SHOGUN
3.2.1
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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 59 of file NeuralConvolutionalLayer.h.
Public Member Functions | |
CNeuralConvolutionalLayer () | |
CNeuralConvolutionalLayer (EConvMapActivationFunction function, int32_t num_maps, int32_t input_width, int32_t input_height, 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) | |
virtual | ~CNeuralConvolutionalLayer () |
virtual void | set_batch_size (int32_t batch_size) |
virtual void | initialize (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 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 float64_t | compute_error (SGMatrix< float64_t > targets) |
virtual void | dropout_activations () |
virtual float64_t | compute_contraction_term (SGVector< float64_t > parameters) |
virtual int32_t | get_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 CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_copy () const |
virtual bool | is_generic (EPrimitiveType *generic) const |
template<class T > | |
void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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void | set_generic () |
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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) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
SGStringList< char > | get_modelsel_names () |
void | print_modsel_params () |
char * | get_modsel_param_descr (const char *param_name) |
index_t | get_modsel_param_index (const char *param_name) |
void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
virtual void | update_parameter_hash () |
virtual bool | parameter_hash_changed () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
virtual CSGObject * | clone () |
Public Attributes | |
bool | is_training |
float64_t | dropout_prop |
float64_t | contraction_coefficient |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
ParameterMap * | m_parameter_map |
uint32_t | m_hash |
Protected Member Functions | |
virtual TParameter * | migrate (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_maps |
int32_t | m_input_width |
int32_t | m_input_height |
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_buffer |
SGMatrix< float64_t > | m_max_indices |
int32_t | m_num_neurons |
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 | input_width, | ||
int32_t | input_height, | ||
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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Constuctor
function | Activation function |
num_maps | Number of feature maps |
input_width | Width of the input |
input_height | Height of the input |
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 96 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 1185 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 1302 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 139 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 116 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 228 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 in CNeuralLinearLayer, and CNeuralSoftmaxLayer.
Definition at line 192 of file NeuralLayer.h.
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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 140 of file NeuralConvolutionalLayer.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 146 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 88 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 182 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 1206 of file SGObject.cpp.
Gets the layer's activation gradients, a matrix of size num_neurons * batch_size
Definition at line 256 of file NeuralLayer.h.
Gets the layer's activations, a matrix of size num_neurons * batch_size
Definition at line 249 of file NeuralLayer.h.
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Gets the layer's local gradients, a matrix of size num_neurons * batch_size
Definition at line 266 of file NeuralLayer.h.
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Definition at line 1077 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 1101 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 1114 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 190 of file NeuralConvolutionalLayer.h.
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Gets the number of neurons in the layer
Definition at line 237 of file NeuralLayer.h.
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Gets the number of parameters used in this layer
Definition at line 243 of file NeuralLayer.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 84 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 93 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 243 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 113 of file NeuralLayer.h.
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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)
file_version | parameter version of the file |
current_version | version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 648 of file SGObject.cpp.
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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
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
Definition at line 489 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 320 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 occurres. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1004 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 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.
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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
param_base | set of TParameter instances that are mapped to the provided target parameter infos |
base_version | version of the parameter base |
target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 686 of file SGObject.cpp.
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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
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
Definition at line 893 of file SGObject.cpp.
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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)
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
replacement | (used as output) here the TParameter instance which is returned by migration is created into |
to_migrate | the only source that is used for migration |
old_name | with this parameter, a name change may be specified |
Definition at line 833 of file SGObject.cpp.
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Definition at line 209 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 1053 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 255 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 261 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 occurres. |
Reimplemented in CKernel.
Definition at line 1014 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 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.
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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 68 of file NeuralConvolutionalLayer.cpp.
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Definition at line 38 of file SGObject.cpp.
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Definition at line 43 of file SGObject.cpp.
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Definition at line 48 of file SGObject.cpp.
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Definition at line 53 of file SGObject.cpp.
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Definition at line 58 of file SGObject.cpp.
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Definition at line 63 of file SGObject.cpp.
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Definition at line 68 of file SGObject.cpp.
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Definition at line 73 of file SGObject.cpp.
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Definition at line 78 of file SGObject.cpp.
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Definition at line 83 of file SGObject.cpp.
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Definition at line 88 of file SGObject.cpp.
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Definition at line 93 of file SGObject.cpp.
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Definition at line 98 of file SGObject.cpp.
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Definition at line 103 of file SGObject.cpp.
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Definition at line 108 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 189 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 230 of file SGObject.cpp.
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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 140 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 250 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 196 of file SGObject.cpp.
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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 294 of file NeuralLayer.h.
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probabilty of dropping out a neuron in the layer
Definition at line 283 of file NeuralLayer.h.
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io
Definition at line 461 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 280 of file NeuralLayer.h.
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The map's activation function
Definition at line 224 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 322 of file NeuralLayer.h.
activations of the neurons in this layer size num_neurons * batch_size
Definition at line 317 of file NeuralLayer.h.
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number of training/test cases the network is currently working with
Definition at line 312 of file NeuralLayer.h.
Buffer for activation calculation
Definition at line 233 of file NeuralConvolutionalLayer.h.
Holds the output of convolution
Definition at line 227 of file NeuralConvolutionalLayer.h.
Gradients of the error with respect to the convolution's output
Definition at line 230 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 334 of file NeuralLayer.h.
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parameters wrt which we can compute gradients
Definition at line 476 of file SGObject.h.
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Hash of parameter values
Definition at line 482 of file SGObject.h.
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Height of the input
Definition at line 203 of file NeuralConvolutionalLayer.h.
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Indices of the layers that are connected to this layer as input
Definition at line 304 of file NeuralLayer.h.
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Number of neurons in the layers that are connected to this layer as input
Definition at line 309 of file NeuralLayer.h.
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Width of the input
Definition at line 200 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 328 of file NeuralLayer.h.
Row indices of the max elements for each pooling region
Definition at line 236 of file NeuralConvolutionalLayer.h.
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model selection parameters
Definition at line 473 of file SGObject.h.
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Number of feature maps
Definition at line 197 of file NeuralConvolutionalLayer.h.
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Number of neurons in this layer
Definition at line 298 of file NeuralLayer.h.
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Number of neurons in this layer
Definition at line 301 of file NeuralLayer.h.
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map for different parameter versions
Definition at line 479 of file SGObject.h.
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parameters
Definition at line 470 of file SGObject.h.
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Height of the pooling region
Definition at line 215 of file NeuralConvolutionalLayer.h.
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Width of the pooling region
Definition at line 212 of file NeuralConvolutionalLayer.h.
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Radius of the convolution filter on the x (width) axis
Definition at line 206 of file NeuralConvolutionalLayer.h.
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Radius of the convolution filter on the y (height) axis
Definition at line 209 of file NeuralConvolutionalLayer.h.
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Stride in the x direction
Definition at line 218 of file NeuralConvolutionalLayer.h.
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Stride in the y direcetion
Definition at line 221 of file NeuralConvolutionalLayer.h.
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parallel
Definition at line 464 of file SGObject.h.
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version
Definition at line 467 of file SGObject.h.