Softmax< InputDataType, OutputDataType > Class Template Reference

Implementation of the Softmax layer. More...

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

Detailed Description

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

Implementation of the Softmax layer.

The softmax function takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers. It should be used for inference only and not with NLL loss (use LogSoftMax instead).

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).

Definition at line 38 of file softmax.hpp.

Constructor & Destructor Documentation

Softmax

(

)

Create the Softmax object.

Member Function Documentation

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.

Using the results from the feed forward pass.

Parameters

input	The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

InputDataType& Delta ( ) const

inline

Get the delta.

Definition at line 76 of file softmax.hpp.

InputDataType& Delta ( )

inline

Modify the delta.

Definition at line 78 of file softmax.hpp.

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.

Parameters

input	Input data used for evaluating the specified function.
output	Resulting output activation.

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 71 of file softmax.hpp.

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 73 of file softmax.hpp.

void serialize	(	Archive &	,
		const unsigned	int
	)

Serialize the layer.

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

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