steepness_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 "fann.h"
#include <stdio.h>

void train_on_steepness_file(struct fann *ann, char *filename,
							 unsigned int max_epochs, unsigned int epochs_between_reports,
							 float desired_error, float steepness_start,
							 float steepness_step, float steepness_end)
{
	float error;
	unsigned int i;

	struct fann_train_data *data = fann_read_train_from_file(filename);

	if(epochs_between_reports)
	{
		printf("Max epochs %8d. Desired error: %.10f\n", max_epochs, desired_error);
	}

	fann_set_activation_steepness_hidden(ann, steepness_start);
	fann_set_activation_steepness_output(ann, steepness_start);
	for(i = 1; i <= max_epochs; i++)
	{
		/* train */
		error = fann_train_epoch(ann, data);

		/* print current output */
		if(epochs_between_reports &&
		   (i % epochs_between_reports == 0 || i == max_epochs || i == 1 || error < desired_error))
		{
			printf("Epochs     %8d. Current error: %.10f\n", i, error);
		}

		if(error < desired_error)
		{
			steepness_start += steepness_step;
			if(steepness_start <= steepness_end)
			{
				printf("Steepness: %f\n", steepness_start);
				fann_set_activation_steepness_hidden(ann, steepness_start);
				fann_set_activation_steepness_output(ann, steepness_start);
			}
			else
			{
				break;
			}
		}
	}
	fann_destroy_train(data);
}

int main()
{
	const unsigned int num_input = 2;
	const unsigned int num_output = 1;
	const unsigned int num_layers = 3;
	const unsigned int num_neurons_hidden = 3;
	const float desired_error = (const float) 0.001;
	const unsigned int max_epochs = 500000;
	const unsigned int epochs_between_reports = 1000;
	unsigned int i;
	fann_type *calc_out;

	struct fann_train_data *data;

	struct fann *ann = fann_create_standard(num_layers,
								   num_input, num_neurons_hidden, num_output);

	data = fann_read_train_from_file("xor.data");

	fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
	fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);

	fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP);

	train_on_steepness_file(ann, "xor.data", max_epochs,
							epochs_between_reports, desired_error, (float) 1.0, (float) 0.1,
							(float) 20.0);

	fann_set_activation_function_hidden(ann, FANN_THRESHOLD_SYMMETRIC);
	fann_set_activation_function_output(ann, FANN_THRESHOLD_SYMMETRIC);

	for(i = 0; i != fann_length_train_data(data); i++)
	{
		calc_out = fann_run(ann, data->input[i]);
		printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n",
			   data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
			   (float) fann_abs(calc_out[0] - data->output[i][0]));
	}


	fann_save(ann, "xor_float.net");

	fann_destroy(ann);
	fann_destroy_train(data);

	return 0;
}