Examples with NeuralNetConfiguration org.deeplearning4j.nn.conf.NeuralNetConfiguration used on opensource projects

Example 81 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TrainModule method getModelData.

private Result getModelData(String str) {
    Long lastUpdateTime = lastUpdateForSession.get(currentSessionID);
    if (lastUpdateTime == null)
        lastUpdateTime = -1L;
    //TODO validation
    int layerIdx = Integer.parseInt(str);
    I18N i18N = I18NProvider.getInstance();
    //Model info for layer
    boolean noData = currentSessionID == null;
    //First pass (optimize later): query all data...
    StatsStorage ss = (noData ? null : knownSessionIDs.get(currentSessionID));
    String wid = getWorkerIdForIndex(currentWorkerIdx);
    if (wid == null) {
        noData = true;
    }
    Map<String, Object> result = new HashMap<>();
    result.put("updateTimestamp", lastUpdateTime);
    Triple<MultiLayerConfiguration, ComputationGraphConfiguration, NeuralNetConfiguration> conf = getConfig();
    if (conf == null) {
        return ok(Json.toJson(result));
    }
    TrainModuleUtils.GraphInfo gi = getGraphInfo();
    if (gi == null) {
        return ok(Json.toJson(result));
    }
    // Get static layer info
    String[][] layerInfoTable = getLayerInfoTable(layerIdx, gi, i18N, noData, ss, wid);
    result.put("layerInfo", layerInfoTable);
    //First: get all data, and subsample it if necessary, to avoid returning too many points...
    List<Persistable> updates = (noData ? null : ss.getAllUpdatesAfter(currentSessionID, StatsListener.TYPE_ID, wid, 0));
    List<Integer> iterationCounts = null;
    boolean needToHandleLegacyIterCounts = false;
    if (updates != null && updates.size() > maxChartPoints) {
        int subsamplingFrequency = updates.size() / maxChartPoints;
        List<Persistable> subsampled = new ArrayList<>();
        iterationCounts = new ArrayList<>();
        int pCount = -1;
        int lastUpdateIdx = updates.size() - 1;
        int lastIterCount = -1;
        for (Persistable p : updates) {
            if (!(p instanceof StatsReport))
                continue;
            ;
            StatsReport sr = (StatsReport) p;
            pCount++;
            int iterCount = sr.getIterationCount();
            if (iterCount <= lastIterCount) {
                needToHandleLegacyIterCounts = true;
            }
            lastIterCount = iterCount;
            if (pCount > 0 && subsamplingFrequency > 1 && pCount % subsamplingFrequency != 0) {
                //Skip this to subsample the data
                if (pCount != lastUpdateIdx)
                    //Always keep the most recent value
                    continue;
            }
            subsampled.add(p);
            iterationCounts.add(iterCount);
        }
        updates = subsampled;
    } else if (updates != null) {
        int offset = 0;
        iterationCounts = new ArrayList<>(updates.size());
        int lastIterCount = -1;
        for (Persistable p : updates) {
            if (!(p instanceof StatsReport))
                continue;
            ;
            StatsReport sr = (StatsReport) p;
            int iterCount = sr.getIterationCount();
            if (iterCount <= lastIterCount) {
                needToHandleLegacyIterCounts = true;
            }
            iterationCounts.add(iterCount);
        }
    }
    //Now, it should use the proper iteration counts
    if (needToHandleLegacyIterCounts) {
        cleanLegacyIterationCounts(iterationCounts);
    }
    //Get mean magnitudes line chart
    ModelType mt;
    if (conf.getFirst() != null)
        mt = ModelType.MLN;
    else if (conf.getSecond() != null)
        mt = ModelType.CG;
    else
        mt = ModelType.Layer;
    MeanMagnitudes mm = getLayerMeanMagnitudes(layerIdx, gi, updates, iterationCounts, mt);
    Map<String, Object> mmRatioMap = new HashMap<>();
    mmRatioMap.put("layerParamNames", mm.getRatios().keySet());
    mmRatioMap.put("iterCounts", mm.getIterations());
    mmRatioMap.put("ratios", mm.getRatios());
    mmRatioMap.put("paramMM", mm.getParamMM());
    mmRatioMap.put("updateMM", mm.getUpdateMM());
    result.put("meanMag", mmRatioMap);
    //Get activations line chart for layer
    Triple<int[], float[], float[]> activationsData = getLayerActivations(layerIdx, gi, updates, iterationCounts);
    Map<String, Object> activationMap = new HashMap<>();
    activationMap.put("iterCount", activationsData.getFirst());
    activationMap.put("mean", activationsData.getSecond());
    activationMap.put("stdev", activationsData.getThird());
    result.put("activations", activationMap);
    //Get learning rate vs. time chart for layer
    Map<String, Object> lrs = getLayerLearningRates(layerIdx, gi, updates, iterationCounts, mt);
    result.put("learningRates", lrs);
    //Parameters histogram data
    Persistable lastUpdate = (updates != null && updates.size() > 0 ? updates.get(updates.size() - 1) : null);
    Map<String, Object> paramHistograms = getHistograms(layerIdx, gi, StatsType.Parameters, lastUpdate);
    result.put("paramHist", paramHistograms);
    //Updates histogram data
    Map<String, Object> updateHistograms = getHistograms(layerIdx, gi, StatsType.Updates, lastUpdate);
    result.put("updateHist", updateHistograms);
    return ok(Json.toJson(result));
}

Example 82 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class TestSerialization method testModelSerde.

@Test
public void testModelSerde() throws Exception {
    ObjectMapper mapper = getMapper();
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().momentum(0.9f).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1000).learningRate(1e-1f).layer(new org.deeplearning4j.nn.conf.layers.AutoEncoder.Builder().nIn(4).nOut(3).corruptionLevel(0.6).sparsity(0.5).lossFunction(LossFunctions.LossFunction.RECONSTRUCTION_CROSSENTROPY).build()).build();
    DataSet d2 = new IrisDataSetIterator(150, 150).next();
    INDArray input = d2.getFeatureMatrix();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    AutoEncoder da = (AutoEncoder) conf.getLayer().instantiate(conf, Arrays.asList(new ScoreIterationListener(1), new HistogramIterationListener(1)), 0, params, true);
    da.setInput(input);
    ModelAndGradient g = new ModelAndGradient(da);
    String json = mapper.writeValueAsString(g);
    ModelAndGradient read = mapper.readValue(json, ModelAndGradient.class);
    assertEquals(g, read);
}

Example 83 with NeuralNetConfiguration

use of org.deeplearning4j.nn.conf.NeuralNetConfiguration in project deeplearning4j by deeplearning4j.

the class RBMTests method getRBMLayer.

private static RBM getRBMLayer(int nIn, int nOut, HiddenUnit hiddenUnit, VisibleUnit visibleUnit, INDArray params, boolean pretrain, boolean initialize, int iterations, LossFunctions.LossFunction lossFunctions) {
    org.deeplearning4j.nn.conf.layers.RBM layer = new org.deeplearning4j.nn.conf.layers.RBM.Builder(hiddenUnit, visibleUnit).nIn(nIn).nOut(nOut).learningRate(1e-1f).lossFunction(lossFunctions).build();
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().iterations(iterations).seed(42).layer(layer).build();
    conf.setPretrain(pretrain);
    return (RBM) conf.getLayer().instantiate(conf, null, 0, params, initialize);
}