CDeepBeliefNetwork Class Reference

Detailed Description

A Deep Belief Network.

A Deep Belief Network [Hinton, 2006] is a multilayer probabilistic generative models. It consists of hidden layers and visible layers. The top hidden layer and the layer below it form a Restricted Boltzmann Machine. The rest of connections in the network are directed connections that go from a hidden layer into a visible layer or another hidden layer.

The network can be pre-trained by treating it as a stack of RBMs. Each hidden layer along with the layer below it form an RBM. Each RBM is then trained using (persistent) contrastive divergence. Pre-training often provides a good initialization for the network's parameters.

After pre-training, the parameters can be fine-tuned using a variant of the wake-sleep algorithm [Hinton, 2006].

The DBN can be used to initialize the parameters of a neural network using convert_to_neural_network().

Samples can be drawn from the model by starting with a random state for the top hidden layer, performing some steps of Gibbs sampling in the top RBM to obtain the states of the top hidden layer and then using those to infer the states of the lower layers using a down-pass.

Steps for using the DBN class:

• Specify the number of visible units and their type using the constructor
• Add the hidden layers using add_visible_layer() and add_hidden_layer().
• Initialize the DBN using initialize_neural_network()
• Pre-train the DBN using pre_train()
• Optionally do some unsupervised fine-tuning using train()
• If needed, call convert_to_neural_network() to convert the DBN into a neural network

Definition at line 91 of file DeepBeliefNetwork.h.

Inheritance diagram for CDeepBeliefNetwork:

Public Member Functions
	CDeepBeliefNetwork ()
	CDeepBeliefNetwork (int32_t num_visible_units, ERBMVisibleUnitType unit_type=RBMVUT_BINARY)
virtual	~CDeepBeliefNetwork ()
virtual void	add_hidden_layer (int32_t num_units)
virtual void	initialize_neural_network (float64_t sigma=0.01)
virtual void	set_batch_size (int32_t batch_size)
virtual void	pre_train (CDenseFeatures< float64_t > *features)
virtual void	pre_train (int32_t index, CDenseFeatures< float64_t > *features)
virtual void	train (CDenseFeatures< float64_t > *features)
virtual CDenseFeatures < float64_t > *	transform (CDenseFeatures< float64_t > *features, int32_t i=-1)
virtual CDenseFeatures < float64_t > *	sample (int32_t num_gibbs_steps=1, int32_t batch_size=1)
virtual void	reset_chain ()
virtual CNeuralNetwork *	convert_to_neural_network (CNeuralLayer *output_layer=NULL, float64_t sigma=0.01)
virtual SGMatrix< float64_t >	get_weights (int32_t index, SGVector< float64_t > p=SGVector< float64_t >())
virtual SGVector< float64_t >	get_biases (int32_t index, SGVector< float64_t > p=SGVector< float64_t >())
virtual const char *	get_name () const
virtual CSGObject *	shallow_copy () const
virtual CSGObject *	deep_copy () const
virtual bool	is_generic (EPrimitiveType *generic) const
template<class T >
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
template<>
void	set_generic ()
void	unset_generic ()
virtual void	print_serializable (const char *prefix="")
virtual bool	save_serializable (CSerializableFile *file, const char *prefix="")
virtual bool	load_serializable (CSerializableFile *file, const char *prefix="")
void	set_global_io (SGIO *io)
SGIO *	get_global_io ()
void	set_global_parallel (Parallel *parallel)
Parallel *	get_global_parallel ()
void	set_global_version (Version *version)
Version *	get_global_version ()
SGStringList< char >	get_modelsel_names ()
void	print_modsel_params ()
char *	get_modsel_param_descr (const char *param_name)
index_t	get_modsel_param_index (const char *param_name)
void	build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual void	update_parameter_hash ()
virtual bool	parameter_hash_changed ()
virtual bool	equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
virtual CSGObject *	clone ()

Public Attributes
SGVector< int32_t >	pt_cd_num_steps
SGVector< bool >	pt_cd_persistent
SGVector< bool >	pt_cd_sample_visible
SGVector< float64_t >	pt_l2_coefficient
SGVector< float64_t >	pt_l1_coefficient
SGVector< int32_t >	pt_monitoring_interval
SGVector< int32_t >	pt_monitoring_method
SGVector< int32_t >	pt_max_num_epochs
SGVector< int32_t >	pt_gd_mini_batch_size
SGVector< float64_t >	pt_gd_learning_rate
SGVector< float64_t >	pt_gd_learning_rate_decay
SGVector< float64_t >	pt_gd_momentum
int32_t	cd_num_steps
int32_t	monitoring_interval
int32_t	max_num_epochs
int32_t	gd_mini_batch_size
float64_t	gd_learning_rate
float64_t	gd_learning_rate_decay
float64_t	gd_momentum
SGIO *	io
Parallel *	parallel
Version *	version
Parameter *	m_parameters
Parameter *	m_model_selection_parameters
Parameter *	m_gradient_parameters
uint32_t	m_hash

Protected Member Functions
virtual void	down_step (int32_t index, SGVector< float64_t > params, SGMatrix< float64_t > input, SGMatrix< float64_t > result, bool sample_states=true)
virtual void	up_step (int32_t index, SGVector< float64_t > params, SGMatrix< float64_t > input, SGMatrix< float64_t > result, bool sample_states=true)
virtual void	wake_sleep (SGMatrix< float64_t > data, CRBM *top_rbm, SGMatrixList< float64_t > sleep_states, SGMatrixList< float64_t > wake_states, SGMatrixList< float64_t > psleep_states, SGMatrixList< float64_t > pwake_states, SGVector< float64_t > gen_params, SGVector< float64_t > rec_params, SGVector< float64_t > gen_gradients, SGVector< float64_t > rec_gradients)
virtual void	load_serializable_pre () throw (ShogunException)
virtual void	load_serializable_post () throw (ShogunException)
virtual void	save_serializable_pre () throw (ShogunException)
virtual void	save_serializable_post () throw (ShogunException)

Protected Attributes
ERBMVisibleUnitType	m_visible_units_type
int32_t	m_num_layers
CDynamicArray< int32_t > *	m_layer_sizes
SGMatrixList< float64_t >	m_states
int32_t	m_batch_size
SGVector< float64_t >	m_params
int32_t	m_num_params
SGVector< int32_t >	m_bias_index_offsets
SGVector< int32_t >	m_weights_index_offsets
float64_t	m_sigma

Constructor & Destructor Documentation

CDeepBeliefNetwork

(

)

default constructor

Definition at line 51 of file DeepBeliefNetwork.cpp.

CDeepBeliefNetwork	(	int32_t	num_visible_units,
		ERBMVisibleUnitType	unit_type = RBMVUT_BINARY
	)

Creates a network with one layer of visible units

Parameters

num_visible_units	Number of visible units
unit_type	Type of visible units

Definition at line 56 of file DeepBeliefNetwork.cpp.

~CDeepBeliefNetwork ( )

virtual

Definition at line 65 of file DeepBeliefNetwork.cpp.

Member Function Documentation

void add_hidden_layer ( int32_t  num_units )

virtual

Adds a layer of hidden units. The layer is connected to the layer that was added directly before it.

Parameters

num_units

Number of hidden units

Definition at line 70 of file DeepBeliefNetwork.cpp.

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > *  dict )

inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters

dict	dictionary of parameters to be built.

Definition at line 597 of file SGObject.cpp.

CSGObject * clone ( )

virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns

an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 714 of file SGObject.cpp.

CNeuralNetwork * convert_to_neural_network ( CNeuralLayer *  output_layer = NULL, float64_t  sigma = 0.01  )

virtual

Converts the DBN into a neural network with the same structure and parameters. The visible layer in the DBN is converted into a CNeuralInputLayer object, and the hidden layers are converted into CNeuralLogisticLayer objects. An output layer can also be stacked on top of the last hidden layer

Parameters

output_layer	An output layer
sigma	Standard deviation of the gaussian used to initialize the parameters of the output layer

Returns

Neural network inititialized using the DBN

Definition at line 360 of file DeepBeliefNetwork.cpp.

CSGObject * deep_copy ( ) const

virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 198 of file SGObject.cpp.

void down_step ( int32_t  index, SGVector< float64_t >  params, SGMatrix< float64_t >  input, SGMatrix< float64_t >  result, bool  sample_states = true  )

protectedvirtual

Computes the states of some layer using the states of the layer above it

Definition at line 393 of file DeepBeliefNetwork.cpp.

bool equals ( CSGObject *  other, float64_t  accuracy = 0.0, bool  tolerant = false  )

virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters

other	object to compare with
accuracy	accuracy to use for comparison (optional)
tolerant	allows linient check on float equality (within accuracy)

Returns

true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

SGVector< float64_t > get_biases ( int32_t  index, SGVector< float64_t >  p = SGVector<float64_t>()  )

virtual

Returns the bias vector of layer i

Parameters

index	Layer index
p	If specified, the bias vector is extracted from it instead of m_params

Definition at line 558 of file DeepBeliefNetwork.cpp.

SGIO * get_global_io ( )

inherited

get the io object

Returns

io object

Definition at line 235 of file SGObject.cpp.

Parallel * get_global_parallel ( )

inherited

get the parallel object

Returns

parallel object

Definition at line 277 of file SGObject.cpp.

Version * get_global_version ( )

inherited

get the version object

Returns

version object

Definition at line 290 of file SGObject.cpp.

SGStringList< char > get_modelsel_names ( )

inherited

Returns

vector of names of all parameters which are registered for model selection

Definition at line 498 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name )

inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters

param_name

name of the parameter

Returns

description of the parameter

Definition at line 522 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name )

inherited

Returns index of model selection parameter with provided index

Parameters

param_name

name of model selection parameter

Returns

index of model selection parameter with provided name, -1 if there is no such

Definition at line 535 of file SGObject.cpp.

virtual const char* get_name ( ) const

virtual

Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.

Returns

name of the SGSerializable

Implements CSGObject.

Definition at line 214 of file DeepBeliefNetwork.h.

SGMatrix< float64_t > get_weights ( int32_t  index, SGVector< float64_t >  p = SGVector<float64_t>()  )

virtual

Returns the weights matrix between layer i and i+1

Parameters

index	Layer index
p	If specified, the weight matrix is extracted from it instead of m_params

Definition at line 547 of file DeepBeliefNetwork.cpp.

void initialize_neural_network ( float64_t  sigma = 0.01 )

virtual

Initializes the DBN

Parameters

sigma

Standard deviation of the gaussian used to initialize the weights

Definition at line 76 of file DeepBeliefNetwork.cpp.

bool is_generic ( EPrimitiveType *  generic ) const

virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters

generic

set to the type of the generic if returning TRUE

Returns

TRUE if a class template.

Definition at line 296 of file SGObject.cpp.

bool load_serializable ( CSerializableFile *  file, const char *  prefix = ""  )

virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters

file	where to load from
prefix	prefix for members

Returns

TRUE if done, otherwise FALSE

Definition at line 369 of file SGObject.cpp.

void load_serializable_post ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 426 of file SGObject.cpp.

void load_serializable_pre ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 421 of file SGObject.cpp.

bool parameter_hash_changed ( )

virtualinherited

Returns

whether parameter combination has changed since last update

Definition at line 262 of file SGObject.cpp.

void pre_train ( CDenseFeatures< float64_t > *  features )

virtual

Pre-trains the DBN as a stack of RBMs

Parameters

features

Input features. Should have as many features as the number of visible units in the DBN

Definition at line 149 of file DeepBeliefNetwork.cpp.

void pre_train ( int32_t  index, CDenseFeatures< float64_t > *  features  )

virtual

Pre-trains a single RBM

Parameters

index	Index of the RBM
features	Input features. Should have as many features as the total number of visible units in the DBN

Definition at line 159 of file DeepBeliefNetwork.cpp.

void print_modsel_params ( )

inherited

prints all parameter registered for model selection and their type

Definition at line 474 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "" )

virtualinherited

prints registered parameters out

Parameters

prefix

prefix for members

Definition at line 308 of file SGObject.cpp.

void reset_chain ( )

virtual

Resets the state of the markov chain used for sampling

Definition at line 352 of file DeepBeliefNetwork.cpp.

CDenseFeatures< float64_t > * sample ( int32_t  num_gibbs_steps = 1, int32_t  batch_size = 1  )

virtual

Draws samples from the marginal distribution of the visible units. The sampling starts from the values DBN's internal state and result of the sampling is stored there too.

Parameters

num_gibbs_steps	Number of Gibbs sampling steps for the top RBM.
batch_size	Number of samples to be drawn. A seperate chain is used for each sample

Returns

Sampled states of the visible units

Definition at line 333 of file DeepBeliefNetwork.cpp.

bool save_serializable ( CSerializableFile *  file, const char *  prefix = ""  )

virtualinherited

Save this object to file.

Parameters

file	where to save the object; will be closed during returning if PREFIX is an empty string.
prefix	prefix for members

Returns

TRUE if done, otherwise FALSE

Definition at line 314 of file SGObject.cpp.

void save_serializable_post ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 436 of file SGObject.cpp.

void save_serializable_pre ( ) throw ( ShogunException )

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions

ShogunException

will be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 431 of file SGObject.cpp.

void set_batch_size ( int32_t  batch_size )

virtual

Sets the number of train/test cases the RBM will deal with

Parameters

batch_size

Batch size

Definition at line 135 of file DeepBeliefNetwork.cpp.

void set_generic ( )

inherited

Definition at line 41 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 46 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 51 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 56 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 61 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 66 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 71 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 76 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 81 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 86 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 91 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 96 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 101 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 106 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 111 of file SGObject.cpp.

void set_generic ( )

inherited

set generic type to T

void set_global_io ( SGIO *  io )

inherited

set the io object

Parameters

io	io object to use

Definition at line 228 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel )

inherited

set the parallel object

Parameters

parallel

parallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version *  version )

inherited

set the version object

Parameters

version

version object to use

Definition at line 283 of file SGObject.cpp.

CSGObject * shallow_copy ( ) const

virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 192 of file SGObject.cpp.

void train ( CDenseFeatures< float64_t > *  features )

virtual

Trains the DBN using the variant of the wake-sleep algorithm described in [A Fast Learning Algorithm for Deep Belief Nets, Hinton, 2006].

Parameters

features

Input features. Should have as many features as the total number of visible units in the DBN

Definition at line 210 of file DeepBeliefNetwork.cpp.

CDenseFeatures< float64_t > * transform ( CDenseFeatures< float64_t > *  features, int32_t  i = -1  )

virtual

Applies the DBN as a features transformation

Forward-propagates the input features through the DBN and returns the Mean activations of the \( i^{th} \) hidden layer

Parameters

features	Input features. Should have as many features as the number of visible units in the DBN
i	Index of the hidden layer. If -1, the activations of the last hidden layer is returned

Returns

Mean activations of the \( i^{th} \) hidden layer

Definition at line 316 of file DeepBeliefNetwork.cpp.

void unset_generic ( )

inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 303 of file SGObject.cpp.

void up_step ( int32_t  index, SGVector< float64_t >  params, SGMatrix< float64_t >  input, SGMatrix< float64_t >  result, bool  sample_states = true  )

protectedvirtual

Computes the states of some layer using the states of the layer below it

Definition at line 443 of file DeepBeliefNetwork.cpp.

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

void wake_sleep ( SGMatrix< float64_t >  data, CRBM *  top_rbm, SGMatrixList< float64_t >  sleep_states, SGMatrixList< float64_t >  wake_states, SGMatrixList< float64_t >  psleep_states, SGMatrixList< float64_t >  pwake_states, SGVector< float64_t >  gen_params, SGVector< float64_t >  rec_params, SGVector< float64_t >  gen_gradients, SGVector< float64_t >  rec_gradients  )

protectedvirtual

Computes the gradients using the wake-sleep algorithm

Definition at line 473 of file DeepBeliefNetwork.cpp.

Member Data Documentation

int32_t cd_num_steps

Number of Gibbs sampling steps performed before each weight update during wake-sleep training. Default value is 1.

Definition at line 306 of file DeepBeliefNetwork.h.

float64_t gd_learning_rate

Gradient descent learning rate for wake-sleep training, defualt value 0.1

Definition at line 325 of file DeepBeliefNetwork.h.

float64_t gd_learning_rate_decay

Gradient descent learning rate decay for wake-sleep training. The learning rate is updated at each iteration i according to: alpha(i)=decay*alpha(i-1) Default value is 1.0 (no decay)

Definition at line 332 of file DeepBeliefNetwork.h.

int32_t gd_mini_batch_size

Size of the mini-batch used during gradient descent wake-sleep training, If 0 full-batch training is performed Default value is 0

Definition at line 322 of file DeepBeliefNetwork.h.

float64_t gd_momentum

gradient descent momentum multiplier for wake-sleep training

default value is 0.9

For more details on momentum, see this paper [Sutskever, 2013]

Definition at line 342 of file DeepBeliefNetwork.h.

SGIO* io

inherited

io

Definition at line 369 of file SGObject.h.

int32_t m_batch_size

protected

Number of train/test cases the network is currently dealing with

Definition at line 358 of file DeepBeliefNetwork.h.

SGVector<int32_t> m_bias_index_offsets

protected

Index at which the bias of each layer is stored in the parameters vector

Definition at line 367 of file DeepBeliefNetwork.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

CDynamicArray<int32_t>* m_layer_sizes

protected

Size of each layer

Definition at line 352 of file DeepBeliefNetwork.h.

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 381 of file SGObject.h.

int32_t m_num_layers

protected

Number of layers

Definition at line 349 of file DeepBeliefNetwork.h.

int32_t m_num_params

protected

Number of parameters

Definition at line 364 of file DeepBeliefNetwork.h.

Parameter* m_parameters

inherited

parameters

Definition at line 378 of file SGObject.h.

SGVector<float64_t> m_params

protected

Parameters of the network

Definition at line 361 of file DeepBeliefNetwork.h.

float64_t m_sigma

protected

Standard deviation of the gaussian used to initialize the parameters

Definition at line 376 of file DeepBeliefNetwork.h.

SGMatrixList<float64_t> m_states

protected

States of each layer

Definition at line 355 of file DeepBeliefNetwork.h.

ERBMVisibleUnitType m_visible_units_type

protected

Type of the visible units

Definition at line 346 of file DeepBeliefNetwork.h.

SGVector<int32_t> m_weights_index_offsets

protected

Index at which the weights of each hidden layer is stored in the parameters vector

Definition at line 372 of file DeepBeliefNetwork.h.

int32_t max_num_epochs

Maximum number of iterations over the training set during wake-sleep training. Defualt value is 1

Definition at line 316 of file DeepBeliefNetwork.h.

int32_t monitoring_interval

Number of weight updates between each evaluation of the reconstruction error during wake-sleep training. Default value is 10.

Definition at line 311 of file DeepBeliefNetwork.h.

Parallel* parallel

inherited

parallel

Definition at line 372 of file SGObject.h.

SGVector<int32_t> pt_cd_num_steps

CRBM::cd_num_steps for pre-training each RBM. Default value is 1 for all RBMs

Definition at line 246 of file DeepBeliefNetwork.h.

SGVector<bool> pt_cd_persistent

CRBM::cd_persistent for pre-training each RBM. Default value is true for all RBMs

Definition at line 251 of file DeepBeliefNetwork.h.

SGVector<bool> pt_cd_sample_visible

CRBM::cd_sample_visible for pre-training each RBM. Default value is false for all RBMs

Definition at line 256 of file DeepBeliefNetwork.h.

SGVector<float64_t> pt_gd_learning_rate

CRBM::gd_learning_rate for pre-training each RBM. Default value is 0.1 for all RBMs

Definition at line 291 of file DeepBeliefNetwork.h.

SGVector<float64_t> pt_gd_learning_rate_decay

CRBM::gd_learning_rate_decay for pre-training each RBM. Default value is 1.0 for all RBMs

Definition at line 296 of file DeepBeliefNetwork.h.

SGVector<int32_t> pt_gd_mini_batch_size

CRBM::gd_mini_batch_size for pre-training each RBM. Default value is 0 for all RBMs

Definition at line 286 of file DeepBeliefNetwork.h.

SGVector<float64_t> pt_gd_momentum

CRBM::gd_momentum for pre-training each RBM. Default value is 0.9 for all RBMs

Definition at line 301 of file DeepBeliefNetwork.h.

SGVector<float64_t> pt_l1_coefficient

CRBM::l1_coefficient for pre-training each RBM. Default value is 0.0 for all RBMs

Definition at line 266 of file DeepBeliefNetwork.h.

SGVector<float64_t> pt_l2_coefficient

CRBM::l2_coefficient for pre-training each RBM. Default value is 0.0 for all RBMs

Definition at line 261 of file DeepBeliefNetwork.h.

SGVector<int32_t> pt_max_num_epochs

CRBM::max_num_epochs for pre-training each RBM. Default value is 1 for all RBMs

Definition at line 281 of file DeepBeliefNetwork.h.

SGVector<int32_t> pt_monitoring_interval

CRBM::monitoring_interval for pre-training each RBM. Default value is 10 for all RBMs

Definition at line 271 of file DeepBeliefNetwork.h.

SGVector<int32_t> pt_monitoring_method

CRBM::monitoring_method for pre-training each RBM. Default value is RBMMM_RECONSTRUCTION_ERROR for all RBMs

Definition at line 276 of file DeepBeliefNetwork.h.

Version* version

inherited

version

Definition at line 375 of file SGObject.h.

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

• DeepBeliefNetwork.h
• DeepBeliefNetwork.cpp