| activate() | GClasses::GLayerConvolutional2D | virtual |
| activation() | GClasses::GLayerConvolutional2D | inlinevirtual |
| backPropError(GNeuralNetLayer *pUpStreamLayer, size_t inputStart=0) | GClasses::GLayerConvolutional2D | virtual |
| baseDomNode(GDom *pDoc) | GClasses::GNeuralNetLayer | protected |
| bias() | GClasses::GLayerConvolutional2D | inline |
| biasDelta() | GClasses::GLayerConvolutional2D | inline |
| computeError(const double *pTarget) | GClasses::GLayerConvolutional2D | virtual |
| copyBiasToNet() | GClasses::GLayerConvolutional2D | virtual |
| copySingleNeuronWeights(size_t source, size_t dest) | GClasses::GNeuralNetLayer | inlinevirtual |
| copyWeights(GNeuralNetLayer *pSource) | GClasses::GLayerConvolutional2D | virtual |
| countWeights() | GClasses::GLayerConvolutional2D | virtual |
| deactivateError() | GClasses::GLayerConvolutional2D | virtual |
| deserialize(GDomNode *pNode) | GClasses::GNeuralNetLayer | static |
| diminishWeights(double amount, bool regularizeBiases) | GClasses::GLayerConvolutional2D | virtual |
| dropConnect(GRand &rand, double probOfDrop) | GClasses::GLayerConvolutional2D | virtual |
| dropOut(GRand &rand, double probOfDrop) | GClasses::GLayerConvolutional2D | virtual |
| error() | GClasses::GLayerConvolutional2D | inlinevirtual |
| feedForward(const double *pIn) | GClasses::GNeuralNetLayer | |
| feedIn(const double *pIn, size_t inputStart, size_t inputCount) | GClasses::GLayerConvolutional2D | virtual |
| GClasses::GNeuralNetLayer::feedIn(GNeuralNetLayer *pUpStreamLayer, size_t inputStart) | GClasses::GNeuralNetLayer | inlinevirtual |
| feedThrough(const GMatrix &data) | GClasses::GNeuralNetLayer | |
| getWeightsSingleNeuron(size_t outputNode, double *&weights) | GClasses::GNeuralNetLayer | inlinevirtual |
| GLayerConvolutional2D(size_t inputCols, size_t inputRows, size_t inputChannels, size_t kernelSize, size_t kernelsPerChannel, GActivationFunction *pActivationFunction=NULL) | GClasses::GLayerConvolutional2D | |
| GLayerConvolutional2D(GDomNode *pNode) | GClasses::GLayerConvolutional2D | |
| GNeuralNetLayer() | GClasses::GNeuralNetLayer | inline |
| inputs() | GClasses::GLayerConvolutional2D | inlinevirtual |
| kernels() | GClasses::GLayerConvolutional2D | inline |
| m_activation | GClasses::GLayerConvolutional2D | protected |
| m_bias | GClasses::GLayerConvolutional2D | protected |
| m_delta | GClasses::GLayerConvolutional2D | protected |
| m_inputChannels | GClasses::GLayerConvolutional2D | protected |
| m_inputCols | GClasses::GLayerConvolutional2D | protected |
| m_inputRows | GClasses::GLayerConvolutional2D | protected |
| m_kernelCount | GClasses::GLayerConvolutional2D | protected |
| m_kernels | GClasses::GLayerConvolutional2D | protected |
| m_kernelsPerChannel | GClasses::GLayerConvolutional2D | protected |
| m_outputCols | GClasses::GLayerConvolutional2D | protected |
| m_outputRows | GClasses::GLayerConvolutional2D | protected |
| m_pActivationFunction | GClasses::GLayerConvolutional2D | protected |
| maxNorm(double max) | GClasses::GLayerConvolutional2D | virtual |
| net() | GClasses::GLayerConvolutional2D | inline |
| outputs() | GClasses::GLayerConvolutional2D | inlinevirtual |
| perturbWeights(GRand &rand, double deviation, size_t start, size_t count) | GClasses::GLayerConvolutional2D | virtual |
| renormalizeInput(size_t input, double oldMin, double oldMax, double newMin=0.0, double newMax=1.0) | GClasses::GLayerConvolutional2D | virtual |
| resetWeights(GRand &rand) | GClasses::GLayerConvolutional2D | virtual |
| resize(size_t inputs, size_t outputs, GRand *pRand=NULL, double deviation=0.03) | GClasses::GLayerConvolutional2D | virtual |
| scaleUnitIncomingWeights(size_t unit, double scalar) | GClasses::GLayerConvolutional2D | virtual |
| scaleUnitOutgoingWeights(size_t input, double scalar) | GClasses::GLayerConvolutional2D | virtual |
| scaleWeights(double factor, bool scaleBiases) | GClasses::GLayerConvolutional2D | virtual |
| serialize(GDom *pDoc) | GClasses::GLayerConvolutional2D | virtual |
| setWeightsSingleNeuron(size_t outputNode, const double *weights) | GClasses::GNeuralNetLayer | inlinevirtual |
| type() | GClasses::GLayerConvolutional2D | inlinevirtual |
| unitIncomingWeightsL1Norm(size_t unit) | GClasses::GLayerConvolutional2D | virtual |
| unitIncomingWeightsL2Norm(size_t unit) | GClasses::GLayerConvolutional2D | virtual |
| unitOutgoingWeightsL1Norm(size_t input) | GClasses::GLayerConvolutional2D | virtual |
| unitOutgoingWeightsL2Norm(size_t input) | GClasses::GLayerConvolutional2D | virtual |
| updateBias(double learningRate, double momentum) | GClasses::GLayerConvolutional2D | virtual |
| updateWeights(const double *pUpStreamActivation, size_t inputStart, size_t inputCount, double learningRate, double momentum) | GClasses::GLayerConvolutional2D | virtual |
| GClasses::GNeuralNetLayer::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) | GClasses::GLayerConvolutional2D | virtual |
| GClasses::GNeuralNetLayer::updateWeightsAndRestoreDroppedOnes(GNeuralNetLayer *pUpStreamLayer, size_t inputStart, double learningRate, double momentum) | GClasses::GNeuralNetLayer | inlinevirtual |
| usesGPU() | GClasses::GNeuralNetLayer | inlinevirtual |
| vectorToWeights(const double *pVector) | GClasses::GLayerConvolutional2D | virtual |
| weightsToVector(double *pOutVector) | GClasses::GLayerConvolutional2D | virtual |
| ~GLayerConvolutional2D() | GClasses::GLayerConvolutional2D | virtual |
| ~GNeuralNetLayer() | GClasses::GNeuralNetLayer | inlinevirtual |
1.8.7