org.deeplearning4j.arbiter.layers.DenseLayerSpace Java Examples

private static MultiLayerSpace getMultiLayerSpaceMnist() {
    return new MultiLayerSpace.Builder()
            .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())
            .addLayer(new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128))
                    .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
                    .build(), new IntegerParameterSpace(0, 1), true) //0 to 1 layers
            .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
            .setInputType(InputType.convolutionalFlat(28, 28, 1))
            .build();
}
 

@Test
public void testComputationGraphSpaceJson() {
    ParameterSpace<Integer> p = new IntegerParameterSpace(10, 100);
    ComputationGraphSpace cgs =
                    new ComputationGraphSpace.Builder()
                                    .updater(new AdamSpace(new DiscreteParameterSpace<>(0.1, 0.5, 1.0)))
                                    .seed(12345).addInputs("in")
                                    .addLayer("0", new DenseLayerSpace.Builder()
                                                    .nIn(new IntegerParameterSpace(1, 100)).nOut(p).build(), "in")
                                    .addLayer("1", new DenseLayerSpace.Builder().nIn(p).nOut(10).build(), "0")
                                    .addLayer("2", new OutputLayerSpace.Builder().iLossFunction(
                                                    LossFunctions.LossFunction.MCXENT.getILossFunction()).nIn(10)
                                                    .nOut(5).build(), "1")
                                    .setOutputs("2").build();

    String asJson = cgs.toJson();
    ComputationGraphSpace fromJson = ComputationGraphSpace.fromJson(asJson);

    assertEquals(cgs, fromJson);
}
 

@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(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 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(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
public void testMultiLayerSpaceJson() {
    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 DenseLayerSpace.Builder().nIn(1).nOut(new IntegerParameterSpace(5, 30))
                                    .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.SOFTPLUS,
                                                    Activation.LEAKYRELU))
                                    .build(), new IntegerParameterSpace(1, 2), true) //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), true) //0 to 1 layers
                    .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                                    .iLossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build())
                    .setInputType(InputType.convolutional(28, 28, 1)).build();

    String asJson = mls.toJson();
    //        System.out.println(asJson);

    MultiLayerSpace fromJson = MultiLayerSpace.fromJson(asJson);

    assertEquals(mls, fromJson);
}
 

@Test
public void testBasic() {

    ComputationGraphConfiguration expected = new NeuralNetConfiguration.Builder()
                    .updater(new Sgd(0.005))
                    .seed(12345)
                    .graphBuilder().addInputs("in")
                    .addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in")
                    .addLayer("1", new DenseLayer.Builder().nIn(10).nOut(10).build(), "0").addLayer("2",
                                    new OutputLayer.Builder().lossFunction(LossFunction.MCXENT)
                                            .activation(Activation.SOFTMAX)
                                            .nIn(10).nOut(5)
                                                    .build(),
                                    "1")
                    .setOutputs("2").build();

    ComputationGraphSpace cgs = new ComputationGraphSpace.Builder()
                    .updater(new Sgd(0.005))
                    .seed(12345).addInputs("in")
                    .addLayer("0", new DenseLayerSpace.Builder().nIn(10).nOut(10).build(), "in")
                    .addLayer("1", new DenseLayerSpace.Builder().nIn(10).nOut(10).build(), "0")
                    .addLayer("2", new OutputLayerSpace.Builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10).nOut(5)
                                    .build(), "1")
                    .setOutputs("2").setInputTypes(InputType.feedForward(10))
                    .build();

    int nParams = cgs.numParameters();
    assertEquals(0, nParams);

    ComputationGraphConfiguration conf = cgs.getValue(new double[0]).getConfiguration();

    assertEquals(expected, conf);
}
 

@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 -----");
}
 

@Test
    public void testCliRunner() throws Exception {
        ArbiterCliRunner cliRunner = new ArbiterCliRunner();

        //Define: network config (hyperparameter space)
        MultiLayerSpace mls = new MultiLayerSpace.Builder()
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1)))
                .l2(new ContinuousParameterSpace(0.0001, 0.01))
                .addLayer(new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2,10))
                        .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
                        .build())
                .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
                        .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                .numEpochs(3).build();
         assertEquals(mls,MultiLayerSpace.fromJson(mls.toJson()));
        //Define configuration:
        Map<String,Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY,TestDataFactoryProviderMnist.class.getCanonicalName());

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


//        String modelSavePath = FilenameUtils.concat(System.getProperty("java.io.tmpdir"),"ArbiterDL4JTest/");
        String modelSavePath = new File(System.getProperty("java.io.tmpdir"),"ArbiterDL4JTest/").getAbsolutePath();
        File dir = new File(modelSavePath);
        if(!dir.exists())
            dir.mkdirs();
        String configPath = System.getProperty("java.io.tmpdir") + File.separator + UUID.randomUUID().toString() + ".json";
        OptimizationConfiguration configuration
                = new OptimizationConfiguration.Builder()
                .candidateGenerator(candidateGenerator)
                .dataProvider(dataProvider)
                .modelSaver(new FileModelSaver(modelSavePath))
                .scoreFunction(new TestSetLossScoreFunction())
                .terminationConditions(new MaxTimeCondition(30, TimeUnit.SECONDS),
                        new MaxCandidatesCondition(5))
                .build();
        assertEquals(configuration,OptimizationConfiguration.fromJson(configuration.toJson()));

        FileUtils.writeStringToFile(new File(configPath),configuration.toJson());
//        System.out.println(configuration.toJson());
        configuration.toJson();

        log.info("Starting test");
        cliRunner.runMain(
                "--dataSetIteratorClass",
                TestDataFactoryProviderMnist.class.getCanonicalName(),
                "--neuralNetType",
                ArbiterCliRunner.MULTI_LAYER_NETWORK,
                "--optimizationConfigPath",
                configPath
        );
    }
 

@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);
}
 

@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 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 testOptimizationFromJsonDataSource() {
    for(boolean withProperties : new boolean[]{false, true}) {
        //Define: network config (hyperparameter space)
        ComputationGraphSpace cgs = new ComputationGraphSpace.Builder()
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new AdaMaxSpace(new ContinuousParameterSpace(0.0001, 0.1)))
                .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in")
                .setInputTypes(InputType.feedForward(4))
                .addLayer("first",
                        new DenseLayerSpace.Builder().nIn(4).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(3).activation(Activation.SOFTMAX)
                        .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "first")
                .setOutputs("out").build();

        //Define configuration:
        Map<String, Object> commands = new HashMap<>();
        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());

        CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, commands);

        Properties p = new Properties();
        p.setProperty("minibatch", "16");

        OptimizationConfiguration configuration =
                new OptimizationConfiguration.Builder().candidateGenerator(candidateGenerator)
                        .dataSource(MnistDataSource.class, (withProperties ? p : null))
                        .scoreFunction(new TestSetLossScoreFunction())
                        .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES),
                                new MaxCandidatesCondition(100))
                        .build();

        String json = configuration.toJson();
        OptimizationConfiguration loadConf = OptimizationConfiguration.fromJson(json);
        assertEquals(configuration, loadConf);
        assertNotNull(loadConf.getDataSource());
        if(withProperties){
            assertNotNull(loadConf.getDataSourceProperties());
        }
    }
}
 

@Test
public void testOptimizationFromJson() {
    EarlyStoppingConfiguration<ComputationGraph> esConf =
                    new EarlyStoppingConfiguration.Builder<ComputationGraph>()
                                    .epochTerminationConditions(new MaxEpochsTerminationCondition(100))
                                    .scoreCalculator(new DataSetLossCalculatorCG(new IrisDataSetIterator(150, 150),
                                                    true))
                                    .modelSaver(new InMemoryModelSaver<ComputationGraph>()).build();

    //Define: network config (hyperparameter space)
    ComputationGraphSpace cgs = new ComputationGraphSpace.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new AdaMaxSpace(new ContinuousParameterSpace(0.0001, 0.1)))
                    .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in")
                    .setInputTypes(InputType.feedForward(4))
                    .addLayer("first",
                                    new DenseLayerSpace.Builder().nIn(4).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(3).activation(Activation.SOFTMAX)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "first")
                    .setOutputs("out").earlyStoppingConfiguration(esConf).build();

    //Define configuration:
    Map<String, Object> commands = new HashMap<>();
    commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());

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


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

    String json = configuration.toJson();
    OptimizationConfiguration loadConf = OptimizationConfiguration.fromJson(json);
    assertEquals(configuration, loadConf);
}
 

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


    //Define: network config (hyperparameter space)
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1)))
            .l2(new ContinuousParameterSpace(0.0001, 0.01))
            .addLayer(
                    new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(250, 500))
                            .activation(new DiscreteParameterSpace<>(Activation.RELU,
                                    Activation.TANH))
                            .build(),
                    new IntegerParameterSpace(1, 2)) //1-2 identical layers (except nIn)
            .addLayer(new OCNNLayerSpace.Builder().nu(new ContinuousParameterSpace(0.0001, 0.1))
                    .numHidden(new DiscreteParameterSpace<Integer>(784 / 2,784 / 4))
                    .activation(Activation.HARDSIGMOID)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
            .setInputType(InputType.convolutionalFlat(28,28,1))
            .build();

    //Define configuration:

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


    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest3\\").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();


    //candidate generation: uncomment execute if you want to run
    IOptimizationRunner runner = new LocalOptimizationRunner(configuration,
            new MultiLayerNetworkTaskCreator(new ClassificationEvaluator()));

    Candidate candidate = candidateGenerator.getCandidate();

    // runner.execute();
    System.out.println("----- COMPLETE -----");
}
 

@Test
@Ignore
public void testLocalExecutionEarlyStopping() throws Exception {
    EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>()
                    .epochTerminationConditions(new MaxEpochsTerminationCondition(100))
                    .scoreCalculator(new DataSetLossCalculator(new IrisDataSetIterator(150, 150), true))
                    .modelSaver(new InMemoryModelSaver()).build();
    Map<String, Object> commands = new HashMap<>();
    commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());


    //Define: network config (hyperparameter space)
    MultiLayerSpace mls = new MultiLayerSpace.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1)))
                    .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())
                    .earlyStoppingConfiguration(esConf).build();

    //Define configuration:

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


    String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest2\\").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, 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) 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);
}
 

@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);
}
 

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);

    }
 

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);

    }