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

A
	activation_function_enum, FANN
C
	callback_type, FANN
	Cascade Training
	cascadetrain_on_data, neural_net
	cascadetrain_on_file, neural_net
	clear_scaling_params, neural_net
	connection, FANN
	copy_from_struct_fann, neural_net
	create_from_file, neural_net
	create_shortcut, neural_net
	create_shortcut_array, neural_net
	create_sparse, neural_net
	create_sparse_array, neural_net
	create_standard, neural_net
	create_standard_array, neural_net
	create_train_from_callback, training_data
	Creation,Destruction&Execution
D
	descale_input, neural_net
	descale_output, neural_net
	descale_train, neural_net
	destroy neural_net training_data
E
	Enumerations, FANN
	Enumerations and Constants
	Error Handling
	error_function_enum, FANN
	ERRORFUNC_LINEAR, FANN
	ERRORFUNC_TANH, FANN

The activation functions used for the neurons during training.

This callback function can be called during training when using neural_net::train_on_data, neural_net::train_on_file or neural_net::cascadetrain_on_data.

void cascadetrain_on_data( const training_data & data,
unsigned int max_neurons,
unsigned int neurons_between_reports,
float desired_error )

Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.

void cascadetrain_on_file( const std:: string & filename,
unsigned int max_neurons,
unsigned int neurons_between_reports,
float desired_error )

Does the same as cascadetrain_on_data, but reads the training data directly from a file.

bool clear_scaling_params()

Clears scaling parameters.

Describes a connection between two neurons and its weight

void copy_from_struct_fann( struct fann * other )

Set the internal fann struct to a copy of other

bool create_from_file( const std:: string & configuration_file )

Constructs a backpropagation neural network from a configuration file, which have been saved by save.

bool create_shortcut( unsigned int num_layers,
... )

Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.

bool create_shortcut_array( unsigned int num_layers,
const unsigned int * layers )

Just like create_shortcut, but with an array of layer sizes instead of individual parameters.

bool create_sparse( float connection_rate,
unsigned int num_layers,
... )

Creates a standard backpropagation neural network, which is not fully connected.

bool create_sparse_array( float connection_rate,
unsigned int num_layers,
const unsigned int * layers )

Just like create_sparse, but with an array of layer sizes instead of individual parameters.

bool create_standard( unsigned int num_layers,
... )

Creates a standard fully connected backpropagation neural network.

bool create_standard_array( unsigned int num_layers,
const unsigned int * layers )

Just like create_standard, but with an array of layer sizes instead of individual parameters.

void create_train_from_callback(
    unsigned int num_data,
    unsigned int num_input,
    unsigned int num_output,
    void (FANN_API *user_function)( unsigned int, unsigned int, unsigned int, fann_type * , fann_type * )
)

Creates the training data struct from a user supplied function.

void descale_input( fann_type * input_vector )

Scale data in input vector after get it from ann based on previously calculated parameters.

void descale_output( fann_type * output_vector )

Scale data in output vector after get it from ann based on previously calculated parameters.

void descale_train( training_data & data )

Descale input and output data based on previously calculated parameters.

void destroy()

Destructs the entire network.

void destroy_train()

Destructs the training data.

Error function used during training.

Standard linear error function.

Tanh error function, usually better but can require a lower learning rate.