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fann_cascadetrain_on_data »
« XOR training
PHP Kılavuzu
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FANN
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Fann İşlevleri
İçindekiler
fann_cascadetrain_on_data
— Trains on an entire dataset, for a period of time using the Cascade2 training algorithm
fann_cascadetrain_on_file
— Trains on an entire dataset read from file, for a period of time using the Cascade2 training algorithm
fann_clear_scaling_params
— Clears scaling parameters
fann_copy
— Creates a copy of a fann structure
fann_create_from_file
— Constructs a backpropagation neural network from a configuration file
fann_create_shortcut
— Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
fann_create_shortcut_array
— Creates a standard backpropagation neural network which is not fully connectected and has shortcut connections
fann_create_sparse
— Creates a standard backpropagation neural network, which is not fully connected
fann_create_sparse_array
— Creates a standard backpropagation neural network, which is not fully connected using an array of layer sizes
fann_create_standard
— Creates a standard fully connected backpropagation neural network
fann_create_standard_array
— Creates a standard fully connected backpropagation neural network using an array of layer sizes
fann_create_train
— Creates an empty training data struct
fann_create_train_from_callback
— Creates the training data struct from a user supplied function
fann_descale_input
— Scale data in input vector after get it from ann based on previously calculated parameters
fann_descale_output
— Scale data in output vector after get it from ann based on previously calculated parameters
fann_descale_train
— Descale input and output data based on previously calculated parameters
fann_destroy
— Destroys the entire network and properly freeing all the associated memory
fann_destroy_train
— Destructs the training data
fann_duplicate_train_data
— Returns an exact copy of a fann train data
fann_get_activation_function
— Returns the activation function
fann_get_activation_steepness
— Returns the activation steepness for supplied neuron and layer number
fann_get_bias_array
— Get the number of bias in each layer in the network
fann_get_bit_fail
— The number of fail bits
fann_get_bit_fail_limit
— Returns the bit fail limit used during training
fann_get_cascade_activation_functions
— Returns the cascade activation functions
fann_get_cascade_activation_functions_count
— Returns the number of cascade activation functions
fann_get_cascade_activation_steepnesses
— Returns the cascade activation steepnesses
fann_get_cascade_activation_steepnesses_count
— The number of activation steepnesses
fann_get_cascade_candidate_change_fraction
— Returns the cascade candidate change fraction
fann_get_cascade_candidate_limit
— Return the candidate limit
fann_get_cascade_candidate_stagnation_epochs
— Returns the number of cascade candidate stagnation epochs
fann_get_cascade_max_cand_epochs
— Returns the maximum candidate epochs
fann_get_cascade_max_out_epochs
— Returns the maximum out epochs
fann_get_cascade_min_cand_epochs
— Returns the minimum candidate epochs
fann_get_cascade_min_out_epochs
— Returns the minimum out epochs
fann_get_cascade_num_candidate_groups
— Returns the number of candidate groups
fann_get_cascade_num_candidates
— Returns the number of candidates used during training
fann_get_cascade_output_change_fraction
— Returns the cascade output change fraction
fann_get_cascade_output_stagnation_epochs
— Returns the number of cascade output stagnation epochs
fann_get_cascade_weight_multiplier
— Returns the weight multiplier
fann_get_connection_array
— Get connections in the network
fann_get_connection_rate
— Get the connection rate used when the network was created
fann_get_errno
— Returns the last error number
fann_get_errstr
— Returns the last errstr
fann_get_layer_array
— Get the number of neurons in each layer in the network
fann_get_learning_momentum
— Returns the learning momentum
fann_get_learning_rate
— Returns the learning rate
fann_get_MSE
— Reads the mean square error from the network
fann_get_network_type
— Get the type of neural network it was created as
fann_get_num_input
— Get the number of input neurons
fann_get_num_layers
— Get the number of layers in the neural network
fann_get_num_output
— Get the number of output neurons
fann_get_quickprop_decay
— Returns the decay which is a factor that weights should decrease in each iteration during quickprop training
fann_get_quickprop_mu
— Returns the mu factor
fann_get_rprop_decrease_factor
— Returns the increase factor used during RPROP training
fann_get_rprop_delta_max
— Returns the maximum step-size
fann_get_rprop_delta_min
— Returns the minimum step-size
fann_get_rprop_delta_zero
— Returns the initial step-size
fann_get_rprop_increase_factor
— Returns the increase factor used during RPROP training
fann_get_sarprop_step_error_shift
— Returns the sarprop step error shift
fann_get_sarprop_step_error_threshold_factor
— Returns the sarprop step error threshold factor
fann_get_sarprop_temperature
— Returns the sarprop temperature
fann_get_sarprop_weight_decay_shift
— Returns the sarprop weight decay shift
fann_get_total_connections
— Get the total number of connections in the entire network
fann_get_total_neurons
— Get the total number of neurons in the entire network
fann_get_train_error_function
— Returns the error function used during training
fann_get_train_stop_function
— Returns the stop function used during training
fann_get_training_algorithm
— Returns the training algorithm
fann_init_weights
— Initialize the weights using Widrow + Nguyen’s algorithm
fann_length_train_data
— Returns the number of training patterns in the train data
fann_merge_train_data
— Merges the train data
fann_num_input_train_data
— Returns the number of inputs in each of the training patterns in the train data
fann_num_output_train_data
— Returns the number of outputs in each of the training patterns in the train data
fann_print_error
— Prints the error string
fann_randomize_weights
— Give each connection a random weight between min_weight and max_weight
fann_read_train_from_file
— Reads a file that stores training data
fann_reset_errno
— Resets the last error number
fann_reset_errstr
— Resets the last error string
fann_reset_MSE
— Resets the mean square error from the network
fann_run
— Will run input through the neural network
fann_save
— Saves the entire network to a configuration file
fann_save_train
— Save the training structure to a file
fann_scale_input
— Scale data in input vector before feed it to ann based on previously calculated parameters
fann_scale_input_train_data
— Scales the inputs in the training data to the specified range
fann_scale_output
— Scale data in output vector before feed it to ann based on previously calculated parameters
fann_scale_output_train_data
— Scales the outputs in the training data to the specified range
fann_scale_train
— Scale input and output data based on previously calculated parameters
fann_scale_train_data
— Scales the inputs and outputs in the training data to the specified range
fann_set_activation_function
— Sets the activation function for supplied neuron and layer
fann_set_activation_function_hidden
— Sets the activation function for all of the hidden layers
fann_set_activation_function_layer
— Sets the activation function for all the neurons in the supplied layer
fann_set_activation_function_output
— Sets the activation function for the output layer
fann_set_activation_steepness
— Sets the activation steepness for supplied neuron and layer number
fann_set_activation_steepness_hidden
— Sets the steepness of the activation steepness for all neurons in the all hidden layers
fann_set_activation_steepness_layer
— Sets the activation steepness for all of the neurons in the supplied layer number
fann_set_activation_steepness_output
— Sets the steepness of the activation steepness in the output layer
fann_set_bit_fail_limit
— Set the bit fail limit used during training
fann_set_callback
— Sets the callback function for use during training
fann_set_cascade_activation_functions
— Sets the array of cascade candidate activation functions
fann_set_cascade_activation_steepnesses
— Sets the array of cascade candidate activation steepnesses
fann_set_cascade_candidate_change_fraction
— Sets the cascade candidate change fraction
fann_set_cascade_candidate_limit
— Sets the candidate limit
fann_set_cascade_candidate_stagnation_epochs
— Sets the number of cascade candidate stagnation epochs
fann_set_cascade_max_cand_epochs
— Sets the max candidate epochs
fann_set_cascade_max_out_epochs
— Sets the maximum out epochs
fann_set_cascade_min_cand_epochs
— Sets the min candidate epochs
fann_set_cascade_min_out_epochs
— Sets the minimum out epochs
fann_set_cascade_num_candidate_groups
— Sets the number of candidate groups
fann_set_cascade_output_change_fraction
— Sets the cascade output change fraction
fann_set_cascade_output_stagnation_epochs
— Sets the number of cascade output stagnation epochs
fann_set_cascade_weight_multiplier
— Sets the weight multiplier
fann_set_error_log
— Sets where the errors are logged to
fann_set_input_scaling_params
— Calculate input scaling parameters for future use based on training data
fann_set_learning_momentum
— Sets the learning momentum
fann_set_learning_rate
— Sets the learning rate
fann_set_output_scaling_params
— Calculate output scaling parameters for future use based on training data
fann_set_quickprop_decay
— Sets the quickprop decay factor
fann_set_quickprop_mu
— Sets the quickprop mu factor
fann_set_rprop_decrease_factor
— Sets the decrease factor used during RPROP training
fann_set_rprop_delta_max
— Sets the maximum step-size
fann_set_rprop_delta_min
— Sets the minimum step-size
fann_set_rprop_delta_zero
— Sets the initial step-size
fann_set_rprop_increase_factor
— Sets the increase factor used during RPROP training
fann_set_sarprop_step_error_shift
— Sets the sarprop step error shift
fann_set_sarprop_step_error_threshold_factor
— Sets the sarprop step error threshold factor
fann_set_sarprop_temperature
— Sets the sarprop temperature
fann_set_sarprop_weight_decay_shift
— Sets the sarprop weight decay shift
fann_set_scaling_params
— Calculate input and output scaling parameters for future use based on training data
fann_set_train_error_function
— Sets the error function used during training
fann_set_train_stop_function
— Sets the stop function used during training
fann_set_training_algorithm
— Sets the training algorithm
fann_set_weight
— Set a connection in the network
fann_set_weight_array
— Set connections in the network
fann_shuffle_train_data
— Shuffles training data, randomizing the order
fann_subset_train_data
— Returns an copy of a subset of the train data
fann_test
— Test with a set of inputs, and a set of desired outputs
fann_test_data
— Test a set of training data and calculates the MSE for the training data
fann_train
— Train one iteration with a set of inputs, and a set of desired outputs
fann_train_epoch
— Train one epoch with a set of training data
fann_train_on_data
— Trains on an entire dataset for a period of time
fann_train_on_file
— Trains on an entire dataset, which is read from file, for a period of time
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