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neural_net::neural_net(float connection_rate,unsigned int num_layers,neural_net::neural_net(network_type_enum net_type,unsigned int num_layers,
A
 activation_function_enum
C
 callback_type
 Cascade Training
 cascadetrain_on_data, neural_net
 cascadetrain_on_file, neural_net
 clear_scaling_params, neural_net
 connection
 copy_from_struct_fann-DEPRECATED, neural_net
 create_from_file-DEPRECATED, neural_net
 create_shortcut-DEPRECATED, neural_net
 create_shortcut_array-DEPRECATED, neural_net
 create_sparse-DEPRECATED, neural_net
 create_sparse_array-DEPRECATED, neural_net
 create_standard-DEPRECATED, neural_net
 create_standard_array-DEPRECATED, 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_nettraining_data
 disable_seed_rand, neural_net
E
 enable_seed_rand, neural_net
 Enumerations
 Enumerations and Constants
 Error Handling
 error_function_enum
 ERRORFUNC_LINEAR
 ERRORFUNC_TANH
neural_net(float connection_rate,
unsigned int num_layers,
 ...)
Creates a standard backpropagation neural network, which is sparsely connected, this will default the network_type_enum to LAYER
neural_net(network_type_enum net_type,
unsigned int num_layers,
 ...)
Creates a neural network of the desired network_type_enum net_type.
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
Set the internal fann struct to a copy of other
Constructs a backpropagation neural network from a configuration file, which have been saved by save.
Creates a standard backpropagation neural network, which is fully connected and which also has shortcut connections.
Just like create_shortcut, but with an array of layer sizes instead of individual parameters.
Creates a standard backpropagation neural network, which is not fully connected.
Just like create_sparse, but with an array of layer sizes instead of individual parameters.
Creates a standard fully connected backpropagation neural network.
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.
void disable_seed_rand()
Disables the automatic random generator seeding that happens in FANN.
void enable_seed_rand()
Enables the automatic random generator seeding that happens in FANN.
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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