Using a neural network class in WEKA in Java code

Question

Hi, I want to do simple training and testing using the Neural Network in the WEKA library.

But, I believe that this is not trivial, but its difference from the NaiveBayes class in its library.

Does anyone have an example of how to use this class in java code?

Answer 1

The following steps may help you:

• Add Weka Libraries

Download Weka from http://www.cs.waikato.ac.nz/ml/weka/downloading.html .

From the package, find "Weka.jar" and add it to the project.

Java code snippet

2. Building a neural classifier

   public void simpleWekaTrain(String filepath)
   {
   try{
   //Reading training arff or csv file
   FileReader trainreader = new FileReader(filepath);
   Instances train = new Instances(trainreader);
   train.setClassIndex(train.numAttributes() – 1);
   //Instance of NN
   MultilayerPerceptron mlp = new MultilayerPerceptron();
   //Setting Parameters
   mlp.setLearningRate(0.1);
   mlp.setMomentum(0.2);
   mlp.setTrainingTime(2000);
   mlp.setHiddenLayers("3?);
   mlp.buildClassifier(train);
   }
   catch(Exception ex){
   ex.printStackTrace();
   }
   }
   

Another way to set parameters,

    mlp.setOptions(Utils.splitOptions("-L 0.1 -M 0.2 -N 2000 -V 0 -S 0 -E 20 -H 3?));

Where

L = Learning Rate
M = Momentum
N = Training Time or Epochs
H = Hidden Layers
etc.

    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(mlp, train);
    System.out.println(eval.errorRate()); //Printing Training Mean root squared Error
    System.out.println(eval.toSummaryString()); //Summary of Training

K-Fold

    eval.crossValidateModel(mlp, train, kfolds, new Random(1));

4. /

   Instances datapredict = new Instances(
   new BufferedReader(
   new FileReader(<Predictdatapath>)));
   datapredict.setClassIndex(datapredict.numAttributes() – 1);
   Instances predicteddata = new Instances(datapredict);
   //Predict Part
   for (int i = 0; i < datapredict.numInstances(); i++) {
   double clsLabel = mlp.classifyInstance(datapredict.instance(i));
   predicteddata.instance(i).setClassValue(clsLabel);
   }
   //Storing again in arff
   BufferedWriter writer = new BufferedWriter(
   new FileWriter(<Output File Path>));
   writer.write(predicteddata.toString());
   writer.newLine();
   writer.flush();
   writer.close();
   

Answer 2

, " NeuralNetwork WEKA, NeuralNetwork, " MultilayerPerceptron "

, . , WEKA ??? T__T

, , .

http://weka.8497.n7.nabble.com/Multi-layer-perception-td2896.html

Ps. , , !