Fast Artificial Neural Network Library (FANN)
Reference Manual for latest CVS release
FANN HOME
Reference Manual
Tutorials
Download FANN
Index
Index
$#! · 0-9 · A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z

S
 save, neural_net
fann_save
 save_to_fixed, neural_net
fann_save_to_fixed
 save_train, training_data
fann_save_train
 save_train_to_fixed, training_data
fann_save_train_to_fixed
 scale_input, neural_net
fann_scale_input
 scale_input_train_data, training_data
fann_scale_input_train_data
 scale_output, neural_net
fann_scale_output
 scale_output_train_data, training_data
fann_scale_output_train_data
 scale_train, neural_net
fann_scale_train
 scale_train_data, training_data
fann_scale_train_data
 Self-Organizing Maps and Growing Neural Gas
 set_activation_function, neural_net
fann_set_activation_function
 set_activation_function_hidden, neural_net
fann_set_activation_function_hidden
 set_activation_function_layer, neural_net
fann_set_activation_function_layer
 set_activation_function_output, neural_net
fann_set_activation_function_output
 set_activation_steepness, neural_net
fann_set_activation_steepness
 set_activation_steepness_hidden, neural_net
fann_set_activation_steepness_hidden
 set_activation_steepness_layer, neural_net
fann_set_activation_steepness_layer
 set_activation_steepness_output, neural_net
fann_set_activation_steepness_output
 set_bit_fail_limit, neural_net
fann_set_bit_fail_limit
 set_callback, neural_net
fann_set_callback
 set_cascade_activation_functions, neural_net
fann_set_cascade_activation_functions
 set_cascade_activation_steepnesses, neural_net
fann_set_cascade_activation_steepnesses
 set_cascade_candidate_change_fraction, neural_net
fann_set_cascade_candidate_change_fraction
 set_cascade_candidate_limit, neural_net
fann_set_cascade_candidate_limit
 set_cascade_candidate_stagnation_epochs, neural_net
fann_set_cascade_candidate_stagnation_epochs
 set_cascade_max_cand_epochs, neural_net
fann_set_cascade_max_cand_epochs
 set_cascade_max_out_epochs, neural_net
fann_set_cascade_max_out_epochs
fann_set_cascade_min_cand_epochs
fann_set_cascade_min_out_epochs
 set_cascade_num_candidate_groups, neural_net
fann_set_cascade_num_candidate_groups
 set_cascade_output_change_fraction, neural_net
fann_set_cascade_output_change_fraction
 set_cascade_output_stagnation_epochs, neural_net
fann_set_cascade_output_stagnation_epochs
 set_cascade_weight_multiplier, neural_net
fann_set_cascade_weight_multiplier
 set_error_log, neural_net
fann_set_error_log
 set_input_scaling_params, neural_net
fann_set_input_scaling_params
 set_learning_momentum, neural_net
fann_set_learning_momentum
 set_learning_rate, neural_net
fann_set_learning_rate
 set_output_scaling_params, neural_net
fann_set_output_scaling_params
 set_quickprop_decay, neural_net
fann_set_quickprop_decay
 set_quickprop_mu, neural_net
fann_set_quickprop_mu
 set_rprop_decrease_factor, neural_net
fann_set_rprop_decrease_factor
 set_rprop_delta_max, neural_net
fann_set_rprop_delta_max
 set_rprop_delta_min, neural_net
fann_set_rprop_delta_min
 set_rprop_delta_zero, neural_net
fann_set_rprop_delta_zero
 set_rprop_increase_factor, neural_net
fann_set_rprop_increase_factor
 set_sarprop_step_error_shift, neural_net
fann_set_sarprop_step_error_shift
 set_sarprop_step_error_threshold_factor, neural_net
fann_set_sarprop_step_error_threshold_factor
 set_sarprop_temperature, neural_net
fann_set_sarprop_temperature
 set_sarprop_weight_decay_shift, neural_net
fann_set_sarprop_weight_decay_shift
 set_scaling_params, neural_net
fann_set_scaling_params
 set_train_data, training_data
 set_train_error_function, neural_net
fann_set_train_error_function
 set_train_stop_function, neural_net
fann_set_train_stop_function
 set_training_algorithm, neural_net
fann_set_training_algorithm
fann_set_user_data
 set_weight, neural_net
fann_set_weight
 set_weight_array, neural_net
fann_set_weight_array
fann_setup_descr
fann_setup_layer_descr
 Shader
 SHORTCUT
 shuffle_train_data, training_data
fann_shuffle_train_data
FANN_SIGMOID
fann_cpp.hfann_data.h
FANN_SIGMOID_STEPWISE
fann_cpp.hfann_data.h
FANN_SIGMOID_SYMMETRIC
fann_cpp.hfann_data.h
FANN_SIGMOID_SYMMETRIC_STEPWISE
FANN_SIN
FANN_SIN_SYMMETRIC
fann_cpp.hfann_data.h
fann_som_learning_decay_enum
FANN_SOM_LEARNING_DECAY_NAMES
fann_som_neighborhood_enum
FANN_SOM_NEIGHBORHOOD_NAMES
fann_som_topology_enum
FANN_SOM_TOPOLOGY_NAMES
fann_sparse_compute_MSE
fann_sparse_layer_constructor
fann_sparse_neuron_backprop
fann_sparse_neuron_constructor
fann_sparse_neuron_run
fann_sparse_neuron_update
 stop_function_enum, FANN
 STOPFUNC_BIT
FANN_STOPFUNC_BIT
fann_stopfunc_enum
 STOPFUNC_MSE
FANN_STOPFUNC_MSE
FANN_STOPFUNC_NAMES
 struct fann
 struct fann_error
 struct fann_train_data
 subset_train_data, training_data
fann_subset_train_data
bool save(const std::string &configuration_file)
Save the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save(struct fann *ann,
const char *configuration_file)
Save the entire network to a configuration file.
int save_to_fixed(const std::string &configuration_file)
Saves the entire network to a configuration file.
FANN_EXTERNAL int FANN_API fann_save_to_fixed(struct fann *ann,
const char *configuration_file)
Saves the entire network to a configuration file.
bool save_train(const std::string &filename)
Save the training structure to a file, with the format as specified in read_train_from_file
FANN_EXTERNAL int FANN_API fann_save_train(struct fann_train_data *data,
const char *filename)
Save the training structure to a file, with the format as specified in fann_read_train_from_file
bool save_train_to_fixed(const std::string &filename,
unsigned int decimal_point)
Saves the training structure to a fixed point data file.
FANN_EXTERNAL int FANN_API fann_save_train_to_fixed(
   struct fann_train_data *data,
   const char *filename,
   unsigned int decimal_point
)
Saves the training structure to a fixed point data file.
void scale_input(fann_type *input_vector)
Scale data in input vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_input(struct fann *ann,
fann_type *input_vector)
Scale data in input vector before feed it to ann based on previously calculated parameters.
void scale_input_train_data(fann_type new_min,
fann_type new_max)
Scales the inputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_input_train_data(
   struct fann_train_data *train_data,
   fann_type new_min,
   fann_type new_max
)
Scales the inputs in the training data to the specified range.
void scale_output(fann_type *output_vector)
Scale data in output vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_output(struct fann *ann,
fann_type *output_vector)
Scale data in output vector before feed it to ann based on previously calculated parameters.
void scale_output_train_data(fann_type new_min,
fann_type new_max)
Scales the outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_output_train_data(
   struct fann_train_data *train_data,
   fann_type new_min,
   fann_type new_max
)
Scales the outputs in the training data to the specified range.
void scale_train(training_data &data)
Scale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_train(struct fann *ann,
struct fann_train_data *data)
Scale input and output data based on previously calculated parameters.
void scale_train_data(fann_type new_min,
fann_type new_max)
Scales the inputs and outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_train_data(
   struct fann_train_data *train_data,
   fann_type new_min,
   fann_type new_max
)
Scales the inputs and outputs in the training data to the specified range.
This section will briefly explain usage of the Self-Organizing Map and Growing Neural Gas algorithms.
void set_activation_function(activation_function_enum activation_function,
int layer,
int neuron)
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function(
   struct fann *ann,
   enum fann_activationfunc_enum activation_function,
   int layer,
   int neuron
)
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
void set_activation_function_hidden(
   activation_function_enum activation_function
)
Set the activation function for all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden(
   struct fann *ann,
   enum fann_activationfunc_enum activation_function
)
Set the activation function for all of the hidden layers.
void set_activation_function_layer(
   activation_function_enum activation_function,
   int layer
)
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function_layer(
   struct fann *ann,
   enum fann_activationfunc_enum activation_function,
   int layer
)
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
void set_activation_function_output(
   activation_function_enum activation_function
)
Set the activation function for the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_function_output(
   struct fann *ann,
   enum fann_activationfunc_enum activation_function
)
Set the activation function for the output layer.
void set_activation_steepness(fann_type steepness,
int layer,
int neuron)
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness(struct fann *ann,
fann_type steepness,
int layer,
int neuron)
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
void set_activation_steepness_hidden(fann_type steepness)
Set the steepness of the activation steepness in all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden(
   struct fann *ann,
   fann_type steepness
)
Set the steepness of the activation steepness in all of the hidden layers.
void set_activation_steepness_layer(fann_type steepness,
int layer)
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer(
   struct fann *ann,
   fann_type steepness,
   int layer
)
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
void set_activation_steepness_output(fann_type steepness)
Set the steepness of the activation steepness in the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output(
   struct fann *ann,
   fann_type steepness
)
Set the steepness of the activation steepness in the output layer.
void set_bit_fail_limit(fann_type bit_fail_limit)
Set the bit fail limit used during training.
FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit(struct fann *ann,
fann_type bit_fail_limit)
Set the bit fail limit used during training.
void set_callback(callback_type callback,
void *user_data)
Sets the callback function for use during training.
FANN_EXTERNAL void FANN_API fann_set_callback(struct fann *ann,
fann_callback_type callback)
Sets the callback function for use during training.
void set_cascade_activation_functions(
   activation_function_enum *cascade_activation_functions,
   unsigned int cascade_activation_functions_count
)
Sets the array of cascade candidate activation functions.
FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions(
   struct fann *ann,
   enum fann_activationfunc_enum *cascade_activation_functions,
   unsigned int cascade_activation_functions_count
)
Sets the array of cascade candidate activation functions.
void set_cascade_activation_steepnesses(
   fann_type *cascade_activation_steepnesses,
   unsigned int cascade_activation_steepnesses_count
)
Sets the array of cascade candidate activation steepnesses.
FANN_EXTERNAL void FANN_API fann_set_cascade_activation_steepnesses(
   struct fann *ann,
   fann_type *cascade_activation_steepnesses,
   unsigned int cascade_activation_steepnesses_count
)
Sets the array of cascade candidate activation steepnesses.
void set_cascade_candidate_change_fraction(
   float cascade_candidate_change_fraction
)
Sets the cascade candidate change fraction.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_change_fraction(
   struct fann *ann,
   float cascade_candidate_change_fraction
)
Sets the cascade candidate change fraction.
void set_cascade_candidate_limit(fann_type cascade_candidate_limit)
Sets the candidate limit.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_limit(
   struct fann *ann,
   fann_type cascade_candidate_limit
)
Sets the candidate limit.
void set_cascade_candidate_stagnation_epochs(
   unsigned int cascade_candidate_stagnation_epochs
)
Sets the number of cascade candidate stagnation epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_stagnation_epochs(
   struct fann *ann,
   unsigned int cascade_candidate_stagnation_epochs
)
Sets the number of cascade candidate stagnation epochs.
void set_cascade_max_cand_epochs(unsigned int cascade_max_cand_epochs)
Sets the max candidate epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_max_cand_epochs(
   struct fann *ann,
   unsigned int cascade_max_cand_epochs
)
Sets the max candidate epochs.
void set_cascade_max_out_epochs(unsigned int cascade_max_out_epochs)
Sets the maximum out epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_max_out_epochs(
   struct fann *ann,
   unsigned int cascade_max_out_epochs
)
Sets the maximum out epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_min_cand_epochs(
   struct fann *ann,
   unsigned int cascade_min_cand_epochs
)
Sets the min candidate epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_min_out_epochs(
   struct fann *ann,
   unsigned int cascade_min_out_epochs
)
Sets the minimum out epochs.
void set_cascade_num_candidate_groups(
   unsigned int cascade_num_candidate_groups
)
Sets the number of candidate groups.
FANN_EXTERNAL void FANN_API fann_set_cascade_num_candidate_groups(
   struct fann *ann,
   unsigned int cascade_num_candidate_groups
)
Sets the number of candidate groups.
void set_cascade_output_change_fraction(float cascade_output_change_fraction)
Sets the cascade output change fraction.
FANN_EXTERNAL void FANN_API fann_set_cascade_output_change_fraction(
   struct fann *ann,
   float cascade_output_change_fraction
)
Sets the cascade output change fraction.
void set_cascade_output_stagnation_epochs(
   unsigned int cascade_output_stagnation_epochs
)
Sets the number of cascade output stagnation epochs.
FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs(
   struct fann *ann,
   unsigned int cascade_output_stagnation_epochs
)
Sets the number of cascade output stagnation epochs.
void set_cascade_weight_multiplier(fann_type cascade_weight_multiplier)
Sets the weight multiplier.
FANN_EXTERNAL void FANN_API fann_set_cascade_weight_multiplier(
   struct fann *ann,
   fann_type cascade_weight_multiplier
)
Sets the weight multiplier.
void set_error_log(FILE *log_file)
Change where errors are logged to.
FANN_EXTERNAL void FANN_API fann_set_error_log(struct fann *ann,
FILE *log_file)
Change where errors are logged to.
bool set_input_scaling_params(const training_data &data,
float new_input_min,
float new_input_max)
Calculate scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_input_scaling_params(
   struct fann *ann,
   const struct fann_train_data *data,
   float new_input_min,
   float new_input_max
)
Calculate input scaling parameters for future use based on training data.
void set_learning_momentum(float learning_momentum)
Set the learning momentum.
FANN_EXTERNAL void FANN_API fann_set_learning_momentum(struct fann *ann,
float learning_momentum)
Set the learning momentum.
void set_learning_rate(float learning_rate)
Set the learning rate.
FANN_EXTERNAL void FANN_API fann_set_learning_rate(struct fann *ann,
float learning_rate)
Set the learning rate.
bool set_output_scaling_params(const training_data &data,
float new_output_min,
float new_output_max)
Calculate scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_output_scaling_params(
   struct fann *ann,
   const struct fann_train_data *data,
   float new_output_min,
   float new_output_max
)
Calculate output scaling parameters for future use based on training data.
void set_quickprop_decay(float quickprop_decay)
Sets the quickprop decay factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_decay(struct fann *ann,
float quickprop_decay)
Sets the quickprop decay factor.
void set_quickprop_mu(float quickprop_mu)
Sets the quickprop mu factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_mu(struct fann *ann,
float quickprop_mu)
Sets the quickprop mu factor.
void set_rprop_decrease_factor(float rprop_decrease_factor)
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor(
   struct fann *ann,
   float rprop_decrease_factor
)
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
void set_rprop_delta_max(float rprop_delta_max)
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max(struct fann *ann,
float rprop_delta_max)
The maximum step-size is a positive number determining how large the maximum step-size may be.
void set_rprop_delta_min(float rprop_delta_min)
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min(struct fann *ann,
float rprop_delta_min)
The minimum step-size is a small positive number determining how small the minimum step-size may be.
void set_rprop_delta_zero(float rprop_delta_zero)
The initial step-size is a small positive number determining how small the initial step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_zero(struct fann *ann,
float rprop_delta_max)
The initial step-size is a positive number determining the initial step size.
void set_rprop_increase_factor(float rprop_increase_factor)
The increase factor used during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor(
   struct fann *ann,
   float rprop_increase_factor
)
The increase factor used during RPROP training.
void set_sarprop_step_error_shift(float sarprop_step_error_shift)
Set the sarprop step error shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_shift(
   struct fann *ann,
   float sarprop_step_error_shift
)
Set the sarprop step error shift.
void set_sarprop_step_error_threshold_factor(
   float sarprop_step_error_threshold_factor
)
Set the sarprop step error threshold factor.
FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_threshold_factor(
   struct fann *ann,
   float sarprop_step_error_threshold_factor
)
Set the sarprop step error threshold factor.
void set_sarprop_temperature(float sarprop_temperature)
Set the sarprop_temperature.
FANN_EXTERNAL void FANN_API fann_set_sarprop_temperature(
   struct fann *ann,
   float sarprop_temperature
)
Set the sarprop_temperature.
void set_sarprop_weight_decay_shift(float sarprop_weight_decay_shift)
Set the sarprop weight decay shift.
FANN_EXTERNAL void FANN_API fann_set_sarprop_weight_decay_shift(
   struct fann *ann,
   float sarprop_weight_decay_shift
)
Set the sarprop weight decay shift.
bool set_scaling_params(const training_data &data,
float new_input_min,
float new_input_max,
float new_output_min,
float new_output_max)
Calculate scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_scaling_params(
   struct fann *ann,
   const struct fann_train_data *data,
   float new_input_min,
   float new_input_max,
   float new_output_min,
   float new_output_max
)
Calculate input and output scaling parameters for future use based on training data.
void set_train_data(unsigned int num_data,
unsigned int num_input,
fann_type **input,
unsigned int num_output,
fann_type **output)
Set the training data to the input and output data provided.
void set_train_error_function(error_function_enum train_error_function)
Set the error function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_error_function(
   struct fann *ann,
   enum fann_errorfunc_enum train_error_function
)
Set the error function used during training.
void set_train_stop_function(stop_function_enum train_stop_function)
Set the stop function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_stop_function(
   struct fann *ann,
   enum fann_stopfunc_enum train_stop_function
)
Set the stop function used during training.
void set_training_algorithm(training_algorithm_enum training_algorithm)
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm(
   struct fann *ann,
   enum fann_train_enum training_algorithm
)
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_user_data(struct fann *ann,
void *user_data)
Store a pointer to user defined data.
void set_weight(unsigned int from_neuron,
unsigned int to_neuron,
fann_type weight)
Set a connection in the network.
FANN_EXTERNAL void FANN_API fann_set_weight(struct fann *ann,
unsigned int from_neuron,
unsigned int to_neuron,
fann_type weight)
Set a connection in the network.
void set_weight_array(connection *connections,
unsigned int num_connections)
Set connections in the network.
FANN_EXTERNAL void FANN_API fann_set_weight_array(
   struct fann *ann,
   struct fann_connection *connections,
   unsigned int num_connections
)
Set connections in the network.
FANN_EXTERNAL int FANN_API fann_setup_descr(struct fann_descr *descr,
unsigned int num_hidden_layers,
unsigned int num_inputs)
This function populates a network descriptor.
FANN_EXTERNAL int FANN_API fann_setup_layer_descr(
   struct fann_layer_descr *layer_descr,
   const char *layer_type,
   unsigned int num_MIMO_neurons,
   void *private_data
)
This function populates a layer descriptor.
To exploit the fact that the shader can perform operations on vectors of size 4 using one instruction the values on a texture are stored in the rgba channels giving 4 values per texel.
Each layer has connections to all following layers
void shuffle_train_data()
Shuffles training data, randomizing the order.
FANN_EXTERNAL void FANN_API fann_shuffle_train_data(
   struct fann_train_data *train_data
)
Shuffles training data, randomizing the order.
Sigmoid activation function.
Stepwise linear approximation to sigmoid.
Symmetric sigmoid activation function, aka.
Stepwise linear approximation to symmetric sigmoid.
Periodical sinus activation function.
Periodical sinus activation function.
Definition of function determining decay of learning rate, if the network is a Self-Organizing Map.
Constant array consisting of the names for the learning decay types for a SOM, so that the name of an learning decay type can be received by:
Definition of neighborhood function, determining which nodes are affected by learning updates, if the network is a Self-Organizing Map.
Constant array consisting of the names for the neighborhood types for a SOM, so that the name of an neighborhood type can be received by:
Definition of topology type if the network is a Self-Organizing Map
Constant array consisting of the names for the topology types for a SOM, so that the name of an topology type can be received by:
Compute the error on a MIMO Neuron after forward propagation of a certain input vector i.e.
Allocates room inside the layer for neurons and connections.
Train the MIMO neuron: this function backpropagates the error to the previous layer and computes the weight update.
FANN_EXTERNAL int FANN_API fann_sparse_neuron_constructor(
   struct fann *ann,
   struct fann_layer *layer,
   struct fann_neuron *neuron,
   struct fann_neuron_descr *descr
)
Allocates room inside the neuron for the connections.
Does a forward iteration on the neuron.
FANN_EXTERNAL void FANN_API fann_sparse_neuron_update(
   struct fann *ann,
   struct fann_neuron *neuron
)
the weight update stored in the neuron is applied.
Stop criteria used during training.
Stop criteria is number of bits that fail.
Stop criteria is number of bits that fail.
Stop criteria used during training.
Stop criteria is Mean Square Error (MSE) value.
Stop criteria is Mean Square Error (MSE) value.
Constant array consisting of the names for the training stop functions, so that the name of a stop function can be received by:
The fast artificial neural network(fann) structure.
Structure used to store error-related information, both struct fann and struct fann_train_data can be casted to this type.
Structure used to store data, for use with training.
void subset_train_data(unsigned int pos,
unsigned int length)
Changes the training data to a subset, starting at position pos and length elements forward.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data(
   struct fann_train_data *data,
   unsigned int pos,
   unsigned int length
)
Returns an copy of a subset of the struct fann_train_data, starting at position pos and length elements forward.
TOP
Created and managed by Steffen Nissen.
SourceForge.net Logo
Generated by Natural Docs.