Package com.github.neuralnetworks.calculation.neuronfunctions

Examples of com.github.neuralnetworks.calculation.neuronfunctions.BernoulliDistribution

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    public static AparapiCDTrainer cdSoftReLUTrainer(RBM rbm, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, int gibbsSampling, boolean isPersistentCD) {
  rbm.setLayerCalculator(NNFactory.rbmSoftReluSoftRelu(rbm));

  RBMLayerCalculator lc = NNFactory.rbmSigmoidSigmoid(rbm);
  ConnectionCalculatorFullyConnected cc = (ConnectionCalculatorFullyConnected) lc.getConnectionCalculator(rbm.getInputLayer());
  cc.addPreTransferFunction(new BernoulliDistribution());

  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, isPersistentCD));
    }
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    public static AparapiCDTrainer cdSigmoidTrainer(RBM rbm, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, int gibbsSampling, boolean isPersistentCD) {
  rbm.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(rbm));

  RBMLayerCalculator lc = NNFactory.rbmSigmoidSigmoid(rbm);
  ConnectionCalculatorFullyConnected cc = (ConnectionCalculatorFullyConnected) lc.getConnectionCalculator(rbm.getInputLayer());
  cc.addPreTransferFunction(new BernoulliDistribution());

  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, isPersistentCD));
    }
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    public static AparapiCDTrainer cdSoftReLUTrainer(RBM rbm, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, int gibbsSampling, int trainingBatchSize, int epochs, boolean isPersistentCD) {
  rbm.setLayerCalculator(NNFactory.lcSoftRelu(rbm, null));

  RBMLayerCalculator lc = NNFactory.rbmSoftReluSoftRelu(rbm, trainingBatchSize);
  ConnectionCalculatorFullyConnected cc = (ConnectionCalculatorFullyConnected) lc.getNegPhaseHiddenToVisibleCC();
  cc.addPreTransferFunction(new BernoulliDistribution());

  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, trainingBatchSize, epochs, isPersistentCD));
    }
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    public static AparapiCDTrainer cdSigmoidBinaryTrainer(RBM rbm, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, int gibbsSampling, int trainingBatchSize, int epochs, boolean isPersistentCD) {
  rbm.setLayerCalculator(NNFactory.lcSigmoid(rbm, null));

  RBMLayerCalculator lc = NNFactory.rbmSigmoidSigmoid(rbm, trainingBatchSize);
  ConnectionCalculatorFullyConnected cc = (ConnectionCalculatorFullyConnected) lc.getNegPhaseHiddenToVisibleCC();
  cc.addPreTransferFunction(new BernoulliDistribution());

  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, trainingBatchSize, epochs, isPersistentCD));
    }
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Related Classes of com.github.neuralnetworks.calculation.neuronfunctions.BernoulliDistribution

  • com.github.neuralnetworks.training.TrainerFactory
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