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https://github.com/lxsang/antd-lua-plugin
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117 lines
3.4 KiB
C
117 lines
3.4 KiB
C
/*
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Fast Artificial Neural Network Library (fann)
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Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*/
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#include "fann.h"
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#include <stdio.h>
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void train_on_steepness_file(struct fann *ann, char *filename,
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unsigned int max_epochs, unsigned int epochs_between_reports,
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float desired_error, float steepness_start,
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float steepness_step, float steepness_end)
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{
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float error;
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unsigned int i;
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struct fann_train_data *data = fann_read_train_from_file(filename);
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if(epochs_between_reports)
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{
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printf("Max epochs %8d. Desired error: %.10f\n", max_epochs, desired_error);
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}
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fann_set_activation_steepness_hidden(ann, steepness_start);
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fann_set_activation_steepness_output(ann, steepness_start);
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for(i = 1; i <= max_epochs; i++)
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{
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/* train */
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error = fann_train_epoch(ann, data);
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/* print current output */
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if(epochs_between_reports &&
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(i % epochs_between_reports == 0 || i == max_epochs || i == 1 || error < desired_error))
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{
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printf("Epochs %8d. Current error: %.10f\n", i, error);
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}
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if(error < desired_error)
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{
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steepness_start += steepness_step;
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if(steepness_start <= steepness_end)
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{
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printf("Steepness: %f\n", steepness_start);
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fann_set_activation_steepness_hidden(ann, steepness_start);
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fann_set_activation_steepness_output(ann, steepness_start);
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}
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else
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{
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break;
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}
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}
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}
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fann_destroy_train(data);
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}
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int main()
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{
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const unsigned int num_input = 2;
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const unsigned int num_output = 1;
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const unsigned int num_layers = 3;
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const unsigned int num_neurons_hidden = 3;
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const float desired_error = (const float) 0.001;
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const unsigned int max_epochs = 500000;
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const unsigned int epochs_between_reports = 1000;
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unsigned int i;
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fann_type *calc_out;
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struct fann_train_data *data;
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struct fann *ann = fann_create_standard(num_layers,
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num_input, num_neurons_hidden, num_output);
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data = fann_read_train_from_file("xor.data");
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fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
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fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
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fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP);
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train_on_steepness_file(ann, "xor.data", max_epochs,
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epochs_between_reports, desired_error, (float) 1.0, (float) 0.1,
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(float) 20.0);
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fann_set_activation_function_hidden(ann, FANN_THRESHOLD_SYMMETRIC);
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fann_set_activation_function_output(ann, FANN_THRESHOLD_SYMMETRIC);
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for(i = 0; i != fann_length_train_data(data); i++)
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{
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calc_out = fann_run(ann, data->input[i]);
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printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n",
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data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
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(float) fann_abs(calc_out[0] - data->output[i][0]));
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}
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fann_save(ann, "xor_float.net");
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fann_destroy(ann);
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fann_destroy_train(data);
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return 0;
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}
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