mlpack::ann Namespace Reference

Artificial Neural Network. More...

Classes
class	Add
	Implementation of the Add module class. More...
class	AddMerge
	Implementation of the AddMerge module class. More...
class	AddVisitor
	AddVisitor exposes the Add() method of the given module. More...
class	BackwardVisitor
	BackwardVisitor executes the Backward() function given the input, error and delta parameter. More...
class	BaseLayer
	Implementation of the base layer. More...
class	Concat
	Implementation of the Concat class. More...
class	ConcatPerformance
	Implementation of the concat performance class. More...
class	Constant
	Implementation of the constant layer. More...
class	Convolution
	Implementation of the Convolution class. More...
class	DeleteVisitor
	DeleteVisitor executes the destructor of the instantiated object. More...
class	DeltaVisitor
	DeltaVisitor exposes the delta parameter of the given module. More...
class	DeterministicSetVisitor
	DeterministicSetVisitor set the deterministic parameter given the deterministic value. More...
class	DropConnect
	The DropConnect layer is a regularizer that randomly with probability ratio sets the connection values to zero and scales the remaining elements by factor 1 /(1 - ratio). More...
class	Dropout
	The dropout layer is a regularizer that randomly with probability ratio sets input values to zero and scales the remaining elements by factor 1 / (1 - ratio). More...
class	ELU
	The ELU activation function, defined by. More...
class	FFN
	Implementation of a standard feed forward network. More...
class	FFTConvolution
	Computes the two-dimensional convolution through fft. More...
class	ForwardVisitor
	ForwardVisitor executes the Forward() function given the input and output parameter. More...
class	FullConvolution
class	Glimpse
	The glimpse layer returns a retina-like representation (down-scaled cropped images) of increasing scale around a given location in a given image. More...
class	GradientSetVisitor
	GradientSetVisitor update the gradient parameter given the gradient set. More...
class	GradientUpdateVisitor
	GradientUpdateVisitor update the gradient parameter given the gradient set. More...
class	GradientVisitor
	SearchModeVisitor executes the Gradient() method of the given module using the input and delta parameter. More...
class	GradientZeroVisitor
class	HardTanH
	The Hard Tanh activation function, defined by. More...
class	IdentityFunction
	The identity function, defined by. More...
class	Join
	Implementation of the Join module class. More...
class	KathirvalavakumarSubavathiInitialization
	This class is used to initialize the weight matrix with the method proposed by T. More...
class	LayerTraits
	This is a template class that can provide information about various layers. More...
class	LeakyReLU
	The LeakyReLU activation function, defined by. More...
class	Linear
	Implementation of the Linear layer class. More...
class	LinearNoBias
	Implementation of the LinearNoBias class. More...
class	LoadOutputParameterVisitor
	LoadOutputParameterVisitor restores the output parameter using the given parameter set. More...
class	LogisticFunction
	The logistic function, defined by. More...
class	LogSoftMax
	Implementation of the log softmax layer. More...
class	Lookup
	Implementation of the Lookup class. More...
class	LSTM
	An implementation of a lstm network layer. More...
class	MaxPooling
	Implementation of the MaxPooling layer. More...
class	MaxPoolingRule
class	MeanPooling
	Implementation of the MeanPooling. More...
class	MeanPoolingRule
class	MeanSquaredError
	The mean squared error performance function measures the network's performance according to the mean of squared errors. More...
class	MultiplyConstant
	Implementation of the multiply constant layer. More...
class	NaiveConvolution
	Computes the two-dimensional convolution. More...
class	NegativeLogLikelihood
	Implementation of the negative log likelihood layer. More...
class	NguyenWidrowInitialization
	This class is used to initialize the weight matrix with the Nguyen-Widrow method. More...
class	OivsInitialization
	This class is used to initialize the weight matrix with the oivs method. More...
class	OrthogonalInitialization
	This class is used to initialize the weight matrix with the orthogonal matrix initialization. More...
class	OutputHeightVisitor
	OutputWidthVisitor exposes the OutputHeight() method of the given module. More...
class	OutputParameterVisitor
	OutputParameterVisitor exposes the output parameter of the given module. More...
class	OutputWidthVisitor
	OutputWidthVisitor exposes the OutputWidth() method of the given module. More...
class	ParametersSetVisitor
	ParametersSetVisitor update the parameters set using the given matrix. More...
class	ParametersVisitor
	ParametersVisitor exposes the parameters set of the given module and stores the parameters set into the given matrix. More...
class	PReLU
	The PReLU activation function, defined by (where alpha is trainable) More...
class	RandomInitialization
	This class is used to initialize randomly the weight matrix. More...
class	RectifierFunction
	The rectifier function, defined by. More...
class	Recurrent
	Implementation of the RecurrentLayer class. More...
class	RecurrentAttention
	This class implements the Recurrent Model for Visual Attention, using a variety of possible layer implementations. More...
class	ReinforceNormal
	Implementation of the reinforce normal layer. More...
class	ResetVisitor
	ResetVisitor executes the Reset() function. More...
class	RewardSetVisitor
	RewardSetVisitor set the reward parameter given the reward value. More...
class	RNN
	Implementation of a standard recurrent neural network container. More...
class	SaveOutputParameterVisitor
	SaveOutputParameterVisitor saves the output parameter into the given parameter set. More...
class	Select
	The select module selects the specified column from a given input matrix. More...
class	Sequential
	Implementation of the Sequential class. More...
class	SetInputHeightVisitor
	SetInputHeightVisitor updates the input height parameter with the given input height. More...
class	SetInputWidthVisitor
	SetInputWidthVisitor updates the input width parameter with the given input width. More...
class	SoftplusFunction
	The softplus function, defined by. More...
class	SoftsignFunction
	The softsign function, defined by. More...
class	SVDConvolution
	Computes the two-dimensional convolution using singular value decomposition. More...
class	TanhFunction
	The tanh function, defined by. More...
class	ValidConvolution
class	VRClassReward
	Implementation of the variance reduced classification reinforcement layer. More...
class	WeightSetVisitor
	WeightSetVisitor update the module parameters given the parameters set. More...
class	WeightSizeVisitor
	WeightSizeVisitor returns the number of weights of the given module. More...
class	ZeroInitialization
	This class is used to initialize randomly the weight matrix. More...

Typedefs
template<class ActivationFunction = IdentityFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	IdentityLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Identity-Layer using the identity activation function. More...
using	LayerTypes = boost::variant< Add< arma::mat, arma::mat > *, AddMerge< arma::mat, arma::mat > *, BaseLayer< LogisticFunction, arma::mat, arma::mat > *, BaseLayer< IdentityFunction, arma::mat, arma::mat > *, BaseLayer< TanhFunction, arma::mat, arma::mat > *, BaseLayer< RectifierFunction, arma::mat, arma::mat > *, Concat< arma::mat, arma::mat > *, ConcatPerformance< NegativeLogLikelihood< arma::mat, arma::mat >, arma::mat, arma::mat > *, Constant< arma::mat, arma::mat > *, Convolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< FullConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, DropConnect< arma::mat, arma::mat > *, Dropout< arma::mat, arma::mat > *, Glimpse< arma::mat, arma::mat > *, HardTanH< arma::mat, arma::mat > *, Join< arma::mat, arma::mat > *, LeakyReLU< arma::mat, arma::mat > *, Linear< arma::mat, arma::mat > *, LinearNoBias< arma::mat, arma::mat > *, LogSoftMax< arma::mat, arma::mat > *, Lookup< arma::mat, arma::mat > *, LSTM< arma::mat, arma::mat > *, MaxPooling< arma::mat, arma::mat > *, MeanPooling< arma::mat, arma::mat > *, MeanSquaredError< arma::mat, arma::mat > *, MultiplyConstant< arma::mat, arma::mat > *, NegativeLogLikelihood< arma::mat, arma::mat > *, PReLU< arma::mat, arma::mat > *, Recurrent< arma::mat, arma::mat > *, RecurrentAttention< arma::mat, arma::mat > *, ReinforceNormal< arma::mat, arma::mat > *, Select< arma::mat, arma::mat > *, Sequential< arma::mat, arma::mat > *, VRClassReward< arma::mat, arma::mat > * >
template<class ActivationFunction = RectifierFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	ReLULayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard rectified linear unit non-linearity layer. More...
template<class ActivationFunction = LogisticFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	SigmoidLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard Sigmoid-Layer using the logistic activation function. More...
template<class ActivationFunction = TanhFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>
using	TanHLayer = BaseLayer< ActivationFunction, InputDataType, OutputDataType >
	Standard hyperbolic tangent layer. More...

Functions
	HAS_MEM_FUNC (Gradient, HasGradientCheck)
	HAS_MEM_FUNC (Deterministic, HasDeterministicCheck)
	HAS_MEM_FUNC (Parameters, HasParametersCheck)
	HAS_MEM_FUNC (Add, HasAddCheck)
	HAS_MEM_FUNC (Model, HasModelCheck)
	HAS_MEM_FUNC (Location, HasLocationCheck)
	HAS_MEM_FUNC (Reset, HasResetCheck)
	HAS_MEM_FUNC (Reward, HasRewardCheck)
	HAS_MEM_FUNC (InputWidth, HasInputWidth)
	HAS_MEM_FUNC (InputHeight, HasInputHeight)
	HAS_MEM_FUNC (InputHeight, HasRho)

Detailed Description

Artificial Neural Network.

Typedef Documentation

template<class ActivationFunction = IdentityFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>

using mlpack::ann::IdentityLayer = typedef BaseLayer< ActivationFunction, InputDataType, OutputDataType>

Standard Identity-Layer using the identity activation function.

Definition at line 147 of file base_layer.hpp.

using mlpack::ann::LayerTypes = typedef boost::variant< Add<arma::mat, arma::mat>*, AddMerge<arma::mat, arma::mat>*, BaseLayer<LogisticFunction, arma::mat, arma::mat>*, BaseLayer<IdentityFunction, arma::mat, arma::mat>*, BaseLayer<TanhFunction, arma::mat, arma::mat>*, BaseLayer<RectifierFunction, arma::mat, arma::mat>*, Concat<arma::mat, arma::mat>*, ConcatPerformance<NegativeLogLikelihood<arma::mat, arma::mat>, arma::mat, arma::mat>*, Constant<arma::mat, arma::mat>*, Convolution<NaiveConvolution<ValidConvolution>, NaiveConvolution<FullConvolution>, NaiveConvolution<ValidConvolution>, arma::mat, arma::mat>*, DropConnect<arma::mat, arma::mat>*, Dropout<arma::mat, arma::mat>*, Glimpse<arma::mat, arma::mat>*, HardTanH<arma::mat, arma::mat>*, Join<arma::mat, arma::mat>*, LeakyReLU<arma::mat, arma::mat>*, Linear<arma::mat, arma::mat>*, LinearNoBias<arma::mat, arma::mat>*, LogSoftMax<arma::mat, arma::mat>*, Lookup<arma::mat, arma::mat>*, LSTM<arma::mat, arma::mat>*, MaxPooling<arma::mat, arma::mat>*, MeanPooling<arma::mat, arma::mat>*, MeanSquaredError<arma::mat, arma::mat>*, MultiplyConstant<arma::mat, arma::mat>*, NegativeLogLikelihood<arma::mat, arma::mat>*, PReLU<arma::mat, arma::mat>*, Recurrent<arma::mat, arma::mat>*, RecurrentAttention<arma::mat, arma::mat>*, ReinforceNormal<arma::mat, arma::mat>*, Select<arma::mat, arma::mat>*, Sequential<arma::mat, arma::mat>*, VRClassReward<arma::mat, arma::mat>* >

Definition at line 115 of file layer_types.hpp.

template<class ActivationFunction = RectifierFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>

using mlpack::ann::ReLULayer = typedef BaseLayer< ActivationFunction, InputDataType, OutputDataType>

Standard rectified linear unit non-linearity layer.

Definition at line 158 of file base_layer.hpp.

template<class ActivationFunction = LogisticFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>

using mlpack::ann::SigmoidLayer = typedef BaseLayer< ActivationFunction, InputDataType, OutputDataType>

Standard Sigmoid-Layer using the logistic activation function.

Definition at line 136 of file base_layer.hpp.

template<class ActivationFunction = TanhFunction, typename InputDataType = arma::mat, typename OutputDataType = arma::mat>

using mlpack::ann::TanHLayer = typedef BaseLayer< ActivationFunction, InputDataType, OutputDataType>

Standard hyperbolic tangent layer.

Definition at line 169 of file base_layer.hpp.

Function Documentation

mlpack::ann::HAS_MEM_FUNC	(	Gradient	,
		HasGradientCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Deterministic	,
		HasDeterministicCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Parameters	,
		HasParametersCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Add	,
		HasAddCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Model	,
		HasModelCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Location	,
		HasLocationCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Reset	,
		HasResetCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	Reward	,
		HasRewardCheck
	)

mlpack::ann::HAS_MEM_FUNC	(	InputWidth	,
		HasInputWidth
	)

mlpack::ann::HAS_MEM_FUNC	(	InputHeight	,
		HasInputHeight
	)

mlpack::ann::HAS_MEM_FUNC	(	InputHeight	,
		HasRho
	)