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

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

    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++;
      }

AbdelmajidB

       ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);
        recordReader.initialize(train);
        Field f = BaseImageRecordReader.class.getDeclaredField("imageLoader");
        f.setAccessible(true);
        f.set(recordReader, new NativeImageLoader(height, width, 1, BaseImageLoader.MultiPageMode.MINIBATCH));
       
        int labelIndex = 1;     //You have 2 Writables ("columns") - index 0 is features image NDArrayWritable, index 1 is labels image NDArrayWritable

        // DataSet Iterator
        DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, labelIndex, labelIndex, true);

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

Exception in thread "main" java.lang.IllegalStateException: Input and label arrays do not have same shape: [24, 3, 96, 96] vs. [24, 1, 96, 96]
	at org.nd4j.base.Preconditions.throwStateEx(Preconditions.java:641)
	at org.nd4j.base.Preconditions.checkState(Preconditions.java:340)
	at org.deeplearning4j.nn.layers.convolution.CnnLossLayer.backpropGradient(CnnLossLayer.java:80)
	at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doBackward(LayerVertex.java:149)
	at org.deeplearning4j.nn.graph.ComputationGraph.calcBackpropGradients(ComputationGraph.java:2663)
	at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1378)
	at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1338)
	at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:160)
	at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:63)