Fast Artificial Neural Network Library (FANN)
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FANN_GAUSSIAN
fann_cpp.hfann_data.h
FANN_GAUSSIAN_SYMMETRIC
fann_cpp.hfann_data.h
 get_activation_function, neural_net
fann_get_activation_function
 get_activation_steepness, neural_net
fann_get_activation_steepness
 get_bias_array, neural_net
fann_get_bias_array
 get_bit_fail, neural_net
fann_get_bit_fail
 get_bit_fail_limit, neural_net
fann_get_bit_fail_limit
 get_cascade_activation_functions, neural_net
fann_get_cascade_activation_functions
 get_cascade_activation_functions_count, neural_net
fann_get_cascade_activation_functions_count
 get_cascade_activation_steepnesses, neural_net
fann_get_cascade_activation_steepnesses
 get_cascade_activation_steepnesses_count, neural_net
fann_get_cascade_activation_steepnesses_count
 get_cascade_candidate_change_fraction, neural_net
fann_get_cascade_candidate_change_fraction
 get_cascade_candidate_limit, neural_net
fann_get_cascade_candidate_limit
 get_cascade_candidate_stagnation_epochs, neural_net
fann_get_cascade_candidate_stagnation_epochs
 get_cascade_max_cand_epochs, neural_net
fann_get_cascade_max_cand_epochs
 get_cascade_max_out_epochs, neural_net
fann_get_cascade_max_out_epochs
fann_get_cascade_min_cand_epochs
fann_get_cascade_min_out_epochs
 get_cascade_num_candidate_groups, neural_net
fann_get_cascade_num_candidate_groups
 get_cascade_num_candidates, neural_net
fann_get_cascade_num_candidates
 get_cascade_output_change_fraction, neural_net
fann_get_cascade_output_change_fraction
 get_cascade_output_stagnation_epochs, neural_net
fann_get_cascade_output_stagnation_epochs
 get_cascade_weight_multiplier, neural_net
fann_get_cascade_weight_multiplier
 get_connection_array, neural_net
fann_get_connection_array
 get_connection_rate, neural_net
fann_get_connection_rate
 get_decimal_point, neural_net
fann_get_decimal_point
 get_errno, neural_net
fann_get_errno
 get_errstr, neural_net
fann_get_errstr
 get_input, training_data
 get_layer_array, neural_net
fann_get_layer_array
 get_learning_momentum, neural_net
fann_get_learning_momentum
 get_learning_rate, neural_net
fann_get_learning_rate
 get_MSE, neural_net
fann_get_MSE
 get_multiplier, neural_net
fann_get_multiplier
 get_network_type, neural_net
fann_get_network_type
 get_num_input, neural_net
fann_get_num_input
 get_num_layers, neural_net
fann_get_num_layers
 get_num_output, neural_net
fann_get_num_output
 get_output, training_data
 get_quickprop_decay, neural_net
fann_get_quickprop_decay
 get_quickprop_mu, neural_net
fann_get_quickprop_mu
 get_rprop_decrease_factor, neural_net
fann_get_rprop_decrease_factor
 get_rprop_delta_max, neural_net
fann_get_rprop_delta_max
 get_rprop_delta_min, neural_net
fann_get_rprop_delta_min
 get_rprop_delta_zero, neural_net
fann_get_rprop_delta_zero
 get_rprop_increase_factor, neural_net
fann_get_rprop_increase_factor
 get_sarprop_step_error_shift, neural_net
fann_get_sarprop_step_error_shift
 get_sarprop_step_error_threshold_factor, neural_net
fann_get_sarprop_step_error_threshold_factor
 get_sarprop_temperature, neural_net
fann_get_sarprop_temperature
 get_sarprop_weight_decay_shift, neural_net
fann_get_sarprop_weight_decay_shift
 get_total_connections, neural_net
fann_get_total_connections
 get_total_neurons, neural_net
fann_get_total_neurons
 get_train_error_function, neural_net
fann_get_train_error_function
 get_train_stop_function, neural_net
fann_get_train_stop_function
 get_training_algorithm, neural_net
fann_get_training_algorithm
fann_get_user_data
 Getting Help
 Getting Started
Gaussian activation function.
Symmetric gaussian activation function.
activation_function_enum get_activation_function(int layer,
int neuron)
Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function(
   struct fann *ann,
   int layer,
   int neuron
)
Get the activation function 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 activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness(
   struct fann *ann,
   int layer,
   int neuron
)
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
void get_bias_array(unsigned int *bias)
Get the number of bias in each layer in the network.
FANN_EXTERNAL void FANN_API fann_get_bias_array(struct fann *ann,
unsigned int *bias)
Get the number of bias in each layer in the network.
unsigned int get_bit_fail()
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).
FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail(struct fann *ann)
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit).
fann_type get_bit_fail_limit()
Returns the bit fail limit used during training.
FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit(struct fann *ann)
Returns the bit fail limit used during training.
activation_function_enum * get_cascade_activation_functions()
The cascade activation functions array is an array of the different activation functions used by the candidates.
FANN_EXTERNAL enum fann_activationfunc_enum * FANN_API fann_get_cascade_activation_functions(
   struct fann *ann
)
The cascade activation functions array is an array of the different activation functions used by the candidates.
unsigned int get_cascade_activation_functions_count()
The number of activation functions in the get_cascade_activation_functions array.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_functions_count(
   struct fann *ann
)
The number of activation functions in the fann_get_cascade_activation_functions array.
fann_type *get_cascade_activation_steepnesses()
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
FANN_EXTERNAL fann_type * FANN_API fann_get_cascade_activation_steepnesses(
   struct fann *ann
)
The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
unsigned int get_cascade_activation_steepnesses_count()
The number of activation steepnesses in the get_cascade_activation_functions array.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_steepnesses_count(
   struct fann *ann
)
The number of activation steepnesses in the fann_get_cascade_activation_functions array.
float get_cascade_candidate_change_fraction()
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.
FANN_EXTERNAL float FANN_API fann_get_cascade_candidate_change_fraction(
   struct fann *ann
)
The cascade candidate change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_candidate_stagnation_epochs during training of the candidate neurons, in order for the training not to stagnate.
fann_type get_cascade_candidate_limit()
The candidate limit is a limit for how much the candidate neuron may be trained.
FANN_EXTERNAL fann_type FANN_API fann_get_cascade_candidate_limit(
   struct fann *ann
)
The candidate limit is a limit for how much the candidate neuron may be trained.
unsigned int get_cascade_candidate_stagnation_epochs()
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.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_candidate_stagnation_epochs(
   struct fann *ann
)
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 fann_get_cascade_candidate_change_fraction.
unsigned int get_cascade_max_cand_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.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_cand_epochs(
   struct fann *ann
)
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_out_epochs()
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_out_epochs(
   struct fann *ann
)
The maximum out epochs determines the maximum number of epochs the output connections may be trained after adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_cand_epochs(
   struct fann *ann
)
The minimum candidate epochs determines the minimum number of epochs the input connections to the candidates may be trained before adding a new candidate neuron.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_out_epochs(
   struct fann *ann
)
The minimum out epochs determines the minimum number of epochs the output connections must be trained after adding a new candidate neuron.
unsigned int get_cascade_num_candidate_groups()
The number of candidate groups is the number of groups of identical candidates which will be used during training.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidate_groups(
   struct fann *ann
)
The number of candidate groups is the number of groups of identical candidates which will be used during training.
unsigned int get_cascade_num_candidates()
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).
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates(
   struct fann *ann
)
The number of candidates used during training (calculated by multiplying fann_get_cascade_activation_functions_count, fann_get_cascade_activation_steepnesses_count and fann_get_cascade_num_candidate_groups).
float get_cascade_output_change_fraction()
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.
FANN_EXTERNAL float FANN_API fann_get_cascade_output_change_fraction(
   struct fann *ann
)
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE value should change within fann_get_cascade_output_stagnation_epochs during training of the output connections, in order for the training not to stagnate.
unsigned int get_cascade_output_stagnation_epochs()
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.
FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_output_stagnation_epochs(
   struct fann *ann
)
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 fann_get_cascade_output_change_fraction.
fann_type get_cascade_weight_multiplier()
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_EXTERNAL fann_type FANN_API fann_get_cascade_weight_multiplier(
   struct fann *ann
)
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.
void get_connection_array(connection *connections)
Get the connections in the network.
FANN_EXTERNAL void FANN_API fann_get_connection_array(
   struct fann *ann,
   struct fann_connection *connections
)
Get the connections in the network.
float get_connection_rate()
Get the connection rate used when the network was created
FANN_EXTERNAL float FANN_API fann_get_connection_rate(struct fann *ann)
Get the connection rate used when the network was created
unsigned int get_decimal_point()
Returns the position of the decimal point in the ann.
FANN_EXTERNAL unsigned int FANN_API fann_get_decimal_point(struct fann *ann)
Returns the position of the decimal point in the ann.
unsigned int get_errno()
Returns the last error number.
FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(
   struct fann_error *errdat
)
Returns the last error number.
std::string get_errstr()
Returns the last errstr.
FANN_EXTERNAL char *FANN_API fann_get_errstr(struct fann_error *errdat)
Returns the last errstr.
fann_type **get_input()
A pointer to the array of input training data
void get_layer_array(unsigned int *layers)
Get the number of neurons in each layer in the network.
FANN_EXTERNAL void FANN_API fann_get_layer_array(struct fann *ann,
unsigned int *layers)
Get the number of neurons in each layer in the network.
float get_learning_momentum()
Get the learning momentum.
FANN_EXTERNAL float FANN_API fann_get_learning_momentum(struct fann *ann)
Get the learning momentum.
float get_learning_rate()
Return the learning rate.
FANN_EXTERNAL float FANN_API fann_get_learning_rate(struct fann *ann)
Return the learning rate.
float get_MSE()
Reads the mean square error from the network.
FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann)
Reads the mean square error from the network.
unsigned int get_multiplier()
Returns the multiplier that fix point data is multiplied with.
FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier(struct fann *ann)
returns the multiplier that fix point data is multiplied with.
network_type_enum get_network_type()
Get the type of neural network it was created as.
FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type(
   struct fann *ann
)
Get the type of neural network it was created as.
unsigned int get_num_input()
Get the number of input neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_input(struct fann *ann)
Get the number of input neurons.
unsigned int get_num_layers()
Get the number of layers in the network
FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers(struct fann *ann)
Get the number of layers in the network
unsigned int get_num_output()
Get the number of output neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_output(struct fann *ann)
Get the number of output neurons.
fann_type **get_output()
A pointer to the array of output training data
float get_quickprop_decay()
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_decay(struct fann *ann)
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_mu()
The mu factor is used to increase and decrease the step-size during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_mu(struct fann *ann)
The mu factor is used to increase and decrease the step-size during quickprop training.
float get_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 float FANN_API fann_get_rprop_decrease_factor(struct fann *ann)
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
float get_rprop_delta_max()
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max(struct fann *ann)
The maximum step-size is a positive number determining how large the maximum step-size may be.
float get_rprop_delta_min()
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min(struct fann *ann)
The minimum step-size is a small positive number determining how small the minimum step-size may be.
float get_rprop_delta_zero()
The initial step-size is a small positive number determining how small the initial step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_zero(struct fann *ann)
The initial step-size is a positive number determining the initial step size.
float get_rprop_increase_factor()
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor(struct fann *ann)
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
float get_sarprop_step_error_shift()
The get sarprop step error shift.
FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_shift(
   struct fann *ann
)
The get sarprop step error shift.
float get_sarprop_step_error_threshold_factor()
The sarprop step error threshold factor.
FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_threshold_factor(
   struct fann *ann
)
The sarprop step error threshold factor.
float get_sarprop_temperature()
The sarprop weight decay shift.
FANN_EXTERNAL float FANN_API fann_get_sarprop_temperature(struct fann *ann)
The sarprop weight decay shift.
float get_sarprop_weight_decay_shift()
The sarprop weight decay shift.
FANN_EXTERNAL float FANN_API fann_get_sarprop_weight_decay_shift(
   struct fann *ann
)
The sarprop weight decay shift.
unsigned int get_total_connections()
Get the total number of connections in the entire network.
FANN_EXTERNAL unsigned int FANN_API fann_get_total_connections(
   struct fann *ann
)
Get the total number of connections in the entire network.
unsigned int get_total_neurons()
Get the total number of neurons in the entire network.
FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons(struct fann *ann)
Get the total number of neurons in the entire network.
error_function_enum get_train_error_function()
Returns the error function used during training.
FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function(
   struct fann *ann
)
Returns the error function used during training.
stop_function_enum get_train_stop_function()
Returns the the stop function used during training.
FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function(
   struct fann *ann
)
Returns the the stop function used during training.
training_algorithm_enum get_training_algorithm()
Return the training algorithm as described by FANN::training_algorithm_enum.
FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm(
   struct fann *ann
)
Return the training algorithm as described by fann_train_enum.
FANN_EXTERNAL void * FANN_API fann_get_user_data(struct fann *ann)
Get a pointer to user defined data that was previously set with fann_set_user_data.
If after reading the documentation you are still having problems, or have a question that is not covered in the documentation, please consult the fann-general mailing list.
An ANN is normally run in two different modes, a training mode and an execution mode.
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Created and managed by Steffen Nissen.
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