BaseLayer< ActivationFunction, InputDataType, OutputDataType > Class Template Reference

Implementation of the base layer. More...

Public Member Functions
	BaseLayer ()
	Create the BaseLayer object. More...
template < typename eT >
void	Backward (const arma::Mat< eT > &input, 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...
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...
OutputDataType const &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template < typename Archive >
void	serialize (Archive &, const uint32_t)
	Serialize the layer. More...

Detailed Description

template

<

class

ActivationFunction

=

LogisticFunction

,

typename

InputDataType

=

arma::mat

,

typename

OutputDataType

=

arma::mat

>

class mlpack::ann::BaseLayer< ActivationFunction, InputDataType, OutputDataType >

Implementation of the base layer.

The base layer works as a metaclass which attaches various functions to the embedding layer.

A few convenience typedefs are given:

• SigmoidLayer
• IdentityLayer
• ReLULayer
• TanHLayer
• SoftplusLayer
• HardSigmoidLayer
• SwishLayer
• MishLayer
• LiSHTLayer
• GELULayer
• ELiSHLayer
• ElliotLayer
• GaussianLayer
• HardSwishLayer
• TanhExpLayer

Template Parameters

ActivationFunction	Activation function used for the embedding layer.
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).

Definition at line 69 of file base_layer.hpp.

Constructor & Destructor Documentation

BaseLayer()

BaseLayer ( )

inline

Create the BaseLayer object.

Definition at line 75 of file base_layer.hpp.

Member Function Documentation

Backward()

void Backward ( const arma::Mat< eT > &  input, const arma::Mat< eT > &  gy, arma::Mat< eT > &  g  )

inline

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.

Definition at line 103 of file base_layer.hpp.

Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 118 of file base_layer.hpp.

Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 120 of file base_layer.hpp.

Forward()

void Forward ( const InputType &  input, OutputType &  output  )

inline

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.

Definition at line 88 of file base_layer.hpp.

OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 113 of file base_layer.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 115 of file base_layer.hpp.

serialize()

void serialize ( Archive &  , const uint32_t    )

inline

Serialize the layer.

Definition at line 126 of file base_layer.hpp.

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

• /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/base_layer.hpp