diff --git a/elastic-ctr/client/api/python/elasticctr/elastic_ctr_api.py b/elastic-ctr/client/api/python/elasticctr/elastic_ctr_api.py
index a8101f8b9394f9f143ebf7a77e25d2d2ed6652d0..b9ef7ef402bcb5dc92d1a6f21ec875e6ef85c8d7 100644
--- a/elastic-ctr/client/api/python/elasticctr/elastic_ctr_api.py
+++ b/elastic-ctr/client/api/python/elasticctr/elastic_ctr_api.py
@@ -50,6 +50,9 @@ class ElasticCTRAPI(object):
         self._instances += instance
         return instance
 
+    def clear(self):
+        self._instances = []
+
     def add_slot(self, instance, slot, feasigns):
         if not isinstance(instance, list):
             print("add slot: parameter invalid: instance should be list")
diff --git a/elastic-ctr/client/demo/elastic_ctr.py b/elastic-ctr/client/demo/elastic_ctr.py
index 30873437c67d58c3a816ad344019bbeaa7503d54..91695fd6656345b26a76807e0d3c258ba3adf81f 100644
--- a/elastic-ctr/client/demo/elastic_ctr.py
+++ b/elastic-ctr/client/demo/elastic_ctr.py
@@ -43,6 +43,7 @@ def data_reader(data_file, samples, labels):
             sample = {}
             line = line.rstrip('\n')
             feature_slots = line.split(' ')
+            labels.append(int(feature_slots[1]))
             feature_slots = feature_slots[2:]
             feature_slot_maps = [x.split(':') for x in feature_slots]
 
@@ -89,7 +90,9 @@ if __name__ == "__main__":
 
     ret = data_reader(sys.argv[4], samples, labels)
 
+    correct = 0
     for i in range(0, len(samples) - BATCH_SIZE, BATCH_SIZE):
+        api.clear()
         batch = samples[i:i + BATCH_SIZE]
         instances = []
         for sample in batch:
@@ -102,5 +105,22 @@ if __name__ == "__main__":
                     api.add_slot(instance, k, v)
 
         ret = api.inference()
-        print(ret)
-        sys.exit(0)
+        ret = json.loads(ret)
+        predictions = ret["predictions"]
+
+        idx = 0
+        for x in predictions:
+            if x["prob0"] >= x["prob1"]:
+                pred = 0
+            else:
+                pred = 1
+
+            if labels[i + idx] == pred:
+                correct += 1
+            else:
+                print("id=%d predict incorrect: pred=%d label=%d (%f %f)" %
+                      (i + idx, pred, labels[i + idx], x["prob0"], x["prob1"]))
+
+            idx = idx + 1
+
+    print("Acc=%f" % (float(correct) / len(samples)))