Index

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.
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.
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