求大神帮忙看看程序哪里有问题

mmf827950280

1. # 载入FD001数据集
   
2. file_load = 'd:/python_keras/predict aircraft engineer RUL/Dataset/PM_train_guiyihua.csv'
   
3. train_df = pd.read_csv(file_load, encoding='gbk')
   
4. # print(train_df)
   
5. new_features = ['cycle_norm', 's4', 's11', 's12', 's9', 's7', 's14', 's15',
   
6.                 's21', 's2', 's3', 's20', 'setting1', 's13', 's8', 's17']
   
7. x_t = train_df[new_features]
   
8. y_t = train_df['RUL']
   
9. 
   
10. # 模型参数设置
    
11. # 对模型进行优化（每层神经元数量， 每层丢弃率, 迭代步数， 批次样本数）
    
12. d = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
    
13. neuron = [1024, 512, 256, 128, 64, 32]
    
14. epochs = [10, 20, 50, 100, 1000]
    
15. batch_size = [10, 25, 50, 100, 200]
    
16. seq_length = [10, 20, 30, 50]
    
17. 
    
18. def model_t_c(d, n1, n2):
    
19.     model = Sequential()
    
20.     model.add(GRU(n1, input_shape=(1, 16)))
    
21.     model.add(Dropout(d))
    
22.     model.add(Dense(n2, activation='sigmoid'))
    
23.     model.add(Dense(1, activation='linear'))
    
24. 
    
25.     model.compile(optimizer='rmsprop', loss='mse', metrics='mae')
    
26.     model.summary()
    
27.     return model
    
28. 
    
29. model_t = model_t_c(0.2, 256, 256)
    
30. 
    
31. # 贝叶斯优化
    
32. hyperparameters = {"d": Real(0, 1, prior='log-uniform'), "n1":Real(0, 1e4, prior='uniform'), "n2":Real(0, 1e4, prior='uniform')}
    
33. Kmodel = KerasClassifier(build_fn=model_t, verbose=1)
    
34. bayesian = BayesSearchCV(estimator = Kmodel, search_spaces = hyperparameters, n_iter = 200, cv = 5, random_state=42, n_jobs = -1, scoring='neg_mean_squared_error')
    
35. bayesian.fit(x_t, y_t)
    
36. print(f'Best parameters: {bayesian.best_params_}')
    
37. print(f'Best score: {bayesian.best_score_}')

复制代码

执行上述代码之后会出现下面的错误，目前没有找到解决方案
TypeError: can't pickle _thread.RLock objects

suchocolate

pickle只能保存python基本类型的对象，如列表，字典。thread.RLock应该是模块中定义的对象，所以不能保存。