RecurrentAttention< InputDataType, OutputDataType > Class Template Reference

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

Public Member Functions

 RecurrentAttention ()
 Default constructor: this will not give a usable RecurrentAttention object, so be sure to set all the parameters before use. More...

 
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 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...

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

 
OutputDataType & Delta ()
 Modify the delta. More...

 
bool Deterministic () const
 The value of the deterministic parameter. More...

 
bool & Deterministic ()
 Modify the value of the deterministic parameter. 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 > &, const arma::Mat< eT > &, arma::Mat< eT > &)
 
OutputDataType const & Gradient () const
 Get the gradient. More...

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

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

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

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

 
size_t OutSize () const
 Get the module output size. More...

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

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

 
size_t const & Rho () const
 Get the number of steps to backpropagate through time. 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::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
InputDataTypeType of the input data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).
OutputDataTypeType of the output data (arma::colvec, arma::mat, arma::sp_mat or arma::cube).

Definition at line 205 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ RecurrentAttention() [1/2]

Default constructor: this will not give a usable RecurrentAttention object, so be sure to set all the parameters before use.

◆ RecurrentAttention() [2/2]

RecurrentAttention ( const size_t  outSize,
const RNNModuleType &  rnn,
const ActionModuleType &  action,
const size_t  rho 
)

Create the RecurrentAttention object using the specified modules.

Parameters
outSizeThe module output size.
rnnThe recurrent neural network module.
actionThe action module.
rhoMaximum number of steps to backpropagate through time (BPTT).

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 trough f.

Using the results from the feed forward pass.

Parameters
*(input) The propagated input activation.
gyThe backpropagated error.
gThe calculated gradient.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const
inline

Get the delta.

Definition at line 133 of file recurrent_attention.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )
inline

Modify the delta.

Definition at line 135 of file recurrent_attention.hpp.

◆ Deterministic() [1/2]

bool Deterministic ( ) const
inline

The value of the deterministic parameter.

Definition at line 118 of file recurrent_attention.hpp.

◆ Deterministic() [2/2]

bool& Deterministic ( )
inline

Modify the value of the deterministic parameter.

Definition at line 120 of file recurrent_attention.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
inputInput data used for evaluating the specified function.
outputResulting output activation.

◆ Gradient() [1/3]

void Gradient ( const arma::Mat< eT > &  ,
const arma::Mat< eT > &  ,
arma::Mat< eT > &   
)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const
inline

Get the gradient.

Definition at line 138 of file recurrent_attention.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )
inline

Modify the gradient.

Definition at line 140 of file recurrent_attention.hpp.

◆ Model()

std::vector<LayerTypes<> >& Model ( )
inline

Get the model modules.

Definition at line 115 of file recurrent_attention.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 128 of file recurrent_attention.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )
inline

Modify the output parameter.

Definition at line 130 of file recurrent_attention.hpp.

◆ OutSize()

size_t OutSize ( ) const
inline

Get the module output size.

Definition at line 143 of file recurrent_attention.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const
inline

Get the parameters.

Definition at line 123 of file recurrent_attention.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )
inline

Modify the parameters.

Definition at line 125 of file recurrent_attention.hpp.

◆ Rho()

size_t const& Rho ( ) const
inline

Get the number of steps to backpropagate through time.

Definition at line 146 of file recurrent_attention.hpp.

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

◆ serialize()

void serialize ( Archive &  ar,
const uint32_t   
)

The documentation for this class was generated from the following files:
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/layer_types.hpp
  • /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/ann/layer/recurrent_attention.hpp