papperwing

java.lang.UnsupportedOperationException
	at org.datavec.api.writable.ArrayWritable.toDouble(ArrayWritable.java:37)
	at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.getDataSet(RecordReaderDataSetIterator.java:261)
	at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:186)
	at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:389)
	at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:52)
	at cz.muni.fi.imageNet.manager.ImageNetRunner.trainModel(ImageNetRunner.java:68)
	at cz.muni.fi.imageNet.Service.ImageNetAPI.getModel(ImageNetAPI.java:67)
	at cz.sa.dovolena.server.task.imagenet.ImageNetTask.execute(ImageNetTask.java:73)
	at cz.sa.dovolena.server.task.DovolenaTaskThread.run(DovolenaTaskThread.java:59)

papperwing

public NeuralNetModel trainModel(final NeuralNetModel model, DataSet dataset) {

        final DataSetIterator trainIterator = prepareDataSetIterator(dataset);
        model.getModel().init();

        final Evaluation eval = new Evaluation();
        //TODO: přidat listenery na statistiky
        for (int n = 0; n < conf.getEpoch(); n++) {
            while (trainIterator.hasNext()) {
                final org.nd4j.linalg.dataset.DataSet next = trainIterator.next();

papperwing

/**
     * Method takes parameters and create {@link NetworkConfiguration}.
     *
     * @param seed
     * @param iterations
     * @param learningRate
     * @param layerSize
     * @return
     */
    public NetworkConfiguration generateConfiguration(