Implementation of the AdaptiveMeanPooling layer. More...

Inheritance diagram for AdaptiveMeanPoolingType< MatType >:

Public Member Functions
	AdaptiveMeanPoolingType ()
	Create the AdaptiveMeanPooling object. More...

	AdaptiveMeanPoolingType (const size_t outputWidth, const size_t outputHeight)
	Create the AdaptiveMeanPooling object. More...

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

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

virtual	~AdaptiveMeanPoolingType ()

void	Backward (const MatType &input, const MatType &gy, MatType &g)
	Ordinary feed backward pass of a neural network, using 3rd-order tensors as input, calculating the function f(x) by propagating x backwards through f. More...

AdaptiveMeanPoolingType *	Clone () const
	Clone the AdaptiveMeanPoolingType object. More...

void	ComputeOutputDimensions ()
	Compute the size of the output given `InputDimensions()`. 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...

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

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

size_t const &	OutputHeight () const
	Get the output height. More...

size_t &	OutputHeight ()
	Modify the output height. More...

size_t const &	OutputWidth () const
	Get the output width. More...

size_t &	OutputWidth ()
	Modify the output width. More...

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

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	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 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 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::AdaptiveMeanPoolingType< MatType >

Implementation of the AdaptiveMeanPooling layer.

The AdaptiveMeanPooling layer works similarly to MeanPooling layer, but it adaptively changes the size of the pooling region to minimize the amount of computation. In MeanPooling, we specifies the kernel and stride size whereas in AdaptiveMeanPooling, we specify the output size of the pooling region.

Template Parameters

MatType Matrix representation to accept as input and use for computation.

Definition at line 36 of file adaptive_mean_pooling.hpp.

Constructor & Destructor Documentation

◆ AdaptiveMeanPoolingType() [1/4]

AdaptiveMeanPoolingType ( )

Create the AdaptiveMeanPooling object.

Referenced by AdaptiveMeanPoolingType< MatType >::Clone(), and AdaptiveMeanPoolingType< MatType >::~AdaptiveMeanPoolingType().

◆ AdaptiveMeanPoolingType() [2/4]

AdaptiveMeanPoolingType	(	const size_t	outputWidth,
		const size_t	outputHeight
	)

Create the AdaptiveMeanPooling object.

Parameters

outputWidth	Width of the output.
outputHeight	Height of the output.

◆ ~AdaptiveMeanPoolingType()

virtual ~AdaptiveMeanPoolingType ( )

inlinevirtual

Definition at line 52 of file adaptive_mean_pooling.hpp.

References AdaptiveMeanPoolingType< MatType >::AdaptiveMeanPoolingType(), and AdaptiveMeanPoolingType< MatType >::operator=().

◆ AdaptiveMeanPoolingType() [3/4]

AdaptiveMeanPoolingType ( const AdaptiveMeanPoolingType< MatType > & other )

Copy the given AdaptiveMeanPoolingType.

◆ AdaptiveMeanPoolingType() [4/4]

AdaptiveMeanPoolingType ( AdaptiveMeanPoolingType< MatType > && other )

Take ownership of the given AdaptiveMeanPoolingType.

Member Function Documentation

◆ Backward()

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

virtual

Ordinary feed backward pass of a neural network, using 3rd-order tensors as input, 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.

Reimplemented from Layer< MatType >.

Referenced by AdaptiveMeanPoolingType< MatType >::Clone().

◆ Clone()

AdaptiveMeanPoolingType* Clone ( ) const

inlinevirtual

Clone the AdaptiveMeanPoolingType object.

This handles polymorphism correctly.

Implements Layer< MatType >.

Definition at line 68 of file adaptive_mean_pooling.hpp.

References AdaptiveMeanPoolingType< MatType >::AdaptiveMeanPoolingType(), AdaptiveMeanPoolingType< MatType >::Backward(), and AdaptiveMeanPoolingType< MatType >::Forward().

◆ ComputeOutputDimensions()

void ComputeOutputDimensions ( )

virtual

Compute the size of the output given InputDimensions().

Reimplemented from Layer< MatType >.

Referenced by AdaptiveMeanPoolingType< MatType >::OutputHeight().

◆ 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

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

Reimplemented from Layer< MatType >.

Referenced by AdaptiveMeanPoolingType< MatType >::Clone().

◆ operator=() [1/2]

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

Copy the given AdaptiveMeanPoolingType.

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

◆ operator=() [2/2]

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

Take ownership of the given AdaptiveMeanPoolingType.

◆ OutputHeight() [1/2]

size_t const& OutputHeight ( ) const

inline

Get the output height.

Definition at line 101 of file adaptive_mean_pooling.hpp.

◆ OutputHeight() [2/2]

size_t& OutputHeight ( )

inline

Modify the output height.

Definition at line 103 of file adaptive_mean_pooling.hpp.

References AdaptiveMeanPoolingType< MatType >::ComputeOutputDimensions(), and AdaptiveMeanPoolingType< MatType >::serialize().

◆ OutputWidth() [1/2]

size_t const& OutputWidth ( ) const

inline

Get the output width.

Definition at line 96 of file adaptive_mean_pooling.hpp.

◆ OutputWidth() [2/2]

size_t& OutputWidth ( )

inline

Modify the output width.

Definition at line 98 of file adaptive_mean_pooling.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t	version
	)

Serialize the layer.

Referenced by AdaptiveMeanPoolingType< MatType >::OutputHeight().

The documentation for this class was generated from the following file:

/home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/adaptive_mean_pooling.hpp

Public Member Functions

Additional Inherited Members

Detailed Description

template<typenameMatType=arma::mat> class mlpack::ann::AdaptiveMeanPoolingType< MatType >

Constructor & Destructor Documentation

◆ AdaptiveMeanPoolingType() [1/4]

◆ AdaptiveMeanPoolingType() [2/4]

◆ ~AdaptiveMeanPoolingType()

◆ AdaptiveMeanPoolingType() [3/4]

◆ AdaptiveMeanPoolingType() [4/4]

Member Function Documentation

◆ Backward()

◆ Clone()

◆ ComputeOutputDimensions()

◆ Forward()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ OutputHeight() [1/2]

◆ OutputHeight() [2/2]

◆ OutputWidth() [1/2]

◆ OutputWidth() [2/2]

◆ serialize()

template
<
typename
MatType
=
arma::mat
>

class mlpack::ann::AdaptiveMeanPoolingType< MatType >