mlpack::ann::BaseLayer< ActivationFunction, InputDataType, OutputDataType > Class Template Reference

Implementation of the base layer. More...

#include <base_layer.hpp>

Public Member Functions
	BaseLayer ()
	Create the BaseLayer object.
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 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 &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
template<typename Archive >
void	serialize (Archive &, const uint32_t)
	Serialize the layer.

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
• SILULayer

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).

Member Function Documentation

Backward()

template<class ActivationFunction = LogisticFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>

template<typename eT >

void mlpack::ann::BaseLayer< ActivationFunction, InputDataType, OutputDataType >::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.

Forward()

template<class ActivationFunction = LogisticFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>

template<typename InputType , typename OutputType >

void mlpack::ann::BaseLayer< ActivationFunction, InputDataType, OutputDataType >::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.

---

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

• src/mlpack/methods/ann/layer/base_layer.hpp