Logo ROOT   6.19/01
Reference Guide
TMVA::DNN Namespace Reference

Namespaces

 Blas
 
 CNN
 
 RNN
 

Classes

class  Batch
 The Batch class encapsulates one mini-batch. More...
 
class  ClassificationSettings
 Settings for classificationused to distinguish between different function signatures. More...
 
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  TCuda
 The TCuda architecture class. More...
 
class  TCudaDeviceBuffer
 TCudaDeviceBuffer. More...
 
class  TCudaDeviceReference
 TCudaDeviceReference. More...
 
class  TCudaHostBuffer
 TCudaHostBuffer. More...
 
class  TCudaMatrix
 TCudaMatrix 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...
 
class  TDevice
 TDevice. More...
 
class  TDLGradientDescent
 
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 > >
 
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 > & >
 
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  EActivationFunction {
  EActivationFunction::kIdentity = 0, EActivationFunction::kRelu = 1, EActivationFunction::kSigmoid = 2, EActivationFunction::kTanh = 3,
  EActivationFunction::kSymmRelu = 4, EActivationFunction::kSoftSign = 5, EActivationFunction::kGauss = 6
}
 Enum that represents layer activation functions. More...
 
enum  EInitialization {
  EInitialization::kGauss = 'G', EInitialization::kUniform = 'U', EInitialization::kIdentity = 'I', EInitialization::kZero = 'Z',
  EInitialization::kGlorotNormal = 'X', EInitialization::kGlorotUniform = 'F'
}
 
enum  ELossFunction { ELossFunction::kCrossEntropy = 'C', ELossFunction::kMeanSquaredError = 'R', ELossFunction::kSoftmaxCrossEntropy = 'S' }
 Enum that represents objective functions for the net, i.e. More...
 
enum  EnumFunction {
  EnumFunction::ZERO = '0', EnumFunction::LINEAR = 'L', EnumFunction::TANH = 'T', EnumFunction::RELU = 'R',
  EnumFunction::SYMMRELU = 'r', EnumFunction::TANHSHIFT = 't', EnumFunction::SIGMOID = 's', EnumFunction::SOFTSIGN = 'S',
  EnumFunction::GAUSS = 'G', EnumFunction::GAUSSCOMPLEMENT = 'C'
}
 
enum  EnumRegularization { EnumRegularization::NONE, EnumRegularization::L1, EnumRegularization::L2, EnumRegularization::L1MAX }
 
enum  EOptimizer {
  EOptimizer::kSGD = 0, EOptimizer::kAdam = 1, EOptimizer::kAdagrad = 2, EOptimizer::kRMSProp = 3,
  EOptimizer::kAdadelta = 4
}
 Enum representing the optimizer used for training. More...
 
enum  EOutputFunction { EOutputFunction::kIdentity = 'I', EOutputFunction::kSigmoid = 'S', EOutputFunction::kSoftmax = 'M' }
 Enum that represents output functions. More...
 
enum  ERegularization { ERegularization::kNone = '0', ERegularization::kL1 = '1', ERegularization::kL2 = '2' }
 Enum representing the regularization type applied for a given layer. More...
 
enum  MinimizerType { fSteepest }
 < list all the minimizer types More...
 
enum  ModeErrorFunction { ModeErrorFunction::SUMOFSQUARES = 'S', ModeErrorFunction::CROSSENTROPY = 'C', ModeErrorFunction::CROSSENTROPY_MUTUALEXCLUSIVE = 'M' }
 error functions to be chosen from More...
 
enum  ModeOutput { ModeOutput::FETCH }
 
enum  ModeOutputValues : int { ModeOutputValues::DIRECT = 0x01, ModeOutputValues::SIGMOID = 0x02, ModeOutputValues::SOFTMAX = 0x04, ModeOutputValues::BATCHNORMALIZATION = 0x08 }
 
enum  WeightInitializationStrategy { WeightInitializationStrategy::XAVIER, WeightInitializationStrategy::TEST, WeightInitializationStrategy::LAYERSIZE, WeightInitializationStrategy::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. More...
 
template<typename ItValue , typename Fnc >
void applyFunctions (ItValue itValue, ItValue itValueEnd, Fnc fnc)
 apply the activation functions More...
 
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 More...
 
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<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" More...
 
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 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" More...
 
template<typename ItSource , typename ItWeight , typename ItPrev >
void applyWeightsBackwards (ItSource itCurrBegin, ItSource itCurrEnd, ItWeight itWeight, ItPrev itPrevBegin, ItPrev itPrevEnd)
 
template<typename LAYERDATA >
void backward (LAYERDATA &prevLayerData, LAYERDATA &currLayerData)
 backward application of the weights (back-propagation of the error) More...
 
template<EnumRegularization Regularization>
double computeRegularization (double weight, const double &factorWeightDecay)
 compute the regularization (L1, L2) More...
 
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 More...
 
void cudaError (cudaError_t code, const char *file, int line, bool abort=true)
 Function to check cuda return code. More...
 
template<typename Architecture >
auto debugTensor (const std::vector< typename Architecture::Matrix_t > &A, const std::string name="tensor") -> void
 
template<typename Architecture_t >
void 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 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 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 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 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 LAYERDATA >
void forward (const LAYERDATA &prevLayerData, LAYERDATA &currLayerData)
 apply the weights (and functions) in forward direction of the DNN More...
 
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. More...
 
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) More...
 
double studenttDouble (double distributionParameter)
 
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 More...
 
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 Container , typename T >
void uniformDouble (Container &container, T maxValue)
 
double uniformDouble (double minValue, double maxValue)
 
template<typename T >
uniformFromTo (T from, T to)
 
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 More...
 
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 More...
 
template<typename LAYERDATA >
void update (const LAYERDATA &prevLayerData, LAYERDATA &currLayerData, double factorWeightDecay, EnumRegularization regularization)
 update the node values More...
 
template<typename ItWeight >
double weightDecay (double error, ItWeight itWeight, ItWeight itWeightEnd, double factorWeightDecay, EnumRegularization eRegularization)
 compute the weight decay for regularization (L1 or L2) More...
 

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 * s*s * (*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 * s*s * (*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 220 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.

◆ pass_through_type

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

Definition at line 1301 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 42 of file TensorDataLoader.h.

◆ TMVAInput_t

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

Definition at line 40 of file DataLoader.h.

Enumeration Type Documentation

◆ EActivationFunction

enum TMVA::DNN::EActivationFunction
strong

Enum that represents layer activation functions.

Enumerator
kIdentity 
kRelu 
kSigmoid 
kTanh 
kSymmRelu 
kSoftSign 
kGauss 

Definition at line 31 of file Functions.h.

◆ EInitialization

enum TMVA::DNN::EInitialization
strong
Enumerator
kGauss 
kUniform 
kIdentity 
kZero 
kGlorotNormal 
kGlorotUniform 

Definition at line 70 of file Functions.h.

◆ ELossFunction

enum 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 54 of file Functions.h.

◆ EnumFunction

enum TMVA::DNN::EnumFunction
strong
Enumerator
ZERO 
LINEAR 
TANH 
RELU 
SYMMRELU 
TANHSHIFT 
SIGMOID 
SOFTSIGN 
GAUSS 
GAUSSCOMPLEMENT 

Definition at line 157 of file NeuralNet.h.

◆ EnumRegularization

enum TMVA::DNN::EnumRegularization
strong
Enumerator
NONE 
L1 
L2 
L1MAX 

Definition at line 173 of file NeuralNet.h.

◆ EOptimizer

enum TMVA::DNN::EOptimizer
strong

Enum representing the optimizer used for training.

Enumerator
kSGD 
kAdam 
kAdagrad 
kRMSProp 
kAdadelta 

Definition at line 80 of file Functions.h.

◆ EOutputFunction

enum TMVA::DNN::EOutputFunction
strong

Enum that represents output functions.

Enumerator
kIdentity 
kSigmoid 
kSoftmax 

Definition at line 43 of file Functions.h.

◆ ERegularization

enum TMVA::DNN::ERegularization
strong

Enum representing the regularization type applied for a given layer.

Enumerator
kNone 
kL1 
kL2 

Definition at line 62 of file Functions.h.

◆ MinimizerType

enum TMVA::DNN::MinimizerType

< list all the minimizer types

Enumerator
fSteepest 

SGD.

Definition at line 321 of file NeuralNet.h.

◆ ModeErrorFunction

enum TMVA::DNN::ModeErrorFunction
strong

error functions to be chosen from

Enumerator
SUMOFSQUARES 
CROSSENTROPY 
CROSSENTROPY_MUTUALEXCLUSIVE 

Definition at line 1045 of file NeuralNet.h.

◆ ModeOutput

enum TMVA::DNN::ModeOutput
strong
Enumerator
FETCH 

Definition at line 1036 of file NeuralNet.h.

◆ ModeOutputValues

enum TMVA::DNN::ModeOutputValues : int
strong
Enumerator
DIRECT 
SIGMOID 
SOFTMAX 
BATCHNORMALIZATION 

Definition at line 179 of file NeuralNet.h.

◆ WeightInitializationStrategy

enum TMVA::DNN::WeightInitializationStrategy
strong

weight initialization strategies to be chosen from

Enumerator
XAVIER 
TEST 
LAYERSIZE 
XAVIERUNIFORM 

Definition at line 1056 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 236 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<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.

◆ applyWeights() [2/2]

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

◆ applyWeightsBackwards() [1/2]

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.

◆ applyWeightsBackwards() [2/2]

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

◆ 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 188 of file CudaMatrix.h.

◆ debugTensor()

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

Definition at line 742 of file DeepNet.h.

◆ evaluate() [1/3]

template<typename Architecture_t >
void TMVA::DNN::evaluate ( typename Architecture_t::Matrix_t &  A,
EActivationFunction  f 
)
inline

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

Definition at line 96 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 matrix A.

Definition at line 153 of file Functions.h.

◆ evaluate() [3/3]

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 176 of file Functions.h.

◆ evaluateDerivative()

template<typename Architecture_t >
void TMVA::DNN::evaluateDerivative ( typename Architecture_t::Matrix_t &  B,
EActivationFunction  f,
const typename Architecture_t::Matrix_t &  A 
)
inline

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

Definition at line 122 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 193 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 260 of file Functions.h.

◆ isFlagSet()

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

Definition at line 213 of file NeuralNet.h.

◆ operator &()

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

Definition at line 200 of file NeuralNet.h.

◆ operator &=()

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

Definition at line 205 of file NeuralNet.h.

◆ operator|()

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

Definition at line 189 of file NeuralNet.h.

◆ operator|=()

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

Definition at line 194 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 216 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 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.

◆ sumOfSquares() [2/2]

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 
)

◆ uniformDouble() [1/2]

template<typename Container , typename T >
void TMVA::DNN::uniformDouble ( Container &  container,
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 ( from,
to 
)

Definition at line 34 of file NeuralNet.icc.

◆ update() [1/3]

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() [2/3]

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.

◆ update() [3/3]

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.

◆ 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.