Implementation of the Convolution class. More...

Public Member Functions
	Convolution ()
	Create the Convolution object. More...

	Convolution (const size_t inSize, const size_t outSize, const size_t kW, const size_t kH, const size_t dW=1, const size_t dH=1, const size_t padW=0, const size_t padH=0, const size_t inputWidth=0, const size_t inputHeight=0)
	Create the Convolution object using the specified number of input maps, output maps, filter size, stride and padding parameter. More...

template < typename eT >
void	Backward (const arma::Mat< eT > &&, arma::Mat< eT > &&gy, arma::Mat< eT > &&g)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...

arma::mat &	Bias ()
	Modify the bias weights of the layer. More...

OutputDataType const &	Delta () const
	Get the delta. More...

OutputDataType &	Delta ()
	Modify the delta. More...

template < typename eT >
void	Forward (const arma::Mat< eT > &&input, arma::Mat< eT > &&output)
	Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

template < typename eT >
void	Gradient (const arma::Mat< eT > &&, arma::Mat< eT > &&error, arma::Mat< eT > &&gradient)

OutputDataType const &	Gradient () const
	Get the gradient. More...

OutputDataType &	Gradient ()
	Modify the gradient. More...

size_t const &	InputHeight () const
	Get the input height. More...

size_t &	InputHeight ()
	Modify the input height. More...

InputDataType const &	InputParameter () const
	Get the input parameter. More...

InputDataType &	InputParameter ()
	Modify the input parameter. More...

size_t const &	InputWidth () const
	Get the input width. More...

size_t &	InputWidth ()
	Modify input the width. More...

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

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

OutputDataType const &	OutputParameter () const
	Get the output parameter. More...

OutputDataType &	OutputParameter ()
	Modify the output parameter. More...

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

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

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

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

void	Reset ()

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

Detailed Description

template<typename ForwardConvolutionRule = NaiveConvolution<ValidConvolution>, typename BackwardConvolutionRule = NaiveConvolution<FullConvolution>, typename GradientConvolutionRule = NaiveConvolution<ValidConvolution>, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, InputDataType, OutputDataType >

Implementation of the Convolution class.

The Convolution class represents a single layer of a neural network.

Template Parameters

ForwardConvolutionRule	Convolution to perform forward process.
BackwardConvolutionRule	Convolution to perform backward process.
GradientConvolutionRule	Convolution to calculate gradient.
InputDataType	Type of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataType	Type of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Definition at line 47 of file convolution.hpp.

Constructor & Destructor Documentation

◆ Convolution() [1/2]

Convolution ( )

Create the Convolution object.

◆ Convolution() [2/2]

Convolution	(	const size_t	inSize,
		const size_t	outSize,
		const size_t	kW,
		const size_t	kH,
		const size_t	dW = `1`,
		const size_t	dH = `1`,
		const size_t	padW = `0`,
		const size_t	padH = `0`,
		const size_t	inputWidth = `0`,
		const size_t	inputHeight = `0`
	)

Create the Convolution object using the specified number of input maps, output maps, filter size, stride and padding parameter.

Parameters

inSize	The number of input maps.
outSize	The number of output maps.
kW	Width of the filter/kernel.
kH	Height of the filter/kernel.
dW	Stride of filter application in the x direction.
dH	Stride of filter application in the y direction.
padW	Padding width of the input.
padH	Padding height of the input.
inputWidth	The width of the input data.
inputHeight	The height of the input data.

Member Function Documentation

◆ Backward()

void Backward	(	const arma::Mat< eT > &&	,
		arma::Mat< eT > &&	gy,
		arma::Mat< eT > &&	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.

◆ Bias()

arma::mat& Bias ( )

inline

Modify the bias weights of the layer.

Definition at line 166 of file convolution.hpp.

References Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, InputDataType, OutputDataType >::serialize().

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 136 of file convolution.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 138 of file convolution.hpp.

◆ Forward()

void Forward	(	const arma::Mat< eT > &&	input,
		arma::Mat< eT > &&	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.

◆ Gradient() [1/3]

void Gradient	(	const arma::Mat< eT > &&	,
		arma::Mat< eT > &&	error,
		arma::Mat< eT > &&	gradient
	)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 141 of file convolution.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 143 of file convolution.hpp.

◆ InputHeight() [1/2]

size_t const& InputHeight ( ) const

inline

Get the input height.

Definition at line 151 of file convolution.hpp.

◆ InputHeight() [2/2]

size_t& InputHeight ( )

inline

Modify the input height.

Definition at line 153 of file convolution.hpp.

◆ InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 126 of file convolution.hpp.

◆ InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 128 of file convolution.hpp.

◆ InputWidth() [1/2]

size_t const& InputWidth ( ) const

inline

Get the input width.

Definition at line 146 of file convolution.hpp.

◆ InputWidth() [2/2]

size_t& InputWidth ( )

inline

Modify input the width.

Definition at line 148 of file convolution.hpp.

◆ OutputHeight() [1/2]

size_t const& OutputHeight ( ) const

inline

Get the output height.

Definition at line 161 of file convolution.hpp.

◆ OutputHeight() [2/2]

size_t& OutputHeight ( )

inline

Modify the output height.

Definition at line 163 of file convolution.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 131 of file convolution.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 133 of file convolution.hpp.

◆ OutputWidth() [1/2]

size_t const& OutputWidth ( ) const

inline

Get the output width.

Definition at line 156 of file convolution.hpp.

◆ OutputWidth() [2/2]

size_t& OutputWidth ( )

inline

Modify the output width.

Definition at line 158 of file convolution.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 121 of file convolution.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 123 of file convolution.hpp.

◆ Reset()

void Reset ( )

◆ serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

Referenced by Convolution< ForwardConvolutionRule, BackwardConvolutionRule, GradientConvolutionRule, InputDataType, OutputDataType >::Bias().

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

/home/jenkins-mlpack/mlpack.org/_src/mlpack-3.2.1/src/mlpack/methods/ann/layer/convolution.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ Convolution() [1/2]

◆ Convolution() [2/2]

Member Function Documentation

◆ Backward()

◆ Bias()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Forward()

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InputHeight() [1/2]

◆ InputHeight() [2/2]

◆ InputParameter() [1/2]

◆ InputParameter() [2/2]

◆ InputWidth() [1/2]

◆ InputWidth() [2/2]

◆ OutputHeight() [1/2]

◆ OutputHeight() [2/2]

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ OutputWidth() [1/2]

◆ OutputWidth() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Reset()

◆ serialize()