Implementation of the Linear layer class. More...

Public Member Functions
	Linear ()
	Create the Linear object. More...

	Linear (const size_t inSize, const size_t outSize, RegularizerType regularizer=RegularizerType())
	Create the Linear layer object using the specified number of units. More...

	Linear (const Linear &layer)
	Copy constructor. More...

	Linear (Linear &&)
	Move constructor. More...

template < typename eT >
void	Backward (const arma::Mat< eT > &, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f. More...

OutputDataType const &	Bias () const
	Get the bias of the layer. More...

OutputDataType &	Bias ()
	Modify the bias weights of the layer. More...

OutputDataType const &	Delta () const
	Get the delta. More...

OutputDataType &	Delta ()
	Modify the delta. More...

template < typename eT >
void	Forward (const arma::Mat< eT > &input, arma::Mat< eT > &output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

template < typename eT >
void	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &error, arma::Mat< eT > &gradient)

OutputDataType const &	Gradient () const
	Get the gradient. More...

OutputDataType &	Gradient ()
	Modify the gradient. More...

InputDataType const &	InputParameter () const
	Get the input parameter. More...

InputDataType &	InputParameter ()
	Modify the input parameter. More...

size_t	InputShape () const
	Get the shape of the input. More...

size_t	InputSize () const
	Get the input size. More...

Linear &	operator= (const Linear &layer)
	Copy assignment operator. More...

Linear &	operator= (Linear &&layer)
	Move assignment operator. More...

OutputDataType const &	OutputParameter () const
	Get the output parameter. More...

OutputDataType &	OutputParameter ()
	Modify the output parameter. More...

size_t	OutputSize () const
	Get the output size. More...

OutputDataType const &	Parameters () const
	Get the parameters. More...

OutputDataType &	Parameters ()
	Modify the parameters. More...

void	Reset ()

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

OutputDataType const &	Weight () const
	Get the weight of the layer. More...

OutputDataType &	Weight ()
	Modify the weight of the layer. More...

size_t	WeightSize () const
	Get the size of the weights. More...

Detailed Description

template
<
typename
InputDataType
=
arma::mat
,
typename
OutputDataType
=
arma::mat
,
typename
RegularizerType
=
NoRegularizer
>

class mlpack::ann::Linear< InputDataType, OutputDataType, RegularizerType >

Implementation of the Linear layer class.

The Linear class represents a single layer of a neural network.

Template Parameters

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

Definition at line 95 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ Linear() [1/4]

Linear ( )

Create the Linear object.

◆ Linear() [2/4]

Linear	(	const size_t	inSize,
		const size_t	outSize,
		RegularizerType	regularizer = `RegularizerType()`
	)

Create the Linear layer object using the specified number of units.

Parameters

inSize	The number of input units.
outSize	The number of output units.
regularizer	The regularizer to use, optional.

◆ Linear() [3/4]

Linear ( const Linear< InputDataType, OutputDataType, RegularizerType > & layer )

Copy constructor.

◆ Linear() [4/4]

Linear ( Linear< InputDataType, OutputDataType, RegularizerType > && )

Move constructor.

Member Function Documentation

◆ Backward()

void Backward	(	const arma::Mat< eT > &	,
		const arma::Mat< eT > &	gy,
		arma::Mat< eT > &	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.

◆ Bias() [1/2]

OutputDataType const& Bias ( ) const

inline

Get the bias of the layer.

Definition at line 145 of file linear.hpp.

◆ Bias() [2/2]

OutputDataType& Bias ( )

inline

Modify the bias weights of the layer.

Definition at line 147 of file linear.hpp.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 124 of file linear.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 126 of file linear.hpp.

◆ Forward()

void Forward	(	const arma::Mat< eT > &	input,
		arma::Mat< eT > &	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.

◆ Gradient() [1/3]

void Gradient	(	const arma::Mat< eT > &	input,
		const arma::Mat< eT > &	error,
		arma::Mat< eT > &	gradient
	)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 135 of file linear.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 137 of file linear.hpp.

◆ InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 114 of file linear.hpp.

◆ InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 116 of file linear.hpp.

◆ InputShape()

size_t InputShape ( ) const

inline

Get the shape of the input.

Definition at line 156 of file linear.hpp.

References Linear< InputDataType, OutputDataType, RegularizerType >::serialize().

◆ InputSize()

size_t InputSize ( ) const

inline

Get the input size.

Definition at line 129 of file linear.hpp.

◆ operator=() [1/2]

Linear& operator= ( const Linear< InputDataType, OutputDataType, RegularizerType > & layer )

Copy assignment operator.

◆ operator=() [2/2]

Linear& operator= ( Linear< InputDataType, OutputDataType, RegularizerType > && layer )

Move assignment operator.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 119 of file linear.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 121 of file linear.hpp.

◆ OutputSize()

size_t OutputSize ( ) const

inline

Get the output size.

Definition at line 132 of file linear.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 109 of file linear.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 111 of file linear.hpp.

◆ Reset()

void Reset ( )

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by Linear< InputDataType, OutputDataType, RegularizerType >::InputShape().

◆ Weight() [1/2]

OutputDataType const& Weight ( ) const

inline

Get the weight of the layer.

Definition at line 140 of file linear.hpp.

◆ Weight() [2/2]

OutputDataType& Weight ( )

inline

Modify the weight of the layer.

Definition at line 142 of file linear.hpp.

◆ WeightSize()

size_t WeightSize ( ) const

inline

Get the size of the weights.

Definition at line 150 of file linear.hpp.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/layer_types.hpp
/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/linear.hpp

Public Member Functions

Detailed Description

template<typenameInputDataType=arma::mat,typenameOutputDataType=arma::mat,typenameRegularizerType=NoRegularizer> class mlpack::ann::Linear< InputDataType, OutputDataType, RegularizerType >

Constructor & Destructor Documentation

◆ Linear() [1/4]

◆ Linear() [2/4]

◆ Linear() [3/4]

◆ Linear() [4/4]

Member Function Documentation

◆ Backward()

◆ Bias() [1/2]

◆ Bias() [2/2]

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Forward()

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InputParameter() [1/2]

◆ InputParameter() [2/2]

◆ InputShape()

◆ InputSize()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ OutputSize()

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Reset()

◆ serialize()

◆ Weight() [1/2]

◆ Weight() [2/2]

◆ WeightSize()

template
<
typename
InputDataType
=
arma::mat
,
typename
OutputDataType
=
arma::mat
,
typename
RegularizerType
=
NoRegularizer
>

class mlpack::ann::Linear< InputDataType, OutputDataType, RegularizerType >