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CDeepBeliefNetwork Class Reference

Detailed Description

A Deep Belief Network.

A [Deep Belief Network](http://www.scholarpedia.org/article/Deep_belief_networks) [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:

Definition at line 90 of file DeepBeliefNetwork.h.

Inheritance diagram for CDeepBeliefNetwork:
[legend]

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 CNeuralNetworkconvert_to_neural_network (CNeuralLayer *output_layer=NULL, float64_t sigma=0.01)
 
virtual SGMatrix< float64_tget_weights (int32_t index, SGVector< float64_t > p=SGVector< float64_t >())
 
virtual SGVector< float64_tget_biases (int32_t index, SGVector< float64_t > p=SGVector< float64_t >())
 
virtual const char * get_name () const
 
virtual CSGObjectshallow_copy () const
 
virtual CSGObjectdeep_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)
 
SGIOget_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallelget_global_parallel ()
 
void set_global_version (Version *version)
 
Versionget_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)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
get (const std::string &name) const
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObjectclone ()
 

Public Attributes

SGVector< int32_t > pt_cd_num_steps
 
SGVector< bool > pt_cd_persistent
 
SGVector< bool > pt_cd_sample_visible
 
SGVector< float64_tpt_l2_coefficient
 
SGVector< float64_tpt_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_tpt_gd_learning_rate
 
SGVector< float64_tpt_gd_learning_rate_decay
 
SGVector< float64_tpt_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
 
SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_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)
 
template<typename T >
void register_param (Tag< T > &_tag, const T &value)
 
template<typename T >
void register_param (const std::string &name, const T &value)
 

Protected Attributes

ERBMVisibleUnitType m_visible_units_type
 
int32_t m_num_layers
 
CDynamicArray< int32_t > * m_layer_sizes
 
SGMatrixList< float64_tm_states
 
int32_t m_batch_size
 
SGVector< float64_tm_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

default constructor

Definition at line 50 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_unitsNumber of visible units
unit_typeType of visible units

Definition at line 55 of file DeepBeliefNetwork.cpp.

~CDeepBeliefNetwork ( )
virtual

Definition at line 64 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_unitsNumber of hidden units

Definition at line 69 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
dictdictionary of parameters to be built.

Definition at line 630 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 747 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_layerAn output layer
sigmaStandard deviation of the gaussian used to initialize the parameters of the output layer
Returns
Neural network inititialized using the DBN

Definition at line 359 of file DeepBeliefNetwork.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 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 392 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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

T get ( const Tag< T > &  _tag) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string &  name) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

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

Returns the bias vector of layer i

Parameters
indexLayer index
pIf specified, the bias vector is extracted from it instead of m_params

Definition at line 557 of file DeepBeliefNetwork.cpp.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 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 531 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_namename of the parameter
Returns
description of the parameter

Definition at line 555 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_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 568 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 213 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
indexLayer index
pIf specified, the weight matrix is extracted from it instead of m_params

Definition at line 546 of file DeepBeliefNetwork.cpp.

bool has ( const std::string &  name) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > &  tag) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
tagtag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

bool has ( const std::string &  name) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

void initialize_neural_network ( float64_t  sigma = 0.01)
virtual

Initializes the DBN

Parameters
sigmaStandard deviation of the gaussian used to initialize the weights

Definition at line 75 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
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 329 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
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 402 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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 459 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
ShogunExceptionwill 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 454 of file SGObject.cpp.

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 295 of file SGObject.cpp.

void pre_train ( CDenseFeatures< float64_t > *  features)
virtual

Pre-trains the DBN as a stack of RBMs

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

Definition at line 148 of file DeepBeliefNetwork.cpp.

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

Pre-trains a single RBM

Parameters
indexIndex of the RBM
featuresInput features. Should have as many features as the total number of visible units in the DBN

Definition at line 158 of file DeepBeliefNetwork.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "")
virtualinherited

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

void register_param ( Tag< T > &  _tag,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

void register_param ( const std::string &  name,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

void reset_chain ( )
virtual

Resets the state of the markov chain used for sampling

Definition at line 351 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_stepsNumber of Gibbs sampling steps for the top RBM.
batch_sizeNumber of samples to be drawn. A seperate chain is used for each sample
Returns
Sampled states of the visible units

Definition at line 332 of file DeepBeliefNetwork.cpp.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 347 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 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
ShogunExceptionwill 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 464 of file SGObject.cpp.

void set ( const Tag< T > &  _tag,
const T &  value 
)
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

void set ( const std::string &  name,
const T &  value 
)
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_batch_size ( int32_t  batch_size)
virtual

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

Parameters
batch_sizeBatch size

Definition at line 134 of file DeepBeliefNetwork.cpp.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 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
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 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 225 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
featuresInput features. Should have as many features as the total number of visible units in the DBN

Definition at line 209 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
featuresInput features. Should have as many features as the number of visible units in the DBN
iIndex 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 315 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 336 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 442 of file DeepBeliefNetwork.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 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 472 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 305 of file DeepBeliefNetwork.h.

float64_t gd_learning_rate

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

Definition at line 324 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 331 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 321 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](http://jmlr.org/proceedings/papers/v28/sutskever13.html) [Sutskever, 2013]

Definition at line 341 of file DeepBeliefNetwork.h.

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

int32_t m_batch_size
protected

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

Definition at line 357 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 366 of file DeepBeliefNetwork.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

CDynamicArray<int32_t>* m_layer_sizes
protected

Size of each layer

Definition at line 351 of file DeepBeliefNetwork.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

int32_t m_num_layers
protected

Number of layers

Definition at line 348 of file DeepBeliefNetwork.h.

int32_t m_num_params
protected

Number of parameters

Definition at line 363 of file DeepBeliefNetwork.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

SGVector<float64_t> m_params
protected

Parameters of the network

Definition at line 360 of file DeepBeliefNetwork.h.

float64_t m_sigma
protected

Standard deviation of the gaussian used to initialize the parameters

Definition at line 375 of file DeepBeliefNetwork.h.

SGMatrixList<float64_t> m_states
protected

States of each layer

Definition at line 354 of file DeepBeliefNetwork.h.

ERBMVisibleUnitType m_visible_units_type
protected

Type of the visible units

Definition at line 345 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 371 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 315 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 310 of file DeepBeliefNetwork.h.

Parallel* parallel
inherited

parallel

Definition at line 540 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 245 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 250 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 255 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 290 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 295 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 285 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 300 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 265 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 260 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 280 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 270 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 275 of file DeepBeliefNetwork.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation