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...

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

virtual Layer &	operator= (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

InputType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputType	Type 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

stdev Standard 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

input	The propagated input activation.
*	(gy) The backpropagated error.
g	The calculated gradient.

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

◆ Clone()

ReinforceNormalType* Clone ( ) const

inlinevirtual

Clone the ReinforceNormalType object. This handles polymorphism correctly.

Implements Layer< InputType, OutputType >.

Definition at line 46 of file reinforce_normal.hpp.

References ReinforceNormalType< InputType, OutputType >::Backward(), ReinforceNormalType< InputType, OutputType >::Forward(), and ReinforceNormalType< InputType, OutputType >::ReinforceNormalType().

◆ 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

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

Serialize the layer.

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

◆ 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

Public Member Functions

Additional Inherited Members

Detailed Description

template<typenameInputType=arma::mat,typenameOutputType=arma::mat> class mlpack::ann::ReinforceNormalType< InputType, OutputType >

Constructor & Destructor Documentation

◆ ReinforceNormalType()

Member Function Documentation

◆ Backward()

◆ Clone()

◆ Forward()

◆ Reward() [1/2]

◆ Reward() [2/2]

◆ serialize()

◆ StandardDeviation()

template
<
typename
InputType
=
arma::mat
,
typename
OutputType
=
arma::mat
>

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