Java 使用deeplearning4j在Iris数据集上的糟糕性能

我正在按照他们的一些教程进行

deeplearning4j

实验。iris数据集是众所周知的，通过使用Weka（使用RandomForest或MultilayerPerceptron），我可以很容易地使F-度量值达到几乎1（以下示例中为0.97）：

我在

deeplearning4j

方面不太成功：

Examples labeled as 0 classified by model as 0: 4 times
Examples labeled as 1 classified by model as 0: 12 times
Examples labeled as 2 classified by model as 0: 14 times

Warning: class 1 was never predicted by the model. This class was excluded from the average precision
Warning: class 2 was never predicted by the model. This class was excluded from the average precision

Accuracy:  0.1333  Precision: 0.1333  Recall:    0.3333  F1 Score: 0.1905

以下是我正在使用的代码（在Scala中）：

import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator
import org.deeplearning4j.eval.Evaluation
import org.deeplearning4j.nn.api.{Layer, OptimizationAlgorithm}
import org.deeplearning4j.nn.conf.{Updater, NeuralNetConfiguration}
import org.deeplearning4j.nn.conf.layers.{OutputLayer, RBM}
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork
import org.deeplearning4j.nn.weights.WeightInit
import org.deeplearning4j.ui.weights.HistogramIterationListener
import org.nd4j.linalg.factory.Nd4j
import org.nd4j.linalg.lossfunctions.LossFunctions

object Main extends App {
  Nd4j.MAX_SLICES_TO_PRINT = -1
  Nd4j.MAX_ELEMENTS_PER_SLICE = -1
  Nd4j.ENFORCE_NUMERICAL_STABILITY = true

  val inputNum = 4
  var outputNum = 3
  var numSamples = 150
  var batchSize = 150
  var iterations = 1000
  var seed = 321
  var listenerFreq = iterations/5
  val learningRate = 1e-6

  println("Load data....")

  val iter = new IrisDataSetIterator(batchSize, numSamples)
  val iris = iter.next()
  iris.shuffle()
  iris.normalizeZeroMeanZeroUnitVariance()

  val testAndTrain = iris.splitTestAndTrain(0.80)
  val train = testAndTrain.getTrain
  val test = testAndTrain.getTest

  println("Build model....")

  val RMSE_XENT = LossFunctions.LossFunction.RMSE_XENT

  val conf = new NeuralNetConfiguration.Builder()
    .seed(seed)
    .iterations(iterations)
    .learningRate(learningRate)
    .l1(1e-1).regularization(true).l2(2e-4)
    .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
    .useDropConnect(true)
    .list(2)
    .layer(0, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN)
      .nIn(inputNum).nOut(3).k(1).activation("relu").weightInit(WeightInit.XAVIER).lossFunction(RMSE_XENT)
      .updater(Updater.ADAGRAD).dropOut(0.5)
      .build())
    .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
      .nIn(3).nOut(outputNum).activation("softmax").build())
    .build()

  val model = new MultiLayerNetwork(conf)
  model.init()
  model.setListeners(new HistogramIterationListener(listenerFreq))

  println("Train model....")
  model.fit(train.getFeatureMatrix)

  println("Evaluate model....")
  val eval = new Evaluation(outputNum)
  val output = model.output(test.getFeatureMatrix, Layer.TrainingMode.TEST)

  (0 until output.rows()).foreach { i =>
    val actual = train.getLabels.getRow(i).toString.trim()
    val predicted = output.getRow(i).toString.trim()
    println("actual " + actual + " vs predicted " + predicted)
  }

  eval.eval(test.getLabels, output)
  println(eval.stats())
}

---

您可以尝试删除正则化：

.l1（1e-1）。正则化（真）。l2（2e-4） 这是我的代码：

公共多层网络构建模型（）{
int lowerSize=特征尺寸；
List hiddenLayers=new ArrayList（）；
对于（int i=0；i

RBMs仅适用于输入数据的特定分布（例如图像和可能的建议）。此外，RBM的可表达性非常低，您只有三个单元，这可能不足以从输入分布中捕获任何内容。这里有一个有趣的解释：我可能会从像Weka这样的完全连接的层开始，然后从那里开始。你好Hugo，dl4j开发者建议从官方网站在gitter上提问。

import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator
import org.deeplearning4j.eval.Evaluation
import org.deeplearning4j.nn.api.{Layer, OptimizationAlgorithm}
import org.deeplearning4j.nn.conf.{Updater, NeuralNetConfiguration}
import org.deeplearning4j.nn.conf.layers.{OutputLayer, RBM}
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork
import org.deeplearning4j.nn.weights.WeightInit
import org.deeplearning4j.ui.weights.HistogramIterationListener
import org.nd4j.linalg.factory.Nd4j
import org.nd4j.linalg.lossfunctions.LossFunctions

object Main extends App {
  Nd4j.MAX_SLICES_TO_PRINT = -1
  Nd4j.MAX_ELEMENTS_PER_SLICE = -1
  Nd4j.ENFORCE_NUMERICAL_STABILITY = true

  val inputNum = 4
  var outputNum = 3
  var numSamples = 150
  var batchSize = 150
  var iterations = 1000
  var seed = 321
  var listenerFreq = iterations/5
  val learningRate = 1e-6

  println("Load data....")

  val iter = new IrisDataSetIterator(batchSize, numSamples)
  val iris = iter.next()
  iris.shuffle()
  iris.normalizeZeroMeanZeroUnitVariance()

  val testAndTrain = iris.splitTestAndTrain(0.80)
  val train = testAndTrain.getTrain
  val test = testAndTrain.getTest

  println("Build model....")

  val RMSE_XENT = LossFunctions.LossFunction.RMSE_XENT

  val conf = new NeuralNetConfiguration.Builder()
    .seed(seed)
    .iterations(iterations)
    .learningRate(learningRate)
    .l1(1e-1).regularization(true).l2(2e-4)
    .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
    .useDropConnect(true)
    .list(2)
    .layer(0, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN)
      .nIn(inputNum).nOut(3).k(1).activation("relu").weightInit(WeightInit.XAVIER).lossFunction(RMSE_XENT)
      .updater(Updater.ADAGRAD).dropOut(0.5)
      .build())
    .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
      .nIn(3).nOut(outputNum).activation("softmax").build())
    .build()

  val model = new MultiLayerNetwork(conf)
  model.init()
  model.setListeners(new HistogramIterationListener(listenerFreq))

  println("Train model....")
  model.fit(train.getFeatureMatrix)

  println("Evaluate model....")
  val eval = new Evaluation(outputNum)
  val output = model.output(test.getFeatureMatrix, Layer.TrainingMode.TEST)

  (0 until output.rows()).foreach { i =>
    val actual = train.getLabels.getRow(i).toString.trim()
    val predicted = output.getRow(i).toString.trim()
    println("actual " + actual + " vs predicted " + predicted)
  }

  eval.eval(test.getLabels, output)
  println(eval.stats())
}

public MultiLayerNetwork buildModel() {
    int lowerSize = featureSize;
    List<DenseLayer> hiddenLayers = new ArrayList<DenseLayer>();
    for (int i = 0; i < hiddenLayersDim.length; i++) {
        int higherSize = hiddenLayersDim[i];
        hiddenLayers.add(new DenseLayer.Builder().nIn(lowerSize).nOut(higherSize)
                .activation(activationType).weightInit(weightInit).build());
        lowerSize = higherSize;
    }
    NeuralNetConfiguration.ListBuilder listBuilder = new NeuralNetConfiguration.Builder()
            .seed(seed)
            .iterations(iterations)
            .optimizationAlgo(algorithm)
            .learningRate(learningRate)
            .updater(updater)
            .list();
    int layerCount = 0;
    for (int i = 0; i < hiddenLayers.size(); i++) {
        listBuilder.layer(layerCount, hiddenLayers.get(i));
        layerCount += 1;
    }
    listBuilder.layer(layerCount, new OutputLayer.Builder(lossFunction)
            .weightInit(weightInit)
            .activation("softmax")
            .nIn(lowerSize).nOut(labelSize).build());
    MultiLayerConfiguration conf = listBuilder.pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    net.setListeners(new ScoreIterationListener(1));
    this.net = net;
    return net;
}