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 kernelWidth, const size_t kernelHeight, const size_t strideWidth=1, const size_t strideHeight=1, const size_t padW=0, const size_t padH=0, const size_t inputWidth=0, const size_t inputHeight=0, const std::string &paddingType="None")
	Create the Convolution object using the specified number of input maps, output maps, filter size, stride and padding parameter. More...

	Convolution (const size_t inSize, const size_t outSize, const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth, const size_t strideHeight, const std::tuple< size_t, size_t > &padW, const std::tuple< size_t, size_t > &padH, const size_t inputWidth=0, const size_t inputHeight=0, const std::string &paddingType="None")
	Create the Convolution object using the specified number of input maps, output maps, filter size, stride and padding parameter. More...

template
void	Backward (const arma::Mat< eT > &, const 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 const &	Bias () const
	Get the bias of the layer. More...

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

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

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

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

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

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

size_t	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	InputSize () const
	Get the input size. More...

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

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

size_t	KernelHeight () const
	Get the kernel height. More...

size_t &	KernelHeight ()
	Modify the kernel height. More...

size_t	KernelWidth () const
	Get the kernel width. More...

size_t &	KernelWidth ()
	Modify the kernel width. More...

size_t	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	OutputSize () const
	Get the output size. More...

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

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

size_t	PadHBottom () const
	Get the bottom padding height. More...

size_t &	PadHBottom ()
	Modify the bottom padding height. More...

size_t	PadHTop () const
	Get the top padding height. More...

size_t &	PadHTop ()
	Modify the top padding height. More...

size_t	PadWLeft () const
	Get the left padding width. More...

size_t &	PadWLeft ()
	Modify the left padding width. More...

size_t	PadWRight () const
	Get the right padding width. More...

size_t &	PadWRight ()
	Modify the right padding width. More...

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

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

void	Reset ()

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

size_t	StrideHeight () const
	Get the stride height. More...

size_t &	StrideHeight ()
	Modify the stride height. More...

size_t	StrideWidth () const
	Get the stride width. More...

size_t &	StrideWidth ()
	Modify the stride width. More...

arma::cube const &	Weight () const
	Get the weight of the layer. More...

arma::cube &	Weight ()
	Modify the weight of the layer. More...

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

Detailed Description

template, typename BackwardConvolutionRule = NaiveConvolution, typename GradientConvolutionRule = NaiveConvolution, 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 48 of file convolution.hpp.

Constructor & Destructor Documentation

◆ Convolution() [1/3]

Convolution ( )

Create the Convolution object.

◆ Convolution() [2/3]

Convolution	(	const size_t	inSize,
		const size_t	outSize,
		const size_t	kernelWidth,
		const size_t	kernelHeight,
		const size_t	strideWidth = `1`,
		const size_t	strideHeight = `1`,
		const size_t	padW = `0`,
		const size_t	padH = `0`,
		const size_t	inputWidth = `0`,
		const size_t	inputHeight = `0`,
		const std::string &	paddingType = `"None"`
	)

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.
kernelWidth	Width of the filter/kernel.
kernelHeight	Height of the filter/kernel.
strideWidth	Stride of filter application in the x direction.
strideHeight	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.
paddingType	The type of padding (Valid or Same). Defaults to None.

◆ Convolution() [3/3]

Convolution	(	const size_t	inSize,
		const size_t	outSize,
		const size_t	kernelWidth,
		const size_t	kernelHeight,
		const size_t	strideWidth,
		const size_t	strideHeight,
		const std::tuple< size_t, size_t > &	padW,
		const std::tuple< size_t, size_t > &	padH,
		const size_t	inputWidth = `0`,
		const size_t	inputHeight = `0`,
		const std::string &	paddingType = `"None"`
	)

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.
kernelWidth	Width of the filter/kernel.
kernelHeight	Height of the filter/kernel.
strideWidth	Stride of filter application in the x direction.
strideHeight	Stride of filter application in the y direction.
padW	A two-value tuple indicating padding widths of the input. First value is padding at left side. Second value is padding on right side.
padH	A two-value tuple indicating padding heights of the input. First value is padding at top. Second value is padding on bottom.
inputWidth	The width of the input data.
inputHeight	The height of the input data.
paddingType	The type of padding (Valid or Same). Defaults to None.

Member Function Documentation

◆ Backward()

void Backward	(	const arma::Mat< eT > &	,
		const 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() [1/2]

arma::mat const& Bias ( ) const

inline

Get the bias of the layer.

Definition at line 166 of file convolution.hpp.

◆ Bias() [2/2]

arma::mat& Bias ( )

inline

Modify the bias of the layer.

Definition at line 168 of file convolution.hpp.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 181 of file convolution.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 183 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 > &	,
		const arma::Mat< eT > &	error,
		arma::Mat< eT > &	gradient
	)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 186 of file convolution.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 188 of file convolution.hpp.

◆ InputHeight() [1/2]

size_t InputHeight ( ) const

inline

Get the input height.

Definition at line 196 of file convolution.hpp.

◆ InputHeight() [2/2]

size_t& InputHeight ( )

inline

Modify the input height.

Definition at line 198 of file convolution.hpp.

◆ InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 171 of file convolution.hpp.

◆ InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 173 of file convolution.hpp.

◆ InputSize()

size_t InputSize ( ) const

inline

Get the input size.

Definition at line 211 of file convolution.hpp.

◆ InputWidth() [1/2]

size_t InputWidth ( ) const

inline

Get the input width.

Definition at line 191 of file convolution.hpp.

◆ InputWidth() [2/2]

size_t& InputWidth ( )

inline

Modify input the width.

Definition at line 193 of file convolution.hpp.

◆ KernelHeight() [1/2]

size_t KernelHeight ( ) const

inline

Get the kernel height.

Definition at line 222 of file convolution.hpp.

◆ KernelHeight() [2/2]

size_t& KernelHeight ( )

inline

Modify the kernel height.

Definition at line 224 of file convolution.hpp.

◆ KernelWidth() [1/2]

size_t KernelWidth ( ) const

inline

Get the kernel width.

Definition at line 217 of file convolution.hpp.

◆ KernelWidth() [2/2]

size_t& KernelWidth ( )

inline

Modify the kernel width.

Definition at line 219 of file convolution.hpp.

◆ OutputHeight() [1/2]

size_t OutputHeight ( ) const

inline

Get the output height.

Definition at line 206 of file convolution.hpp.

◆ OutputHeight() [2/2]

size_t& OutputHeight ( )

inline

Modify the output height.

Definition at line 208 of file convolution.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 176 of file convolution.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 178 of file convolution.hpp.

◆ OutputSize()

size_t OutputSize ( ) const

inline

Get the output size.

Definition at line 214 of file convolution.hpp.

◆ OutputWidth() [1/2]

size_t OutputWidth ( ) const

inline

Get the output width.

Definition at line 201 of file convolution.hpp.

◆ OutputWidth() [2/2]

size_t& OutputWidth ( )

inline

Modify the output width.

Definition at line 203 of file convolution.hpp.

◆ PadHBottom() [1/2]

size_t PadHBottom ( ) const

inline

Get the bottom padding height.

Definition at line 242 of file convolution.hpp.

◆ PadHBottom() [2/2]

size_t& PadHBottom ( )

inline

Modify the bottom padding height.

Definition at line 244 of file convolution.hpp.

◆ PadHTop() [1/2]

size_t PadHTop ( ) const

inline

Get the top padding height.

Definition at line 237 of file convolution.hpp.

◆ PadHTop() [2/2]

size_t& PadHTop ( )

inline

Modify the top padding height.

Definition at line 239 of file convolution.hpp.

◆ PadWLeft() [1/2]

size_t PadWLeft ( ) const

inline

Get the left padding width.

Definition at line 247 of file convolution.hpp.

◆ PadWLeft() [2/2]

size_t& PadWLeft ( )

inline

Modify the left padding width.

Definition at line 249 of file convolution.hpp.

◆ PadWRight() [1/2]

size_t PadWRight ( ) const

inline

Get the right padding width.

Definition at line 252 of file convolution.hpp.

◆ PadWRight() [2/2]

size_t& PadWRight ( )

inline

Modify the right padding width.

Definition at line 254 of file convolution.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 156 of file convolution.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 158 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 >::WeightSize().

◆ StrideHeight() [1/2]

size_t StrideHeight ( ) const

inline

Get the stride height.

Definition at line 232 of file convolution.hpp.

◆ StrideHeight() [2/2]

size_t& StrideHeight ( )

inline

Modify the stride height.

Definition at line 234 of file convolution.hpp.

◆ StrideWidth() [1/2]

size_t StrideWidth ( ) const

inline

Get the stride width.

Definition at line 227 of file convolution.hpp.

◆ StrideWidth() [2/2]

size_t& StrideWidth ( )

inline

Modify the stride width.

Definition at line 229 of file convolution.hpp.

◆ Weight() [1/2]

arma::cube const& Weight ( ) const

inline

Get the weight of the layer.

Definition at line 161 of file convolution.hpp.

◆ Weight() [2/2]

arma::cube& Weight ( )

inline

Modify the weight of the layer.

Definition at line 163 of file convolution.hpp.

◆ WeightSize()

size_t WeightSize ( ) const

inline

Get size of weights for the layer.

Definition at line 257 of file convolution.hpp.

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

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

/home/jenkins-mlpack/mlpack.org/_src/mlpack-3.4.2/src/mlpack/methods/ann/layer/convolution.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ Convolution() [1/3]

◆ Convolution() [2/3]

◆ Convolution() [3/3]

Member Function Documentation

◆ Backward()

◆ Bias() [1/2]

◆ Bias() [2/2]

◆ 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]

◆ InputSize()

◆ InputWidth() [1/2]

◆ InputWidth() [2/2]

◆ KernelHeight() [1/2]

◆ KernelHeight() [2/2]

◆ KernelWidth() [1/2]

◆ KernelWidth() [2/2]

◆ OutputHeight() [1/2]

◆ OutputHeight() [2/2]

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ OutputSize()

◆ OutputWidth() [1/2]

◆ OutputWidth() [2/2]

◆ PadHBottom() [1/2]

◆ PadHBottom() [2/2]

◆ PadHTop() [1/2]

◆ PadHTop() [2/2]

◆ PadWLeft() [1/2]

◆ PadWLeft() [2/2]

◆ PadWRight() [1/2]

◆ PadWRight() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Reset()

◆ serialize()

◆ StrideHeight() [1/2]

◆ StrideHeight() [2/2]

◆ StrideWidth() [1/2]

◆ StrideWidth() [2/2]

◆ Weight() [1/2]

◆ Weight() [2/2]

◆ WeightSize()