LayerData holds the data of one layer.

LayerData holds the data of one layer, but not its layout

Definition at line 436 of file NeuralNet.h.

Public Types
typedef DropContainer::const_iterator	const_dropout_iterator

typedef function_container_type::const_iterator	const_function_iterator_type

typedef container_type::const_iterator	const_iterator_type

typedef std::vector< double >	container_type

typedef std::vector< std::function< double(double)> >	function_container_type

typedef function_container_type::iterator	function_iterator_type

typedef container_type::iterator	iterator_type

Public Member Functions
	LayerData (const LayerData &other)
	copy c'tor of LayerData

	LayerData (const_iterator_type itInputBegin, const_iterator_type itInputEnd, ModeOutputValues eModeOutput=ModeOutputValues::DIRECT)
	c'tor of LayerData

	LayerData (LayerData &&other)
	move c'tor of LayerData

	LayerData (size_t inputSize)
	c'tor of LayerData

	LayerData (size_t size, const_iterator_type itWeightBegin, iterator_type itGradientBegin, std::shared_ptr< std::function< double(double)> > activationFunction, std::shared_ptr< std::function< double(double)> > inverseActivationFunction, ModeOutputValues eModeOutput=ModeOutputValues::DIRECT)
	c'tor of LayerData

	LayerData (size_t size, const_iterator_type itWeightBegin, std::shared_ptr< std::function< double(double)> > activationFunction, ModeOutputValues eModeOutput=ModeOutputValues::DIRECT)
	c'tor of LayerData

	~LayerData ()

std::shared_ptr< std::function< double(double)> >	activationFunction () const

void	clear ()
	clear the values and the deltas

void	clearDropOut ()
	clear the drop-out-data for this layer

iterator_type	deltasBegin ()
	returns iterator to the begin of the deltas (back-propagation)

const_iterator_type	deltasBegin () const
	returns const iterator to the begin of the deltas (back-propagation)

iterator_type	deltasEnd ()
	returns iterator to the end of the deltas (back-propagation)

const_iterator_type	deltasEnd () const
	returns const iterator to the end of the deltas (back-propagation)

const_dropout_iterator	dropOut () const
	return the begin of the drop-out information

iterator_type	gradientsBegin ()
	returns iterator to the begin of the gradients

const_iterator_type	gradientsBegin () const
	returns const iterator to the begin of the gradients

bool	hasDropOut () const
	has this layer drop-out turned on?

std::shared_ptr< std::function< double(double)> >	inverseActivationFunction () const

ModeOutputValues	outputMode () const
	returns the output mode

container_type	probabilities () const
	computes the probabilities from the current node values and returns them

template<typename Iterator >
void	setDropOut (Iterator itDrop)
	set the drop-out info for this layer

void	setInput (const_iterator_type itInputBegin, const_iterator_type itInputEnd)
	change the input iterators

size_t	size () const
	return the size of the layer

iterator_type	valueGradientsBegin ()
	returns iterator to the begin of the gradients of the node values

const_iterator_type	valueGradientsBegin () const
	returns const iterator to the begin of the gradients

iterator_type	valueGradientsEnd ()
	returns iterator to the end of the gradients of the node values

const_iterator_type	valueGradientsEnd () const
	returns const iterator to the end of the gradients

iterator_type	valuesBegin ()
	returns iterator to the begin of the (node) values

const_iterator_type	valuesBegin () const
	returns const iterator to the begin of the (node) values

iterator_type	valuesEnd ()
	returns iterator to the end of the (node) values

const_iterator_type	valuesEnd () const
	returns iterator to the end of the (node) values

const_iterator_type	weightsBegin () const
	returns const iterator to the begin of the weights for this layer

Private Member Functions
container_type	computeProbabilities () const
	compute the probabilities from the node values

Private Attributes
std::shared_ptr< std::function< double(double)> >	m_activationFunction
	activation function for this layer

std::vector< double >	m_deltas
	stores the deltas for the DNN training

ModeOutputValues	m_eModeOutput
	stores the output mode (DIRECT, SIGMOID, SOFTMAX)

bool	m_hasDropOut
	dropOut is turned on?

bool	m_hasGradients
	does this layer have gradients (only if in training mode)

bool	m_hasWeights
	does this layer have weights (it does not if it is the input layer)

std::shared_ptr< std::function< double(double)> >	m_inverseActivationFunction
	inverse activation function for this layer

bool	m_isInputLayer
	is this layer an input layer

const_iterator_type	m_itConstWeightBegin
	const iterator to the first weight of this layer in the weight vector

const_dropout_iterator	m_itDropOut
	iterator to a container indicating if the corresponding node is to be dropped

iterator_type	m_itGradientBegin
	iterator to the first gradient of this layer in the gradient vector

const_iterator_type	m_itInputBegin
	iterator to the first of the nodes in the input node vector

const_iterator_type	m_itInputEnd
	iterator to the end of the nodes in the input node vector

size_t	m_size

std::vector< double >	m_valueGradients
	stores the gradients of the values (nodes)

std::vector< double >	m_values
	stores the values of the nodes in this layer

#include <TMVA/NeuralNet.h>

Member Typedef Documentation

◆ const_dropout_iterator

typedef DropContainer::const_iterator TMVA::DNN::LayerData::const_dropout_iterator

Definition at line 448 of file NeuralNet.h.

◆ const_function_iterator_type

typedef function_container_type::const_iterator TMVA::DNN::LayerData::const_function_iterator_type

Definition at line 446 of file NeuralNet.h.

◆ const_iterator_type

typedef container_type::const_iterator TMVA::DNN::LayerData::const_iterator_type

Definition at line 442 of file NeuralNet.h.

◆ container_type

typedef std::vector<double> TMVA::DNN::LayerData::container_type

Definition at line 439 of file NeuralNet.h.

◆ function_container_type

typedef std::vector<std::function<double(double)> > TMVA::DNN::LayerData::function_container_type

Definition at line 444 of file NeuralNet.h.

◆ function_iterator_type

typedef function_container_type::iterator TMVA::DNN::LayerData::function_iterator_type

Definition at line 445 of file NeuralNet.h.

◆ iterator_type

typedef container_type::iterator TMVA::DNN::LayerData::iterator_type

Definition at line 441 of file NeuralNet.h.

Constructor & Destructor Documentation

◆ LayerData() [1/6]

TMVA::DNN::LayerData::LayerData	(	const_iterator_type	itInputBegin,
		const_iterator_type	itInputEnd,
		ModeOutputValues	eModeOutput = `ModeOutputValues::DIRECT`
	)

c'tor of LayerData

C'tor of LayerData for the input layer

Parameters

itInputBegin	iterator to the begin of a vector which holds the values of the nodes of the neural net
itInputEnd	iterator to the end of a vector which holdsd the values of the nodes of the neural net
eModeOutput	indicates a potential tranformation of the output values before further computation DIRECT does not further transformation; SIGMOID applies a sigmoid transformation to each output value (to create a probability); SOFTMAX applies a softmax transformation to all output values (mutually exclusive probability)

Definition at line 81 of file NeuralNet.cxx.

◆ LayerData() [2/6]

TMVA::DNN::LayerData::LayerData ( size_t inputSize )

c'tor of LayerData

C'tor of LayerData for the input layer

Parameters

inputSize input size of this layer

Definition at line 68 of file NeuralNet.cxx.

◆ ~LayerData()

TMVA::DNN::LayerData::~LayerData ( )

inline

Definition at line 471 of file NeuralNet.h.

◆ LayerData() [3/6]

TMVA::DNN::LayerData::LayerData	(	size_t	size,
		const_iterator_type	itWeightBegin,
		iterator_type	itGradientBegin,
		std::shared_ptr< std::function< double(double)> >	activationFunction,
		std::shared_ptr< std::function< double(double)> >	inverseActivationFunction,
		ModeOutputValues	eModeOutput = `ModeOutputValues::DIRECT`
	)

c'tor of LayerData

C'tor of LayerData for all layers which are not the input layer; Used during the training of the DNN

Parameters

size	size of the layer
itWeightBegin	indicates the start of the weights for this layer on the weight vector
itGradientBegin	indicates the start of the gradients for this layer on the gradient vector
activationFunction	indicates activation functions for this layer
inverseActivationFunction	indicates the inverse activation functions for this layer
eModeOutput	indicates a potential tranformation of the output values before further computation DIRECT does not further transformation; SIGMOID applies a sigmoid transformation to each output value (to create a probability); SOFTMAX applies a softmax transformation to all output values (mutually exclusive probability)

Definition at line 97 of file NeuralNet.cxx.

◆ LayerData() [4/6]

TMVA::DNN::LayerData::LayerData	(	size_t	size,
		const_iterator_type	itWeightBegin,
		std::shared_ptr< std::function< double(double)> >	activationFunction,
		ModeOutputValues	eModeOutput = `ModeOutputValues::DIRECT`
	)

c'tor of LayerData

C'tor of LayerData for all layers which are not the input layer; Used during the application of the DNN

Parameters

size	size of the layer
itWeightBegin	indicates the start of the weights for this layer on the weight vector
activationFunction	indicates the activation function for this layer
eModeOutput	indicates a potential tranformation of the output values before further computation DIRECT does not further transformation; SIGMOID applies a sigmoid transformation to each output value (to create a probability); SOFTMAX applies a softmax transformation to all output values (mutually exclusive probability)

Definition at line 122 of file NeuralNet.cxx.

◆ LayerData() [5/6]

TMVA::DNN::LayerData::LayerData ( const LayerData & other )

inline

copy c'tor of LayerData

Definition at line 515 of file NeuralNet.h.

◆ LayerData() [6/6]

TMVA::DNN::LayerData::LayerData ( LayerData && other )

inline

move c'tor of LayerData

Definition at line 538 of file NeuralNet.h.

Member Function Documentation

◆ activationFunction()

std::shared_ptr< std::function< double(double)> > TMVA::DNN::LayerData::activationFunction ( ) const

inline

Definition at line 607 of file NeuralNet.h.

◆ clear()

void TMVA::DNN::LayerData::clear ( )

inline

clear the values and the deltas

Definition at line 576 of file NeuralNet.h.

◆ clearDropOut()

void TMVA::DNN::LayerData::clearDropOut ( )

inline

clear the drop-out-data for this layer

Definition at line 620 of file NeuralNet.h.

◆ computeProbabilities()

LayerData::container_type TMVA::DNN::LayerData::computeProbabilities ( ) const

private

compute the probabilities from the node values

Definition at line 140 of file NeuralNet.cxx.

◆ deltasBegin() [1/2]

iterator_type TMVA::DNN::LayerData::deltasBegin ( )

inline

returns iterator to the begin of the deltas (back-propagation)

Definition at line 591 of file NeuralNet.h.

◆ deltasBegin() [2/2]

const_iterator_type TMVA::DNN::LayerData::deltasBegin ( ) const

inline

returns const iterator to the begin of the deltas (back-propagation)

Definition at line 594 of file NeuralNet.h.

◆ deltasEnd() [1/2]

iterator_type TMVA::DNN::LayerData::deltasEnd ( )

inline

returns iterator to the end of the deltas (back-propagation)

Definition at line 592 of file NeuralNet.h.

◆ deltasEnd() [2/2]

const_iterator_type TMVA::DNN::LayerData::deltasEnd ( ) const

inline

returns const iterator to the end of the deltas (back-propagation)

Definition at line 595 of file NeuralNet.h.

◆ dropOut()

const_dropout_iterator TMVA::DNN::LayerData::dropOut ( ) const

inline

return the begin of the drop-out information

Definition at line 623 of file NeuralNet.h.

◆ gradientsBegin() [1/2]

iterator_type TMVA::DNN::LayerData::gradientsBegin ( )

inline

returns iterator to the begin of the gradients

Definition at line 603 of file NeuralNet.h.

◆ gradientsBegin() [2/2]

const_iterator_type TMVA::DNN::LayerData::gradientsBegin ( ) const

inline

returns const iterator to the begin of the gradients

Definition at line 604 of file NeuralNet.h.

◆ hasDropOut()

bool TMVA::DNN::LayerData::hasDropOut ( ) const

inline

has this layer drop-out turned on?

Definition at line 622 of file NeuralNet.h.

◆ inverseActivationFunction()

std::shared_ptr< std::function< double(double)> > TMVA::DNN::LayerData::inverseActivationFunction ( ) const

inline

Definition at line 608 of file NeuralNet.h.

◆ outputMode()

ModeOutputValues TMVA::DNN::LayerData::outputMode ( ) const

inline

returns the output mode

Definition at line 588 of file NeuralNet.h.

◆ probabilities()

container_type TMVA::DNN::LayerData::probabilities ( ) const

inline

computes the probabilities from the current node values and returns them

Definition at line 589 of file NeuralNet.h.

◆ setDropOut()

template<typename Iterator >

void TMVA::DNN::LayerData::setDropOut ( Iterator itDrop )

inline

set the drop-out info for this layer

Definition at line 614 of file NeuralNet.h.

◆ setInput()

void TMVA::DNN::LayerData::setInput	(	const_iterator_type	itInputBegin,
		const_iterator_type	itInputEnd
	)

inline

change the input iterators

Parameters

itInputBegin	indicates the start of the input node vector
itInputEnd	indicates the end of the input node vector

Definition at line 565 of file NeuralNet.h.

◆ size()

size_t TMVA::DNN::LayerData::size ( ) const

inline

return the size of the layer

Definition at line 625 of file NeuralNet.h.

◆ valueGradientsBegin() [1/2]

iterator_type TMVA::DNN::LayerData::valueGradientsBegin ( )

inline

returns iterator to the begin of the gradients of the node values

Definition at line 597 of file NeuralNet.h.

◆ valueGradientsBegin() [2/2]

const_iterator_type TMVA::DNN::LayerData::valueGradientsBegin ( ) const

inline

returns const iterator to the begin of the gradients

Definition at line 600 of file NeuralNet.h.

◆ valueGradientsEnd() [1/2]

iterator_type TMVA::DNN::LayerData::valueGradientsEnd ( )

inline

returns iterator to the end of the gradients of the node values

Definition at line 598 of file NeuralNet.h.

◆ valueGradientsEnd() [2/2]

const_iterator_type TMVA::DNN::LayerData::valueGradientsEnd ( ) const

inline

returns const iterator to the end of the gradients

Definition at line 601 of file NeuralNet.h.

◆ valuesBegin() [1/2]

iterator_type TMVA::DNN::LayerData::valuesBegin ( )

inline

returns iterator to the begin of the (node) values

Definition at line 585 of file NeuralNet.h.

◆ valuesBegin() [2/2]

const_iterator_type TMVA::DNN::LayerData::valuesBegin ( ) const

inline

returns const iterator to the begin of the (node) values

Definition at line 582 of file NeuralNet.h.

◆ valuesEnd() [1/2]

iterator_type TMVA::DNN::LayerData::valuesEnd ( )

inline

returns iterator to the end of the (node) values

Definition at line 586 of file NeuralNet.h.

◆ valuesEnd() [2/2]

const_iterator_type TMVA::DNN::LayerData::valuesEnd ( ) const

inline

returns iterator to the end of the (node) values

Definition at line 583 of file NeuralNet.h.

◆ weightsBegin()

const_iterator_type TMVA::DNN::LayerData::weightsBegin ( ) const

inline

returns const iterator to the begin of the weights for this layer

Definition at line 605 of file NeuralNet.h.

Member Data Documentation

◆ m_activationFunction

std::shared_ptr<std::function<double(double)> > TMVA::DNN::LayerData::m_activationFunction

private

activation function for this layer

Definition at line 651 of file NeuralNet.h.

◆ m_deltas

std::vector<double> TMVA::DNN::LayerData::m_deltas

private

stores the deltas for the DNN training

Definition at line 642 of file NeuralNet.h.

◆ m_eModeOutput

ModeOutputValues TMVA::DNN::LayerData::m_eModeOutput

private

stores the output mode (DIRECT, SIGMOID, SOFTMAX)

Definition at line 658 of file NeuralNet.h.

◆ m_hasDropOut

bool TMVA::DNN::LayerData::m_hasDropOut

private

dropOut is turned on?

Definition at line 646 of file NeuralNet.h.

◆ m_hasGradients

bool TMVA::DNN::LayerData::m_hasGradients

private

does this layer have gradients (only if in training mode)

Definition at line 656 of file NeuralNet.h.

◆ m_hasWeights

bool TMVA::DNN::LayerData::m_hasWeights

private

does this layer have weights (it does not if it is the input layer)

Definition at line 655 of file NeuralNet.h.

◆ m_inverseActivationFunction

std::shared_ptr<std::function<double(double)> > TMVA::DNN::LayerData::m_inverseActivationFunction

private

inverse activation function for this layer

Definition at line 652 of file NeuralNet.h.

◆ m_isInputLayer

bool TMVA::DNN::LayerData::m_isInputLayer

private

is this layer an input layer

Definition at line 654 of file NeuralNet.h.

◆ m_itConstWeightBegin

const_iterator_type TMVA::DNN::LayerData::m_itConstWeightBegin

private

const iterator to the first weight of this layer in the weight vector

Definition at line 648 of file NeuralNet.h.

◆ m_itDropOut

const_dropout_iterator TMVA::DNN::LayerData::m_itDropOut

private

iterator to a container indicating if the corresponding node is to be dropped

Definition at line 645 of file NeuralNet.h.

◆ m_itGradientBegin

iterator_type TMVA::DNN::LayerData::m_itGradientBegin

private

iterator to the first gradient of this layer in the gradient vector

Definition at line 649 of file NeuralNet.h.

◆ m_itInputBegin

const_iterator_type TMVA::DNN::LayerData::m_itInputBegin

private

iterator to the first of the nodes in the input node vector

Definition at line 639 of file NeuralNet.h.

◆ m_itInputEnd

const_iterator_type TMVA::DNN::LayerData::m_itInputEnd

private

iterator to the end of the nodes in the input node vector

Definition at line 640 of file NeuralNet.h.

◆ m_size

size_t TMVA::DNN::LayerData::m_size

private

Definition at line 637 of file NeuralNet.h.

◆ m_valueGradients

std::vector<double> TMVA::DNN::LayerData::m_valueGradients

private

stores the gradients of the values (nodes)

Definition at line 643 of file NeuralNet.h.

◆ m_values

std::vector<double> TMVA::DNN::LayerData::m_values

private

stores the values of the nodes in this layer

Definition at line 644 of file NeuralNet.h.

Libraries for TMVA::DNN::LayerData:

[legend]

The documentation for this class was generated from the following files:

tmva/tmva/inc/TMVA/NeuralNet.h
tmva/tmva/src/NeuralNet.cxx

Public Types

Public Member Functions

Private Member Functions

Private Attributes

Member Typedef Documentation

◆ const_dropout_iterator

◆ const_function_iterator_type

◆ const_iterator_type

◆ container_type

◆ function_container_type

◆ function_iterator_type

◆ iterator_type

Constructor & Destructor Documentation

◆ LayerData() [1/6]

◆ LayerData() [2/6]

◆ ~LayerData()

◆ LayerData() [3/6]

◆ LayerData() [4/6]

◆ LayerData() [5/6]

◆ LayerData() [6/6]

Member Function Documentation

◆ activationFunction()

◆ clear()

◆ clearDropOut()

◆ computeProbabilities()

◆ deltasBegin() [1/2]

◆ deltasBegin() [2/2]

◆ deltasEnd() [1/2]

◆ deltasEnd() [2/2]

◆ dropOut()

◆ gradientsBegin() [1/2]

◆ gradientsBegin() [2/2]

◆ hasDropOut()

◆ inverseActivationFunction()

◆ outputMode()

◆ probabilities()

◆ setDropOut()

◆ setInput()

◆ size()

◆ valueGradientsBegin() [1/2]

◆ valueGradientsBegin() [2/2]

◆ valueGradientsEnd() [1/2]

◆ valueGradientsEnd() [2/2]

◆ valuesBegin() [1/2]

◆ valuesBegin() [2/2]

◆ valuesEnd() [1/2]

◆ valuesEnd() [2/2]

◆ weightsBegin()

Member Data Documentation

◆ m_activationFunction

◆ m_deltas

◆ m_eModeOutput

◆ m_hasDropOut

◆ m_hasGradients

◆ m_hasWeights

◆ m_inverseActivationFunction

◆ m_isInputLayer

◆ m_itConstWeightBegin

◆ m_itDropOut

◆ m_itGradientBegin

◆ m_itInputBegin

◆ m_itInputEnd

◆ m_size

◆ m_valueGradients

◆ m_values