diff --git a/twml/twml/contrib/metrics/metrics.py b/twml/twml/contrib/metrics/metrics.py
index dea1a5273..10693dd37 100644
--- a/twml/twml/contrib/metrics/metrics.py
+++ b/twml/twml/contrib/metrics/metrics.py
@@ -31,7 +31,8 @@ def get_eval_metric_ops(graph_output, labels, weights):
       else:
         predcol_list=list(predcols)
       for col in predcol_list:
-        assert 0 <= col < graph_output['output'].shape[class_dim], 'Invalid Prediction Column Index !'
+        if not (0 <= col < graph_output['output'].shape[class_dim]):
+          raise ValueError('Invalid Prediction Column Index !')
       preds  = tf.gather(graph_output['output'], indices=predcol_list, axis=class_dim)     # [batchSz, num_col]
       labels = tf.gather(labels, indices=predcol_list, axis=class_dim)                     # [batchSz, num_col]
 
@@ -55,7 +56,8 @@ def mean_numeric_label_topK(labels, predictions, weights, name, topK_id):
   return tf.metrics.mean(values=top_k_labels, name=name)
 
 def mean_gated_numeric_label_topK(labels, predictions, weights, name, topK_id, bar=2.0):
-  assert isinstance(bar, int) or isinstance(bar, float), "bar must be int or float"
+  if not (isinstance(bar, int) or isinstance(bar, float)):
+    raise TypeError("bar must be int or float")
   top_k_labels  = tf.gather(params=labels, indices=topK_id, axis=0)                # [topK, 1]
   gated_top_k_labels  = tf.cast(top_k_labels > bar*1.0, tf.int32)
   return tf.metrics.mean(values=gated_top_k_labels, name=name)
@@ -108,8 +110,10 @@ def get_eval_metric_ops(graph_output, labels, weights):
     if predcol is None:
       pred = graph_output['output']
     else:
-      assert 0 <= predcol < graph_output['output'].shape[1], 'Invalid Prediction Column Index !'
-      assert labelcol is not None
+      if not (0 <= predcol < graph_output['output'].shape[1]):
+        raise ValueError('Invalid Prediction Column Index !')
+      if labelcol is None:
+        raise ValueError('labelcol must be provided when predcol is set')
       pred   = tf.reshape(graph_output['output'][:, predcol], shape=[-1, 1])
       labels = tf.reshape(labels[:, labelcol], shape=[-1, 1])
     numOut = graph_output['output'].shape[1]