Implementation of the MiniBatchDiscrimination layer. More...

Public Member Functions
	MiniBatchDiscrimination ()
	Create the MiniBatchDiscrimination object. More...

	MiniBatchDiscrimination (const size_t inSize, const size_t outSize, const size_t features)
	Create the MiniBatchDiscrimination layer object using the specified number of units. 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, calculating the function f(x) by propagating x backwards through f. More...

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

OutputDataType &	Delta ()
	Modify the delta. 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	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &, arma::Mat< eT > &gradient)
	Calculate the gradient using the output delta and the input activation. More...

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

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

InputDataType const &	InputParameter () const
	Get the input parameter. More...

InputDataType &	InputParameter ()
	Modify the input parameter. More...

size_t	InputShape () const
	Get the shape of the input. More...

OutputDataType const &	OutputParameter () const
	Get the output parameter. More...

OutputDataType &	OutputParameter ()
	Modify the output parameter. More...

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

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

void	Reset ()
	Reset the layer parameter. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the layer. More...

Detailed Description

template
<
typename
InputDataType
=
arma::mat
,
typename
OutputDataType
=
arma::mat
>

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

Implementation of the MiniBatchDiscrimination layer.

MiniBatchDiscrimination is a layer of the discriminator that allows the discriminator to look at multiple data examples in combination and perform what is called as mini-batch discrimination. This helps prevent the collapse of the generator parameters to a setting where it emits the same point. This happens because normally a discriminator will process each example independently and there will be no mechanism to diversify the outputs of the generator.

For more information, see the following.

@article{Goodfellow2016,
  author  = {Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung,
             Alec Radford, Xi Chen},
  title   = {Improved Techniques for Training GANs},
  year    = {2016},
  url     = {https://arxiv.org/abs/1606.03498},
}

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 124 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ MiniBatchDiscrimination() [1/2]

MiniBatchDiscrimination ( )

Create the MiniBatchDiscrimination object.

◆ MiniBatchDiscrimination() [2/2]

MiniBatchDiscrimination	(	const size_t	inSize,
		const size_t	outSize,
		const size_t	features
	)

Create the MiniBatchDiscrimination layer object using the specified number of units.

Parameters

inSize	The number of input units.
outSize	The number of output units.
features	The number of features to compute for each dimension.

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.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 128 of file minibatch_discrimination.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 130 of file minibatch_discrimination.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 > &	input,
		const arma::Mat< eT > &	,
		arma::Mat< eT > &	gradient
	)

Calculate the gradient using the output delta and the input activation.

Parameters

input	The input parameter used for calculating the gradient.
*	(error) The calculated error.
gradient	The calculated gradient.

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const

inline

Get the gradient.

Definition at line 133 of file minibatch_discrimination.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 135 of file minibatch_discrimination.hpp.

◆ InputParameter() [1/2]

InputDataType const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 118 of file minibatch_discrimination.hpp.

◆ InputParameter() [2/2]

InputDataType& InputParameter ( )

inline

Modify the input parameter.

Definition at line 120 of file minibatch_discrimination.hpp.

◆ InputShape()

size_t InputShape ( ) const

inline

Get the shape of the input.

Definition at line 138 of file minibatch_discrimination.hpp.

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

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 123 of file minibatch_discrimination.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 125 of file minibatch_discrimination.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 113 of file minibatch_discrimination.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 115 of file minibatch_discrimination.hpp.

◆ Reset()

void Reset ( )

Reset the layer parameter.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the layer.

Referenced by MiniBatchDiscrimination< InputDataType, OutputDataType >::InputShape().

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/layer_types.hpp
/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/minibatch_discrimination.hpp

Public Member Functions

Detailed Description

template<typenameInputDataType=arma::mat,typenameOutputDataType=arma::mat> class mlpack::ann::MiniBatchDiscrimination< InputDataType, OutputDataType >

Constructor & Destructor Documentation

◆ MiniBatchDiscrimination() [1/2]

◆ MiniBatchDiscrimination() [2/2]

Member Function Documentation

◆ Backward()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Forward()

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InputParameter() [1/2]

◆ InputParameter() [2/2]

◆ InputShape()

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Reset()

◆ serialize()

template
<
typename
InputDataType
=
arma::mat
,
typename
OutputDataType
=
arma::mat
>

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