ReinforceNormalType< InputType, OutputType > Class Template Reference

Implementation of the reinforce normal layer. More...

Inheritance diagram for ReinforceNormalType< InputType, OutputType >:

Public Member Functions

 ReinforceNormalType (const double stdev=1.0)
 Create the ReinforceNormal object. More...

 
void Backward (const InputType &input, const OutputType &, OutputType &g)
 Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...

 
ReinforceNormalTypeClone () const
 Clone the ReinforceNormalType object. This handles polymorphism correctly. More...

 
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...

 
double const & Reward () const
 Get the value of the reward parameter. More...

 
double & Reward ()
 Modify the value of the deterministic parameter. More...

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

 
double const & StandardDeviation () const
 Get the standard deviation used during forward and backward pass. More...

 
- Public Member Functions inherited from Layer< InputType, OutputType >
 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 Backward (const InputType &, const InputType &, InputType &)
 Performs a backpropagation step through the layer, with respect to the given input. More...

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

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

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

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

 
virtual void Gradient (const InputType &, const InputType &, InputType &)
 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 InputType & Parameters () const
 Get the parameters. More...

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

 
void serialize (Archive &ar, const uint32_t)
 Serialize the layer. More...

 
virtual void SetWeights (typename InputType ::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< InputType, OutputType >
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
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

class mlpack::ann::ReinforceNormalType< InputType, OutputType >

Implementation of the reinforce normal layer.

The reinforce normal layer implements the REINFORCE algorithm for the normal distribution.

Template Parameters
InputTypeType of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputTypeType of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Definition at line 35 of file reinforce_normal.hpp.

Constructor & Destructor Documentation

◆ ReinforceNormalType()

ReinforceNormalType ( const double  stdev = 1.0)

Create the ReinforceNormal object.

Parameters
stdevStandard deviation used during the forward and backward pass.

Referenced by ReinforceNormalType< InputType, OutputType >::Clone().

Member Function Documentation

◆ Backward()

void Backward ( const InputType &  input,
const OutputType &  ,
OutputType &  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.
*(gy) The backpropagated error.
gThe calculated gradient.

Referenced by ReinforceNormalType< InputType, OutputType >::Clone().

◆ Clone()

◆ 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.

Referenced by ReinforceNormalType< InputType, OutputType >::Clone().

◆ Reward() [1/2]

double const& Reward ( ) const
inline

Get the value of the reward parameter.

Definition at line 71 of file reinforce_normal.hpp.

◆ Reward() [2/2]

double& Reward ( )
inline

Modify the value of the deterministic parameter.

Definition at line 73 of file reinforce_normal.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const uint32_t   
)

◆ StandardDeviation()

double const& StandardDeviation ( ) const
inline

Get the standard deviation used during forward and backward pass.

Definition at line 76 of file reinforce_normal.hpp.

References ReinforceNormalType< InputType, OutputType >::serialize().


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