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
ActivationFunctionActivation function used for the embedding layer.
InputDataTypeType of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataTypeType 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
inputThe propagated input activation.
gyThe backpropagated error.
gThe 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
inputInput data used for evaluating the specified function.
outputResulting 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.


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