RCNN

network:
	GraphBuilder graphBuilder = builder.graphBuilder().backpropType(BackpropType.Standard).addInputs("inputs")
		          .addLayer("cnn1",
		            new ConvolutionLayer.Builder(new int[] { kernelSize1, kernelSize1 },
		            		new int[] { cnnStride1, cnnStride1 },
		            		new int[] { padding, padding })
		    		.nIn(channels)
		    		.nOut(64)
inputPreProcessors.put("cnn1", new RnnToCnnPreProcessor(501, 501, channels));

feature mask:
			double[] inputMask = new double[sampleCount * size];
			for (int i = 0; i < 30; i++) {
				inputMask[i] = 1;
			}
			featuresMask = Nd4j.create(inputMask, new int[] { size, sampleCount });
			DataSet dataSet = new DataSet(featureData, labelData, featuresMask, labelsMask);