# 神經網路:計算簡單的加法

`//// fann_test.c :// FANN 測試//// 到下面的網站下載FANN庫:// Fast Artificial Neural Network Library (fann)// http://leenissen.dk/fann/// 下載連結: Download FANN ---> C Source Code and Windows DLL files//#include "../fann-2.0.0/src/include/doublefann.h"#ifdef _DEBUG#pragma comment(lib, "../fann-2.0.0/MicrosoftWindowsDll/bin/fanndoubleMTd.lib")#else#pragma comment(lib, "../fann-2.0.0/MicrosoftWindowsDll/bin/fanndoubleMT.lib")#endif// 訓練:// 加法神經網路// c = a+b;void train(){const unsigned int num_input = 2; // 輸入項個數const unsigned int num_output = 1; // 輸出項個數const unsigned int num_layers = 3;const unsigned int num_neurons_hidden = 3;const float desired_error = (const float) 0.00000001;const unsigned int max_epochs = 500000; // 最多執行次數const unsigned int epochs_between_reports = 10000; // 報告頻率struct fann *ann;int Num = 200;float Mf = Num*3.f;int i;double a, b, c;FILE *fp;fopen_s(&fp, "add.fann", "w");fprintf_s(fp, "%d 2 1\n", Num);// 產生訓練檔案for(i=1; i<=Num; i++){// 產生2個數, 要求在(0,1)之間a = i/Mf;b = (i+1)/Mf;c = a+b; // 要求在(0,1)之間// 輸入內容寫到訓練檔案fprintf_s(fp, "%lf %lf\n%lf\n", a, b, c);}fclose(fp);// 樣本訓練ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);fann_set_activation_function_hidden(ann, FANN_LINEAR );fann_set_activation_function_output(ann, FANN_LINEAR );fann_train_on_file(ann, "add.fann", max_epochs, epochs_between_reports, desired_error);fann_save(ann, "add.fann.net");fann_destroy(ann);}// 執行:// 測試void exec(double a, double b){struct fann *ann;fann_type *calc_out;fann_type input[2];ann = fann_create_from_file("add.fann.net");input[0] = a;input[1] = b;calc_out = fann_run(ann, input);fann_destroy(ann);printf("a=%f\nb=%f\nc=%f\n期望值c=%f\n\n", input[0], input[1], calc_out[0], input[0]+input[1]);}//// 主程式//int main(){// 下面的方法只需調用一次, 然後注釋掉// train();exec(0.354,0.58934);exec(0.21469,0.3914968);exec(0.130,0.44);exec(-0.3654,0.58455);exec(0.365420,-0.95);return 0;}`

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