This class represents a recurrent layer of neurons. More...
#include <recurrent_layer.h>
Public Types | |
enum class | ActivationFunction { Threshold , SymmetricThreshold , Logistic , HyperbolicTangent , Linear , RectifiedLinear , ExponentialLinear , ScaledExponentialLinear , SoftPlus , SoftSign , HardSigmoid } |
Enumeration of the available activation functions for the recurrent layer. 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 | |
RecurrentLayer () | |
RecurrentLayer (const Index &, const Index &) | |
virtual | ~RecurrentLayer () |
bool | is_empty () const |
Index | get_inputs_number () const |
Returns the number of inputs to the layer. More... | |
Index | get_neurons_number () const |
Returns the size of the neurons vector. More... | |
const Tensor< type, 1 > & | get_hidden_states () const |
Returns the hidden states of the layer. More... | |
Index | get_timesteps () const |
Tensor< type, 1 > | get_biases () const |
const Tensor< type, 2 > & | get_input_weights () const |
const Tensor< type, 2 > & | get_recurrent_weights () const |
Index | get_biases_number () const |
Index | get_input_weights_number () const |
Index | get_recurrent_weights_number () const |
Index | get_parameters_number () const |
Returns the number of parameters (biases and weights) of the layer. More... | |
Tensor< type, 1 > | get_parameters () const |
Tensor< type, 2 > | get_biases (const Tensor< type, 1 > &) const |
Tensor< type, 2 > | get_input_weights (const Tensor< type, 1 > &) const |
Tensor< type, 2 > | get_recurrent_weights (const Tensor< type, 1 > &) const |
const RecurrentLayer::ActivationFunction & | get_activation_function () const |
Returns the activation function of the layer. More... | |
string | write_activation_function () const |
const bool & | get_display () const |
void | set () |
void | set (const Index &, const Index &) |
void | set (const RecurrentLayer &) |
void | set_default () |
void | set_inputs_number (const Index &) |
void | set_neurons_number (const Index &) |
void | set_input_shape (const Tensor< Index, 1 > &) |
void | set_timesteps (const Index &) |
void | set_biases (const Tensor< type, 1 > &) |
void | set_input_weights (const Tensor< type, 2 > &) |
void | set_recurrent_weights (const Tensor< type, 2 > &) |
void | set_parameters (const Tensor< type, 1 > &, const Index &=0) |
void | set_activation_function (const ActivationFunction &) |
void | set_activation_function (const string &) |
void | set_display (const bool &) |
void | set_hidden_states_constant (const type &) |
void | set_biases_constant (const type &) |
void | set_input_weights_constant (const type &) |
void | set_recurrent_weights_constant (const type &) |
void | initialize_input_weights_Glorot (const type &, const type &) |
void | set_parameters_constant (const type &) |
void | set_parameters_random () |
void | calculate_combinations (const Tensor< type, 1 > &, const Tensor< type, 2 > &, const Tensor< type, 2 > &, const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | calculate_activations (const Tensor< type, 1 > &, Tensor< type, 1 > &) const |
void | calculate_activations_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) 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 > &, const Tensor< type, 1 >, LayerForwardPropagation *) |
void | calculate_hidden_delta (LayerForwardPropagation *, LayerBackPropagation *, LayerForwardPropagation *, LayerBackPropagation *) const |
void | calculate_hidden_delta_perceptron (PerceptronLayerForwardPropagation *, PerceptronLayerBackPropagation *, RecurrentLayerBackPropagation *) const |
void | calculate_hidden_delta_probabilistic (ProbabilisticLayerForwardPropagation *, ProbabilisticLayerBackPropagation *, RecurrentLayerBackPropagation *) const |
void | insert_gradient (LayerBackPropagation *, const Index &, Tensor< type, 1 > &) const |
void | calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagation *) const |
void | calculate_biases_error_gradient (const Tensor< type, 2 > &, RecurrentLayerForwardPropagation *, RecurrentLayerBackPropagation *) const |
void | calculate_input_weights_error_gradient (const Tensor< type, 2 > &, RecurrentLayerForwardPropagation *, RecurrentLayerBackPropagation *) const |
void | calculate_recurrent_weights_error_gradient (const Tensor< type, 2 > &, RecurrentLayerForwardPropagation *, RecurrentLayerBackPropagation *) const |
string | write_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const |
string | write_activation_function_expression () 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_lm (LayerForwardPropagation *, LayerBackPropagationLM *, LayerBackPropagationLM *) const |
virtual void | calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation *, LayerBackPropagation *) const |
virtual void | calculate_squared_errors_Jacobian_lm (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagationLM *) |
virtual void | insert_squared_errors_Jacobian_lm (LayerBackPropagationLM *, const Index &, Tensor< type, 2 > &) const |
virtual Index | get_synaptic_weights_number () const |
Returns the number of layer's synaptic weights. More... | |
Type | get_type () const |
string | get_type_string () const |
Takes the type of layer used by the model. More... | |
virtual string | write_expression_c () const |
Protected Attributes | |
Index | timesteps = 1 |
Tensor< type, 1 > | biases |
Tensor< type, 2 > | input_weights |
Tensor< type, 2 > | recurrent_weights |
This matrix containing conection strengths from a recurrent layer inputs to its neurons. More... | |
ActivationFunction | activation_function = ActivationFunction::HyperbolicTangent |
Activation function variable. More... | |
Tensor< type, 1 > | hidden_states |
bool | display = true |
Display messages to screen. More... | |
Protected Attributes inherited from Layer | |
ThreadPool * | 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 recurrent layer of neurons.
Layers of neurons will be used to construct multilayer neurons.
Definition at line 45 of file recurrent_layer.h.
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strong |
Enumeration of the available activation functions for the recurrent layer.
Definition at line 52 of file recurrent_layer.h.
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explicit |
Default constructor. It creates a empty layer object, with no neurons. This constructor also initializes the rest of class members to their default values.
Definition at line 18 of file recurrent_layer.cpp.
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explicit |
Layer architecture constructor. It creates a layer object with given numbers of inputs and neurons. The parameters are initialized at random. This constructor also initializes the rest of class members to their default values.
new_inputs_number | Number of inputs in the layer. |
new_neurons_number | Number of neurons in the layer. |
Definition at line 33 of file recurrent_layer.cpp.
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virtual |
Destructor. This destructor does not delete any pointer.
Definition at line 44 of file recurrent_layer.cpp.
void calculate_activations | ( | const Tensor< type, 1 > & | combinations_1d, |
Tensor< type, 1 > & | activations_1d | ||
) | const |
Definition at line 628 of file recurrent_layer.cpp.
void calculate_activations_derivatives | ( | const Tensor< type, 1 > & | combinations_1d, |
Tensor< type, 1 > & | activations_1d, | ||
Tensor< type, 1 > & | activations_derivatives_1d | ||
) | const |
Definition at line 663 of file recurrent_layer.cpp.
void calculate_biases_error_gradient | ( | const Tensor< type, 2 > & | inputs, |
RecurrentLayerForwardPropagation * | forward_propagation, | ||
RecurrentLayerBackPropagation * | back_propagation | ||
) | const |
Definition at line 1021 of file recurrent_layer.cpp.
void calculate_combinations | ( | const Tensor< type, 1 > & | inputs, |
const Tensor< type, 2 > & | input_weights, | ||
const Tensor< type, 2 > & | recurrent_weights, | ||
const Tensor< type, 1 > & | biases, | ||
Tensor< type, 1 > & | combinations | ||
) | const |
Definition at line 614 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 1001 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 845 of file recurrent_layer.cpp.
void calculate_hidden_delta_perceptron | ( | PerceptronLayerForwardPropagation * | next_forward_propagation, |
PerceptronLayerBackPropagation * | next_back_propagation, | ||
RecurrentLayerBackPropagation * | back_propagation | ||
) | const |
Definition at line 888 of file recurrent_layer.cpp.
void calculate_hidden_delta_probabilistic | ( | ProbabilisticLayerForwardPropagation * | next_forward_propagation, |
ProbabilisticLayerBackPropagation * | next_back_propagation, | ||
RecurrentLayerBackPropagation * | back_propagation | ||
) | const |
Definition at line 900 of file recurrent_layer.cpp.
void calculate_input_weights_error_gradient | ( | const Tensor< type, 2 > & | inputs, |
RecurrentLayerForwardPropagation * | forward_propagation, | ||
RecurrentLayerBackPropagation * | back_propagation | ||
) | const |
Definition at line 1062 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 797 of file recurrent_layer.cpp.
void calculate_recurrent_weights_error_gradient | ( | const Tensor< type, 2 > & | inputs, |
RecurrentLayerForwardPropagation * | forward_propagation, | ||
RecurrentLayerBackPropagation * | back_propagation | ||
) | const |
Definition at line 1122 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 751 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 714 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 1259 of file recurrent_layer.cpp.
const RecurrentLayer::ActivationFunction & get_activation_function | ( | ) | const |
Returns the activation function of the layer.
Definition at line 174 of file recurrent_layer.cpp.
Tensor< type, 1 > get_biases | ( | ) | const |
Returns the biases from all the recurrent neurons in the layer. The format is a vector of real values. The size of this vector is the number of neurons in the layer.
Definition at line 94 of file recurrent_layer.cpp.
Tensor< type, 2 > get_biases | ( | const Tensor< type, 1 > & | parameters | ) | const |
Returns the biases from all the recurrent in the layer. The format is a vector of real values. The size of this vector is the number of neurons in the layer.
Definition at line 184 of file recurrent_layer.cpp.
Index get_biases_number | ( | ) | const |
Definition at line 122 of file recurrent_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 279 of file recurrent_layer.cpp.
const Tensor< type, 1 > & get_hidden_states | ( | ) | const |
Returns the hidden states of the layer.
Definition at line 67 of file recurrent_layer.cpp.
const Tensor< type, 2 > & get_input_weights | ( | ) | const |
Returns the weights from the recurrent layer. The format is a matrix of real values. The number of rows is the number of neurons in the layer. The number of columns is the number of inputs to the layer.
Definition at line 105 of file recurrent_layer.cpp.
Tensor< type, 2 > get_input_weights | ( | const Tensor< type, 1 > & | parameters | ) | const |
Returns the weights from the recurrent layer. The format is a matrix of real values. The number of rows is the number of inputs in the layer. The number of columns is the number of neurons to the layer.
Definition at line 204 of file recurrent_layer.cpp.
Index get_input_weights_number | ( | ) | const |
Definition at line 128 of file recurrent_layer.cpp.
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Returns the number of inputs to the layer.
Reimplemented from Layer.
Definition at line 51 of file recurrent_layer.cpp.
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Returns the size of the neurons vector.
Reimplemented from Layer.
Definition at line 59 of file recurrent_layer.cpp.
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virtual |
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 144 of file recurrent_layer.cpp.
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virtual |
Returns the number of parameters (biases and weights) of the layer.
Reimplemented from Layer.
Definition at line 75 of file recurrent_layer.cpp.
const Tensor< type, 2 > & get_recurrent_weights | ( | ) | const |
Returns the recurrent weights from the recurrent layer. The format is a matrix of real values. The number of rows is the number of neurons in the layer. The number of columns is the number of neurons to the layer.
Definition at line 116 of file recurrent_layer.cpp.
Tensor< type, 2 > get_recurrent_weights | ( | const Tensor< type, 1 > & | parameters | ) | const |
Returns the recurrent weights from the recurrent layer. The format is a matrix of real values. The number of rows is the number of neurons in the layer. The number of columns is the number of neurons to the layer.
Definition at line 224 of file recurrent_layer.cpp.
Index get_recurrent_weights_number | ( | ) | const |
Definition at line 134 of file recurrent_layer.cpp.
Index get_timesteps | ( | ) | const |
Definition at line 84 of file recurrent_layer.cpp.
void initialize_input_weights_Glorot | ( | const type & | , |
const type & | |||
) |
Definition at line 556 of file recurrent_layer.cpp.
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virtual |
Reimplemented from Layer.
Definition at line 973 of file recurrent_layer.cpp.
void set | ( | ) |
Sets an empty layer, wihtout any neuron. It also sets the rest of members to their default values.
Definition at line 288 of file recurrent_layer.cpp.
void set | ( | const Index & | new_inputs_number, |
const Index & | new_neurons_number | ||
) |
Sets new numbers of inputs and neurons in the layer. It also sets the rest of members to their default values.
new_inputs_number | Number of inputs. |
new_neurons_number | Number of neuron. |
Definition at line 299 of file recurrent_layer.cpp.
void set | ( | const RecurrentLayer & | other_neuron_layer | ) |
Sets the members of this neuron layer object with those from other neuron layer object.
other_neuron_layer | RecurrentLayer object to be copied. |
Definition at line 320 of file recurrent_layer.cpp.
void set_activation_function | ( | const ActivationFunction & | new_activation_function | ) |
This class sets a new activation(or transfer) function in a single layer.
new_activation_function | Activation function for the layer. |
Definition at line 438 of file recurrent_layer.cpp.
void set_activation_function | ( | const string & | new_activation_function_name | ) |
Sets a new activation(or transfer) function in a single layer. The second argument is a string containing the name of the function("Logistic", "HyperbolicTangent", "Threshold", etc).
new_activation_function_name | Activation function for that layer. |
Definition at line 448 of file recurrent_layer.cpp.
void set_biases | ( | const Tensor< type, 1 > & | new_biases | ) |
Definition at line 389 of file recurrent_layer.cpp.
void set_biases_constant | ( | const type & | value | ) |
Initializes the biases of all the neurons in the layer of neurons with a given value.
value | Biases initialization value. |
Definition at line 530 of file recurrent_layer.cpp.
void set_default | ( | ) |
Sets those members not related to the vector of neurons to their default value.
Definition at line 337 of file recurrent_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 512 of file recurrent_layer.cpp.
void set_hidden_states_constant | ( | const type & | value | ) |
Initializes the hidden states of in the layer of neurons with a given value.
value | Hidden states initialization value. |
Definition at line 521 of file recurrent_layer.cpp.
void set_input_shape | ( | const Tensor< Index, 1 > & | size | ) |
Definition at line 359 of file recurrent_layer.cpp.
void set_input_weights | ( | const Tensor< type, 2 > & | new_input_weights | ) |
Definition at line 395 of file recurrent_layer.cpp.
void set_input_weights_constant | ( | const type & | value | ) |
Initializes the input weights of all the neurons in the layer of neurons neuron with a given value.
value | Input weights initialization value. |
Definition at line 539 of file recurrent_layer.cpp.
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virtual |
Sets a new number of inputs in the layer. The new synaptic weights are initialized at random.
new_inputs_number | Number of layer inputs. |
Reimplemented from Layer.
Definition at line 351 of file recurrent_layer.cpp.
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virtual |
Sets a new number neurons in the layer. All the parameters are also initialized at random.
new_neurons_number | New number of neurons in the layer. |
Reimplemented from Layer.
Definition at line 371 of file recurrent_layer.cpp.
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virtual |
Sets the parameters of this layer.
new_parameters | Parameters vector for that layer. |
index | Index for this layer. |
Reimplemented from Layer.
Definition at line 411 of file recurrent_layer.cpp.
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Initializes all the biases, input weights and recurrent weights in the neural newtork with a given value.
value | Parameters initialization value. |
Reimplemented from Layer.
Definition at line 565 of file recurrent_layer.cpp.
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virtual |
Initializes all the biases and input weights in the layer of neurons at random with values comprised between -1 and 1 values.
Reimplemented from Layer.
Definition at line 580 of file recurrent_layer.cpp.
void set_recurrent_weights | ( | const Tensor< type, 2 > & | new_recurrent_weights | ) |
Definition at line 401 of file recurrent_layer.cpp.
void set_recurrent_weights_constant | ( | const type & | value | ) |
Initializes the recurrent weights of all the neurons in the layer of neurons neuron with a given value.
value | Synaptic weights initialization value. |
Definition at line 548 of file recurrent_layer.cpp.
void set_timesteps | ( | const Index & | new_timesteps | ) |
Definition at line 383 of file recurrent_layer.cpp.
string write_activation_function | ( | ) | const |
Returns a string with the name of the layer activation function. This can be: Logistic, HyperbolicTangent, Threshold, SymmetricThreshold, Linear, RectifiedLinear, ScaledExponentialLinear.
Definition at line 245 of file recurrent_layer.cpp.
string write_activation_function_expression | ( | ) | const |
Definition at line 1246 of file recurrent_layer.cpp.
string write_activations_python | ( | ) | const |
Definition at line 1449 of file recurrent_layer.cpp.
string write_combinations_python | ( | ) | const |
Definition at line 1417 of file recurrent_layer.cpp.
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Returns a string with the expression of the inputs-outputs relationship of the layer.
inputs_names | Vector of strings with the name of the layer inputs. |
outputs_names | Vector of strings with the name of the layer outputs. |
Reimplemented from Layer.
Definition at line 1189 of file recurrent_layer.cpp.
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Reimplemented from Layer.
Definition at line 1516 of file recurrent_layer.cpp.
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Reimplemented from Layer.
Definition at line 1352 of file recurrent_layer.cpp.
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Activation function variable.
Definition at line 243 of file recurrent_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 233 of file recurrent_layer.h.
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Display messages to screen.
Definition at line 249 of file recurrent_layer.h.
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Definition at line 245 of file recurrent_layer.h.
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Definition at line 235 of file recurrent_layer.h.
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This matrix containing conection strengths from a recurrent layer inputs to its neurons.
Definition at line 239 of file recurrent_layer.h.
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Definition at line 228 of file recurrent_layer.h.