Functions.h File Reference

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Namespaces
	TMVA
	Abstract ClassifierFactory template that handles arbitrary types.
	TMVA::DNN

Enumerations
enum	TMVA::DNN::EActivationFunction {   TMVA::DNN::EActivationFunction::kIdentity = 0, TMVA::DNN::EActivationFunction::kRelu = 1, TMVA::DNN::EActivationFunction::kSigmoid = 2, TMVA::DNN::EActivationFunction::kTanh = 3,   TMVA::DNN::EActivationFunction::kSymmRelu = 4, TMVA::DNN::EActivationFunction::kSoftSign = 5, TMVA::DNN::EActivationFunction::kGauss = 6 }
	Enum that represents layer activation functions. More...
enum	TMVA::DNN::EInitialization {   TMVA::DNN::EInitialization::kGauss = 'G', TMVA::DNN::EInitialization::kUniform = 'U', TMVA::DNN::EInitialization::kIdentity = 'I', TMVA::DNN::EInitialization::kZero = 'Z',   TMVA::DNN::EInitialization::kGlorotNormal = 'X', TMVA::DNN::EInitialization::kGlorotUniform = 'F' }
enum	TMVA::DNN::ELossFunction { TMVA::DNN::ELossFunction::kCrossEntropy = 'C', TMVA::DNN::ELossFunction::kMeanSquaredError = 'R', TMVA::DNN::ELossFunction::kSoftmaxCrossEntropy = 'S' }
	Enum that represents objective functions for the net, i.e. More...
enum	TMVA::DNN::EOutputFunction { TMVA::DNN::EOutputFunction::kIdentity = 'I', TMVA::DNN::EOutputFunction::kSigmoid = 'S', TMVA::DNN::EOutputFunction::kSoftmax = 'M' }
	Enum that represents output functions. More...
enum	TMVA::DNN::ERegularization { TMVA::DNN::ERegularization::kNone = '0', TMVA::DNN::ERegularization::kL1 = '1', TMVA::DNN::ERegularization::kL2 = '2' }
	Enum representing the regularization type applied for a given layer. More...

Functions
template<typename Architecture_t >
void	TMVA::DNN::addRegularizationGradients (typename Architecture_t::Matrix_t &A, const typename Architecture_t::Matrix_t &W, typename Architecture_t::Scalar_t weightDecay, ERegularization R)
	Add the regularization gradient corresponding to weight matrix W, to the matrix A. More...
template<typename Architecture_t >
void	TMVA::DNN::evaluate (typename Architecture_t::Matrix_t &A, EActivationFunction f)
	Apply the given activation function to each value in the given matrix A. More...
template<typename Architecture_t >
void	TMVA::DNN::evaluate (typename Architecture_t::Matrix_t &A, EOutputFunction f, const typename Architecture_t::Matrix_t &X)
	Apply the given output function to each value in the given matrix A. More...
template<typename Architecture_t >
auto	TMVA::DNN::evaluate (ELossFunction f, const typename Architecture_t::Matrix_t &Y, const typename Architecture_t::Matrix_t &output, const typename Architecture_t::Matrix_t &weights) -> decltype(Architecture_t::CrossEntropy(Y, output, weights))
	Compute the value of the objective function f for given activations of the ouput layer and the truth Y. More...
template<typename Architecture_t >
void	TMVA::DNN::evaluateDerivative (typename Architecture_t::Matrix_t &B, EActivationFunction f, const typename Architecture_t::Matrix_t &A)
	Compute the first partial derivative of the activation function for the values given in matrix A and write the results into B. More...
template<typename Architecture_t >
void	TMVA::DNN::evaluateGradients (typename Architecture_t::Matrix_t &dY, ELossFunction f, const typename Architecture_t::Matrix_t &Y, const typename Architecture_t::Matrix_t &output, const typename Architecture_t::Matrix_t &weights)
	Compute the gradient of the given output function f for given activations output of the output layer and truth Y and write the results into dY. More...
template<typename Architecture_t >
void	TMVA::DNN::initialize (typename Architecture_t::Matrix_t &A, EInitialization m)
template<typename Architecture_t >
auto	TMVA::DNN::regularization (const typename Architecture_t::Matrix_t &A, ERegularization R) -> decltype(Architecture_t::L1Regularization(A))
	Evaluate the regularization functional for a given weight matrix. More...