LogSoftMax< InputDataType, OutputDataType > Class Template Reference

Implementation of the log softmax layer. More...

Public Member Functions

 LogSoftMax ()
 Create the LogSoftmax 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 trough 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::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
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 36 of file log_softmax.hpp.

Constructor & Destructor Documentation

◆ LogSoftMax()

Create the LogSoftmax 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 trough 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 74 of file log_softmax.hpp.

◆ Delta() [2/2]

InputDataType& Delta ( )
inline

Modify the delta.

Definition at line 76 of file log_softmax.hpp.

References LogSoftMax< 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 69 of file log_softmax.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )
inline

Modify the output parameter.

Definition at line 71 of file log_softmax.hpp.

◆ serialize()

void serialize ( Archive &  ,
const unsigned  int 
)

Serialize the layer.

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


The documentation for this class was generated from the following file:
  • /home/ryan/src/mlpack.org/_src/mlpack-3.3.2/src/mlpack/methods/ann/layer/log_softmax.hpp