code: public <T extends ROC> T evaluateROC(DataSetIterator iterator, int rocThresholdSteps) {
          Layer outputLayer = getOutputLayer(0);
          if(getConfiguration().isValidateOutputLayerConfig()){
              OutputLayerUtil.validateOutputLayerForClassifierEvaluation(outputLayer.conf().getLayer(), ROC.class);
          }
          return (T)doEvaluation(iterator, new org.deeplearning4j.eval.ROC(rocThresholdSteps))[0];
      }
docs: Evaluate the network (must be a binary classifier) on the specified data, using the {@link ROC} class
  
  @param iterator          Data to evaluate on
  @param rocThresholdSteps Number of threshold steps to use with {@link ROC}
  @return ROC evaluation on the given dataset