Package org.gd.spark.opendl.downpourSGD

Examples of org.gd.spark.opendl.downpourSGD.SGDTrainConfig

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      JavaRDD<SampleVector> rdds = context.parallelize(trainList);
      rdds.count();
      logger.info("RDD ok.");
     
      LR lr = new LR(x_feature, y_feature);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setCgEpochStep(100);
            config.setCgTolerance(0);
            config.setCgMaxIterations(30);
            config.setMaxEpochs(100);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setUseRegularization(true);
            config.setMrDataStorage(StorageLevel.MEMORY_ONLY());
            config.setPrintLoss(true);
            config.setLossCalStep(3);
            config.setParamOutput(true);
            config.setParamOutputStep(3);
            config.setParamOutputPath("wb.bin");
           
            logger.info("Start to train lr.");
            DownpourSGDTrain.train(lr, rdds, config);
           
            int trueCount = 0;
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      List<SampleVector> trainList = new ArrayList<SampleVector>();
      List<SampleVector> testList = new ArrayList<SampleVector>();
      DataInput.splitList(samples, trainList, testList, 0.7);
     
      LR lr = new LR(x_feature, y_feature);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setCgEpochStep(100);
            config.setCgTolerance(0);
            config.setCgMaxIterations(30);
            config.setMaxEpochs(100);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setUseRegularization(true);
            config.setPrintLoss(true);
           
            logger.info("Start to train lr.");
            DownpourSGDTrain.train(lr, trainList, config);
           
            int trueCount = 0;
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      List<SampleVector> trainList = new ArrayList<SampleVector>();
      List<SampleVector> testList = new ArrayList<SampleVector>();
      DataInput.splitList(samples, trainList, testList, 0.7);
     
      AutoEncoder da = new AutoEncoder(x_feature, n_hidden);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setDoCorruption(false);
            config.setCorruption_level(0.25);
            config.setCgEpochStep(50);
            config.setCgTolerance(0);
            config.setCgMaxIterations(30);
            config.setMaxEpochs(50);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setUseRegularization(true);
            config.setPrintLoss(true);
           
            logger.info("Start to train dA.");
            DownpourSGDTrain.train(da, trainList, config);
           
            double[] reconstruct_x = new double[x_feature];
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      List<SampleVector> trainList = new ArrayList<SampleVector>();
      List<SampleVector> testList = new ArrayList<SampleVector>();
      DataInput.splitList(samples, trainList, testList, 0.7);
     
      RBM rbm = new RBM(x_feature, n_hidden);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setCgEpochStep(50);
            config.setCgTolerance(0);
            config.setCgMaxIterations(30);
            config.setMaxEpochs(50);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setPrintLoss(true);
           
            logger.info("Start to train RBM.");
            DownpourSGDTrain.train(rbm, trainList, config);
           
            double[] reconstruct_x = new double[x_feature];
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      int[] hiddens = new int[1];
            hiddens[0] = 160;
           
      BP bp = new BP(x_feature, y_feature, hiddens);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setCgEpochStep(50);
            config.setCgTolerance(0);
            config.setCgMaxIterations(30);
            config.setMaxEpochs(50);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setUseRegularization(true);
            config.setPrintLoss(true);
            config.setCgInitStepSize(1.0);

            logger.info("Start to train bp.");
            DownpourSGDTrain.train(bp, trainList, config);
           
//            int trueCount = 0;
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      JavaRDD<SampleVector> rdds = context.parallelize(trainList);
      rdds.count();
      logger.info("RDD ok.");
     
      AutoEncoder da = new AutoEncoder(x_feature, n_hidden);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setDoCorruption(true);
            config.setCorruption_level(0.25);
            config.setCgEpochStep(50);
            config.setCgTolerance(0);
            config.setCgMaxIterations(10);
            config.setMaxEpochs(50);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setUseRegularization(true);
            config.setMrDataStorage(StorageLevel.MEMORY_ONLY());
            config.setPrintLoss(true);
            config.setLossCalStep(3);
           
            logger.info("Start to train dA.");
            DownpourSGDTrain.train(da, rdds, config);
           
            double[] reconstruct_x = new double[x_feature];
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      JavaRDD<SampleVector> rdds = context.parallelize(trainList);
      rdds.count();
      logger.info("RDD ok.");
     
      RBM rbm = new RBM(x_feature, n_hidden);
            SGDTrainConfig config = new SGDTrainConfig();
            config.setUseCG(true);
            config.setCgEpochStep(50);
            config.setCgTolerance(0);
            config.setCgMaxIterations(10);
            config.setMaxEpochs(50);
            config.setNbrModelReplica(4);
            config.setMinLoss(0.01);
            config.setMrDataStorage(StorageLevel.MEMORY_ONLY());
            config.setPrintLoss(true);
            config.setLossCalStep(3);
           
            logger.info("Start to train RBM.");
            DownpourSGDTrain.train(rbm, rdds, config);
           
            double[] reconstruct_x = new double[x_feature];
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Related Classes of org.gd.spark.opendl.downpourSGD.SGDTrainConfig

  • org.gd.spark.opendl.example.spark.dATest
  • org.gd.spark.opendl.example.spark.LRTest
  • org.gd.spark.opendl.example.spark.RBMTest
  • org.gd.spark.opendl.example.standalone.BPTest
  • org.gd.spark.opendl.example.standalone.dATest
  • org.gd.spark.opendl.example.standalone.LRTest
  • org.gd.spark.opendl.example.standalone.RBMTest
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