LogSoftMaxType< MatType > Class Template Reference

Implementation of the log softmax layer. More...

Inheritance diagram for LogSoftMaxType< MatType >:

Public Member Functions

 LogSoftMaxType ()
 Create the LogSoftmax layer. More...

 
 LogSoftMaxType (const LogSoftMaxType &other)
 Copy the given LogSoftMaxType. More...

 
 LogSoftMaxType (LogSoftMaxType &&other)
 Take ownership of the given LogSoftMaxType. More...

 
virtual ~LogSoftMaxType ()
 
void Backward (const MatType &input, const MatType &gy, MatType &g)
 Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards trough f. More...

 
LogSoftMaxTypeClone () const
 Clone the LogSoftMaxType object. This handles polymorphism correctly. More...

 
void Forward (const MatType &input, MatType &output)
 Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

 
LogSoftMaxTypeoperator= (const LogSoftMaxType &other)
 Copy the given LogSoftMaxType. More...

 
LogSoftMaxTypeoperator= (LogSoftMaxType &&other)
 Take ownership of the given LogSoftMaxType. More...

 
template
<
typename
Archive
>
void serialize (Archive &ar, const uint32_t)
 
- Public Member Functions inherited from Layer< MatType >
 Layer ()
 Default constructor. More...

 
 Layer (const Layer &layer)
 Copy constructor. This is not responsible for copying weights! More...

 
 Layer (Layer &&layer)
 Move constructor. This is not responsible for moving weights! More...

 
virtual ~Layer ()
 Default deconstructor. More...

 
virtual void ComputeOutputDimensions ()
 Compute the output dimensions. More...

 
virtual void CustomInitialize (MatType &, const size_t)
 Override the weight matrix of the layer. More...

 
virtual void Forward (const MatType &, const MatType &)
 Takes an input and output object, and computes the corresponding loss of the layer. More...

 
virtual void Gradient (const MatType &, const MatType &, MatType &)
 Computing the gradient of the layer with respect to its own input. More...

 
const std::vector< size_t > & InputDimensions () const
 Get the input dimensions. More...

 
std::vector< size_t > & InputDimensions ()
 Modify the input dimensions. More...

 
virtual double Loss ()
 Get the layer loss. More...

 
virtual Layeroperator= (const Layer &layer)
 Copy assignment operator. This is not responsible for copying weights! More...

 
virtual Layeroperator= (Layer &&layer)
 Move assignment operator. This is not responsible for moving weights! More...

 
const std::vector< size_t > & OutputDimensions ()
 Get the output dimensions. More...

 
virtual size_t OutputSize () final
 Get the number of elements in the output from this layer. More...

 
virtual const MatType & Parameters () const
 Get the parameters. More...

 
virtual MatType & Parameters ()
 Set the parameters. More...

 
template
<
typename
Archive
>
void serialize (Archive &ar, const uint32_t)
 Serialize the layer. More...

 
virtual void SetWeights (typename MatType::elem_type *)
 Reset the layer parameter. More...

 
virtual bool const & Training () const
 Get whether the layer is currently in training mode. More...

 
virtual bool & Training ()
 Modify whether the layer is currently in training mode. More...

 
virtual size_t WeightSize () const
 Get the total number of trainable weights in the layer. More...

 

Additional Inherited Members

- Protected Attributes inherited from Layer< MatType >
std::vector< size_t > inputDimensions
 Logical input dimensions of each point. More...

 
std::vector< size_t > outputDimensions
 Logical output dimensions of each point. More...

 
bool training
 If true, the layer is in training mode; otherwise, it is in testing mode. More...

 
bool validOutputDimensions
 This is true if ComputeOutputDimensions() has been called, and outputDimensions can be considered to be up-to-date. More...

 

Detailed Description


template
<
typename
MatType
=
arma::mat
>

class mlpack::ann::LogSoftMaxType< MatType >

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
MatTypeMatrix representation to accept as input and use for computation.

Definition at line 33 of file log_softmax.hpp.

Constructor & Destructor Documentation

◆ LogSoftMaxType() [1/3]

◆ ~LogSoftMaxType()

◆ LogSoftMaxType() [2/3]

LogSoftMaxType ( const LogSoftMaxType< MatType > &  other)

Copy the given LogSoftMaxType.

◆ LogSoftMaxType() [3/3]

LogSoftMaxType ( LogSoftMaxType< MatType > &&  other)

Take ownership of the given LogSoftMaxType.

Member Function Documentation

◆ Backward()

void Backward ( const MatType &  input,
const MatType &  gy,
MatType &  g 
)
virtual

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.

Reimplemented from Layer< MatType >.

Referenced by LogSoftMaxType< MatType >::~LogSoftMaxType().

◆ Clone()

LogSoftMaxType* Clone ( ) const
inlinevirtual

Clone the LogSoftMaxType object. This handles polymorphism correctly.

Implements Layer< MatType >.

Definition at line 42 of file log_softmax.hpp.

References LogSoftMaxType< MatType >::LogSoftMaxType().

◆ Forward()

void Forward ( const MatType &  input,
MatType &  output 
)
virtual

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.

Reimplemented from Layer< MatType >.

Referenced by LogSoftMaxType< MatType >::~LogSoftMaxType().

◆ operator=() [1/2]

LogSoftMaxType& operator= ( const LogSoftMaxType< MatType > &  other)

◆ operator=() [2/2]

LogSoftMaxType& operator= ( LogSoftMaxType< MatType > &&  other)

Take ownership of the given LogSoftMaxType.

◆ serialize()

void serialize ( Archive &  ar,
const uint32_t   
)
inline

Definition at line 77 of file log_softmax.hpp.


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