AbdelmajidB

 private static final Logger log = LoggerFactory.getLogger(SemanticSegmentation.class);
    private static int height = 96;
    private static int width = 96;
    private static int channels = 1;
    private static int batchSize = 22;
    
    private static long seed = 1234;
    private static Random rng = new Random(seed);
    private static int epochs = 1;
    public static final String DATA_PATH= "C:/Users/bismi/Documents/dl4j/brain-tumor-segmentation/src/main/resources";

AbdelmajidB

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;

AbdelmajidB

public class SemanticSegmentation {
	private static final Logger log = LoggerFactory.getLogger(SemanticSegmentation.class);
	private static int height = 256;
    private static int width = 256;
    private static int channels = 1;
    private static int batchSize = 6;
    
    private static long seed = 1234;
    private static Random rng = new Random(seed);
    private static int epochs = 100;

AbdelmajidB

21:05:24.437 [main] INFO ma.enset.brain_tumor_segmentation.MyPathLabelGenerator - C:/Users/bismi/Documents/dl4j/brain-tumor-segmentation/src/main/resources/testI/rois/1.png
21:05:24.557 [main] INFO ma.enset.brain_tumor_segmentation.SemanticSegmentationLoad2 - 

========================Evaluation Metrics========================
 # of classes:    2
 Accuracy:        0,9990
 Precision:       0,9318
 Recall:          1,0000
 F1 Score:        0,9647
Precision, recall & F1: reported for positive class (class 1 - "1") only

AbdelmajidB

21:03:21.537 [main] INFO ma.enset.brain_tumor_segmentation.MyPathLabelGenerator - C:/Users/bismi/Documents/dl4j/brain-tumor-segmentation/src/main/resources/testI/rois/1.png
21:03:21.540 [main] DEBUG org.nd4j.jita.handler.impl.CudaZeroHandler - Creating bucketID: 2
21:03:21.677 [main] INFO ma.enset.brain_tumor_segmentation.SemanticSegmentationLoad2 - 

========================Evaluation Metrics========================
 # of classes:    2
 Accuracy:        0,1186
 Precision:       0,0149
 Recall:          1,0000
 F1 Score:        0,0294

AbdelmajidB

package ma.enset.brain_tumor_segmentation;

import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Map;

AbdelmajidB

package ma.enset.brain_tumor_segmentation;

import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Map;

AbdelmajidB

        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();

AbdelmajidB

public class MyPathLabelGenerator implements PathLabelGenerator{
    protected static final Logger log = LoggerFactory.getLogger(MyPathLabelGenerator.class);

    String labelsDir;
	private static NativeImageLoader imageLoader = new NativeImageLoader(96, 96, 1);
    File file;
	public MyPathLabelGenerator(String path) {
		labelsDir=path;
	}
	

AbdelmajidB

    while (Iter.hasNext()) {
          DataSet next = Iter.next(); 
          INDArray out2d = modelT.outputSingle(next.getFeatures()).permute(0,2,3,1).dup().reshape('c',height*width,1);
          INDArray labels2d = next.getLabels().permute(0,2,3,1).dup().reshape('c',height*width,1);
          if(k==0) {
        	   e.eval(labels2d, out2d);
        	   log.info(e.stats());
           }
           k++;
      }