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May 14, 2019 01:59
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package ma.enset.brain_tumor_segmentation; | |
import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
import org.deeplearning4j.nn.conf.CacheMode; | |
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; | |
import org.deeplearning4j.nn.conf.ConvolutionMode; | |
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
import org.deeplearning4j.nn.conf.WorkspaceMode; | |
import org.deeplearning4j.nn.conf.inputs.InputType; | |
import org.deeplearning4j.nn.conf.layers.Cnn3DLossLayer; | |
import org.deeplearning4j.nn.conf.layers.Convolution3D; | |
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; | |
import org.deeplearning4j.nn.conf.layers.DropoutLayer; | |
import org.deeplearning4j.nn.conf.layers.Subsampling3DLayer; | |
import org.deeplearning4j.nn.conf.layers.Upsampling3D; | |
import org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat; | |
import org.deeplearning4j.nn.graph.ComputationGraph; | |
import org.deeplearning4j.nn.weights.WeightInit; | |
import org.nd4j.linalg.activations.Activation; | |
import org.nd4j.linalg.api.buffer.DataType; | |
import org.nd4j.linalg.api.ndarray.INDArray; | |
import org.nd4j.linalg.factory.Nd4j; | |
import org.nd4j.linalg.learning.config.AdaDelta; | |
import org.nd4j.linalg.learning.config.IUpdater; | |
import org.nd4j.linalg.lossfunctions.LossFunctions; | |
public class App { | |
public static void main(String[] args) { | |
App app=new App(); | |
ComputationGraphConfiguration.GraphBuilder graph = app.unetBuilder(); | |
graph.addInputs("input").setInputTypes(InputType.convolutional3D(DataFormat.NCDHW,app.inputShape[0],app.inputShape[1],app.inputShape[2],app.inputShape[3])); | |
ComputationGraphConfiguration conf = graph.build(); | |
ComputationGraph cg = new ComputationGraph(conf); | |
cg.init(); | |
INDArray in = Nd4j.create(DataType.FLOAT, 1, 64, 64, 64, 64); | |
INDArray out = cg.outputSingle(in); | |
} | |
private static long seed = 1234; | |
private int[] inputShape = new int[] {180,256, 256,1}; | |
private CacheMode cacheMode = CacheMode.NONE; | |
private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED; | |
private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST; | |
private WeightInit weightInit = WeightInit.RELU; | |
private IUpdater updater = new AdaDelta(); | |
public ComputationGraphConfiguration.GraphBuilder unetBuilder() { | |
ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed) | |
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) | |
.updater(updater) | |
.weightInit(weightInit) | |
.l2(5e-5) | |
.miniBatch(true) | |
.cacheMode(cacheMode) | |
.trainingWorkspaceMode(workspaceMode) | |
.inferenceWorkspaceMode(workspaceMode) | |
.graphBuilder(); | |
graph | |
.addLayer("conv1-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(32).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "input") | |
.addLayer("conv1-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(64).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv1-1") | |
.addLayer("pool1", new Subsampling3DLayer.Builder(Subsampling3DLayer.PoolingType.MAX).kernelSize(2,2,2) | |
.build(), "conv1-2") | |
.addLayer("conv2-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(64).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "pool1") | |
.addLayer("conv2-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(128).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv2-1") | |
.addLayer("pool2", new Subsampling3DLayer.Builder(Subsampling3DLayer.PoolingType.MAX).kernelSize(2,2,2) | |
.build(), "conv2-2") | |
.addLayer("conv3-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(128).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "pool2") | |
.addLayer("conv3-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(256).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv3-1") | |
.addLayer("drop3", new DropoutLayer.Builder(0.5).build(), "conv3-2") | |
.addLayer("pool3", new Subsampling3DLayer.Builder(Subsampling3DLayer.PoolingType.MAX).kernelSize(2,2,2) | |
.build(), "drop3") | |
.addLayer("conv4-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(256).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "pool3") | |
.addLayer("conv4-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(512).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv4-1") | |
.addLayer("drop4", new DropoutLayer.Builder(0.5).build(), "conv4-2") | |
// up5 | |
.addLayer("up5-1", new Upsampling3D.Builder(2).build(), "drop4") | |
.addLayer("up5-2", new Convolution3D.Builder(2,2,2).stride(1,1,1).nOut(512).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "up5-1") | |
.addVertex("merge5", new MergeVertex(), "drop3", "up5-2") | |
.addLayer("conv5-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(256).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "merge5") | |
.addLayer("conv5-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(256).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv5-1") | |
// up6 | |
.addLayer("up6-1", new Upsampling3D.Builder(2).build(), "conv5-2") | |
.addLayer("up6-2", new Convolution3D.Builder(2,2,2).stride(1,1,1).nOut(256).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "up6-1") | |
.addVertex("merge6", new MergeVertex(), "conv2-2", "up6-2") | |
.addLayer("conv6-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(128).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "merge6") | |
.addLayer("conv6-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(128).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv6-1") | |
// up7 | |
.addLayer("up7-1", new Upsampling3D.Builder(2).build(), "conv6-2") | |
.addLayer("up7-2", new Convolution3D.Builder(2,2,2).stride(1,1,1).nOut(128).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "up7-1") | |
.addVertex("merge7", new MergeVertex(), "conv1-2", "up7-2") | |
.addLayer("conv7-1", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(64).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "merge7") | |
.addLayer("conv7-2", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(64).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv7-1") | |
.addLayer("conv7-3", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(2) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.RELU).build(), "conv7-2") | |
.addLayer("conv8", new Convolution3D.Builder(3,3,3).stride(1,1,1).nOut(1).dataFormat(Convolution3D.DataFormat.NCDHW) | |
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode) | |
.activation(Activation.IDENTITY).build(), "conv7-3") | |
.addLayer("output", new Cnn3DLossLayer.Builder(DataFormat.NCDHW).lossFunction(LossFunctions.LossFunction.XENT) | |
.activation(Activation.SIGMOID).build(), "conv8") | |
.setOutputs("output"); | |
return graph; | |
} | |
} |
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i want the all code source please