FastLSTM< InputDataType, OutputDataType > Class Template Reference

An implementation of a faster version of the Fast LSTM network layer. More...

Public Types

typedef OutputDataType::elem_type ElemType
 
typedef InputDataType::elem_type InputElemType
 

Public Member Functions

 FastLSTM ()
 Create the Fast LSTM object. More...

 
 FastLSTM (const FastLSTM &layer)
 Copy Constructor. More...

 
 FastLSTM (FastLSTM &&layer)
 Move Constructor. More...

 
 FastLSTM (const size_t inSize, const size_t outSize, const size_t rho=std::numeric_limits< size_t >::max())
 Create the Fast LSTM layer object using the specified parameters. More...

 
template
<
typename
InputType
,
typename
ErrorType
,
typename
GradientType
>
void Backward (const InputType &input, const ErrorType &gy, GradientType &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...

 
template
<
typename
InputType
,
typename
OutputType
>
void Forward (const InputType &input, OutputType &output)
 Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

 
template
<
typename
InputType
,
typename
ErrorType
,
typename
GradientType
>
void Gradient (const InputType &input, const ErrorType &error, GradientType &gradient)
 
OutputDataType const & Gradient () const
 Get the gradient. More...

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

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

 
size_t InSize () const
 Get the number of input units. More...

 
FastLSTMoperator= (const FastLSTM &layer)
 Copy assignment operator. More...

 
FastLSTMoperator= (FastLSTM &&layer)
 Move assignment operator. More...

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

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

 
size_t OutSize () const
 Get the number of output units. More...

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

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

 
void Reset ()
 
void ResetCell (const size_t size)
 
size_t Rho () const
 Get the maximum number of steps to backpropagate through time (BPTT). More...

 
size_t & Rho ()
 Modify the maximum number of steps to backpropagate through time (BPTT). More...

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

 
size_t WeightSize () const
 Get the size of the weight matrix. More...

 

Detailed Description


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

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

An implementation of a faster version of the Fast LSTM network layer.

Basically by combining the calculation of the input, forget, output gates and hidden state in a single step. The standard formula changes as follows:

\begin{eqnarray} i &=& sigmoid(W \cdot x + W \cdot h + b) \\ f &=& sigmoid(W \cdot x + W \cdot h + b) \\ z &=& tanh(W \cdot x + W \cdot h + b) \\ c &=& f \cdot c + i \cdot z \\ o &=& sigmoid(W \cdot x + W \cdot h + b) \\ h &=& o \cdot tanh(c) \end{eqnarray}

Note that FastLSTM network layer does not use peephole connections between the cell and gates.

Note also that if a FastLSTM layer is desired as the first layer of a neural network, an IdentityLayer should be added to the network as the first layer, and then the FastLSTM layer should be added.

For more information, see the following.

@article{Hochreiter1997,
author = {Hochreiter, Sepp and Schmidhuber, J\"{u}rgen},
title = {Long Short-term Memory},
journal = {Neural Comput.},
year = {1997},
url = {https://www.bioinf.jku.at/publications/older/2604.pdf}
}
See also
LSTM for a standard implementation of the LSTM layer.
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 66 of file fast_lstm.hpp.

Member Typedef Documentation

◆ ElemType

typedef OutputDataType::elem_type ElemType

Definition at line 71 of file fast_lstm.hpp.

◆ InputElemType

typedef InputDataType::elem_type InputElemType

Definition at line 70 of file fast_lstm.hpp.

Constructor & Destructor Documentation

◆ FastLSTM() [1/4]

FastLSTM ( )

Create the Fast LSTM object.

◆ FastLSTM() [2/4]

FastLSTM ( const FastLSTM< InputDataType, OutputDataType > &  layer)

Copy Constructor.

◆ FastLSTM() [3/4]

FastLSTM ( FastLSTM< InputDataType, OutputDataType > &&  layer)

Move Constructor.

◆ FastLSTM() [4/4]

FastLSTM ( const size_t  inSize,
const size_t  outSize,
const size_t  rho = std::numeric_limits< size_t >::max() 
)

Create the Fast LSTM layer object using the specified parameters.

Parameters
inSizeThe number of input units.
outSizeThe number of output units.
rhoMaximum number of steps to backpropagate through time (BPTT).

Member Function Documentation

◆ Backward()

void Backward ( const InputType &  input,
const ErrorType &  gy,
GradientType &  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
inputThe propagated input activation.
gyThe backpropagated error.
gThe calculated gradient.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const
inline

Get the delta.

Definition at line 164 of file fast_lstm.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )
inline

Modify the delta.

Definition at line 166 of file fast_lstm.hpp.

◆ Forward()

void Forward ( const InputType &  input,
OutputType &  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 InputType &  input,
const ErrorType &  error,
GradientType &  gradient 
)

◆ Gradient() [2/3]

OutputDataType const& Gradient ( ) const
inline

Get the gradient.

Definition at line 169 of file fast_lstm.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )
inline

Modify the gradient.

Definition at line 171 of file fast_lstm.hpp.

◆ InputShape()

size_t InputShape ( ) const
inline

Get the shape of the input.

Definition at line 186 of file fast_lstm.hpp.

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

◆ InSize()

size_t InSize ( ) const
inline

Get the number of input units.

Definition at line 174 of file fast_lstm.hpp.

◆ operator=() [1/2]

FastLSTM& operator= ( const FastLSTM< InputDataType, OutputDataType > &  layer)

Copy assignment operator.

◆ operator=() [2/2]

FastLSTM& operator= ( FastLSTM< InputDataType, OutputDataType > &&  layer)

Move assignment operator.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const
inline

Get the output parameter.

Definition at line 159 of file fast_lstm.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )
inline

Modify the output parameter.

Definition at line 161 of file fast_lstm.hpp.

◆ OutSize()

size_t OutSize ( ) const
inline

Get the number of output units.

Definition at line 177 of file fast_lstm.hpp.

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const
inline

Get the parameters.

Definition at line 154 of file fast_lstm.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )
inline

Modify the parameters.

Definition at line 156 of file fast_lstm.hpp.

◆ Reset()

void Reset ( )

◆ ResetCell()

void ResetCell ( const size_t  size)

◆ Rho() [1/2]

size_t Rho ( ) const
inline

Get the maximum number of steps to backpropagate through time (BPTT).

Definition at line 149 of file fast_lstm.hpp.

◆ Rho() [2/2]

size_t& Rho ( )
inline

Modify the maximum number of steps to backpropagate through time (BPTT).

Definition at line 151 of file fast_lstm.hpp.

◆ serialize()

void serialize ( Archive &  ar,
const uint32_t   
)

Serialize the layer.

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

◆ WeightSize()

size_t WeightSize ( ) const
inline

Get the size of the weight matrix.

Definition at line 180 of file fast_lstm.hpp.


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