基于C++版本的Bp神经网络，数据拟合，预测

#include "BpNet.h" using namespace std; BpNet::BpNet() { srand((unsigned)time(NULL)); // 随机数种子 error = 100.f; // error初始值，极大值即可 // 初始化输入层 for (int i = 0; i < innode; i++) { inputLayer[i] = new inputNode(); for (int j = 0; j < hidenode; j++) { inputLayer[i]->weight.push_back(get_11Random()); inputLayer[i]->wDeltaSum.push_back(0.f); } } // 初始化隐藏层 for (int i = 0; i < hidelayer; i++) { if (i == hidelayer - 1) { for (int j = 0; j < hidenode; j++) { hiddenLayer[i][j] = new hiddenNode(); hiddenLayer[i][j]->bias = get_11Random(); for (int k = 0; k < outnode; k++) { hiddenLayer[i][j]->weight.push_back(get_11Random()); hiddenLayer[i][j]->wDeltaSum.push_back(0.f); } } } else { for (int j = 0; j < hidenode; j++) { hiddenLayer[i][j] = new hiddenNode(); hiddenLayer[i][j]->bias = get_11Random(); for (int k = 0; k < hidenode; k++) { hiddenLayer[i][j]->weight.push_back(get_11Random()); } } } } // 初始化输出层 for (int i = 0; i < outnode; i++) { outputLayer[i] = new outputNode(); outputLayer[i]->bias = get_11Random(); } } void BpNet::forwardPropagationEpoc() { // forward propagation on hidden layer for (int i = 0; i < hidelayer; i++) { if (i == 0) { for (int j = 0; j < hidenode; j++) { double sum = 0.f; for (int k = 0; k < innode; k++) { sum += inputLayer[k]->value * inputLayer[k]->weight[j]; } sum += hiddenLayer[i][j]->bias; hiddenLayer[i][j]->value = sigmoid(sum); } } else { for (int j = 0; j < hidenode; j++) { double sum = 0.f; for (int k = 0; k < hidenode; k++) { sum += hiddenLayer[i - 1][k]->value * hiddenLayer[i - 1][k]->weight[j]; } sum += hiddenLayer[i][j]->bias; hiddenLayer[i][j]->value = sigmoid(sum); } } } // forward propagation on output layer for (int i = 0; i < outnode; i++) { double sum = 0.f; for (int j = 0; j < hidenode; j++) { sum += hiddenLayer[hidelayer - 1][j]->value * hiddenLayer[hidelayer - 1][j]->weight[i]; } sum += outputLayer[i]->bias; outputLayer[i]->value = sigmoid(sum); } } void BpNet::backPropagationEpoc() { // backward propagation on output layer // -- compute delta for (int i = 0; i < outnode; i++) { double tmpe = fabs(outputLayer[i]->value - outputLayer[i]->rightout); error += tmpe * tmpe / 2; outputLayer[i]->delta = (outputLayer[i]->value - outputLayer[i]->rightout)*(1 - outputLayer[i]->value)*outputLayer[i]->value; } // backward propagation on hidden layer // -- compute delta for (int i = hidelayer - 1; i >= 0; i--) // 反向计算 { if (i == hidelayer - 1) { for (int j = 0; j < hidenode; j++) { double sum = 0.f; for (int k = 0; k < outnode; k++) { sum += outputLayer[k]->delta * hiddenLayer[i][j]->weight[k]; } hiddenLayer[i][j]->delta = sum * (1 - hiddenLayer[i][j]->value) * hiddenLayer[i][j]->value; } } else { for (int j = 0; j < hidenode; j++) { double sum = 0.f; for (int k = 0; k < hidenode; k++) { sum += hiddenLayer[i + 1][k]->delta * hiddenLayer[i][j]->weight[k]; } hiddenLayer[i][j]->delta = sum * (1 - hiddenLayer[i][j]->value) * hiddenLayer[i][j]->value; } } } // backward propagation on input layer // -- update weight delta sum for (int i = 0; i < innode; i++) { for (int j = 0; j < hidenode; j++) { inputLayer[i]->wDeltaSum[j] += inputLayer[i]->value * hiddenLayer[0][j]->delta; } } // backward propagation on hidden layer // -- update weight delta sum & bias delta sum for (int i = 0; i < hidelayer; i++) { if (i == hidelayer - 1) { for (int j = 0; j < hidenode; j++) { hiddenLayer[i][j]->bDeltaSum += hiddenLayer[i][j]->delta; for (int k = 0; k < outnode; k++) { hiddenLayer[i][j]->wDeltaSum[k] += hiddenLayer[i][j]->value * outputLayer[k]->delta; } } } else { for (int j = 0; j < hidenode; j++) { hiddenLayer[i][j]->bDeltaSum += hiddenLayer[i][j]->delta; for (int k = 0; k < hidenode; k++) { hiddenLayer[i][j]->wDeltaSum[k] += hiddenLayer[i][j]->value * hiddenLayer[i + 1][k]->delta; } } } } // backward propagation on output layer // -- update bias delta sum for (int i = 0; i < outnode; i++) outputLayer[i]->bDeltaSum += outputLayer[i]->delta; } void BpNet::training(static vector<sample> sampleGroup, double threshold) { int sampleNum = sampleGroup.size(); while (error > threshold) //for (int curTrainingTime = 0; curTrainingTime < trainingTime; curTrainingTime++) { cout << "training error: " << error << endl; error = 0.f; // initialize delta sum for (int i = 0; i < innode; i++) inputLayer[i]->wDeltaSum.assign(inputLayer[i]->wDeltaSum.size(), 0.f); for (int i = 0; i < hidelayer; i++) { for (int j = 0; j < hidenode; j++) { hiddenLayer[i][j]->wDeltaSum.assign(hiddenLayer[i][j]->wDeltaSum.size(), 0.f); hiddenLayer[i][j]->bDeltaSum = 0.f; } } for (int i = 0; i < outnode; i++) outputLayer[i]->bDeltaSum = 0.f; for (int iter = 0; iter < sampleNum; iter++) { setInput(sampleGroup[iter].in); setOutput(sampleGroup[iter].out); forwardPropagationEpoc(); backPropagationEpoc(); } // backward propagation on input layer // -- update weight for (int i = 0; i < innode; i++) { for (int j = 0; j < hidenode; j++) { inputLayer[i]->weight[j] -= learningRate * inputLayer[i]->wDeltaSum[j] / sampleNum; } } // backward propagation on hidden layer // -- update weight & bias for (int i = 0; i < hidelayer; i++) { if (i == hidelayer - 1) { for (int j = 0; j < hidenode; j++) { // bias hiddenLayer[i][j]->bias -= learningRate * hiddenLayer[i][j]->bDeltaSum / sampleNum; // weight for (int k = 0; k < outnode; k++) { hiddenLayer[i][j]->weight[k] -= learningRate * hiddenLayer[i][j]->wDeltaSum[k] / sampleNum; } } } else { for (int j = 0; j < hidenode; j++) { // bias hiddenLayer[i][j]->bias -= learningRate * hiddenLayer[i][j]->bDeltaSum / sampleNum; // weight for (int k = 0; k < hidenode; k++) { hiddenLayer[i][j]->weight[k] -= learningRate * hiddenLayer[i][j]->wDeltaSum[k] / sampleNum; } } } } // backward propagation on output layer // -- update bias for (int i = 0; i < outnode; i++) { outputLayer[i]->bias -= learningRate * outputLayer[i]->bDeltaSum / sampleNum; } } } void BpNet::predict(vector<sample>& testGroup) { int testNum = testGroup.size(); for (int iter = 0; iter < testNum; iter++) { testGroup[iter].out.clear(); setInput(testGroup[iter].in); // forward propagation on hidden layer for (int i = 0; i < hidelayer; i++) { if (i == 0) { for (int j = 0; j < hidenode; j++) { double sum = 0.f; for (int k = 0; k < innode; k++) { sum += inputLayer[k]->value * inputLayer[k]->weight[j]; } sum += hiddenLayer[i][j]->bias; hiddenLayer[i][j]->value = sigmoid(sum); } } else { for (int j = 0; j < hidenode; j++) { double sum = 0.f; for (int k = 0; k < hidenode; k++) { sum += hiddenLayer[i - 1][k]->value * hiddenLayer[i - 1][k]->weight[j]; } sum += hiddenLayer[i][j]->bias; hiddenLayer[i][j]->value = sigmoid(sum); } } } // forward propagation on output layer for (int i = 0; i < outnode; i++) { double sum = 0.f; for (int j = 0; j < hidenode; j++) { sum += hiddenLayer[hidelayer - 1][j]->value * hiddenLayer[hidelayer - 1][j]->weight[i]; } sum += outputLayer[i]->bias