神经网络C语言实现

#i n c l u d e"" #include <>const double e = ;//设置一个神经网络//有一个隐藏层（含有两个节点）//输出层有一个节点//输入数据是二维（两个节点）//一个样本数据为：x = , 标签为//初始权值输入节点1到隐藏层：，//输入节点2到隐藏层：,//隐藏层到输出层初始权值为：,//学习速率为1double changeWeightFromHiddenToOutput(double cost,double output,double hiddenLayerCode){double result=0;result = cost*output*(1-output)*hiddenLayerCode;return result;}double changeWeightFromInputToHidden(double cost,double output,double weightOfHiddenCodeToOutput,double weightOfHiddenCode,double inputNum)double result=0;result = cost*output*(1-output)*weightOfHiddenCodeToOutput*weightOfHiddenC ode*(1-weightOfHiddenCode)*inputNum;return result;}double sigmoidFunction(double x){double result=0;result = 1/(1+pow(e,-x));return result;}double costFunction(double originalSignal,double outputOfOurCalculation){//此处采取的损失函数是最小二乘法double cost=0;cost = (1/*(originalSignal-outputOfOurCalculation)*(originalSignal-outpu tOfOurCalculation);return cost;double upDateWeightFunction(double originalValue,double gradient) {double updatedWeight=originalValue;updatedWeight = updatedWeight - fabs(gradient);return updatedWeight;}int main(void){double weightFromInputToHidden[][2]={,,,};double weightFromHiddenToOutput[]={,};double firstHiddenCode,secondHiddenCode,outputCode;double inputValue[] ={,};double originalSignal = ;double cost=0;double weightChangeNum=0;double addWeightSum = 0;firstHiddenCode = 0;secondHiddenCode = 0;outputCode = 0;//前向传播addWeightSum = weightFromInputToHidden[0][0]*inputValue[0] +weightFromInputToHidden[1][0]*inputValue[1];firstHiddenCode = sigmoidFunction(addWeightSum);addWeightSum = weightFromInputToHidden[0][1]*inputValue[0] + weightFromInputToHidden[1][1]*inputValue[1];secondHiddenCode = sigmoidFunction(addWeightSum);addWeightSum = weightFromHiddenToOutput[0]*firstHiddenCode + weightFromHiddenToOutput[1]*secondHiddenCode;outputCode = sigmoidFunction(addWeightSum);//计算误差cost = costFunction(originalSignal,outputCode);printf("\n\n(0)firNode:[%f] secNode:[%f] outNode:[%f] cost:%f",firstHiddenCode,secondHiddenCode,outputCode,cost);printf("\n\n\t\t输入到隐藏层的权值:\t\t");for(int i=0;i<2;i++){printf("\n\t\t");for(int j=0;j<2;j++)printf(" %lf ",weightFromInputToHidden[i][j]);}printf("\n\n\t\t隐藏层到输出的权值:\n\t\t");for(i=0;i<2;i++){printf(" {%lf} ",weightFromHiddenToOutput[i]);}for(int iteration = 0;iteration<1;iteration++){//更新隐藏层到输出层权值//weightChangeNum为相应权值的梯度weightChangeNum = changeWeightFromHiddenToOutput(cost,outputCode,firstHiddenCode);weightFromHiddenToOutput[0] = upDateWeightFunction(weightFromHiddenToOutput[0],weightChangeNum) ;weightChangeNum = changeWeightFromHiddenToOutput(cost,outputCode,secondHiddenCode);weightFromHiddenToOutput[1] = upDateWeightFunction(weightFromHiddenToOutput[1],weightChangeNum) ;//更新输入层到隐藏层的权值weightChangeNum = changeWeightFromInputToHidden(cost,outputCode,weightFromHiddenToO utput[0],firstHiddenCode,inputValue[0]);weightFromInputToHidden[0][0] = upDateWeightFunction(weightFromInputToHidden[0][0],weightChangeNum);weightChangeNum = changeWeightFromInputToHidden(cost,outputCode,weightFromHiddenToO utput[1],secondHiddenCode,inputValue[0]);weightFromInputToHidden[0][1] = upDateWeightFunction(weightFromInputToHidden[0][1],weightChangeNu m);weightChangeNum = changeWeightFromInputToHidden(cost,outputCode,weightFromHiddenToO utput[0],firstHiddenCode,inputValue[1]);weightFromInputToHidden[1][0] = upDateWeightFunction(weightFromInputToHidden[1][0],weightChangeNu m);weightChangeNum = changeWeightFromInputToHidden(cost,outputCode,weightFromHiddenToO utput[1],secondHiddenCode,inputValue[1]);weightFromInputToHidden[1][1] = upDateWeightFunction(weightFromInputToHidden[1][1],weightChangeNu m);//再次进行前向传播addWeightSum = weightFromInputToHidden[0][0]*inputValue[0] + weightFromInputToHidden[1][0]*inputValue[1];firstHiddenCode = sigmoidFunction(addWeightSum);addWeightSum = weightFromInputToHidden[0][1]*inputValue[0] + weightFromInputToHidden[1][1]*inputValue[1];secondHiddenCode = sigmoidFunction(addWeightSum);//输出addWeightSum = weightFromHiddenToOutput[0]*firstHiddenCode + weightFromHiddenToOutput[1]*secondHiddenCode;outputCode = sigmoidFunction(addWeightSum);//计算误差cost = costFunction(originalSignal,outputCode);printf("\n\n(%d)firNode:[%f] secNode:[%f] outNode:[%f] cost:%f",iteration+1,firstHiddenCode,secondHiddenCode,outputCode, cost);printf("\n\n\t\t输入到隐藏层的权值:\t\t");for(int i=0;i<2;i++){printf("\n\t\t");for(int j=0;j<2;j++)printf(" %lf ",weightFromInputToHidden[i][j]);}printf("\n\n\t\t隐藏层到输出的权值:\n\t\t");for(i=0;i<2;i++){printf(" {%lf} ",weightFromHiddenToOutput[i]);}if(cost<{printf("\n\n误差已经小于了！停止^_^\n");break;}}printf("\n\n\t\t");}。