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

Package tv.floe.metronome.deeplearning.dbn

Class tv.floe.metronome.deeplearning.dbn.DeepBeliefNetwork

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

tv.floe.metronome.deeplearning.dbn.DeepBeliefNetwork.preTrain()
This is where we work through each RBM layer, learning an unsupervised representation of the data This unsupervised learning method runs contrastive divergence on each RBM layer in the network. @param trainingRecords the input to train on @param k the k to use for running the RBM contrastive divergence.The typical tip is that the higher k is the closer to the model you will be approximating due to more sampling. K = 1 usually gives very good results and is the default in quite a few situations. The training input to each successive layer is the activations of the hidden layer of the previous pretraining layer's RBM Hidden Neurons

      
      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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