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OpenANN
1.1.0
An open source library for artificial neural networks.
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All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Pages
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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::Net, including all inherited members.
| addLayer(Layer *layer) | OpenANN::Net | |
| addOutputLayer(Layer *layer) | OpenANN::Net | |
| alphaBetaFilterLayer(double deltaT, double stdDev=0.05) | OpenANN::Net | |
| architecture | OpenANN::Net | protected |
| backpropagate() | OpenANN::Net | protected |
| compressedLayer(int units, int params, ActivationFunction act, const std::string &compression, double stdDev=0.05, bool bias=true) | OpenANN::Net | |
| compressedOutputLayer(int units, int params, ActivationFunction act, const std::string &compression, double stdDev=0.05, bool bias=true) | OpenANN::Net | |
| convolutionalLayer(int featureMaps, int kernelRows, int kernelCols, ActivationFunction act, double stdDev=0.05, bool bias=true) | OpenANN::Net | |
| currentParameters() | OpenANN::Net | virtual |
| deleteTrainSet | OpenANN::Learner | protected |
| deleteValidSet | OpenANN::Learner | protected |
| derivatives | OpenANN::Net | protected |
| dimension() | OpenANN::Net | virtual |
| dropout | OpenANN::Net | protected |
| dropoutLayer(double dropoutProbability) | OpenANN::Net | |
| error(unsigned int n) | OpenANN::Net | virtual |
| error() | OpenANN::Net | 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 |
| errorFunction | OpenANN::Net | protected |
| errorGradient(int n, double &value, Eigen::VectorXd &grad) | OpenANN::Net | virtual |
| errorGradient(double &value, Eigen::VectorXd &grad) | OpenANN::Net | virtual |
| errorGradient(std::vector< int >::const_iterator startN, std::vector< int >::const_iterator endN, double &value, Eigen::VectorXd &grad) | OpenANN::Net | virtual |
| examples() | OpenANN::Net | virtual |
| extremeLayer(int units, ActivationFunction act, double stdDev=5.0, bool bias=true) | OpenANN::Net | |
| finishedIteration() | OpenANN::Net | virtual |
| forwardPropagate(double *error) | OpenANN::Net | protected |
| fullyConnectedLayer(int units, ActivationFunction act, double stdDev=0.05, bool bias=true) | OpenANN::Net | |
| getLayer(unsigned int l) | OpenANN::Net | |
| getOutputInfo(unsigned int l) | OpenANN::Net | |
| gradient(unsigned int n) | OpenANN::Net | virtual |
| gradient() | OpenANN::Net | 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 |
| infos | OpenANN::Net | protected |
| initialize() | OpenANN::Net | virtual |
| initialized | OpenANN::Net | protected |
| initializeNetwork() | OpenANN::Net | protected |
| inputLayer(int dim1, int dim2=1, int dim3=1) | OpenANN::Net | |
| intrinsicPlasticityLayer(double targetMean, double stdDev=1.0) | OpenANN::Net | |
| L | OpenANN::Net | protected |
| layers | OpenANN::Net | protected |
| Learner() | OpenANN::Learner | |
| load(const std::string &fileName) | OpenANN::Net | |
| load(std::istream &stream) | OpenANN::Net | |
| localReponseNormalizationLayer(double k, int n, double alpha, double beta) | OpenANN::Net | |
| maxPoolingLayer(int kernelRows, int kernelCols) | OpenANN::Net | |
| N | OpenANN::Learner | protected |
| Net() | OpenANN::Net | |
| numberOflayers() | OpenANN::Net | |
| operator()(const Eigen::VectorXd &x) | OpenANN::Net | virtual |
| operator()(const Eigen::MatrixXd &X) | OpenANN::Net | virtual |
| outputLayer(int units, ActivationFunction act, double stdDev=0.05, bool bias=true) | OpenANN::Net | |
| P | OpenANN::Net | protected |
| parameters | OpenANN::Net | protected |
| parameterVector | OpenANN::Net | protected |
| propagateDataSet(DataSet &dataSet, int l) | OpenANN::Net | |
| providesGradient() | OpenANN::Net | virtual |
| providesInitialization() | OpenANN::Net | virtual |
| regularization | OpenANN::Net | protected |
| removeTrainingSet() | OpenANN::Learner | virtual |
| removeValidationSet() | OpenANN::Learner | virtual |
| restrictedBoltzmannMachineLayer(int H, int cdN=1, double stdDev=0.01, bool backprop=true) | OpenANN::Net | |
| save(const std::string &fileName) | OpenANN::Net | |
| save(std::ostream &stream) | OpenANN::Net | |
| setErrorFunction(ErrorFunction errorFunction) | OpenANN::Net | |
| setParameters(const Eigen::VectorXd ¶meters) | OpenANN::Net | virtual |
| setRegularization(double l1Penalty=0.0, double l2Penalty=0.0, double maxSquaredWeightNorm=0.0) | OpenANN::Net | |
| sparseAutoEncoderLayer(int H, double beta, double rho, ActivationFunction act) | OpenANN::Net | |
| subsamplingLayer(int kernelRows, int kernelCols, ActivationFunction act, double stdDev=0.05, bool bias=true) | OpenANN::Net | |
| tempError | OpenANN::Net | protected |
| tempGradient | OpenANN::Net | protected |
| tempInput | OpenANN::Net | protected |
| tempOutput | OpenANN::Net | protected |
| trainingSet(Eigen::MatrixXd &input, Eigen::MatrixXd &output) | OpenANN::Learner | virtual |
| trainingSet(DataSet &trainingSet) | OpenANN::Learner | virtual |
| trainSet | OpenANN::Learner | protected |
| useDropout(bool activate=true) | OpenANN::Net | |
| validationSet(Eigen::MatrixXd &input, Eigen::MatrixXd &output) | OpenANN::Learner | virtual |
| validationSet(DataSet &validationSet) | OpenANN::Learner | virtual |
| validSet | OpenANN::Learner | protected |
| ~Learner() | OpenANN::Learner | virtual |
| ~Net() | OpenANN::Net | virtual |
| ~Optimizable() | OpenANN::Optimizable | inlinevirtual |
1.8.4