This class represents a layer of probabilistic neurons. More...
#include <probabilistic_layer.h>
Public Types | |
enum class | ActivationFunction { Binary , Logistic , Competitive , Softmax } |
Enumeration of available methods for interpreting variables as probabilities. More... | |
Public Types inherited from Layer | |
enum class | Type { Scaling , Convolutional , Perceptron , Pooling , Probabilistic , LongShortTermMemory , Recurrent , Unscaling , Bounding } |
This enumeration represents the possible types of layers. More... | |
Public Member Functions | |
ProbabilisticLayer () | |
ProbabilisticLayer (const Index &, const Index &) | |
virtual | ~ProbabilisticLayer () |
Index | get_inputs_number () const |
Returns the number of inputs. More... | |
Index | get_neurons_number () const |
Index | get_biases_number () const |
Index | get_synaptic_weights_number () const |
Returns the number of layer's synaptic weights. More... | |
const type & | get_decision_threshold () const |
Returns the decision threshold. More... | |
const ActivationFunction & | get_activation_function () const |
string | write_activation_function () const |
string | write_activation_function_text () const |
const bool & | get_display () const |
void | set () |
void | set (const Index &, const Index &) |
void | set (const ProbabilisticLayer &) |
void | set_inputs_number (const Index &) |
void | set_neurons_number (const Index &) |
void | set_biases (const Tensor< type, 2 > &) |
void | set_synaptic_weights (const Tensor< type, 2 > &) |
void | set_parameters (const Tensor< type, 1 > &, const Index &index=0) |
void | set_decision_threshold (const type &) |
void | set_activation_function (const ActivationFunction &) |
void | set_activation_function (const string &) |
virtual void | set_default () |
const Tensor< type, 2 > & | get_biases () const |
Returns the biases of the layer. More... | |
const Tensor< type, 2 > & | get_synaptic_weights () const |
Returns the synaptic weights of the layer. More... | |
Tensor< type, 2 > | get_biases (Tensor< type, 1 > &) const |
Tensor< type, 2 > | get_synaptic_weights (Tensor< type, 1 > &) const |
Index | get_parameters_number () const |
Returns the number of parameters(biases and synaptic weights) of the layer. More... | |
Tensor< type, 1 > | get_parameters () const |
void | set_display (const bool &) |
void | set_biases_constant (const type &) |
void | set_synaptic_weights_constant (const type &) |
void | set_synaptic_weights_constant_Glorot () |
void | set_parameters_constant (const type &) |
void | set_parameters_random () |
void | insert_parameters (const Tensor< type, 1 > &, const Index &) |
void | calculate_combinations (const Tensor< type, 2 > &, const Tensor< type, 2 > &, const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | calculate_activations (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | calculate_activations_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const |
Tensor< type, 2 > | calculate_outputs (const Tensor< type, 2 > &) |
void | forward_propagate (const Tensor< type, 2 > &, LayerForwardPropagation *) |
void | forward_propagate (const Tensor< type, 2 > &, Tensor< type, 1 >, LayerForwardPropagation *) |
void | calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagation *) const |
void | insert_gradient (LayerBackPropagation *, const Index &, Tensor< type, 1 > &) const |
void | calculate_squared_errors_Jacobian_lm (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagationLM *) |
void | insert_squared_errors_Jacobian_lm (LayerBackPropagationLM *, const Index &, Tensor< type, 2 > &) const |
string | write_binary_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_logistic_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_competitive_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_softmax_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_no_probabilistic_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_combinations (const Tensor< string, 1 > &) const |
string | write_activations (const Tensor< string, 1 > &) const |
string | write_expression_c () const |
string | write_combinations_c () const |
string | write_activations_c () const |
string | write_expression_python () const |
string | write_combinations_python () const |
string | write_activations_python () const |
void | from_XML (const tinyxml2::XMLDocument &) |
void | write_XML (tinyxml2::XMLPrinter &) const |
Public Member Functions inherited from Layer | |
string | get_name () const |
void | set_threads_number (const int &) |
virtual Tensor< type, 2 > | calculate_outputs_from4D (const Tensor< type, 4 > &) |
virtual Tensor< type, 4 > | calculate_outputs_4D (const Tensor< type, 4 > &) |
virtual void | forward_propagate (const Tensor< type, 4 > &, LayerForwardPropagation *) |
virtual void | forward_propagate (const Tensor< type, 4 > &, Tensor< type, 1 >, LayerForwardPropagation *) |
virtual void | calculate_hidden_delta (LayerForwardPropagation *, LayerBackPropagation *, LayerBackPropagation *) const |
virtual void | calculate_hidden_delta_lm (LayerForwardPropagation *, LayerBackPropagationLM *, LayerBackPropagationLM *) const |
virtual void | calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation *, LayerBackPropagation *) const |
Type | get_type () const |
string | get_type_string () const |
Takes the type of layer used by the model. More... | |
Protected Attributes | |
Tensor< type, 2 > | biases |
Tensor< type, 2 > | synaptic_weights |
This matrix containing conection strengths from a layer's inputs to its neurons. More... | |
ActivationFunction | activation_function = ActivationFunction::Logistic |
Activation function variable. More... | |
type | decision_threshold |
bool | display = true |
Display messages to screen. More... | |
Protected Attributes inherited from Layer | |
NonBlockingThreadPool * | non_blocking_thread_pool = nullptr |
ThreadPoolDevice * | thread_pool_device = nullptr |
string | layer_name = "layer" |
Layer name. More... | |
Type | layer_type = Type::Perceptron |
Layer type. More... | |
const Eigen::array< IndexPair< Index >, 1 > | A_BT = {IndexPair<Index>(1, 1)} |
const Eigen::array< IndexPair< Index >, 1 > | AT_B = {IndexPair<Index>(0, 0)} |
const Eigen::array< IndexPair< Index >, 1 > | A_B = {IndexPair<Index>(1, 0)} |
Additional Inherited Members | |
Protected Member Functions inherited from Layer | |
void | hard_sigmoid (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | hyperbolic_tangent (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | logistic (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | symmetric_threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | rectified_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | scaled_exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | soft_plus (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | soft_sign (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | softmax (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | binary (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | competitive (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | hard_sigmoid_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | hyperbolic_tangent_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | logistic_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | symmetric_threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | rectified_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | scaled_exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | soft_plus_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | soft_sign_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | hard_sigmoid (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | hyperbolic_tangent (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | logistic (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | symmetric_threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | rectified_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | scaled_exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | soft_plus (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | soft_sign (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | softmax (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | binary (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | competitive (const Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | hard_sigmoid_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | hyperbolic_tangent_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | symmetric_threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | rectified_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | scaled_exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | soft_plus_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | soft_sign_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const |
void | logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const |
void | softmax_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const |
void | linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | logistic (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | hyperbolic_tangent (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | symmetric_threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | rectified_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | scaled_exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | soft_plus (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | soft_sign (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | hard_sigmoid (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | logistic_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | hyperbolic_tangent_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | symmetric_threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | rectified_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | scaled_exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | soft_plus_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | soft_sign_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | hard_sigmoid_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
void | exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const |
This class represents a layer of probabilistic neurons.
The neural network defined in OpenNN includes a probabilistic layer for those problems when the outptus are to be interpreted as probabilities. It does not has Synaptic weights or Biases
Definition at line 49 of file probabilistic_layer.h.
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Enumeration of available methods for interpreting variables as probabilities.
Definition at line 68 of file probabilistic_layer.h.
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Default constructor. It creates a probabilistic layer object with zero probabilistic neurons. It does not has Synaptic weights or Biases
Definition at line 18 of file probabilistic_layer.cpp.
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Probabilistic neurons number constructor. It creates a probabilistic layer with a given size.
new_neurons_number | Number of neurons in the layer. |
Definition at line 28 of file probabilistic_layer.cpp.
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Destructor. This destructor does not delete any pointer.
Definition at line 42 of file probabilistic_layer.cpp.
void calculate_activations | ( | const Tensor< type, 2 > & | combinations, |
Tensor< type, 2 > & | activations | ||
) | const |
Definition at line 585 of file probabilistic_layer.cpp.
void calculate_activations_derivatives | ( | const Tensor< type, 2 > & | combinations, |
Tensor< type, 2 > & | activations, | ||
Tensor< type, 3 > & | activations_derivatives | ||
) | const |
Definition at line 640 of file probabilistic_layer.cpp.
void calculate_combinations | ( | const Tensor< type, 2 > & | inputs, |
const Tensor< type, 2 > & | biases, | ||
const Tensor< type, 2 > & | synaptic_weights, | ||
Tensor< type, 2 > & | combinations | ||
) | const |
Definition at line 566 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 749 of file probabilistic_layer.cpp.
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This method processes the input to the probabilistic layer in order to obtain a set of outputs which can be interpreted as probabilities. This posprocessing is performed according to the probabilistic method to be used.
inputs | Set of inputs to the probabilistic layer. |
Reimplemented from Layer.
Definition at line 680 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 827 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 695 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 708 of file probabilistic_layer.cpp.
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Deserializes a TinyXML document into this probabilistic layer object.
document | XML document containing the member data. |
Reimplemented from Layer.
Definition at line 994 of file probabilistic_layer.cpp.
const ProbabilisticLayer::ActivationFunction & get_activation_function | ( | ) | const |
Returns the method to be used for interpreting the outputs as probabilistic values. The methods available for that are Binary, Probability, Competitive and Softmax.
Definition at line 84 of file probabilistic_layer.cpp.
const Tensor< type, 2 > & get_biases | ( | ) | const |
Returns the biases of the layer.
Definition at line 169 of file probabilistic_layer.cpp.
Tensor< type, 2 > get_biases | ( | Tensor< type, 1 > & | parameters | ) | const |
Returns the biases from a given vector of paramters for the layer.
parameters | Parameters of the layer. |
Definition at line 186 of file probabilistic_layer.cpp.
Index get_biases_number | ( | ) | const |
Definition at line 59 of file probabilistic_layer.cpp.
const type & get_decision_threshold | ( | ) | const |
Returns the decision threshold.
Definition at line 75 of file probabilistic_layer.cpp.
const bool & get_display | ( | ) | const |
Returns true if messages from this class are to be displayed on the screen, or false if messages from this class are not to be displayed on the screen.
Definition at line 161 of file probabilistic_layer.cpp.
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Returns the number of inputs.
Reimplemented from Layer.
Definition at line 47 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 53 of file probabilistic_layer.cpp.
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Returns a single vector with all the layer parameters. The format is a vector of real values. The size is the number of parameters in the layer.
Reimplemented from Layer.
Definition at line 223 of file probabilistic_layer.cpp.
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Returns the number of parameters(biases and synaptic weights) of the layer.
Reimplemented from Layer.
Definition at line 213 of file probabilistic_layer.cpp.
const Tensor< type, 2 > & get_synaptic_weights | ( | ) | const |
Returns the synaptic weights of the layer.
Definition at line 177 of file probabilistic_layer.cpp.
Tensor< type, 2 > get_synaptic_weights | ( | Tensor< type, 1 > & | parameters | ) | const |
Returns the synaptic weights from a given vector of paramters for the layer.
parameters | Parameters of the layer. |
Definition at line 199 of file probabilistic_layer.cpp.
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Returns the number of layer's synaptic weights.
Reimplemented from Layer.
Definition at line 67 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 809 of file probabilistic_layer.cpp.
void insert_parameters | ( | const Tensor< type, 1 > & | parameters, |
const Index & | |||
) |
Definition at line 556 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 897 of file probabilistic_layer.cpp.
void set | ( | ) |
Sets a probabilistic layer with zero probabilistic neurons. It also sets the rest of members to their default values.
Definition at line 245 of file probabilistic_layer.cpp.
void set | ( | const Index & | new_inputs_number, |
const Index & | new_neurons_number | ||
) |
Resizes the size of the probabilistic layer. It also sets the rest of class members to their default values.
new_neurons_number | New size for the probabilistic layer. |
Definition at line 259 of file probabilistic_layer.cpp.
void set | ( | const ProbabilisticLayer & | other_probabilistic_layer | ) |
Sets this object to be equal to another object of the same class.
other_probabilistic_layer | Probabilistic layer object to be copied. |
Definition at line 274 of file probabilistic_layer.cpp.
void set_activation_function | ( | const ActivationFunction & | new_activation_function | ) |
Sets the chosen method for probabilistic postprocessing. Current probabilistic methods include Binary, Probability, Competitive and Softmax.
new_activation_function | Method for interpreting the outputs as probabilistic values. |
Definition at line 395 of file probabilistic_layer.cpp.
void set_activation_function | ( | const string & | new_activation_function | ) |
Sets a new method for probabilistic processing from a string with the name. Current probabilistic methods include Competitive and Softmax.
new_activation_function | Method for interpreting the outputs as probabilistic values. |
Definition at line 455 of file probabilistic_layer.cpp.
void set_biases | ( | const Tensor< type, 2 > & | new_biases | ) |
Definition at line 306 of file probabilistic_layer.cpp.
void set_biases_constant | ( | const type & | value | ) |
Initializes the biases of all the neurons in the probabilistic layer with a given value.
value | Biases initialization value. |
Definition at line 500 of file probabilistic_layer.cpp.
void set_decision_threshold | ( | const type & | new_decision_threshold | ) |
Sets a new threshold value for discriminating between two classes.
new_decision_threshold | New discriminating value. It must be comprised between 0 and 1. |
Definition at line 331 of file probabilistic_layer.cpp.
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Sets the members to their default values:
Definition at line 368 of file probabilistic_layer.cpp.
void set_display | ( | const bool & | new_display | ) |
Sets a new display value. If it is set to true messages from this class are to be displayed on the screen; if it is set to false messages from this class are not to be displayed on the screen.
new_display | Display value. |
Definition at line 491 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 286 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 296 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 318 of file probabilistic_layer.cpp.
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Initializes all the biases and synaptic weights in the neural newtork with a given value.
value | Parameters initialization value. |
Reimplemented from Layer.
Definition at line 524 of file probabilistic_layer.cpp.
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Initializes all the biases and synaptic weights in the neural newtork at random with values comprised between -1 and +1.
Reimplemented from Layer.
Definition at line 535 of file probabilistic_layer.cpp.
void set_synaptic_weights | ( | const Tensor< type, 2 > & | new_synaptic_weights | ) |
Definition at line 312 of file probabilistic_layer.cpp.
void set_synaptic_weights_constant | ( | const type & | value | ) |
Initializes the synaptic weights of all the neurons in the probabilistic layer with a given value.
value | Synaptic weights initialization value. |
Definition at line 509 of file probabilistic_layer.cpp.
void set_synaptic_weights_constant_Glorot | ( | ) |
Definition at line 515 of file probabilistic_layer.cpp.
string write_activation_function | ( | ) | const |
Returns a string with the probabilistic method for the outputs ("Competitive", "Softmax" or "NoProbabilistic").
Definition at line 93 of file probabilistic_layer.cpp.
string write_activation_function_text | ( | ) | const |
Returns a string with the probabilistic method for the outputs to be included in some text ("competitive", "softmax" or "no probabilistic").
Definition at line 127 of file probabilistic_layer.cpp.
string write_activations | ( | const Tensor< string, 1 > & | outputs_names | ) | const |
Definition at line 1416 of file probabilistic_layer.cpp.
string write_activations_c | ( | ) | const |
Definition at line 1239 of file probabilistic_layer.cpp.
string write_activations_python | ( | ) | const |
Definition at line 1323 of file probabilistic_layer.cpp.
string write_binary_expression | ( | const Tensor< string, 1 > & | inputs_names, |
const Tensor< string, 1 > & | outputs_names | ||
) | const |
Returns a string with the expression of the binary probabilistic outputs function.
inputs_names | Names of inputs to the probabilistic layer. |
outputs_names | Names of outputs to the probabilistic layer. |
Definition at line 1133 of file probabilistic_layer.cpp.
string write_combinations | ( | const Tensor< string, 1 > & | inputs_names | ) | const |
Definition at line 1391 of file probabilistic_layer.cpp.
string write_combinations_c | ( | ) | const |
Definition at line 1214 of file probabilistic_layer.cpp.
string write_combinations_python | ( | ) | const |
Definition at line 1296 of file probabilistic_layer.cpp.
string write_competitive_expression | ( | const Tensor< string, 1 > & | inputs_names, |
const Tensor< string, 1 > & | outputs_names | ||
) | const |
Returns a string with the expression of the competitive probabilistic outputs function.
inputs_names | Names of inputs to the probabilistic layer. |
outputs_names | Names of outputs to the probabilistic layer. |
Definition at line 1168 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 1515 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 1483 of file probabilistic_layer.cpp.
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Reimplemented from Layer.
Definition at line 1499 of file probabilistic_layer.cpp.
string write_logistic_expression | ( | const Tensor< string, 1 > & | inputs_names, |
const Tensor< string, 1 > & | outputs_names | ||
) | const |
Returns a string with the expression of the probability outputs function.
inputs_names | Names of inputs to the probabilistic layer. |
outputs_names | Names of outputs to the probabilistic layer. |
Definition at line 1151 of file probabilistic_layer.cpp.
string write_no_probabilistic_expression | ( | const Tensor< string, 1 > & | inputs_names, |
const Tensor< string, 1 > & | outputs_names | ||
) | const |
Returns a string with the expression of the no probabilistic outputs function.
inputs_names | Names of inputs to the probabilistic layer. |
outputs_names | Names of outputs to the probabilistic layer. |
Definition at line 1201 of file probabilistic_layer.cpp.
string write_softmax_expression | ( | const Tensor< string, 1 > & | inputs_names, |
const Tensor< string, 1 > & | outputs_names | ||
) | const |
Returns a string with the expression of the softmax probabilistic outputs function.
inputs_names | Names of inputs to the probabilistic layer. |
outputs_names | Names of outputs to the probabilistic layer. |
Definition at line 1184 of file probabilistic_layer.cpp.
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Serializes the probabilistic layer object into a XML document of the TinyXML library without keep the DOM tree in memory. See the OpenNN manual for more information about the format of this document.
Reimplemented from Layer.
Definition at line 916 of file probabilistic_layer.cpp.
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Activation function variable.
Definition at line 217 of file probabilistic_layer.h.
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Bias is a neuron parameter that is summed with the neuron's weighted inputs and passed through the neuron's trabsfer function to generate the neuron's output.
Definition at line 209 of file probabilistic_layer.h.
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Definition at line 219 of file probabilistic_layer.h.
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Display messages to screen.
Definition at line 223 of file probabilistic_layer.h.
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This matrix containing conection strengths from a layer's inputs to its neurons.
Definition at line 213 of file probabilistic_layer.h.