Dropout Class Reference

Implements a dropout layer for machine learning, which randomly sets input values to zero during training. More...

#include <Dropout.h>

Inheritance diagram for Dropout:

Public Member Functions
	Dropout (float p)
	Constructor for Dropout.
void	apply (const SelectivityVector &rows, std::vector< VectorPtr > &args, const TypePtr &type, exec::EvalCtx &context, VectorPtr &output) const override
	Applies the dropout function to the input data.
float *	getTensor () const override
	Returns the tensor associated with the function.
void	setWeight (float p)
	Sets the dropout probability.
Public Member Functions inherited from MLFunction
virtual	~MLFunction ()=default
	Virtual destructor.
virtual std::vector< int >	getDims ()
	Returns the dimensions of the function.
virtual std::string	getFuncName ()
	Returns the name of the function.
virtual int	getNumDims ()
	Returns the number of dimensions of the function.
virtual CostEstimate	getCost (std::vector< int > inputDims)
	Estimates the computational cost of applying the function.

Static Public Member Functions
static std::vector< std::shared_ptr< exec::FunctionSignature > >	signatures ()
	Returns the function signatures.
static std::string	getName ()
	Returns the name of the function.

Additional Inherited Members
Protected Member Functions inherited from MLFunction
double	getWeightedCost (std::string name, float cost)
	Calculates the weighted cost of the function.
std::vector< double >	getCoefficientVector (std::string name)
	Retrieves the cost coefficients for the function.
Protected Attributes inherited from MLFunction
std::vector< int >	dims
	Dimensions of the function.

Detailed Description

Implements a dropout layer for machine learning, which randomly sets input values to zero during training.

Constructor & Destructor Documentation

Dropout()

Dropout::Dropout ( float p )

inline

Constructor for Dropout.

Parameters

p	The probability of dropping out an input value (setting it to zero).

Member Function Documentation

apply()

void Dropout::apply ( const SelectivityVector & rows, std::vector< VectorPtr > & args, const TypePtr & type, exec::EvalCtx & context, VectorPtr & output ) const

inlineoverride

Applies the dropout function to the input data.

Parameters

rows	Selectivity vector indicating which rows to process.
args	Vector of input arguments.
type	Type of the output vector.
context	Evaluation context.
output	Output vector to store the results.

getName()

static std::string Dropout::getName ( )

inlinestatic

Returns the name of the function.

Returns

Function name.

getTensor()

float * Dropout::getTensor ( ) const

inlineoverridevirtual

Returns the tensor associated with the function.

Returns

Pointer to the tensor.

Implements MLFunction.

setWeight()

void Dropout::setWeight ( float p )

inline

Sets the dropout probability.

Parameters

p	The probability of dropping out an input value (setting it to zero).

signatures()

static std::vector< std::shared_ptr< exec::FunctionSignature > > Dropout::signatures ( )

inlinestatic

Returns the function signatures.

Returns

Vector of function signatures.

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

• Dropout.h