Activation actv = Activation.IDENTITY;
		Activation outA = Activation.HARDSIGMOID;
		MultiLayerConfiguration mlp = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER)
				.activation(actv).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(updater)
				.list()
				.layer(0,
						new ConvolutionLayer.Builder().nIn(1).nOut(6).weightInit(WeightInit.XAVIER)
								.activation(actv).stride(1, 1).kernelSize(5, 5).build())
				.layer(1,
						new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2)
								.build())
				.layer(2,
						new ConvolutionLayer.Builder().nIn(6).nOut(16).weightInit(WeightInit.XAVIER)
								.activation(actv).stride(1, 1).kernelSize(5, 5).build())
				.layer(3,
						new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2)
								.build())
				.layer(4,
						new DenseLayer.Builder().nIn(256).nOut(16).activation(actv).weightInit(WeightInit.XAVIER)
								.build())
				.layer(5,
						new DenseLayer.Builder().nIn(16).nOut(256).activation(actv).weightInit(WeightInit.XAVIER)
								.build())
				.layer(6,
						new Deconvolution2D.Builder().nIn(16).nOut(6).weightInit(WeightInit.XAVIER)
								.activation(actv).stride(1, 1).kernelSize(5, 5).build())
				.layer(7,
						new Deconvolution2D.Builder().nIn(6).nOut(6).weightInit(WeightInit.XAVIER)
								.activation(actv).stride(1, 1).kernelSize(5, 5).build())
				.layer(8,
						new Deconvolution2D.Builder().nIn(6).nOut(1).weightInit(WeightInit.XAVIER)
								.activation(actv).stride(1, 1).kernelSize(5, 5).build())
				.layer(9,
						new LossLayer.Builder().lossFunction(LossFunction.MSE).build())
				.inputPreProcessor(6, new FeedForwardToCnnPreProcessor(16, 16))
				.setInputType(InputType.convolutionalFlat(28, 28, 1))
				.build();