diff --git a/gnes/encoder/numeric/quantizer.py b/gnes/encoder/numeric/quantizer.py
index ebfeb395..3be5b8f1 100644
--- a/gnes/encoder/numeric/quantizer.py
+++ b/gnes/encoder/numeric/quantizer.py
@@ -36,8 +36,7 @@ def __init__(self, dim_per_byte: int, cluster_per_byte: int = 255,
         self.upper_bound = upper_bound
         self.lower_bound = lower_bound
         self.partition_method = partition_method
-        self.centroids = None
-        self._get_centroids()
+        self.centroids = self._get_centroids()
 
     def _get_centroids(self):
         """
@@ -52,7 +51,7 @@ def _get_centroids(self):
         if self.upper_bound < self.lower_bound:
             raise ValueError("upper bound is smaller than lower bound")
 
-        self.centroids = []
+        centroids = []
         num_sample_per_dim = np.ceil(pow(self.num_clusters, 1 / self.dim_per_byte)).astype(np.uint8)
         if self.partition_method == 'average':
             axis_point = np.linspace(self.lower_bound, self.upper_bound, num=num_sample_per_dim+1,
@@ -65,16 +64,13 @@ def _get_centroids(self):
             raise NotImplementedError
 
         for item in product(*coordinates):
-            self.centroids.append(list(item))
-        self.centroids = self.centroids[:self.num_clusters]
+            centroids.append(list(item))
+        return centroids[:self.num_clusters]
 
     @batching
     def encode(self, vecs: np.ndarray, *args, **kwargs) -> np.ndarray:
+        self._check_bound(vecs)
         num_bytes = self._get_num_bytes(vecs)
-        max_value, min_value = self._get_max_min_value(vecs)
-
-        self._check_bound(max_value, min_value)
-
         x = np.reshape(vecs, [vecs.shape[0], num_bytes, 1, self.dim_per_byte])
         x = np.sum(np.square(x - self.centroids), -1)
         # start from 1
@@ -93,7 +89,8 @@ def _get_num_bytes(self, vecs: np.ndarray):
     def _get_max_min_value(vecs):
         return np.amax(vecs, axis=None), np.amin(vecs, axis=None)
 
-    def _check_bound(self, max_value, min_value):
+    def _check_bound(self, vecs):
+        max_value, min_value = self._get_max_min_value(vecs)
         if self.upper_bound < max_value:
             raise Warning("upper bound (=%.3f) is smaller than max value of input data (=%.3f), you should choose"
                                 "a bigger value for upper bound" % (self.upper_bound, max_value))