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

Soft Shrink operator is defined as,

\begin{eqnarray*} f(x) &=& \begin{cases} x - \lambda & : x > \lambda \\ x + \lambda & : x < -\lambda \\ 0 & : otherwise. \\ \end{cases} \\ f'(x) &=& \begin{cases} 1 & : x > \lambda \\ 1 & : x < -\lambda \\ 0 & : otherwise. \end{cases} \end{eqnarray*}

. More...

#include <softshrink.hpp>

Public Member Functions
	SoftShrink (const double lambda=0.5)
	Create Soft Shrink object using specified hyperparameter lambda. More...
template<typename InputType , typename OutputType >
void	Forward (const InputType &input, OutputType &output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...
template<typename DataType >
void	Backward (const DataType &input, DataType &gy, DataType &g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...
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.
double const &	Lambda () const
	Get the hyperparameter lambda.
double &	Lambda ()
	Modify the hyperparameter lambda.
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::SoftShrink< InputDataType, OutputDataType >

Soft Shrink operator is defined as,

\begin{eqnarray*} f(x) &=& \begin{cases} x - \lambda & : x > \lambda \\ x + \lambda & : x < -\lambda \\ 0 & : otherwise. \\ \end{cases} \\ f'(x) &=& \begin{cases} 1 & : x > \lambda \\ 1 & : x < -\lambda \\ 0 & : otherwise. \end{cases} \end{eqnarray*}

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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

SoftShrink()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::SoftShrink< InputDataType, OutputDataType >::SoftShrink

(

const double

lambda = 0.5

)

Create Soft Shrink object using specified hyperparameter lambda.

Parameters

lambda

The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. It is proportional to the noise level entered by the user. And it is calculated by multiplying the noise level sigma of the input(noisy image) and a coefficient 'a' which is one of the training parameters. Default value of lambda is 0.5.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename DataType >

void mlpack::ann::SoftShrink< InputDataType, OutputDataType >::Backward	(	const DataType &	input,
		DataType &	gy,
		DataType &	g
	)

Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f.

Using the results from the feed forward pass.

Parameters

input	The propagated input activation f(x).
gy	The backpropagated error.
g	The calculated gradient

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename InputType , typename OutputType >

void mlpack::ann::SoftShrink< InputDataType, OutputDataType >::Forward	(	const InputType &	input,
		OutputType &	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 Soft Shrink 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/softshrink.hpp
• src/mlpack/methods/ann/layer/softshrink_impl.hpp