Index - Fast Artificial Neural Network Library (FANN)

A
	activation_function_enum, FANN
fann_	activationfunc_enum
FANN_	ACTIVATIONFUNC_NAMES
	Adjusting Parameters
	Adjusting Parameters During Training
	Advanced Usage
	Artificial Neural Networks
	Avoid Over-Fitting
C
	C Library Installation
	callback_type, FANN
fann_	callback_type
	Cascade Training
	cascadetrain_on_data, neural_net
fann_	cascadetrain_on_data
	cascadetrain_on_file, neural_net
fann_	cascadetrain_on_file
	Changes
	clear_scaling_params, neural_net
fann_	clear_scaling_params
	Compiling from source
	Compiling into PHP
	connection, FANN
fann_	connection, struct fann
fann_	copy
	copy_from_struct_fann, neural_net
FANN_	COS
FANN_	COS_SYMMETRIC fann_cpp.h fann_data.h
	create_from_file, neural_net
fann_	create_from_file
	create_shortcut, neural_net
fann_	create_shortcut
	create_shortcut_array, neural_net
fann_	create_shortcut_array
	create_sparse, neural_net
fann_	create_sparse
	create_sparse_array, neural_net
fann_	create_sparse_array
	create_standard, neural_net
fann_	create_standard
	create_standard_array, neural_net
fann_	create_standard_array
fann_	create_train
	create_train_from_callback, training_data
fann_	create_train_from_callback
	Creation,Destruction&Execution
D
	DEB Installation
	Delphi Bindings
	Delphi Install
	descale_input, neural_net
fann_	descale_input
	descale_output, neural_net
fann_	descale_output
	descale_train, neural_net
fann_	descale_train
	destroy neural_net training_data
fann_	destroy
fann_	destroy_train
fann_	duplicate_train_data

The activation functions used for the neurons during training.

Constant array consisting of the names for the activation function, so that the name of an activation function can be received by:

Several different parameters exists in an ANN, these parameters are given defaults in the fann library, but they can be adjusted at runtime.

If a very low mean square error is required it can sometimes be a good idea to gradually decrease the learning rate during training, in order to make the adjusting of weights more subtle.

This section describes some of the low-level functions and how they can be used to obtain more control of the fann library.

It is not possible (at the moment) to make an artificial brain, but it is possible to make simplified artificial neurons and artificial neural networks.

With the knowledge of how to train and test an ANN, a new approach to training can be introduced.

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.

This callback function can be called during training when using fann_train_on_data, fann_train_on_file or fann_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.

FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(
    struct fann * ann,
    struct fann_train_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.

FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(
    struct fann * ann,
    const char * filename,
    unsigned int max_neurons,
    unsigned int neurons_between_reports,
    float desired_error
)

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

bool clear_scaling_params()

Clears scaling parameters.

FANN_EXTERNAL int FANN_API fann_clear_scaling_params( struct fann * ann )

Clears scaling parameters.

Compiling FANN from source code entails the standard GNU autotools technique.

Please only use this method if using the methods outlined in Using PEAR have failed.

Describes a connection between two neurons and its weight

FANN_EXTERNAL struct fann * FANN_API fann_copy( struct fann * ann )

Creates a copy of a fann structure.

void copy_from_struct_fann( const struct fann * other )

Set the internal fann struct to a copy of other

Periodical cosinus activation function.

bool create_from_file( const std:: string & configuration_file )

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

FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(
const char * configuration_file
)

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

FANN_EXTERNAL struct fann *FANN_API fann_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.

FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(
unsigned int num_layers,
const unsigned int * layers
)

Just like fann_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.

FANN_EXTERNAL struct fann *FANN_API fann_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.

FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(
    float connection_rate,
    unsigned int num_layers,
    const unsigned int * layers
)

Just like fann_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.

FANN_EXTERNAL struct fann *FANN_API fann_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.

FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array(
unsigned int num_layers,
const unsigned int * layers
)

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

FANN_EXTERNAL struct fann_train_data * FANN_API fann_create_train(
    unsigned int num_data,
    unsigned int num_input,
    unsigned int num_output
)

Creates an empty training data struct.

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.

FANN_EXTERNAL struct fann_train_data * FANN_API fann_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.

DEBs are packages for the Debian Linux distribution.

These functions allow you to interact with the FANN library from Delphi.

Make sure to install the fann library first.

void descale_input( fann_type * input_vector )

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

FANN_EXTERNAL void FANN_API fann_descale_input( struct fann * ann,
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.

FANN_EXTERNAL void FANN_API fann_descale_output( struct fann * ann,
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.

FANN_EXTERNAL void FANN_API fann_descale_train( struct fann * ann,
struct fann_train_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.

FANN_EXTERNAL void FANN_API fann_destroy( struct fann * ann )

Destroys the entire network and properly freeing all the associated memmory.

FANN_EXTERNAL void FANN_API fann_destroy_train(
struct fann_train_data * train_data
)

Destructs the training data and properly deallocates all of the associated data.

FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data(
struct fann_train_data * data
)

Returns an exact copy of a struct fann_train_data.