Examples with GridExecutioner org.nd4j.linalg.api.ops.executioner.GridExecutioner used on opensource projects

Example 26 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class ParameterAveragingTrainingWorker method getFinalResult.

@Override
public ParameterAveragingTrainingResult getFinalResult(ComputationGraph network) {
    INDArray updaterState = null;
    if (saveUpdater) {
        ComputationGraphUpdater u = network.getUpdater();
        if (u != null)
            updaterState = u.getStateViewArray();
    }
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueueBlocking();
    Collection<StorageMetaData> storageMetaData = null;
    Collection<Persistable> listenerStaticInfo = null;
    Collection<Persistable> listenerUpdates = null;
    if (listenerRouterProvider != null) {
        StatsStorageRouter r = listenerRouterProvider.getRouter();
        if (r instanceof VanillaStatsStorageRouter) {
            //TODO this is ugly... need to find a better solution
            VanillaStatsStorageRouter ssr = (VanillaStatsStorageRouter) r;
            storageMetaData = ssr.getStorageMetaData();
            listenerStaticInfo = ssr.getStaticInfo();
            listenerUpdates = ssr.getUpdates();
        }
    }
    return new ParameterAveragingTrainingResult(network.params(), updaterState, network.score(), storageMetaData, listenerStaticInfo, listenerUpdates);
}

Example 27 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class ParameterAveragingTrainingWorker method getFinalResult.

@Override
public ParameterAveragingTrainingResult getFinalResult(MultiLayerNetwork network) {
    INDArray updaterState = null;
    if (saveUpdater) {
        Updater u = network.getUpdater();
        if (u != null)
            updaterState = u.getStateViewArray();
    }
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueueBlocking();
    Collection<StorageMetaData> storageMetaData = null;
    Collection<Persistable> listenerStaticInfo = null;
    Collection<Persistable> listenerUpdates = null;
    if (listenerRouterProvider != null) {
        StatsStorageRouter r = listenerRouterProvider.getRouter();
        if (r instanceof VanillaStatsStorageRouter) {
            //TODO this is ugly... need to find a better solution
            VanillaStatsStorageRouter ssr = (VanillaStatsStorageRouter) r;
            storageMetaData = ssr.getStorageMetaData();
            listenerStaticInfo = ssr.getStaticInfo();
            listenerUpdates = ssr.getUpdates();
        }
    }
    return new ParameterAveragingTrainingResult(network.params(), updaterState, network.score(), storageMetaData, listenerStaticInfo, listenerUpdates);
}

Example 28 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class ParameterAveragingElementAddFunction method call.

@Override
public ParameterAveragingAggregationTuple call(ParameterAveragingAggregationTuple tuple, ParameterAveragingTrainingResult result) throws Exception {
    if (tuple == null) {
        return ParameterAveragingAggregationTuple.builder().parametersSum(result.getParameters()).updaterStateSum(result.getUpdaterState()).scoreSum(result.getScore()).aggregationsCount(1).sparkTrainingStats(result.getSparkTrainingStats()).listenerMetaData(result.getListenerMetaData()).listenerStaticInfo(result.getListenerStaticInfo()).listenerUpdates(result.getListenerUpdates()).build();
    }
    INDArray params = tuple.getParametersSum().addi(result.getParameters());
    INDArray updaterStateSum;
    if (tuple.getUpdaterStateSum() == null) {
        updaterStateSum = result.getUpdaterState();
    } else {
        updaterStateSum = tuple.getUpdaterStateSum();
        if (result.getUpdaterState() != null)
            updaterStateSum.addi(result.getUpdaterState());
    }
    double scoreSum = tuple.getScoreSum() + result.getScore();
    SparkTrainingStats stats = tuple.getSparkTrainingStats();
    if (result.getSparkTrainingStats() != null) {
        if (stats == null)
            stats = result.getSparkTrainingStats();
        else
            stats.addOtherTrainingStats(result.getSparkTrainingStats());
    }
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueueBlocking();
    Collection<StorageMetaData> listenerMetaData = tuple.getListenerMetaData();
    if (listenerMetaData == null)
        listenerMetaData = result.getListenerMetaData();
    else {
        Collection<StorageMetaData> newMeta = result.getListenerMetaData();
        if (newMeta != null)
            listenerMetaData.addAll(newMeta);
    }
    Collection<Persistable> listenerStaticInfo = tuple.getListenerStaticInfo();
    if (listenerStaticInfo == null)
        listenerStaticInfo = result.getListenerStaticInfo();
    else {
        Collection<Persistable> newStatic = tuple.getListenerStaticInfo();
        if (newStatic != null)
            listenerStaticInfo.addAll(newStatic);
    }
    Collection<Persistable> listenerUpdates = tuple.getListenerUpdates();
    if (listenerUpdates == null)
        listenerUpdates = result.getListenerUpdates();
    else {
        Collection<Persistable> newUpdates = result.getListenerUpdates();
        if (newUpdates != null)
            listenerUpdates.addAll(newUpdates);
    }
    return new ParameterAveragingAggregationTuple(params, updaterStateSum, scoreSum, tuple.getAggregationsCount() + 1, stats, listenerMetaData, listenerStaticInfo, listenerUpdates);
}