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
	Algorithmic Overview
	Artificial Neural Networks
	Avoid Over-Fitting
B
	Background
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
fann_	connect_layer
fann_	connect_layers
	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
fann_	create_from_descr
	create_from_file, neural_net
fann_	create_from_file
fann_	create_gng
	create_shortcut, neural_net
fann_	create_shortcut
	create_shortcut_array, neural_net
fann_	create_shortcut_array
fann_	create_som
	create_sparse, neural_net
fann_	create_sparse
	create_sparse_array, neural_net
fann_	create_sparse_array
fann_	create_sparse_array_typed
	create_standard, neural_net
fann_	create_standard
	create_standard_array, neural_net
fann_	create_standard_array
fann_	create_standard_array_typed
fann_	create_train
	create_train_from_callback, training_data
fann_	create_train_from_callback
	Creation,Destruction&Execution

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.

Self-Organizing Maps (SOM) is an unsupervised learning method that uses vector quantization to allow visualization and clustering of data.

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.

The idea of running neural networks on the gpu is to exploit that many shader programs can run in parallell on the gpu.

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.

FANN_EXTERNAL void FANN_API fann_connect_layer( struct fann_layer * layer,
unsigned int num_inputs,
fann_type * inputs )

connect a layer to an input vector

FANN_EXTERNAL void FANN_API fann_connect_layers(
struct fann_layer * parent_layer,
struct fann_layer * child_layer
)

connect a layer to another layer’s output

Describes a connection between two neurons and its weight

FANN_EXTERNAL struct fann * FANN_API fann_copy( const 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.

FANN_EXTERNAL struct fann *FANN_API fann_create_from_descr(
struct fann_descr * descr
)

Creates the network from a description structure

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.

FANN_EXTERNAL struct fann *FANN_API fann_create_gng( unsigned int num_input )

Creates a Growing Neural Gas clusterer

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.

FANN_EXTERNAL struct fann *FANN_API fann_create_som( unsigned int width,
unsigned int height,
unsigned int num_input )

Creates a Self-Organizing Map

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.

FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array_typed(
    const char * layer_type,
    const char * neuron_type,
    float connection_rate,
    unsigned int num_layers,
    const unsigned int * layers
)

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 *FANN_API fann_create_standard_array_typed(
    const char * layer_type,
    const char * neuron_type,
    unsigned int num_layers,
    const unsigned int * layers
)

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.