fann_create -- Create a new artificial neural network, and return a pointer to it.
fann_create_array -- Create a new artificial neural network, and return a pointer to it.
fann_destroy -- Destroy an ANN.
fann_run -- Run (execute) an ANN.
fann_randomize_weights -- Give each connection a random weight.
fann_init_weights -- Initialize the weight of each connection.

5.2. Input/Output

fann_save -- Save an ANN to a file.
fann_save_to_fixed -- Save an ANN to a fixed-point file.
fann_create_from_file -- Load an ANN from a file.

5.3. Training

fann_train -- Train an ANN.
fann_test -- Tests an ANN.
fann_get_MSE -- Return the mean square error of an ANN.
fann_reset_MSE -- Reset the mean square error of an ANN.

5.4. Training Data

fann_read_train_from_file -- Read training data from a file.
fann_save_train -- Save training data.
fann_save_train_to_fixed -- Save training data as fixed point.
fann_destroy_train -- Destroy training data.
fann_train_on_data -- Train an ANN.
fann_train_on_data_callback -- Train an ANN.
fann_train_on_file -- Train an ANN.
fann_train_on_file_callback -- Train an ANN.
fann_shuffle_train_data -- Shuffle the training data.
fann_merge_train_data -- Merge two sets of training data.
fann_duplicate_train_data -- Copies a set of training data.

5.5. Options

fann_get_learning_rate -- Retrieve learning rate from a network.
fann_set_learning_rate -- Set a network's learning rate.
fann_get_activation_function_hidden -- Get the activation function used in the hidden layers.
fann_set_activation_function_hidden -- Set the activation function for the hidden layers.
fann_get_activation_function_output -- Get the activation function of the output layer.
fann_set_activation_function_output -- Set the activation function for the output layer.
fann_get_activation_hidden_steepness -- Retrieve the steepness of the activation function of the hidden layers.
fann_set_activation_hidden_steepness -- Set the steepness of the activation function of the hidden layers.
fann_get_activation_output_steepness -- Retrieve the steepness of the activation function of the hidden layers.
fann_set_activation_output_steepness -- Set the steepness of the activation function of the hidden layers.
fann_get_num_input -- Get the number of neurons in the input layer.
fann_get_num_output -- Get number of neurons in the output layer.
fann_get_total_neurons -- Get the total number of neurons in a network.
fann_get_total_connections -- Get the total number of connections in a network.
fann_get_decimal_point -- Get the position of the decimal point.
fann_get_multiplier -- Get the multiplier.

5.6. Error Handling

fann_get_errno -- Return the numerical representation of the last error.
fann_get_errstr -- Return the last error.
fann_reset_errno -- Reset the last error number.
fann_reset_errstr -- Reset the last error string.
fann_set_error_log -- Set the error log to a file descriptor.
fann_print_error -- Print the last error to the error log.

5.7. Data Structures

struct fann -- Describes a neural network.
struct fann_train_data -- Describes a set of training data.
struct fann_error -- Describes an error.
struct fann_neuron -- Describes an individual neuron.
struct fann_layer -- Describes a layer in a network.

5.8. Constants

Activation Functions -- Constants representing activation functions.
Error Codes -- Constants representing errors.

5.9. Internal Functions

5.9.1. Creation And Destruction
5.9.2. Input/Output
5.9.3. Training Data
5.9.4. Error Handling
5.9.5. Options

5.10. Deprecated Functions

5.10.1. Mean Square Error

6. PHP Extension

6.1. API Reference

fann_create -- Creates an artificial neural network.
fann_train -- Train an artificial neural network.
fann_save -- Save an artificial neural network to a file.
fann_run -- Run an artificial neural network.
fann_randomize_weights -- Randomize the weights of the neurons in the network.
fann_init_weights -- Initialize the weight of each connection.
fann_get_MSE -- Get the mean squared error.
fann_get_num_input -- Get the number of input neurons.
fann_get_num_output -- Get the number of output neurons.
fann_get_total_neurons -- Get the total number of neurons.
fann_get_total_connections -- Get the total number of connections.
fann_get_learning_rate -- Get the learning rate.
fann_get_activation_function_hidden -- Get the activation function of the hidden neurons.
fann_get_activation_function_output -- Get the activation function of the output neurons.
fann_get_activation_hidden_steepness -- Get the steepness of the activation function for the hidden neurons.
fann_get_activation_output_steepness -- Get the steepness of the activation function for the output neurons.
fann_set_learning_rate -- Set the learning rate.
fann_set_activation_function_hidden -- Set the activation function for the hidden neurons.
fann_set_activation_function_output -- Set the activation function for the output neurons.
fann_set_activation_hidden_steepness -- Set the steepness of the activation function for the hidden neurons.
fann_set_activation_output_steepness -- Set the steepness of the activation function for the output neurons.

7. Python Bindings

7.1. Python Install

Bibliography

List of Examples
1-1. Simple training example
1-2. Simple execution example
2-1. The internals of the fann_train_on_file function, without writing the status line.
2-2. Test all of the data in a file and calculates the mean square error.
3-1. An example of a program written to support training in both fixed point and floating point numbers
3-2. An example of a program written to support both fixed point and floating point numbers
5-1. fann_create_array example
6-1. fann_create from scratch
6-2. fann_create loading from a file
6-1. fann_create from training data
6-1. fann_runExample

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