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

Package com.github.neuralnetworks.calculation.neuronfunctions

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

com.github.neuralnetworks.calculation.neuronfunctions.ConnectionCalculatorFullyConnected
Default implementation of Connection calculator for fully connected layers Biases are also added After all the input functions are calculated there is a list of activation functions that can be applied to the result This class differs from LayerCalculatorImpl in the fact that LayerCalculatorImpl traverses the graph of layers, where ConnectionCalculatorImpl only deals with the connections passed as parameter !!! Important !!! The results of the calculations are represented as matrices (Matrix). This is done, because it is assumed that implementations will provide a way for calculating many input results at once. Each column of the matrix represents a single input. For example if the network is trained to classify MNIST images, each column of the input matrix will represent single MNIST image.


    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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com.github.neuralnetworks.architecture.Connections

com.github.neuralnetworks.architecture.GraphConnections

com.github.neuralnetworks.architecture.types.NNFactory

com.github.neuralnetworks.calculation.ConnectionCalculator

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