u-net.java

        ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
        		.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(new Adam(0.0001))
                .weightInit(new NormalDistribution(0.0, 0.01))
                .biasInit(0)
                .miniBatch(true)
                .cacheMode(cacheMode)
                .trainingWorkspaceMode(workspaceMode)
                .inferenceWorkspaceMode(workspaceMode)
                .graphBuilder();
        
        graph  
                .addLayer("conv1-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)// tester 32 (https://link.springer.com/content/pdf/10.1007%2Fs42452-019-0694-y.pdf)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "input")
                .addLayer("conv1-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv1-1")
                .addLayer("pool1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
                        .build(), "conv1-2")

                .addLayer("conv2-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "pool1")
                .addLayer("conv2-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv2-1")
                .addLayer("pool2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
                        .build(), "conv2-2")

                .addLayer("conv3-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "pool2")
                .addLayer("conv3-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv3-1")
                .addLayer("pool3", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
                        .build(), "conv3-2")

                .addLayer("conv4-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "pool3")
                .addLayer("conv4-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv4-1")
                .addLayer("drop4", new DropoutLayer.Builder(0.5).build(), "conv4-2")
                .addLayer("pool4", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
                        .build(), "drop4")

                .addLayer("conv5-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "pool4")
                .addLayer("conv5-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv5-1")
                .addLayer("drop5", new DropoutLayer.Builder(0.5).build(), "conv5-2")

                // up6
                .addLayer("up6-1", new Upsampling2D.Builder(2).build(), "drop5")
                .addLayer("up6-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(512)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "up6-1")
                .addVertex("merge6", new MergeVertex(), "drop4", "up6-2")
                .addLayer("conv6-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "merge6")
                .addLayer("conv6-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv6-1")

                // up7
                .addLayer("up7-1", new Upsampling2D.Builder(2).build(), "conv6-2")
                .addLayer("up7-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(256)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "up7-1")
                .addVertex("merge7", new MergeVertex(), "conv3-2", "up7-2")
                .addLayer("conv7-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "merge7")
                .addLayer("conv7-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv7-1")

                // up8
                .addLayer("up8-1", new Upsampling2D.Builder(2).build(), "conv7-2")
                .addLayer("up8-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(128)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "up8-1")
                .addVertex("merge8", new MergeVertex(), "conv2-2", "up8-2")
                .addLayer("conv8-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "merge8")
                .addLayer("conv8-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv8-1")

                // up9
                .addLayer("up9-1", new Upsampling2D.Builder(2).build(), "conv8-2")
                .addLayer("up9-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(64)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "up9-1")
                .addVertex("merge9", new MergeVertex(), "conv1-2", "up9-2")
                .addLayer("conv9-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "merge9")
                .addLayer("conv9-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.RELU).build(), "conv9-1")
   
                .addLayer("conv9-3", new ConvolutionLayer.Builder(1,1).stride(1,1).nOut(1)
                        .convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
                        .activation(Activation.SIGMOID).build(), "conv9-2")

           
                .addLayer("output", new SameDiffDiceLossLayer(), "conv9-3")
                
                .setOutputs("output");
        
        return graph;
    }