Index

A |
| activation_function_enum, FANN
C |
| callback_type, FANN
| Cascade Training
| cascadetrain_on_data, neural_net
| cascadetrain_on_file, neural_net
| clear_scaling_params, neural_net
| connection, FANN
| copy_from_struct_fann, neural_net
| create_from_file, neural_net
| create_shortcut, neural_net
| create_shortcut_array, neural_net
| create_sparse, neural_net
| create_sparse_array, neural_net
| create_standard, neural_net
| create_standard_array, neural_net
| create_train_from_callback, training_data
| Creation,Destruction&Execution
D |
| descale_input, neural_net
| descale_output, neural_net
| descale_train, neural_net
| destroy neural_net training_data
E |
| Enumerations, FANN
| Enumerations and Constants
| Error Handling
| error_function_enum, FANN
| ERRORFUNC_LINEAR, FANN
| ERRORFUNC_TANH, FANN

The activation functions used for the neurons during training.
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.

> 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.

> 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.

> bool clear_scaling_params()

Clears scaling parameters.
Describes a connection between two neurons and its weight

> void copy_from_struct_fann( | struct | fann | * | other | )

Set the internal fann struct to a copy of other

> bool create_from_file( | const std:: | string | & | configuration_file | )

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

> 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.

> bool 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.

> bool 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.

> 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.

> void descale_input( | 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.

> void descale_train( | training_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.
Error function used during training.
Standard linear error function.
Tanh error function, usually better but can require a lower learning rate.
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