Hard Shrink operator is defined as,. More...

Inheritance diagram for HardShrinkType< InputType, OutputType >:

Public Member Functions
	HardShrinkType (const double lambda=0.5)
	Create HardShrink object using specified hyperparameter lambda. More...

void	Backward (const InputType &input, const OutputType &gy, OutputType &g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...

HardShrinkType *	Clone () const
	Clone the HardShrinkType object. This handles polymorphism correctly. More...

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...

double const &	Lambda () const
	Get the hyperparameter lambda. More...

double &	Lambda ()
	Modify the hyperparameter lambda. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

Public Member Functions inherited from Layer< InputType, OutputType >
	Layer ()
	Default constructor. More...

	Layer (const Layer &layer)
	Copy constructor. This is not responsible for copying weights! More...

	Layer (Layer &&layer)
	Move constructor. This is not responsible for moving weights! More...

virtual	~Layer ()
	Default deconstructor. More...

virtual void	Backward (const InputType &, const InputType &, InputType &)
	Performs a backpropagation step through the layer, with respect to the given input. More...

virtual void	ComputeOutputDimensions ()
	Compute the output dimensions. More...

virtual void	CustomInitialize (InputType &, const size_t)
	Override the weight matrix of the layer. More...

virtual void	Forward (const InputType &, InputType &)
	Takes an input object, and computes the corresponding output of the layer. More...

virtual void	Forward (const InputType &, const InputType &)
	Takes an input and output object, and computes the corresponding loss of the layer. More...

virtual void	Gradient (const InputType &, const InputType &, InputType &)
	Computing the gradient of the layer with respect to its own input. More...

const std::vector< size_t > &	InputDimensions () const
	Get the input dimensions. More...

std::vector< size_t > &	InputDimensions ()
	Modify the input dimensions. More...

virtual double	Loss ()
	Get the layer loss. More...

virtual Layer &	operator= (const Layer &layer)
	Copy assignment operator. This is not responsible for copying weights! More...

virtual Layer &	operator= (Layer &&layer)
	Move assignment operator. This is not responsible for moving weights! More...

const std::vector< size_t > &	OutputDimensions ()
	Get the output dimensions. More...

virtual size_t	OutputSize () final
	Get the number of elements in the output from this layer. More...

virtual const InputType &	Parameters () const
	Get the parameters. More...

virtual InputType &	Parameters ()
	Set the parameters. More...

void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

virtual void	SetWeights (typename InputType ::elem_type *)
	Reset the layer parameter. More...

virtual bool const &	Training () const
	Get whether the layer is currently in training mode. More...

virtual bool &	Training ()
	Modify whether the layer is currently in training mode. More...

virtual size_t	WeightSize () const
	Get the total number of trainable weights in the layer. More...

Additional Inherited Members
Protected Attributes inherited from Layer< InputType, OutputType >
std::vector< size_t >	inputDimensions
	Logical input dimensions of each point. More...

std::vector< size_t >	outputDimensions
	Logical output dimensions of each point. More...

bool	training
	If true, the layer is in training mode; otherwise, it is in testing mode. More...

bool	validOutputDimensions
	This is `true` if `ComputeOutputDimensions()` has been called, and `outputDimensions` can be considered to be up-to-date. More...

Detailed Description

template
<
typename
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

class mlpack::ann::HardShrinkType< InputType, OutputType >

Hard Shrink operator is defined as,.

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

$\lambda$ is set to 0.5 by default.

Template Parameters

InputType	The type of the layer's inputs. The layer automatically cast inputs to this type (Default: arma::mat).
OutputType	The type of the computation which also causes the output to also be in this type. The type also allows the computation and weight type to differ from the input type (Default: arma::mat).

Definition at line 50 of file hardshrink.hpp.

Constructor & Destructor Documentation

◆ HardShrinkType()

HardShrinkType ( const double lambda = 0.5 )

Create HardShrink object using specified hyperparameter lambda.

Parameters

lambda 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.

Referenced by HardShrinkType< InputType, OutputType >::Clone().

Member Function Documentation

◆ Backward()

void Backward	(	const InputType &	input,
		const OutputType &	gy,
		OutputType &	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.

Referenced by HardShrinkType< InputType, OutputType >::Clone().

◆ Clone()

HardShrinkType* Clone ( ) const

inlinevirtual

Clone the HardShrinkType object. This handles polymorphism correctly.

Implements Layer< InputType, OutputType >.

Definition at line 63 of file hardshrink.hpp.

References HardShrinkType< InputType, OutputType >::Backward(), HardShrinkType< InputType, OutputType >::Forward(), and HardShrinkType< InputType, OutputType >::HardShrinkType().

◆ Forward()

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.

Parameters

input	Input data used for evaluating the Hard Shrink function.
output	Resulting output activation.

Referenced by HardShrinkType< InputType, OutputType >::Clone().

◆ Lambda() [1/2]

double const& Lambda ( ) const

inline

Get the hyperparameter lambda.

Definition at line 86 of file hardshrink.hpp.

◆ Lambda() [2/2]

double& Lambda ( )

inline

Modify the hyperparameter lambda.

Definition at line 88 of file hardshrink.hpp.

References HardShrinkType< InputType, OutputType >::serialize().

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by HardShrinkType< InputType, OutputType >::Lambda().

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

/home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/not_adapted/hardshrink.hpp

Public Member Functions

Additional Inherited Members

Detailed Description

template<typenameInputType=arma::mat,typenameOutputType=arma::mat> class mlpack::ann::HardShrinkType< InputType, OutputType >

Constructor & Destructor Documentation

◆ HardShrinkType()

Member Function Documentation

◆ Backward()

◆ Clone()

◆ Forward()

◆ Lambda() [1/2]

◆ Lambda() [2/2]

◆ serialize()

template
<
typename
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

class mlpack::ann::HardShrinkType< InputType, OutputType >