mlpack::ann::NoisyLinear< InputDataType, OutputDataType > Class Template Reference

Implementation of the NoisyLinear layer class. More...

#include <noisylinear.hpp>

Public Member Functions
	NoisyLinear ()
	Create the NoisyLinear object.
	NoisyLinear (const size_t inSize, const size_t outSize)
	Create the NoisyLinear layer object using the specified number of units. More...
	NoisyLinear (const NoisyLinear &)
	Copy constructor.
	NoisyLinear (NoisyLinear &&)
	Move constructor.
NoisyLinear &	operator= (const NoisyLinear &layer)
	Operator= copy constructor.
NoisyLinear &	operator= (NoisyLinear &&layer)
	Operator= move constructor.
void	Reset ()
void	ResetNoise ()
void	ResetParameters ()
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	InputShape () const
	Get the shape of the input.
arma::mat &	Bias ()
	Modify the bias weights of the layer.
size_t	WeightSize () const
	Get size of weights.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::NoisyLinear< InputDataType, OutputDataType >

Implementation of the NoisyLinear layer class.

It represents a single layer of a neural network, with parametric noise added to its weights.

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

NoisyLinear()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::NoisyLinear< InputDataType, OutputDataType >::NoisyLinear	(	const size_t	inSize,
		const size_t	outSize
	)

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

Parameters

inSize	The number of input units.
outSize	The number of output units.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::NoisyLinear< InputDataType, OutputDataType >::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 >

template<typename eT >

void mlpack::ann::NoisyLinear< InputDataType, OutputDataType >::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.

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

• src/mlpack/methods/ann/layer/layer_types.hpp
• src/mlpack/methods/ann/layer/noisylinear.hpp
• src/mlpack/methods/ann/layer/noisylinear_impl.hpp