In the code at the bottom, when lstmLayerSize is 1 then I get the following error:

  c must be f order, offset 0 and have length == c.data.length

However no error is received when lstmLayerSize > 2. The reason for this is not apparent
and the error does not make sense.

To reproduce this error, create a array `input` of size 1x1x1 with say value 1.0 and run the following command:

    List<INDArray> result = this.net.feedForward(input);

public RecurrentNeuralNetwork(int nIn, int nOut) {

		int lstmLayerSize = 5;

		MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
				.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
				.iterations(1)
				.learningRate(0.2)
				.momentum(0.5)
				.rmsDecay(0.95)
				.seed(12345)
				.regularization(true)
				.l2(0.001)
				.list(3)
				.layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(lstmLayerSize)
						.updater(Updater.RMSPROP)
						.activation("hardtanh").weightInit(WeightInit.DISTRIBUTION)
						.dist(new UniformDistribution(-0.08, 0.08)).build())
				.layer(1, new GravesLSTM.Builder().nIn(lstmLayerSize).nOut(lstmLayerSize)
						.updater(Updater.RMSPROP)
						.activation("hardtanh").weightInit(WeightInit.DISTRIBUTION)
						.dist(new UniformDistribution(-0.08, 0.08)).build())
				 .layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation("softmax")
						.updater(Updater.RMSPROP)
						.nIn(lstmLayerSize).nOut(nOut).weightInit(WeightInit.DISTRIBUTION)
						.dist(new UniformDistribution(-0.08, 0.08)).build())
				.pretrain(false).backprop(true)
				.build();

		System.out.println("Configuration: "+conf.toString());
		this.net = new MultiLayerNetwork(conf);

		this.net.init();
		this.net.setListeners(new ScoreIterationListener(1));
}