Package tv.floe.metronome.deeplearning.dbn

Examples of tv.floe.metronome.deeplearning.dbn.DeepBeliefNetwork.preTrain()


     
      System.out.println( "PreTrain: Batch Mode, Processed Total " + recordsProcessed + ", Elapsed Time " + watch.toString() );
     
      batchWatch.reset();
      batchWatch.start();
      dbn.preTrain( recordBatch.getFirst(), 1, learningRate, preTrainEpochs);
      batchWatch.stop();
     
      System.out.println( "Batch Training Elapsed Time " + batchWatch.toString() );

      System.out.println( "DBN Network Stats:\n" + dbn.generateNetworkSizeReport() );
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      System.out.println( "FineTune: Batch Mode, Processed Total " + recordsProcessed + ", Elapsed Time " + watch.toString() );
     
     
      dbn.finetune( recordBatch.getSecond(), learningRate, fineTuneEpochs );

      dbn.preTrain( recordBatch.getFirst(), 1, learningRate, preTrainEpochs);
      dbn.finetune( recordBatch.getSecond(), learningRate, fineTuneEpochs );
     
      dbn.preTrain( recordBatch.getFirst(), 1, learningRate, preTrainEpochs);
      dbn.finetune( recordBatch.getSecond(), learningRate, fineTuneEpochs );
     
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      dbn.finetune( recordBatch.getSecond(), learningRate, fineTuneEpochs );

      dbn.preTrain( recordBatch.getFirst(), 1, learningRate, preTrainEpochs);
      dbn.finetune( recordBatch.getSecond(), learningRate, fineTuneEpochs );
     
      dbn.preTrain( recordBatch.getFirst(), 1, learningRate, preTrainEpochs);
      dbn.finetune( recordBatch.getSecond(), learningRate, fineTuneEpochs );
     
      /*     
      if (fetcher.hasNext()) {
        first = fetcher.next();
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      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() );
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    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();
      }
     
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//    dbn.addWeightTransform(0, new MultiplyScalar(100));
//    dbn.addWeightTransform(1, new MultiplyScalar(10));

   
   
    dbn.preTrain(x_xor_Matrix,k, preTrainLr, preTrainEpochs);
   
   
   
    dbn.finetune(y_xor_Matrix,fineTuneLr, fineTuneEpochs);
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//    dbn.addWeightTransform(0, new MultiplyScalar(100));
//    dbn.addWeightTransform(1, new MultiplyScalar(10));

   
   
    dbn.preTrain(x_toy_Matrix,k, preTrainLr, preTrainEpochs);
   
   
   
    dbn.finetune(y_toy_Matrix,fineTuneLr, fineTuneEpochs);
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    DeepBeliefNetwork dbn = new DeepBeliefNetwork(nIns, hiddenLayerSizes, nOuts, hiddenLayerSizes.length, rng ); //, Matrix input, Matrix labels);

   
   
    dbn.preTrain(x_xor_Matrix,k, preTrainLr, preTrainEpochs);
    dbn.finetune(y_xor_Matrix,fineTuneLr, fineTuneEpochs);

   
   
   
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    DeepBeliefNetwork dbn = new DeepBeliefNetwork(nIns, hiddenLayerSizes, nOuts, hiddenLayerSizes.length, rng ); //, Matrix input, Matrix labels);

   
   
    dbn.preTrain( x_xor_Matrix,k, preTrainLr, preTrainEpochs );
    dbn.finetune( y_xor_Matrix,fineTuneLr, fineTuneEpochs );

   
   
   
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