diff --git a/exps/NAS-Bench-201/dist-setup.py b/exps/NAS-Bench-201/dist-setup.py
index a62447e..8ea4782 100644
--- a/exps/NAS-Bench-201/dist-setup.py
+++ b/exps/NAS-Bench-201/dist-setup.py
@@ -1,7 +1,9 @@
 #####################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
 #####################################################
-# [2020.03.09] Upgrade to v1.2
+# [2020.02.25] Initialize the API as v1.1
+# [2020.03.09] Upgrade the API to v1.2
+# [2020.03.16] Upgrade the API to v1.3
 import os
 from setuptools import setup
 
@@ -13,7 +15,7 @@ def read(fname='README.md'):
 
 setup(
     name = "nas_bench_201",
-    version = "1.2",
+    version = "1.3",
     author = "Xuanyi Dong",
     author_email = "dongxuanyi888@gmail.com",
     description = "API for NAS-Bench-201 (a benchmark for neural architecture search).",
diff --git a/exps/NAS-Bench-201/test-weights.py b/exps/NAS-Bench-201/test-weights.py
index 0ba08d1..abc973f 100644
--- a/exps/NAS-Bench-201/test-weights.py
+++ b/exps/NAS-Bench-201/test-weights.py
@@ -37,7 +37,8 @@ def evaluate(api, weight_dir, data: str, use_12epochs_result: bool):
   final_val_accs = OrderedDict({'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
   final_test_accs = OrderedDict({'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
   for idx in range(len(api)):
-    info = api.get_more_info(idx, data, use_12epochs_result=use_12epochs_result, is_random=False)
+    # info = api.get_more_info(idx, data, use_12epochs_result=use_12epochs_result, is_random=False)
+    # import pdb; pdb.set_trace()
     for key in ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']:
       info = api.get_more_info(idx, key, use_12epochs_result=False, is_random=False)
       if key == 'cifar10-valid':
@@ -50,7 +51,7 @@ def evaluate(api, weight_dir, data: str, use_12epochs_result: bool):
     config = api.get_net_config(idx, data)
     net = get_cell_based_tiny_net(config)
     api.reload(weight_dir, idx)
-    params = api.get_net_param(idx, data, None)
+    params = api.get_net_param(idx, data, None, use_12epochs_result=use_12epochs_result)
     cur_norms = []
     for seed, param in params.items():
       with torch.no_grad():