DiceLoss< InputDataType, OutputDataType > Class Template Reference

The dice loss performance function measures the network's performance according to the dice coefficient between the input and target distributions. More...

Public Member Functions
	DiceLoss (const double smooth=1)
	Create the DiceLoss object. More...
template < typename InputType , typename TargetType , typename OutputType >
void	Backward (const InputType &input, const TargetType &target, OutputType &output)
	Ordinary feed backward pass of a neural network. More...
template < typename InputType , typename TargetType >
InputType::elem_type	Forward (const InputType &input, const TargetType &target)
	Computes the dice loss function. More...
OutputDataType &	OutputParameter () const
	Get the output parameter. More...
OutputDataType &	OutputParameter ()
	Modify the output parameter. More...
template < typename Archive >
void	serialize (Archive &ar, const unsigned int)
	Serialize the layer. More...
double	Smooth () const
	Get the smooth. More...
double &	Smooth ()
	Modify the smooth. More...

Detailed Description

template

<

typename

InputDataType

=

arma::mat

,

typename

OutputDataType

=

arma::mat

>

class mlpack::ann::DiceLoss< InputDataType, OutputDataType >

The dice loss performance function measures the network's performance according to the dice coefficient between the input and target distributions.

For more information see the following.

@article{Milletari2016,
  author    = {Fausto Milletari and Nassir Navab and Seyed{-}Ahmad Ahmadi},
  title     = {V-Net: Fully Convolutional Neural Networks for
               Volumetric Medical Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1606.04797},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.04797},
  archivePrefix = {arXiv},
  eprint    = {1606.04797},
}

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).

Definition at line 50 of file dice_loss.hpp.

Constructor & Destructor Documentation

DiceLoss()

DiceLoss

(

const double

smooth = 1

)

Create the DiceLoss object.

Parameters

smooth

The Laplace smoothing parameter.

Member Function Documentation

Backward()

void Backward	(	const InputType &	input,
		const TargetType &	target,
		OutputType &	output
	)

Ordinary feed backward pass of a neural network.

Parameters

input	The propagated input activation.
target	The target vector.
output	The calculated error.

Forward()

InputType::elem_type Forward	(	const InputType &	input,
		const TargetType &	target
	)

Computes the dice loss function.

Parameters

input	Input data used for evaluating the specified function.
target	The target vector.

OutputParameter() [1/2]

OutputDataType& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 83 of file dice_loss.hpp.

OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 85 of file dice_loss.hpp.

serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

Referenced by DiceLoss< InputDataType, OutputDataType >::Smooth().

Smooth() [1/2]

double Smooth ( ) const

inline

Get the smooth.

Definition at line 88 of file dice_loss.hpp.

Smooth() [2/2]

double& Smooth ( )

inline

Modify the smooth.

Definition at line 90 of file dice_loss.hpp.

References DiceLoss< InputDataType, OutputDataType >::serialize().

---

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

• /var/www/mlpack.ratml.org/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/loss_functions/dice_loss.hpp