使用多种训练方法训练ANN与Encog

Question

我想知道在使用弹性传播训练之前是否使用遗传算法,粒子群优化和模拟退火训练前馈神经网络确实可以改善结果.

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);

正如您所看到的那样,一系列训练算法可以改善整体训练.

如果有意义并且代码是否正确,请告诉我.它似乎工作但我想确定,因为有时我看到GA的进展从PSO重置.

谢谢

Answer 1

这似乎合乎逻辑，但行不通。

使用 RPROP 的默认参数，此序列不太可能起作用。原因是在之前的训练之后，神经网络的权重将接近局部最优。由于接近局部最优，只有权重的微小变化就会更接近最优（降低错误率）。默认情况下，RPROP 在权重矩阵中使用初始更新值 0.1。对于如此接近最佳状态的网络来说，这是一个巨大的价值。你现在正在“在瓷器店里放出一头公牛”。第一次迭代将使网络远离最优状态，并且本质上将开始新的全局搜索。

降低初始更新值应该有帮助。我不确定是多少。您可能需要使用您的数据查看列车的平均 RPROP 权重更新值以了解情况。或者尝试将其设置得非常小，然后逐步恢复。