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

This class implements the Recurrent Model for Visual Attention, using a variety of possible layer implementations. More...

#include <recurrent_attention.hpp>

Public Member Functions
	RecurrentAttention ()
	Default constructor: this will not give a usable RecurrentAttention object, so be sure to set all the parameters before use.
template<typename RNNModuleType , typename ActionModuleType >
	RecurrentAttention (const size_t outSize, const RNNModuleType &rnn, const ActionModuleType &action, const size_t rho)
	Create the RecurrentAttention object using the specified modules. 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	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 trough f. More...
template<typename eT >
void	Gradient (const arma::Mat< eT > &, const arma::Mat< eT > &, arma::Mat< eT > &)
std::vector< LayerTypes<> > &	Model ()
	Get the model modules.
bool	Deterministic () const
	The value of the deterministic parameter.
bool &	Deterministic ()
	Modify the value of the deterministic parameter.
OutputDataType const &	Parameters () const
	Get the parameters.
OutputDataType &	Parameters ()
	Modify the parameters.
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.
OutputDataType const &	Gradient () const
	Get the gradient.
OutputDataType &	Gradient ()
	Modify the gradient.
size_t	OutSize () const
	Get the module output size.
size_t const &	Rho () const
	Get the number of steps to backpropagate through time.
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::RecurrentAttention< InputDataType, OutputDataType >

This class implements the Recurrent Model for Visual Attention, using a variety of possible layer implementations.

For more information, see the following paper.

@article{MnihHGK14,
  title   = {Recurrent Models of Visual Attention},
  author  = {Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu},
  journal = {CoRR},
  volume  = {abs/1406.6247},
  year    = {2014},
  url     = {https://arxiv.org/abs/1406.6247}
}

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

RecurrentAttention()

template<typename InputDataType , typename OutputDataType >

template<typename RNNModuleType , typename ActionModuleType >

mlpack::ann::RecurrentAttention< InputDataType, OutputDataType >::RecurrentAttention	(	const size_t	outSize,
		const RNNModuleType &	rnn,
		const ActionModuleType &	action,
		const size_t	rho
	)

Create the RecurrentAttention object using the specified modules.

Parameters

outSize	The module output size.
rnn	The recurrent neural network module.
action	The action module.
rho	Maximum number of steps to backpropagate through time (BPTT).

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::RecurrentAttention< InputDataType, OutputDataType >::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 trough f.

Using the results from the feed forward pass.

Parameters

*	(input) The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::RecurrentAttention< InputDataType, OutputDataType >::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.

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

• src/mlpack/methods/ann/layer/layer_types.hpp
• src/mlpack/methods/ann/layer/recurrent_attention.hpp
• src/mlpack/methods/ann/layer/recurrent_attention_impl.hpp