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

The dropout layer is a regularizer that randomly with probability 'ratio' sets input values to zero and scales the remaining elements by factor 1 / (1 - ratio) rather than during test time so as to keep the expected sum same. More...

#include <dropout.hpp>

Public Member Functions
	Dropout (const double ratio=0.5)
	Create the Dropout object using the specified ratio parameter. More...
	Dropout (const Dropout &layer)
	Copy Constructor.
	Dropout (const Dropout &&)
	Move Constructor.
Dropout &	operator= (const Dropout &layer)
	Copy assignment operator.
Dropout &	operator= (Dropout &&layer)
	Move assignment operator.
template<typename eT >
void	Forward (const arma::Mat< eT > &input, arma::Mat< eT > &output)
	Ordinary feed forward pass of the dropout layer. More...
template<typename eT >
void	Backward (const arma::Mat< eT > &, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
	Ordinary feed backward pass of the dropout layer. More...
OutputDataType const &	OutputParameter () const
	Get the output parameter.
OutputDataType &	OutputParameter ()
	Modify the output parameter.
OutputDataType const &	Delta () const
	Get the detla.
OutputDataType &	Delta ()
	Modify the delta.
bool	Deterministic () const
	The value of the deterministic parameter.
bool &	Deterministic ()
	Modify the value of the deterministic parameter.
double	Ratio () const
	The probability of setting a value to zero.
void	Ratio (const double r)
	Modify the probability of setting a value to zero.
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::Dropout< InputDataType, OutputDataType >

The dropout layer is a regularizer that randomly with probability 'ratio' sets input values to zero and scales the remaining elements by factor 1 / (1 - ratio) rather than during test time so as to keep the expected sum same.

In the deterministic mode (during testing), there is no change in the input.

Note: During training you should set deterministic to false and during testing you should set deterministic to true.

For more information, see the following.

@article{Hinton2012,
  author  = {Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky,
             Ilya Sutskever, Ruslan Salakhutdinov},
  title   = {Improving neural networks by preventing co-adaptation of feature
             detectors},
  journal = {CoRR},
  volume  = {abs/1207.0580},
  year    = {2012},
  url     = {https://arxiv.org/abs/1207.0580}
}

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

Dropout()

template<typename InputDataType , typename OutputDataType >

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

(

const double

ratio = 0.5

)

Create the Dropout object using the specified ratio parameter.

Parameters

ratio

The probability of setting a value to zero.

Member Function Documentation

Backward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

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

Ordinary feed backward pass of the dropout layer.

Parameters

*	(input) The propagated input activation.
gy	The backpropagated error.
g	The calculated gradient.

Forward()

template<typename InputDataType , typename OutputDataType >

template<typename eT >

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

Ordinary feed forward pass of the dropout layer.

Parameters

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

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

• src/mlpack/methods/ann/layer/dropout.hpp
• src/mlpack/methods/ann/layer/dropout_impl.hpp