FANN Creation/ ExecutionThe FANN library is designed to be very easy to use. A feedforward ann can be created by a simple fann_create_standard function, while other ANNs can be created just as easily. The ANNs can be trained by fann_train_on_file and executed by fann_run. All of this can be done without much knowledge of the internals of ANNs, although the ANNs created will still be powerfull and effective. If you have more knowledge about ANNs, and desire more control, almost every part of the ANNs can be parametized to create specialized and highly optimal ANNs. Summary | The FANN library is designed to be very easy to use. | | | | Creates a standard fully connected backpropagation neural network. | | | | Creates a standard backpropagation neural network, which is not fully connected. | | Just like fann_create_sparse, but with an array of layer sizes instead of individual parameters. | | Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections. | | | | Destroys the entire network and properly freeing all the associated memmory. | | Creates a copy of a fann structure. | | Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer. | | Give each connection a random weight between min_weight and max_weight | | Initialize the weights using Widrow + Nguyen’s algorithm. | | Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann. | | | | Prints all of the parameters and options of the ANN | | Get the number of input neurons. | | Get the number of output neurons. | | Get the total number of neurons in the entire network. | | Get the total number of connections in the entire network. | | Get the type of neural network it was created as. | | Get the connection rate used when the network was created | | Get the number of layers in the network | | Get the number of neurons in each layer in the network. | | Get the number of bias in each layer in the network. | | Get the connections in the network. | | Set connections in the network. | | Set a connection in the network. | | Store a pointer to user defined data. | | | | Returns the position of the decimal point in the ann. | | returns the multiplier that fix point data is multiplied with. |
Creation, Destruction & Executionfann_create_standard| FANN_EXTERNAL struct fann *FANN_API fann_create_standard( | | | unsigned | int | num_layers, | | | | | ... | | ) |
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Creates a standard fully connected backpropagation neural network. There will be a bias neuron in each layer (except the output layer), and this bias neuron will be connected to all neurons in the next layer. When running the network, the bias nodes always emits 1. To destroy a struct fann use the fann_destroy function. Parameters| num_layers | The total number of layers including the input and the output layer. | | ... | Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer. |
ReturnsA pointer to the newly created struct fann. Example// Creating an ANN with 2 input neurons, 1 output neuron,
// and two hidden neurons with 8 and 9 neurons
struct fann *ann = fann_create_standard(4, 2, 8, 9, 1);
See alsofann_create_standard_array, fann_create_sparse, fann_create_shortcut This function appears in FANN >= 2.0.0. fann_create_standard_array| FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array( | | | unsigned | int | | num_layers, | | | const unsigned | int | * | layers | | ) |
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Just like fann_create_standard, but with an array of layer sizes instead of individual parameters. Example// Creating an ANN with 2 input neurons, 1 output neuron,
// and two hidden neurons with 8 and 9 neurons
unsigned int layers[4] = {2, 8, 9, 1};
struct fann *ann = fann_create_standard_array(4, layers);
See alsofann_create_standard, fann_create_sparse, fann_create_shortcut This function appears in FANN >= 2.0.0. fann_create_sparse| FANN_EXTERNAL struct fann *FANN_API fann_create_sparse( | | | | float | connection_rate, | | | unsigned | int | num_layers, | | | | | ... | | ) |
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Creates a standard backpropagation neural network, which is not fully connected. Parameters| connection_rate | The connection rate controls how many connections there will be in the network. If the connection rate is set to 1, the network will be fully connected, but if it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield the same result as fann_create_standard | | num_layers | The total number of layers including the input and the output layer. | | ... | Integer values determining the number of neurons in each layer starting with the input layer and ending with the output layer. |
ReturnsA pointer to the newly created struct fann. See alsofann_create_sparse_array, fann_create_standard, fann_create_shortcut This function appears in FANN >= 2.0.0. fann_create_shortcut| FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut( | | | unsigned | int | num_layers, | | | | | ... | | ) |
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Creates a standard backpropagation neural network, which is not fully connected and which also has shortcut connections. Shortcut connections are connections that skip layers. A fully connected network with shortcut connections, is a network where all neurons are connected to all neurons in later layers. Including direct connections from the input layer to the output layer. See fann_create_standard for a description of the parameters. See alsofann_create_shortcut_array, fann_create_standard, fann_create_sparse, This function appears in FANN >= 2.0.0. fann_destroy| FANN_EXTERNAL void FANN_API fann_destroy( | struct | fann | * | ann | ) |
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Destroys the entire network and properly freeing all the associated memmory. This function appears in FANN >= 1.0.0. fann_copy| FANN_EXTERNAL struct fann * FANN_API fann_copy( | struct | fann | * | ann | ) |
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Creates a copy of a fann structure. Data in the user data fann_set_user_data is not copied, but the user data pointer is copied. This function appears in FANN >= 2.2.0. fann_run| FANN_EXTERNAL fann_type * FANN_API fann_run( | struct | fann | * | ann, | | | fann_type | * | input | ) |
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Will run input through the neural network, returning an array of outputs, the number of which being equal to the number of neurons in the output layer. See alsofann_test This function appears in FANN >= 1.0.0. fann_randomize_weights| FANN_EXTERNAL void FANN_API fann_randomize_weights( | struct | fann | * | ann, | | | fann_type | | min_weight, | | | fann_type | | max_weight | ) |
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Give each connection a random weight between min_weight and max_weight From the beginning the weights are random between -0.1 and 0.1. See alsofann_init_weights This function appears in FANN >= 1.0.0. fann_init_weights| FANN_EXTERNAL void FANN_API fann_init_weights( | | | struct | fann | * | ann, | | | struct | fann_train_data | * | train_data | | ) |
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Initialize the weights using Widrow + Nguyen’s algorithm. This function behaves similarly to fann_randomize_weights. It will use the algorithm developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up training. This technique is not always successful, and in some cases can be less efficient than a purely random initialization. The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, data, which is the training data that will be used to train the network. See alsofann_randomize_weights, fann_read_train_from_file This function appears in FANN >= 1.1.0. fann_print_connections| FANN_EXTERNAL void FANN_API fann_print_connections( | struct | fann | * | ann | ) |
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Will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann. The output from fann_print_connections on a small (2 2 1) network trained on the xor problem Layer / Neuron 012345
L 1 / N 3 BBa...
L 1 / N 4 BBA...
L 1 / N 5 ......
L 2 / N 6 ...BBA
L 2 / N 7 ......
This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. “.” is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has connection from the three neurons in the previous layer as is visible in the first two lines. The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5 as is visible in the fourth line. To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to. This function appears in FANN >= 1.2.0. fann_print_parameters| FANN_EXTERNAL void FANN_API fann_print_parameters( | struct | fann | * | ann | ) |
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Prints all of the parameters and options of the ANN This function appears in FANN >= 1.2.0. fann_get_num_input| FANN_EXTERNAL unsigned int FANN_API fann_get_num_input( | struct | fann | * | ann | ) |
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Get the number of input neurons. This function appears in FANN >= 1.0.0. fann_get_num_output| FANN_EXTERNAL unsigned int FANN_API fann_get_num_output( | struct | fann | * | ann | ) |
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Get the number of output neurons. This function appears in FANN >= 1.0.0. fann_get_total_neurons| FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons( | struct | fann | * | ann | ) |
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Get the total number of neurons in the entire network. This number does also include the bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons. This function appears in FANN >= 1.0.0. fann_get_total_connections| FANN_EXTERNAL unsigned int FANN_API fann_get_total_connections( | | | struct | fann | * | ann | | ) |
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Get the total number of connections in the entire network. This function appears in FANN >= 1.0.0. fann_get_network_type| FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type( | | | struct | fann | * | ann | | ) |
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Get the type of neural network it was created as. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
ReturnsThe neural network type from enum fann_network_type_enum See Alsofann_network_type_enum This function appears in FANN >= 2.1.0 fann_get_connection_rate| FANN_EXTERNAL float FANN_API fann_get_connection_rate( | struct | fann | * | ann | ) |
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Get the connection rate used when the network was created Parameters| ann | A previously created neural network structure of type struct fann pointer. |
ReturnsThe connection rate This function appears in FANN >= 2.1.0 fann_get_num_layers| FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers( | struct | fann | * | ann | ) |
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Get the number of layers in the network Parameters| ann | A previously created neural network structure of type struct fann pointer. |
ReturnsThe number of layers in the neural network Example// Obtain the number of layers in a neural network
struct fann *ann = fann_create_standard(4, 2, 8, 9, 1);
unsigned int num_layers = fann_get_num_layers(ann);
This function appears in FANN >= 2.1.0 fann_get_layer_array| FANN_EXTERNAL void FANN_API fann_get_layer_array( | struct | fann | * | ann, | | unsigned | int | * | layers | ) |
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Get the number of neurons in each layer in the network. Bias is not included so the layers match the fann_create functions. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
The layers array must be preallocated to at least sizeof(unsigned int) * fann_num_layers() long. This function appears in FANN >= 2.1.0 fann_get_bias_array| FANN_EXTERNAL void FANN_API fann_get_bias_array( | struct | fann | * | ann, | | unsigned | int | * | bias | ) |
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Get the number of bias in each layer in the network. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
The bias array must be preallocated to at least sizeof(unsigned int) * fann_num_layers() long. This function appears in FANN >= 2.1.0 fann_get_connection_array| FANN_EXTERNAL void FANN_API fann_get_connection_array( | | | struct | fann | * | ann, | | | struct | fann_connection | * | connections | | ) |
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Get the connections in the network. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
The connections array must be preallocated to at least sizeof(struct fann_connection) * fann_get_total_connections() long. This function appears in FANN >= 2.1.0 fann_set_weight_array| FANN_EXTERNAL void FANN_API fann_set_weight_array( | | | struct | fann | * | ann, | | | struct | fann_connection | * | connections, | | | unsigned | int | | num_connections | | ) |
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Set connections in the network. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
Only the weights can be changed, connections and weights are ignored if they do not already exist in the network. The array must have sizeof(struct fann_connection) * num_connections size. This function appears in FANN >= 2.1.0 fann_set_weight| FANN_EXTERNAL void FANN_API fann_set_weight( | struct | fann | * | ann, | | unsigned | int | | from_neuron, | | unsigned | int | | to_neuron, | | | fann_type | | weight | ) |
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Set a connection in the network. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
Only the weights can be changed. The connection/weight is ignored if it does not already exist in the network. This function appears in FANN >= 2.1.0 fann_set_user_data| FANN_EXTERNAL void FANN_API fann_set_user_data( | struct | fann | * | ann, | | | void | * | user_data | ) |
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Store a pointer to user defined data. The pointer can be retrieved with fann_get_user_data for example in a callback. It is the user’s responsibility to allocate and deallocate any data that the pointer might point to. Parameters| ann | A previously created neural network structure of type struct fann pointer. | | user_data | A void pointer to user defined data. |
This function appears in FANN >= 2.1.0 fann_get_user_data| FANN_EXTERNAL void * FANN_API fann_get_user_data( | struct | fann | * | ann | ) |
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Get a pointer to user defined data that was previously set with fann_set_user_data. It is the user’s responsibility to allocate and deallocate any data that the pointer might point to. Parameters| ann | A previously created neural network structure of type struct fann pointer. |
ReturnsA void pointer to user defined data. This function appears in FANN >= 2.1.0 fann_get_multiplier| FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier( | struct | fann | * | ann | ) |
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returns the multiplier that fix point data is multiplied with. This function is only available when the ANN is in fixed point mode. The multiplier is the used to convert between floating point and fixed point notation. A floating point number is multiplied with the multiplier in order to get the fixed point number and visa versa. The multiplier is described in greater detail in the tutorial Fixed Point Usage. See alsoFixed Point Usage, fann_get_decimal_point, fann_save_to_fixed, fann_save_train_to_fixed This function appears in FANN >= 1.0.0. |