for code review

fluid/chinese_ner/train.py

@@ -141,7 +141,8 @@ def ner_net(word_dict_len, label_dict_len):
             label=target,
             param_attr=fluid.ParamAttr(
                 name='crfw',
-                learning_rate=0.2, ))
+                learning_rate=0.2,
+            ))
         avg_cost = fluid.layers.mean(x=crf_cost)
         return avg_cost, emission
 
@@ -165,9 +166,11 @@ def test2(exe, chunk_evaluator, inference_program, test_data, place,
         target = to_lodtensor(map(lambda x: x[2], data), place)
         result_list = exe.run(
             inference_program,
-            feed={"word": word,
-                  "mention": mention,
-                  "target": target},
+            feed={
+                "word": word,
+                "mention": mention,
+                "target": target
+            },
             fetch_list=cur_fetch_list)
         number_infer = np.array(result_list[0])
         number_label = np.array(result_list[1])
@@ -186,14 +189,16 @@ def test(test_exe, chunk_evaluator, inference_program, test_data, place,
         target = to_lodtensor(map(lambda x: x[2], data), place)
         result_list = test_exe.run(
             fetch_list=cur_fetch_list,
-            feed={"word": word,
-                  "mention": mention,
-                  "target": target})
+            feed={
+                "word": word,
+                "mention": mention,
+                "target": target
+            })
         number_infer = np.array(result_list[0])
         number_label = np.array(result_list[1])
         number_correct = np.array(result_list[2])
-        chunk_evaluator.update(number_infer.sum(),
-                               number_label.sum(), number_correct.sum())
+        chunk_evaluator.update(number_infer.sum(), number_label.sum(),
+                               number_correct.sum())
     return chunk_evaluator.eval()
 
 
@@ -208,12 +213,11 @@ def main(train_data_file, test_data_file, model_save_dir, num_passes):
     main = fluid.Program()
     startup = fluid.Program()
     with fluid.program_guard(main, startup):
-        avg_cost, feature_out, word, mention, target = ner_net(word_dict_len,
-                                                               label_dict_len)
+        avg_cost, feature_out, word, mention, target = ner_net(
+            word_dict_len, label_dict_len)
 
         crf_decode = fluid.layers.crf_decoding(
-            input=feature_out, param_attr=fluid.ParamAttr(
-                name='crfw'))
+            input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
         sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
         sgd_optimizer.minimize(avg_cost)
@@ -277,20 +281,20 @@ def main(train_data_file, test_data_file, model_save_dir, num_passes):
                 except StopIteration:
                     break
             end_time = time.time()
-            print("pass_id:" + str(pass_id) + ", time_cost:" + str(
-                end_time - start_time) + "s")
+            print("pass_id:" + str(pass_id) + ", time_cost:" +
+                  str(end_time - start_time) + "s")
             precision, recall, f1_score = chunk_evaluator.eval()
-            print("[Train] precision:" + str(precision) + ", recall:" + str(
-                recall) + ", f1:" + str(f1_score))
+            print("[Train] precision:" + str(precision) + ", recall:" +
+                  str(recall) + ", f1:" + str(f1_score))
             p, r, f1 = test2(
                 exe, chunk_evaluator, inference_program, test_reader, place,
                 [num_infer_chunks, num_label_chunks, num_correct_chunks])
-            print("[Test] precision:" + str(p) + ", recall:" + str(r) + ", f1:"
-                  + str(f1))
+            print("[Test] precision:" + str(p) + ", recall:" + str(r) +
+                  ", f1:" + str(f1))
             save_dirname = os.path.join(model_save_dir,
                                         "params_pass_%d" % pass_id)
-            fluid.io.save_inference_model(
-                save_dirname, ['word', 'mention'], [crf_decode], exe)
+            fluid.io.save_inference_model(save_dirname, ['word', 'mention'],
+                                          [crf_decode], exe)
 
 
 if __name__ == "__main__":