diff --git a/keras_wrapper/extra/evaluation.py b/keras_wrapper/extra/evaluation.py
index 6bc7181..ee79f30 100644
--- a/keras_wrapper/extra/evaluation.py
+++ b/keras_wrapper/extra/evaluation.py
@@ -242,24 +242,53 @@ def multiclass_metrics(pred_list, verbose, extra_vars, split):
 
     # Compute accuracy
     accuracy = sklearn_metrics.accuracy_score(y_gt, y_pred)
-    accuracy_balanced = sklearn_metrics.accuracy_score(y_gt, y_pred, sample_weight=sample_weights)
+    accuracy_3 = __top_k_accuracy(y_gt, y_pred, 3)
+    accuracy_5 = __top_k_accuracy(y_gt, y_pred, 5)
+    #accuracy_balanced = sklearn_metrics.accuracy_score(y_gt, y_pred, sample_weight=sample_weights, )
+
+    # The following two lines should both provide the same measure (balanced accuracy)
+    #_, accuracy_balanced, _, _ = sklearn_metrics.precision_recall_fscore_support(y_gt, y_pred, average='macro')
+    accuracy_balanced = sklearn_metrics.balanced_accuracy_score(y_gt, y_pred)
+
     # Compute Precision, Recall and F1 score
     precision, recall, f1, _ = sklearn_metrics.precision_recall_fscore_support(y_gt, y_pred, average='micro')
 
     if verbose > 0:
         logging.info('Accuracy: %f' % accuracy)
+        logging.info('Accuracy top-3: %f' % accuracy_3)
+        logging.info('Accuracy top-5: %f' % accuracy_5)
         logging.info('Balanced Accuracy: %f' % accuracy_balanced)
         logging.info('Precision: %f' % precision)
         logging.info('Recall: %f' % recall)
         logging.info('F1 score: %f' % f1)
 
     return {'accuracy': accuracy,
+            'accuracy_top_3': accuracy_3,
+            'accuracy_top_5': accuracy_5,
             'accuracy_balanced': accuracy_balanced,
             'precision': precision,
             'recall': recall,
             'f1': f1}
 
 
+def __top_k_accuracy(truths, preds, k):
+    """
+    Both preds and truths are same shape m by n (m is number of predictions and n is number of classes)
+
+    :param preds:
+    :param truths:
+    :param k:
+    :return:
+    """
+    best_k = np.argsort(preds, axis=1)[:, -k:]
+    ts = np.argmax(truths, axis=1)
+    successes = 0
+    for i in range(ts.shape[0]):
+      if ts[i] in best_k[i,:]:
+        successes += 1
+    return float(successes)/ts.shape[0]
+
+
 def semantic_segmentation_accuracy(pred_list, verbose, extra_vars, split):
     """
     Semantic Segmentation Accuracy metric