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

Implementation of the LSTM module class. More...

#include <lstm.hpp>

Public Member Functions
	LSTM ()
	Create the LSTM object.
	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...
	LSTM (const LSTM &layer)
	Copy constructor.
	LSTM (LSTM &&)
	Move constructor.
LSTM &	operator= (const LSTM &layer)
	Copy assignment operator.
LSTM &	operator= (LSTM &&layer)
	Move assignment operator.
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 OutputType >
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<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...
void	Reset ()
void	ResetCell (const size_t size)
template<typename InputType , typename ErrorType , typename GradientType >
void	Gradient (const InputType &input, const ErrorType &error, GradientType &gradient)
size_t	Rho () const
	Get the maximum number of steps to backpropagate through time (BPTT).
size_t &	Rho ()
	Modify the maximum number of steps to backpropagate through time (BPTT).
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	InSize () const
	Get the number of input units.
size_t	OutSize () const
	Get the number of output units.
size_t	WeightSize () const
	Get the size of the weights.
size_t	InputShape () const
	Get the shape of the input.
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::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).

Constructor & Destructor Documentation

LSTM()

template<typename InputDataType , typename OutputDataType >

mlpack::ann::LSTM< InputDataType, OutputDataType >::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()

template<typename InputDataType , typename OutputDataType >

template<typename InputType , typename ErrorType , typename GradientType >

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

Forward() [1/2]

template<typename InputDataType , typename OutputDataType >

template<typename InputType , typename OutputType >

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

Locally-stored cellState.

Forward() [2/2]

template<typename InputDataType , typename OutputDataType >

template<typename InputType , typename OutputType >

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

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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/lstm.hpp
• src/mlpack/methods/ann/layer/lstm_impl.hpp