Functions.h File Reference

This graph shows which files directly or indirectly include this file:

Namespaces
namespace	TMVA
	create variable transformations
namespace	TMVA::DNN

Enumerations
enum class	TMVA::DNN::EActivationFunction {   TMVA::DNN::kIdentity = 0 , TMVA::DNN::kRelu = 1 , TMVA::DNN::kSigmoid = 2 , TMVA::DNN::kTanh = 3 ,   TMVA::DNN::kSymmRelu = 4 , TMVA::DNN::kSoftSign = 5 , TMVA::DNN::kGauss = 6 , TMVA::DNN::kFastTanh = 7 }
	Enum that represents layer activation functions. More...
enum class	TMVA::DNN::EInitialization {   TMVA::DNN::kGauss = 'G' , TMVA::DNN::kUniform = 'U' , TMVA::DNN::kIdentity = 'I' , TMVA::DNN::kZero = 'Z' ,   TMVA::DNN::kGlorotNormal = 'X' , TMVA::DNN::kGlorotUniform = 'F' }
enum class	TMVA::DNN::ELossFunction { TMVA::DNN::kCrossEntropy = 'C' , TMVA::DNN::kMeanSquaredError = 'R' , TMVA::DNN::kSoftmaxCrossEntropy = 'S' }
	Enum that represents objective functions for the net, i.e. More...
enum class	TMVA::DNN::EOptimizer {   TMVA::DNN::kSGD = 0 , TMVA::DNN::kAdam = 1 , TMVA::DNN::kAdagrad = 2 , TMVA::DNN::kRMSProp = 3 ,   TMVA::DNN::kAdadelta = 4 }
	Enum representing the optimizer used for training. More...
enum class	TMVA::DNN::EOutputFunction { TMVA::DNN::kIdentity = 'I' , TMVA::DNN::kSigmoid = 'S' , TMVA::DNN::kSoftmax = 'M' }
	Enum that represents output functions. More...
enum class	TMVA::DNN::ERegularization { TMVA::DNN::kNone = '0' , TMVA::DNN::kL1 = '1' , TMVA::DNN::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.
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.
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 tensor A.
template<typename Architecture_t >
void	TMVA::DNN::evaluate (typename Architecture_t::Tensor_t &A, EActivationFunction f)
	Apply the given activation function to each value in the given tensor A.
template<typename Architecture_t >
void	TMVA::DNN::evaluateDerivative (typename Architecture_t::Tensor_t &B, EActivationFunction f, const typename Architecture_t::Tensor_t &A)
	Compute the first partial derivative of the activation function for the values given in tensor A and write the results into B.
template<typename Architecture_t >
void	TMVA::DNN::evaluateDerivativeMatrix (typename Architecture_t::Matrix_t &B, EActivationFunction f, const typename Architecture_t::Matrix_t &A)
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.
template<typename Architecture_t >
void	TMVA::DNN::evaluateMatrix (typename Architecture_t::Matrix_t &A, EActivationFunction f)
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.