diff --git a/src/main/java/edu/snu/reef/dolphin/neuralnet/NeuralNetworkTask.java b/src/main/java/edu/snu/reef/dolphin/neuralnet/NeuralNetworkTask.java
index 6247a8e..2d99618 100644
--- a/src/main/java/edu/snu/reef/dolphin/neuralnet/NeuralNetworkTask.java
+++ b/src/main/java/edu/snu/reef/dolphin/neuralnet/NeuralNetworkTask.java
@@ -38,7 +38,8 @@ public final class NeuralNetworkTask implements Task {
 
   private static final Logger LOG = Logger.getLogger(NeuralNetworkTask.class.getName());
 
-  private final Validator validator;
+  private final Validator crossValidator;
+  private final Validator trainingValidator;
   private final DataParser<List<Pair<Pair<INDArray, Integer>, Boolean>>> dataParser;
   private final NeuralNetwork neuralNetwork;
   private final int maxIterations;
@@ -88,10 +89,17 @@ public void reset() {
     /**
      * @return the prediction accuracy of model.
      */
-    public float getStats() {
+    public float getAccuracy() {
       return correctNum / (float) totalNum;
     }
 
+    /**
+     * @return the prediction error of model.
+     */
+    public float getError() {
+      return 1 - getAccuracy();
+    }
+
     /**
      * @return the total number of samples that are used for evaluation.
      */
@@ -103,13 +111,13 @@ public int getTotalNum() {
   @Inject
   NeuralNetworkTask(final DataParser<List<Pair<Pair<INDArray, Integer>, Boolean>>> dataParser,
                     final NeuralNetwork neuralNetwork,
-                    @Parameter(MaxIterations.class) final int maxIterations,
-                    final Validator validator) {
+                    @Parameter(MaxIterations.class) final int maxIterations) {
     super();
     this.dataParser = dataParser;
     this.neuralNetwork = neuralNetwork;
     this.maxIterations = maxIterations;
-    this.validator = validator;
+    this.trainingValidator = new Validator(neuralNetwork);
+    this.crossValidator = new Validator(neuralNetwork);
   }
 
   /** {@inheritDoc} */
@@ -125,17 +133,21 @@ public byte[] call(final byte[] bytes) throws Exception {
         final int label = data.getFirst().getSecond();
         final boolean isValidation = data.getSecond();
         if (isValidation) {
-          validator.validate(input, label);
+          crossValidator.validate(input, label);
         } else {
           neuralNetwork.train(input, label);
+          trainingValidator.validate(input, label);
         }
       }
       LOG.log(Level.INFO, "=========================================================");
       LOG.log(Level.INFO, "Iteration: {0}", String.valueOf(i));
-      LOG.log(Level.INFO, "Result: {0}", String.valueOf(validator.getStats()));
-      LOG.log(Level.INFO, "# of validation inputs: {0}", String.valueOf(validator.getTotalNum()));
+      LOG.log(Level.INFO, "Training Error: {0}", String.valueOf(trainingValidator.getError()));
+      LOG.log(Level.INFO, "Cross Validation Error: {0}", String.valueOf(crossValidator.getError()));
+      LOG.log(Level.INFO, "# of training inputs: {0}", String.valueOf(trainingValidator.getTotalNum()));
+      LOG.log(Level.INFO, "# of validation inputs: {0}", String.valueOf(crossValidator.getTotalNum()));
       LOG.log(Level.INFO, "=========================================================");
-      validator.reset();
+      crossValidator.reset();
+      trainingValidator.reset();
     }
 
     return null;