Crate fann_sys [−] [src]

Raw bindings to C functions of the Fast Artificial Neural Network library

Creation/Execution

The FANN library is designed to be very easy to use. A feedforward ANN can be created by a simple fann_create_standard function, while other ANNs can be created just as easily. The ANNs can be trained by fann_train_on_file and executed by fann_run.

All of this can be done without much knowledge of the internals of ANNs, although the ANNs created will still be powerful and effective. If you have more knowledge about ANNs, and desire more control, almost every part of the ANNs can be parametrized to create specialized and highly optimal ANNs.

Training

There are many different ways of training neural networks and the FANN library supports a number of different approaches.

Two fundementally different approaches are the most commonly used:

Fixed topology training - The size and topology of the ANN is determined in advance and the training alters the weights in order to minimize the difference between the desired output values and the actual output values. This kind of training is supported by fann_train_on_data.
Evolving topology training - The training start out with an empty ANN, only consisting of input and output neurons. Hidden neurons and connections are added during training, in order to achieve the same goal as for fixed topology training. This kind of training is supported by FANN Cascade Training.

Cascade Training

Cascade training differs from ordinary training in the sense that it starts with an empty neural network and then adds neurons one by one, while it trains the neural network. The main benefit of this approach is that you do not have to guess the number of hidden layers and neurons prior to training, but cascade training has also proved better at solving some problems.

The basic idea of cascade training is that a number of candidate neurons are trained separate from the real network, then the most promising of these candidate neurons is inserted into the neural network. Then the output connections are trained and new candidate neurons are prepared. The candidate neurons are created as shortcut connected neurons in a new hidden layer, which means that the final neural network will consist of a number of hidden layers with one shortcut connected neuron in each.

File Input/Output

It is possible to save an entire ann to a file with fann_save for future loading with fann_create_from_file.

Error Handling

Errors from the FANN library are usually reported on stderr. It is however possible to redirect these error messages to a file, or completely ignore them with the fann_set_error_log function.

It is also possible to inspect the last error message by using the fann_get_errno and fann_get_errstr functions.

Datatypes

The two main datatypes used in the FANN library are fann, which represents an artificial neural network, and fann_train_data, which represents training data.

Reexports

pub use fann_errno_enum::*;

pub use fann_train_enum::*;

pub use fann_activationfunc_enum::*;

pub use fann_errorfunc_enum::*;

pub use fann_stopfunc_enum::*;

pub use fann_nettype_enum::*;

Structs

fann	The fast artificial neural network (`fann`) structure.
fann_connection	Describes a connection between two neurons and its weight.
fann_error	Structure used to store error-related information, both `fann` and `fann_train_data` can be cast to this type.
fann_train_data	Structure used to store data, for use with training.

Enums

fann_activationfunc_enum	The activation functions used for the neurons during training. The activation functions can either be defined for a group of neurons by `fann_set_activation_function_hidden` and `fann_set_activation_function_output`, or it can be defined for a single neuron by `fann_set_activation_function`.
fann_errno_enum	Error events on fann and fann_train_data.
fann_errorfunc_enum	Error function used during training.
fann_nettype_enum	Definition of network types used by `fann_get_network_type`.
fann_stopfunc_enum	Stop criteria used during training.
fann_train_enum	The Training algorithms used when training on `fann_train_data` with functions like `fann_train_on_data` or `fann_train_on_file`. The incremental training alters the weights after each time it is presented an input pattern, while batch only alters the weights once after it has been presented to all the patterns.

Statics

fann_default_error_log

Functions

fann_cascadetrain_on_data	Trains on an entire dataset, for a period of time using the Cascade2 training algorithm. This algorithm adds neurons to the neural network while training, which means that it needs to start with an ANN without any hidden layers. The neural network should also use shortcut connections, so `fann_create_shortcut` should be used to create the ANN like this:
fann_cascadetrain_on_file	Does the same as `fann_cascadetrain_on_data`, but reads the training data directly from a file.
fann_clear_scaling_params	Clears scaling parameters.
fann_create_from_file	Constructs a backpropagation neural network from a configuration file, which has been saved by `fann_save`.
fann_create_shortcut	Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections.
fann_create_shortcut_array	Just like `fann_create_shortcut`, but with an array of layer sizes instead of individual parameters.
fann_create_sparse	Creates a standard backpropagation neural network, which is not fully connected.
fann_create_sparse_array	Just like `fann_create_sparse`, but with an array of layer sizes instead of individual parameters.
fann_create_standard	Creates a standard fully connected backpropagation neural network.
fann_create_standard_array	Just like `fann_create_standard`, but with an array of layer sizes instead of individual parameters.
fann_create_train_from_callback	Creates the training data struct from a user supplied function. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input,output) and returns the set.
fann_descale_input	Scale data in input vector after getting it from the ANN based on previously calculated parameters.
fann_descale_output	Scale data in output vector after getting it from the ANN based on previously calculated parameters.
fann_descale_train	Descale input and output data based on previously calculated parameters.
fann_destroy	Destroys the entire network, properly freeing all the associated memory.
fann_destroy_train	Destructs the training data and properly deallocates all of the associated data. Be sure to call this function when finished using the training data.
fann_duplicate_train_data	Returns an exact copy of a `fann_train_data`.
fann_get_MSE	Reads the mean square error from the network.
fann_get_activation_function	Get the activation function for neuron number `neuron` in layer number `layer`, counting the input layer as layer 0.
fann_get_activation_steepness	Get the activation steepness for neuron number `neuron` in layer number `layer`, counting the input layer as layer 0.
fann_get_bias_array	Get the number of bias in each layer in the network.
fann_get_bit_fail	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`). The bits are counted in all of the training data, so this number can be higher than the number of training data.
fann_get_bit_fail_limit	Returns the bit fail limit used during training.
fann_get_cascade_activation_functions	The cascade activation functions array is an array of the different activation functions used by the candidates.
fann_get_cascade_activation_functions_count	The number of activation functions in the `fann_get_cascade_activation_functions` array.
fann_get_cascade_activation_steepnesses	The cascade activation steepnesses array is an array of the different activation functions used by the candidates.
fann_get_cascade_activation_steepnesses_count	The number of activation steepnesses in the `fann_get_cascade_activation_functions` array.
fann_get_cascade_candidate_change_fraction	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. If the training stagnates, the training of the candidate neurons will be ended and the best candidate will be selected.
fann_get_cascade_candidate_limit	The candidate limit is a limit for how much the candidate neuron may be trained. The limit is a limit on the proportion between the MSE and candidate score.
fann_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 `fann_get_cascade_candidate_change_fraction`.
fann_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_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_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_get_cascade_num_candidates	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`).
fann_get_cascade_output_change_fraction	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. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared.
fann_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 `fann_get_cascade_output_change_fraction`.
fann_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. This parameter is usually between 0 and 1, and is used to make the training a bit less aggressive.
fann_get_connection_array	Get the connections in the network.
fann_get_connection_rate	Get the connection rate used when the network was created.
fann_get_errno	Returns the last error number.
fann_get_errstr	Returns the last error string.
fann_get_layer_array	Get the number of neurons in each layer in the network.
fann_get_learning_momentum	Get the learning momentum.
fann_get_learning_rate	Return the learning rate.
fann_get_network_type	Get the type of neural network it was created as.
fann_get_num_input	Get the number of input neurons.
fann_get_num_layers	Get the number of layers in the network.
fann_get_num_output	Get the number of output neurons.
fann_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. This is used to make sure that the weights do not become too high during training.
fann_get_quickprop_mu	The mu factor is used to increase and decrease the step size during quickprop training. The mu factor should always be above 1, since it would otherwise decrease the step size when it was supposed to increase it.
fann_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_get_rprop_delta_max	The maximum step size is a positive number determining how large the maximum step size may be.
fann_get_rprop_delta_min	The minimum step size is a small positive number determining how small the minimum step size may be.
fann_get_rprop_delta_zero	The initial step size is a positive number determining the initial step size.
fann_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_get_total_connections	Get the total number of connections in the entire network.
fann_get_total_neurons	Get the total number of neurons in the entire network. This number does also include the bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons.
fann_get_train_error_function	Returns the error function used during training.
fann_get_train_stop_function	Returns the the stop function used during training.
fann_get_training_algorithm	Return the training algorithm as described by `fann_train_enum`. This training algorithm is used by `fann_train_on_data` and associated functions.
fann_get_user_data	Get a pointer to user defined data that was previously set with `fann_set_user_data`. It is the user's responsibility to allocate and deallocate any data that the pointer might point to.
fann_init_weights	Initialize the weights using Widrow + Nguyen's algorithm.
fann_length_train_data	Returns the number of training patterns in the `fann_train_data`.
fann_merge_train_data	Merges the data from `data1` and `data2` into a new `fann_train_data`.
fann_num_input_train_data	Returns the number of inputs in each of the training patterns in the `fann_train_data`.
fann_num_output_train_data	Returns the number of outputs in each of the training patterns in the `fann_train_data`.
fann_print_connections	Prints the connections of the ANN in a compact matrix, for easy viewing of the internals of the ANN.
fann_print_error	Prints the last error to `stderr`.
fann_print_parameters	Prints all of the parameters and options of the ANN.
fann_randomize_weights	Give each connection a random weight between `min_weight` and `max_weight`.
fann_read_train_from_file	Reads a file that stores training data.
fann_reset_MSE	Resets the mean square error from the network.
fann_reset_errno	Resets the last error number.
fann_reset_errstr	Resets the last error string.
fann_run	Runs 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_save	Save the entire network to a configuration file.
fann_save_to_fixed	Saves the entire network to a configuration file. But it is saved in fixed point format no matter which format it is currently in.
fann_save_train	Save the training structure to a file, with the format specified in `fann_read_train_from_file`
fann_save_train_to_fixed	Saves the training structure to a fixed point data file.
fann_scale_input	Scale data in input vector before feeding it to the ANN based on previously calculated parameters.
fann_scale_input_train_data	Scales the inputs in the training data to the specified range.
fann_scale_output	Scale data in output vector before feeding it to the ANN based on previously calculated parameters.
fann_scale_output_train_data	Scales the outputs in the training data to the specified range.
fann_scale_train	Scale input and output data based on previously calculated parameters.
fann_scale_train_data	Scales the inputs and outputs in the training data to the specified range.
fann_set_activation_function	Set the activation function for neuron number `neuron` in layer number `layer`, counting the input layer as layer 0.
fann_set_activation_function_hidden	Set the activation function for all of the hidden layers.
fann_set_activation_function_layer	Set the activation function for all the neurons in the layer number `layer`, counting the input layer as layer 0.
fann_set_activation_function_output	Set the activation function for the output layer.
fann_set_activation_steepness	Set the activation steepness for neuron number `neuron` in layer number `layer`, counting the input layer as layer 0.
fann_set_activation_steepness_hidden	Set the steepness of the activation steepness in all of the hidden layers.
fann_set_activation_steepness_layer	Set the activation steepness for all neurons in layer number `layer`, counting the input layer as layer 0.
fann_set_activation_steepness_output	Set the steepness of the activation steepness in the output layer.
fann_set_bit_fail_limit	Set the bit fail limit used during training.
fann_set_callback	Sets the callback function for use during training.
fann_set_cascade_activation_functions	Sets the array of cascade candidate activation functions. The array must be just as long as defined by the count.
fann_set_cascade_activation_steepnesses	Sets the array of cascade candidate activation steepnesses. The array must be just as long as defined by the count.
fann_set_cascade_candidate_change_fraction	Sets the cascade candidate change fraction.
fann_set_cascade_candidate_limit	Sets the candidate limit.
fann_set_cascade_candidate_stagnation_epochs	Sets the number of cascade candidate stagnation epochs.
fann_set_cascade_max_cand_epochs	Sets the max candidate epochs.
fann_set_cascade_max_out_epochs	Sets the maximum out epochs.
fann_set_cascade_num_candidate_groups	Sets the number of candidate groups.
fann_set_cascade_output_change_fraction	Sets the cascade output change fraction.
fann_set_cascade_output_stagnation_epochs	Sets the number of cascade output stagnation epochs.
fann_set_cascade_weight_multiplier	Sets the weight multiplier.
fann_set_error_log	Change where errors are logged to. Both `fann` and `fann_data` can be cast to `fann_error`, so this function can be used to set either of these.
fann_set_input_scaling_params	Calculate input scaling parameters for future use based on training data.
fann_set_learning_momentum	Set the learning momentum.
fann_set_learning_rate	Set the learning rate.
fann_set_output_scaling_params	Calculate output scaling parameters for future use based on training data.
fann_set_quickprop_decay	Sets the quickprop decay factor.
fann_set_quickprop_mu	Sets the quickprop mu factor.
fann_set_rprop_decrease_factor	The decrease factor is a value smaller than 1, which is used to decrease the step size during RPROP training.
fann_set_rprop_delta_max	The maximum step size is a positive number determining how large the maximum step size may be.
fann_set_rprop_delta_min	The minimum step size is a small positive number determining how small the minimum step size may be.
fann_set_rprop_delta_zero	The initial step size is a positive number determining the initial step size.
fann_set_rprop_increase_factor	The increase factor used during RPROP training.
fann_set_scaling_params	Calculate input and output scaling parameters for future use based on training data.
fann_set_train_error_function	Set the error function used during training.
fann_set_train_stop_function	Set the stop function used during training.
fann_set_training_algorithm	Set the training algorithm.
fann_set_user_data	Store a pointer to user defined data. The pointer can be retrieved with `fann_get_user_data` for example in a callback. It is the user's responsibility to allocate and deallocate any data that the pointer might point to.
fann_set_weight	Set a connection in the network.
fann_set_weight_array	Set connections in the network.
fann_shuffle_train_data	Shuffles training data, randomizing the order. This is recommended for incremental training, while it has no influence during batch training.
fann_subset_train_data	Returns an copy of a subset of the `fann_train_data`, starting at position `pos` and `length` elements forward.
fann_test	Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error, but does not change the network in any way.
fann_test_data	Tests a set of training data and calculates the MSE for the training data.
fann_train	Train one iteration with a set of inputs, and a set of desired outputs. This training is always incremental training (see `fann_train_enum`), since only one pattern is presented.
fann_train_epoch	Train one epoch with a set of training data.
fann_train_on_data	Trains on an entire dataset, for a period of time.
fann_train_on_file	Does the same as `fann_train_on_data`, but reads the training data directly from a file.

Type Definitions

fann_callback_type	This callback function can be called during training when using `fann_train_on_data`, `fann_train_on_file` or `fann_cascadetrain_on_data`.
fann_type	The type of weights, inputs and outputs in a neural network. In the Rust bindings, it is defined as `c_float` by default, and as `c_double`, if the `double` feature is configured.
fann_type_internal