Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
activation_function_enum get_activation_function( int layer, int neuron )
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
fann_type get_activation_steepness( int layer, int neuron )
Get the number of bias in each layer in the network.
void get_bias_array( unsigned int * bias )
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see get_bit_fail_limit, set_bit_fail_limit).
unsigned int get_bit_fail()
Returns the bit fail limit used during training.
fann_type get_bit_fail_limit()
The cascade activation functions array is an array of the different activation functions used by the candidates.
activation_function_enum * get_cascade_activation_functions()
The number of activation functions in the get_cascade_activation_functions array.
unsigned int get_cascade_activation_functions_count()
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
fann_type *get_cascade_activation_steepnesses()
The number of activation steepnesses in the get_cascade_activation_functions array.
unsigned int get_cascade_activation_steepnesses_count()
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the get_MSE value should change within get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate.
float get_cascade_candidate_change_fraction()
The candidate limit is a limit for how much the candidate neuron may be trained.
fann_type get_cascade_candidate_limit()
The number of cascade candidate stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of get_cascade_candidate_change_fraction.
unsigned int get_cascade_candidate_stagnation_epochs()
The maximum candidate epochs determines the maximum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
unsigned int get_cascade_max_cand_epochs()
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
unsigned int get_cascade_max_out_epochs()
The number of candidate groups is the number of groups of identical candidates which will be used during training.
unsigned int get_cascade_num_candidate_groups()
The number of candidates used during training (calculated by multiplying get_cascade_activation_functions_count, get_cascade_activation_steepnesses_count and get_cascade_num_candidate_groups).
unsigned int get_cascade_num_candidates()
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the get_MSE value should change within get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate.
float get_cascade_output_change_fraction()
The number of cascade output stagnation epochs determines the number of epochs training is allowed to continue without changing the MSE by a fraction of get_cascade_output_change_fraction.
unsigned int get_cascade_output_stagnation_epochs()
The weight multiplier is a parameter which is used to multiply the weights from the candidate neuron before adding the neuron to the neural network.
fann_type get_cascade_weight_multiplier()
Get the connections in the network.
void get_connection_array( connection * connections )
Get the connection rate used when the network was created
float get_connection_rate()
Returns the position of the decimal point in the ann.
unsigned int get_decimal_point()
Returns the last error number.
unsigned int get_errno()
Returns the last errstr.
std::string get_errstr()
A pointer to the array of input training data
fann_type **get_input()
Get the number of neurons in each layer in the network.
void get_layer_array( unsigned int * layers )
Get the learning momentum.
float get_learning_momentum()
Return the learning rate.
float get_learning_rate()
Reads the mean square error from the network.
float get_MSE()
Returns the multiplier that fix point data is multiplied with.
unsigned int get_multiplier()
Get the type of neural network it was created as.
network_type_enum get_network_type()
Get the number of input neurons.
unsigned int get_num_input()
Get the number of layers in the network
unsigned int get_num_layers()
Get the number of output neurons.
unsigned int get_num_output()
A pointer to the array of output training data
fann_type **get_output()
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.
float get_quickprop_decay()
The mu factor is used to increase and decrease the step-size during quickprop training.
float get_quickprop_mu()
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
float get_rprop_decrease_factor()
The maximum step-size is a positive number determining how large the maximum step-size may be.
float get_rprop_delta_max()
The minimum step-size is a small positive number determining how small the minimum step-size may be.
float get_rprop_delta_min()
The initial step-size is a small positive number determining how small the initial step-size may be.
float get_rprop_delta_zero()
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
float get_rprop_increase_factor()
The get sarprop step error shift.
float get_sarprop_step_error_shift()
The sarprop step error threshold factor.
float get_sarprop_step_error_threshold_factor()
The sarprop weight decay shift.
float get_sarprop_temperature()
The sarprop weight decay shift.
float get_sarprop_weight_decay_shift()
Get the total number of connections in the entire network.
unsigned int get_total_connections()
Get the total number of neurons in the entire network.
unsigned int get_total_neurons()
Returns the error function used during training.
error_function_enum get_train_error_function()
Returns the the stop function used during training.
stop_function_enum get_train_stop_function()
Return the training algorithm as described by FANN::training_algorithm_enum.
training_algorithm_enum get_training_algorithm()
Initialize the weights using Widrow + Nguyen’s algorithm.
void init_weights( const training_data & data )
Returns the number of training patterns in the training_data.
unsigned int length_train_data()
Merges the data into the data contained in the training_data.
void merge_train_data( const training_data & data )
Encapsulation of a neural network struct fann and associated C API functions.
class neural_net
Default constructor creates an empty neural net.
neural_net( ) : ann(NULL)
Provides automatic cleanup of data.
#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~neural_net()
Returns the number of inputs in each of the training patterns in the training_data.
unsigned int num_input_train_data()
Returns the number of outputs in each of the training patterns in the struct fann_train_data.
unsigned int num_output_train_data()
Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
void print_connections()
Prints the last error to stderr.
void print_error()
Prints all of the parameters and options of the neural network
void print_parameters()
Give each connection a random weight between min_weight and max_weight
void randomize_weights( fann_type min_weight, fann_type max_weight )
Reads a file that stores training data.
bool read_train_from_file( const std:: string & filename )
Resets the last error number.
void reset_errno()
Resets the last error string.
void reset_errstr()
Resets the mean square error from the network.
void reset_MSE()
Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer.
fann_type* run( fann_type * input )