mlpack::ann::Softmin< InputDataType, OutputDataType > Class Template Reference

Implementation of the Softmin layer. More...

#include <softmin.hpp>

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

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::Softmin< InputDataType, OutputDataType >

Implementation of the Softmin layer.

The Softmin function takes as a input a vector of K real numbers, rescaling them so that the elements of the K-dimensional output vector lie in the range [0, 1] and sum to 1.

Template Parameters

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<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::Softmin< InputDataType, OutputDataType >::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 through 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<typename InputDataType , typename OutputDataType >

template<typename InputType , typename OutputType >

void mlpack::ann::Softmin< InputDataType, OutputDataType >::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 specified function.
output	Resulting output activation.

---

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

• src/mlpack/methods/ann/layer/softmin.hpp
• src/mlpack/methods/ann/layer/softmin_impl.hpp