update timer

src/main.py

@@ -47,11 +47,26 @@ def main():
     # print(next(iter(predictions)))
 
     test_300 = test_df.sample(300)
+    test_300_2 = test_df.sample(300)
+    test_300_3 = test_df.sample(300)
+
+    predict_fn = tf.contrib.predictor.from_saved_model(save_path)
+
     time_1 = timeit.default_timer()
-    model_predict(test_300, save_path)
+    model_predict(test_300, predict_fn)
     total_1 = (timeit.default_timer() - time_1)
     print("prediction cost {:.5f} s".format(total_1))
 
+    time_2 = timeit.default_timer()
+    model_predict(test_300_2, predict_fn)
+    total_2 = (timeit.default_timer() - time_2)
+    print("prediction cost {:.5f} s".format(total_2))
+
+    time_3 = timeit.default_timer()
+    model_predict(test_300_3, predict_fn)
+    total_3 = (timeit.default_timer() - time_3)
+    print("prediction cost {:.5f} s".format(total_3))
+
     total_time = (time.time() - time_begin) / 60
     print("cost {:.2f} mins at {}".format(total_time, datetime.now()))
 

src/models/esmm/model.py

@@ -89,8 +89,8 @@ def _bytes_feature(value):
     return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
 
 
-def model_predict(inputs, model_path):
-    predict_fn = tf.contrib.predictor.from_saved_model(model_path)
+def model_predict(inputs, predict_fn):
+
     int_columns = [
         "active_type", "active_days", "card_id", "is_pure_author", "is_have_reply", "is_have_pure_reply", "content_level",
         "topic_num", "favor_num", "vote_num"