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CNeuralNetwork Class Reference

Detailed Description

A generic multi-layer neural network.

A Neural network is constructed using an array of CNeuralLayer objects. The NeuralLayer class defines the interface necessary for forward and backpropagation.

The network can be constructed as any arbitrary directed acyclic graph.

How to use the network:

Supported feature types: CDenseFeatures<float64_t> Supported label types:

The neural network can be trained using L-BFGS (default) or mini-batch gradient descent.

NOTE: LBFGS does not work properly when using dropout/max-norm regularization due to their stochastic nature. Use gradient descent instead.

During training, the error at each iteration is logged as MSG_INFO. (to turn on info messages call io.set_loglevel(MSG_INFO)).

The network stores the parameters of all the layers in a single array. This makes it easy to train a network of any combination of arbitrary layer types using any optimization method (gradient descent, L-BFGS, ..)

All the matrices the network (and related classes) deal with are in column-major format

When implemnting new layer types, the function check_gradients() can be used to make sure the gradient computations are correct.

Definition at line 107 of file NeuralNetwork.h.

Inheritance diagram for CNeuralNetwork:
Inheritance graph
[legend]

Public Member Functions

 CNeuralNetwork ()
 CNeuralNetwork (CDynamicObjectArray *layers)
virtual void set_layers (CDynamicObjectArray *layers)
virtual void connect (int32_t i, int32_t j)
virtual void quick_connect ()
virtual void disconnect (int32_t i, int32_t j)
virtual void disconnect_all ()
virtual void initialize (float64_t sigma=0.01f)
virtual ~CNeuralNetwork ()
virtual CBinaryLabelsapply_binary (CFeatures *data)
virtual CRegressionLabelsapply_regression (CFeatures *data)
virtual CMulticlassLabelsapply_multiclass (CFeatures *data)
virtual CDenseFeatures
< float64_t > * 
transform (CDenseFeatures< float64_t > *data)
virtual void set_labels (CLabels *lab)
virtual EMachineType get_classifier_type ()
virtual EProblemType get_machine_problem_type () const
virtual float64_t check_gradients (float64_t approx_epsilon=1.0e-3, float64_t s=1.0e-9)
SGVector< float64_t > * get_layer_parameters (int32_t i)
int32_t get_num_parameters ()
SGVector< float64_tget_parameters ()
int32_t get_num_inputs ()
int32_t get_num_outputs ()
CDynamicObjectArrayget_layers ()
virtual const char * get_name () const
virtual bool train (CFeatures *data=NULL)
virtual CLabelsapply (CFeatures *data=NULL)
virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)
virtual CLatentLabelsapply_latent (CFeatures *data=NULL)
virtual CLabelsget_labels ()
void set_max_train_time (float64_t t)
float64_t get_max_train_time ()
void set_solver_type (ESolverType st)
ESolverType get_solver_type ()
virtual void set_store_model_features (bool store_model)
virtual bool train_locked (SGVector< index_t > indices)
virtual float64_t apply_one (int32_t i)
virtual CLabelsapply_locked (SGVector< index_t > indices)
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)
virtual CMulticlassLabelsapply_locked_multiclass (SGVector< index_t > indices)
virtual CStructuredLabelsapply_locked_structured (SGVector< index_t > indices)
virtual CLatentLabelsapply_locked_latent (SGVector< index_t > indices)
virtual void data_lock (CLabels *labs, CFeatures *features)
virtual void post_lock (CLabels *labs, CFeatures *features)
virtual void data_unlock ()
virtual bool supports_locking () const
bool is_data_locked () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
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)
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

ENNOptimizationMethod optimization_method
float64_t l2_coefficient
float64_t l1_coefficient
float64_t dropout_hidden
float64_t dropout_input
float64_t max_norm
float64_t epsilon
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
float64_t gd_error_damping_coeff
SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)
virtual bool train_gradient_descent (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets)
virtual bool train_lbfgs (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets)
virtual SGMatrix< float64_tforward_propagate (CFeatures *data, int32_t j=-1)
virtual SGMatrix< float64_tforward_propagate (SGMatrix< float64_t > inputs, int32_t j=-1)
virtual void set_batch_size (int32_t batch_size)
virtual float64_t compute_gradients (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets, SGVector< float64_t > gradients)
virtual float64_t compute_error (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets)
virtual float64_t compute_error (SGMatrix< float64_t > targets)
virtual bool is_label_valid (CLabels *lab) const
CNeuralLayerget_layer (int32_t i)
SGMatrix< float64_tfeatures_to_matrix (CFeatures *features)
SGMatrix< float64_tlabels_to_matrix (CLabels *labs)
virtual void store_model_features ()
virtual bool train_require_labels () const
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
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

int32_t m_num_inputs
int32_t m_num_layers
CDynamicObjectArraym_layers
SGMatrix< bool > m_adj_matrix
int32_t m_total_num_parameters
SGVector< float64_tm_params
SGVector< bool > m_param_regularizable
SGVector< int32_t > m_index_offsets
int32_t m_batch_size
bool m_is_training
float64_t m_max_train_time
CLabelsm_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

Friends

class CDeepBeliefNetwork

Constructor & Destructor Documentation

default constuctor

Definition at line 43 of file NeuralNetwork.cpp.

Sets the layers of the network

Parameters
layersAn array of CNeuralLayer objects specifying the layers of the network. Must contain at least one input layer. The last layer in the array is treated as the output layer

Definition at line 49 of file NeuralNetwork.cpp.

~CNeuralNetwork ( )
virtual

Definition at line 151 of file NeuralNetwork.cpp.

Member Function Documentation

CLabels * apply ( CFeatures data = NULL)
virtualinherited

apply machine to data if data is not specified apply to the current features

Parameters
data(test)data to be classified
Returns
classified labels

Definition at line 160 of file Machine.cpp.

CBinaryLabels * apply_binary ( CFeatures data)
virtual

apply machine to data in means of binary classification problem

Reimplemented from CMachine.

Definition at line 156 of file NeuralNetwork.cpp.

CLatentLabels * apply_latent ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 240 of file Machine.cpp.

CLabels * apply_locked ( SGVector< index_t indices)
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters
indicesindex vector (of locked features) that is predicted

Definition at line 195 of file Machine.cpp.

CBinaryLabels * apply_locked_binary ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for binary problems

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 246 of file Machine.cpp.

CLatentLabels * apply_locked_latent ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for latent problems

Definition at line 274 of file Machine.cpp.

CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for multiclass problems

Definition at line 260 of file Machine.cpp.

CRegressionLabels * apply_locked_regression ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for regression problems

Reimplemented in CKernelMachine.

Definition at line 253 of file Machine.cpp.

CStructuredLabels * apply_locked_structured ( SGVector< index_t indices)
virtualinherited

applies a locked machine on a set of indices for structured problems

Definition at line 267 of file Machine.cpp.

CMulticlassLabels * apply_multiclass ( CFeatures data)
virtual

apply machine to data in means of multiclass classification problem

Reimplemented from CMachine.

Definition at line 191 of file NeuralNetwork.cpp.

virtual float64_t apply_one ( int32_t  i)
virtualinherited
CRegressionLabels * apply_regression ( CFeatures data)
virtual

apply machine to data in means of regression problem

Reimplemented from CMachine.

Definition at line 179 of file NeuralNetwork.cpp.

CStructuredLabels * apply_structured ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 234 of file Machine.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 1185 of file SGObject.cpp.

float64_t check_gradients ( float64_t  approx_epsilon = 1.0e-3,
float64_t  s = 1.0e-9 
)
virtual

Checks if the gradients computed using backpropagation are correct by comparing them with gradients computed using numerical approximation. Used for testing purposes only.

Gradients are numerically approximated according to:

\[ c = max(\epsilon x, s) \]

\[ f'(x) = \frac{f(x + c)-f(x - c)}{2c} \]

Parameters
approx_epsilonConstant used during gradient approximation
sSome small value, used to prevent division by zero
Returns
Average difference between numerical gradients and backpropagation gradients

Definition at line 512 of file NeuralNetwork.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 1302 of file SGObject.cpp.

float64_t compute_error ( SGMatrix< float64_t inputs,
SGMatrix< float64_t targets 
)
protectedvirtual

Forward propagates the inputs and computes the error between the output layer's activations and the given target activations.

Parameters
inputsinputs to the network, a matrix of size m_num_inputs*m_batch_size
targetsdesired values for the network's output, matrix of size num_neurons_output_layer*batch_size

Definition at line 504 of file NeuralNetwork.cpp.

float64_t compute_error ( SGMatrix< float64_t targets)
protectedvirtual

Computes the error between the output layer's activations and the given target activations.

Parameters
targetsdesired values for the network's output, matrix of size num_neurons_output_layer*batch_size

Reimplemented in CDeepAutoencoder, and CAutoencoder.

Definition at line 477 of file NeuralNetwork.cpp.

float64_t compute_gradients ( SGMatrix< float64_t inputs,
SGMatrix< float64_t targets,
SGVector< float64_t gradients 
)
protectedvirtual

Applies backpropagation to compute the gradients of the error with repsect to every parameter in the network.

Parameters
inputsinputs to the network, a matrix of size m_num_inputs*m_batch_size
targetsdesired values for the output layer's activations. matrix of size m_layers[m_num_layers-1].get_num_neurons()*m_batch_size
gradientsarray to be filled with gradient values.
Returns
error between the targets and the activations of the last layer

Definition at line 425 of file NeuralNetwork.cpp.

void connect ( int32_t  i,
int32_t  j 
)
virtual

Connects layer i as input to layer j. In order for forward and backpropagation to work correctly, i must be less that j

Definition at line 73 of file NeuralNetwork.cpp.

void data_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called

Only possible if supports_locking() returns true

Parameters
labslabels used for locking
featuresfeatures used for locking

Reimplemented in CKernelMachine.

Definition at line 120 of file Machine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 151 of file Machine.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 146 of file SGObject.cpp.

void disconnect ( int32_t  i,
int32_t  j 
)
virtual

Disconnects layer i from layer j

Definition at line 86 of file NeuralNetwork.cpp.

void disconnect_all ( )
virtual

Removes all connections in the network

Definition at line 91 of file NeuralNetwork.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 1206 of file SGObject.cpp.

SGMatrix< float64_t > features_to_matrix ( CFeatures features)
protected

Ensures the given features are suitable for use with the network and returns their feature matrix

Definition at line 572 of file NeuralNetwork.cpp.

SGMatrix< float64_t > forward_propagate ( CFeatures data,
int32_t  j = -1 
)
protectedvirtual

Applies forward propagation, computes the activations of each layer up to layer j

Parameters
datainput features
jlayer index at which the propagation should stop. If -1, the propagation continues up to the last layer
Returns
activations of the last layer

Definition at line 397 of file NeuralNetwork.cpp.

SGMatrix< float64_t > forward_propagate ( SGMatrix< float64_t inputs,
int32_t  j = -1 
)
protectedvirtual

Applies forward propagation, computes the activations of each layer up to layer j

Parameters
inputsinputs to the network, a matrix of size m_num_inputs*m_batch_size
jlayer index at which the propagation should stop. If -1, the propagation continues up to the last layer
Returns
activations of the last layer

Definition at line 404 of file NeuralNetwork.cpp.

virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type CT_NEURALNETWORK

Reimplemented from CMachine.

Definition at line 185 of file NeuralNetwork.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 183 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 224 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 237 of file SGObject.cpp.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 84 of file Machine.cpp.

CNeuralLayer * get_layer ( int32_t  i)
protected

returns a pointer to layer i in the network

Definition at line 681 of file NeuralNetwork.cpp.

SGVector< float64_t > * get_layer_parameters ( int32_t  i)

returns a copy of a layer's parameters array

Parameters
iindex of the layer

Definition at line 670 of file NeuralNetwork.cpp.

CDynamicObjectArray * get_layers ( )

Returns an array holding the network's layers

Definition at line 702 of file NeuralNetwork.cpp.

EProblemType get_machine_problem_type ( ) const
virtual

returns type of problem machine solves

Reimplemented from CMachine.

Definition at line 633 of file NeuralNetwork.cpp.

float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 95 of file Machine.cpp.

SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1077 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 1101 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 1114 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

Reimplemented from CMachine.

Reimplemented in CDeepAutoencoder, and CAutoencoder.

Definition at line 229 of file NeuralNetwork.h.

int32_t get_num_inputs ( )

returns the number of inputs the network takes

Definition at line 221 of file NeuralNetwork.h.

int32_t get_num_outputs ( )

returns the number of neurons in the output layer

Definition at line 697 of file NeuralNetwork.cpp.

int32_t get_num_parameters ( )

returns the totat number of parameters in the network

Definition at line 215 of file NeuralNetwork.h.

SGVector<float64_t> get_parameters ( )

return the network's parameter array

Definition at line 218 of file NeuralNetwork.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 110 of file Machine.cpp.

void initialize ( float64_t  sigma = 0.01f)
virtual

Initializes the network

Parameters
sigmastandard deviation of the gaussian used to randomly initialize the parameters

Definition at line 96 of file NeuralNetwork.cpp.

bool is_data_locked ( ) const
inherited
Returns
whether this machine is locked

Definition at line 294 of file Machine.h.

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 243 of file SGObject.cpp.

bool is_label_valid ( CLabels lab) const
protectedvirtual

check whether the labels is valid.

Subclasses can override this to implement their check of label types.

Parameters
labthe labels being checked, guaranteed to be non-NULL

Reimplemented from CMachine.

Definition at line 647 of file NeuralNetwork.cpp.

SGMatrix< float64_t > labels_to_matrix ( CLabels labs)
protected

converts the given labels into a matrix suitable for use with network

Returns
matrix of size get_num_outputs()*num_labels

Definition at line 588 of file NeuralNetwork.cpp.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
)
inherited

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 648 of file SGObject.cpp.

DynArray< TParameter * > * load_file_parameters ( const SGParamInfo param_info,
int32_t  file_version,
CSerializableFile file,
const char *  prefix = "" 
)
inherited

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 489 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 320 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 occurres.

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

Definition at line 1004 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 occurres.

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

Definition at line 999 of file SGObject.cpp.

void map_parameters ( DynArray< TParameter * > *  param_base,
int32_t &  base_version,
DynArray< const SGParamInfo * > *  target_param_infos 
)
inherited

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 686 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
)
protectedvirtualinherited

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 893 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
)
protectedvirtualinherited

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 833 of file SGObject.cpp.

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

Definition at line 209 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

post lock

Reimplemented in CMultitaskLinearMachine.

Definition at line 285 of file Machine.h.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1053 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 255 of file SGObject.cpp.

void quick_connect ( )
virtual

Connects each layer to the layer after it. That is, connects layer i to as input to layer i+1 for all i.

Definition at line 79 of file NeuralNetwork.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 261 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 occurres.

Reimplemented in CKernel.

Definition at line 1014 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 occurres.

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

Definition at line 1009 of file SGObject.cpp.

void set_batch_size ( int32_t  batch_size)
protectedvirtual

Sets the batch size (the number of train/test cases) the network is expected to deal with. Allocates memory for the activations, local gradients, input gradients if necessary (if the batch size is different from it's previous value)

Parameters
batch_sizenumber of train/test cases the network is expected to deal with.

Definition at line 562 of file NeuralNetwork.cpp.

void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 38 of file SGObject.cpp.

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 176 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 189 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 230 of file SGObject.cpp.

void set_labels ( CLabels lab)
virtual

set labels

Parameters
lablabels

Reimplemented from CMachine.

Definition at line 654 of file NeuralNetwork.cpp.

void set_layers ( CDynamicObjectArray layers)
virtual

Sets the layers of the network

Parameters
layersAn array of CNeuralLayer objects specifying the layers of the network. Must contain at least one input layer. The last layer in the array is treated as the output layer

Definition at line 55 of file NeuralNetwork.cpp.

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 90 of file Machine.cpp.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver type

Definition at line 105 of file Machine.cpp.

void set_store_model_features ( bool  store_model)
virtualinherited

Setter for store-model-features-after-training flag

Parameters
store_modelwhether model should be stored after training

Definition at line 115 of file Machine.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 140 of file SGObject.cpp.

virtual void store_model_features ( )
protectedvirtualinherited

Stores feature data of underlying model. After this method has been called, it is possible to change the machine's feature data and call apply(), which is then performed on the training feature data that is part of the machine's model.

Base method, has to be implemented in order to allow cross-validation and model selection.

NOT IMPLEMENTED! Has to be done in subclasses

Reimplemented in CKernelMachine, CKNN, CLinearMulticlassMachine, CTreeMachine< T >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CLinearMachine, CGaussianProcessMachine, CHierarchical, CDistanceMachine, CKernelMulticlassMachine, and CLinearStructuredOutputMachine.

Definition at line 333 of file Machine.h.

virtual bool supports_locking ( ) const
virtualinherited
Returns
whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 291 of file Machine.h.

bool train ( CFeatures data = NULL)
virtualinherited

train machine

Parameters
datatraining data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training.
Returns
whether training was successful

Reimplemented in CRelaxedTree, CAutoencoder, CSGDQN, and COnlineSVMSGD.

Definition at line 47 of file Machine.cpp.

bool train_gradient_descent ( SGMatrix< float64_t inputs,
SGMatrix< float64_t targets 
)
protectedvirtual

trains the network using gradient descent

Definition at line 244 of file NeuralNetwork.cpp.

bool train_lbfgs ( SGMatrix< float64_t inputs,
SGMatrix< float64_t targets 
)
protectedvirtual

trains the network using L-BFGS

Definition at line 328 of file NeuralNetwork.cpp.

virtual bool train_locked ( SGVector< index_t indices)
virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

NOT IMPLEMENTED

Parameters
indicesindex vector (of locked features) that is used for training
Returns
whether training was successful

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 237 of file Machine.h.

bool train_machine ( CFeatures data = NULL)
protectedvirtual

trains the network

Reimplemented from CMachine.

Definition at line 212 of file NeuralNetwork.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented in COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.

Definition at line 352 of file Machine.h.

CDenseFeatures< float64_t > * transform ( CDenseFeatures< float64_t > *  data)
virtual

Applies the network as a feature transformation

Forward-propagates the data through the network and returns the activations of the last layer

Parameters
dataInput features
Returns
Transformed features

Reimplemented in CAutoencoder, and CDeepAutoencoder.

Definition at line 205 of file NeuralNetwork.cpp.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 250 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 196 of file SGObject.cpp.

Friends And Related Function Documentation

friend class CDeepBeliefNetwork
friend

Definition at line 109 of file NeuralNetwork.h.

Member Data Documentation

float64_t dropout_hidden

Probabilty that a hidden layer neuron will be dropped out When using this, the recommended value is 0.5

default value 0.0 (no dropout)

For more details on dropout, see paper [Hinton, 2012]

Definition at line 372 of file NeuralNetwork.h.

float64_t dropout_input

Probabilty that a input layer neuron will be dropped out When using this, a good value might be 0.2

default value 0.0 (no dropout)

For more details on dropout, see this paper [Hinton, 2012]

Definition at line 382 of file NeuralNetwork.h.

float64_t epsilon

convergence criteria training stops when (E'- E)/E < epsilon where E is the error at the current iterations and E' is the error at the previous iteration default value is 1.0e-5

Definition at line 397 of file NeuralNetwork.h.

float64_t gd_error_damping_coeff

Used to damp the error fluctuations when stochastic gradient descent is used. damping is done according to: error_damped(i) = c*error(i) + (1-c)*error_damped(i-1) where c is the damping coefficient

If -1, the damping coefficient is automatically computed according to: c = 0.99*gd_mini_batch_size/training_set_size + 1e-2;

default value is -1

Definition at line 441 of file NeuralNetwork.h.

float64_t gd_learning_rate

gradient descent learning rate, defualt value 0.1

Definition at line 412 of file NeuralNetwork.h.

float64_t gd_learning_rate_decay

gradient descent learning rate decay 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 419 of file NeuralNetwork.h.

int32_t gd_mini_batch_size

size of the mini-batch used during gradient descent training, if 0 full-batch training is performed default value is 0

Definition at line 409 of file NeuralNetwork.h.

float64_t gd_momentum

gradient descent momentum multiplier

default value is 0.9

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

Definition at line 429 of file NeuralNetwork.h.

SGIO* io
inherited

io

Definition at line 461 of file SGObject.h.

float64_t l1_coefficient

L1 Regularization coeff, default value is 0.0

Definition at line 362 of file NeuralNetwork.h.

float64_t l2_coefficient

L2 Regularization coeff, default value is 0.0

Definition at line 359 of file NeuralNetwork.h.

SGMatrix<bool> m_adj_matrix
protected

Describes the connections in the network: if there's a connection from layer i to layer j then m_adj_matrix(i,j) = 1.

Definition at line 455 of file NeuralNetwork.h.

int32_t m_batch_size
protected

number of train/test cases the network is expected to deal with. Default value is 1

Definition at line 477 of file NeuralNetwork.h.

bool m_data_locked
protectedinherited

whether data is locked

Definition at line 368 of file Machine.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 476 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 482 of file SGObject.h.

SGVector<int32_t> m_index_offsets
protected

offsets specifying where each layer's parameters and parameter gradients are stored, i.e layer i's parameters are stored at m_params + m_index_offsets[i]

Definition at line 472 of file NeuralNetwork.h.

bool m_is_training
protected

True if the network is currently being trained initial value is false

Definition at line 482 of file NeuralNetwork.h.

CLabels* m_labels
protectedinherited

labels

Definition at line 359 of file Machine.h.

CDynamicObjectArray* m_layers
protected

network's layers

Definition at line 450 of file NeuralNetwork.h.

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 356 of file Machine.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 473 of file SGObject.h.

int32_t m_num_inputs
protected

number of neurons in the input layer

Definition at line 444 of file NeuralNetwork.h.

int32_t m_num_layers
protected

number of layer

Definition at line 447 of file NeuralNetwork.h.

SGVector<bool> m_param_regularizable
protected

Array that specifies which parameters are to be regularized. This is used to turn off regularization for bias parameters

Definition at line 466 of file NeuralNetwork.h.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 479 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 470 of file SGObject.h.

SGVector<float64_t> m_params
protected

array where all the parameters of the network are stored

Definition at line 461 of file NeuralNetwork.h.

ESolverType m_solver_type
protectedinherited

solver type

Definition at line 362 of file Machine.h.

bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 365 of file Machine.h.

int32_t m_total_num_parameters
protected

total number of parameters in the network

Definition at line 458 of file NeuralNetwork.h.

float64_t max_norm

Maximum allowable L2 norm for a neurons weights *When using this, a good value might be 15

default value -1 (max-norm regularization disabled)

Definition at line 389 of file NeuralNetwork.h.

int32_t max_num_epochs

maximum number of iterations over the training set. If 0, training will continue until convergence. defualt value is 0

Definition at line 403 of file NeuralNetwork.h.

ENNOptimizationMethod optimization_method

Optimization method, default is NNOM_LBFGS

Definition at line 356 of file NeuralNetwork.h.

Parallel* parallel
inherited

parallel

Definition at line 464 of file SGObject.h.

Version* version
inherited

version

Definition at line 467 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation