SHOGUN  4.2.0
CNeuralConvolutionalLayer Class Reference

## Detailed Description

Main component in convolutional neural networks

This layer type of consists of multiple feature maps. Each feature map computes its activations using by convolving its filter with the inputs, adding a bias, and then applying a non-linearity. Activations of each feature map can be max-pooled, that is, the map is divided into regions of a certain size and then the maximum activation is taken from each region.

All layer that are connected to this layer as input must have the same size.

During convolution, the inputs are implicitly padded with zeros on the sides

The layer assumes that its input images are in column major format

Definition at line 75 of file NeuralConvolutionalLayer.h.

Inheritance diagram for CNeuralConvolutionalLayer:
[legend]

## Public Member Functions

CNeuralConvolutionalLayer ()

CNeuralConvolutionalLayer (EConvMapActivationFunction function, int32_t num_maps, int32_t radius_x, int32_t radius_y, int32_t pooling_width=1, int32_t pooling_height=1, int32_t stride_x=1, int32_t stride_y=1, EInitializationMode initialization_mode=NORMAL)

virtual ~CNeuralConvolutionalLayer ()

virtual void set_batch_size (int32_t batch_size)

virtual void initialize_neural_layer (CDynamicObjectArray *layers, SGVector< int32_t > input_indices)

virtual void initialize_parameters (SGVector< float64_t > parameters, SGVector< bool > parameter_regularizable, float64_t sigma)

virtual void compute_activations (SGVector< float64_t > parameters, CDynamicObjectArray *layers)

virtual void compute_gradients (SGVector< float64_t > parameters, SGMatrix< float64_t > targets, CDynamicObjectArray *layers, SGVector< float64_t > parameter_gradients)

virtual float64_t compute_error (SGMatrix< float64_t > targets)

virtual void enforce_max_norm (SGVector< float64_t > parameters, float64_t max_norm)

virtual const char * get_name () const

virtual bool is_input ()

virtual void compute_activations (SGMatrix< float64_t > inputs)

virtual void dropout_activations ()

virtual float64_t compute_contraction_term (SGVector< float64_t > parameters)

virtual int32_t get_num_neurons ()

virtual int32_t get_width ()

virtual int32_t get_height ()

virtual void set_num_neurons (int32_t num_neurons)

virtual int32_t get_num_parameters ()

virtual SGMatrix< float64_tget_activations ()

virtual SGVector< int32_t > get_input_indices ()

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)

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

bool is_training

float64_t dropout_prop

float64_t contraction_coefficient

ENLAutoencoderPosition autoencoder_position

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

uint32_t m_hash

## Protected Member Functions

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_maps

int32_t m_input_width

int32_t m_input_height

int32_t m_input_num_channels

int32_t m_pooling_width

int32_t m_pooling_height

int32_t m_stride_x

int32_t m_stride_y

EConvMapActivationFunction m_activation_function

SGMatrix< float64_tm_convolution_output

SGMatrix< float64_tm_max_indices

EInitializationMode m_initialization_mode

int32_t m_num_neurons

int32_t m_width

int32_t m_height

int32_t m_num_parameters

SGVector< int32_t > m_input_indices

SGVector< int32_t > m_input_sizes

int32_t m_batch_size

SGMatrix< float64_tm_activations

## Constructor & Destructor Documentation

 CNeuralConvolutionalLayer ( )

default constructor

Definition at line 40 of file NeuralConvolutionalLayer.cpp.

 CNeuralConvolutionalLayer ( EConvMapActivationFunction function, int32_t num_maps, int32_t radius_x, int32_t radius_y, int32_t pooling_width = 1, int32_t pooling_height = 1, int32_t stride_x = 1, int32_t stride_y = 1, EInitializationMode initialization_mode = NORMAL )

Constuctor

Parameters
 function Activation function num_maps Number of feature maps radius_x Radius of the convolution filter on the x (width) axis. The filter size on the x-axis equals (2*radius_x+1) radius_y Radius of the convolution filter on the y (height) axis. The filter size on the y-axis equals (2*radius_y+1) pooling_width Width of the pooling region pooling_height Height of the pooling region stride_x Stride in the x direction for convolution stride_y Stride in the y direction for convolution

Definition at line 45 of file NeuralConvolutionalLayer.cpp.

 virtual ~CNeuralConvolutionalLayer ( )
virtual

Definition at line 108 of file NeuralConvolutionalLayer.h.

## Member Function Documentation

 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.

 virtual void compute_activations ( SGMatrix< float64_t > inputs )
virtualinherited

Computes the activations of the neurons in this layer, results should be stored in m_activations. To be used only with input layers

Parameters
 inputs activations of the neurons in the previous layer, matrix of size previous_layer_num_neurons * batch_size

Reimplemented in CNeuralInputLayer.

Definition at line 153 of file NeuralLayer.h.

 void compute_activations ( SGVector< float64_t > parameters, CDynamicObjectArray * layers )
virtual

Computes the activations of the neurons in this layer, results should be stored in m_activations. To be used only with non-input layers

Parameters
 parameters Vector of size get_num_parameters(), contains the parameters of the layer layers Array of layers that form the network that this layer is being used with

Reimplemented from CNeuralLayer.

Definition at line 157 of file NeuralConvolutionalLayer.cpp.

 virtual float64_t compute_contraction_term ( SGVector< float64_t > parameters )
virtualinherited

Computes

$\frac{\lambda}{N} \sum_{k=0}^{N-1} \left \| J(x_k) \right \|^2_F$

where $$\left \| J(x_k)) \right \|^2_F$$ is the Frobenius norm of the Jacobian of the activations of the hidden layer with respect to its inputs, $$N$$ is the batch size, and $$\lambda$$ is the contraction coefficient.

Should be implemented by layers that support being used as a hidden layer in a contractive autoencoder.

Parameters
 parameters Vector of size get_num_parameters(), contains the parameters of the layer

Reimplemented in CNeuralLinearLayer, CNeuralLogisticLayer, and CNeuralRectifiedLinearLayer.

Definition at line 242 of file NeuralLayer.h.

 float64_t compute_error ( SGMatrix< float64_t > targets )
virtual

Computes the error between the layer's current activations and the given target activations. Should only be used with output layers

Parameters
 targets desired values for the layer's activations, matrix of size num_neurons*batch_size

Reimplemented from CNeuralLayer.

Definition at line 235 of file NeuralConvolutionalLayer.cpp.

 void compute_gradients ( SGVector< float64_t > parameters, SGMatrix< float64_t > targets, CDynamicObjectArray * layers, SGVector< float64_t > parameter_gradients )
virtual

Computes the gradients that are relevent to this layer:

• The gradients of the error with respect to the layer's parameters -The gradients of the error with respect to the layer's inputs

Deriving classes should make sure to account for dropout [Hinton, 2012] during gradient computations

Parameters
 parameters Vector of size get_num_parameters(), contains the parameters of the layer targets a matrix of size num_neurons*batch_size. If the layer is being used as an output layer, targets is the desired values for the layer's activations, otherwise it's an empty matrix layers Array of layers that form the network that this layer is being used with parameter_gradients Vector of size get_num_parameters(). To be filled with gradients of the error with respect to each parameter of the layer

Reimplemented from CNeuralLayer.

Definition at line 182 of file NeuralConvolutionalLayer.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 dropout_activations ( )
virtualinherited

Applies dropout [Hinton, 2012] to the activations of the layer

If is_training is true, fills m_dropout_mask with random values (according to dropout_prop) and multiplies it into the activations, otherwise, multiplies the activations by (1-dropout_prop) to compensate for using dropout during training

Definition at line 90 of file NeuralLayer.cpp.

 void enforce_max_norm ( SGVector< float64_t > parameters, float64_t max_norm )
virtual

Constrains the weights of each neuron in the layer to have an L2 norm of at most max_norm

Parameters
 parameters pointer to the layer's parameters, array of size get_num_parameters() max_norm maximum allowable norm for a neuron's weights

Reimplemented from CNeuralLayer.

Definition at line 246 of file NeuralConvolutionalLayer.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.

virtualinherited

Gets the layer's activation gradients, a matrix of size num_neurons * batch_size

Returns

Definition at line 294 of file NeuralLayer.h.

 virtual SGMatrix get_activations ( )
virtualinherited

Gets the layer's activations, a matrix of size num_neurons * batch_size

Returns
layer's activations

Definition at line 287 of file NeuralLayer.h.

 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.

 virtual int32_t get_height ( )
virtualinherited

Returns the height assuming that the layer's activations are interpreted as images (i.e for convolutional nets)

Returns
Height

Definition at line 265 of file NeuralLayer.h.

 virtual SGVector get_input_indices ( )
virtualinherited

Gets the indices of the layers that are connected to this layer as input

Returns
layer's input indices

Definition at line 313 of file NeuralLayer.h.

virtualinherited

Gets the layer's local gradients, a matrix of size num_neurons * batch_size

Returns

Definition at line 304 of file NeuralLayer.h.

 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

Reimplemented from CNeuralLayer.

Definition at line 210 of file NeuralConvolutionalLayer.h.

 virtual int32_t get_num_neurons ( )
virtualinherited

Gets the number of neurons in the layer

Returns
number of neurons in the layer

Definition at line 251 of file NeuralLayer.h.

 virtual int32_t get_num_parameters ( )
virtualinherited

Gets the number of parameters used in this layer

Returns
number of parameters used in this layer

Definition at line 281 of file NeuralLayer.h.

 virtual int32_t get_width ( )
virtualinherited

Returns the width assuming that the layer's activations are interpreted as images (i.e for convolutional nets)

Returns
Width

Definition at line 258 of file NeuralLayer.h.

 void initialize_neural_layer ( CDynamicObjectArray * layers, SGVector< int32_t > input_indices )
virtual

Initializes the layer, computes the number of parameters needed for the layer

Parameters
 layers Array of layers that form the network that this layer is being used with input_indices Indices of the layers that are connected to this layer as input

Reimplemented from CNeuralLayer.

Definition at line 82 of file NeuralConvolutionalLayer.cpp.

 void initialize_parameters ( SGVector< float64_t > parameters, SGVector< bool > parameter_regularizable, float64_t sigma )
virtual

Initializes the layer's parameters. The layer should fill the given arrays with the initial value for its parameters

Parameters
 parameters Vector of size get_num_parameters() parameter_regularizable Vector of size get_num_parameters(). This controls which of the layer's parameter are subject to regularization, i.e to turn off regularization for parameter i, set parameter_regularizable[i] = false. This is usally used to turn off regularization for bias parameters. sigma standard deviation of the gaussian used to random the parameters

Reimplemented from CNeuralLayer.

Definition at line 125 of file NeuralConvolutionalLayer.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.

 virtual bool is_input ( )
virtualinherited

returns true if the layer is an input layer. Input layers are the root layers of a network, that is, they don't receive signals from other layers, they receive signals from the inputs features to the network.

Local and activation gradients are not computed for input layers

Reimplemented in CNeuralInputLayer.

Definition at line 127 of file NeuralLayer.h.

 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.

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.

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 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.

 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.

Definition at line 431 of file SGObject.cpp.

 void set_batch_size ( int32_t batch_size )
virtual

Sets the batch_size and allocates memory for m_activations and m_input_gradients accordingly. Must be called before forward or backward propagation is performed

Parameters
 batch_size number of training/test cases the network is currently working with

Reimplemented from CNeuralLayer.

Definition at line 64 of file NeuralConvolutionalLayer.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.

 virtual void set_num_neurons ( int32_t num_neurons )
virtualinherited

Gets the number of neurons in the layer

Parameters
 num_neurons number of neurons in the layer

Definition at line 271 of file NeuralLayer.h.

 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 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 update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

## Member Data Documentation

 ENLAutoencoderPosition autoencoder_position
inherited

For autoencoders, specifies the position of the layer in the autoencoder, i.e an encoding layer or a decoding layer. Default value is NLAP_NONE

Definition at line 343 of file NeuralLayer.h.

 float64_t contraction_coefficient
inherited

For hidden layers in a contractive autoencoders [Rifai, 2011] a term:

$\frac{\lambda}{N} \sum_{k=0}^{N-1} \left \| J(x_k) \right \|^2_F$

is added to the error, where $$\left \| J(x_k)) \right \|^2_F$$ is the Frobenius norm of the Jacobian of the activations of the hidden layer with respect to its inputs, $$N$$ is the batch size, and $$\lambda$$ is the contraction coefficient.

Default value is 0.0.

Definition at line 338 of file NeuralLayer.h.

 float64_t dropout_prop
inherited

probabilty of dropping out a neuron in the layer

Definition at line 327 of file NeuralLayer.h.

 SGIO* io
inherited

io

Definition at line 369 of file SGObject.h.

 bool is_training
inherited

Should be true if the layer is currently used during training initial value is false

Definition at line 324 of file NeuralLayer.h.

 EConvMapActivationFunction m_activation_function
protected

The map's activation function

Definition at line 247 of file NeuralConvolutionalLayer.h.

protectedinherited

gradients of the error with respect to the layer's inputs size previous_layer_num_neurons * batch_size

Definition at line 381 of file NeuralLayer.h.

 SGMatrix m_activations
protectedinherited

activations of the neurons in this layer size num_neurons * batch_size

Definition at line 376 of file NeuralLayer.h.

 int32_t m_batch_size
protectedinherited

number of training/test cases the network is currently working with

Definition at line 371 of file NeuralLayer.h.

 SGMatrix m_convolution_output
protected

Holds the output of convolution

Definition at line 250 of file NeuralConvolutionalLayer.h.

protected

Gradients of the error with respect to the convolution's output

Definition at line 253 of file NeuralConvolutionalLayer.h.

protectedinherited

binary mask that determines whether a neuron will be kept or dropped out during the current iteration of training size num_neurons * batch_size

Definition at line 393 of file NeuralLayer.h.

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.

 int32_t m_height
protectedinherited

Width of the image (if the layer's activations are to be interpreted as images. Default value is 1

Definition at line 357 of file NeuralLayer.h.

 EInitializationMode m_initialization_mode
protected

Parameters initialization mode

Definition at line 259 of file NeuralConvolutionalLayer.h.

 int32_t m_input_height
protected

Height of the input

Definition at line 223 of file NeuralConvolutionalLayer.h.

 SGVector m_input_indices
protectedinherited

Indices of the layers that are connected to this layer as input

Definition at line 363 of file NeuralLayer.h.

 int32_t m_input_num_channels
protected

Total number channels in the inputs

Definition at line 226 of file NeuralConvolutionalLayer.h.

 SGVector m_input_sizes
protectedinherited

Number of neurons in the layers that are connected to this layer as input

Definition at line 368 of file NeuralLayer.h.

 int32_t m_input_width
protected

Width of the input

Definition at line 220 of file NeuralConvolutionalLayer.h.

protectedinherited

gradients of the error with respect to the layer's pre-activations, this is usually used as a buffer when computing the input gradients size num_neurons * batch_size

Definition at line 387 of file NeuralLayer.h.

 SGMatrix m_max_indices
protected

Row indices of the max elements for each pooling region

Definition at line 256 of file NeuralConvolutionalLayer.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 381 of file SGObject.h.

 int32_t m_num_maps
protected

Number of feature maps

Definition at line 217 of file NeuralConvolutionalLayer.h.

 int32_t m_num_neurons
protectedinherited

Number of neurons in this layer

Definition at line 347 of file NeuralLayer.h.

 int32_t m_num_parameters
protectedinherited

Number of neurons in this layer

Definition at line 360 of file NeuralLayer.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 378 of file SGObject.h.

 int32_t m_pooling_height
protected

Height of the pooling region

Definition at line 238 of file NeuralConvolutionalLayer.h.

 int32_t m_pooling_width
protected

Width of the pooling region

Definition at line 235 of file NeuralConvolutionalLayer.h.

protected

Radius of the convolution filter on the x (width) axis

Definition at line 229 of file NeuralConvolutionalLayer.h.

protected

Radius of the convolution filter on the y (height) axis

Definition at line 232 of file NeuralConvolutionalLayer.h.

 int32_t m_stride_x
protected

Stride in the x direction

Definition at line 241 of file NeuralConvolutionalLayer.h.

 int32_t m_stride_y
protected

Stride in the y direcetion

Definition at line 244 of file NeuralConvolutionalLayer.h.

 int32_t m_width
protectedinherited

Width of the image (if the layer's activations are to be interpreted as images. Default value is m_num_neurons

Definition at line 352 of file NeuralLayer.h.

 Parallel* parallel
inherited

parallel

Definition at line 372 of file SGObject.h.

 Version* version
inherited

version

Definition at line 375 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation