Conv net config

gistfile1.txt

        NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
                .seed(123)
                .miniBatch(true) 
                .cacheMode(CacheMode.DEVICE) 
                .learningRate(.01) 
                .updater(new Nesterovs(0.9)) 
                .iterations(1) 
                .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer) 
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .regularization(true); // KEEP?
                
        int[] kernelArray = new int[]{3,3};
        int[] strideArray = new int[]{1,1};
        int[] zeroPaddingArray = new int[]{0,0}; 
		int processWidth = 4;
        
        ListBuilder listBuilder = builder.list(); // Building the DL4J network

        listBuilder = listBuilder.layer(0, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
        		.name("cnn1")
        		.convolutionMode(ConvolutionMode.Strict)
        		.nIn(2) // 2 input channels
        		.nOut(processWidth)
        		.weightInit(WeightInit.XAVIER_UNIFORM)
        		.activation(Activation.RELU)
        		.learningRate(1e-2) 
        		.biasInit(1e-2) 
        		.biasLearningRate(1e-2*2).build());

        listBuilder = listBuilder.layer(1, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
        		.name("cnn2")
        		.convolutionMode(ConvolutionMode.Strict)
        		.nOut(processWidth)
        		.weightInit(WeightInit.XAVIER_UNIFORM)
        		.activation(Activation.RELU)
        		.learningRate(1e-2) 
        		.biasInit(1e-2) 
        		.biasLearningRate(1e-2*2).build());
				
        listBuilder = listBuilder.layer(2, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
        		.name("cnn3")
        		.convolutionMode(ConvolutionMode.Strict)
        		.nOut(processWidth)
        		.weightInit(WeightInit.XAVIER_UNIFORM)
        		.activation(Activation.RELU)
        		.learningRate(1e-2) 
        		.biasInit(1e-2) 
        		.biasLearningRate(1e-2*2).build());
				
        listBuilder = listBuilder.layer(3, new ConvolutionLayer.Builder(kernelArray, strideArray, zeroPaddingArray)
        		.name("cnn4")
        		.convolutionMode(ConvolutionMode.Strict)
        		.nOut(processWidth)
        		.weightInit(WeightInit.XAVIER_UNIFORM)
        		.activation(Activation.RELU)
        		.learningRate(1e-2) 
        		.biasInit(1e-2) 
        		.biasLearningRate(1e-2*2).build());
				
		listBuilder = listBuilder
				.layer(4, new OutputLayer.Builder(LossFunctions.LossFunction.MSE) 
						.name("output")
						.nOut(1)
						.activation(Activation.TANH)
						.build());

		MultiLayerConfiguration conf = listBuilder
				.backprop(true)
				.pretrain(false)
				.setInputType(InputType.convolutional(20, 10, 2))
				.build();

		// For some reason, this model works
		MultiLayerNetwork niceModel = new MultiLayerNetwork(conf);
		niceModel.init();