多线程java和神经网络：同时读取2个csv文件
我正在从事一个神经网络项目，该项目需要使用不同的训练集运行两个神经网络程序实例，特别是同时运行两个实例。为此，我在java plus encog库中使用多线程来实现ANN。所以我创建了两个线程，每个线程都包含ANN实现，但是有两个不同的CSV文件。我有一部分结果是有效的，它返回CSV文件中每个组件的最小值和最大值，但问题是ANN的输出只计算一个文件。以下是我的实现： ReadfileMT.java多线程java和神经网络：同时读取2个csv文件,java,multithreading,encog,Java,Multithreading,Encog,我正在从事一个神经网络项目，该项目需要使用不同的训练集运行两个神经网络程序实例，特别是同时运行两个实例。为此，我在java plus encog库中使用多线程来实现ANN。所以我创建了两个线程，每个线程都包含ANN实现，但是有两个不同的CSV文件。我有一部分结果是有效的，它返回CSV文件中每个组件的最小值和最大值，但问题是ANN的输出只计算一个文件。以下是我的实现： ReadfileMT.java public class ReadFileMT implements Runnable {
public class ReadFileMT implements Runnable {
public static void dumpFieldInfo(EncogAnalyst analyst) {
System.out.println("Fields found in file:");
for (AnalystField field : analyst.getScript().getNormalize()
.getNormalizedFields())
{
StringBuilder line = new StringBuilder();
line.append(field.getName());
line.append(",action=");
line.append(field.getAction());
line.append(",min=");
line.append(field.getActualLow());
line.append(",max=");
line.append(field.getActualHigh());
System.out.println(line.toString());
}
}
public void run() {
File sourceFile = new File("d:\\data\\F21.csv");
File targetFile = new File("d:\\data\\F2_norm.csv");
EncogAnalyst analyst = new EncogAnalyst();
AnalystWizard wizard = new AnalystWizard(analyst);
AnalystField targetField = wizard.getTargetField();
wizard.setTargetField("Old_Resp");
wizard.wizard(sourceFile, true, AnalystFileFormat.DECPNT_COMMA);
dumpFieldInfo(analyst);
final AnalystNormalizeCSV norm = new AnalystNormalizeCSV();
norm.analyze(sourceFile, true, CSVFormat.ENGLISH, analyst);
norm.setProduceOutputHeaders(true);
norm.normalize(targetFile);
// Encog.getInstance().shutdown();
//*****************************Read from the csv file**************************************************
final BasicNetwork network = EncogUtility.simpleFeedForward(4, 4, 0, 1,
false);
network.addLayer(new BasicLayer(new ActivationSigmoid(),false,4));
network.addLayer(newBasicLayer(newActivationSigmoid(),false,4));
network.addLayer(newBasicLayer(newActivationSigmoid(),false,1));
network.getStructure().finalizeStructure();
network.reset();
//create training data
final MLDataSet trainingSet = TrainingSetUtil.loadCSVTOMemory(
CSVFormat.ENGLISH, "c:\\temp\\F2_norm.csv",false, 4, 1);
// train the neural network
System.out.println();
System.out.println("Training Network");
final Backpropagationtrain=newBackpropagation
(network,trainingSet,0.05,0.9);
train.fixFlatSpot(false);
int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:" +train.getError());
epoch++;
} while(train.getError() > 0.01);
train.finishTraining();
//final Train train=newResilientPropagation(network,trainingSet);
/*int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:"
+ train.getError() * 100 + "%");
epoch++;
} while (train.getError() > 0.015);*/
/*int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:" +train.getError());
epoch++;
} while(train.getError() > 0.01);
train.finishTraining();*/
// test the neural network
System.out.println("Neural Network Results:");
for(MLDataPair pair: trainingSet ) {
final MLData output = network.compute(pair.getInput());
System.out.println(pair.getInput().getData(0)+",
"+pair.getInput().getData(1)+","+ pair.getInput().getData(2)
+","+ pair.getInput().getData(3)
+ ", actual=" + output.getData(0) + ",ideal="
+pair.getIdeal().getData(0));
}
Encog.getInstance().shutdown();
}
}
public class ReadFileMT2 implements Runnable {
public static void dumpFieldInfo(EncogAnalyst analyst) {
System.out.println("Fields found in file:");
for (AnalystField field : analyst.getScript().getNormalize()
.getNormalizedFields())
{
StringBuilder line = new StringBuilder();
line.append(field.getName());
line.append(",action=");
line.append(field.getAction());
line.append(",min=");
line.append(field.getActualLow());
line.append(",max=");
line.append(field.getActualHigh());
System.out.println(line.toString());
}
}
public void run() {
File sourceFile = new File("d:\\data\\RespTime.csv");
File targetFile =newFile("d:\\data\\RespTime_norm.csv");
EncogAnalyst analyst = new EncogAnalyst();
AnalystWizard wizard = new AnalystWizard(analyst);
AnalystField targetField = wizard.getTargetField();
wizard.setTargetField("Old_Resp");
wizard.wizard(sourceFile, true, AnalystFileFormat.DECPNT_COMMA);
dumpFieldInfo(analyst);
final AnalystNormalizeCSV norm = new AnalystNormalizeCSV();
norm.analyze(sourceFile, true, CSVFormat.ENGLISH, analyst);
norm.setProduceOutputHeaders(true);
norm.normalize(targetFile);
// Encog.getInstance().shutdown();
//******Read from the csv file*************************
final BasicNetwork network = EncogUtility.simpleFeedForward(4, 4, 0, 1,
false);
network.addLayer(newBasicLayer(newActivationSigmoid(),false,4));
network.addLayer(newBasicLayer(newActivationSigmoid(),false,4));
network.addLayer(newBasicLayer(newActivationSigmoid(),false,1));
network.getStructure().finalizeStructure();
network.reset();
//create training data
final MLDataSet trainingSet = TrainingSetUtil.loadCSVTOMemory(
CSVFormat.ENGLISH, "c:\\temp\\RespTime_norm.csv",false, 4, 1);
// train the neural network
System.out.println();
System.out.println("Training Network");
final Backpropagation train = new Backpropagation
(network,trainingSet,0.05, 0.9);
train.fixFlatSpot(false);
int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:" +train.getError());
epoch++;
} while(train.getError() > 0.01);
train.finishTraining();
/*int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:"
+ train.getError() * 100 + "%");
epoch++;
} while (train.getError() > 0.015);*/
/*int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:" +train.getError());
epoch++;
} while(train.getError() > 0.01);
train.finishTraining();*/
// test the neural network
System.out.println("Neural Network Results:");
for(MLDataPair pair: trainingSet ) {
final MLData output = network.compute(pair.getInput());
System.out.println(pair.getInput().getData(0) + ","
+pair.getInput().getData(1)+",
"+ pair.getInput().getData(2)+
","+ pair.getInput().getData(3)
+ ", actual=" +output.getData(0)+",ideal="+
pair.getIdeal().getData(0));
}
Encog.getInstance().shutdown();
}
}
public static void main(String[] args) {
ReadFileMT obj1 = new ReadFileMT();
ReadFileMT2 obj2 = new ReadFileMT2();
Thread t1 = new Thread(obj1);
Thread t2 = new Thread(obj2);
t1.start();
t2.start();
}
}
我不明白怎么了。备注：我是parralel编程新手请添加您想要实现的具体目标：您想要将结果拆分为不同的文件还是将其写入一个文件中（按特定顺序）我使用神经网络预测时间序列，所以我有两个CSV，数据不同我希望ANN给我预测parralel中的两个CSV文件没有拆分文件它们被拆分，总结起来我希望parralel中运行的同一个ANN的两个实例，每个都使用CSV文件进行预测请告诉我我不能实现我想要的？有人帮我pleaze pleaze pleaze你是否尝试分别运行每个方法？每种方法都正确吗？请添加您想要实现的目标：是将结果拆分到不同的文件中，还是将结果写入一个文件中（按特定顺序）我使用神经网络预测时间序列，所以我有两个CSV，数据不同我希望ANN给我预测parralel中的两个CSV文件没有拆分文件它们被拆分，总结起来我希望parralel中运行的同一个ANN的两个实例，每个都使用CSV文件进行预测请告诉我我不能实现我想要的？有人帮我pleaze pleaze pleaze你是否尝试分别运行每个方法？每种方法都正确吗？