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

Implementation of the AdaptiveMeanPooling. More...

#include <adaptive_mean_pooling.hpp>

Public Member Functions
	AdaptiveMeanPooling ()
	Create the AdaptiveMeanPooling object.
	AdaptiveMeanPooling (const size_t outputWidth, const size_t outputHeight)
	Create the AdaptiveMeanPooling object. More...
	AdaptiveMeanPooling (const std::tuple< size_t, size_t > &outputShape)
	Create the AdaptiveMeanPooling object. 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 > &input, 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	WeightSize () const
	Get the size of the weights.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t version)
	Serialize the layer.

Detailed Description

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

Implementation of the AdaptiveMeanPooling.

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

AdaptiveMeanPooling() [1/2]

template<typename InputDataType , typename OutputDataType >

mlpack::ann::AdaptiveMeanPooling< InputDataType, OutputDataType >::AdaptiveMeanPooling	(	const size_t	outputWidth,
		const size_t	outputHeight
	)

Create the AdaptiveMeanPooling object.

Parameters

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

AdaptiveMeanPooling() [2/2]

template<typename InputDataType , typename OutputDataType >

mlpack::ann::AdaptiveMeanPooling< InputDataType, OutputDataType >::AdaptiveMeanPooling

(

const std::tuple< size_t, size_t > &

outputShape

)

Create the AdaptiveMeanPooling object.

Parameters

outputShape

A two-value tuple indicating width and height of the output.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::AdaptiveMeanPooling< InputDataType, OutputDataType >::Backward	(	const arma::Mat< eT > &	input,
		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::AdaptiveMeanPooling< 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/adaptive_mean_pooling.hpp
• src/mlpack/methods/ann/layer/adaptive_mean_pooling_impl.hpp