HardShrink< InputDataType, OutputDataType > Class Template Reference

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

Public Member Functions
	HardShrink (const double lambda=0.5)
	Create HardShrink object using specified hyperparameter lambda. 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 &	Delta () const
	Get the delta. More...
OutputDataType &	Delta ()
	Modify the delta. 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...
double const &	Lambda () const
	Get the hyperparameter lambda. More...
double &	Lambda ()
	Modify the hyperparameter lambda. More...
OutputDataType const &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

Detailed Description

template

<

typename

InputDataType

=

arma::mat

,

typename

OutputDataType

=

arma::mat

>

class mlpack::ann::HardShrink< InputDataType, OutputDataType >

Hard Shrink operator is defined as,.

is set to 0.5 by default.

Definition at line 45 of file hardshrink.hpp.

Constructor & Destructor Documentation

HardShrink()

HardShrink

(

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.

Member Function Documentation

Backward()

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.

Using the results from the feed forward pass.

Parameters

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

Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 88 of file hardshrink.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 90 of file hardshrink.hpp.

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.

Lambda() [1/2]

double const& Lambda ( ) const

inline

Get the hyperparameter lambda.

Definition at line 93 of file hardshrink.hpp.

Lambda() [2/2]

double& Lambda ( )

inline

Modify the hyperparameter lambda.

Definition at line 95 of file hardshrink.hpp.

References HardShrink< InputDataType, OutputDataType >::serialize().

OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 83 of file hardshrink.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 85 of file hardshrink.hpp.

serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by HardShrink< InputDataType, OutputDataType >::Lambda().

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

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/hardshrink.hpp