LinearNoBias< InputDataType, OutputDataType, RegularizerType > Class Template Reference

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 > &&, 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, 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	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 unsigned int)
	Serialize the layer. 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 78 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.

Member Function Documentation

Backward()

void Backward	(	const arma::Mat< eT > &&	,
		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 110 of file linear_no_bias.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 112 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,
		arma::Mat< eT > &&	error,
		arma::Mat< eT > &&	gradient
	)

Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 121 of file linear_no_bias.hpp.

Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 123 of file linear_no_bias.hpp.

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

InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 100 of file linear_no_bias.hpp.

InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 102 of file linear_no_bias.hpp.

InputSize()

size_t InputSize ( ) const

inline

Get the input size.

Definition at line 115 of file linear_no_bias.hpp.

OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 105 of file linear_no_bias.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 107 of file linear_no_bias.hpp.

OutputSize()

size_t OutputSize ( ) const

inline

Get the output size.

Definition at line 118 of file linear_no_bias.hpp.

Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 95 of file linear_no_bias.hpp.

Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 97 of file linear_no_bias.hpp.

Reset()

void Reset

(

)

serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

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

---

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

• /home/ryan/src/mlpack.org-go/_src/mlpack-git/src/mlpack/methods/ann/layer/layer_types.hpp
• /home/ryan/src/mlpack.org-go/_src/mlpack-git/src/mlpack/methods/ann/layer/linear_no_bias.hpp