我想知道在使用弹性传播训练之前使用遗传算法，粒子群优化和模拟退火训练前馈神经网络是否确实可以改善结果。
这是我正在使用的代码：
CalculateScore score = new TrainingSetScore(trainingSet);
StopTrainingStrategy stop = new StopTrainingStrategy();
StopTrainingStrategy stopGA = new StopTrainingStrategy();
StopTrainingStrategy stopSIM = new StopTrainingStrategy();
StopTrainingStrategy stopPSO = new StopTrainingStrategy();
Randomizer randomizer = new NguyenWidrowRandomizer();
//Backpropagation train = new Backpropagation((BasicNetwork) network, trainingSet, 0.2, 0.1);
// LevenbergMarquardtTraining train = new LevenbergMarquardtTraining((BasicNetwork) network, trainingSet);
int population = 500;
MLTrain trainGA = new MLMethodGeneticAlgorithm(new MethodFactory(){
@Override
public MLMethod factor() {
final BasicNetwork result = createNetwork();
((MLResettable)result).reset();
return result;
}}, score,population);
Date dStart = new Date();
int epochGA = 0;
trainGA.addStrategy(stopGA);
do{
trainGA.iteration();
if(writeOnStdOut)
System.out.println("Epoch Genetic #" + epochGA + " Error:" + trainGA.getError());
epochGA++;//0000001
previousError = trainGA.getError();
Date dtemp = new Date();
totsecs = ((double)(dtemp.getTime()-dStart.getTime())/1000);
} while(previousError > maximumAcceptedErrorTreshold && epochGA < (maxIterations/5) && !stopGA.shouldStop() && totsecs < (secs/3));
NeuralPSO trainPSO = new NeuralPSO((BasicNetwork) network, randomizer, score, 100);
int epochPSO = 0;
trainPSO.addStrategy(stopPSO);
dStart = new Date();
do{
trainPSO.iteration();
if(writeOnStdOut)
System.out.println("Epoch Particle Swarm #" + epochPSO + " Error:" + trainPSO.getError());
epochPSO++;//0000001
previousError = trainPSO.getError();
Date dtemp = new Date();
totsecs = ((double)(dtemp.getTime()-dStart.getTime())/1000);
} while(previousError > maximumAcceptedErrorTreshold && epochPSO < (maxIterations/5) && !stopPSO.shouldStop() && totsecs < (secs/3));
MLTrain trainSIM = new NeuralSimulatedAnnealing((MLEncodable) network, score, startTemperature, stopTemperature, cycles);
int epochSA = 0;
trainSIM.addStrategy(stopSIM);
dStart = new Date();
do{
trainSIM.iteration();
if(writeOnStdOut)
System.out.println("Epoch Simulated Annealing #" + epochSA + " Error:" + trainSIM.getError());
epochSA++;//0000001
previousError = trainSIM.getError();
Date dtemp = new Date();
totsecs = ((double)(dtemp.getTime()-dStart.getTime())/1000);
} while(previousError > maximumAcceptedErrorTreshold && epochSA < (maxIterations/5) && !stopSIM.shouldStop() && totsecs < (secs/3));
previousError = 0;
BasicTraining train = getTraining(method,(BasicNetwork) network, trainingSet);
//train.addStrategy(new Greedy());
//trainAlt.addStrategy(new Greedy());
HybridStrategy strAnneal = new HybridStrategy(trainSIM);
train.addStrategy(strAnneal);
//train.addStrategy(strGenetic);
//train.addStrategy(strPSO);
train.addStrategy(stop);
//
// Backpropagation train = new Backpropagation((ContainsFlat) network, trainingSet, 0.7, 0.3);
dStart = new Date();
int epoch = 1;
do {
train.iteration();
if(writeOnStdOut)
System.out.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;//0000001
if(Math.abs(train.getError()-previousError)<0.0000001) iterationWithoutImprovement++; else iterationWithoutImprovement = 0;
previousError = train.getError();
Date dtemp = new Date();
totsecs = ((double)(dtemp.getTime()-dStart.getTime())/1000);
} while(previousError > maximumAcceptedErrorTreshold && epoch < maxIterations && !stop.shouldStop() && totsecs < secs);//&& iterationWithoutImprovement < maxiter);
如您所见，有一系列训练算法可以改善整体训练。
请让我知道这是否有意义以及代码是否正确。
它似乎正在运行，但是我想确定，因为有时我看到GA的进度已从PSO中重置。
谢谢
最佳答案
似乎合乎逻辑，但是它不起作用。
使用RPROP的默认参数，此序列可能无法正常工作。原因是在您先前的训练之后，神经网络的权重将接近局部最优值。由于接近局部最优，因此权重的很小变化将更接近最优（错误率更低）。默认情况下，RPROP在权重矩阵上使用的initialUpdate值为0.1。对于如此接近最佳状态的网络而言，这是一个巨大的价值。您正在“此时在中国商店释放一头公牛”。第一次迭代将使网络远离最佳状态，并从根本上开始新的全局搜索。
降低initialUpdate值应该有帮助。我不确定多少。您可能希望通过数据查看火车的平均RPROP重量更新值，以获取想法。或者尝试将其设置得很小，然后再备份。