Implementation of the LSTM module class. More...

Public Member Functions
	LSTM ()
	Create the LSTM object. More...

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

template
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
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
void	Forward (const InputType &input, OutputType &output, OutputType &cellState, bool useCellState=false)
	Ordinary feed-forward pass of a neural network, evaluating the function f(x) by propagating the activity forward through f. More...

template
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	InSize () const
	Get the number of input units. 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
void	serialize (Archive &ar, const unsigned int)
	Serialize the layer. More...

Detailed Description

template
class mlpack::ann::LSTM< InputDataType, OutputDataType >

Implementation of the LSTM module class.

The implementation corresponds to the following algorithm:

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

Note that if an LSTM 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 LSTM layer should be added.

For more information, see the following.

@article{Graves2013,
  author  = {Alex Graves and Abdel{-}rahman Mohamed and Geoffrey E. Hinton},
  title   = {Speech Recognition with Deep Recurrent Neural Networks},
  journal = CoRR},
  year    = {2013},
  url     = {http://arxiv.org/abs/1303.5778},
}

See also: FastLSTM for a faster LSTM version which combines the calculation of the input, forget, output gates and hidden state in a single step.

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).

Definition at line 76 of file layer_types.hpp.

Constructor & Destructor Documentation

◆ LSTM() [1/2]

LSTM ( )

Create the LSTM object.

◆ LSTM() [2/2]

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

Create the LSTM layer object using the specified parameters.

Parameters

inSize	The number of input units.
outSize	The number of output units.
rho	Maximum 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

input	The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

◆ Delta() [1/2]

OutputDataType const& Delta ( ) const

inline

Get the delta.

Definition at line 159 of file lstm.hpp.

◆ Delta() [2/2]

OutputDataType& Delta ( )

inline

Modify the delta.

Definition at line 161 of file lstm.hpp.

◆ Forward() [1/2]

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

input	Input data used for evaluating the specified function.
output	Resulting output activation.

◆ Forward() [2/2]

void Forward	(	const InputType &	input,
		OutputType &	output,
		OutputType &	cellState,
		bool	useCellState = `false`
	)

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.
cellState	Cell state of the LSTM.
useCellState	Use the cellState passed in the LSTM cell.

◆ 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 164 of file lstm.hpp.

◆ Gradient() [3/3]

OutputDataType& Gradient ( )

inline

Modify the gradient.

Definition at line 166 of file lstm.hpp.

◆ InSize()

size_t InSize ( ) const

inline

Get the number of input units.

Definition at line 169 of file lstm.hpp.

◆ OutputParameter() [1/2]

OutputDataType const& OutputParameter ( ) const

inline

Get the output parameter.

Definition at line 154 of file lstm.hpp.

◆ OutputParameter() [2/2]

OutputDataType& OutputParameter ( )

inline

Modify the output parameter.

Definition at line 156 of file lstm.hpp.

◆ OutSize()

size_t OutSize ( ) const

inline

Get the number of output units.

Definition at line 172 of file lstm.hpp.

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

◆ Parameters() [1/2]

OutputDataType const& Parameters ( ) const

inline

Get the parameters.

Definition at line 149 of file lstm.hpp.

◆ Parameters() [2/2]

OutputDataType& Parameters ( )

inline

Modify the parameters.

Definition at line 151 of file 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 144 of file lstm.hpp.

◆ Rho() [2/2]

size_t& Rho ( )

inline

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

Definition at line 146 of file lstm.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the layer.

Referenced by LSTM< InputDataType, OutputDataType >::OutSize().

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

/home/jenkins-mlpack/mlpack.org/_src/mlpack-3.4.2/src/mlpack/methods/ann/layer/layer_types.hpp
/home/jenkins-mlpack/mlpack.org/_src/mlpack-3.4.2/src/mlpack/methods/ann/layer/lstm.hpp

Public Member Functions

Detailed Description

template class mlpack::ann::LSTM< InputDataType, OutputDataType >

Constructor & Destructor Documentation

◆ LSTM() [1/2]

◆ LSTM() [2/2]

Member Function Documentation

◆ Backward()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ Forward() [1/2]

◆ Forward() [2/2]

◆ Gradient() [1/3]

◆ Gradient() [2/3]

◆ Gradient() [3/3]

◆ InSize()

◆ OutputParameter() [1/2]

◆ OutputParameter() [2/2]

◆ OutSize()

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ Reset()

◆ ResetCell()

◆ Rho() [1/2]

◆ Rho() [2/2]

◆ serialize()

template
class mlpack::ann::LSTM< InputDataType, OutputDataType >