The select module selects the specified dimensions from a given input point. More...

Inheritance diagram for SelectType< InputType, OutputType >:

Public Member Functions
	SelectType (const size_t index=0, const size_t elements=0)
	Create the Select object. More...

void	Backward (const InputType &, const OutputType &gy, OutputType &g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f. More...

SelectType *	Clone () const
	Clone the SelectType object. This handles polymorphism correctly. More...

void	Forward (const InputType &input, OutputType &output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

const size_t &	Index () const
	Get the column index. More...

const size_t &	NumElements () const
	Get the number of elements selected. More...

const std::vector< size_t >	OutputDimensions () const

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

Public Member Functions inherited from Layer< InputType, OutputType >
	Layer ()
	Default constructor. More...

	Layer (const Layer &layer)
	Copy constructor. This is not responsible for copying weights! More...

	Layer (Layer &&layer)
	Move constructor. This is not responsible for moving weights! More...

virtual	~Layer ()
	Default deconstructor. More...

virtual void	Backward (const InputType &, const InputType &, InputType &)
	Performs a backpropagation step through the layer, with respect to the given input. More...

virtual void	ComputeOutputDimensions ()
	Compute the output dimensions. More...

virtual void	CustomInitialize (InputType &, const size_t)
	Override the weight matrix of the layer. More...

virtual void	Forward (const InputType &, InputType &)
	Takes an input object, and computes the corresponding output of the layer. More...

virtual void	Forward (const InputType &, const InputType &)
	Takes an input and output object, and computes the corresponding loss of the layer. More...

virtual void	Gradient (const InputType &, const InputType &, InputType &)
	Computing the gradient of the layer with respect to its own input. More...

const std::vector< size_t > &	InputDimensions () const
	Get the input dimensions. More...

std::vector< size_t > &	InputDimensions ()
	Modify the input dimensions. More...

virtual double	Loss ()
	Get the layer loss. More...

virtual Layer &	operator= (const Layer &layer)
	Copy assignment operator. This is not responsible for copying weights! More...

virtual Layer &	operator= (Layer &&layer)
	Move assignment operator. This is not responsible for moving weights! More...

const std::vector< size_t > &	OutputDimensions ()
	Get the output dimensions. More...

virtual size_t	OutputSize () final
	Get the number of elements in the output from this layer. More...

virtual const InputType &	Parameters () const
	Get the parameters. More...

virtual InputType &	Parameters ()
	Set the parameters. More...

void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

virtual void	SetWeights (typename InputType ::elem_type *)
	Reset the layer parameter. More...

virtual bool const &	Training () const
	Get whether the layer is currently in training mode. More...

virtual bool &	Training ()
	Modify whether the layer is currently in training mode. More...

virtual size_t	WeightSize () const
	Get the total number of trainable weights in the layer. More...

Additional Inherited Members
Protected Attributes inherited from Layer< InputType, OutputType >
std::vector< size_t >	inputDimensions
	Logical input dimensions of each point. More...

std::vector< size_t >	outputDimensions
	Logical output dimensions of each point. More...

bool	training
	If true, the layer is in training mode; otherwise, it is in testing mode. More...

bool	validOutputDimensions
	This is `true` if `ComputeOutputDimensions()` has been called, and `outputDimensions` can be considered to be up-to-date. More...

Detailed Description

template
<
typename
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

class mlpack::ann::SelectType< InputType, OutputType >

The select module selects the specified dimensions from a given input point.

Template Parameters

InputType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputType	Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Definition at line 34 of file select.hpp.

Constructor & Destructor Documentation

◆ SelectType()

SelectType	(	const size_t	index = `0`,
		const size_t	elements = `0`
	)

Create the Select object.

Parameters

index	The first dimension to extract from the input.
elements	The number of elements that should be used. If 0 is given, then all dimensions starting with index up to the number of dimensions are used.

Referenced by SelectType< InputType, OutputType >::Clone().

Member Function Documentation

◆ Backward()

void Backward	(	const InputType &	,
		const OutputType &	gy,
		OutputType &	g
	)

Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f.

Using the results from the feed forward pass.

Parameters

*	(input) The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

Referenced by SelectType< InputType, OutputType >::Clone().

◆ Clone()

SelectType* Clone ( ) const

inlinevirtual

Clone the SelectType object. This handles polymorphism correctly.

Implements Layer< InputType, OutputType >.

Definition at line 48 of file select.hpp.

References SelectType< InputType, OutputType >::Backward(), SelectType< InputType, OutputType >::Forward(), and SelectType< InputType, OutputType >::SelectType().

◆ Forward()

void Forward	(	const InputType &	input,
		OutputType &	output
	)

Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f.

Parameters

input	Input data used for evaluating the specified function.
output	Resulting output activation.

Referenced by SelectType< InputType, OutputType >::Clone().

◆ Index()

const size_t& Index ( ) const

inline

Get the column index.

Definition at line 73 of file select.hpp.

◆ NumElements()

const size_t& NumElements ( ) const

inline

Get the number of elements selected.

Definition at line 76 of file select.hpp.

References SelectType< InputType, OutputType >::serialize().

◆ OutputDimensions()

const std::vector<size_t> OutputDimensions ( ) const

inline

Definition at line 84 of file select.hpp.

References Layer< InputType, OutputType >::inputDimensions, and Layer< InputType, OutputType >::outputDimensions.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by SelectType< InputType, OutputType >::NumElements().

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

/home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/not_adapted/select.hpp

Public Member Functions

Additional Inherited Members

Detailed Description

template<typenameInputType=arma::mat,typenameOutputType=arma::mat> class mlpack::ann::SelectType< InputType, OutputType >

Constructor & Destructor Documentation

◆ SelectType()

Member Function Documentation

◆ Backward()

◆ Clone()

◆ Forward()

◆ Index()

◆ NumElements()

◆ OutputDimensions()

◆ serialize()

template
<
typename
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

class mlpack::ann::SelectType< InputType, OutputType >