOptimizationRunnerStatusListener(server));

    runner.execute();


    System.out.println("----- COMPLETE -----");
}
 

@Test
@Ignore
public void testBasicMnistCompGraph() throws Exception {

    ComputationGraphSpace cgs = new ComputationGraphSpace.Builder()
            .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
            .l2(new ContinuousParameterSpace(0.0001, 0.05))
            .addInputs("in")
            .addLayer("0",
                    new ConvolutionLayerSpace.Builder().nIn(1)
                            .nOut(new IntegerParameterSpace(5, 30))
                            .kernelSize(new DiscreteParameterSpace<>(new int[]{3, 3},
                                    new int[]{4, 4}, new int[]{5, 5}))
                            .stride(new DiscreteParameterSpace<>(new int[]{1, 1},
                                    new int[]{2, 2}))
                            .activation(new DiscreteParameterSpace<>(Activation.RELU,
                                    Activation.SOFTPLUS, Activation.LEAKYRELU))
                            .build(), "in")
            .addLayer("1", new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128))
                    .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
                    .build(), "0")
            .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "1")
            .setOutputs("out")
            .setInputTypes(InputType.convolutionalFlat(28, 28, 1))
            .build();

    //Define configuration:
    CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs);
    DataProvider dataProvider = new MnistDataSetProvider();


    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnistCG\\").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    if (!f.exists())
        throw new RuntimeException();

    OptimizationConfiguration configuration =
            new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
                    .modelSaver(new FileModelSaver(modelSavePath))
                    .scoreFunction(new TestSetLossScoreFunction(true))
                    .terminationConditions(new MaxTimeCondition(120, TimeUnit.MINUTES),
                            new MaxCandidatesCondition(100))
                    .build();

    IOptimizationRunner runner =
            new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator());

    StatsStorage ss = new InMemoryStatsStorage();
    StatusListener sl = new ArbiterStatusListener(ss);
    runner.addListeners(sl);

    UIServer.getInstance().attach(ss);

    runner.execute();
    Thread.sleep(100000);
}
 

public static void main(String[] args) {

        ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01);
        ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11);
        MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder()
                .updater(new AdamSpace(learningRateParam))
                //  .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(11)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(layerSizeParam)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new OutputLayerSpace.Builder()
                        .activation(Activation.SIGMOID)
                        .lossFunction(LossFunctions.LossFunction.XENT)
                        .nOut(1)
                        .build())
                .build();

        Map<String,Object> dataParams = new HashMap<>();
        dataParams.put("batchSize",new Integer(10));

        Map<String,Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, HyperParameterTuningArbiterUiExample.ExampleDataSource.class.getCanonicalName());

        CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams);

        Properties dataSourceProperties = new Properties();
        dataSourceProperties.setProperty("minibatchSize", "64");

        ResultSaver modelSaver = new FileModelSaver("resources/");
        ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY);


        TerminationCondition[] conditions = {
                new MaxTimeCondition(120, TimeUnit.MINUTES),
                new MaxCandidatesCondition(30)

        };

        OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder()
                .candidateGenerator(candidateGenerator)
                .dataSource(HyperParameterTuningArbiterUiExample.ExampleDataSource.class,dataSourceProperties)
                .modelSaver(modelSaver)
                .scoreFunction(scoreFunction)
                .terminationConditions(conditions)
                .build();

        IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator());
        //Uncomment this if you want to store the model.
        StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j"));
        runner.addListeners(new ArbiterStatusListener(ss));
        UIServer.getInstance().attach(ss);
        //runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss)
        runner.execute();

        //Print the best hyper params

        double bestScore = runner.bestScore();
        int bestCandidateIndex = runner.bestScoreCandidateIndex();
        int numberOfConfigsEvaluated = runner.numCandidatesCompleted();

        String s = "Best score: " + bestScore + "\n" +
                "Index of model with best score: " + bestCandidateIndex + "\n" +
                "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n";

        System.out.println(s);

    }
 

@Test
@Ignore
public void testBasicMnistDataSource() throws InterruptedException {
    ParameterSpace<Double> learningRateHyperparam = new ContinuousParameterSpace(0.0001, 0.1);
    ParameterSpace<Integer> layerSizeHyperparam = new IntegerParameterSpace(16, 256);

    MultiLayerSpace hyperparameterSpace = new MultiLayerSpace.Builder()
            .weightInit(WeightInit.XAVIER)
            .l2(0.0001)
            .updater(new SgdSpace(learningRateHyperparam))
            .addLayer(new DenseLayerSpace.Builder()
                    .nIn(784)
                    .activation(Activation.LEAKYRELU)
                    .nOut(layerSizeHyperparam)
                    .build())
            .addLayer(new OutputLayerSpace.Builder()
                    .nOut(10)
                    .activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT)
                    .build())
            .build();
    CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperparameterSpace, null);
    ScoreFunction scoreFunction = new EvaluationScoreFunction(Evaluation.Metric.ACCURACY);
    TerminationCondition[] terminationConditions = {
            new MaxTimeCondition(5, TimeUnit.MINUTES),
            new MaxCandidatesCondition(2)};

    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnist\\").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    if (!f.exists())
        throw new RuntimeException();
    Class<? extends DataSource> ds = MnistDataSource.class;
    Properties dsp = new Properties();
    dsp.setProperty("minibatch", "8");
    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataSource(ds, dsp)
            .modelSaver(new FileModelSaver(modelSavePath))
            .scoreFunction(scoreFunction)
            .terminationConditions(terminationConditions)
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());

    StatsStorage ss = new InMemoryStatsStorage();
    StatusListener sl = new ArbiterStatusListener(ss);
    runner.addListeners(sl);

    UIServer.getInstance().attach(ss);

    runner.execute();
    Thread.sleep(90000);
}
 

@Test
public void testROCScoreFunctions() throws Exception {


    for (boolean auc : new boolean[]{true, false}) {
        for (ROCScoreFunction.ROCType rocType : ROCScoreFunction.ROCType.values()) {
            String msg = (auc ? "AUC" : "AUPRC") + " - " + rocType;
            log.info("Starting: " + msg);

            ParameterSpace<Double> lr = new ContinuousParameterSpace(1e-5, 1e-3);

            int nOut = (rocType == ROCScoreFunction.ROCType.ROC ? 2 : 10);
            LossFunctions.LossFunction lf = (rocType == ROCScoreFunction.ROCType.BINARY ?
                    LossFunctions.LossFunction.XENT : LossFunctions.LossFunction.MCXENT);
            Activation a = (rocType == ROCScoreFunction.ROCType.BINARY ? Activation.SIGMOID : Activation.SOFTMAX);
            MultiLayerSpace mls = new MultiLayerSpace.Builder()
                    .trainingWorkspaceMode(WorkspaceMode.NONE)
                    .inferenceWorkspaceMode(WorkspaceMode.NONE)
                    .updater(new AdamSpace(lr))
                    .weightInit(WeightInit.XAVIER)
                    .layer(new OutputLayerSpace.Builder().nIn(784).nOut(nOut)
                            .activation(a)
                            .lossFunction(lf).build())
                    .build();

            CandidateGenerator cg = new RandomSearchGenerator(mls);
            ResultSaver rs = new InMemoryResultSaver();
            ScoreFunction sf = new ROCScoreFunction(rocType, (auc ? ROCScoreFunction.Metric.AUC : ROCScoreFunction.Metric.AUPRC));


            OptimizationConfiguration oc = new OptimizationConfiguration.Builder()
                    .candidateGenerator(cg)
                    .dataProvider(new DP(rocType))
                    .modelSaver(rs)
                    .scoreFunction(sf)
                    .terminationConditions(new MaxCandidatesCondition(3))
                    .rngSeed(12345)
                    .build();

            IOptimizationRunner runner = new LocalOptimizationRunner(oc, new MultiLayerNetworkTaskCreator());
            runner.execute();

            List<ResultReference> list = runner.getResults();

            for (ResultReference rr : list) {
                DataSetIterator testIter = new MnistDataSetIterator(4, 16, false, false, false, 12345);
                testIter.setPreProcessor(new PreProc(rocType));

                OptimizationResult or = rr.getResult();
                MultiLayerNetwork net = (MultiLayerNetwork) or.getResultReference().getResultModel();

                double expScore;
                switch (rocType){
                    case ROC:
                        if(auc){
                            expScore = net.doEvaluation(testIter, new ROC())[0].calculateAUC();
                        } else {
                            expScore = net.doEvaluation(testIter, new ROC())[0].calculateAUCPR();
                        }
                        break;
                    case BINARY:
                        if(auc){
                            expScore = net.doEvaluation(testIter, new ROCBinary())[0].calculateAverageAuc();
                        } else {
                            expScore = net.doEvaluation(testIter, new ROCBinary())[0].calculateAverageAUCPR();
                        }
                        break;
                    case MULTICLASS:
                        if(auc){
                            expScore = net.doEvaluation(testIter, new ROCMultiClass())[0].calculateAverageAUC();
                        } else {
                            expScore = net.doEvaluation(testIter, new ROCMultiClass())[0].calculateAverageAUCPR();
                        }
                        break;
                    default:
                        throw new RuntimeException();
                }


                DataSetIterator iter = new MnistDataSetIterator(4, 16, false, false, false, 12345);
                iter.setPreProcessor(new PreProc(rocType));

                assertEquals(msg, expScore, or.getScore(), 1e-4);
            }
        }
    }
}
 

@Test
@org.junit.Ignore
public void testLocalExecutionGridSearch() throws Exception {

    //Define: network config (hyperparameter space)
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
                    .l2(new ContinuousParameterSpace(0.0001, 0.01))
                    .addLayer(
                                    new DenseLayerSpace.Builder().nIn(4).nOut(new IntegerParameterSpace(2, 10))
                                                    .activation(new DiscreteParameterSpace<>(Activation.RELU,
                                                                    Activation.TANH))
                                                    .build(),
                                    new IntegerParameterSpace(1, 2)) //1-2 identical layers (except nIn)
                    .addLayer(new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                    .numEpochs(3).build();
    Map<String, Object> commands = new HashMap<>();
    commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());

    CandidateGenerator candidateGenerator = new GridSearchCandidateGenerator(mls, 5,
                    GridSearchCandidateGenerator.Mode.Sequential, commands);
    DataProvider dataProvider = new DataSetIteratorFactoryProvider();

    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest/").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    f.deleteOnExit();
    if (!f.exists())
        throw new RuntimeException();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                    .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
                    .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(new TestSetLossScoreFunction())
                    .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES),
                                    new MaxCandidatesCondition(100))
                    .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration,
                    new MultiLayerNetworkTaskCreator(new ClassificationEvaluator()));

    runner.execute();

    System.out.println("----- COMPLETE -----");
}
 

public static void main(String[] args) {

        ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01);
        ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11);
        MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder()
                .updater(new AdamSpace(learningRateParam))
                //  .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(11)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(layerSizeParam)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new OutputLayerSpace.Builder()
                        .activation(Activation.SIGMOID)
                        .lossFunction(LossFunctions.LossFunction.XENT)
                        .nOut(1)
                        .build())
                .build();

        Map<String,Object> dataParams = new HashMap<>();
        dataParams.put("batchSize",new Integer(10));

        Map<String,Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY,ExampleDataSource.class.getCanonicalName());

        CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams);

        Properties dataSourceProperties = new Properties();
        dataSourceProperties.setProperty("minibatchSize", "64");

        ResultSaver modelSaver = new FileModelSaver("resources/");
        ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY);


        TerminationCondition[] conditions = {
                new MaxTimeCondition(120, TimeUnit.MINUTES),
                new MaxCandidatesCondition(30)

        };

        OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder()
                .candidateGenerator(candidateGenerator)
                .dataSource(ExampleDataSource.class,dataSourceProperties)
                .modelSaver(modelSaver)
                .scoreFunction(scoreFunction)
                .terminationConditions(conditions)
                .build();

        IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator());
        //Uncomment this if you want to store the model.
        //StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j"));
        //runner.addListeners(new ArbiterStatusListener(ss));
        //UIServer.getInstance().attach(ss);
        runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss)
        runner.execute();

        //Print the best hyper params

        double bestScore = runner.bestScore();
        int bestCandidateIndex = runner.bestScoreCandidateIndex();
        int numberOfConfigsEvaluated = runner.numCandidatesCompleted();

        String s = "Best score: " + bestScore + "\n" +
                "Index of model with best score: " + bestCandidateIndex + "\n" +
                "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n";

        System.out.println(s);

    }
 

@Test
public void testLocalExecution() throws Exception {
    Map<String, Object> commands = new HashMap<>();
    commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());

    //Define: network config (hyperparameter space)
    ComputationGraphSpace mls = new ComputationGraphSpace.Builder()
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1)))
            .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in")
            .setInputTypes(InputType.feedForward(4))
            .addLayer("layer0",
                    new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10))
                            .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
                            .build(),
                    "in")
            .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "layer0")
            .setOutputs("out").numEpochs(3).build();

    //Define configuration:
    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands);
    DataProvider dataProvider = new DataSetIteratorFactoryProvider();

    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest\\").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    f.deleteOnExit();
    if (!f.exists())
        throw new RuntimeException();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
            .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(ScoreFunctions.testSetLoss(true))
            .terminationConditions(new MaxTimeCondition(30, TimeUnit.SECONDS),
                    new MaxCandidatesCondition(3))
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration,
            new ComputationGraphTaskCreator(new ClassificationEvaluator()));

    runner.execute();

    assertEquals(0, runner.numCandidatesFailed());
    assertTrue(runner.numCandidatesCompleted() > 0);
}
 

@Test
public void testLocalExecutionMDS() throws Exception {
    //Define: network config (hyperparameter space)
    ComputationGraphSpace mls = new ComputationGraphSpace.Builder()
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1)))
            .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in")
            .setInputTypes(InputType.feedForward(784))
            .addLayer("layer0",
                    new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10))
                            .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
                            .build(),
                    "in")
            .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "layer0")
            .setOutputs("out").numEpochs(3).build();

    //Define configuration:
    CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, null);

    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest\\").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    f.deleteOnExit();
    if (!f.exists())
        throw new RuntimeException();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator)
            .dataProvider(new TestMdsDataProvider(1, 32))
            .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(ScoreFunctions.testSetLoss(true))
            .terminationConditions(new MaxTimeCondition(30, TimeUnit.SECONDS),
                    new MaxCandidatesCondition(3))
            .scoreFunction(ScoreFunctions.testSetAccuracy())
            .build();

    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator());

    runner.execute();

    assertEquals(0, runner.numCandidatesFailed());
    assertTrue(runner.numCandidatesCompleted() > 0);
}
 

@Test
public void testLocalExecutionEarlyStopping() throws Exception {
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
            .epochTerminationConditions(new MaxEpochsTerminationCondition(2))
            .scoreCalculator(new ScoreProvider())
            .modelSaver(new InMemoryModelSaver()).build();
    Map<String, Object> commands = new HashMap<>();
    commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());

    //Define: network config (hyperparameter space)
    ComputationGraphSpace cgs = new ComputationGraphSpace.Builder()
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .updater(new AdamSpace(new ContinuousParameterSpace(0.0001, 0.1)))
            .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in")
            .setInputTypes(InputType.feedForward(784))
            .addLayer("first",
                    new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10))
                            .activation(new DiscreteParameterSpace<>(Activation.RELU,
                                    Activation.TANH))
                            .build(),
                    "in") //1-2 identical layers (except nIn)
            .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "first")
            .setOutputs("out").earlyStoppingConfiguration(esConf).build();

    //Define configuration:

    CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, commands);
    DataProvider dataProvider = new DataSetIteratorFactoryProvider();


    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest2CG\\").getAbsolutePath();

    File f = new File(modelSavePath);
    if (f.exists())
        f.delete();
    f.mkdir();
    f.deleteOnExit();
    if (!f.exists())
        throw new RuntimeException();

    OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
            .candidateGenerator(candidateGenerator)
            .dataProvider(dataProvider)
            .scoreFunction(ScoreFunctions.testSetF1())
            .modelSaver(new FileModelSaver(modelSavePath))
            .terminationConditions(new MaxTimeCondition(15, TimeUnit.SECONDS),
                    new MaxCandidatesCondition(3))
            .build();


    IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator());
    runner.execute();

    assertEquals(0, runner.numCandidatesFailed());
    assertTrue(runner.numCandidatesCompleted() > 0);
}
 

public static void main(String[] args) {

        ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01);
        ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11);
        MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder()
                .updater(new AdamSpace(learningRateParam))
                //  .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(11)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(layerSizeParam)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new OutputLayerSpace.Builder()
                        .activation(Activation.SIGMOID)
                        .lossFunction(LossFunctions.LossFunction.XENT)
                        .nOut(1)
                        .build())
                .build();

        Map<String,Object> dataParams = new HashMap<>();
        dataParams.put("batchSize",new Integer(10));

        Map<String,Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, HyperParameterTuningArbiterUiExample.ExampleDataSource.class.getCanonicalName());

        CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams);

        Properties dataSourceProperties = new Properties();
        dataSourceProperties.setProperty("minibatchSize", "64");

        ResultSaver modelSaver = new FileModelSaver("resources/");
        ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY);


        TerminationCondition[] conditions = {
                new MaxTimeCondition(120, TimeUnit.MINUTES),
                new MaxCandidatesCondition(30)

        };

        OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder()
                .candidateGenerator(candidateGenerator)
                .dataSource(HyperParameterTuningArbiterUiExample.ExampleDataSource.class,dataSourceProperties)
                .modelSaver(modelSaver)
                .scoreFunction(scoreFunction)
                .terminationConditions(conditions)
                .build();

        IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator());
        //Uncomment this if you want to store the model.
        StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j"));
        runner.addListeners(new ArbiterStatusListener(ss));
        UIServer.getInstance().attach(ss);
        //runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss)
        runner.execute();

        //Print the best hyper params

        double bestScore = runner.bestScore();
        int bestCandidateIndex = runner.bestScoreCandidateIndex();
        int numberOfConfigsEvaluated = runner.numCandidatesCompleted();

        String s = "Best score: " + bestScore + "\n" +
                "Index of model with best score: " + bestCandidateIndex + "\n" +
                "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n";

        System.out.println(s);

    }
 

public static void main(String[] args) {

        ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01);
        ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11);
        MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder()
                .updater(new AdamSpace(learningRateParam))
                //  .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(11)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new DenseLayerSpace.Builder()
                        .activation(Activation.RELU)
                        .nIn(layerSizeParam)
                        .nOut(layerSizeParam)
                        .build())
                .addLayer(new OutputLayerSpace.Builder()
                        .activation(Activation.SIGMOID)
                        .lossFunction(LossFunctions.LossFunction.XENT)
                        .nOut(1)
                        .build())
                .build();

        Map<String,Object> dataParams = new HashMap<>();
        dataParams.put("batchSize",new Integer(10));

        Map<String,Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY,ExampleDataSource.class.getCanonicalName());

        CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams);

        Properties dataSourceProperties = new Properties();
        dataSourceProperties.setProperty("minibatchSize", "64");

        ResultSaver modelSaver = new FileModelSaver("resources/");
        ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY);


        TerminationCondition[] conditions = {
                new MaxTimeCondition(120, TimeUnit.MINUTES),
                new MaxCandidatesCondition(30)

        };

        OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder()
                .candidateGenerator(candidateGenerator)
                .dataSource(ExampleDataSource.class,dataSourceProperties)
                .modelSaver(modelSaver)
                .scoreFunction(scoreFunction)
                .terminationConditions(conditions)
                .build();

        IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator());
        //Uncomment this if you want to store the model.
        //StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j"));
        //runner.addListeners(new ArbiterStatusListener(ss));
        //UIServer.getInstance().attach(ss);
        runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss)
        runner.execute();

        //Print the best hyper params

        double bestScore = runner.bestScore();
        int bestCandidateIndex = runner.bestScoreCandidateIndex();
        int numberOfConfigsEvaluated = runner.numCandidatesCompleted();

        String s = "Best score: " + bestScore + "\n" +
                "Index of model with best score: " + bestCandidateIndex + "\n" +
                "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n";

        System.out.println(s);

    }