Implementation of the Sequential class. More...

Public Member Functions
	Sequential (const bool model=true)
	Create the Sequential object using the specified parameters. More...

	~Sequential ()
	Destroy the Sequential object. More...

template<class LayerType , class... Args>
void	Add (Args... args)

void	Add (LayerTypes< CustomLayers... > layer)

template < typename eT >
void	Backward (const arma::Mat< eT > &&, 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...

void	DeleteModules ()

arma::mat const &	Delta () const
	Get the delta. More...

arma::mat &	Delta ()
	Modify the delta. More...

template < typename eT >
void	Forward (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 (arma::Mat< eT > &&input, arma::Mat< eT > &&error, arma::Mat< eT > &&)

arma::mat const &	Gradient () const
	Get the gradient. More...

arma::mat &	Gradient ()
	Modify the gradient. More...

arma::mat const &	InputParameter () const
	Get the input parameter. More...

arma::mat &	InputParameter ()
	Modify the input parameter. More...

std::vector< LayerTypes< CustomLayers... > > &	Model ()
	Return the model modules. More...

arma::mat const &	OutputParameter () const
	Get the output parameter. More...

arma::mat &	OutputParameter ()
	Modify the output parameter. More...

const arma::mat &	Parameters () const
	Return the initial point for the optimization. More...

arma::mat &	Parameters ()
	Modify the initial point for the optimization. More...

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

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, bool Residual = false, typename... CustomLayers>
class mlpack::ann::Sequential< InputDataType, OutputDataType, Residual, CustomLayers >

Implementation of the Sequential class.

The sequential class works as a feed-forward fully connected network container which plugs various layers together.

This class can also be used as a container for a residual block. In that case, the sizes of the input and output matrices of this class should be equal. A typedef has been added for use as a Residual<> class.

For more information, refer the following paper.

@article{He15,
  author    = {Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  year      = {2015},
  url       = {https://arxiv.org/abs/1512.03385},
  eprint    = {1512.03385},
}

Note: If this class is used as the first layer of a network, it should be preceded by IdentityLayer<>.

Note: This class should at least have two layers for a call to its Gradient() function.

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).
Residual	If true, use the object as a Residual block.

Definition at line 106 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ Sequential()

Sequential ( const bool model = true )

Create the Sequential object using the specified parameters.

Parameters

model Expose the all network modules.

◆ ~Sequential()

~Sequential ( )

Destroy the Sequential object.

Member Function Documentation

◆ Add() [1/2]

void Add ( Args... args )

inline

Definition at line 126 of file sequential.hpp.

◆ Add() [2/2]

void Add ( LayerTypes< CustomLayers... > layer )

inline

Definition at line 133 of file sequential.hpp.

References Sequential< InputDataType, OutputDataType, Residual, CustomLayers >::DeleteModules().

◆ Backward()

void Backward	(	const arma::Mat< eT > &&	,
		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.

◆ DeleteModules()

void DeleteModules ( )

Referenced by Sequential< InputDataType, OutputDataType, Residual, CustomLayers >::Add().

◆ Delta() [1/2]

arma::mat const& Delta ( ) const

inline

Get the delta.

Definition at line 167 of file sequential.hpp.

◆ Delta() [2/2]

arma::mat& Delta ( )

inline

Modify the delta.

Definition at line 169 of file sequential.hpp.

◆ Forward()

void Forward	(	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	(	arma::Mat< eT > &&	input,
		arma::Mat< eT > &&	error,
		arma::Mat< eT > &&
	)

◆ Gradient() [2/3]

arma::mat const& Gradient ( ) const

inline

Get the gradient.

Definition at line 172 of file sequential.hpp.

◆ Gradient() [3/3]

arma::mat& Gradient ( )

inline

Modify the gradient.

Definition at line 174 of file sequential.hpp.

References Sequential< InputDataType, OutputDataType, Residual, CustomLayers >::serialize().

◆ InputParameter() [1/2]

arma::mat const& InputParameter ( ) const

inline

Get the input parameter.

Definition at line 157 of file sequential.hpp.

◆ InputParameter() [2/2]

arma::mat& InputParameter ( )

inline

Modify the input parameter.

Definition at line 159 of file sequential.hpp.

◆ Model()

std::vector<LayerTypes<CustomLayers...> >& Model ( )

inline

Return the model modules.

Definition at line 141 of file sequential.hpp.

◆ OutputParameter() [1/2]

arma::mat const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 162 of file sequential.hpp.

◆ OutputParameter() [2/2]

arma::mat& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 164 of file sequential.hpp.

◆ Parameters() [1/2]

const arma::mat& Parameters ( ) const

inline

Return the initial point for the optimization.

Definition at line 152 of file sequential.hpp.

◆ Parameters() [2/2]

arma::mat& Parameters ( )

inline

Modify the initial point for the optimization.

Definition at line 154 of file sequential.hpp.

◆ serialize()

void serialize	(	Archive &	,
		const unsigned	int
	)

Serialize the layer.

Referenced by Sequential< InputDataType, OutputDataType, Residual, CustomLayers >::Gradient().

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

/home/ryan/src/mlpack.org/_src/mlpack-3.2.2/src/mlpack/methods/ann/layer/layer_types.hpp
/home/ryan/src/mlpack.org/_src/mlpack-3.2.2/src/mlpack/methods/ann/layer/sequential.hpp

Public Member Functions

Detailed Description

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, bool Residual = false, typename... CustomLayers> class mlpack::ann::Sequential< InputDataType, OutputDataType, Residual, CustomLayers >

Constructor & Destructor Documentation

◆ Sequential()

◆ ~Sequential()

Member Function Documentation

◆ Add() [1/2]

◆ Add() [2/2]

◆ Backward()

◆ DeleteModules()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Forward()

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InputParameter() [1/2]

◆ InputParameter() [2/2]

◆ Model()

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ serialize()

template<typename InputDataType = arma::mat, typename OutputDataType = arma::mat, bool Residual = false, typename... CustomLayers>
class mlpack::ann::Sequential< InputDataType, OutputDataType, Residual, CustomLayers >