CNN Neural Net

MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
                .seed(123)
                .iterations(iterations)
                .regularization(true).l2(.0005) //Arbitrary
                .learningRate(0.005)
                .weightInit(WeightInit.XAVIER)
                .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(Updater.NESTEROVS).momentum(0.5)
                .list(6)
                .layer(0, new ConvolutionLayer.Builder(5,5)
                        .nIn(11*200).nOut(20).activation("identity").padding(2,2).stride(1,1).dropOut(0.5).build()
                )
                .layer(1,new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG)
                        .kernelSize(4,4).stride(1,1).build()
                )
                .layer(2, new ConvolutionLayer.Builder(5,5)
                        .nOut(20).activation("identity").padding(2,2).stride(1,1).dropOut(0.5).build()
                )
                .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG)
                        .kernelSize(2,2).stride(1,1).build()
                )
                .layer(4, new DenseLayer.Builder().activation("relu").nOut(120).build())
                .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nOut(num_people).activation("softmax").build())
                .backprop(true).pretrain(false);
        new ConvolutionLayerSetup(builder,11,200,1);