java - Encog 入力レイヤーのサイズ制限

Question

Encog フレームワークと Java を使用して、画像認識システムを実行しています。それでも、Downsample の幅と高さを 100 より大きく設定すると、

 java.lang.NegativeArraySizeException

ネットワークを作成しようとしているとき。

入力層のニューロン数に制限はありますか?

    public class PlateNetwork {

    protected final List<RawImage> imageList;
    protected ImageMLDataSet imageMLDataSet;
    protected Downsample downsample;
    protected Size downsampleSize;
    protected int outputLayerSize;
    protected BasicNetwork network;

    public PlateNetwork () {
        imageList = new ArrayList<>();
        outputLayerSize = Neuron.getTotalNeurons();
        downsample = new SimpleIntensityDownsample();
        downsampleSize = new Size(200, 150);
        imageMLDataSet = new ImageMLDataSet(downsample, false, 1, -1);
    }

    public void processNN() {
        inputImages();
        createNetwork();
        initTraining();
    }

    private void inputImages() {
        RawImage rawImage;
        File[] inputImages = Global.inputFolder.listFiles();
        int inputLength = inputImages.length;

        for (int i = 0; i < inputLength; i++) {
            rawImage = new RawImage(inputImages[i], Neuron.BOL_PLATE);
            imageList.add(rawImage);
            imageMLDataSet.add(rawImage.getImageMLData(), rawImage.getIdeal());
        }
    }


    private void createNetwork() {
        final int inputLayerSize = downsampleSize.getArea();
        final int hiddenLayerSize = (inputLayerSize + outputLayerSize) * 2/3;
        final int hiddenLayer1Neurons = hiddenLayerSize;
        final int hiddenLayer2Neurons = hiddenLayerSize;

        imageMLDataSet.downsample(downsampleSize.getHeight(), downsampleSize.getWidth());
        network = EncogUtility.simpleFeedForward( imageMLDataSet.getInputSize(),
                                                  hiddenLayer1Neurons,
                                                  hiddenLayer2Neurons,
                                                  imageMLDataSet.getIdealSize(),
                                                  true);
    }

    private void initTraining() {
        final int trainingMinutes = 1;
        final double strategyError = 0.25;
        final int strategyCycles = 50;

        final ResilientPropagation train = new ResilientPropagation(network, imageMLDataSet);
        train.addStrategy(new ResetStrategy(strategyError, strategyCycles));

        EncogUtility.trainConsole(train, network, imageMLDataSet, trainingMinutes);
        System.out.println("Training Stopped...");
    }

}