Implementation of the MultiplyMerge module class. More...

Inheritance diagram for MultiplyMergeType< InputType, OutputType >:

[legend]

Public Member Functions
	MultiplyMergeType (const bool model=false, const bool run=true)
	Create the MultiplyMerge object using the specified parameters. More...

	~MultiplyMergeType ()
	Destructor to release allocated memory. More...

void	Backward (const InputType &, const OutputType &gy, OutputType &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. More...

MultiplyMergeType *	Clone () const
	Clone the MultiplyMergeType object. This handles polymorphism correctly. More...

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

void	Gradient (const InputType &input, const OutputType &error, OutputType &gradient)

	MultiplyMerge (const MultiplyMerge &layer)
	Copy Constructor. More...

	MultiplyMerge (MultiplyMerge &&layer)
	Move Constructor. More...

MultiplyMerge &	operator= (const MultiplyMerge &layer)
	Copy assignment operator. More...

MultiplyMerge &	operator= (MultiplyMerge &&layer)
	Move assignment operator. More...

OutputType const &	Parameters () const
	Get the parameters. More...

OutputType &	Parameters ()
	Modify the parameters. More...

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

size_t	WeightSize () const
	Get the size of the weights. More...

Public Member Functions inherited from MultiLayer< InputType, OutputType >
	MultiLayer ()
	Create an empty MultiLayer that holds no layers of its own. More...

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

	MultiLayer (MultiLayer &&other)
	Take ownership of the layers of the given MultiLayer. More...

virtual	~MultiLayer ()
	Virtual destructor: delete all held layers. More...

void	Add (Args... args)

void	Add (Layer< InputType > *layer)

virtual void	Backward (const InputType &input, const InputType &gy, InputType &g)
	Perform a backward pass with the given data. More...

virtual void	ComputeOutputDimensions ()
	Compute the output dimensions of the MultiLayer using `InputDimensions()`. More...

virtual void	Forward (const InputType &input, InputType &output)
	Perform a forward pass with the given input data. More...

void	Forward (const InputType &input, InputType &output, const size_t start, const size_t end)
	Perform a forward pass with the given input data, but only on a subset of the layers in the MultiLayer. More...

virtual void	Gradient (const InputType &input, const InputType &error, InputType &gradient)
	Compute the gradients of each layer. More...

virtual double	Loss () const
	Compute the loss that should be added to the objective. More...

const std::vector< Layer< InputType > *>	Network () const
	Get the network (series of layers) held by this MultiLayer. More...

std::vector< Layer< InputType > *> &	Network ()
	Modify the network (series of layers) held by this MultiLayer. More...

MultiLayer &	operator= (const MultiLayer &other)
	Copy the given MultiLayer. More...

MultiLayer &	operator= (MultiLayer &&other)
	Take ownership of the given MultiLayer. More...

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

virtual void	SetWeights (typename InputType ::elem_type *weightsPtr)
	Set the weights of the layer to use the memory given as `weightsPtr`. More...

Public Member Functions inherited from Layer< InputType >
	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	Forward (const InputType &, const InputType &)
	Takes an input and output object, and computes the corresponding loss of the layer. 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...

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

Additional Inherited Members
Protected Member Functions inherited from MultiLayer< InputType, OutputType >
void	InitializeBackwardPassMemory (const size_t batchSize)
	Initialize memory that will be used by each layer for the backwards pass, assuming that the input will have the given `batchSize`. More...

void	InitializeForwardPassMemory (const size_t batchSize)
	Initialize memory that will be used by each layer for the forward pass, assuming that the input will have the given `batchSize`. More...

void	InitializeGradientPassMemory (InputType &gradient)
	Initialize memory for the gradient pass. More...

Protected Attributes inherited from MultiLayer< InputType, OutputType >
size_t	inSize

InputType	layerDeltaMatrix
	This matrix stores all of the backwards pass results of each layer when Backward() is called. More...

std::vector< InputType >	layerDeltas
	These are aliases of `layerDeltaMatrix` for each layer. More...

std::vector< InputType >	layerGradients
	Gradient aliases for each layer. More...

InputType	layerOutputMatrix
	This matrix stores all of the outputs of each layer when Forward() is called. More...

std::vector< InputType >	layerOutputs
	These are aliases of `layerOutputMatrix` for each layer. More...

std::vector< Layer< InputType > *>	network
	The internally-held network. More...

size_t	totalInputSize

size_t	totalOutputSize

Protected Attributes inherited from Layer< InputType >
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::MultiplyMergeType< InputType, OutputType >

Implementation of the MultiplyMerge module class.

The MultiplyMerge class multiplies the output of various modules element-wise.

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 40 of file multiply_merge.hpp.

Constructor & Destructor Documentation

◆ MultiplyMergeType()

MultiplyMergeType	(	const bool	model = `false`,
		const bool	run = `true`
	)

Create the MultiplyMerge object using the specified parameters.

Parameters

model	Expose all the network modules.
run	Call the Forward/Backward method before the output is merged.

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

◆ ~MultiplyMergeType()

~MultiplyMergeType ( )

Destructor to release allocated memory.

Member Function Documentation

◆ Backward()

void Backward	(	const InputType &	,
		const OutputType &	gy,
		OutputType &	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

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

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

◆ Clone()

MultiplyMergeType* Clone ( ) const

inlinevirtual

Clone the MultiplyMergeType object. This handles polymorphism correctly.

Reimplemented from MultiLayer< InputType, OutputType >.

Definition at line 67 of file multiply_merge.hpp.

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

◆ Forward()

void Forward	(	const InputType &	,
		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 MultiplyMergeType< InputType, OutputType >::Clone().

◆ Gradient()

void Gradient	(	const InputType &	input,
		const OutputType &	error,
		OutputType &	gradient
	)

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

◆ MultiplyMerge() [1/2]

MultiplyMerge ( const MultiplyMerge & layer )

Copy Constructor.

◆ MultiplyMerge() [2/2]

MultiplyMerge ( MultiplyMerge && layer )

Move Constructor.

◆ operator=() [1/2]

MultiplyMerge& operator= ( const MultiplyMerge & layer )

Copy assignment operator.

◆ operator=() [2/2]

MultiplyMerge& operator= ( MultiplyMerge && layer )

Move assignment operator.

◆ Parameters() [1/2]

OutputType const& Parameters ( ) const

inlinevirtual

Get the parameters.

Reimplemented from Layer< InputType >.

Definition at line 103 of file multiply_merge.hpp.

◆ Parameters() [2/2]

OutputType& Parameters ( )

inlinevirtual

Modify the parameters.

Reimplemented from Layer< InputType >.

Definition at line 105 of file multiply_merge.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by MultiplyMergeType< InputType, OutputType >::WeightSize().

◆ WeightSize()

size_t WeightSize ( ) const

inlinevirtual

Get the size of the weights.

Reimplemented from MultiLayer< InputType, OutputType >.

Definition at line 108 of file multiply_merge.hpp.

References MultiplyMergeType< 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/multiply_merge.hpp

Public Member Functions

Additional Inherited Members

Detailed Description

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

Constructor & Destructor Documentation

◆ MultiplyMergeType()

◆ ~MultiplyMergeType()

Member Function Documentation

◆ Backward()

◆ Clone()

◆ Forward()

◆ Gradient()

◆ MultiplyMerge() [1/2]

◆ MultiplyMerge() [2/2]

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ serialize()

◆ WeightSize()

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

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