Examples of org.gd.spark.opendl.downpourSGD.TiedWeightLayer.RBM$RBMOptimizer

      
      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];
            double totalError = 0;
            for(SampleVector test : testList) {
              rbm.reconstruct(test.getX(), reconstruct_x);
              totalError += ClassVerify.squaredError(test.getX(), reconstruct_x);
            }
            logger.info("Mean square error is " + totalError / testList.size());
    } catch(Throwable e) {
      logger.error("", e);

      JavaSparkContext context = SparkContextBuild.getContext(args);
      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];
            double totalError = 0;
            for(SampleVector test : testList) {
              rbm.reconstruct(test.getX(), reconstruct_x);
              totalError += ClassVerify.squaredError(test.getX(), reconstruct_x);
            }
            logger.info("Mean square error is " + totalError / testList.size());
    } catch(Throwable e) {
      logger.error("", e);

Examples of org.gd.spark.opendl.downpourSGD.TiedWeightLayer.RBM$RBMOptimizer

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