FANN Training |
FANN TrainingThere are many different ways of training neural networks and the FANN library supports a number of different approaches. Two fundementally different approaches are the most commonly used
Summary
struct fann_train_dataStructure used to store data, for use with training. The data inside this structure should never be manipulated directly, but should use some of the supplied functions in Training Data Manipulation. The training data structure is very usefull for storing data during training and testing of a neural network. See alsofann_read_train_from_file, fann_train_on_data, fann_destroy_train FANN TrainingSummary
fann_train
Train one iteration with a set of inputs, and a set of desired outputs. This training is always incremental training (see fann_train_enum), since only one pattern is presented. Parameters
See alsofann_train_on_data, fann_train_epoch This function appears in FANN >= 1.0.0. fann_test
Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error, but does not change the network in any way. See alsoThis function appears in FANN >= 1.0.0. fann_get_MSE
Reads the mean square error from the network. Reads the mean square error from the network. This value is calculated during training or testing, and can therefore sometimes be a bit off if the weights have been changed since the last calculation of the value. See alsoThis function appears in FANN >= 1.1.0. fann_get_bit_fail
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit). The bits are counted in all of the training data, so this number can be higher than the number of training data. This value is reset by fann_reset_MSE and updated by all the same functions which also updates the MSE value (e.g. fann_test_data, fann_train_epoch) See alsofann_stopfunc_enum, fann_get_MSE This function appears in FANN >= 2.0.0 fann_reset_MSE
Resets the mean square error from the network. This function also resets the number of bits that fail. See alsofann_get_MSE, fann_get_bit_fail_limit This function appears in FANN >= 1.1.0 fann_train_on_data
Trains on an entire dataset, for a period of time. This training uses the training algorithm chosen by fann_set_training_algorithm, and the parameters set for these training algorithms. Parameters
Instead of printing out reports every epochs_between_reports, a callback function can be called (see fann_set_callback). See alsofann_train_on_file, fann_train_epoch, Parameters This function appears in FANN >= 1.0.0. fann_train_on_file
Does the same as fann_train_on_data, but reads the training data directly from a file. See alsoThis function appears in FANN >= 1.0.0. fann_train_epoch
Train one epoch with a set of training data. Train one epoch with the training data stored in data. One epoch is where all of the training data is considered exactly once. This function returns the MSE error as it is calculated either before or during the actual training. This is not the actual MSE after the training epoch, but since calculating this will require to go through the entire training set once more, it is more than adequate to use this value during training. The training algorithm used by this function is chosen by the fann_set_training_algorithm function. See alsofann_train_on_data, fann_test_data This function appears in FANN >= 1.2.0. fann_test_data
Test a set of training data and calculates the MSE for the training data. This function updates the MSE and the bit fail values. See alsofann_test, fann_get_MSE, fann_get_bit_fail This function appears in FANN >= 1.2.0. fann_read_train_from_file
Reads a file that stores training data. The file must be formatted likenum_train_data num_input num_output See alsofann_train_on_data, fann_destroy_train, fann_save_train This function appears in FANN >= 1.0.0 fann_create_train_from_callback
Creates the training data struct from a user supplied function. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input,output) and returns the set. Parameters
Parameters for the user function
See alsofann_read_train_from_file, fann_train_on_data, fann_destroy_train, fann_save_train This function appears in FANN >= 2.1.0 fann_scale_train
Scale input and output data based on previously calculated parameters. Parameters
See alsofann_descale_train, fann_set_scaling_params This function appears in FANN >= 2.1.0 fann_descale_train
Descale input and output data based on previously calculated parameters. Parameters
See alsofann_scale_train, fann_set_scaling_params This function appears in FANN >= 2.1.0 fann_set_input_scaling_params
Calculate input scaling parameters for future use based on training data. Parameters
See alsofann_set_output_scaling_params This function appears in FANN >= 2.1.0 fann_set_output_scaling_params
Calculate output scaling parameters for future use based on training data. Parameters
See alsoThis function appears in FANN >= 2.1.0 fann_set_scaling_params
Calculate input and output scaling parameters for future use based on training data. Parameters
See alsofann_set_input_scaling_params, fann_set_output_scaling_params This function appears in FANN >= 2.1.0 fann_scale_input
Scale data in input vector before feed it to ann based on previously calculated parameters. Parameters
See alsofann_descale_input, fann_scale_output This function appears in FANN >= 2.1.0 fann_scale_output
Scale data in output vector before feed it to ann based on previously calculated parameters. Parameters
See alsofann_descale_output, fann_scale_input This function appears in FANN >= 2.1.0 fann_descale_input
Scale data in input vector after get it from ann based on previously calculated parameters. Parameters
See alsofann_scale_input, fann_descale_output This function appears in FANN >= 2.1.0 fann_descale_output
Scale data in output vector after get it from ann based on previously calculated parameters. Parameters
See alsofann_scale_output, fann_descale_input This function appears in FANN >= 2.1.0 fann_scale_input_train_data
Scales the inputs in the training data to the specified range. See alsofann_scale_output_train_data, fann_scale_train_data This function appears in FANN >= 2.0.0. fann_scale_output_train_data
Scales the outputs in the training data to the specified range. See alsofann_scale_input_train_data, fann_scale_train_data This function appears in FANN >= 2.0.0. fann_scale_train_data
Scales the inputs and outputs in the training data to the specified range. See alsofann_scale_output_train_data, fann_scale_input_train_data This function appears in FANN >= 2.0.0. fann_merge_train_data
Merges the data from data1 and data2 into a new struct fann_train_data. This function appears in FANN >= 1.1.0. fann_duplicate_train_data
Returns an exact copy of a struct fann_train_data. This function appears in FANN >= 1.1.0. fann_subset_train_data
Returns an copy of a subset of the struct fann_train_data, starting at position pos and length elements forward. fann_subset_train_data(train_data, 0, fann_length_train_data(train_data)) Will do the same as fann_duplicate_train_data. See alsoThis function appears in FANN >= 2.0.0. fann_length_train_data
Returns the number of training patterns in the struct fann_train_data. This function appears in FANN >= 2.0.0. fann_num_input_train_data
Returns the number of inputs in each of the training patterns in the struct fann_train_data. See also<fann_num_train_data>, fann_num_output_train_data This function appears in FANN >= 2.0.0. fann_num_output_train_data
Returns the number of outputs in each of the training patterns in the struct fann_train_data. See also<fann_num_train_data>, fann_num_input_train_data This function appears in FANN >= 2.0.0. fann_save_train
Save the training structure to a file, with the format as specified in fann_read_train_from_file ReturnThe function returns 0 on success and -1 on failure. See alsofann_read_train_from_file, fann_save_train_to_fixed This function appears in FANN >= 1.0.0. fann_save_train_to_fixed
Saves the training structure to a fixed point data file. This function is very usefull for testing the quality of a fixed point network. ReturnThe function returns 0 on success and -1 on failure. See alsoThis function appears in FANN >= 1.0.0. fann_get_training_algorithm
Return the training algorithm as described by fann_train_enum. This training algorithm is used by fann_train_on_data and associated functions. Note that this algorithm is also used during fann_cascadetrain_on_data, although only FANN_TRAIN_RPROP and FANN_TRAIN_QUICKPROP is allowed during cascade training. The default training algorithm is FANN_TRAIN_RPROP. See alsofann_set_training_algorithm, fann_train_enum This function appears in FANN >= 1.0.0. fann_set_training_algorithm
Set the training algorithm. More info available in fann_get_training_algorithm This function appears in FANN >= 1.0.0. fann_get_learning_rate
Return the learning rate. The learning rate is used to determine how aggressive training should be for some of the training algorithms (FANN_TRAIN_INCREMENTAL, FANN_TRAIN_BATCH, FANN_TRAIN_QUICKPROP). Do however note that it is not used in FANN_TRAIN_RPROP. The default learning rate is 0.7. See alsofann_set_learning_rate, fann_set_training_algorithm This function appears in FANN >= 1.0.0. fann_set_learning_rate
Set the learning rate. More info available in fann_get_learning_rate This function appears in FANN >= 1.0.0. fann_get_learning_momentum
Get the learning momentum. The learning momentum can be used to speed up FANN_TRAIN_INCREMENTAL training. A too high momentum will however not benefit training. Setting momentum to 0 will be the same as not using the momentum parameter. The recommended value of this parameter is between 0.0 and 1.0. The default momentum is 0. See alsofann_set_learning_momentum, fann_set_training_algorithm This function appears in FANN >= 2.0.0. fann_set_learning_momentum
Set the learning momentum. More info available in fann_get_learning_momentum This function appears in FANN >= 2.0.0. fann_get_activation_function
Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0. It is not possible to get activation functions for the neurons in the input layer. Information about the individual activation functions is available at fann_activationfunc_enum. ReturnsThe activation function for the neuron or -1 if the neuron is not defined in the neural network. See alsofann_set_activation_function_layer, fann_set_activation_function_hidden, fann_set_activation_function_output, fann_set_activation_steepness, fann_set_activation_function This function appears in FANN >= 2.1.0 fann_set_activation_function
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0. It is not possible to set activation functions for the neurons in the input layer. When choosing an activation function it is important to note that the activation functions have different range. FANN_SIGMOID is e.g. in the 0 - 1 range while FANN_SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN_LINEAR is unbound. Information about the individual activation functions is available at fann_activationfunc_enum. The default activation function is FANN_SIGMOID_STEPWISE. See alsofann_set_activation_function_layer, fann_set_activation_function_hidden, fann_set_activation_function_output, fann_set_activation_steepness, fann_get_activation_function This function appears in FANN >= 2.0.0. fann_set_activation_function_layer
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0. It is not possible to set activation functions for the neurons in the input layer. See alsofann_set_activation_function, fann_set_activation_function_hidden, fann_set_activation_function_output, fann_set_activation_steepness_layer This function appears in FANN >= 2.0.0. fann_set_activation_function_hidden
Set the activation function for all of the hidden layers. See alsofann_set_activation_function, fann_set_activation_function_layer, fann_set_activation_function_output, fann_set_activation_steepness_hidden This function appears in FANN >= 1.0.0. fann_set_activation_function_output
Set the activation function for the output layer. See alsofann_set_activation_function, fann_set_activation_function_layer, fann_set_activation_function_hidden, fann_set_activation_steepness_output This function appears in FANN >= 1.0.0. fann_get_activation_steepness
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0. It is not possible to get activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. ReturnsThe activation steepness for the neuron or -1 if the neuron is not defined in the neural network. See alsofann_set_activation_steepness_layer, fann_set_activation_steepness_hidden, fann_set_activation_steepness_output, fann_set_activation_function, fann_set_activation_steepness This function appears in FANN >= 2.1.0 fann_set_activation_steepness
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0. It is not possible to set activation steepness for the neurons in the input layer. The steepness of an activation function says something about how fast the activation function goes from the minimum to the maximum. A high value for the activation function will also give a more agressive training. When training neural networks where the output values should be at the extremes (usually 0 and 1, depending on the activation function), a steep activation function can be used (e.g. 1.0). The default activation steepness is 0.5. See alsofann_set_activation_steepness_layer, fann_set_activation_steepness_hidden, fann_set_activation_steepness_output, fann_set_activation_function, fann_get_activation_steepness This function appears in FANN >= 2.0.0. fann_set_activation_steepness_layer
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0. It is not possible to set activation steepness for the neurons in the input layer. See alsofann_set_activation_steepness, fann_set_activation_steepness_hidden, fann_set_activation_steepness_output, fann_set_activation_function_layer This function appears in FANN >= 2.0.0. fann_set_activation_steepness_hidden
Set the steepness of the activation steepness in all of the hidden layers. See alsofann_set_activation_steepness, fann_set_activation_steepness_layer, fann_set_activation_steepness_output, fann_set_activation_function_hidden This function appears in FANN >= 1.2.0. fann_set_activation_steepness_output
Set the steepness of the activation steepness in the output layer. See alsofann_set_activation_steepness, fann_set_activation_steepness_layer, fann_set_activation_steepness_hidden, fann_set_activation_function_output This function appears in FANN >= 1.2.0. fann_get_train_error_function
Returns the error function used during training. The error functions is described further in fann_errorfunc_enum The default error function is FANN_ERRORFUNC_TANH See alsoThis function appears in FANN >= 1.2.0. fann_set_train_error_function
Set the error function used during training. The error functions is described further in fann_errorfunc_enum See alsoThis function appears in FANN >= 1.2.0. fann_get_train_stop_function
Returns the the stop function used during training. The stop function is described further in fann_stopfunc_enum The default stop function is FANN_STOPFUNC_MSE See alsofann_get_train_stop_function, fann_get_bit_fail_limit This function appears in FANN >= 2.0.0. fann_set_train_stop_function
Set the stop function used during training. Returns the the stop function used during training. The stop function is described further in fann_stopfunc_enum See alsoThis function appears in FANN >= 2.0.0. fann_get_bit_fail_limit
Returns the bit fail limit used during training. The bit fail limit is used during training where the fann_stopfunc_enum is set to FANN_STOPFUNC_BIT. The limit is the maximum accepted difference between the desired output and the actual output during training. Each output that diverges more than this limit is counted as an error bit. This difference is divided by two when dealing with symmetric activation functions, so that symmetric and not symmetric activation functions can use the same limit. The default bit fail limit is 0.35. See alsoThis function appears in FANN >= 2.0.0. fann_set_callback
Sets the callback function for use during training. See fann_callback_type for more information about the callback function. The default callback function simply prints out some status information. This function appears in FANN >= 2.0.0. fann_get_quickprop_decay
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training. This is used to make sure that the weights do not become too high during training. The default decay is -0.0001. See alsoThis function appears in FANN >= 1.2.0. fann_get_quickprop_mu
The mu factor is used to increase and decrease the step-size during quickprop training. The mu factor should always be above 1, since it would otherwise decrease the step-size when it was suppose to increase it. The default mu factor is 1.75. See alsoThis function appears in FANN >= 1.2.0. fann_get_rprop_increase_factor
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training. The default increase factor is 1.2. See alsofann_set_rprop_increase_factor This function appears in FANN >= 1.2.0. fann_set_rprop_increase_factor
The increase factor used during RPROP training. See alsofann_get_rprop_increase_factor This function appears in FANN >= 1.2.0. fann_get_rprop_decrease_factor
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. The default decrease factor is 0.5. See alsofann_set_rprop_decrease_factor This function appears in FANN >= 1.2.0. fann_set_rprop_decrease_factor
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. See alsofann_get_rprop_decrease_factor This function appears in FANN >= 1.2.0. fann_get_rprop_delta_max
The maximum step-size is a positive number determining how large the maximum step-size may be. The default delta max is 50.0. See alsofann_set_rprop_delta_max, fann_get_rprop_delta_min This function appears in FANN >= 1.2.0. fann_set_rprop_delta_max
The maximum step-size is a positive number determining how large the maximum step-size may be. See alsofann_get_rprop_delta_max, fann_get_rprop_delta_min This function appears in FANN >= 1.2.0. |
Train one iteration with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL void FANN_API fann_train( struct fann * ann, fann_type * input, fann_type * desired_output )
Test with a set of inputs, and a set of desired outputs.
FANN_EXTERNAL fann_type * FANN_API fann_test( struct fann * ann, fann_type * input, fann_type * desired_output )
Reads the mean square error from the network.
FANN_EXTERNAL float FANN_API fann_get_MSE( struct fann * ann )
The number of fail bits; means the number of output neurons which differ more than the bit fail limit (see fann_get_bit_fail_limit, fann_set_bit_fail_limit).
FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail( struct fann * ann )
Returns the bit fail limit used during training.
FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit( struct fann * ann )
Set the bit fail limit used during training.
FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit( struct fann * ann, fann_type bit_fail_limit )
Resets the mean square error from the network.
FANN_EXTERNAL void FANN_API fann_reset_MSE( struct fann * ann )
Trains on an entire dataset, for a period of time.
FANN_EXTERNAL void FANN_API fann_train_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Does the same as fann_train_on_data, but reads the training data directly from a file.
FANN_EXTERNAL void FANN_API fann_train_on_file( struct fann * ann, const char * filename, unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error )
Train one epoch with a set of training data.
FANN_EXTERNAL float FANN_API fann_train_epoch( struct fann * ann, struct fann_train_data * data )
Test a set of training data and calculates the MSE for the training data.
FANN_EXTERNAL float FANN_API fann_test_data( struct fann * ann, struct fann_train_data * data )
Reads a file that stores training data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file( const char * filename )
Creates the training data struct from a user supplied function.
FANN_EXTERNAL struct fann_train_data * FANN_API fann_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 * ) )
Destructs the training data and properly deallocates all of the associated data.
FANN_EXTERNAL void FANN_API fann_destroy_train( struct fann_train_data * train_data )
Shuffles training data, randomizing the order.
FANN_EXTERNAL void FANN_API fann_shuffle_train_data( struct fann_train_data * train_data )
Scale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_train( struct fann * ann, struct fann_train_data * data )
Descale input and output data based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_train( struct fann * ann, struct fann_train_data * data )
Calculate input scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_input_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_input_min, float new_input_max )
Calculate output scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_output_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_output_min, float new_output_max )
Calculate input and output scaling parameters for future use based on training data.
FANN_EXTERNAL int FANN_API fann_set_scaling_params( struct fann * ann, const struct fann_train_data * data, float new_input_min, float new_input_max, float new_output_min, float new_output_max )
Clears scaling parameters.
FANN_EXTERNAL int FANN_API fann_clear_scaling_params( struct fann * ann )
Scale data in input vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_input( struct fann * ann, fann_type * input_vector )
Scale data in output vector before feed it to ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_scale_output( struct fann * ann, fann_type * output_vector )
Scale data in input vector after get it from ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_input( struct fann * ann, fann_type * input_vector )
Scale data in output vector after get it from ann based on previously calculated parameters.
FANN_EXTERNAL void FANN_API fann_descale_output( struct fann * ann, fann_type * output_vector )
Scales the inputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_input_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Scales the outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_output_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Scales the inputs and outputs in the training data to the specified range.
FANN_EXTERNAL void FANN_API fann_scale_train_data( struct fann_train_data * train_data, fann_type new_min, fann_type new_max )
Merges the data from data1 and data2 into a new struct fann_train_data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_merge_train_data( struct fann_train_data * data1, struct fann_train_data * data2 )
Returns an exact copy of a struct fann_train_data.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_duplicate_train_data( struct fann_train_data * data )
Returns an copy of a subset of the struct fann_train_data, starting at position pos and length elements forward.
FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data( struct fann_train_data * data, unsigned int pos, unsigned int length )
Returns the number of training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_length_train_data( struct fann_train_data * data )
Returns the number of inputs in each of the training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data( struct fann_train_data * data )
Returns the number of outputs in each of the training patterns in the struct fann_train_data.
FANN_EXTERNAL unsigned int FANN_API fann_num_output_train_data( struct fann_train_data * data )
Save the training structure to a file, with the format as specified in fann_read_train_from_file
FANN_EXTERNAL int FANN_API fann_save_train( struct fann_train_data * data, const char * filename )
Saves the training structure to a fixed point data file.
FANN_EXTERNAL int FANN_API fann_save_train_to_fixed( struct fann_train_data * data, const char * filename, unsigned int decimal_point )
Return the training algorithm as described by fann_train_enum.
FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm( struct fann * ann )
Set the training algorithm.
FANN_EXTERNAL void FANN_API fann_set_training_algorithm( struct fann * ann, enum fann_train_enum training_algorithm )
Return the learning rate.
FANN_EXTERNAL float FANN_API fann_get_learning_rate( struct fann * ann )
Set the learning rate.
FANN_EXTERNAL void FANN_API fann_set_learning_rate( struct fann * ann, float learning_rate )
Get the learning momentum.
FANN_EXTERNAL float FANN_API fann_get_learning_momentum( struct fann * ann )
Set the learning momentum.
FANN_EXTERNAL void FANN_API fann_set_learning_momentum( struct fann * ann, float learning_momentum )
Get the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function( struct fann * ann, int layer, int neuron )
Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer, int neuron )
Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_function_layer( struct fann * ann, enum fann_activationfunc_enum activation_function, int layer )
Set the activation function for all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden( struct fann * ann, enum fann_activationfunc_enum activation_function )
Set the activation function for the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_function_output( struct fann * ann, enum fann_activationfunc_enum activation_function )
Get the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness( struct fann * ann, int layer, int neuron )
Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness( struct fann * ann, fann_type steepness, int layer, int neuron )
Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer( struct fann * ann, fann_type steepness, int layer )
Set the steepness of the activation steepness in all of the hidden layers.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden( struct fann * ann, fann_type steepness )
Set the steepness of the activation steepness in the output layer.
FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output( struct fann * ann, fann_type steepness )
Returns the error function used during training.
FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function( struct fann * ann )
Set the error function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_error_function( struct fann * ann, enum fann_errorfunc_enum train_error_function )
Returns the the stop function used during training.
FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function( struct fann * ann )
Set the stop function used during training.
FANN_EXTERNAL void FANN_API fann_set_train_stop_function( struct fann * ann, enum fann_stopfunc_enum train_stop_function )
Sets the callback function for use during training.
FANN_EXTERNAL void FANN_API fann_set_callback( struct fann * ann, fann_callback_type callback )
The decay is a small negative valued number which is the factor that the weights should become smaller in each iteration during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_decay( struct fann * ann )
Sets the quickprop decay factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_decay( struct fann * ann, float quickprop_decay )
The mu factor is used to increase and decrease the step-size during quickprop training.
FANN_EXTERNAL float FANN_API fann_get_quickprop_mu( struct fann * ann )
Sets the quickprop mu factor.
FANN_EXTERNAL void FANN_API fann_set_quickprop_mu( struct fann * ann, float quickprop_mu )
The increase factor is a value larger than 1, which is used to increase the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor( struct fann * ann )
The increase factor used during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor( struct fann * ann, float rprop_increase_factor )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL float FANN_API fann_get_rprop_decrease_factor( struct fann * ann )
The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training.
FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor( struct fann * ann, float rprop_decrease_factor )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min( struct fann * ann )
The minimum step-size is a small positive number determining how small the minimum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min( struct fann * ann, float rprop_delta_min )
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max( struct fann * ann )
The maximum step-size is a positive number determining how large the maximum step-size may be.
FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max( struct fann * ann, float rprop_delta_max )
Get the number of input neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_input( struct fann * ann )
Get the number of output neurons.
FANN_EXTERNAL unsigned int FANN_API fann_get_num_output( struct fann * ann )
Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data( struct fann * ann, struct fann_train_data * data, unsigned int max_neurons, unsigned int neurons_between_reports, float desired_error )