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

Example 41 with ComputationGraphConfiguration

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

the class TestRenders method testHistogramComputationGraph.

@Test
public void testHistogramComputationGraph() throws Exception {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder().addInputs("input").addLayer("cnn1", new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(3).build(), "input").addLayer("cnn2", new ConvolutionLayer.Builder(4, 4).stride(2, 2).padding(1, 1).nIn(1).nOut(3).build(), "input").addLayer("max1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).build(), "cnn1", "cnn2").addLayer("output", new OutputLayer.Builder().nIn(7 * 7 * 6).nOut(10).build(), "max1").setOutputs("output").inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(28, 28, 1)).inputPreProcessor("cnn2", new FeedForwardToCnnPreProcessor(28, 28, 1)).inputPreProcessor("output", new CnnToFeedForwardPreProcessor(7, 7, 6)).pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    graph.setListeners(new HistogramIterationListener(1), new ScoreIterationListener(1));
    DataSetIterator mnist = new MnistDataSetIterator(32, 640, false, true, false, 12345);
    graph.fit(mnist);
}

Example 42 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration 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 43 with ComputationGraphConfiguration

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

the class TestPlayUI method testUICompGraph.

@Test
@Ignore
public void testUICompGraph() throws Exception {
    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").pretrain(false).backprop(true).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);
        Thread.sleep(100);
    }
    Thread.sleep(100000);
}

Example 44 with ComputationGraphConfiguration

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

the class TestEarlyStoppingCompGraph method testNoImprovementNEpochsTermination.

@Test
public void testNoImprovementNEpochsTermination() {
    //Idea: terminate training if score (test set loss) does not improve for 5 consecutive epochs
    //Simulate this by setting LR = 0.0
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(0.0).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in").setOutputs("0").pretrain(false).backprop(true).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.setListeners(new ScoreIterationListener(1));
    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>().epochTerminationConditions(new MaxEpochsTerminationCondition(100), new ScoreImprovementEpochTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS), //Initial score is ~2.5
    new MaxScoreIterationTerminationCondition(7.5)).scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();
    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter);
    EarlyStoppingResult result = trainer.fit();
    //Expect no score change due to 0 LR -> terminate after 6 total epochs
    assertEquals(6, result.getTotalEpochs());
    assertEquals(0, result.getBestModelEpoch());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
    String expDetails = new ScoreImprovementEpochTerminationCondition(5).toString();
    assertEquals(expDetails, result.getTerminationDetails());
}

Example 45 with ComputationGraphConfiguration

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

the class TestEarlyStoppingCompGraph method testEarlyStoppingIris.

@Test
public void testEarlyStoppingIris() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in").setOutputs("0").pretrain(false).backprop(true).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.setListeners(new ScoreIterationListener(1));
    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>().epochTerminationConditions(new MaxEpochsTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();
    IEarlyStoppingTrainer<ComputationGraph> trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter);
    EarlyStoppingResult<ComputationGraph> result = trainer.fit();
    System.out.println(result);
    assertEquals(5, result.getTotalEpochs());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
    Map<Integer, Double> scoreVsIter = result.getScoreVsEpoch();
    assertEquals(5, scoreVsIter.size());
    String expDetails = esConf.getEpochTerminationConditions().get(0).toString();
    assertEquals(expDetails, result.getTerminationDetails());
    ComputationGraph out = result.getBestModel();
    assertNotNull(out);
    //Check that best score actually matches (returned model vs. manually calculated score)
    ComputationGraph bestNetwork = result.getBestModel();
    irisIter.reset();
    double score = bestNetwork.score(irisIter.next());
    assertEquals(result.getBestModelScore(), score, 1e-2);
}