Examples of MnistDataSetIterator

tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistDataSetIterator

Examples of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistDataSetIterator

    
  }
  
  @Test
  public void testMnist() throws Exception {
    MnistDataSetIterator fetcher = new MnistDataSetIterator(100,200);
    MersenneTwister rand = new MersenneTwister(123);


    double learningRate = 0.001;
    
    int[] batchSteps = { 250, 200, 150, 100, 50, 25, 5 };
    
    DataSet first = fetcher.next();
/*
    RestrictedBoltzmannMachine da = new RBM.Builder().numberOfVisible(784).numHidden(400).withRandom(rand).renderWeights(1000)
        .useRegularization(false)
        .withMomentum(0).build();
*/

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Examples of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistDataSetIterator

    int batchSize = 100 * datasetSize;
    int totalNumExamples = 100 * datasetSize;
    
    
    
    MnistDataSetIterator fetcher = new MnistDataSetIterator( batchSize, totalNumExamples );
    DataSet recordBatch = fetcher.next();
    
    
    Map<Integer, Integer> filter = new HashMap<Integer, Integer>();
    for (int x = 0; x < classIndexes.length; x++ ) {

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Examples of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistDataSetIterator

        
    int batchSize = 50;
    boolean showNetworkStats = true;
    
    // mini-batches through dataset
    MnistDataSetIterator fetcher = new MnistDataSetIterator( batchSize, totalNumExamples );
    DataSet first = fetcher.next();
    int numIns = first.getFirst().numCols();
    int numLabels = first.getSecond().numCols();


    int n_layers = hiddenLayerSizes.length;
    RandomGenerator rng = new MersenneTwister(123);
    
    
    DeepBeliefNetwork dbn = new DeepBeliefNetwork( numIns, hiddenLayerSizes, numLabels, n_layers, rng ); //, Matrix input, Matrix labels);
        
    dbn.useRegularization = false;
    dbn.setSparsity(0.01);
    dbn.setMomentum(0);
    
    
    int recordsProcessed = 0;
    int batchesProcessed = 0;
    long totalBatchProcessingTime = 0;
    
    StopWatch watch = new StopWatch();
    watch.start();
    
    StopWatch batchWatch = new StopWatch();
    
    
    do  {
      
      recordsProcessed += batchSize;
      batchesProcessed++;
      
      System.out.println( "PreTrain: Batch Mode, Processed Total " + recordsProcessed + ", Elapsed Time " + watch.toString() );
      
      batchWatch.reset();
      batchWatch.start();
      dbn.preTrain( first.getFirst(), 1, learningRate, preTrainEpochs);
      batchWatch.stop();
      
      totalBatchProcessingTime += batchWatch.getTime();
      
      System.out.println( "Batch Training Elapsed Time " + batchWatch.toString() );


      //System.out.println( "DBN Network Stats:\n" + dbn.generateNetworkSizeReport() );


      
      if (fetcher.hasNext()) {
        first = fetcher.next();
      }
      
    } while (fetcher.hasNext());


    double avgBatchTime = totalBatchProcessingTime / batchesProcessed;
    double avgBatchSeconds = avgBatchTime / 1000;
    double avgBatchMinutes = avgBatchSeconds / 60;

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Examples of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistDataSetIterator

    int totalNumExamples = 50;
    //int rowLimit = 100;
        
    int batchSize = 10;
    // mini-batches through dataset
    MnistDataSetIterator fetcher = new MnistDataSetIterator( batchSize, totalNumExamples );
    DataSet first = fetcher.next();
    int numIns = first.getFirst().numCols();
    int numLabels = first.getSecond().numCols();


    int n_layers = hiddenLayerSizes.length;
    RandomGenerator rng = new MersenneTwister(123);
    
    
    DeepBeliefNetwork dbn = new DeepBeliefNetwork( numIns, hiddenLayerSizes, numLabels, n_layers, rng ); //, Matrix input, Matrix labels);
        
    int recordsProcessed = 0;
    
    do  {
      
      recordsProcessed += batchSize;
      
      System.out.println( "PreTrain: Batch Mode, Processed Total " + recordsProcessed );
      dbn.preTrain( first.getFirst(), 1, learningRate, preTrainEpochs);


      if (fetcher.hasNext()) {
        first = fetcher.next();
      }
      
    } while (fetcher.hasNext());


    fetcher.reset();
    first = fetcher.next();
    
    recordsProcessed = 0;
    
    do {
      
      recordsProcessed += batchSize;
      
      System.out.println( "FineTune: Batch Mode, Processed Total " + recordsProcessed );
      
      
      dbn.finetune( first.getSecond(), learningRate, fineTuneEpochs );
      
      if (fetcher.hasNext()) {
        first = fetcher.next();
      }
      
    } while (fetcher.hasNext());
    
    System.out.println("----------- Training Complete! -----------");
    
    // save model

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Examples of tv.floe.metronome.deeplearning.datasets.iterator.impl.MnistDataSetIterator

  }
  
  @Test
  public void testMNISTRenderPath() throws Exception {
    
    MnistDataSetIterator fetcher = new MnistDataSetIterator(100,200);
    MersenneTwister rand = new MersenneTwister(123);


    double learningRate = 0.001;
    
    int[] batchSteps = { 250, 200, 150, 100, 50, 25, 5 };
    
    DataSet first = fetcher.next();
/*
    RestrictedBoltzmannMachine da = new RBM.Builder().numberOfVisible(784).numHidden(400).withRandom(rand).renderWeights(1000)
        .useRegularization(false)
        .withMomentum(0).build();
*/

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