cascade_train.c

python 神经网络 数据挖掘 python实现的神经网络算法

/*
  Fast Artificial Neural Network Library (fann)
  Copyright (C) 2003 Steffen Nissen ([e-mail])
  
  This library is free software; you can redistribute it and/or
  modify it under the terms of the GNU Lesser General Public
  License as published by the Free Software Foundation; either
  version 2.1 of the License, or (at your option) any later version.
  
  This library is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
  Lesser General Public License for more details.
  
  You should have received a copy of the GNU Lesser General Public
  License along with this library; if not, write to the Free Software
  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
*/

#include <stdio.h>

#include "fann.h"


int main()
{
	struct fann *ann;
	struct fann_train_data *train_data, *test_data;
	const float desired_error = (const float) 0.001;
	unsigned int max_neurons = 10;
	unsigned int neurons_between_reports = 1;
	fann_type *steepnesses = NULL;
	
	printf("Reading data.\n");
	
	train_data = fann_read_train_from_file("../benchmarks/datasets/parity8.train");
	test_data = fann_read_train_from_file("../benchmarks/datasets/parity8.test");
	
	fann_scale_train_data(train_data, -1, 1);
	fann_scale_train_data(test_data, -1, 1);
	
	printf("Creating network.\n");
	
	ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data));
	
	fann_set_learning_rate(ann, 0.1);
	fann_set_quickprop_decay(ann, 0.0);
	fann_set_quickprop_mu(ann, 2.0);
	fann_set_cascade_weight_multiplier(ann, 1);
	fann_set_cascade_max_out_epochs(ann, 150);
	fann_set_bit_fail_limit(ann, 0.35);
	fann_set_activation_steepness_output(ann, 1);

	fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP);

	fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);

	fann_set_activation_function_output(ann, FANN_LINEAR_PIECE);
	fann_set_activation_function_output(ann, FANN_LINEAR_PIECE_SYMMETRIC);
	fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
	fann_set_activation_function_output(ann, FANN_LINEAR);

	fann_set_train_error_function(ann, FANN_ERRORFUNC_TANH);
	fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);


	fann_randomize_weights(ann, 0.1, 0.1);
	
/*
	fann_set_cascade_weight_multiplier(ann, 0.4);
 	fann_set_cascade_candidate_limit(ann, 1000.0);
	*/
	fann_set_cascade_output_change_fraction(ann, 0.01);
	fann_set_cascade_candidate_change_fraction(ann, 0.01);
	
	/*
	steepnesses = (fann_type *)calloc(1,  sizeof(fann_type));
	steepnesses[0] = (fann_type)1;
	fann_set_cascade_activation_steepnesses(ann, steepnesses, 1);
	*/	
	
	fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
	fann_print_parameters(ann);
	
	printf("Training network.\n");

	fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error);
	
	fann_print_connections(ann);
	
	printf("\nTrain error: %f, Test error: %f\n\n", fann_test_data(ann, train_data),
		fann_test_data(ann, test_data));
	
	printf("Saving network.\n");
	
	fann_save(ann, "two_spiral.net");
	
	printf("Cleaning up.\n");
	fann_destroy_train(train_data);
	fann_destroy_train(test_data);
	fann_destroy(ann);
	
	return 0;
}