A layer of Gaussian neurons. More...

#include <shark/Unsupervised/RBM/Neuronlayers/GaussianLayer.h>

Inheritance diagram for shark::GaussianLayer:

Public Types
typedef RealSpace	StateSpace
	the bias terms associated with the neurons More...

typedef RealVector	SufficientStatistics
	The sufficient statistics for the Guassian Layer stores the mean of the neuron and the inverse temperature. More...

typedef Batch< SufficientStatistics >::type	StatisticsBatch
	Sufficient statistics of a batch of data. More...

Public Types inherited from shark::IParameterizable<>
typedef RealVector	ParameterVectorType

Public Member Functions
const RealVector &	bias () const
	Returns the bias values of the units. More...

RealVector &	bias ()
	Returns the bias values of the units. More...

void	resize (std::size_t newSize)
	Resizes this neuron layer. More...

std::size_t	size () const
	Returns the number of neurons of this layer. More...

template<class Input , class BetaVector >
void	sufficientStatistics (Input const &input, StatisticsBatch &statistics, BetaVector const &beta) const
	Takes the input of the neuron and estimates the expectation of the response of the neuron. More...

template<class Matrix , class Rng >
void	sample (StatisticsBatch const &statistics, Matrix &state, double alpha, Rng &rng) const
	Given a the precomputed statistics (the mean of the Gaussian), the elements of the vector are sampled. This happens either with Gibbs-Sampling or Flip-the-State sampling. For alpha= 0 gibbs sampling is performed. That is the next state for neuron i is directly taken from the conditional distribution of the i-th neuron. In the case of alpha=1, flip-the-state sampling is performed, which takes the last state into account and tries to do deterministically jump into states with higher probability. THIS IS NOT IMPLEMENTED YET and alpha is ignored! More...

template<class Matrix >
RealVector	logProbability (StatisticsBatch const &statistics, Matrix const &state) const
	Computes the log of the probability of the given states in the conditional distribution. More...

template<class Matrix >
Matrix const &	phi (Matrix const &state) const
	Transforms the current state of the neurons for the multiplication with the weight matrix of the RBM, i.e. calculates the value of the phi-function used in the interaction term. In the case of Gaussian neurons the phi-function is just the identity. More...

RealMatrix const &	expectedPhiValue (StatisticsBatch const &statistics) const
	Returns the expectation of the phi-function. More...

RealMatrix const &	mean (StatisticsBatch const &statistics) const
	Returns the mean given the state of the connected layer, i.e. in this case the mean of the Gaussian. More...

template<class Matrix , class BetaVector >
RealVector	energyTerm (Matrix const &state, BetaVector const &beta) const
	The energy term this neuron adds to the energy function for a batch of inputs. More...

template<class Input >
double	logMarginalize (const Input &inputs, double beta) const
	Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result. More...

template<class Vector , class SampleBatch >
void	expectedParameterDerivative (Vector &derivative, SampleBatch const &samples) const
	Calculates the expectation of the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights. The expectation is taken with respect to the conditional probability distribution of the layer given the state of the connected layer. More...

template<class Vector , class SampleBatch , class Vector2 >
void	expectedParameterDerivative (Vector &derivative, SampleBatch const &samples, Vector2 const &weights) const

template<class Vector , class SampleBatch >
void	parameterDerivative (Vector &derivative, SampleBatch const &samples) const
	Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights. More...

template<class Vector , class SampleBatch , class WeightVector >
void	parameterDerivative (Vector &derivative, SampleBatch const &samples, WeightVector const &weights) const
	Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights. More...

RealVector	parameterVector () const
	Returns the vector with the parameters associated with the neurons in the layer. More...

void	setParameterVector (RealVector const &newParameters)
	Returns the vector with the parameters associated with the neurons in the layer. More...

std::size_t	numberOfParameters () const
	Returns the number of the parameters associated with the neurons in the layer. More...

void	read (InArchive &archive)
	Reads the bias parameters from an archive. More...

void	write (OutArchive &archive) const
	Writes the bias parameters to an archive. More...

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor. More...

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...). More...

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...). More...

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Public Member Functions inherited from shark::IParameterizable<>
virtual	~IParameterizable ()

Detailed Description

A layer of Gaussian neurons.

For a Gaussian neuron/variable the conditional probability distribution of the state of the variable given the state of the other layer is given by a Gaussian distribution with the input of the neuron as mean and unit variance.

Definition at line 48 of file GaussianLayer.h.

Member Typedef Documentation

◆ StateSpace

typedef RealSpace shark::GaussianLayer::StateSpace

the bias terms associated with the neurons

the state space of this neuron is binary

Definition at line 53 of file GaussianLayer.h.

◆ StatisticsBatch

typedef Batch<SufficientStatistics>::type shark::GaussianLayer::StatisticsBatch

Sufficient statistics of a batch of data.

Definition at line 58 of file GaussianLayer.h.

◆ SufficientStatistics

typedef RealVector shark::GaussianLayer::SufficientStatistics

The sufficient statistics for the Guassian Layer stores the mean of the neuron and the inverse temperature.

Definition at line 56 of file GaussianLayer.h.

Member Function Documentation

◆ bias() [1/2]

const RealVector& shark::GaussianLayer::bias ( ) const

inline

Returns the bias values of the units.

Definition at line 61 of file GaussianLayer.h.

◆ bias() [2/2]

RealVector& shark::GaussianLayer::bias ( )

inline

Returns the bias values of the units.

Definition at line 65 of file GaussianLayer.h.

◆ energyTerm()

template<class Matrix , class BetaVector >

RealVector shark::GaussianLayer::energyTerm	(	Matrix const &	state,
		BetaVector const &	beta
	)		const

inline

The energy term this neuron adds to the energy function for a batch of inputs.

Parameters

state	the state of the neuron layer
beta	the inverse temperature of the i-th state

Returns: the energy term of the neuron layer

Definition at line 179 of file GaussianLayer.h.

References shark::batchSize(), size(), and SIZE_CHECK.

◆ expectedParameterDerivative() [1/2]

template<class Vector , class SampleBatch >

void shark::GaussianLayer::expectedParameterDerivative	(	Vector &	derivative,
		SampleBatch const &	samples
	)		const

inline

Calculates the expectation of the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights. The expectation is taken with respect to the conditional probability distribution of the layer given the state of the connected layer.

This function takes a batch of samples and extracts the required informations out of it.

Parameters

derivative	the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives
samples	the samples from which the informations can be extracted

Definition at line 229 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ expectedParameterDerivative() [2/2]

template<class Vector , class SampleBatch , class Vector2 >

void shark::GaussianLayer::expectedParameterDerivative	(	Vector &	derivative,
		SampleBatch const &	samples,
		Vector2 const &	weights
	)		const

inline

Definition at line 235 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ expectedPhiValue()

RealMatrix const& shark::GaussianLayer::expectedPhiValue ( StatisticsBatch const & statistics ) const

inline

Returns the expectation of the phi-function.

Parameters

statistics the sufficient statistics (the mean of the distribution).

Definition at line 161 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ logMarginalize()

template<class Input >

double shark::GaussianLayer::logMarginalize	(	const Input &	inputs,
		double	beta
	)		const

inline

Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result.

This function is called by Energy when the unnormalized marginal probability of the connected layer is to be computed. This function calculates the part which depends on the neurons which are to be marginalized out. (In the case of the binary hidden neuron, this is the term \( \log \sum_h e^{\vec h^T W \vec v+ \vec h^T \vec c} \)). The rest is calculated by the energy function. In the general case of a hidden layer, this function calculates \( \log \int_h e^(\phi_h(\vec h)^T W \phi_v(\vec v)+f_h(\vec h) ) \) where f_h is the energy term of this.

Parameters

inputs	the inputs of the neurons they get from the other layer
beta	the inverse temperature of the RBM

Returns: the marginal distribution of the connected layer

Definition at line 209 of file GaussianLayer.h.

References beta, size(), SIZE_CHECK, shark::sqr(), and shark::SQRT_2_PI.

◆ logProbability()

template<class Matrix >

RealVector shark::GaussianLayer::logProbability	(	StatisticsBatch const &	statistics,
		Matrix const &	state
	)		const

inline

Computes the log of the probability of the given states in the conditional distribution.

Currently it is only possible to compute the case with alpha=0

Parameters

statistics	the statistics of the conditional distribution
state	the state to check

Definition at line 132 of file GaussianLayer.h.

References size(), SIZE_CHECK, and shark::sqr().

◆ mean()

RealMatrix const& shark::GaussianLayer::mean ( StatisticsBatch const & statistics ) const

inline

Returns the mean given the state of the connected layer, i.e. in this case the mean of the Gaussian.

Parameters

statistics the sufficient statistics of the layer for a whole batch

Definition at line 168 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ numberOfParameters()

std::size_t shark::GaussianLayer::numberOfParameters ( ) const

inlinevirtual

Returns the number of the parameters associated with the neurons in the layer.

Reimplemented from shark::IParameterizable<>.

Definition at line 274 of file GaussianLayer.h.

References size().

◆ parameterDerivative() [1/2]

template<class Vector , class SampleBatch >

void shark::GaussianLayer::parameterDerivative	(	Vector &	derivative,
		SampleBatch const &	samples
	)		const

inline

Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights.

This function takes a batch of samples and extracts the required informations out of it.

Parameters

derivative	the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives
samples	the sample from which the informations can be extracted

Definition at line 246 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ parameterDerivative() [2/2]

template<class Vector , class SampleBatch , class WeightVector >

void shark::GaussianLayer::parameterDerivative	(	Vector &	derivative,
		SampleBatch const &	samples,
		WeightVector const &	weights
	)		const

inline

Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters - the bias weights.

This function takes a batch of samples and calculates a weighted derivative

Parameters

derivative	the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives
samples	the sample from which the informations can be extracted
weights	the weights for the single sample derivatives

Definition at line 258 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ parameterVector()

RealVector shark::GaussianLayer::parameterVector ( ) const

inlinevirtual

Returns the vector with the parameters associated with the neurons in the layer.

Reimplemented from shark::IParameterizable<>.

Definition at line 264 of file GaussianLayer.h.

◆ phi()

template<class Matrix >

Matrix const& shark::GaussianLayer::phi ( Matrix const & state ) const

inline

Transforms the current state of the neurons for the multiplication with the weight matrix of the RBM, i.e. calculates the value of the phi-function used in the interaction term. In the case of Gaussian neurons the phi-function is just the identity.

Parameters

state the state matrix of the neuron layer

Returns: the value of the phi-function

Definition at line 153 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ read()

void shark::GaussianLayer::read ( InArchive & archive )

inlinevirtual

Reads the bias parameters from an archive.

Reimplemented from shark::ISerializable.

Definition at line 279 of file GaussianLayer.h.

◆ resize()

void shark::GaussianLayer::resize ( std::size_t newSize )

inline

Resizes this neuron layer.

Parameters

newSize number of neurons in the layer

Definition at line 72 of file GaussianLayer.h.

◆ sample()

template<class Matrix , class Rng >

void shark::GaussianLayer::sample	(	StatisticsBatch const &	statistics,
		Matrix &	state,
		double	alpha,
		Rng &	rng
	)		const

inline

Given a the precomputed statistics (the mean of the Gaussian), the elements of the vector are sampled. This happens either with Gibbs-Sampling or Flip-the-State sampling. For alpha= 0 gibbs sampling is performed. That is the next state for neuron i is directly taken from the conditional distribution of the i-th neuron. In the case of alpha=1, flip-the-state sampling is performed, which takes the last state into account and tries to do deterministically jump into states with higher probability. THIS IS NOT IMPLEMENTED YET and alpha is ignored!

Parameters

statistics	sufficient statistics containing the mean of the conditional Gaussian distribution of the neurons
state	the state matrix that will hold the sampled states
alpha	factor changing from gibbs to flip-the state sampling. 0<=alpha<=1
rng	the random number generator used for sampling

Definition at line 110 of file GaussianLayer.h.

References shark::random::gauss(), SHARK_CRITICAL_REGION, size(), and SIZE_CHECK.

◆ setParameterVector()

void shark::GaussianLayer::setParameterVector ( RealVector const & newParameters )

inlinevirtual

Returns the vector with the parameters associated with the neurons in the layer.

Reimplemented from shark::IParameterizable<>.

Definition at line 269 of file GaussianLayer.h.

◆ size()

std::size_t shark::GaussianLayer::size ( ) const

inline

Returns the number of neurons of this layer.

Definition at line 77 of file GaussianLayer.h.

Referenced by energyTerm(), expectedParameterDerivative(), expectedPhiValue(), logMarginalize(), logProbability(), mean(), numberOfParameters(), parameterDerivative(), phi(), sample(), and sufficientStatistics().

◆ sufficientStatistics()

template<class Input , class BetaVector >

void shark::GaussianLayer::sufficientStatistics	(	Input const &	input,
		StatisticsBatch &	statistics,
		BetaVector const &	beta
	)		const

inline

Takes the input of the neuron and estimates the expectation of the response of the neuron.

Parameters

input	the batch of inputs of the neuron
statistics	sufficient statistics containing the mean of the resulting Gaussian distribution
beta	the inverse Temperature of the RBM (typically 1) for the whole batch

Definition at line 87 of file GaussianLayer.h.

References size(), and SIZE_CHECK.

◆ write()

void shark::GaussianLayer::write ( OutArchive & archive ) const

inlinevirtual

Writes the bias parameters to an archive.

Reimplemented from shark::ISerializable.

Definition at line 283 of file GaussianLayer.h.

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

include/shark/Unsupervised/RBM/Neuronlayers/GaussianLayer.h

Public Types

Public Member Functions

Detailed Description

Member Typedef Documentation

◆ StateSpace

◆ StatisticsBatch

◆ SufficientStatistics

Member Function Documentation

◆ bias() [1/2]

◆ bias() [2/2]

◆ energyTerm()

◆ expectedParameterDerivative() [1/2]

◆ expectedParameterDerivative() [2/2]

◆ expectedPhiValue()

◆ logMarginalize()

◆ logProbability()

◆ mean()

◆ numberOfParameters()

◆ parameterDerivative() [1/2]

◆ parameterDerivative() [2/2]

◆ parameterVector()

◆ phi()

◆ read()

◆ resize()

◆ sample()

◆ setParameterVector()

◆ size()

◆ sufficientStatistics()

◆ write()