2025-05-22 11:00:08,282 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:00:08,613 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 11:00:11,212 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:00:14,107 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:00:14,110 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:00:14,392 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 11:00:14,401 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 11:00:14,415 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 11:00:14,419 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 11:00:14,423 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 11:00:14,427 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 11:00:14,463 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 11:00:14,463 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 11:00:14,463 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 11:00:14,463 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 11:00:14,464 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 11:00:14,464 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 11:00:15,484 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 11:00:15,484 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 11:00:15,485 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 11:00:15,485 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - 训练集损失函数为：[9.0069e-01 3.1528e-01 9.7720e-02 2.6780e-02 6.4900e-03 1.4300e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 11:00:28,458 - tf_lstm.py - INFO - 训练集损失函数为：[9.0124e-01 3.1763e-01 9.9520e-02 2.7860e-02 7.1800e-03 1.9800e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 11:00:28,459 - tf_lstm.py - INFO - 验证集损失函数为：[5.0957e-01 1.6535e-01 4.7580e-02 1.2060e-02 2.7500e-03 6.1000e-04 1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 11:00:28,459 - tf_lstm.py - INFO - 验证集损失函数为：[0.51216 0.16791 0.0493 0.01325 0.00369 0.00147 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 11:00:28,518 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e934cf784a6b4a7b99c0b - insert_trained_model_into_mongo 2025-05-22 11:00:28,528 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e934c6e3c6f67919b8a61 - insert_trained_model_into_mongo 2025-05-22 11:00:28,553 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e934c6e3c6f67919b8a63 - insert_scaler_model_into_mongo 2025-05-22 11:00:28,559 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e934cf784a6b4a7b99c0d - insert_scaler_model_into_mongo 2025-05-22 11:00:29,840 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task 2025-05-22 11:07:40,679 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:07:44,165 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 11:08:09,067 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:13:00,030 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:13:00,144 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 11:13:02,718 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:13:05,631 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:13:05,632 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 11:13:05,855 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 11:13:05,855 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 11:13:05,873 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 11:13:05,873 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 11:13:05,881 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 11:13:05,881 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 11:13:05,909 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 11:13:05,909 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 11:13:05,909 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 11:13:05,909 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 11:13:05,909 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 11:13:05,910 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 11:13:06,849 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 11:13:06,850 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 11:13:06,872 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 11:13:06,872 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 11:13:19,649 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 11:13:19,649 - tf_lstm.py - INFO - 训练集损失函数为：[9.0405e-01 3.1641e-01 9.7950e-02 2.6820e-02 6.5000e-03 1.4300e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 11:13:19,649 - tf_lstm.py - INFO - 验证集损失函数为：[5.1157e-01 1.6582e-01 4.7650e-02 1.2070e-02 2.7500e-03 6.1000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 11:13:19,659 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 11:13:19,660 - tf_lstm.py - INFO - 训练集损失函数为：[9.0396e-01 3.1831e-01 9.9550e-02 2.7800e-02 7.1400e-03 1.9600e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 11:13:19,660 - tf_lstm.py - INFO - 验证集损失函数为：[0.51355 0.16811 0.04924 0.01319 0.00367 0.00146 0.00101 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 11:13:19,705 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e964ff0d6786d32a727ec - insert_trained_model_into_mongo 2025-05-22 11:13:19,709 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682e964f3e4b222ab549997c - insert_trained_model_into_mongo 2025-05-22 11:13:19,728 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e964ff0d6786d32a727ee - insert_scaler_model_into_mongo 2025-05-22 11:13:19,733 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682e964f3e4b222ab549997e - insert_scaler_model_into_mongo 2025-05-22 11:13:21,022 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task 2025-05-22 13:06:09,682 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:06:09,939 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 13:06:12,715 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:06:15,661 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:06:15,662 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:06:15,932 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 13:06:15,932 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 13:06:15,959 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:06:15,959 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:06:15,967 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 13:06:15,968 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 13:06:16,004 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:06:16,004 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:06:16,005 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:06:16,005 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:06:16,005 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:06:16,005 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:06:17,007 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:06:17,007 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:06:17,007 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:06:17,007 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:06:29,989 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:06:29,990 - tf_lstm.py - INFO - 训练集损失函数为：[9.0225e-01 3.1749e-01 9.9030e-02 2.7310e-02 6.6500e-03 1.4700e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 13:06:29,990 - tf_lstm.py - INFO - 验证集损失函数为：[0.50924 0.16612 0.04859 0.013 0.00362 0.00146 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 13:06:29,990 - tf_lstm.py - INFO - 验证集损失函数为：[5.1195e-01 1.6715e-01 4.8400e-02 1.2340e-02 2.8200e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 13:06:30,043 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb0d6741733b69380e30e - insert_trained_model_into_mongo 2025-05-22 13:06:30,044 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb0d63e5e1c931e47042e - insert_trained_model_into_mongo 2025-05-22 13:06:30,052 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb0d6741733b69380e310 - insert_scaler_model_into_mongo 2025-05-22 13:06:30,052 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb0d63e5e1c931e470430 - insert_scaler_model_into_mongo 2025-05-22 13:06:31,384 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 13:21:28,302 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:21:32,961 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 13:21:55,981 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:22:19,846 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:22:19,965 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:22:32,298 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 13:22:32,299 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 13:22:49,121 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:22:49,123 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:22:59,511 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 13:22:59,519 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 13:22:59,558 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:22:59,558 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:22:59,560 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:22:59,565 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:22:59,565 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:22:59,567 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:23:14,175 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:23:14,175 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:23:14,176 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:23:14,177 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:23:52,897 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:23:52,897 - tf_lstm.py - INFO - 训练集损失函数为：[9.0598e-01 3.1883e-01 9.9640e-02 2.7810e-02 7.1500e-03 1.9700e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 13:23:52,898 - tf_lstm.py - INFO - 验证集损失函数为：[0.51461 0.16825 0.04928 0.0132 0.00367 0.00147 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 13:23:52,944 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:23:52,945 - tf_lstm.py - INFO - 训练集损失函数为：[9.0190e-01 3.1699e-01 9.8690e-02 2.7170e-02 6.6100e-03 1.4600e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 13:23:52,945 - tf_lstm.py - INFO - 验证集损失函数为：[5.1143e-01 1.6671e-01 4.8180e-02 1.2270e-02 2.8100e-03 6.3000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 13:23:58,957 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb4ee215b380c4e277f3d - insert_trained_model_into_mongo 2025-05-22 13:23:58,962 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb4eef0381e0011af450d - insert_trained_model_into_mongo 2025-05-22 13:23:58,970 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb4ee215b380c4e277f3f - insert_scaler_model_into_mongo 2025-05-22 13:23:58,987 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb4eef0381e0011af450f - insert_scaler_model_into_mongo 2025-05-22 13:25:06,867 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:25:10,017 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 13:25:33,151 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:25:56,908 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:25:56,915 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:31:51,198 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:31:51,314 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 13:31:53,876 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:31:56,769 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:31:56,769 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:31:56,973 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 13:31:56,974 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 13:31:56,992 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:31:56,993 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:31:57,000 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 13:31:57,001 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 13:31:57,027 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:31:57,027 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:31:57,028 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:31:57,028 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:31:57,029 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:31:57,029 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:31:57,948 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:31:57,949 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:31:57,957 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:31:57,958 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:32:10,777 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 训练集损失函数为：[8.9570e-01 3.1407e-01 9.7760e-02 2.7200e-02 6.9900e-03 1.9300e-03 8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 训练集损失函数为：[9.0424e-01 3.1647e-01 9.8080e-02 2.6880e-02 6.5100e-03 1.4300e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 验证集损失函数为：[0.5076 0.16537 0.04823 0.01291 0.0036 0.00145 0.00101 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 13:32:10,778 - tf_lstm.py - INFO - 验证集损失函数为：[5.1157e-01 1.6595e-01 4.7750e-02 1.2100e-02 2.7500e-03 6.1000e-04 1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 13:32:10,816 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb6daed827ab3f0037eb2 - insert_trained_model_into_mongo 2025-05-22 13:32:10,816 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb6dad7c91141fbffba57 - insert_trained_model_into_mongo 2025-05-22 13:32:10,848 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb6dad7c91141fbffba59 - insert_scaler_model_into_mongo 2025-05-22 13:32:10,863 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb6daed827ab3f0037eb4 - insert_scaler_model_into_mongo 2025-05-22 13:32:12,164 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 13:36:17,718 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:36:17,835 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 13:36:20,536 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:36:23,520 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:36:23,524 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:36:23,730 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 13:36:23,730 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 13:36:23,748 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:36:23,748 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:36:23,757 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 13:36:23,757 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 13:36:23,784 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:36:23,784 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:36:23,785 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:36:23,786 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:36:23,786 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:36:23,787 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:36:24,750 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:36:24,751 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:36:24,755 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:36:24,755 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:36:37,674 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:36:37,674 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:36:37,675 - tf_lstm.py - INFO - 训练集损失函数为：[9.0215e-01 3.1682e-01 9.8480e-02 2.7050e-02 6.5600e-03 1.4400e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 13:36:37,675 - tf_lstm.py - INFO - 验证集损失函数为：[0.51142 0.16709 0.04888 0.01311 0.00366 0.00147 0.00102 0.00093 0.00091 0.0009 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 13:36:37,675 - tf_lstm.py - INFO - 验证集损失函数为：[5.1143e-01 1.6646e-01 4.8020e-02 1.2190e-02 2.7800e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 13:36:37,713 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb7e5627b101c684d84af - insert_trained_model_into_mongo 2025-05-22 13:36:37,720 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb7e5627b101c684d84b1 - insert_scaler_model_into_mongo 2025-05-22 13:36:37,736 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eb7e5c78eb332b44d57b5 - insert_trained_model_into_mongo 2025-05-22 13:36:37,757 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eb7e5c78eb332b44d57b7 - insert_scaler_model_into_mongo 2025-05-22 13:36:39,035 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 13:43:10,995 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:43:14,416 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 13:43:37,750 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:44:04,231 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:44:04,235 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 13:45:28,979 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 13:45:28,979 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 13:45:29,000 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:45:29,001 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 13:45:29,028 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 13:45:29,029 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 13:45:29,071 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:45:29,072 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:45:29,073 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 13:45:29,073 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 13:45:29,074 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:45:29,075 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 13:45:31,139 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:45:31,140 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:45:31,142 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 13:45:31,143 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 13:46:07,442 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:46:07,442 - tf_lstm.py - INFO - 训练集损失函数为：[8.9715e-01 3.1446e-01 9.7490e-02 2.6710e-02 6.4700e-03 1.4300e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 13:46:07,443 - tf_lstm.py - INFO - 验证集损失函数为：[5.0805e-01 1.6497e-01 4.7450e-02 1.2030e-02 2.7400e-03 6.1000e-04 1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 13:46:07,584 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 13:46:07,584 - tf_lstm.py - INFO - 训练集损失函数为：[8.9563e-01 3.1477e-01 9.8380e-02 2.7490e-02 7.0800e-03 1.9500e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 13:46:07,584 - tf_lstm.py - INFO - 验证集损失函数为：[0.50812 0.16611 0.04868 0.01307 0.00365 0.00146 0.00101 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 13:46:14,116 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eba26770d6ffe7e22a033 - insert_trained_model_into_mongo 2025-05-22 13:46:14,120 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eba26d439ed5d688f8e53 - insert_trained_model_into_mongo 2025-05-22 13:46:14,129 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eba26770d6ffe7e22a035 - insert_scaler_model_into_mongo 2025-05-22 13:46:14,166 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eba26d439ed5d688f8e55 - insert_scaler_model_into_mongo 2025-05-22 14:22:14,267 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:22:14,388 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 14:22:17,074 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:22:19,916 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:22:22,871 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:22:22,872 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:22:23,608 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task 2025-05-22 14:26:54,980 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:26:55,106 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 14:26:57,683 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:00,530 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:03,427 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:03,432 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:04,177 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task 2025-05-22 14:27:41,884 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:42,000 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 14:27:44,598 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:47,308 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:50,203 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:50,204 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 14:27:50,954 - task_worker.py - ERROR - Area 1002 failed: Can only merge Series or DataFrame objects, a was passed - region_task 2025-05-22 15:56:08,387 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:56:08,656 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 15:56:11,294 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:56:14,276 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:56:14,282 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:56:14,515 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 15:56:14,534 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 15:56:14,537 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 15:56:14,546 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 15:56:14,553 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 15:56:14,561 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 15:56:14,583 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 15:56:14,583 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 15:56:14,584 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 15:56:14,588 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 15:56:14,588 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 15:56:14,589 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 15:56:15,565 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 15:56:15,566 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 15:56:15,566 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 15:56:15,566 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 15:56:28,464 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 15:56:28,464 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 训练集损失函数为：[8.9989e-01 3.1527e-01 9.8050e-02 2.7280e-02 7.0200e-03 1.9400e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 训练集损失函数为：[9.0568e-01 3.1746e-01 9.8530e-02 2.7070e-02 6.5900e-03 1.4600e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 验证集损失函数为：[0.50977 0.16589 0.04836 0.01296 0.00362 0.00146 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 15:56:28,465 - tf_lstm.py - INFO - 验证集损失函数为：[5.1289e-01 1.6660e-01 4.8030e-02 1.2220e-02 2.8000e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 15:56:28,507 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8acd5726df85922d205 - insert_trained_model_into_mongo 2025-05-22 15:56:28,522 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8ac2e1adc5f49fe0f09 - insert_trained_model_into_mongo 2025-05-22 15:56:28,529 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8acd5726df85922d207 - insert_scaler_model_into_mongo 2025-05-22 15:56:28,543 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8ac2e1adc5f49fe0f0b - insert_scaler_model_into_mongo 2025-05-22 15:56:29,862 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 15:57:07,940 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:57:08,055 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 15:57:10,632 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:57:13,508 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:57:13,518 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 15:57:13,711 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 15:57:13,718 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 15:57:13,729 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 15:57:13,736 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 15:57:13,737 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 15:57:13,744 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 15:57:13,770 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 15:57:13,770 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 15:57:13,770 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 15:57:13,776 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 15:57:13,776 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 15:57:13,777 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 15:57:14,694 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 15:57:14,694 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 15:57:14,712 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 15:57:14,713 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 15:57:27,628 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 15:57:27,628 - tf_lstm.py - INFO - 训练集损失函数为：[9.1627e-01 3.2273e-01 1.0049e-01 2.7670e-02 6.7300e-03 1.4900e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 15:57:27,628 - tf_lstm.py - INFO - 验证集损失函数为：[5.2048e-01 1.6975e-01 4.9060e-02 1.2500e-02 2.8600e-03 6.3000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 15:57:27,654 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 15:57:27,655 - tf_lstm.py - INFO - 训练集损失函数为：[9.1329e-01 3.2208e-01 1.0066e-01 2.8050e-02 7.1900e-03 1.9700e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 15:57:27,655 - tf_lstm.py - INFO - 验证集损失函数为：[0.51954 0.17006 0.04973 0.01329 0.00368 0.00147 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 15:57:27,670 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8e7d0ad45b5ef1ff99f - insert_trained_model_into_mongo 2025-05-22 15:57:27,693 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ed8e7952fc3d26b39b369 - insert_trained_model_into_mongo 2025-05-22 15:57:27,698 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8e7d0ad45b5ef1ff9a1 - insert_scaler_model_into_mongo 2025-05-22 15:57:27,700 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ed8e7952fc3d26b39b36b - insert_scaler_model_into_mongo 2025-05-22 15:57:28,989 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 16:00:26,751 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:02:27,969 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:02:51,521 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:03:20,174 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:03:20,244 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:03:43,812 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:03:43,818 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:03:43,837 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:03:43,843 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:03:43,869 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:03:43,874 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:03:43,915 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:03:43,916 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:03:43,918 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:03:43,920 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:03:43,920 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:03:43,922 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:03:45,818 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:03:45,820 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:03:45,842 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:03:45,842 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:04:23,192 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 训练集损失函数为：[9.0817e-01 3.2013e-01 1.0016e-01 2.7980e-02 7.1900e-03 1.9700e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:04:23,192 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 验证集损失函数为：[0.51636 0.16911 0.04957 0.01328 0.00369 0.00147 0.00101 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 训练集损失函数为：[9.0438e-01 3.1823e-01 9.9190e-02 2.7320e-02 6.6400e-03 1.4700e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:04:23,193 - tf_lstm.py - INFO - 验证集损失函数为：[5.1324e-01 1.6746e-01 4.8450e-02 1.2330e-02 2.8200e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:04:28,183 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eda8ce1472c691263abcd - insert_trained_model_into_mongo 2025-05-22 16:04:28,184 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eda8ca57967ec2470f538 - insert_trained_model_into_mongo 2025-05-22 16:04:28,234 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eda8ca57967ec2470f53a - insert_scaler_model_into_mongo 2025-05-22 16:04:28,235 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eda8ce1472c691263abcf - insert_scaler_model_into_mongo 2025-05-22 16:15:20,362 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:15:20,482 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:15:23,124 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:15:26,082 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:15:26,083 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:15:26,299 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:15:26,299 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:15:26,318 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:15:26,318 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:15:26,326 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:15:26,326 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:15:26,353 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:15:26,353 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:15:26,353 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:15:26,353 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:15:26,353 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:15:26,353 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:15:27,303 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:15:27,303 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:15:27,303 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:15:27,303 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - 训练集损失函数为：[9.0386e-01 3.1856e-01 9.9750e-02 2.7890e-02 7.1800e-03 1.9800e-03 8.3000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - 训练集损失函数为：[8.9587e-01 3.1399e-01 9.7500e-02 2.6790e-02 6.5000e-03 1.4400e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:15:40,212 - tf_lstm.py - INFO - 验证集损失函数为：[0.51364 0.16831 0.04936 0.01325 0.0037 0.00148 0.00101 0.00092 0.0009 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:15:40,213 - tf_lstm.py - INFO - 验证集损失函数为：[5.0722e-01 1.6484e-01 4.7540e-02 1.2080e-02 2.7600e-03 6.1000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:15:40,256 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edd2c58422a3c9f4e08aa - insert_trained_model_into_mongo 2025-05-22 16:15:40,266 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edd2c1c34fc32963f2881 - insert_trained_model_into_mongo 2025-05-22 16:15:40,287 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edd2c58422a3c9f4e08ac - insert_scaler_model_into_mongo 2025-05-22 16:15:40,289 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edd2c1c34fc32963f2883 - insert_scaler_model_into_mongo 2025-05-22 16:15:41,578 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 16:18:18,375 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:18:18,503 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:18:21,225 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:18:24,210 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:18:24,216 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:18:24,419 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:18:24,420 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:18:24,439 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:18:24,440 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:18:24,447 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:18:24,448 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:18:24,475 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:18:24,475 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:18:24,476 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:18:24,476 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:18:24,476 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:18:24,477 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:18:25,429 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:18:25,429 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:18:25,431 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:18:25,431 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:18:38,854 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:18:38,854 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:18:38,854 - tf_lstm.py - INFO - 训练集损失函数为：[8.9569e-01 3.1485e-01 9.8280e-02 2.7410e-02 7.0500e-03 1.9500e-03 8.2000e-04 6.0000e-04 5.6000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:18:38,855 - tf_lstm.py - INFO - 训练集损失函数为：[9.1019e-01 3.2047e-01 9.9880e-02 2.7530e-02 6.7100e-03 1.4800e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:18:38,855 - tf_lstm.py - INFO - 验证集损失函数为：[0.50823 0.16598 0.0485 0.01302 0.00365 0.00147 0.00102 0.00093 0.00091 0.0009 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:18:38,855 - tf_lstm.py - INFO - 验证集损失函数为：[5.1680e-01 1.6865e-01 4.8800e-02 1.2440e-02 2.8500e-03 6.3000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:18:38,892 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eddde166c956a98e4cdb6 - insert_trained_model_into_mongo 2025-05-22 16:18:38,903 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682eddde9239166b91e13257 - insert_trained_model_into_mongo 2025-05-22 16:18:38,910 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eddde9239166b91e13259 - insert_scaler_model_into_mongo 2025-05-22 16:18:38,924 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682eddde166c956a98e4cdb8 - insert_scaler_model_into_mongo 2025-05-22 16:19:36,327 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:19:36,452 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:19:39,140 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:19:42,056 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:19:42,056 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:19:42,261 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:19:42,261 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:19:42,279 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:19:42,279 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:19:42,287 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:19:42,287 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:19:42,316 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:19:42,316 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:19:42,317 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:19:42,317 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:19:42,317 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:19:42,317 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:19:43,248 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:19:43,249 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:19:43,261 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:19:43,261 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:19:56,132 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:19:56,132 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:19:56,133 - tf_lstm.py - INFO - 训练集损失函数为：[9.0371e-01 3.1890e-01 9.9980e-02 2.7980e-02 7.2000e-03 1.9800e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:19:56,133 - tf_lstm.py - INFO - 验证集损失函数为：[0.51401 0.16865 0.04951 0.01329 0.0037 0.00147 0.00101 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:19:56,133 - tf_lstm.py - INFO - 验证集损失函数为：[5.1199e-01 1.6678e-01 4.8120e-02 1.2220e-02 2.7800e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:19:56,191 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ede2c6f86cd09523a290d - insert_trained_model_into_mongo 2025-05-22 16:19:56,191 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ede2c82774bd11b16d043 - insert_trained_model_into_mongo 2025-05-22 16:19:56,209 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ede2c6f86cd09523a290f - insert_scaler_model_into_mongo 2025-05-22 16:19:56,223 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ede2c82774bd11b16d045 - insert_scaler_model_into_mongo 2025-05-22 16:19:57,513 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 16:23:41,614 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:23:47,120 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:24:10,592 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:24:39,240 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:24:39,242 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:24:39,724 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:24:39,726 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:24:39,745 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:24:39,748 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:24:39,774 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:24:39,776 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:24:39,816 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:24:39,816 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:24:39,817 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:24:39,817 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:24:39,819 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:24:39,819 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:24:41,717 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:24:41,717 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:24:41,748 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:24:41,748 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - 训练集损失函数为：[9.0181e-01 3.1616e-01 9.7980e-02 2.6830e-02 6.4900e-03 1.4300e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:25:18,445 - tf_lstm.py - INFO - 训练集损失函数为：[8.9533e-01 3.1324e-01 9.7340e-02 2.7070e-02 6.9500e-03 1.9200e-03 8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:25:18,446 - tf_lstm.py - INFO - 验证集损失函数为：[5.1082e-01 1.6583e-01 4.7670e-02 1.2070e-02 2.7500e-03 6.1000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:25:18,446 - tf_lstm.py - INFO - 验证集损失函数为：[0.50668 0.16475 0.04801 0.01285 0.00359 0.00145 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:25:18,550 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edf6e4f4d66c1538080fc - insert_trained_model_into_mongo 2025-05-22 16:25:18,562 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edf6e4f4d66c1538080fe - insert_scaler_model_into_mongo 2025-05-22 16:25:18,564 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682edf6ee396be238e929ca2 - insert_trained_model_into_mongo 2025-05-22 16:25:18,576 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682edf6ee396be238e929ca4 - insert_scaler_model_into_mongo 2025-05-22 16:36:42,098 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:37:06,300 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:37:29,931 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:37:56,059 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:37:56,060 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:37:56,556 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:37:56,556 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:37:56,578 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:37:56,578 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:37:56,606 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:37:56,606 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:37:56,647 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:37:56,647 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:37:56,647 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:37:56,649 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:37:56,649 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:37:58,554 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:37:58,554 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:37:58,580 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:37:58,580 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:38:35,605 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:38:35,605 - tf_lstm.py - INFO - 训练集损失函数为：[9.0578e-01 3.1937e-01 1.0006e-01 2.7970e-02 7.1800e-03 1.9700e-03 8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:38:35,606 - tf_lstm.py - INFO - 验证集损失函数为：[0.51488 0.16886 0.04954 0.01328 0.00368 0.00146 0.001 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:38:35,635 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:38:35,635 - tf_lstm.py - INFO - 训练集损失函数为：[8.9900e-01 3.1653e-01 9.8870e-02 2.7350e-02 6.6900e-03 1.4900e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:38:35,636 - tf_lstm.py - INFO - 验证集损失函数为：[5.1022e-01 1.6676e-01 4.8400e-02 1.2390e-02 2.8500e-03 6.4000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:38:35,708 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee28b27b5da75067d35a8 - insert_trained_model_into_mongo 2025-05-22 16:38:35,719 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee28b7a38bab92a0a5a3a - insert_trained_model_into_mongo 2025-05-22 16:38:35,731 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee28b7a38bab92a0a5a3c - insert_scaler_model_into_mongo 2025-05-22 16:38:35,741 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee28b27b5da75067d35aa - insert_scaler_model_into_mongo 2025-05-22 16:41:06,720 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:41:06,848 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:41:09,615 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:41:36,827 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:41:52,435 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:42:15,970 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:42:47,697 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:42:47,703 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:42:48,195 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:42:48,200 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:42:48,218 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:42:48,221 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:42:48,246 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:42:48,249 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:42:48,289 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:42:48,289 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:42:48,291 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:42:48,291 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:42:48,291 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:42:48,293 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:42:50,221 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:42:50,222 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:42:50,251 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:42:50,251 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:43:27,237 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:43:27,238 - tf_lstm.py - INFO - 训练集损失函数为：[8.9134e-01 3.1183e-01 9.6960e-02 2.7010e-02 6.9500e-03 1.9300e-03 8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:43:27,238 - tf_lstm.py - INFO - 验证集损失函数为：[0.50446 0.16402 0.04786 0.01284 0.0036 0.00145 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:43:27,314 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:43:27,314 - tf_lstm.py - INFO - 训练集损失函数为：[8.9997e-01 3.1582e-01 9.7960e-02 2.6850e-02 6.5100e-03 1.4400e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:43:27,314 - tf_lstm.py - INFO - 验证集损失函数为：[5.1007e-01 1.6575e-01 4.7690e-02 1.2090e-02 2.7600e-03 6.1000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:43:27,336 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee3afa37ce5e300dffc58 - insert_trained_model_into_mongo 2025-05-22 16:43:27,385 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee3afa37ce5e300dffc5a - insert_scaler_model_into_mongo 2025-05-22 16:43:27,429 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee3afded77197474391d7 - insert_trained_model_into_mongo 2025-05-22 16:43:27,441 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee3afded77197474391d9 - insert_scaler_model_into_mongo 2025-05-22 16:45:43,854 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 16:46:18,805 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:46:21,969 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:46:46,365 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:47:14,361 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:47:14,373 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:47:15,097 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:47:15,100 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 16:47:15,135 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:47:15,136 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:47:15,169 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:47:15,173 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:47:15,235 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:47:15,235 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:47:15,239 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:47:15,241 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:47:15,241 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:47:15,244 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:47:17,702 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:47:17,703 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:47:17,719 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:47:17,720 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - 训练集损失函数为：[9.0682e-01 3.1992e-01 1.0042e-01 2.8180e-02 7.2700e-03 2.0000e-03 8.3000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:47:59,223 - tf_lstm.py - INFO - 训练集损失函数为：[9.0271e-01 3.1676e-01 9.8510e-02 2.7120e-02 6.6000e-03 1.4600e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:47:59,224 - tf_lstm.py - INFO - 验证集损失函数为：[0.51566 0.1692 0.04981 0.0134 0.00373 0.00148 0.00101 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:47:59,224 - tf_lstm.py - INFO - 验证集损失函数为：[5.1142e-01 1.6643e-01 4.8090e-02 1.2250e-02 2.8000e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:47:59,336 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee4bf35eeabaeac48dbca - insert_trained_model_into_mongo 2025-05-22 16:47:59,349 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee4bf35eeabaeac48dbcc - insert_scaler_model_into_mongo 2025-05-22 16:47:59,372 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee4bf9324837aabe55d44 - insert_scaler_model_into_mongo 2025-05-22 16:51:53,965 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:51:54,086 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:51:56,677 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:51:59,595 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:51:59,599 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:51:59,803 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:51:59,821 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:51:59,829 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 16:51:59,829 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:51:59,857 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:51:59,857 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:51:59,858 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:51:59,858 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:51:59,858 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:51:59,859 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:52:00,807 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:52:00,807 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:52:00,816 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:52:00,816 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:52:13,882 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:52:13,883 - tf_lstm.py - INFO - 训练集损失函数为：[9.0366e-01 3.1743e-01 9.8920e-02 2.7540e-02 7.0700e-03 1.9500e-03 8.2000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:52:13,883 - tf_lstm.py - INFO - 验证集损失函数为：[0.51271 0.16722 0.0488 0.01307 0.00364 0.00146 0.00101 0.00092 0.0009 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:52:13,924 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee5bde140b01fd24f34ff - insert_trained_model_into_mongo 2025-05-22 16:52:13,932 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee5bde140b01fd24f3501 - insert_scaler_model_into_mongo 2025-05-22 16:52:13,956 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:52:13,957 - tf_lstm.py - INFO - 训练集损失函数为：[8.9291e-01 3.1420e-01 9.8070e-02 2.7090e-02 6.6100e-03 1.4600e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:52:13,957 - tf_lstm.py - INFO - 验证集损失函数为：[5.0656e-01 1.6547e-01 4.7970e-02 1.2260e-02 2.8100e-03 6.3000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:52:13,993 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee5bdd1747a06d97e0ef3 - insert_trained_model_into_mongo 2025-05-22 16:52:14,030 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee5bed1747a06d97e0ef5 - insert_scaler_model_into_mongo 2025-05-22 16:52:15,315 - task_worker.py - ERROR - Area 1002 failed: 'Datetime' - region_task 2025-05-22 16:54:08,472 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:54:12,196 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 16:54:36,162 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:55:09,151 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:55:09,153 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 16:55:09,901 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 16:55:09,941 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:55:09,941 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 16:55:09,977 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 16:55:10,026 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:55:10,027 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:55:10,027 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 16:55:10,027 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 16:55:10,029 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:55:10,029 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 16:55:12,772 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:55:12,773 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:55:12,785 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 16:55:12,785 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 16:56:01,718 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 训练集损失函数为：[9.0185e-01 3.1664e-01 9.8400e-02 2.7020e-02 6.5600e-03 1.4500e-03 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 训练集损失函数为：[9.0446e-01 3.1836e-01 9.9350e-02 2.7650e-02 7.0800e-03 1.9400e-03 8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 验证集损失函数为：[5.1114e-01 1.6636e-01 4.7960e-02 1.2180e-02 2.7800e-03 6.2000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 16:56:01,720 - tf_lstm.py - INFO - 验证集损失函数为：[0.51384 0.16792 0.04904 0.0131 0.00363 0.00145 0.001 0.00092 0.0009 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 16:56:01,831 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee6a10749b0ee0fe21ed9 - insert_trained_model_into_mongo 2025-05-22 16:56:01,843 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee6a10749b0ee0fe21edb - insert_scaler_model_into_mongo 2025-05-22 16:56:01,864 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682ee6a13f317b0620e79546 - insert_trained_model_into_mongo 2025-05-22 16:56:01,899 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682ee6a13f317b0620e79548 - insert_scaler_model_into_mongo 2025-05-22 18:30:17,846 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:30:17,971 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 18:30:20,914 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:30:24,052 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:30:24,054 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:30:24,282 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 18:30:24,299 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 18:30:24,300 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 18:30:24,308 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 18:30:24,308 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 18:30:24,335 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 18:30:24,335 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 18:30:24,335 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 18:30:24,335 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 18:30:24,336 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 18:30:24,336 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 18:30:25,304 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 18:30:25,304 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 18:30:25,304 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 18:30:25,305 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - 训练集损失函数为：[9.0965e-01 3.2084e-01 1.0057e-01 2.8150e-02 7.2500e-03 1.9900e-03 8.3000e-04 6.0000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 18:30:39,377 - tf_lstm.py - INFO - 训练集损失函数为：[9.0351e-01 3.1783e-01 9.9160e-02 2.7360e-02 6.6700e-03 1.4700e-03 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training 2025-05-22 18:30:39,378 - tf_lstm.py - INFO - 验证集损失函数为：[0.51728 0.16966 0.04982 0.01337 0.00372 0.00148 0.00101 0.00092 0.0009 0.00089 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082] - training 2025-05-22 18:30:39,378 - tf_lstm.py - INFO - 验证集损失函数为：[5.1250e-01 1.6734e-01 4.8480e-02 1.2370e-02 2.8300e-03 6.3000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 18:30:39,422 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efccfd9cdd24a561b9430 - insert_trained_model_into_mongo 2025-05-22 18:30:39,444 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efccf11d24a1b0567e1cd - insert_trained_model_into_mongo 2025-05-22 18:30:39,447 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efccfd9cdd24a561b9432 - insert_scaler_model_into_mongo 2025-05-22 18:30:39,452 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efccf11d24a1b0567e1cf - insert_scaler_model_into_mongo 2025-05-22 18:35:43,479 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:35:43,599 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main 2025-05-22 18:35:46,348 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:35:49,304 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:35:49,313 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead. - _tfmw_add_deprecation_warning 2025-05-22 18:35:49,509 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources 2025-05-22 18:35:49,518 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources 2025-05-22 18:35:49,527 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 18:35:49,535 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning 2025-05-22 18:35:49,536 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning 2025-05-22 18:35:49,545 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning 2025-05-22 18:35:49,563 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 18:35:49,563 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 18:35:49,564 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 18:35:49,572 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite 2025-05-22 18:35:49,572 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite 2025-05-22 18:35:49,573 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data 2025-05-22 18:35:50,543 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 18:35:50,543 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo 2025-05-22 18:35:50,543 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 18:35:50,543 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init 2025-05-22 18:36:05,459 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 18:36:05,459 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training 2025-05-22 18:36:05,460 - tf_lstm.py - INFO - 训练集损失函数为：[9.0471e-01 3.1927e-01 1.0005e-01 2.7970e-02 7.1900e-03 1.9800e-03 8.3000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training 2025-05-22 18:36:05,460 - tf_lstm.py - INFO - 验证集损失函数为：[5.1764e-01 1.6935e-01 4.9040e-02 1.2470e-02 2.8400e-03 6.3000e-04 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training 2025-05-22 18:36:05,514 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efe15d416e8f832fd0f55 - insert_trained_model_into_mongo 2025-05-22 18:36:05,514 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682efe155dc17e5973916862 - insert_trained_model_into_mongo 2025-05-22 18:36:05,546 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efe155dc17e5973916864 - insert_scaler_model_into_mongo 2025-05-22 18:36:05,561 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682efe15d416e8f832fd0f57 - insert_scaler_model_into_mongo 2025-05-22 18:36:06,898 - task_worker.py - ERROR - Area 1002 failed: 'types.SimpleNamespace' object has no attribute 'cap' - region_task