mlpack::ann::MaxPooling< InputDataType, OutputDataType > Class Template Reference

Implementation of the MaxPooling layer. More...

#include <max_pooling.hpp>

Public Member Functions
	MaxPooling ()
	Create the MaxPooling object.
	MaxPooling (const size_t kernelWidth, const size_t kernelHeight, const size_t strideWidth=1, const size_t strideHeight=1, const bool floor=true)
	Create the MaxPooling object using the specified number of units. 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	Backward (const arma::Mat< eT > &, const arma::Mat< eT > &gy, arma::Mat< eT > &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...
const OutputDataType &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
const OutputDataType &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
size_t	InputWidth () const
	Get the input width.
size_t &	InputWidth ()
	Modify the input width.
size_t	InputHeight () const
	Get the input height.
size_t &	InputHeight ()
	Modify the input height.
size_t	OutputWidth () const
	Get the output width.
size_t &	OutputWidth ()
	Modify the output width.
size_t	OutputHeight () const
	Get the output height.
size_t &	OutputHeight ()
	Modify the output height.
size_t	InputSize () const
	Get the input size.
size_t	OutputSize () const
	Get the output size.
size_t	KernelWidth () const
	Get the kernel width.
size_t &	KernelWidth ()
	Modify the kernel width.
size_t	KernelHeight () const
	Get the kernel height.
size_t &	KernelHeight ()
	Modify the kernel height.
size_t	StrideWidth () const
	Get the stride width.
size_t &	StrideWidth ()
	Modify the stride width.
size_t	StrideHeight () const
	Get the stride height.
size_t &	StrideHeight ()
	Modify the stride height.
bool	Floor () const
	Get the value of the rounding operation.
bool &	Floor ()
	Modify the value of the rounding operation.
bool	Deterministic () const
	Get the value of the deterministic parameter.
bool &	Deterministic ()
	Modify the value of the deterministic parameter.
size_t	WeightSize () const
	Get the size of the weights.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer.

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
class mlpack::ann::MaxPooling< InputDataType, OutputDataType >

Implementation of the MaxPooling layer.

Template Parameters

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).

Constructor & Destructor Documentation

MaxPooling()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::MaxPooling< InputDataType, OutputDataType >::MaxPooling	(	const size_t	kernelWidth,
		const size_t	kernelHeight,
		const size_t	strideWidth = 1,
		const size_t	strideHeight = 1,
		const bool	floor = true
	)

Create the MaxPooling object using the specified number of units.

Parameters

kernelWidth	Width of the pooling window.
kernelHeight	Height of the pooling window.
strideWidth	Width of the stride operation.
strideHeight	Width of the stride operation.
floor	Rounding operator (floor or ceil).

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::MaxPooling< InputDataType, OutputDataType >::Backward	(	const arma::Mat< eT > &	,
		const arma::Mat< eT > &	gy,
		arma::Mat< eT > &	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.

Using the results from the feed forward pass.

Parameters

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

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::MaxPooling< InputDataType, OutputDataType >::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.

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

• src/mlpack/methods/ann/layer/max_pooling.hpp
• src/mlpack/methods/ann/layer/max_pooling_impl.hpp