Problem with DL4J 1D Convolution

TestConv1D.java

import java.util.ArrayList;
import java.util.List;

import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.inputs.*;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.cpu.nativecpu.NDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.DataSetIteratorFactory;
import org.nd4j.linalg.factory.NDArrayFactory;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.learning.config.*;
import gngs.*;
import graxxia.*;


public class TestConv1D {

	public static void main(String[] args) {
		double [][] features = new double [][] {
			{ 1, 1, 1, 1, 1, 1, 1, 1 },
			{ 1, 0, 1, 1, 1, 1, 1, 1 },
			{ 1, 1, 1, 1, 1, 1, 1, 1 },
			{ 1, 0, 1, 1, 1, 1, 1, 1 },
			{ 1, 1, 1, 1, 1, 1, 1, 1 },
			{ 1, 0, 1, 1, 1, 1, 1, 1 },
			{ 1, 1, 1, 1, 1, 1, 1, 1 },
			{ 1, 0, 1, 1, 1, 1, 1, 1 },
			{ 1, 1, 1, 1, 1, 1, 1, 1 },
			{ 1, 0, 1, 1, 1, 1, 1, 1 },
		};
		
		double [][] labels = new double [][] {
			{1, 0},
			{0, 1},
			{1, 0},
			{0, 1},
			{1, 0},
			{0, 1},
			{1, 0},
			{0, 1},
			{1, 0},
			{0, 1},
		};
		
		int data_len = features[0].length;
		
		MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
				.seed(0)
				.l2(0.0005)
				.weightInit(WeightInit.XAVIER)
				.updater(new Adam(1e-3))
				.list()
				.layer(new Convolution1DLayer.Builder(5)
						//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
						.nIn(1)
//						.rnnDataFormat(RNNFormat.NCW)
						.stride(1)
						.nOut(10)
						.activation(Activation.IDENTITY)
						.build())
				.layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
						.nOut(2)
						.activation(Activation.SOFTMAX)
						.build())
				.setInputType(InputType.convolutionalFlat(data_len,1,1 /*, CNN2DFormat.NHWC*/)) //See note below
				.build();
	
		
		DataSet dataset = new DataSet();
		dataset.setFeatures(Nd4j.create(features));
		dataset.setLabels(Nd4j.create(labels));
		
		MultiLayerNetwork model = new MultiLayerNetwork (conf);
		model.init(); 
		model.setListeners(new ScoreIterationListener(10));
		
		List<DataSet> datasets = new ArrayList<DataSet>();
		datasets.add(dataset);

		DataSetIterator trainIter = new ListDataSetIterator(datasets);
		for(int n=0; n<50; n++) { 
		    model.fit(trainIter); 
		}
	}
}