OpenANN
1.1.0
An open source library for artificial neural networks.
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This is the complete list of members for OpenANN::RBM, including all inherited members.
backpropagate(Eigen::MatrixXd *ein, Eigen::MatrixXd *&eout, bool backpropToPrevious) | OpenANN::RBM | virtual |
currentParameters() | OpenANN::RBM | virtual |
deleteTrainSet | OpenANN::Learner | protected |
deleteValidSet | OpenANN::Learner | protected |
dimension() | OpenANN::RBM | virtual |
error() | OpenANN::RBM | virtual |
error(unsigned int n) | OpenANN::RBM | virtual |
OpenANN::Learner::error(unsigned n) | OpenANN::Optimizable | inlinevirtual |
OpenANN::Learner::error(std::vector< int >::const_iterator startN, std::vector< int >::const_iterator endN) | OpenANN::Optimizable | virtual |
errorGradient(std::vector< int >::const_iterator startN, std::vector< int >::const_iterator endN, double &value, Eigen::VectorXd &grad) | OpenANN::RBM | virtual |
OpenANN::Learner::errorGradient(int n, double &value, Eigen::VectorXd &grad) | OpenANN::Optimizable | virtual |
OpenANN::Learner::errorGradient(double &value, Eigen::VectorXd &grad) | OpenANN::Optimizable | virtual |
examples() | OpenANN::RBM | virtual |
finishedIteration() | OpenANN::Optimizable | inlinevirtual |
forwardPropagate(Eigen::MatrixXd *x, Eigen::MatrixXd *&y, bool dropout, double *error=0) | OpenANN::RBM | virtual |
getOutput() | OpenANN::RBM | virtual |
getParameters() | OpenANN::RBM | virtual |
getVisibleProbs() | OpenANN::RBM | |
getVisibleSample() | OpenANN::RBM | |
getWeights() | OpenANN::RBM | |
gradient() | OpenANN::RBM | virtual |
gradient(unsigned int n) | OpenANN::RBM | virtual |
OpenANN::Learner::gradient(unsigned n) | OpenANN::Optimizable | inlinevirtual |
OpenANN::Learner::gradient(std::vector< int >::const_iterator startN, std::vector< int >::const_iterator endN) | OpenANN::Optimizable | virtual |
hiddenUnits() | OpenANN::RBM | |
initialize() | OpenANN::RBM | virtual |
initialize(std::vector< double * > ¶meterPointers, std::vector< double * > ¶meterDerivativePointers) | OpenANN::RBM | virtual |
initializeParameters() | OpenANN::RBM | inlinevirtual |
Learner() | OpenANN::Learner | |
N | OpenANN::Learner | protected |
operator()(const Eigen::VectorXd &x) | OpenANN::RBM | virtual |
operator()(const Eigen::MatrixXd &X) | OpenANN::RBM | virtual |
providesGradient() | OpenANN::RBM | virtual |
providesInitialization() | OpenANN::RBM | virtual |
RBM(int D, int H, int cdN=1, double stdDev=0.01, bool backprop=true, Regularization regularization=Regularization()) | OpenANN::RBM | |
reconstructProb(int n, int steps) | OpenANN::RBM | |
removeTrainingSet() | OpenANN::Learner | virtual |
removeValidationSet() | OpenANN::Learner | virtual |
sampleHgivenV() | OpenANN::RBM | |
sampleVgivenH() | OpenANN::RBM | |
setParameters(const Eigen::VectorXd ¶meters) | OpenANN::RBM | virtual |
trainingSet(Eigen::MatrixXd &input, Eigen::MatrixXd &output) | OpenANN::Learner | virtual |
trainingSet(DataSet &trainingSet) | OpenANN::Learner | virtual |
trainSet | OpenANN::Learner | protected |
updatedParameters() | OpenANN::RBM | inlinevirtual |
validationSet(Eigen::MatrixXd &input, Eigen::MatrixXd &output) | OpenANN::Learner | virtual |
validationSet(DataSet &validationSet) | OpenANN::Learner | virtual |
validSet | OpenANN::Learner | protected |
visibleUnits() | OpenANN::RBM | |
~Layer() | OpenANN::Layer | inlinevirtual |
~Learner() | OpenANN::Learner | virtual |
~Optimizable() | OpenANN::Optimizable | inlinevirtual |
~RBM() | OpenANN::RBM | virtual |