template<typename AData, typename AReal>
class TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >

Definition at line 47 of file TensorDataLoader.h.

Public Member Functions
	TTensorDataLoader (const AData &data, size_t nSamples, size_t batchDepth, size_t batchHeight, size_t batchWidth, size_t nOutputFeatures, std::vector< size_t > inputShape, size_t nStreams=1)
	Constructor. More...

	TTensorDataLoader (const TTensorDataLoader &)=default

	TTensorDataLoader (TTensorDataLoader &&)=default

BatchIterator_t	begin ()

void	CopyTensorInput (std::vector< TMatrixT< AReal > > &tensor, IndexIterator_t sampleIterator)
	Copy input tensor into the given host buffer. More...

void	CopyTensorOutput (TMatrixT< AReal > &matrix, IndexIterator_t sampleIterator)
	Copy output matrix into the given host buffer. More...

void	CopyTensorWeights (TMatrixT< AReal > &matrix, IndexIterator_t sampleIterator)
	Copy weight matrix into the given host buffer. More...

BatchIterator_t	end ()

TTensorBatch< TReference< AReal > >	GetTensorBatch ()
	Return the next batch from the training set. More...

TTensorDataLoader &	operator= (const TTensorDataLoader &)=default

TTensorDataLoader &	operator= (TTensorDataLoader &&)=default

template<typename RNG >
void	Shuffle (RNG &rng)
	Shuffle the order of the samples in the batch. More...

Private Types
using	BatchIterator_t = TTensorBatchIterator< AData, TReference< AReal > >

Private Attributes
size_t	fBatchDepth
	The number of matrices in the tensor. More...

size_t	fBatchHeight
	The number od rows in each matrix. More...

size_t	fBatchIndex
	The index of the batch when there are multiple batches in parallel. More...

size_t	fBatchWidth
	The number of columns in each matrix. More...

const AData &	fData
	The data that should be loaded in the batches. More...

std::vector< size_t >	fInputShape
	Defines the batch depth, no. of channels and spatial dimensions of an input tensor. More...

size_t	fNOutputFeatures
	The number of outputs from the classifier/regressor. More...

size_t	fNSamples
	The total number of samples in the dataset. More...

std::vector< size_t >	fSampleIndices
	Ordering of the samples in the epoch. More...

std::vector< TMatrixT< AReal > >	inputTensor
	The 3D tensor used to keep the input data. More...

TMatrixT< AReal >	outputMatrix
	The matrix used to keep the output. More...

TMatrixT< AReal >	weightMatrix
	The matrix used to keep the batch weights. More...

#include <TMVA/DNN/Architectures/Reference/TensorDataLoader.h>

Member Typedef Documentation

◆ BatchIterator_t

template<typename AData , typename AReal >

using TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::BatchIterator_t = TTensorBatchIterator<AData, TReference<AReal> >

private

Definition at line 49 of file TensorDataLoader.h.

Constructor & Destructor Documentation

◆ TTensorDataLoader() [1/3]

template<typename AData , typename AReal >

TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::TTensorDataLoader	(	const AData &	data,
		size_t	nSamples,
		size_t	batchDepth,
		size_t	batchHeight,
		size_t	batchWidth,
		size_t	nOutputFeatures,
		std::vector< size_t >	inputShape,
		size_t	nStreams = `1`
	)

Constructor.

Definition at line 109 of file TensorDataLoader.h.

◆ TTensorDataLoader() [2/3]

template<typename AData , typename AReal >

TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::TTensorDataLoader ( const TTensorDataLoader< AData, TReference< AReal > > & )

default

◆ TTensorDataLoader() [3/3]

template<typename AData , typename AReal >

TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::TTensorDataLoader ( TTensorDataLoader< AData, TReference< AReal > > && )

default

Member Function Documentation

◆ begin()

template<typename AData , typename AReal >

BatchIterator_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::begin ( )

inline

Definition at line 90 of file TensorDataLoader.h.

◆ CopyTensorInput()

template<typename AData , typename AReal >

void TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::CopyTensorInput	(	std::vector< TMatrixT< AReal > > &	tensor,
		IndexIterator_t	sampleIterator
	)

Copy input tensor into the given host buffer.

Function to be specialized by the architecture-specific backend.

◆ CopyTensorOutput()

template<typename AData , typename AReal >

void TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::CopyTensorOutput	(	TMatrixT< AReal > &	matrix,
		IndexIterator_t	sampleIterator
	)

Copy output matrix into the given host buffer.

Function to be specialized by the architecture-spcific backend.

◆ CopyTensorWeights()

template<typename AData , typename AReal >

void TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::CopyTensorWeights	(	TMatrixT< AReal > &	matrix,
		IndexIterator_t	sampleIterator
	)

Copy weight matrix into the given host buffer.

Function to be specialized by the architecture-spcific backend.

◆ end()

template<typename AData , typename AReal >

BatchIterator_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::end ( )

inline

Definition at line 91 of file TensorDataLoader.h.

◆ GetTensorBatch()

template<typename AData , typename AReal >

auto TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::GetTensorBatch

Return the next batch from the training set.

The TTensorDataLoader object keeps an internal counter that cycles over the batches in the training set.

Definition at line 136 of file TensorDataLoader.h.

◆ operator=() [1/2]

template<typename AData , typename AReal >

TTensorDataLoader & TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::operator= ( const TTensorDataLoader< AData, TReference< AReal > > & )

default

◆ operator=() [2/2]

template<typename AData , typename AReal >

TTensorDataLoader & TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::operator= ( TTensorDataLoader< AData, TReference< AReal > > && )

default

◆ Shuffle()

template<typename AData , typename AReal >

template<typename RNG >

void TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::Shuffle ( RNG & rng )

Shuffle the order of the samples in the batch.

The shuffling is indirect, i.e. only the indices are shuffled. No input data is moved by this routine.

Definition at line 130 of file TensorDataLoader.h.

Member Data Documentation

◆ fBatchDepth

template<typename AData , typename AReal >

size_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fBatchDepth

private

The number of matrices in the tensor.

Definition at line 55 of file TensorDataLoader.h.

◆ fBatchHeight

template<typename AData , typename AReal >

size_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fBatchHeight

private

The number od rows in each matrix.

Definition at line 56 of file TensorDataLoader.h.

◆ fBatchIndex

template<typename AData , typename AReal >

size_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fBatchIndex

private

The index of the batch when there are multiple batches in parallel.

Definition at line 59 of file TensorDataLoader.h.

◆ fBatchWidth

template<typename AData , typename AReal >

size_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fBatchWidth

private

The number of columns in each matrix.

Definition at line 57 of file TensorDataLoader.h.

◆ fData

template<typename AData , typename AReal >

const AData& TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fData

private

The data that should be loaded in the batches.

Definition at line 51 of file TensorDataLoader.h.

◆ fInputShape

template<typename AData , typename AReal >

std::vector<size_t> TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fInputShape

private

Defines the batch depth, no. of channels and spatial dimensions of an input tensor.

Definition at line 61 of file TensorDataLoader.h.

◆ fNOutputFeatures

template<typename AData , typename AReal >

size_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fNOutputFeatures

private

The number of outputs from the classifier/regressor.

Definition at line 58 of file TensorDataLoader.h.

◆ fNSamples

template<typename AData , typename AReal >

size_t TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fNSamples

private

The total number of samples in the dataset.

Definition at line 53 of file TensorDataLoader.h.

◆ fSampleIndices

template<typename AData , typename AReal >

std::vector<size_t> TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::fSampleIndices

private

Ordering of the samples in the epoch.

Definition at line 67 of file TensorDataLoader.h.

◆ inputTensor

template<typename AData , typename AReal >

std::vector<TMatrixT<AReal> > TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::inputTensor

private

The 3D tensor used to keep the input data.

Definition at line 63 of file TensorDataLoader.h.

◆ outputMatrix

template<typename AData , typename AReal >

TMatrixT<AReal> TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::outputMatrix

private

The matrix used to keep the output.

Definition at line 64 of file TensorDataLoader.h.

◆ weightMatrix

template<typename AData , typename AReal >

TMatrixT<AReal> TMVA::DNN::TTensorDataLoader< AData, TReference< AReal > >::weightMatrix

private

The matrix used to keep the batch weights.

Definition at line 65 of file TensorDataLoader.h.

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

tmva/tmva/inc/TMVA/DNN/Architectures/Reference/TensorDataLoader.h

Public Member Functions

Private Types

Private Attributes

Member Typedef Documentation

◆ BatchIterator_t

Constructor & Destructor Documentation

◆ TTensorDataLoader() [1/3]

◆ TTensorDataLoader() [2/3]

◆ TTensorDataLoader() [3/3]

Member Function Documentation

◆ begin()

◆ CopyTensorInput()

◆ CopyTensorOutput()

◆ CopyTensorWeights()

◆ end()

◆ GetTensorBatch()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ Shuffle()

Member Data Documentation

◆ fBatchDepth

◆ fBatchHeight

◆ fBatchIndex

◆ fBatchWidth

◆ fData

◆ fInputShape

◆ fNOutputFeatures

◆ fNSamples

◆ fSampleIndices

◆ inputTensor

◆ outputMatrix

◆ weightMatrix