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

Definition and Implementation of the Bilinear Interpolation Layer. More...

#include <bilinear_interpolation.hpp>

Public Member Functions
	BilinearInterpolation ()
	Create the Bilinear Interpolation object.
	BilinearInterpolation (const size_t inRowSize, const size_t inColSize, const size_t outRowSize, const size_t outColSize, const size_t depth)
	The constructor for the Bilinear Interpolation. More...
template<typename eT >
void	Forward (const arma::Mat< eT > &input, arma::Mat< eT > &output)
	Forward pass through the layer. More...
template<typename eT >
void	Backward (const arma::Mat< eT > &, const arma::Mat< eT > &gradient, arma::Mat< eT > &output)
	Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backwards through f. More...
OutputDataType const &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the delta.
OutputDataType &	Delta ()
	Modify the delta.
size_t const &	InRowSize () const
	Get the row size of the input.
size_t &	InRowSize ()
	Modify the row size of the input.
size_t const &	InColSize () const
	Get the column size of the input.
size_t &	InColSize ()
	Modify the column size of the input.
size_t const &	OutRowSize () const
	Get the row size of the output.
size_t &	OutRowSize ()
	Modify the row size of the output.
size_t const &	OutColSize () const
	Get the column size of the output.
size_t &	OutColSize ()
	Modify the column size of the output.
size_t const &	InDepth () const
	Get the depth of the input.
size_t &	InDepth ()
	Modify the depth of the input.
size_t	InputShape () const
	Get the shape of the input.
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::BilinearInterpolation< InputDataType, OutputDataType >

Definition and Implementation of the Bilinear Interpolation Layer.

Bilinear Interpolation is an mathematical technique, primarily used for scaling purposes. It is an extension of linear interpolation, for interpolating functions of two variables on a rectangular grid. The key idea is to perform linear interpolation first in one direction (e.g., along x-axis), and then again in the other direction (i.e., y-axis), on four different known points in the grid. This way, we represent any arbitrary point, present within the grid, as a function of those four points.

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

BilinearInterpolation()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::BilinearInterpolation< InputDataType, OutputDataType >::BilinearInterpolation	(	const size_t	inRowSize,
		const size_t	inColSize,
		const size_t	outRowSize,
		const size_t	outColSize,
		const size_t	depth
	)

The constructor for the Bilinear Interpolation.

Parameters

inRowSize	Number of input rows.
inColSize	Number of input columns.
outRowSize	Number of output rows.
outColSize	Number of output columns.
depth	Number of input slices.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::BilinearInterpolation< InputDataType, OutputDataType >::Backward	(	const arma::Mat< eT > &	,
		const arma::Mat< eT > &	gradient,
		arma::Mat< eT > &	output
	)

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. Since the layer does not have any learn-able parameters, we just have to down-sample the gradient to make its size compatible with the input size.

Parameters

*	(input) The input matrix.
gradient	The computed backward gradient.
output	The resulting down-sampled output.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::BilinearInterpolation< InputDataType, OutputDataType >::Forward	(	const arma::Mat< eT > &	input,
		arma::Mat< eT > &	output
	)

Forward pass through the layer.

The layer interpolates the matrix using the given Bilinear Interpolation method.

Parameters

input	The input matrix.
output	The resulting interpolated output matrix.

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

• src/mlpack/methods/ann/layer/bilinear_interpolation.hpp
• src/mlpack/methods/ann/layer/bilinear_interpolation_impl.hpp