CNN Multi feature Exception

DL4J
3

@agibsonccc

dataset can be download from here.

DataSet dataSet = new DataSet() ;
dataSet.load(new File());

Features Rank: 4, DataType: DOUBLE, Offset: 0, Order: c, Shape: [43,1,7,2], Stride: [14,14,2,1]
Labels Rank: 3, DataType: DOUBLE, Offset: 0, Order: c, Shape: [43,1,1], Stride: [1,1,1]

Here Model conf:

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
			.trainingWorkspaceMode(WorkspaceMode.ENABLED).inferenceWorkspaceMode(WorkspaceMode.ENABLED)
			.seed(RANDOM_SEED)
			.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
		    .updater(new RmsProp.Builder().learningRate(.001).rmsDecay(.001).build())
			.layer(0, new ConvolutionLayer.Builder(2,2).nIn(1).nOut(10).padding(1,1).stride(1,1).activation(Activation.TANH).build())
		    .layer(1, new SubsamplingLayer.Builder(PoolingType.MAX).kernelSize(1,1) .stride(1,1).build())
		 	.layer(2, new DenseLayer.Builder().nOut(40).activation(Activation.TANH).build())
		 	.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MEAN_ABSOLUTE_ERROR).nOut(1).activation(Activation.TANH)
			.setInputType(InputType.convolutional(7, 7,1))
			.build();
			
	MultiLayerNetwork model = new MultiLayerNetwork(conf);
	model.setInputMiniBatchSize(10);
	model.init();

training sample input dataSet(github file) :
===========INPUT===================
[[[[ 12.2289, 1.0000],
[ 13.4411, 2.0000],
[ 10.6273, 3.0000],
[ 10.4183, 4.0000],
[ 10.9446, 5.0000],
[ 10.3468, 6.0000],
[ 7.5371, 7.0000]]]]
=================OUTPUT==================
[[[7.3623]]]

total 43 is same as above.