This is the complete list of members for GClasses::GNeuralNetLayer, including all inherited members.

activate()=0	GClasses::GNeuralNetLayer	pure virtual
activation()=0	GClasses::GNeuralNetLayer	pure virtual
backPropError(GNeuralNetLayer *pUpStreamLayer, size_t inputStart=0)=0	GClasses::GNeuralNetLayer	pure virtual
baseDomNode(GDom *pDoc)	GClasses::GNeuralNetLayer	protected
computeError(const double *pTarget)=0	GClasses::GNeuralNetLayer	pure virtual
copyBiasToNet()=0	GClasses::GNeuralNetLayer	pure virtual
copySingleNeuronWeights(size_t source, size_t dest)	GClasses::GNeuralNetLayer	inlinevirtual
copyWeights(GNeuralNetLayer *pSource)=0	GClasses::GNeuralNetLayer	pure virtual
countWeights()=0	GClasses::GNeuralNetLayer	pure virtual
deactivateError()=0	GClasses::GNeuralNetLayer	pure virtual
deserialize(GDomNode *pNode)	GClasses::GNeuralNetLayer	static
diminishWeights(double amount, bool regularizeBiases)=0	GClasses::GNeuralNetLayer	pure virtual
dropConnect(GRand &rand, double probOfDrop)=0	GClasses::GNeuralNetLayer	pure virtual
dropOut(GRand &rand, double probOfDrop)=0	GClasses::GNeuralNetLayer	pure virtual
error()=0	GClasses::GNeuralNetLayer	pure virtual
feedForward(const double *pIn)	GClasses::GNeuralNetLayer
feedIn(const double *pIn, size_t inputStart, size_t inputCount)=0	GClasses::GNeuralNetLayer	pure virtual
feedIn(GNeuralNetLayer *pUpStreamLayer, size_t inputStart)	GClasses::GNeuralNetLayer	inlinevirtual
feedThrough(const GMatrix &data)	GClasses::GNeuralNetLayer
getWeightsSingleNeuron(size_t outputNode, double *&weights)	GClasses::GNeuralNetLayer	inlinevirtual
GNeuralNetLayer()	GClasses::GNeuralNetLayer	inline
inputs()=0	GClasses::GNeuralNetLayer	pure virtual
maxNorm(double max)=0	GClasses::GNeuralNetLayer	pure virtual
outputs()=0	GClasses::GNeuralNetLayer	pure virtual
perturbWeights(GRand &rand, double deviation, size_t start=0, size_t count=INVALID_INDEX)=0	GClasses::GNeuralNetLayer	pure virtual
renormalizeInput(size_t input, double oldMin, double oldMax, double newMin=0.0, double newMax=1.0)=0	GClasses::GNeuralNetLayer	pure virtual
resetWeights(GRand &rand)=0	GClasses::GNeuralNetLayer	pure virtual
resize(size_t inputs, size_t outputs, GRand *pRand=NULL, double deviation=0.03)=0	GClasses::GNeuralNetLayer	pure virtual
scaleUnitIncomingWeights(size_t unit, double scalar)=0	GClasses::GNeuralNetLayer	pure virtual
scaleUnitOutgoingWeights(size_t input, double scalar)=0	GClasses::GNeuralNetLayer	pure virtual
scaleWeights(double factor, bool scaleBiases)=0	GClasses::GNeuralNetLayer	pure virtual
serialize(GDom *pDoc)=0	GClasses::GNeuralNetLayer	pure virtual
setWeightsSingleNeuron(size_t outputNode, const double *weights)	GClasses::GNeuralNetLayer	inlinevirtual
type()=0	GClasses::GNeuralNetLayer	pure virtual
unitIncomingWeightsL1Norm(size_t unit)=0	GClasses::GNeuralNetLayer	pure virtual
unitIncomingWeightsL2Norm(size_t unit)=0	GClasses::GNeuralNetLayer	pure virtual
unitOutgoingWeightsL1Norm(size_t input)=0	GClasses::GNeuralNetLayer	pure virtual
unitOutgoingWeightsL2Norm(size_t input)=0	GClasses::GNeuralNetLayer	pure virtual
updateBias(double learningRate, double momentum)=0	GClasses::GNeuralNetLayer	pure virtual
updateWeights(const double *pUpStreamActivation, size_t inputStart, size_t inputCount, double learningRate, double momentum)=0	GClasses::GNeuralNetLayer	pure virtual
updateWeights(GNeuralNetLayer *pUpStreamLayer, size_t inputStart, double learningRate, double momentum)	GClasses::GNeuralNetLayer	inlinevirtual
updateWeightsAndRestoreDroppedOnes(const double *pUpStreamActivation, size_t inputStart, size_t inputCount, double learningRate, double momentum)=0	GClasses::GNeuralNetLayer	pure virtual
updateWeightsAndRestoreDroppedOnes(GNeuralNetLayer *pUpStreamLayer, size_t inputStart, double learningRate, double momentum)	GClasses::GNeuralNetLayer	inlinevirtual
usesGPU()	GClasses::GNeuralNetLayer	inlinevirtual
vectorToWeights(const double *pVector)=0	GClasses::GNeuralNetLayer	pure virtual
weightsToVector(double *pOutVector)=0	GClasses::GNeuralNetLayer	pure virtual
~GNeuralNetLayer()	GClasses::GNeuralNetLayer	inlinevirtual