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

Declaration of the Layer Normalization class. More...

#include <layer_norm.hpp>

Public Member Functions
	LayerNorm ()
	Create the LayerNorm object. More...
	LayerNorm (const size_t size, const double eps=1e-8)
	Create the LayerNorm object for a specified number of input units. More...
void	Reset ()
	Reset the layer parameters.
template<typename eT >
void	Forward (const arma::Mat< eT > &input, arma::Mat< eT > &output)
	Forward pass of Layer Normalization. More...
template<typename eT >
void	Backward (const arma::Mat< eT > &input, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
	Backward pass through the layer. More...
template<typename eT >
void	Gradient (const arma::Mat< eT > &input, const arma::Mat< eT > &error, arma::Mat< eT > &gradient)
	Calculate the gradient using the output delta and the input activations. More...
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.
OutputDataType	Mean ()
	Get the mean across single training data.
OutputDataType	Variance ()
	Get the variance across single training data.
size_t	InSize () const
	Get the number of input units.
double	Epsilon () const
	Get the value of epsilon.
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::LayerNorm< InputDataType, OutputDataType >

Declaration of the Layer Normalization class.

The layer transforms the input data into zero mean and unit variance and then scales and shifts the data by parameters, gamma and beta respectively over a single training data. These parameters are learnt by the network. Layer Normalization is different from Batch Normalization in the way that normalization is done for individual training cases, and the mean and standard deviations are computed across the layer dimensions, as opposed to across the batch.

For more information, refer to the following papers,

@article{Ba16,
  author    = {Jimmy Lei Ba, Jamie Ryan Kiros and Geoffrey E. Hinton},
  title     = {Layer Normalization},
  volume    = {abs/1607.06450},
  year      = {2016},
  url       = {http://arxiv.org/abs/1607.06450},
  eprint    = {1607.06450},
}

@article{Ioffe15,
  author    = {Sergey Ioffe and
               Christian Szegedy},
  title     = {Batch Normalization: Accelerating Deep Network Training by
               Reducing Internal Covariate Shift},
  journal   = {CoRR},
  volume    = {abs/1502.03167},
  year      = {2015},
  url       = {http://arxiv.org/abs/1502.03167},
  eprint    = {1502.03167},
}

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

LayerNorm() [1/2]

template<typename InputDataType , typename OutputDataType >

mlpack::ann::LayerNorm< InputDataType, OutputDataType >::LayerNorm

(

)

Create the LayerNorm object.

Artificial Neural Network.

LayerNorm() [2/2]

template<typename InputDataType , typename OutputDataType >

mlpack::ann::LayerNorm< InputDataType, OutputDataType >::LayerNorm	(	const size_t	size,
		const double	eps = 1e-8
	)

Create the LayerNorm object for a specified number of input units.

Parameters

size	The number of input units.
eps	The epsilon added to variance to ensure numerical stability.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::LayerNorm< InputDataType, OutputDataType >::Backward	(	const arma::Mat< eT > &	input,
		const arma::Mat< eT > &	gy,
		arma::Mat< eT > &	g
	)

Backward pass through the layer.

Parameters

input	The input activations.
gy	The backpropagated error.
g	The calculated gradient.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::LayerNorm< InputDataType, OutputDataType >::Forward	(	const arma::Mat< eT > &	input,
		arma::Mat< eT > &	output
	)

Forward pass of Layer Normalization.

Transforms the input data into zero mean and unit variance, scales the data by a factor gamma and shifts it by beta.

Parameters

input	Input data for the layer.
output	Resulting output activations.

Gradient()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

void mlpack::ann::LayerNorm< InputDataType, OutputDataType >::Gradient	(	const arma::Mat< eT > &	input,
		const arma::Mat< eT > &	error,
		arma::Mat< eT > &	gradient
	)

Calculate the gradient using the output delta and the input activations.

Parameters

input	The input activations.
error	The calculated error.
gradient	The calculated gradient.

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

• src/mlpack/methods/ann/layer/layer_norm.hpp
• src/mlpack/methods/ann/layer/layer_norm_impl.hpp