嗨,我想使用WEKA库中的神经网络进行简单的培训和测试。
但是,我发现它并不是微不足道的,与库中的NaiveBayes类不同。
有人知道如何在Java代码中使用此类吗?
以下步骤可能会为您提供帮助:
从http://www.cs.waikato.ac.nz/ml/weka/downloading.html下载Weka。
从包中找到“ Weka.jar”并添加项目。
Java代码段
构建神经分类器
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();
}
}
另一种设置参数的方式,
mlp.setOptions(Utils.splitOptions(“-L 0.1 -M 0.2 -N 2000 -V 0 -S 0 -E 20 -H 3?));
哪里,
L = Learning Rate
M = Momentum
N = Training Time or Epochs
H = Hidden Layers
etc.
用于评估训练数据,
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
要应用K-fold验证
eval.crossValidateModel(mlp, train, kfolds, new Random(1));
评估/预测未标记的数据
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();
我从互联网上阅读了一些资料,才意识到“如果要在WEKA库中使用NeuralNetwork分类器,则该方法不使用给定的NeuralNetwork类,而应使用“ MultilayerPerceptron”类”
有点棘手,浪费了我的时间。
我希望它对那些为此感到挣扎的人有用。
http://weka.8497.n7.nabble.com/Multi-layer-perception-td2896.html
Ps。如果我错了,请纠正!