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

Implementation of the log softmax layer. More...

#include <log_softmax.hpp>

Public Member Functions
	LogSoftMax ()
	Create the LogSoftmax 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 &	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::LogSoftMax< InputDataType, OutputDataType >

Implementation of the log softmax layer.

The log softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. This layer is meant to be used in combination with the negative log likelihood layer (NegativeLogLikelihoodLayer), which expects that the input contains log-probabilities for each class.

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::LogSoftMax< 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 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<typename InputDataType , typename OutputDataType >

template<typename InputType , typename OutputType >

void mlpack::ann::LogSoftMax< 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.

Fast approximation of exp(-x) for x positive.

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

• src/mlpack/methods/ann/layer/log_softmax.hpp
• src/mlpack/methods/ann/layer/log_softmax_impl.hpp