, including all inherited members.
| backprop(REAL *input, REAL *target) | NN | [private] |
| backpropBLAS(REAL *input, REAL *target) | NN | [private] |
| calcRMSE(REAL *inputs, REAL *targets, int examples) | NN | [private] |
| convertDateToInt(uint day, uint month, uint year, uint hour, uint minute, uint second) | Framework | [static] |
| convertIntToDate(uint date, uint &day, uint &month, uint &year, uint &hour, uint &minute, uint &second, uint &weekday) | Framework | [static] |
| enableErrorFunctionMAE(bool en) | NN | |
| enableRPROP(bool en) | NN | |
| forwardCalculation(REAL *input) | NN | [private] |
| forwardCalculationBLAS(REAL *input) | NN | [private] |
| Framework() | Framework | |
| getAdditionalStartupParameter() | Framework | [static] |
| getBiasIndex(int layer, int neuron) | NN | |
| getDatasetType() | Framework | [static] |
| getFrameworkMode() | Framework | [static] |
| getInitWeight(int fanIn) | NN | [private] |
| getMaxThreads() | Framework | [static] |
| getNrWeights() | NN | |
| getOutputIndex(int layer, int neuron) | NN | |
| getRandomSeed() | Framework | [static] |
| getRMSEProbe() | NN | |
| getRMSETrain() | NN | |
| getWeightIndex(int layer, int neuron, int weight) | NN | |
| getWeightPtr() | NN | |
| initNNWeights(time_t seed) | NN | |
| m_activationFunctionType (defined in NN) | NN | [private] |
| m_adaptiveRPROPlRate (defined in NN) | NN | [private] |
| m_batchSize (defined in NN) | NN | [private] |
| m_d1 (defined in NN) | NN | [private] |
| m_deltaW (defined in NN) | NN | [private] |
| m_deltaWOld (defined in NN) | NN | [private] |
| m_derivates (defined in NN) | NN | [private] |
| m_enableL1Regularization (defined in NN) | NN | [private] |
| m_enableRPROP (defined in NN) | NN | [private] |
| m_errorFunctionMAE (defined in NN) | NN | [private] |
| m_globalEpochs (defined in NN) | NN | [private] |
| m_initWeightFactor (defined in NN) | NN | [private] |
| m_inputsProbe (defined in NN) | NN | [private] |
| m_inputsTrain (defined in NN) | NN | [private] |
| m_learnRate (defined in NN) | NN | [private] |
| m_learnrateDecreaseRate (defined in NN) | NN | [private] |
| m_learnrateDecreaseRateEpoch (defined in NN) | NN | [private] |
| m_learnRateMin (defined in NN) | NN | [private] |
| m_maxEpochs (defined in NN) | NN | [private] |
| m_minUpdateBound (defined in NN) | NN | [private] |
| m_momentum (defined in NN) | NN | [private] |
| m_neuronsPerLayer (defined in NN) | NN | [private] |
| m_normalTrainStopping (defined in NN) | NN | [private] |
| m_nrExamplesProbe (defined in NN) | NN | [private] |
| m_nrExamplesTrain (defined in NN) | NN | [private] |
| m_nrInputs (defined in NN) | NN | [private] |
| m_nrLayer (defined in NN) | NN | [private] |
| m_nrLayWeightOffsets (defined in NN) | NN | [private] |
| m_nrLayWeights (defined in NN) | NN | [private] |
| m_nrOutputs (defined in NN) | NN | [private] |
| m_nrTargets (defined in NN) | NN | [private] |
| m_nrWeights (defined in NN) | NN | [private] |
| m_offsetOutputs (defined in NN) | NN | [private] |
| m_outputs (defined in NN) | NN | [private] |
| m_outputsTmp (defined in NN) | NN | [private] |
| m_RPROP_etaNeg (defined in NN) | NN | [private] |
| m_RPROP_etaPos (defined in NN) | NN | [private] |
| m_RPROP_updateMax (defined in NN) | NN | [private] |
| m_RPROP_updateMin (defined in NN) | NN | [private] |
| m_scaleOutputs (defined in NN) | NN | [private] |
| m_sumSquaredError (defined in NN) | NN | |
| m_sumSquaredErrorSamples (defined in NN) | NN | |
| m_targetsProbe (defined in NN) | NN | [private] |
| m_targetsTrain (defined in NN) | NN | [private] |
| m_useBLAS (defined in NN) | NN | [private] |
| m_weightDecay (defined in NN) | NN | [private] |
| m_weights (defined in NN) | NN | [private] |
| m_weightsBatchUpdate (defined in NN) | NN | [private] |
| m_weightsOld (defined in NN) | NN | [private] |
| m_weightsOldOld (defined in NN) | NN | [private] |
| m_weightsTmp0 (defined in NN) | NN | [private] |
| m_weightsTmp1 (defined in NN) | NN | [private] |
| m_weightsTmp2 (defined in NN) | NN | [private] |
| NN() | NN | |
| predictSingleInput(REAL *input, REAL *output) | NN | |
| printLearnrate() | NN | |
| saveWeights() | NN | [private] |
| setActivationFunctionType(int type) | NN | |
| setAdditionalStartupParameter(char *s) | Framework | [static] |
| setBatchSize(int size) | NN | |
| setDatasetType(bool isClassification) | Framework | [static] |
| setFrameworkMode(int mode) | Framework | [static] |
| setGlobalEpochs(int e) | NN | |
| setInitWeightFactor(REAL factor) | NN | |
| setL1Regularization(bool en) | NN | |
| setLearnrate(REAL learnrate) | NN | |
| setLearnrateMinimum(REAL learnrateMin) | NN | |
| setLearnrateSubtractionValueAfterEveryEpoch(REAL learnrateDecreaseRate) | NN | |
| setLearnrateSubtractionValueAfterEverySample(REAL learnrateDecreaseRate) | NN | |
| setMaxEpochs(int epochs) | NN | |
| setMaxThreads(int n) | Framework | [static] |
| setMinUpdateErrorBound(REAL minUpdateBound) | NN | |
| setMomentum(REAL momentum) | NN | |
| setNNStructure(int nrLayer, int *neuronsPerLayer) | NN | |
| setNormalTrainStopping(bool en) | NN | |
| setNrExamplesProbe(int n) | NN | |
| setNrExamplesTrain(int n) | NN | |
| setNrInputs(int n) | NN | |
| setNrTargets(int n) | NN | |
| setProbeInputs(REAL *inputs) | NN | |
| setProbeTargets(REAL *targets) | NN | |
| setRandomSeed(int n) | Framework | [static] |
| setRPROPMinMaxUpdate(REAL min, REAL max) | NN | |
| setRPROPPosNeg(REAL etaPos, REAL etaNeg) | NN | |
| setScaleOffset(REAL scale, REAL offset) | NN | |
| setTrainInputs(REAL *inputs) | NN | |
| setTrainTargets(REAL *targets) | NN | |
| setWeightDecay(REAL weightDecay) | NN | |
| setWeights(REAL *w) | NN | |
| trainNN() | NN | |
| trainOneEpoch() | NN | |
| useBLASforTraining(bool enable) | NN | |
| ~Framework() | Framework | |
| ~NN() | NN | |
