Namespaces
namespace	Blas

namespace	CNN

namespace	Cuda

namespace	RNN

Classes
class	Batch
	The Batch class encapsulates one mini-batch. More...

class	ClassificationSettings
	Settings for classificationused to distinguish between different function signatures. More...

struct	CudaActivationDescriptor

struct	CudaConvolutionBwdDataAlgo

struct	CudaConvolutionBwdFilterAlgo

struct	CudaConvolutionDescriptor

struct	CudaConvolutionFwdAlgo

struct	CudaDataType

struct	CudaDropoutDescriptor

struct	CudaEmptyDescriptor

struct	CudaFilterDescriptor

struct	CudaPoolingDescriptor

struct	DummyConvolutionBwdDataAlgo

struct	DummyConvolutionBwdFilterAlgo

struct	DummyConvolutionDescriptor

struct	DummyConvolutionFwdAlgo

struct	DummyCudaDataType

struct	DummyDataType

struct	DummyDescriptor

struct	DummyDropoutDescriptor

struct	DummyEmptyDescriptor

struct	DummyFilterDescriptor

struct	DummyPoolingDescriptor

class	Layer
	Layer defines the layout of a layer. More...

class	LayerData
	LayerData holds the data of one layer. More...

class	MeanVariance

class	Net
	neural net More...

class	Settings
	Settings for the training of the neural net. More...

class	Steepest
	Steepest Gradient Descent algorithm (SGD) More...

class	TAdadelta
	Adadelta Optimizer class. More...

class	TAdagrad
	Adagrad Optimizer class. More...

class	TAdam
	Adam Optimizer class. More...

class	TBatch
	TBatch. More...

class	TBatchIterator
	TBatchIterator. More...

class	TBatchNormLayer
	Layer implementing Batch Normalization. More...

class	TCpu
	The TCpu architecture class. More...

class	TCpuBuffer
	TCpuBuffer. More...

class	TCpuMatrix
	The TCpuMatrix class. More...

class	TCpuTensor

class	TCuda
	The TCuda architecture class. More...

class	TCudaDeviceBuffer
	TCudaDeviceBuffer. More...

class	TCudaDeviceReference
	TCudaDeviceReference. More...

class	TCudaHostBuffer
	TCudaHostBuffer. More...

class	TCudaMatrix
	TCudaMatrix Class. More...

class	TCudaTensor
	TCudaTensor Class. More...

class	TDataLoader
	TDataLoader. More...

class	TDataLoader< AData, TReference< AReal > >

class	TDeepNet
	Generic Deep Neural Network class. More...

class	TDenseLayer
	Generic layer class. More...

struct	TDescriptors

class	TDevice
	TDevice. More...

class	TDLGradientDescent

struct	TDNNGenDescriptors

class	TGradientDescent

class	TLayer
	Generic layer class. More...

class	TNet
	Generic neural network class. More...

class	TReference
	The reference architecture class. More...

class	TReshapeLayer

class	TRMSProp
	RMSProp Optimizer class. More...

class	TSGD
	Stochastic Batch Gradient Descent Optimizer class. More...

class	TSharedLayer
	Layer class width shared weight and bias layers. More...

class	TTensorBatch
	TTensorBatch. More...

class	TTensorBatchIterator
	TTensorBatchIterator. More...

class	TTensorDataLoader
	TTensorDataLoader. More...

class	TTensorDataLoader< AData, TReference< AReal > >

struct	TWorkspace

class	VGeneralLayer
	Generic General Layer class. More...

class	VOptimizer
	Generic Optimizer class. More...

Typedefs
typedef std::vector< char >	DropContainer

using	IndexIterator_t = typename std::vector< size_t >::iterator

using	MatrixInput_t = std::tuple< const TMatrixT< Double_t > &, const TMatrixT< Double_t > &, const TMatrixT< Double_t > & >

using	MemoryLayout = TMVA::Experimental::MemoryLayout

typedef std::tuple< Settings &, Batch &, DropContainer & >	pass_through_type

using	TensorInput = std::tuple< const std::vector< TMatrixT< Double_t > > &, const TMatrixT< Double_t > &, const TMatrixT< Double_t > & >

using	TMVAInput_t = std::tuple< const std::vector< Event * > &, const DataSetInfo & >

Enumerations
enum class	EActivationFunction { kIdentity = 0 , kRelu = 1 , kSigmoid = 2 , kTanh = 3 , kSymmRelu = 4 , kSoftSign = 5 , kGauss = 6 , kFastTanh = 7 }
	Enum that represents layer activation functions. More...

enum class	EInitialization { kGauss = 'G' , kUniform = 'U' , kIdentity = 'I' , kZero = 'Z' , kGlorotNormal = 'X' , kGlorotUniform = 'F' }

enum class	ELossFunction { kCrossEntropy = 'C' , kMeanSquaredError = 'R' , kSoftmaxCrossEntropy = 'S' }
	Enum that represents objective functions for the net, i.e. More...

enum class	EnumFunction { ZERO = '0' , LINEAR = 'L' , TANH = 'T' , RELU = 'R' , SYMMRELU = 'r' , TANHSHIFT = 't' , SIGMOID = 's' , SOFTSIGN = 'S' , GAUSS = 'G' , GAUSSCOMPLEMENT = 'C' }

enum class	EnumRegularization { NONE , L1 , L2 , L1MAX }

enum class	EOptimizer { kSGD = 0 , kAdam = 1 , kAdagrad = 2 , kRMSProp = 3 , kAdadelta = 4 }
	Enum representing the optimizer used for training. More...

enum class	EOutputFunction { kIdentity = 'I' , kSigmoid = 'S' , kSoftmax = 'M' }
	Enum that represents output functions. More...

enum class	ERegularization { kNone = '0' , kL1 = '1' , kL2 = '2' }
	Enum representing the regularization type applied for a given layer. More...

enum	MinimizerType { fSteepest }
	< list all the minimizer types More...

enum class	ModeErrorFunction { SUMOFSQUARES = 'S' , CROSSENTROPY = 'C' , CROSSENTROPY_MUTUALEXCLUSIVE = 'M' }
	error functions to be chosen from More...

enum class	ModeOutput { FETCH }

enum class	ModeOutputValues : int { DIRECT = 0x01 , SIGMOID = 0x02 , SOFTMAX = 0x04 , BATCHNORMALIZATION = 0x08 }

enum class	WeightInitializationStrategy { XAVIER , TEST , LAYERSIZE , XAVIERUNIFORM }
	weight initialization strategies to be chosen from More...

Functions
template<typename Architecture_t >
void	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 ItValue , typename Fnc >
void	applyFunctions (ItValue itValue, ItValue itValueEnd, Fnc fnc)
	apply the activation functions

template<typename ItValue , typename Fnc , typename InvFnc , typename ItGradient >
void	applyFunctions (ItValue itValue, ItValue itValueEnd, Fnc fnc, InvFnc invFnc, ItGradient itGradient)
	apply the activation functions and compute the gradient

template<typename ItValue , typename ItFunction >
void	applyFunctions (ItValue itValue, ItValue itValueEnd, ItFunction itFunction)

template<typename ItValue , typename ItFunction , typename ItInverseFunction , typename ItGradient >
void	applyFunctions (ItValue itValue, ItValue itValueEnd, ItFunction itFunction, ItInverseFunction itInverseFunction, ItGradient itGradient)

template<typename ItSource , typename ItWeight , typename ItTarget >
void	applyWeights (ItSource itSourceBegin, ItSource itSourceEnd, ItWeight itWeight, ItTarget itTargetBegin, ItTarget itTargetEnd)

template<bool HasDropOut, typename ItSource , typename ItWeight , typename ItTarget , typename ItDrop >
void	applyWeights (ItSource itSourceBegin, ItSource itSourceEnd, ItWeight itWeight, ItTarget itTargetBegin, ItTarget itTargetEnd, ItDrop itDrop)
	apply weights using drop-out; for no drop out, provide (&bool = true) to itDrop such that *itDrop becomes "true"

template<typename ItSource , typename ItWeight , typename ItPrev >
void	applyWeightsBackwards (ItSource itCurrBegin, ItSource itCurrEnd, ItWeight itWeight, ItPrev itPrevBegin, ItPrev itPrevEnd)

template<bool HasDropOut, typename ItSource , typename ItWeight , typename ItPrev , typename ItDrop >
void	applyWeightsBackwards (ItSource itCurrBegin, ItSource itCurrEnd, ItWeight itWeight, ItPrev itPrevBegin, ItPrev itPrevEnd, ItDrop itDrop)
	apply weights backwards (for backprop); for no drop out, provide (&bool = true) to itDrop such that *itDrop becomes "true"

template<typename LAYERDATA >
void	backward (LAYERDATA &prevLayerData, LAYERDATA &currLayerData)
	backward application of the weights (back-propagation of the error)

template<EnumRegularization Regularization>
double	computeRegularization (double weight, const double &factorWeightDecay)
	compute the regularization (L1, L2)

template<>
double	computeRegularization< EnumRegularization::L1 > (double weight, const double &factorWeightDecay)

template<>
double	computeRegularization< EnumRegularization::L2 > (double weight, const double &factorWeightDecay)

template<typename ItProbability , typename ItTruth , typename ItDelta , typename ItInvActFnc >
double	crossEntropy (ItProbability itProbabilityBegin, ItProbability itProbabilityEnd, ItTruth itTruthBegin, ItTruth, ItDelta itDelta, ItDelta itDeltaEnd, ItInvActFnc, double patternWeight)
	cross entropy error function

void	cudaError (cudaError_t code, const char *file, int line, bool abort=true)
	Function to check cuda return code.

__global__ void	CurandInitializationKernel (unsigned long long seed, curandState_t *state)

template<typename Architecture >
auto	debugTensor (const typename Architecture::Tensor_t &A, const std::string name="tensor") -> void

template<typename Architecture_t >
auto	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	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	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	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	evaluateDerivativeMatrix (typename Architecture_t::Matrix_t &B, EActivationFunction f, const typename Architecture_t::Matrix_t &A)

template<typename Architecture_t >
void	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	evaluateMatrix (typename Architecture_t::Matrix_t &A, EActivationFunction f)

template<typename LAYERDATA >
void	forward (const LAYERDATA &prevLayerData, LAYERDATA &currLayerData)
	apply the weights (and functions) in forward direction of the DNN

double	gaussDouble (double mean, double sigma)

template<typename Architecture_t >
void	initialize (typename Architecture_t::Matrix_t &A, EInitialization m)

template<typename T >
bool	isFlagSet (T flag, T value)

ModeOutputValues	operator& (ModeOutputValues lhs, ModeOutputValues rhs)

ModeOutputValues	operator&= (ModeOutputValues &lhs, ModeOutputValues rhs)

ModeOutputValues	operator\| (ModeOutputValues lhs, ModeOutputValues rhs)

ModeOutputValues	operator\|= (ModeOutputValues &lhs, ModeOutputValues rhs)

int	randomInt (int maxValue)

template<typename Architecture_t >
auto	regularization (const typename Architecture_t::Matrix_t &A, ERegularization R) -> decltype(Architecture_t::L1Regularization(A))
	Evaluate the regularization functional for a given weight matrix.

template<typename ItOutput , typename ItTruth , typename ItDelta , typename ItInvActFnc >
double	softMaxCrossEntropy (ItOutput itProbabilityBegin, ItOutput itProbabilityEnd, ItTruth itTruthBegin, ItTruth, ItDelta itDelta, ItDelta itDeltaEnd, ItInvActFnc, double patternWeight)
	soft-max-cross-entropy error function (for mutual exclusive cross-entropy)

double	studenttDouble (double distributionParameter)

template<typename ItOutput , typename ItTruth , typename ItDelta , typename ItInvActFnc >
double	sumOfSquares (ItOutput itOutputBegin, ItOutput itOutputEnd, ItTruth itTruthBegin, ItTruth itTruthEnd, ItDelta itDelta, ItDelta itDeltaEnd, ItInvActFnc itInvActFnc, double patternWeight)

template<typename ItOutput , typename ItTruth , typename ItDelta , typename InvFnc >
double	sumOfSquares (ItOutput itOutputBegin, ItOutput itOutputEnd, ItTruth itTruthBegin, ItTruth, ItDelta itDelta, ItDelta itDeltaEnd, InvFnc invFnc, double patternWeight)
	sum of squares error function

template<typename Container , typename T >
void	uniformDouble (Container &container, T maxValue)

double	uniformDouble (double minValue, double maxValue)

template<typename T >
T	uniformFromTo (T from, T to)

template<typename LAYERDATA >
void	update (const LAYERDATA &prevLayerData, LAYERDATA &currLayerData, double factorWeightDecay, EnumRegularization regularization)
	update the node values

template<typename ItSource , typename ItDelta , typename ItTargetGradient , typename ItGradient >
void	update (ItSource itSource, ItSource itSourceEnd, ItDelta itTargetDeltaBegin, ItDelta itTargetDeltaEnd, ItTargetGradient itTargetGradientBegin, ItGradient itGradient)
	update the gradients

template<EnumRegularization Regularization, typename ItSource , typename ItDelta , typename ItTargetGradient , typename ItGradient , typename ItWeight >
void	update (ItSource itSource, ItSource itSourceEnd, ItDelta itTargetDeltaBegin, ItDelta itTargetDeltaEnd, ItTargetGradient itTargetGradientBegin, ItGradient itGradient, ItWeight itWeight, double weightDecay)
	update the gradients, using regularization

template<typename ItWeight >
double	weightDecay (double error, ItWeight itWeight, ItWeight itWeightEnd, double factorWeightDecay, EnumRegularization eRegularization)
	compute the weight decay for regularization (L1 or L2)

Variables
std::shared_ptr< std::function< double(double)> >	Gauss = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return exp (-std::pow(value*s,2.0)); })

std::shared_ptr< std::function< double(double)> >	GaussComplement = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return 1.0 - exp (-std::pow(value*s,2.0)); })

std::shared_ptr< std::function< double(double)> >	InvGauss = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return -2.0 * value * ss (*Gauss.get ()) (value); })

std::shared_ptr< std::function< double(double)> >	InvGaussComplement = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return +2.0 * value * ss (*GaussComplement.get ()) (value); })

std::shared_ptr< std::function< double(double)> >	InvLinear = std::make_shared<std::function<double(double)>> ([](double ){ return 1.0; })

std::shared_ptr< std::function< double(double)> >	InvReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.0; return value > margin ? 1.0 : 0; })

std::shared_ptr< std::function< double(double)> >	InvSigmoid = std::make_shared<std::function<double(double)>> ([](double value){ double s = (Sigmoid.get ()) (value); return s(1.0-s); })

std::shared_ptr< std::function< double(double)> >	InvSoftPlus = std::make_shared<std::function<double(double)>> ([](double value){ return 1.0 / (1.0 + std::exp (-value)); })

std::shared_ptr< std::function< double(double)> >	InvSoftSign = std::make_shared<std::function<double(double)>> ([](double value){ return std::pow ((1.0 - fabs (value)),2.0); })

std::shared_ptr< std::function< double(double)> >	InvSymmReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.3; return value > margin ? 1.0 : value < -margin ? 1.0 : 0; })

std::shared_ptr< std::function< double(double)> >	InvTanh = std::make_shared<std::function<double(double)>> ([](double value){ return 1.0 - std::pow (value, 2.0); })

std::shared_ptr< std::function< double(double)> >	InvTanhShift = std::make_shared<std::function<double(double)>> ([](double value){ return 0.3 + (1.0 - std::pow (value, 2.0)); })

std::shared_ptr< std::function< double(double)> >	Linear = std::make_shared<std::function<double(double)>> ([](double value){ return value; })

std::shared_ptr< std::function< double(double)> >	ReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.0; return value > margin ? value-margin : 0; })

std::shared_ptr< std::function< double(double)> >	Sigmoid = std::make_shared<std::function<double(double)>> ([](double value){ value = std::max (-100.0, std::min (100.0,value)); return 1.0/(1.0 + std::exp (-value)); })

std::shared_ptr< std::function< double(double)> >	SoftPlus = std::make_shared<std::function<double(double)>> ([](double value){ return std::log (1.0+ std::exp (value)); })

std::shared_ptr< std::function< double(double)> >	SoftSign = std::make_shared<std::function<double(double)>> ([](double value){ return value / (1.0 + fabs (value)); })

std::shared_ptr< std::function< double(double)> >	SymmReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.3; return value > margin ? value-margin : value < -margin ? value+margin : 0; })

std::shared_ptr< std::function< double(double)> >	Tanh = std::make_shared<std::function<double(double)>> ([](double value){ return tanh (value); })

std::shared_ptr< std::function< double(double)> >	TanhShift = std::make_shared<std::function<double(double)>> ([](double value){ return tanh (value-0.3); })

std::shared_ptr< std::function< double(double)> >	ZeroFnc = std::make_shared<std::function<double(double)>> ([](double ){ return 0; })

Typedef Documentation

◆ DropContainer

typedef std::vector<char> TMVA::DNN::DropContainer

Definition at line 227 of file NeuralNet.h.

◆ IndexIterator_t

typedef typename std::vector< size_t >::iterator TMVA::DNN::IndexIterator_t

Definition at line 42 of file DataLoader.h.

◆ MatrixInput_t

using TMVA::DNN::MatrixInput_t = typedef std::tuple<const TMatrixT<Double_t> &, const TMatrixT<Double_t> &, const TMatrixT<Double_t> &>

Definition at line 38 of file DataLoader.h.

◆ MemoryLayout

using TMVA::DNN::MemoryLayout = typedef TMVA::Experimental::MemoryLayout

Definition at line 56 of file CudaTensor.h.

◆ pass_through_type

typedef std::tuple<Settings&, Batch&, DropContainer&> TMVA::DNN::pass_through_type

Definition at line 1294 of file NeuralNet.h.

◆ TensorInput

using TMVA::DNN::TensorInput = typedef std::tuple<const std::vector<TMatrixT<Double_t> > &, const TMatrixT<Double_t> &, const TMatrixT<Double_t> &>

Definition at line 43 of file TensorDataLoader.h.

◆ TMVAInput_t

typedef std::tuple< const std::vector< Event * > &, const DataSetInfo & > TMVA::DNN::TMVAInput_t

Definition at line 39 of file DataLoader.h.

Enumeration Type Documentation

◆ EActivationFunction

enum class TMVA::DNN::EActivationFunction

strong

Enum that represents layer activation functions.

Enumerator
kIdentity
kRelu
kSigmoid
kTanh
kSymmRelu
kSoftSign
kGauss
kFastTanh

Definition at line 31 of file Functions.h.

◆ EInitialization

enum class TMVA::DNN::EInitialization

strong

Enumerator
kGauss
kUniform
kIdentity
kZero
kGlorotNormal
kGlorotUniform

Definition at line 72 of file Functions.h.

◆ ELossFunction

enum class TMVA::DNN::ELossFunction

strong

Enum that represents objective functions for the net, i.e.

functions that take the output from the last layer in the net together with the truths and return the objective function values that is to be minimized in the training process.

Enumerator
kCrossEntropy
kMeanSquaredError
kSoftmaxCrossEntropy

Definition at line 56 of file Functions.h.

◆ EnumFunction

enum class TMVA::DNN::EnumFunction

strong

Enumerator
ZERO
LINEAR
TANH
RELU
SYMMRELU
TANHSHIFT
SIGMOID
SOFTSIGN
GAUSS
GAUSSCOMPLEMENT

Definition at line 156 of file NeuralNet.h.

◆ EnumRegularization

enum class TMVA::DNN::EnumRegularization

strong

Enumerator
NONE
L1
L2
L1MAX

Definition at line 172 of file NeuralNet.h.

◆ EOptimizer

enum class TMVA::DNN::EOptimizer

strong

Enum representing the optimizer used for training.

Enumerator
kSGD
kAdam
kAdagrad
kRMSProp
kAdadelta

Definition at line 82 of file Functions.h.

◆ EOutputFunction

enum class TMVA::DNN::EOutputFunction

strong

Enum that represents output functions.

Enumerator
kIdentity
kSigmoid
kSoftmax

Definition at line 45 of file Functions.h.

◆ ERegularization

enum class TMVA::DNN::ERegularization

strong

Enum representing the regularization type applied for a given layer.

Enumerator
kNone
kL1
kL2

Definition at line 64 of file Functions.h.

◆ MinimizerType

enum TMVA::DNN::MinimizerType

< list all the minimizer types

Enumerator
fSteepest	SGD.

Definition at line 320 of file NeuralNet.h.

◆ ModeErrorFunction

enum class TMVA::DNN::ModeErrorFunction

strong

error functions to be chosen from

Enumerator
SUMOFSQUARES
CROSSENTROPY
CROSSENTROPY_MUTUALEXCLUSIVE

Definition at line 1038 of file NeuralNet.h.

◆ ModeOutput

enum class TMVA::DNN::ModeOutput

strong

Enumerator
FETCH

Definition at line 1029 of file NeuralNet.h.

◆ ModeOutputValues

enum class TMVA::DNN::ModeOutputValues : int

strong

Enumerator
DIRECT
SIGMOID
SOFTMAX
BATCHNORMALIZATION

Definition at line 178 of file NeuralNet.h.

◆ WeightInitializationStrategy

enum class TMVA::DNN::WeightInitializationStrategy

strong

weight initialization strategies to be chosen from

Enumerator
XAVIER
TEST
LAYERSIZE
XAVIERUNIFORM

Definition at line 1049 of file NeuralNet.h.

Function Documentation

◆ addRegularizationGradients()

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
	)

inline

Add the regularization gradient corresponding to weight matrix W, to the matrix A.

Definition at line 258 of file Functions.h.

◆ applyFunctions() [1/4]

template<typename ItValue , typename Fnc >

void TMVA::DNN::applyFunctions	(	ItValue	itValue,
		ItValue	itValueEnd,
		Fnc	fnc
	)

apply the activation functions

Definition at line 147 of file NeuralNet.icc.

◆ applyFunctions() [2/4]

template<typename ItValue , typename Fnc , typename InvFnc , typename ItGradient >

void TMVA::DNN::applyFunctions	(	ItValue	itValue,
		ItValue	itValueEnd,
		Fnc	fnc,
		InvFnc	invFnc,
		ItGradient	itGradient
	)

apply the activation functions and compute the gradient

Definition at line 164 of file NeuralNet.icc.

◆ applyFunctions() [3/4]

template<typename ItValue , typename ItFunction >

void TMVA::DNN::applyFunctions	(	ItValue	itValue,
		ItValue	itValueEnd,
		ItFunction	itFunction
	)

◆ applyFunctions() [4/4]

template<typename ItValue , typename ItFunction , typename ItInverseFunction , typename ItGradient >

void TMVA::DNN::applyFunctions	(	ItValue	itValue,
		ItValue	itValueEnd,
		ItFunction	itFunction,
		ItInverseFunction	itInverseFunction,
		ItGradient	itGradient
	)

◆ applyWeights() [1/2]

template<typename ItSource , typename ItWeight , typename ItTarget >

void TMVA::DNN::applyWeights	(	ItSource	itSourceBegin,
		ItSource	itSourceEnd,
		ItWeight	itWeight,
		ItTarget	itTargetBegin,
		ItTarget	itTargetEnd
	)

◆ applyWeights() [2/2]

template<bool HasDropOut, typename ItSource , typename ItWeight , typename ItTarget , typename ItDrop >

void TMVA::DNN::applyWeights	(	ItSource	itSourceBegin,
		ItSource	itSourceEnd,
		ItWeight	itWeight,
		ItTarget	itTargetBegin,
		ItTarget	itTargetEnd,
		ItDrop	itDrop
	)

apply weights using drop-out; for no drop out, provide (&bool = true) to itDrop such that *itDrop becomes "true"

itDrop correlates with itSourceBegin

Definition at line 91 of file NeuralNet.icc.

◆ applyWeightsBackwards() [1/2]

template<typename ItSource , typename ItWeight , typename ItPrev >

void TMVA::DNN::applyWeightsBackwards	(	ItSource	itCurrBegin,
		ItSource	itCurrEnd,
		ItWeight	itWeight,
		ItPrev	itPrevBegin,
		ItPrev	itPrevEnd
	)

◆ applyWeightsBackwards() [2/2]

template<bool HasDropOut, typename ItSource , typename ItWeight , typename ItPrev , typename ItDrop >

void TMVA::DNN::applyWeightsBackwards	(	ItSource	itCurrBegin,
		ItSource	itCurrEnd,
		ItWeight	itWeight,
		ItPrev	itPrevBegin,
		ItPrev	itPrevEnd,
		ItDrop	itDrop
	)

apply weights backwards (for backprop); for no drop out, provide (&bool = true) to itDrop such that *itDrop becomes "true"

itDrop correlates with itPrev (to be in agreement with "applyWeights" where it correlates with itSources (same node as itTarget here in applyBackwards)

Definition at line 118 of file NeuralNet.icc.

◆ backward()

template<typename LAYERDATA >

void TMVA::DNN::backward	(	LAYERDATA &	prevLayerData,
		LAYERDATA &	currLayerData
	)

backward application of the weights (back-propagation of the error)

Definition at line 572 of file NeuralNet.icc.

◆ computeRegularization()

template<EnumRegularization Regularization>

double TMVA::DNN::computeRegularization	(	double	weight,
		const double &	factorWeightDecay
	)

inline

compute the regularization (L1, L2)

Definition at line 209 of file NeuralNet.icc.

◆ computeRegularization< EnumRegularization::L1 >()

template<>

double TMVA::DNN::computeRegularization< EnumRegularization::L1 >	(	double	weight,
		const double &	factorWeightDecay
	)

inline

Definition at line 219 of file NeuralNet.icc.

◆ computeRegularization< EnumRegularization::L2 >()

template<>

double TMVA::DNN::computeRegularization< EnumRegularization::L2 >	(	double	weight,
		const double &	factorWeightDecay
	)

inline

Definition at line 226 of file NeuralNet.icc.

◆ crossEntropy()

template<typename ItProbability , typename ItTruth , typename ItDelta , typename ItInvActFnc >

double TMVA::DNN::crossEntropy	(	ItProbability	itProbabilityBegin,
		ItProbability	itProbabilityEnd,
		ItTruth	itTruthBegin,
		ItTruth	,
		ItDelta	itDelta,
		ItDelta	itDeltaEnd,
		ItInvActFnc	,
		double	patternWeight
	)

cross entropy error function

Definition at line 412 of file NeuralNet.icc.

◆ cudaError()

void TMVA::DNN::cudaError	(	cudaError_t	code,
		const char *	file,
		int	line,
		bool	abort = `true`
	)

inline

Function to check cuda return code.

Taken from http://stackoverflow.com/questions/14038589/

Definition at line 198 of file CudaMatrix.h.

◆ CurandInitializationKernel()

__global__ void TMVA::DNN::CurandInitializationKernel	(	unsigned long long	seed,
		curandState_t *	state
	)

Definition at line 26 of file CudaMatrix.cu.

◆ debugTensor()

template<typename Architecture >

auto TMVA::DNN::debugTensor	(	const typename Architecture::Tensor_t &	A,
		const std::string	name = `"tensor"`
	)		-> void

Definition at line 582 of file GeneralLayer.h.

◆ evaluate() [1/3]

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))

inline

Compute the value of the objective function f for given activations of the ouput layer and the truth Y.

Definition at line 198 of file Functions.h.

◆ evaluate() [2/3]

template<typename Architecture_t >

void TMVA::DNN::evaluate	(	typename Architecture_t::Matrix_t &	A,
		EOutputFunction	f,
		const typename Architecture_t::Matrix_t &	X
	)

inline

Apply the given output function to each value in the given tensor A.

Definition at line 175 of file Functions.h.

◆ evaluate() [3/3]

template<typename Architecture_t >

void TMVA::DNN::evaluate	(	typename Architecture_t::Tensor_t &	A,
		EActivationFunction	f
	)

inline

Apply the given activation function to each value in the given tensor A.

Definition at line 98 of file Functions.h.

◆ evaluateDerivative()

template<typename Architecture_t >

void TMVA::DNN::evaluateDerivative	(	typename Architecture_t::Tensor_t &	B,
		EActivationFunction	f,
		const typename Architecture_t::Tensor_t &	A
	)

inline

Compute the first partial derivative of the activation function for the values given in tensor A and write the results into B.

Definition at line 125 of file Functions.h.

◆ evaluateDerivativeMatrix()

template<typename Architecture_t >

void TMVA::DNN::evaluateDerivativeMatrix	(	typename Architecture_t::Matrix_t &	B,
		EActivationFunction	f,
		const typename Architecture_t::Matrix_t &	A
	)

inline

Definition at line 160 of file Functions.h.

◆ evaluateGradients()

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
	)

inline

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.

Definition at line 215 of file Functions.h.

◆ evaluateMatrix()

template<typename Architecture_t >

void TMVA::DNN::evaluateMatrix	(	typename Architecture_t::Matrix_t &	A,
		EActivationFunction	f
	)

inline

Definition at line 152 of file Functions.h.

◆ forward()

template<typename LAYERDATA >

void TMVA::DNN::forward	(	const LAYERDATA &	prevLayerData,
		LAYERDATA &	currLayerData
	)

apply the weights (and functions) in forward direction of the DNN

Definition at line 546 of file NeuralNet.icc.

◆ gaussDouble()

double TMVA::DNN::gaussDouble	(	double	mean,
		double	sigma
	)

Definition at line 35 of file NeuralNet.cxx.

◆ initialize()

template<typename Architecture_t >

void TMVA::DNN::initialize	(	typename Architecture_t::Matrix_t &	A,
		EInitialization	m
	)

inline

Definition at line 282 of file Functions.h.

◆ isFlagSet()

template<typename T >

bool TMVA::DNN::isFlagSet	(	T	flag,
		T	value
	)

Definition at line 212 of file NeuralNet.h.

◆ operator&()

ModeOutputValues TMVA::DNN::operator&	(	ModeOutputValues	lhs,
		ModeOutputValues	rhs
	)

inline

Definition at line 199 of file NeuralNet.h.

◆ operator&=()

ModeOutputValues TMVA::DNN::operator&=	(	ModeOutputValues &	lhs,
		ModeOutputValues	rhs
	)

inline

Definition at line 204 of file NeuralNet.h.

◆ operator|()

ModeOutputValues TMVA::DNN::operator\|	(	ModeOutputValues	lhs,
		ModeOutputValues	rhs
	)

inline

Definition at line 188 of file NeuralNet.h.

◆ operator|=()

ModeOutputValues TMVA::DNN::operator\|=	(	ModeOutputValues &	lhs,
		ModeOutputValues	rhs
	)

inline

Definition at line 193 of file NeuralNet.h.

◆ randomInt()

int TMVA::DNN::randomInt ( int maxValue )

Definition at line 52 of file NeuralNet.cxx.

◆ regularization()

template<typename Architecture_t >

auto TMVA::DNN::regularization	(	const typename Architecture_t::Matrix_t &	A,
		ERegularization	R
	)		-> decltype(Architecture_t::L1Regularization(A))

inline

Evaluate the regularization functional for a given weight matrix.

Definition at line 238 of file Functions.h.

◆ softMaxCrossEntropy()

template<typename ItOutput , typename ItTruth , typename ItDelta , typename ItInvActFnc >

double TMVA::DNN::softMaxCrossEntropy	(	ItOutput	itProbabilityBegin,
		ItOutput	itProbabilityEnd,
		ItTruth	itTruthBegin,
		ItTruth	,
		ItDelta	itDelta,
		ItDelta	itDeltaEnd,
		ItInvActFnc	,
		double	patternWeight
	)

soft-max-cross-entropy error function (for mutual exclusive cross-entropy)

Definition at line 458 of file NeuralNet.icc.

◆ studenttDouble()

double TMVA::DNN::studenttDouble ( double distributionParameter )

Definition at line 60 of file NeuralNet.cxx.

◆ sumOfSquares() [1/2]

template<typename ItOutput , typename ItTruth , typename ItDelta , typename ItInvActFnc >

double TMVA::DNN::sumOfSquares	(	ItOutput	itOutputBegin,
		ItOutput	itOutputEnd,
		ItTruth	itTruthBegin,
		ItTruth	itTruthEnd,
		ItDelta	itDelta,
		ItDelta	itDeltaEnd,
		ItInvActFnc	itInvActFnc,
		double	patternWeight
	)

◆ sumOfSquares() [2/2]

template<typename ItOutput , typename ItTruth , typename ItDelta , typename InvFnc >

double TMVA::DNN::sumOfSquares	(	ItOutput	itOutputBegin,
		ItOutput	itOutputEnd,
		ItTruth	itTruthBegin,
		ItTruth	,
		ItDelta	itDelta,
		ItDelta	itDeltaEnd,
		InvFnc	invFnc,
		double	patternWeight
	)

sum of squares error function

Definition at line 382 of file NeuralNet.icc.

◆ uniformDouble() [1/2]

template<typename Container , typename T >

void TMVA::DNN::uniformDouble	(	Container &	container,
		T	maxValue
	)

Definition at line 42 of file NeuralNet.icc.

◆ uniformDouble() [2/2]

double TMVA::DNN::uniformDouble	(	double	minValue,
		double	maxValue
	)

Definition at line 43 of file NeuralNet.cxx.

◆ uniformFromTo()

template<typename T >

T TMVA::DNN::uniformFromTo	(	T	from,
		T	to
	)

Definition at line 34 of file NeuralNet.icc.

◆ update() [1/3]

template<typename LAYERDATA >

void TMVA::DNN::update	(	const LAYERDATA &	prevLayerData,
		LAYERDATA &	currLayerData,
		double	factorWeightDecay,
		EnumRegularization	regularization
	)

update the node values

Definition at line 600 of file NeuralNet.icc.

◆ update() [2/3]

template<typename ItSource , typename ItDelta , typename ItTargetGradient , typename ItGradient >

void TMVA::DNN::update	(	ItSource	itSource,
		ItSource	itSourceEnd,
		ItDelta	itTargetDeltaBegin,
		ItDelta	itTargetDeltaEnd,
		ItTargetGradient	itTargetGradientBegin,
		ItGradient	itGradient
	)

update the gradients

Definition at line 183 of file NeuralNet.icc.

◆ update() [3/3]

template<EnumRegularization Regularization, typename ItSource , typename ItDelta , typename ItTargetGradient , typename ItGradient , typename ItWeight >

void TMVA::DNN::update	(	ItSource	itSource,
		ItSource	itSourceEnd,
		ItDelta	itTargetDeltaBegin,
		ItDelta	itTargetDeltaEnd,
		ItTargetGradient	itTargetGradientBegin,
		ItGradient	itGradient,
		ItWeight	itWeight,
		double	weightDecay
	)

update the gradients, using regularization

Definition at line 237 of file NeuralNet.icc.

◆ weightDecay()

template<typename ItWeight >

double TMVA::DNN::weightDecay	(	double	error,
		ItWeight	itWeight,
		ItWeight	itWeightEnd,
		double	factorWeightDecay,
		EnumRegularization	eRegularization
	)

compute the weight decay for regularization (L1 or L2)

Definition at line 498 of file NeuralNet.icc.

Variable Documentation

◆ Gauss

std::shared_ptr< std::function< double(double)> > TMVA::DNN::Gauss = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return exp (-std::pow(value*s,2.0)); })

Definition at line 12 of file NeuralNet.cxx.

◆ GaussComplement

std::shared_ptr< std::function< double(double)> > TMVA::DNN::GaussComplement = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return 1.0 - exp (-std::pow(value*s,2.0)); })

Definition at line 13 of file NeuralNet.cxx.

◆ InvGauss

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvGauss = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return -2.0 * value * s*s * (*Gauss.get ()) (value); })

Definition at line 14 of file NeuralNet.cxx.

◆ InvGaussComplement

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvGaussComplement = std::make_shared<std::function<double(double)>> ([](double value){ const double s = 6.0; return +2.0 * value * s*s * (*GaussComplement.get ()) (value); })

Definition at line 15 of file NeuralNet.cxx.

◆ InvLinear

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvLinear = std::make_shared<std::function<double(double)>> ([](double ){ return 1.0; })

Definition at line 16 of file NeuralNet.cxx.

◆ InvReLU

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.0; return value > margin ? 1.0 : 0; })

Definition at line 17 of file NeuralNet.cxx.

◆ InvSigmoid

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvSigmoid = std::make_shared<std::function<double(double)>> ([](double value){ double s = (*Sigmoid.get ()) (value); return s*(1.0-s); })

Definition at line 18 of file NeuralNet.cxx.

◆ InvSoftPlus

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvSoftPlus = std::make_shared<std::function<double(double)>> ([](double value){ return 1.0 / (1.0 + std::exp (-value)); })

Definition at line 19 of file NeuralNet.cxx.

◆ InvSoftSign

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvSoftSign = std::make_shared<std::function<double(double)>> ([](double value){ return std::pow ((1.0 - fabs (value)),2.0); })

Definition at line 20 of file NeuralNet.cxx.

◆ InvSymmReLU

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvSymmReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.3; return value > margin ? 1.0 : value < -margin ? 1.0 : 0; })

Definition at line 21 of file NeuralNet.cxx.

◆ InvTanh

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvTanh = std::make_shared<std::function<double(double)>> ([](double value){ return 1.0 - std::pow (value, 2.0); })

Definition at line 22 of file NeuralNet.cxx.

◆ InvTanhShift

std::shared_ptr< std::function< double(double)> > TMVA::DNN::InvTanhShift = std::make_shared<std::function<double(double)>> ([](double value){ return 0.3 + (1.0 - std::pow (value, 2.0)); })

Definition at line 23 of file NeuralNet.cxx.

◆ Linear

std::shared_ptr< std::function< double(double)> > TMVA::DNN::Linear = std::make_shared<std::function<double(double)>> ([](double value){ return value; })

Definition at line 24 of file NeuralNet.cxx.

◆ ReLU

std::shared_ptr< std::function< double(double)> > TMVA::DNN::ReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.0; return value > margin ? value-margin : 0; })

Definition at line 25 of file NeuralNet.cxx.

◆ Sigmoid

std::shared_ptr< std::function< double(double)> > TMVA::DNN::Sigmoid = std::make_shared<std::function<double(double)>> ([](double value){ value = std::max (-100.0, std::min (100.0,value)); return 1.0/(1.0 + std::exp (-value)); })

Definition at line 26 of file NeuralNet.cxx.

◆ SoftPlus

std::shared_ptr< std::function< double(double)> > TMVA::DNN::SoftPlus = std::make_shared<std::function<double(double)>> ([](double value){ return std::log (1.0+ std::exp (value)); })

Definition at line 27 of file NeuralNet.cxx.

◆ SoftSign

std::shared_ptr< std::function< double(double)> > TMVA::DNN::SoftSign = std::make_shared<std::function<double(double)>> ([](double value){ return value / (1.0 + fabs (value)); })

Definition at line 32 of file NeuralNet.cxx.

◆ SymmReLU

std::shared_ptr< std::function< double(double)> > TMVA::DNN::SymmReLU = std::make_shared<std::function<double(double)>> ([](double value){ const double margin = 0.3; return value > margin ? value-margin : value < -margin ? value+margin : 0; })

Definition at line 30 of file NeuralNet.cxx.

◆ Tanh

std::shared_ptr< std::function< double(double)> > TMVA::DNN::Tanh = std::make_shared<std::function<double(double)>> ([](double value){ return tanh (value); })

Definition at line 29 of file NeuralNet.cxx.

◆ TanhShift

std::shared_ptr< std::function< double(double)> > TMVA::DNN::TanhShift = std::make_shared<std::function<double(double)>> ([](double value){ return tanh (value-0.3); })

Definition at line 31 of file NeuralNet.cxx.

◆ ZeroFnc

std::shared_ptr< std::function< double(double)> > TMVA::DNN::ZeroFnc = std::make_shared<std::function<double(double)>> ([](double ){ return 0; })

Definition at line 28 of file NeuralNet.cxx.

Namespaces

Classes

Typedefs

Enumerations

Functions

Variables

Typedef Documentation

◆ DropContainer

◆ IndexIterator_t

◆ MatrixInput_t

◆ MemoryLayout

◆ pass_through_type

◆ TensorInput

◆ TMVAInput_t

Enumeration Type Documentation

◆ EActivationFunction

◆ EInitialization

◆ ELossFunction

◆ EnumFunction

◆ EnumRegularization

◆ EOptimizer

◆ EOutputFunction

◆ ERegularization

◆ MinimizerType

◆ ModeErrorFunction

◆ ModeOutput

◆ ModeOutputValues

◆ WeightInitializationStrategy

Function Documentation

◆ addRegularizationGradients()

◆ applyFunctions() [1/4]

◆ applyFunctions() [2/4]

◆ applyFunctions() [3/4]

◆ applyFunctions() [4/4]

◆ applyWeights() [1/2]

◆ applyWeights() [2/2]

◆ applyWeightsBackwards() [1/2]

◆ applyWeightsBackwards() [2/2]

◆ backward()

◆ computeRegularization()

◆ computeRegularization< EnumRegularization::L1 >()

◆ computeRegularization< EnumRegularization::L2 >()

◆ crossEntropy()

◆ cudaError()

◆ CurandInitializationKernel()

◆ debugTensor()

◆ evaluate() [1/3]

◆ evaluate() [2/3]

◆ evaluate() [3/3]

◆ evaluateDerivative()

◆ evaluateDerivativeMatrix()

◆ evaluateGradients()

◆ evaluateMatrix()

◆ forward()

◆ gaussDouble()

◆ initialize()

◆ isFlagSet()

◆ operator&()

◆ operator&=()

◆ operator|()

◆ operator|=()

◆ randomInt()

◆ regularization()

◆ softMaxCrossEntropy()

◆ studenttDouble()

◆ sumOfSquares() [1/2]

◆ sumOfSquares() [2/2]

◆ uniformDouble() [1/2]

◆ uniformDouble() [2/2]

◆ uniformFromTo()

◆ update() [1/3]

◆ update() [2/3]

◆ update() [3/3]

◆ weightDecay()

Variable Documentation

◆ Gauss

◆ GaussComplement

◆ InvGauss

◆ InvGaussComplement

◆ InvLinear