SHOGUN
4.2.0
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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.
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_t > | get_activations () |
virtual SGMatrix< float64_t > | get_activation_gradients () |
virtual SGMatrix< float64_t > | get_local_gradients () |
virtual SGVector< int32_t > | get_input_indices () |
virtual CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_copy () const |
virtual bool | is_generic (EPrimitiveType *generic) const |
template<class T > | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
void | unset_generic () |
virtual void | print_serializable (const char *prefix="") |
virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
virtual bool | load_serializable (CSerializableFile *file, const char *prefix="") |
void | set_global_io (SGIO *io) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
SGStringList< char > | get_modelsel_names () |
void | print_modsel_params () |
char * | get_modsel_param_descr (const char *param_name) |
index_t | get_modsel_param_index (const char *param_name) |
void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
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 > | |
T | get (const Tag< T > &_tag) const |
template<typename T , typename U = void> | |
T | 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 CSGObject * | clone () |
Public Attributes | |
bool | is_training |
float64_t | dropout_prop |
float64_t | contraction_coefficient |
ENLAutoencoderPosition | autoencoder_position |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
virtual void | 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 | |
int32_t | m_num_maps |
int32_t | m_input_width |
int32_t | m_input_height |
int32_t | m_input_num_channels |
int32_t | m_radius_x |
int32_t | m_radius_y |
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_t > | m_convolution_output |
SGMatrix< float64_t > | m_convolution_output_gradients |
SGMatrix< float64_t > | m_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_t > | m_activations |
SGMatrix< float64_t > | m_activation_gradients |
SGMatrix< float64_t > | m_local_gradients |
SGMatrix< bool > | m_dropout_mask |
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 , |
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int32_t | pooling_height = 1 , |
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int32_t | stride_x = 1 , |
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int32_t | stride_y = 1 , |
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EInitializationMode | initialization_mode = NORMAL |
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Constuctor
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.
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Definition at line 108 of file NeuralConvolutionalLayer.h.
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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.
dict | dictionary of parameters to be built. |
Definition at line 630 of file SGObject.cpp.
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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.
Definition at line 747 of file SGObject.cpp.
Computes the activations of the neurons in this layer, results should be stored in m_activations. To be used only with input layers
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.
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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 | 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.
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 | 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.
Computes the error between the layer's current activations and the given target activations. Should only be used with output layers
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.
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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
Input gradients for layer i that connects into this layer as input are added to m_layers.element(i).get_activation_gradients()
Deriving classes should make sure to account for dropout [Hinton, 2012] during gradient computations
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.
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A deep copy. All the instance variables will also be copied.
Definition at line 231 of file SGObject.cpp.
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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.
Constrains the weights of each neuron in the layer to have an L2 norm of at most max_norm
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.
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.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 651 of file SGObject.cpp.
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Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
Definition at line 367 of file SGObject.h.
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Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
Definition at line 388 of file SGObject.h.
Gets the layer's activation gradients, a matrix of size num_neurons * batch_size
Definition at line 294 of file NeuralLayer.h.
Gets the layer's activations, a matrix of size num_neurons * batch_size
Definition at line 287 of file NeuralLayer.h.
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Returns the height assuming that the layer's activations are interpreted as images (i.e for convolutional nets)
Definition at line 265 of file NeuralLayer.h.
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Gets the indices of the layers that are connected to this layer as input
Definition at line 313 of file NeuralLayer.h.
Gets the layer's local gradients, a matrix of size num_neurons * batch_size
Definition at line 304 of file NeuralLayer.h.
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Definition at line 531 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 555 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 568 of file SGObject.cpp.
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Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
Reimplemented from CNeuralLayer.
Definition at line 210 of file NeuralConvolutionalLayer.h.
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Gets the number of neurons in the layer
Definition at line 251 of file NeuralLayer.h.
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Gets the number of parameters used in this layer
Definition at line 281 of file NeuralLayer.h.
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Returns the width assuming that the layer's activations are interpreted as images (i.e for convolutional nets)
Definition at line 258 of file NeuralLayer.h.
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Checks if object has a class parameter identified by a name.
name | name of the parameter |
Definition at line 289 of file SGObject.h.
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Checks if object has a class parameter identified by a Tag.
tag | tag of the parameter containing name and type information |
Definition at line 301 of file SGObject.h.
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Checks if a type exists for a class parameter identified by a name.
name | name of the parameter |
Definition at line 312 of file SGObject.h.
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Initializes the layer, computes the number of parameters needed for the layer
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.
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Initializes the layer's parameters. The layer should fill the given arrays with the initial value for its 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.
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If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 329 of file SGObject.cpp.
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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.
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Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
Definition at line 402 of file SGObject.cpp.
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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.
ShogunException | will 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.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 454 of file SGObject.cpp.
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Definition at line 295 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 507 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 341 of file SGObject.cpp.
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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.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 439 of file SGObject.h.
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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.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 452 of file SGObject.h.
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Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
Definition at line 347 of file SGObject.cpp.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 469 of file SGObject.cpp.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 464 of file SGObject.cpp.
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Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 328 of file SGObject.h.
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Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 354 of file SGObject.h.
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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
batch_size | number of training/test cases the network is currently working with |
Reimplemented from CNeuralLayer.
Definition at line 64 of file NeuralConvolutionalLayer.cpp.
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Definition at line 74 of file SGObject.cpp.
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Definition at line 79 of file SGObject.cpp.
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Definition at line 84 of file SGObject.cpp.
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Definition at line 89 of file SGObject.cpp.
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Definition at line 94 of file SGObject.cpp.
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Definition at line 99 of file SGObject.cpp.
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Definition at line 104 of file SGObject.cpp.
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Definition at line 109 of file SGObject.cpp.
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Definition at line 114 of file SGObject.cpp.
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Definition at line 119 of file SGObject.cpp.
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Definition at line 124 of file SGObject.cpp.
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Definition at line 129 of file SGObject.cpp.
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Definition at line 134 of file SGObject.cpp.
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Definition at line 139 of file SGObject.cpp.
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Definition at line 144 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 274 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 316 of file SGObject.cpp.
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Gets the number of neurons in the layer
num_neurons | number of neurons in the layer |
Definition at line 271 of file NeuralLayer.h.
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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.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 336 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
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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.
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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.
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probabilty of dropping out a neuron in the layer
Definition at line 327 of file NeuralLayer.h.
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io
Definition at line 537 of file SGObject.h.
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Should be true if the layer is currently used during training initial value is false
Definition at line 324 of file NeuralLayer.h.
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The map's activation function
Definition at line 247 of file NeuralConvolutionalLayer.h.
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.
activations of the neurons in this layer size num_neurons * batch_size
Definition at line 376 of file NeuralLayer.h.
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number of training/test cases the network is currently working with
Definition at line 371 of file NeuralLayer.h.
Holds the output of convolution
Definition at line 250 of file NeuralConvolutionalLayer.h.
Gradients of the error with respect to the convolution's output
Definition at line 253 of file NeuralConvolutionalLayer.h.
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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.
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parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
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Hash of parameter values
Definition at line 555 of file SGObject.h.
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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.
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Parameters initialization mode
Definition at line 259 of file NeuralConvolutionalLayer.h.
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Height of the input
Definition at line 223 of file NeuralConvolutionalLayer.h.
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Indices of the layers that are connected to this layer as input
Definition at line 363 of file NeuralLayer.h.
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Total number channels in the inputs
Definition at line 226 of file NeuralConvolutionalLayer.h.
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Number of neurons in the layers that are connected to this layer as input
Definition at line 368 of file NeuralLayer.h.
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Width of the input
Definition at line 220 of file NeuralConvolutionalLayer.h.
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.
Row indices of the max elements for each pooling region
Definition at line 256 of file NeuralConvolutionalLayer.h.
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model selection parameters
Definition at line 549 of file SGObject.h.
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Number of feature maps
Definition at line 217 of file NeuralConvolutionalLayer.h.
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Number of neurons in this layer
Definition at line 347 of file NeuralLayer.h.
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Number of neurons in this layer
Definition at line 360 of file NeuralLayer.h.
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parameters
Definition at line 546 of file SGObject.h.
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Height of the pooling region
Definition at line 238 of file NeuralConvolutionalLayer.h.
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Width of the pooling region
Definition at line 235 of file NeuralConvolutionalLayer.h.
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Radius of the convolution filter on the x (width) axis
Definition at line 229 of file NeuralConvolutionalLayer.h.
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Radius of the convolution filter on the y (height) axis
Definition at line 232 of file NeuralConvolutionalLayer.h.
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Stride in the x direction
Definition at line 241 of file NeuralConvolutionalLayer.h.
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Stride in the y direcetion
Definition at line 244 of file NeuralConvolutionalLayer.h.
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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.
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parallel
Definition at line 540 of file SGObject.h.
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version
Definition at line 543 of file SGObject.h.