org.deeplearning4j.ui.api.UIServer Java Examples

@Test
public void testUICompGraph() {

    StatsStorage ss = new InMemoryStatsStorage();

    UIServer uiServer = UIServer.getInstance();
    uiServer.attach(ss);

    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
                    .addLayer("L0", new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build(),
                                    "in")
                    .addLayer("L1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
                                    .activation(Activation.SOFTMAX).nIn(4).nOut(3).build(), "L0")
                    .setOutputs("L1").build();

    ComputationGraph net = new ComputationGraph(conf);
    net.init();

    net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));

    DataSetIterator iter = new IrisDataSetIterator(150, 150);

    for (int i = 0; i < 100; i++) {
        net.fit(iter);
    }
}
 

@Test (expected = DL4JException.class)
public void testUIStartPortAlreadyBound() throws InterruptedException {
    CountDownLatch latch = new CountDownLatch(1);
    //Create HttpServer that binds the same port
    int port = VertxUIServer.DEFAULT_UI_PORT;
    Vertx vertx = Vertx.vertx();
    vertx.createHttpServer()
            .requestHandler(event -> {})
            .listen(port, result -> latch.countDown());
    latch.await();

    try {
        //DL4JException signals that the port cannot be bound, UI server cannot start
        UIServer.getInstance();
    } finally {
        vertx.close();
    }
}
 

public void main(String[] args){
    CLIParams d = new CLIParams();
    new JCommander(d).parse(args);
    instancePort = d.getCliPort();
    UIServer.getInstance(d.isCliMultiSession(), null);
    if(d.isCliEnableRemote()){
        try {
            File tempStatsFile = DL4JFileUtils.createTempFile("dl4j", "UIstats");
            tempStatsFile.delete();
            tempStatsFile.deleteOnExit();
            enableRemoteListener(new FileStatsStorage(tempStatsFile), true);
        } catch(Exception e) {
            log.error("Failed to create temporary file for stats storage",e);
            System.exit(1);
        }
    }
}
 

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

    UIServer s = UIServer.getInstance();
    s.enableRemoteListener();

    Thread.sleep(1000000);
}
 

@Test
public void testUIMultipleSessions() throws Exception {

    for (int session = 0; session < 3; session++) {

        StatsStorage ss = new InMemoryStatsStorage();

        UIServer uiServer = UIServer.getInstance();
        uiServer.attach(ss);

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
                .layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build())
                .layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
                        .activation(Activation.SOFTMAX).nIn(4).nOut(3).build())
                .build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        net.setListeners(new StatsListener(ss, 1), new ScoreIterationListener(1));

        DataSetIterator iter = new IrisDataSetIterator(150, 150);

        for (int i = 0; i < 20; i++) {
            net.fit(iter);
            Thread.sleep(100);
        }
    }
}
 

@Test
public void testUI_VAE() throws Exception {
    //Variational autoencoder - for unsupervised layerwise pretraining

    StatsStorage ss = new InMemoryStatsStorage();

    UIServer uiServer = UIServer.getInstance();
    uiServer.attach(ss);

    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd(1e-5))
                    .list().layer(0,
                                    new VariationalAutoencoder.Builder().nIn(4).nOut(3).encoderLayerSizes(10, 11)
                                                    .decoderLayerSizes(12, 13).weightInit(WeightInit.XAVIER)
                                                    .pzxActivationFunction(Activation.IDENTITY)
                                                    .reconstructionDistribution(
                                                                    new GaussianReconstructionDistribution())
                                                    .activation(Activation.LEAKYRELU).build())
                    .layer(1, new VariationalAutoencoder.Builder().nIn(3).nOut(3).encoderLayerSizes(7)
                                    .decoderLayerSizes(8).weightInit(WeightInit.XAVIER)
                                    .pzxActivationFunction(Activation.IDENTITY)
                                    .reconstructionDistribution(new GaussianReconstructionDistribution())
                                    .activation(Activation.LEAKYRELU).build())
                    .layer(2, new OutputLayer.Builder().nIn(3).nOut(3).build())
                    .build();

    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));

    DataSetIterator iter = new IrisDataSetIterator(150, 150);

    for (int i = 0; i < 50; i++) {
        net.fit(iter);
        Thread.sleep(100);
    }

}
 

@Test
public void testUIAttachDetach() throws Exception {
    StatsStorage ss = new InMemoryStatsStorage();

    UIServer uiServer = UIServer.getInstance();
    uiServer.attach(ss);
    assertFalse(uiServer.getStatsStorageInstances().isEmpty());
    uiServer.detach(ss);
    assertTrue(uiServer.getStatsStorageInstances().isEmpty());
}
 

@Test (expected = DL4JException.class)
public void testUIServerGetInstanceMultipleCalls1() {
    UIServer uiServer = UIServer.getInstance();
    assertFalse(uiServer.isMultiSession());
    UIServer.getInstance(true, null);

}
 

public ConvolutionalIterationListener(StatsStorageRouter ssr, int iterations, boolean openBrowser, String sessionID,
                String workerID) {
    this.ssr = ssr;
    if (sessionID == null) {
        //TODO handle syncing session IDs across different listeners in the same model...
        this.sessionID = UUID.randomUUID().toString();
    } else {
        this.sessionID = sessionID;
    }
    if (workerID == null) {
        this.workerID = UIDProvider.getJVMUID() + "_" + Thread.currentThread().getId();
    } else {
        this.workerID = workerID;
    }

    String subPath = "activations";

    this.freq = iterations;
    this.openBrowser = openBrowser;
    path = "http://localhost:" + UIServer.getInstance().getPort() + "/" + subPath;

    if (openBrowser && ssr instanceof StatsStorage) {
        UIServer.getInstance().attach((StatsStorage) ssr);
    }

    System.out.println("ConvolutionTrainingListener path: " + path);
}
 

@Test(timeout = 300000)
public void testTsne() throws Exception {
    DataTypeUtil.setDTypeForContext(DataType.DOUBLE);
    Nd4j.getRandom().setSeed(123);
    BarnesHutTsne b = new BarnesHutTsne.Builder().stopLyingIteration(10).setMaxIter(10).theta(0.5).learningRate(500)
                    .useAdaGrad(true).build();

    File f = Resources.asFile("/deeplearning4j-core/mnist2500_X.txt");
    INDArray data = Nd4j.readNumpy(f.getAbsolutePath(), "   ").get(NDArrayIndex.interval(0, 100),
                    NDArrayIndex.interval(0, 784));



    ClassPathResource labels = new ClassPathResource("mnist2500_labels.txt");
    List<String> labelsList = IOUtils.readLines(labels.getInputStream()).subList(0, 100);
    b.fit(data);
    File save = new File(System.getProperty("java.io.tmpdir"), "labels-" + UUID.randomUUID().toString());
    System.out.println("Saved to " + save.getAbsolutePath());
    save.deleteOnExit();
    b.saveAsFile(labelsList, save.getAbsolutePath());

    INDArray output = b.getData();
    System.out.println("Coordinates");

    UIServer server = UIServer.getInstance();
    Thread.sleep(10000000000L);
}
 

public static void main(String[] args) throws Exception {
  long startTime = System.currentTimeMillis();
  logger.info("start up time: " + startTime);

  File modelDir = new File(modelDirPath);

  // create dir
  boolean hasDir = modelDir.exists() || modelDir.mkdirs();
  logger.info(modelPath);

  // create model
  ComputationGraph model = createModel();
  // monitor the model score
  UIServer uiServer = UIServer.getInstance();
  StatsStorage statsStorage = new InMemoryStatsStorage();
  uiServer.attach(statsStorage);

  model.setListeners(new ScoreIterationListener(36), new StatsListener(statsStorage));

  // construct the iterator
  MultiDataSetIterator trainMulIterator = new CaptchaSetIterator(batchSize, "train");
  MultiDataSetIterator testMulIterator = new CaptchaSetIterator(batchSize, "test");
  MultiDataSetIterator validateMulIterator = new CaptchaSetIterator(batchSize, "validate");
  // fit
  for (int i = 0; i < epochs; i++) {
    System.out.println("Epoch=====================" + i);
    model.fit(trainMulIterator);
  }
  ModelSerializer.writeModel(model, modelPath, true);
  long endTime = System.currentTimeMillis();
  System.out.println("=============run time=====================" + (endTime - startTime));

  System.out.println("=====eval model=====test==================");
  modelPredict(model, testMulIterator);

  System.out.println("=====eval model=====validate==================");
  modelPredict(model, validateMulIterator);
}
 

public static UIServer getUIServer() {
	if (uiServer == null) {
		uiServer = UIServer.getInstance();
	}
	
	return uiServer;
}
 

@Test
@Ignore //To be run manually
public void testParallelStatsListenerCompatibility() throws Exception {
    UIServer uiServer = UIServer.getInstance();

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

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

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

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

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

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

@Before
public void setUp() throws Exception {
    UIServer.stopInstance();
}
 

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

    List<Persistable> updates = new ArrayList<>();
    List<Persistable> staticInfo = new ArrayList<>();
    List<StorageMetaData> metaData = new ArrayList<>();
    CollectionStatsStorageRouter collectionRouter = new CollectionStatsStorageRouter(metaData, staticInfo, updates);


    UIServer s = UIServer.getInstance();
    Thread.sleep(1000);
    s.enableRemoteListener(collectionRouter, false);


    try(RemoteUIStatsStorageRouter remoteRouter = new RemoteUIStatsStorageRouter("http://localhost:9000")) {    //Use closeable to ensure async thread is shut down

        SbeStatsReport update1 = new SbeStatsReport();
        update1.setMemoryUsePresent(true);
        update1.setDeviceCurrentBytes(new long[]{1, 2});
        update1.setDeviceMaxBytes(new long[]{100, 200});
        update1.reportIterationCount(10);
        update1.reportIDs("sid", "tid", "wid", 123456);
        update1.reportPerformance(10, 20, 30, 40, 50);

        SbeStatsReport update2 = new SbeStatsReport();
        update2.setMemoryUsePresent(true);
        update2.setDeviceCurrentBytes(new long[]{3, 4});
        update2.setDeviceMaxBytes(new long[]{300, 400});
        update2.reportIterationCount(20);
        update2.reportIDs("sid2", "tid2", "wid2", 123456);
        update2.reportPerformance(11, 21, 31, 40, 50);

        StorageMetaData smd1 = new SbeStorageMetaData(123, "sid", "typeid", "wid", "initTypeClass", "updaterTypeClass");
        StorageMetaData smd2 =
                new SbeStorageMetaData(456, "sid2", "typeid2", "wid2", "initTypeClass2", "updaterTypeClass2");

        SbeStatsInitializationReport init1 = new SbeStatsInitializationReport();
        init1.reportIDs("sid", "wid", "tid", 3145253452L);
        init1.reportHardwareInfo(1, 2, 3, 4, null, null, "2344253");
        init1.setHwDeviceTotalMemory(new long[]{1,2});
        init1.setHwDeviceDescription(new String[]{"d1", "d2"});
        init1.setHasHardwareInfo(true);

        remoteRouter.putUpdate(update1);
        Thread.sleep(100);
        remoteRouter.putStorageMetaData(smd1);
        Thread.sleep(100);
        remoteRouter.putStaticInfo(init1);
        Thread.sleep(100);
        remoteRouter.putUpdate(update2);
        Thread.sleep(100);
        remoteRouter.putStorageMetaData(smd2);


        Thread.sleep(2000);

        assertEquals(2, metaData.size());
        assertEquals(2, updates.size());
        assertEquals(1, staticInfo.size());

        assertEquals(Arrays.asList(update1, update2), updates);
        assertEquals(Arrays.asList(smd1, smd2), metaData);
        assertEquals(Collections.singletonList(init1), staticInfo);
    }
}
 

@Test
public void testAutoAttach() throws Exception {

    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
            .addLayer("L0", new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build(),
                    "in")
            .addLayer("L1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
                    .activation(Activation.SOFTMAX).nIn(4).nOut(3).build(), "L0")
            .setOutputs("L1").build();

    ComputationGraph net = new ComputationGraph(conf);
    net.init();

    StatsStorage ss1 = new InMemoryStatsStorage();

    net.setListeners(new StatsListener(ss1, 1, "ss1"));

    DataSetIterator iter = new IrisDataSetIterator(150, 150);

    for (int i = 0; i < 5; i++) {
        net.fit(iter);
    }

    StatsStorage ss2 = new InMemoryStatsStorage();
    net.setListeners(new StatsListener(ss2, 1, "ss2"));

    for (int i = 0; i < 4; i++) {
        net.fit(iter);
    }

    UIServer ui = UIServer.getInstance(true, null);
    try {
        ((VertxUIServer) ui).autoAttachStatsStorageBySessionId(new Function<String, StatsStorage>() {
            @Override
            public StatsStorage apply(String s) {
                if ("ss1".equals(s)) {
                    return ss1;
                } else if ("ss2".equals(s)) {
                    return ss2;
                }
                return null;
            }
        });

        String json1 = IOUtils.toString(new URL("http://localhost:9000/train/ss1/overview/data"),
                StandardCharsets.UTF_8);

        String json2 = IOUtils.toString(new URL("http://localhost:9000/train/ss2/overview/data"),
                StandardCharsets.UTF_8);

        assertNotEquals(json1, json2);

        Map<String, Object> m1 = JsonMappers.getMapper().readValue(json1, Map.class);
        Map<String, Object> m2 = JsonMappers.getMapper().readValue(json2, Map.class);

        List<Object> s1 = (List<Object>) m1.get("scores");
        List<Object> s2 = (List<Object>) m2.get("scores");
        assertEquals(5, s1.size());
        assertEquals(4, s2.size());
    } finally {
        ui.stop();
    }
}
 

@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
@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
public void testUI() throws Exception {
    VertxUIServer uiServer = (VertxUIServer) UIServer.getInstance();
    assertEquals(9000, uiServer.getPort());
    uiServer.stop();
}
 

public static void main(String[] args) throws IOException, InterruptedException {

       final int labelIndex=11;
       final int batchSize=8;
       final int numClasses=2;
       final INDArray weightsArray = Nd4j.create(new double[]{0.57, 0.75});

       final RecordReader recordReader = generateReader(new ClassPathResource("Churn_Modelling.csv").getFile());
       final DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(recordReader,batchSize)
                                                                .classification(labelIndex,numClasses)
                                                                .build();
       final DataNormalization dataNormalization = new NormalizerStandardize();
       dataNormalization.fit(dataSetIterator);
       dataSetIterator.setPreProcessor(dataNormalization);
       final DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,1250,0.8);

       log.info("Building Model------------------->>>>>>>>>");

        final MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
                                                                    .weightInit(WeightInit.RELU_UNIFORM)
                                                                    .updater(new Adam(0.015D))
                                                                    .list()
                                                                    .layer(new DenseLayer.Builder().nIn(11).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(4).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(4).nOut(2).activation(Activation.SOFTMAX).build())
                                                                    .build();

        final UIServer uiServer = UIServer.getInstance();
        final StatsStorage statsStorage = new InMemoryStatsStorage();

        final MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
        multiLayerNetwork.init();
        multiLayerNetwork.setListeners(new ScoreIterationListener(100),
                                       new StatsListener(statsStorage));
        uiServer.attach(statsStorage);
        multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);

        final Evaluation evaluation =  multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
        System.out.println(evaluation.stats());

        final File file = new File("model.zip");
        ModelSerializer.writeModel(multiLayerNetwork,file,true);
        ModelSerializer.addNormalizerToModel(file,dataNormalization);


    }
 

public void setUIServer(UIServer uIServer) {
    this.uIServer = uIServer;
}
 

@Test (expected = DL4JException.class)
public void testUIServerGetInstanceMultipleCalls2() {
    UIServer uiServer = UIServer.getInstance(true, null);
    assertTrue(uiServer.isMultiSession());
    UIServer.getInstance(false, null);
}
 

@Test
public void testUIAutoAttach() throws Exception {
    HashMap<String, StatsStorage> statsStorageForSession = new HashMap<>();

    Function<String, StatsStorage> statsStorageProvider = statsStorageForSession::get;
    UIServer uIServer = UIServer.getInstance(true, statsStorageProvider);

    for (int session = 0; session < 3; session++) {
        int layerSize = session + 4;

        InMemoryStatsStorage ss = new InMemoryStatsStorage();
        String sessionId = Integer.toString(session);
        statsStorageForSession.put(sessionId, ss);
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
                .layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(layerSize).build())
                .layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
                        .activation(Activation.SOFTMAX).nIn(layerSize).nOut(3).build())
                .build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        StatsListener statsListener = new StatsListener(ss, 1);
        statsListener.setSessionID(sessionId);
        net.setListeners(statsListener, new ScoreIterationListener(1));
        uIServer.attach(ss);

        DataSetIterator iter = new IrisDataSetIterator(150, 150);

        for (int i = 0; i < 20; i++) {
            net.fit(iter);
        }

        assertTrue(uIServer.isAttached(statsStorageForSession.get(sessionId)));
        uIServer.detach(ss);
        assertFalse(uIServer.isAttached(statsStorageForSession.get(sessionId)));

        /*
         * Visiting /train/:sessionId to auto-attach StatsStorage
         */
        String sessionUrl = trainingSessionUrl(uIServer.getAddress(), sessionId);
        HttpURLConnection conn = (HttpURLConnection) new URL(sessionUrl).openConnection();
        conn.connect();

        assertEquals(HttpResponseStatus.OK.code(), conn.getResponseCode());
        assertTrue(uIServer.isAttached(statsStorageForSession.get(sessionId)));
    }
}
 

@Before
public void setUp() throws Exception {
    UIServer.stopInstance();
}
 

public static void main(String[] args) throws IOException {
    //number of rows and columns in the input pictures
    final int numRows = 28;
    final int numColumns = 28;
    int outputNum = 10; // number of output classes
    int batchSize = 128; // batch size for each epoch
    int rngSeed = 123; // random number seed for reproducibility
    int numEpochs = 15; // number of epochs to perform
    int listenerFrequency = 1;


    //Get the DataSetIterators:
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);
    DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);


    log.info("Build model....");
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(rngSeed) //include a random seed for reproducibility
            // use stochastic gradient descent as an optimization algorithm
            .updater(new Nesterovs(0.006, 0.9))
            .l2(1e-4)
            .list()
            .layer(0, new DenseLayer.Builder() //create the first, input layer with xavier initialization
                    // batchSize, features
                    .nIn(numRows * numColumns)
                    .nOut(1000)
                    .activation(Activation.RELU)
                    .weightInit(WeightInit.XAVIER)
                    .build())
            .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) //create hidden layer
                    .nIn(1000)
                    .nOut(outputNum)
                    .activation(Activation.SOFTMAX)
                    .weightInit(WeightInit.XAVIER)
                    .build())
            .pretrain(false).backprop(true) //use backpropagation to adjust weights
            .build();

    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    //Initialize the user interface backend
    // 获取一个UI实例
    UIServer uiServer = UIServer.getInstance();

    //Configure where the network information (gradients, activations, score vs. time etc) is to be stored
    //Then add the StatsListener to collect this information from the network, as it trains
    // 训练的存储位置
    StatsStorage statsStorage = new InMemoryStatsStorage();             //Alternative: new FileStatsStorage(File) - see UIStorageExample

    //Attach the StatsStorage instance to the UI: this allows the contents of the StatsStorage to be visualized
    uiServer.attach(statsStorage);
    model.init();
    //print the score with every 1 iteration
    model.setListeners(new StatsListener(statsStorage, listenerFrequency)
            ,new ScoreIterationListener(1)
    );

    log.info("Train model....");
    for( int i=0; i<numEpochs; i++ ){
        model.fit(mnistTrain);
    }


    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(outputNum); //create an evaluation object with 10 possible classes
    while(mnistTest.hasNext()){
        DataSet next = mnistTest.next();
        INDArray output = model.output(next.getFeatures(), false); //get the networks prediction
        eval.eval(next.getLabels(), output); //check the prediction against the true class
    }

    log.info(eval.stats());
    log.info("****************Example finished********************");
}
 

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 IOException, InterruptedException {

       final int labelIndex=11;
       final int batchSize=8;
       final int numClasses=2;
       final INDArray weightsArray = Nd4j.create(new double[]{0.57, 0.75});

       final RecordReader recordReader = generateReader(new ClassPathResource("Churn_Modelling.csv").getFile());
       final DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(recordReader,batchSize)
                                                                .classification(labelIndex,numClasses)
                                                                .build();
       final DataNormalization dataNormalization = new NormalizerStandardize();
       dataNormalization.fit(dataSetIterator);
       dataSetIterator.setPreProcessor(dataNormalization);
       final DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,1250,0.8);

       log.info("Building Model------------------->>>>>>>>>");

        final MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
                                                                    .weightInit(WeightInit.RELU_UNIFORM)
                                                                    .updater(new Adam(0.015D))
                                                                    .list()
                                                                    .layer(new DenseLayer.Builder().nIn(11).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new DenseLayer.Builder().nIn(6).nOut(4).activation(Activation.RELU).dropOut(0.9).build())
                                                                    .layer(new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(4).nOut(2).activation(Activation.SOFTMAX).build())
                                                                    .build();

        final UIServer uiServer = UIServer.getInstance();
        final StatsStorage statsStorage = new InMemoryStatsStorage();

        final MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
        multiLayerNetwork.init();
        multiLayerNetwork.setListeners(new ScoreIterationListener(100),
                                       new StatsListener(statsStorage));
        uiServer.attach(statsStorage);
        multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);

        final Evaluation evaluation =  multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
        System.out.println(evaluation.stats());

        final File file = new File("model.zip");
        ModelSerializer.writeModel(multiLayerNetwork,file,true);
        ModelSerializer.addNormalizerToModel(file,dataNormalization);


    }
 

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
public void testPosting() throws Exception {

    //        File inputFile = Resources.asFile("big/raw_sentences.txt");
    File inputFile = new ClassPathResource("/basic/word2vec_advance.txt").getFile();
    SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
    // Split on white spaces in the line to get words
    TokenizerFactory t = new DefaultTokenizerFactory();
    t.setTokenPreProcessor(new CommonPreprocessor());

    Word2Vec vec = new Word2Vec.Builder().minWordFrequency(1).epochs(1).layerSize(20)
                    .stopWords(new ArrayList<String>()).useAdaGrad(false).negativeSample(5).seed(42).windowSize(5)
                    .iterate(iter).tokenizerFactory(t).build();

    vec.fit();

    File tempFile = File.createTempFile("temp", "w2v");
    tempFile.deleteOnExit();

    WordVectorSerializer.writeWordVectors(vec, tempFile);

    WordVectors vectors = WordVectorSerializer.loadTxtVectors(tempFile);

    UIServer.getInstance(); //Initialize

    UiConnectionInfo uiConnectionInfo =
                    new UiConnectionInfo.Builder().setAddress("localhost").setPort(9000).build();

    BarnesHutTsne tsne = new BarnesHutTsne.Builder().normalize(false).setFinalMomentum(0.8f).numDimension(2)
                    .setMaxIter(10).build();

    vectors.lookupTable().plotVocab(tsne, vectors.lookupTable().getVocabCache().numWords(), uiConnectionInfo);


    Thread.sleep(100000);
}