Index

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T
	test, neural_net
	test_data, neural_net
	train, neural_net
	TRAIN_BATCH, FANN
	train_epoch, neural_net
	TRAIN_INCREMENTAL, FANN
	train_on_data, neural_net
	train_on_file, neural_net
	TRAIN_QUICKPROP, FANN
	TRAIN_RPROP, FANN
	Training
	Training Data Manipulation
	Training Data Training
	training_algorithm_enum, FANN
	training_data Global training_data
~	training_data, training_data
	Types Global FANN struct fann

fann_type * test( fann_type * input,
fann_type * desired_output )

Test with a set of inputs, and a set of desired outputs.

float test_data( const training_data & data )

Test a set of training data and calculates the MSE for the training data.

void train( fann_type * input,
fann_type * desired_output )

Train one iteration with a set of inputs, and a set of desired outputs.

Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set.

float train_epoch( const training_data & data )

Train one epoch with a set of training data.

Standard backpropagation algorithm, where the weights are updated after each training pattern.

void train_on_data( const training_data & data,
unsigned int max_epochs,
unsigned int epochs_between_reports,
float desired_error )

Trains on an entire dataset, for a period of time.

void train_on_file( const std:: string & filename,
unsigned int max_epochs,
unsigned int epochs_between_reports,
float desired_error )

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

A more advanced batch training algorithm which achieves good results for many problems.

The Training algorithms used when training on training_data with functions like neural_net::train_on_data or neural_net::train_on_file.

class training_data

Encapsulation of a training data set struct fann_train_data and associated C API functions.

training_data( ) : train_data(NULL)

Default constructor creates an empty neural net.

#ifdef USE_VIRTUAL_DESTRUCTOR virtual #endif ~training_data()

Provides automatic cleanup of data.