segmentation_unet.java

 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";
    public void run(String[] args) throws Exception {
   	
        log.info("Load data....");
        MyPathLabelGenerator labelMaker = new MyPathLabelGenerator(DATA_PATH);
       
        File trainData = new File(DATA_PATH+"/trainI/images/");
        File testData = new File(DATA_PATH+"/testI/images/");
                
        FileSplit train = new FileSplit(trainData, NativeImageLoader.ALLOWED_FORMATS, rng);
        FileSplit test = new FileSplit(testData, NativeImageLoader.ALLOWED_FORMATS, rng);
            			
        ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);

        recordReader.initialize(train);
 
        int labelIndex = 1;     //You have 2 Writables ("columns") - index 0 is features image NDArrayWritable, index 1 is labels image NDArrayWritable

        // DataSet Iterator
        DataSetIterator dataTrainIter = new RecordReaderDataSetIterator(recordReader, batchSize, labelIndex, labelIndex, true);
        DataSetIterator dataTestIter = new RecordReaderDataSetIterator(recordReader, 1,labelIndex, labelIndex, true);

        //Normalize the  data
        ImagePreProcessingScaler1  scaler = new ImagePreProcessingScaler1(0, 1);
        dataTrainIter.setPreProcessor(scaler);
        
        log.info("Build model....");
        ComputationGraphConfiguration.GraphBuilder graph = unetBuilder();

        graph.addInputs("input").setInputTypes(InputType.convolutional(inputShape[0], inputShape[1], inputShape[2]));
        ComputationGraphConfiguration conf = graph.build();
        ComputationGraph model = new ComputationGraph(conf);
        model.init();

        model.setListeners(new ScoreIterationListener(100));
       
        log.info("Train model....");

	    Instant start = Instant.now();
	   
        for (i=0;i<epochs;i++) {	
           model.fit(dataTrainIter);
        }
        Instant finish = Instant.now();
        log.info("Training time(Minutes): s:"+start.toString()+" f:"+finish.toString()+" duration:"+Duration.between(start, finish).toMinutes());