Detailed Description

template<typename Backend>
class smaug::BatchNormOp< Backend >

Implements the batch normalization layer.

The four different parameter types to BN layers (mean, v ariance, gamma, and beta) are to be provided as separate Tensors. For performance reasons, the variance parameter is often precomputed as 1/sqrt(variance + eps), so hardware does not need to do a square root/division operation.

Template Parameters

Backend The Backend specialization of this Operator.

Definition at line 46 of file backend.h.

Public Types
enum	{ Inputs, Mean, Variance, Gamma, Scaleshift = Gamma, Beta, kNumInputs }

enum	{ Outputs, kNumOutputs }

Public Member Functions
	BatchNormOp (const std::string &name, Workspace *workspace)

void	run () override

TensorShape	inferOutputShape () const

TensorShape	inferWeightsShape () const

void	createWeightsTensors ()

void	createOutputTensors ()

void	createAllTensors () override

int	getNumParameters () const override

std::vector< TensorBase * >	getParameterizableInputs () override

bool	isSamplingSupported () const override

void	setSamplingInfo (const SamplingInfo &_sampling) override

void	run ()

Static Public Attributes
static constexpr float	kEpsilon = 1e-5

Protected Attributes
const std::string	meanName

const std::string	varianceName

const std::string	gammaName

const std::string	betaName

SamplingInfo	sampling

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

smaug/core/backend.h
smaug/operators/batch_norm_op.h

Detailed Description

template<typename Backend> class smaug::BatchNormOp< Backend >

Public Types

Public Member Functions

Static Public Attributes

Protected Attributes

template<typename Backend>
class smaug::BatchNormOp< Backend >