mlpack::ann::LinearNoBias< InputDataType, OutputDataType, RegularizerType > Class Template Reference

Implementation of the LinearNoBias class. More...

#include <linear_no_bias.hpp>

Public Member Functions
	LinearNoBias ()
	Create the LinearNoBias object.
	LinearNoBias (const size_t inSize, const size_t outSize, RegularizerType regularizer=RegularizerType())
	Create the LinearNoBias object using the specified number of units. More...
void	Reset ()
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	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...
template<typename eT >
void	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &error, arma::Mat< eT > &gradient)
OutputDataType const &	Parameters () const
	Get the parameters.
OutputDataType &	Parameters ()
	Modify the parameters.
InputDataType const &	InputParameter () const
	Get the input parameter.
InputDataType &	InputParameter ()
	Modify the input parameter.
OutputDataType const &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
size_t	InputSize () const
	Get the input size.
size_t	OutputSize () const
	Get the output size.
OutputDataType const &	Gradient () const
	Get the gradient.
OutputDataType &	Gradient ()
	Modify the gradient.
size_t	WeightSize () const
	Get the size of the weights.
size_t	InputShape () const
	Get the shape of the input.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

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).

Constructor & Destructor Documentation

LinearNoBias()

template<typename InputDataType , typename OutputDataType , typename RegularizerType >

mlpack::ann::LinearNoBias< InputDataType, OutputDataType, RegularizerType >::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()

template<typename InputDataType , typename OutputDataType , typename RegularizerType >

template<typename eT >

void mlpack::ann::LinearNoBias< InputDataType, OutputDataType, RegularizerType >::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.

Forward()

template<typename InputDataType , typename OutputDataType , typename RegularizerType >

template<typename eT >

void mlpack::ann::LinearNoBias< InputDataType, OutputDataType, RegularizerType >::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.

---

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

• src/mlpack/methods/ann/layer/layer_types.hpp
• src/mlpack/methods/ann/layer/linear_no_bias.hpp
• src/mlpack/methods/ann/layer/linear_no_bias_impl.hpp