Softmin< InputDataType, OutputDataType > Class Template Reference

Implementation of the Softmin layer. More...

Public Member Functions

 Softmin ()
 Create the Softmin 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 uint32_t)
 Serialize the layer. More...

 

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
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 35 of file softmin.hpp.

Constructor & Destructor Documentation

◆ Softmin()

Softmin ( )

Create the Softmin object.

Member Function Documentation

◆ Backward()

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
inputThe propagated input activation.
gyThe backpropagated error.
gThe calculated gradient.

◆ Delta() [1/2]

InputDataType& Delta ( ) const
inline

Get the delta.

Definition at line 73 of file softmin.hpp.

◆ Delta() [2/2]

InputDataType& Delta ( )
inline

Modify the delta.

Definition at line 75 of file softmin.hpp.

References Softmin< InputDataType, OutputDataType >::serialize().

◆ Forward()

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
inputInput data used for evaluating the specified function.
outputResulting output activation.

◆ OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 68 of file softmin.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )
inline

Modify the output parameter.

Definition at line 70 of file softmin.hpp.

◆ serialize()

void serialize ( Archive &  ,
const uint32_t   
)

Serialize the layer.

Referenced by Softmin< InputDataType, OutputDataType >::Delta().


The documentation for this class was generated from the following file:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/softmin.hpp