I'm working with a neural network project that requires to run two instance of the neural network program with different training set specifically two at the same time. For that I'm using multithreading in java plus encog library for the implementation of the ANN. So I created two threads each of them contains the ANN implementation but with two different CSV files. I have part of the result works which returns the min and max for every column in the CSV files but the problem that the output of the ANN is computed for just one files. Here is my implementation:
ReadfileMT.java
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();
}
}
ReadFileMT2.java
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();
}
}
main.java
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();
}
}
I don't understand what is wrong. P.s.: I am new in parallel programming.
