Implementation of the LinearNoBias class. More...

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

	LinearNoBias (const size_t inSize, const size_t outSize, RegularizerType regularizer=RegularizerType())
	Create the LinearNoBias object using the specified number of units. 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 &	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...

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...

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::LinearNoBias< InputDataType, OutputDataType, RegularizerType >

Implementation of the LinearNoBias class.

The LinearNoBias 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 105 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ LinearNoBias() [1/2]

LinearNoBias ( )

Create the LinearNoBias object.

◆ LinearNoBias() [2/2]

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

Create the LinearNoBias 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.

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.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 111 of file linear_no_bias.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 113 of file linear_no_bias.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 122 of file linear_no_bias.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 124 of file linear_no_bias.hpp.

◆ InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 101 of file linear_no_bias.hpp.

◆ InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 103 of file linear_no_bias.hpp.

◆ InputShape()

size_t InputShape ( ) const

inline

Get the shape of the input.

Definition at line 133 of file linear_no_bias.hpp.

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

◆ InputSize()

size_t InputSize ( ) const

inline

Get the input size.

Definition at line 116 of file linear_no_bias.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 106 of file linear_no_bias.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 108 of file linear_no_bias.hpp.

◆ OutputSize()

size_t OutputSize ( ) const

inline

Get the output size.

Definition at line 119 of file linear_no_bias.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 96 of file linear_no_bias.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 98 of file linear_no_bias.hpp.

◆ Reset()

void Reset ( )

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

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

◆ WeightSize()

size_t WeightSize ( ) const

inline

Get the size of the weights.

Definition at line 127 of file linear_no_bias.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_no_bias.hpp

Public Member Functions

Detailed Description

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

Constructor & Destructor Documentation

◆ LinearNoBias() [1/2]

◆ LinearNoBias() [2/2]

Member Function Documentation

◆ Backward()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Forward()

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InputParameter() [1/2]

◆ InputParameter() [2/2]

◆ InputShape()

◆ InputSize()

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ OutputSize()

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Reset()

◆ serialize()

◆ WeightSize()

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

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