Examples with StatsStorage org.deeplearning4j.api.storage.StatsStorage used on opensource projects

Example 6 with StatsStorage

use of org.deeplearning4j.api.storage.StatsStorage in project deeplearning4j by deeplearning4j.

the class PlayUIServer method detach.

@Override
public synchronized void detach(StatsStorage statsStorage) {
    if (statsStorage == null)
        throw new IllegalArgumentException("StatsStorage cannot be null");
    if (!statsStorageInstances.contains(statsStorage))
        //No op
        return;
    boolean found = false;
    for (Pair<StatsStorage, StatsStorageListener> p : listeners) {
        if (p.getFirst() == statsStorage) {
            //Same object, not equality
            statsStorage.deregisterStatsStorageListener(p.getSecond());
            listeners.remove(p);
            found = true;
        }
    }
    for (UIModule uiModule : uiModules) {
        uiModule.onDetach(statsStorage);
    }
    if (found) {
        log.info("StatsStorage instance detached from UI: {}", statsStorage);
    }
}

Example 7 with StatsStorage

use of org.deeplearning4j.api.storage.StatsStorage in project deeplearning4j by deeplearning4j.

the class TestPlayUI method testUIMultipleSessions.

@Test
@Ignore
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).iterations(1).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()).pretrain(false).backprop(true).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 < 20; i++) {
            net.fit(iter);
            Thread.sleep(100);
        }
    }
    Thread.sleep(1000000);
}

Example 8 with StatsStorage

use of org.deeplearning4j.api.storage.StatsStorage in project deeplearning4j by deeplearning4j.

the class TestPlayUI method testUI_RBM.

@Test
@Ignore
public void testUI_RBM() throws Exception {
    //RBM - 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).iterations(1).learningRate(1e-5).list().layer(0, new RBM.Builder().nIn(4).nOut(3).build()).layer(1, new RBM.Builder().nIn(3).nOut(3).build()).layer(2, new OutputLayer.Builder().nIn(3).nOut(3).build()).pretrain(true).backprop(true).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);
    }
    Thread.sleep(100000);
}

Example 9 with StatsStorage

use of org.deeplearning4j.api.storage.StatsStorage in project deeplearning4j by deeplearning4j.

the class FlowListenerModule method getStaticInfo.

private Result getStaticInfo(String sessionID) {
    if (!knownSessionIDs.containsKey(sessionID))
        return ok("Unknown session ID");
    StatsStorage ss = knownSessionIDs.get(sessionID);
    List<Persistable> list = ss.getAllStaticInfos(sessionID, TYPE_ID);
    if (list == null || list.size() == 0)
        return ok();
    Persistable p = list.get(0);
    if (!(p instanceof FlowStaticPersistable))
        return ok();
    FlowStaticPersistable f = (FlowStaticPersistable) p;
    return ok(Json.toJson(f.getModelInfo()));
}

Example 10 with StatsStorage

use of org.deeplearning4j.api.storage.StatsStorage in project deeplearning4j by deeplearning4j.

the class HistogramModule method processRequest.

private Result processRequest(String sessionId) {
    //TODO cache the relevant info and update, rather than querying StatsStorage and building from scratch each time
    StatsStorage ss = knownSessionIDs.get(sessionId);
    if (ss == null) {
        return Results.notFound("Unknown session ID: " + sessionId);
    }
    List<String> workerIDs = ss.listWorkerIDsForSession(sessionId);
    //TODO checks
    StatsInitializationReport initReport = (StatsInitializationReport) ss.getStaticInfo(sessionId, StatsListener.TYPE_ID, workerIDs.get(0));
    if (initReport == null)
        return Results.ok(Json.toJson(Collections.EMPTY_MAP));
    String[] paramNames = initReport.getModelParamNames();
    //Infer layer names from param names...
    Set<String> layerNameSet = new LinkedHashSet<>();
    for (String s : paramNames) {
        String[] split = s.split("_");
        if (!layerNameSet.contains(split[0])) {
            layerNameSet.add(split[0]);
        }
    }
    List<String> layerNameList = new ArrayList<>(layerNameSet);
    List<Persistable> list = ss.getAllUpdatesAfter(sessionId, StatsListener.TYPE_ID, workerIDs.get(0), 0);
    Collections.sort(list, (a, b) -> Long.compare(a.getTimeStamp(), b.getTimeStamp()));
    List<Double> scoreList = new ArrayList<>(list.size());
    //List.get(i) -> layer i. Maps: parameter for the given layer
    List<Map<String, List<Double>>> meanMagHistoryParams = new ArrayList<>();
    //List.get(i) -> layer i. Maps: updates for the given layer
    List<Map<String, List<Double>>> meanMagHistoryUpdates = new ArrayList<>();
    for (int i = 0; i < layerNameList.size(); i++) {
        meanMagHistoryParams.add(new HashMap<>());
        meanMagHistoryUpdates.add(new HashMap<>());
    }
    StatsReport last = null;
    for (Persistable p : list) {
        if (!(p instanceof StatsReport)) {
            log.debug("Encountered unexpected type: {}", p);
            continue;
        }
        StatsReport sp = (StatsReport) p;
        scoreList.add(sp.getScore());
        //Mean magnitudes
        if (sp.hasSummaryStats(StatsType.Parameters, SummaryType.MeanMagnitudes)) {
            updateMeanMagnitudeMaps(sp.getMeanMagnitudes(StatsType.Parameters), layerNameList, meanMagHistoryParams);
        }
        if (sp.hasSummaryStats(StatsType.Updates, SummaryType.MeanMagnitudes)) {
            updateMeanMagnitudeMaps(sp.getMeanMagnitudes(StatsType.Updates), layerNameList, meanMagHistoryUpdates);
        }
        last = sp;
    }
    Map<String, Map> newParams = getHistogram(last.getHistograms(StatsType.Parameters));
    Map<String, Map> newGrad = getHistogram(last.getHistograms(StatsType.Updates));
    double lastScore = (scoreList.size() == 0 ? 0.0 : scoreList.get(scoreList.size() - 1));
    CompactModelAndGradient g = new CompactModelAndGradient();
    g.setGradients(newGrad);
    g.setParameters(newParams);
    g.setScore(lastScore);
    g.setScores(scoreList);
    //        g.setPath(subPath);
    g.setUpdateMagnitudes(meanMagHistoryUpdates);
    g.setParamMagnitudes(meanMagHistoryParams);
    //        g.setLayerNames(layerNames);
    g.setLastUpdateTime(last.getTimeStamp());
    return Results.ok(Json.toJson(g));
}