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2025-05-26
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south-forecast.2025-05-26.0.log

south-forecast.2025-05-26.0.log 34 KB
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  1. 2025-05-26 09:10:54,470 - 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.
    2. 

  2.  - _tfmw_add_deprecation_warning
    3. 

  3. 2025-05-26 09:12:03,996 - 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.
    4. 

  4.  - _tfmw_add_deprecation_warning
    5. 

  5. 2025-05-26 09:12:46,560 - 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.
    6. 

  6.  - _tfmw_add_deprecation_warning
    7. 

  7. 2025-05-26 09:13:35,837 - 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.
    8. 

  8.  - _tfmw_add_deprecation_warning
    9. 

  9. 2025-05-26 09:15:14,753 - 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.
    10. 

  10.  - _tfmw_add_deprecation_warning
    11. 

  11. 2025-05-26 09:15:43,984 - 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.
    12. 

  12.  - _tfmw_add_deprecation_warning
    13. 

  13. 2025-05-26 09:18:39,622 - 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.
    14. 

  14.  - _tfmw_add_deprecation_warning
    15. 

  15. 2025-05-26 09:19:09,611 - 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.
    16. 

  16.  - _tfmw_add_deprecation_warning
    17. 

  17. 2025-05-26 09:19:09,724 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    18. 

  18. 2025-05-26 09:20:01,910 - 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.
    19. 

  19.  - _tfmw_add_deprecation_warning
    20. 

  20. 2025-05-26 09:20:24,979 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    21. 

  21. 2025-05-26 09:20:38,240 - 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.
    22. 

  22.  - _tfmw_add_deprecation_warning
    23. 

  23. 2025-05-26 09:20:38,353 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    24. 

  24. 2025-05-26 09:20:40,863 - 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.
    25. 

  25.  - _tfmw_add_deprecation_warning
    26. 

  26. 2025-05-26 09:20:43,657 - 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.
    27. 

  27.  - _tfmw_add_deprecation_warning
    28. 

  28. 2025-05-26 09:20:43,660 - 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.
    29. 

  29.  - _tfmw_add_deprecation_warning
    30. 

  30. 2025-05-26 09:20:43,772 - task_worker.py - ERROR - Station 1086 failed: 'NoneType' object has no attribute 'nwp' - station_task
    31. 

  31. 2025-05-26 09:20:43,772 - task_worker.py - ERROR - Station 2361 failed: 'NoneType' object has no attribute 'nwp' - station_task
    32. 

  32. 2025-05-26 09:20:44,370 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    33. 

  33. 2025-05-26 09:26:46,082 - 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.
    34. 

  34.  - _tfmw_add_deprecation_warning
    35. 

  35. 2025-05-26 09:27:10,262 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
    36. 

  36. 2025-05-26 09:27:34,736 - 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.
    37. 

  37.  - _tfmw_add_deprecation_warning
    38. 

  38. 2025-05-26 09:28:17,447 - 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.
    39. 

  39.  - _tfmw_add_deprecation_warning
    40. 

  40. 2025-05-26 09:28:17,451 - 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.
    41. 

  41.  - _tfmw_add_deprecation_warning
    42. 

  42. 2025-05-26 09:28:17,831 - task_worker.py - ERROR - Station 1086 failed: 'NoneType' object has no attribute 'nwp' - station_task
    43. 

  43. 2025-05-26 09:28:17,832 - task_worker.py - ERROR - Station 2361 failed: 'NoneType' object has no attribute 'nwp' - station_task
    44. 

  44. 2025-05-26 09:35:28,332 - 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.
    45. 

  45.  - _tfmw_add_deprecation_warning
    46. 

  46. 2025-05-26 09:35:34,654 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    47. 

  47. 2025-05-26 09:36:04,560 - 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.
    48. 

  48.  - _tfmw_add_deprecation_warning
    49. 

  49. 2025-05-26 09:36:12,678 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    50. 

  50. 2025-05-26 09:41:05,002 - 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.
    51. 

  51.  - _tfmw_add_deprecation_warning
    52. 

  52. 2025-05-26 09:41:11,193 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    53. 

  53. 2025-05-26 09:41:44,865 - 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.
    54. 

  54.  - _tfmw_add_deprecation_warning
    55. 

  55. 2025-05-26 09:41:51,449 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    56. 

  56. 2025-05-26 09:46:40,350 - 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.
    57. 

  57.  - _tfmw_add_deprecation_warning
    58. 

  58. 2025-05-26 09:47:08,760 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    59. 

  59. 2025-05-26 09:47:48,682 - 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.
    60. 

  60.  - _tfmw_add_deprecation_warning
    61. 

  61. 2025-05-26 09:47:48,799 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    62. 

  62. 2025-05-26 09:47:48,799 - main.py - INFO - 执行脚本路径: E:\compete\app\model\main.py - main
    63. 

  63. 2025-05-26 09:47:51,328 - 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.
    64. 

  64.  - _tfmw_add_deprecation_warning
    65. 

  65. 2025-05-26 09:47:54,140 - 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.
    66. 

  66.  - _tfmw_add_deprecation_warning
    67. 

  67. 2025-05-26 09:47:54,143 - 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.
    68. 

  68.  - _tfmw_add_deprecation_warning
    69. 

  69. 2025-05-26 09:47:54,265 - task_worker.py - ERROR - Station 1086 failed: 'NoneType' object has no attribute 'nwp' - station_task
    70. 

  70. 2025-05-26 09:47:54,265 - task_worker.py - ERROR - Station 2361 failed: 'NoneType' object has no attribute 'nwp' - station_task
    71. 

  71. 2025-05-26 09:47:54,895 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    72. 

  72. 2025-05-26 10:33:44,175 - 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.
    73. 

  73.  - _tfmw_add_deprecation_warning
    74. 

  74. 2025-05-26 10:38:28,394 - 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.
    75. 

  75.  - _tfmw_add_deprecation_warning
    76. 

  76. 2025-05-26 10:38:40,146 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    77. 

  77. 2025-05-26 10:40:16,560 - 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.
    78. 

  78.  - _tfmw_add_deprecation_warning
    79. 

  79. 2025-05-26 10:40:55,718 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    80. 

  80. 2025-05-26 10:41:24,424 - 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.
    81. 

  81.  - _tfmw_add_deprecation_warning
    82. 

  82. 2025-05-26 10:43:53,243 - 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.
    83. 

  83.  - _tfmw_add_deprecation_warning
    84. 

  84. 2025-05-26 10:43:53,532 - 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.
    85. 

  85.  - _tfmw_add_deprecation_warning
    86. 

  86. 2025-05-26 10:43:53,622 - task_worker.py - ERROR - Station 1086 failed: 'NoneType' object has no attribute 'nwp' - station_task
    87. 

  87. 2025-05-26 10:43:53,622 - task_worker.py - ERROR - Station 2361 failed: 'NoneType' object has no attribute 'nwp' - station_task
    88. 

  88. 2025-05-26 10:45:38,015 - 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.
    89. 

  89.  - _tfmw_add_deprecation_warning
    90. 

  90. 2025-05-26 10:45:38,143 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    91. 

  91. 2025-05-26 10:45:40,780 - 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.
    92. 

  92.  - _tfmw_add_deprecation_warning
    93. 

  93. 2025-05-26 10:45:43,622 - 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.
    94. 

  94.  - _tfmw_add_deprecation_warning
    95. 

  95. 2025-05-26 10:45:43,622 - 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.
    96. 

  96.  - _tfmw_add_deprecation_warning
    97. 

  97. 2025-05-26 10:45:43,733 - task_worker.py - ERROR - Station 1086 failed: 'NoneType' object has no attribute 'nwp' - station_task
    98. 

  98. 2025-05-26 10:45:43,733 - task_worker.py - ERROR - Station 2361 failed: 'NoneType' object has no attribute 'nwp' - station_task
    99. 

  99. 2025-05-26 10:45:44,346 - task_worker.py - ERROR - Area -99 failed: 'NoneType' object has no attribute 'area_id' - region_task
    100. 

  100. 2025-05-26 10:48:52,987 - 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.
    101. 

  101.  - _tfmw_add_deprecation_warning
    102. 

  102. 2025-05-26 10:48:53,102 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    103. 

  103. 2025-05-26 10:48:55,608 - 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.
    104. 

  104.  - _tfmw_add_deprecation_warning
    105. 

  105. 2025-05-26 10:48:58,429 - 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.
    106. 

  106.  - _tfmw_add_deprecation_warning
    107. 

  107. 2025-05-26 10:48:58,433 - 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.
    108. 

  108.  - _tfmw_add_deprecation_warning
    109. 

  109. 2025-05-26 10:48:58,709 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    110. 

  110. 2025-05-26 10:48:58,733 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    111. 

  111. 2025-05-26 10:48:58,734 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    112. 

  112. 2025-05-26 10:48:58,741 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    113. 

  113. 2025-05-26 10:48:58,742 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    114. 

  114. 2025-05-26 10:48:58,779 - 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
    115. 

  115. 2025-05-26 10:48:58,779 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    116. 

  116. 2025-05-26 10:48:58,779 - 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
    117. 

  117. 2025-05-26 10:48:58,779 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    118. 

  118. 2025-05-26 10:48:58,780 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    119. 

  119. 2025-05-26 10:48:58,780 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    120. 

  120. 2025-05-26 10:48:59,746 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    121. 

  121. 2025-05-26 10:48:59,746 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    122. 

  122. 2025-05-26 10:48:59,747 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    123. 

  123. 2025-05-26 10:48:59,747 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    124. 

  124. 2025-05-26 10:49:12,523 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    125. 

  125. 2025-05-26 10:49:12,524 - tf_lstm.py - INFO - 训练集损失函数为：[8.9447e-01 3.1291e-01 9.6900e-02 2.6540e-02 6.4300e-03 1.4200e-03
    126. 

  126.  3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    127. 

  127.  6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    128. 

  128.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    129. 

  129.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    130. 

  130.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    131. 

  131.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    132. 

  132.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    133. 

  133.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    134. 

  134.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    135. 

  135.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    136. 

  136.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    137. 

  137.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    138. 

  138.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    139. 

  139.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    140. 

  140.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    141. 

  141.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    142. 

  142. 2025-05-26 10:49:12,524 - tf_lstm.py - INFO - 训练集损失函数为：[9.0170e-01 3.1713e-01 9.9050e-02 2.7600e-02 7.0700e-03 1.9400e-03
    143. 

  143.  8.1000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    144. 

  144.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    145. 

  145.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    146. 

  146.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    147. 

  147.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    148. 

  148.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    149. 

  149.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    150. 

  150.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    151. 

  151.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    152. 

  152.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    153. 

  153.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    154. 

  154.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    155. 

  155.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    156. 

  156.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    157. 

  157.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    158. 

  158.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    159. 

  159. 2025-05-26 10:49:12,524 - tf_lstm.py - INFO - 验证集损失函数为：[5.0590e-01 1.6401e-01 4.7160e-02 1.1950e-02 2.7200e-03 6.1000e-04
    160. 

  160.  1.7000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    161. 

  161.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    162. 

  162.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    163. 

  163.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    164. 

  164.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    165. 

  165.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    166. 

  166.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    167. 

  167.  8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    168. 

  168.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    169. 

  169.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    170. 

  170.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    171. 

  171.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    172. 

  172.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    173. 

  173.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    174. 

  174.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    175. 

  175.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    176. 

  176. 2025-05-26 10:49:12,524 - tf_lstm.py - INFO - 验证集损失函数为：[0.51183 0.16731 0.04894 0.01308 0.00363 0.00145 0.001   0.00092 0.0009
    177. 

  177.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    178. 

  178.  0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    179. 

  179.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    180. 

  180.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    181. 

  181.  0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    182. 

  182.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    183. 

  183.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00082
    184. 

  184.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    185. 

  185.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    186. 

  186.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    187. 

  187.  0.00082] - training
    188. 

  188. 2025-05-26 10:49:12,642 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 6833d6a876c6370eb176b800 - insert_trained_model_into_mongo
    189. 

  189. 2025-05-26 10:49:12,645 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 6833d6a88a9ccd464b193ac6 - insert_trained_model_into_mongo
    190. 

  190. 2025-05-26 10:49:12,664 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 6833d6a88a9ccd464b193ac8 - insert_scaler_model_into_mongo
    191. 

  191. 2025-05-26 10:49:12,673 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 6833d6a876c6370eb176b802 - insert_scaler_model_into_mongo
    192. 

  192. 2025-05-26 10:49:13,994 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    193. 

  193. 2025-05-26 10:49:14,012 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    194. 

  194. 2025-05-26 10:49:14,019 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    195. 

  195. 2025-05-26 10:49:14,046 - 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
    196. 

  196. 2025-05-26 10:49:14,046 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    197. 

  197. 2025-05-26 10:49:14,046 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    198. 

  198. 2025-05-26 10:49:14,961 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    199. 

  199. 2025-05-26 10:49:14,962 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    200. 

  200. 2025-05-26 10:49:26,513 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    201. 

  201. 2025-05-26 10:49:26,514 - tf_lstm.py - INFO - 训练集损失函数为：[9.0123e-01 3.1717e-01 9.8830e-02 2.7190e-02 6.5700e-03 1.4100e-03
    202. 

  202.  2.7000e-04 5.0000e-05 1.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00
    203. 

  203.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    204. 

  204.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    205. 

  205.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    206. 

  206.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    207. 

  207.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    208. 

  208.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    209. 

  209.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    210. 

  210.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    211. 

  211.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    212. 

  212.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    213. 

  213.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    214. 

  214.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    215. 

  215.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    216. 

  216.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    217. 

  217.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
    218. 

  218. 2025-05-26 10:49:26,514 - tf_lstm.py - INFO - 验证集损失函数为：[5.1155e-01 1.6690e-01 4.8240e-02 1.2230e-02 2.7300e-03 5.4000e-04
    219. 

  219.  1.0000e-04 2.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    220. 

  220.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    221. 

  221.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    222. 

  222.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    223. 

  223.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    224. 

  224.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    225. 

  225.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    226. 

  226.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    227. 

  227.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    228. 

  228.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    229. 

  229.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    230. 

  230.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    231. 

  231.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    232. 

  232.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    233. 

  233.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    234. 

  234.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
    235. 

  235. 2025-05-26 10:49:26,589 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 6833d6b6407675b5bca4c083 - insert_trained_model_into_mongo
    236. 

  236. 2025-05-26 10:49:26,626 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 6833d6b6407675b5bca4c085 - insert_scaler_model_into_mongo
    237. 

  237. 2025-05-26 10:55:40,499 - 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.
    238. 

  238.  - _tfmw_add_deprecation_warning
    239. 

  239. 2025-05-26 10:55:40,612 - main.py - INFO - 输入文件目录: 62/1002/2025-04-21/IN - main
    240. 

  240. 2025-05-26 10:55:43,103 - 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.
    241. 

  241.  - _tfmw_add_deprecation_warning
    242. 

  242. 2025-05-26 10:55:45,883 - 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.
    243. 

  243.  - _tfmw_add_deprecation_warning
    244. 

  244. 2025-05-26 10:55:45,885 - 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.
    245. 

  245.  - _tfmw_add_deprecation_warning
    246. 

  246. 2025-05-26 10:55:46,085 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
    247. 

  247. 2025-05-26 10:55:46,086 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    248. 

  248. 2025-05-26 10:55:46,104 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    249. 

  249. 2025-05-26 10:55:46,105 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    250. 

  250. 2025-05-26 10:55:46,111 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
    251. 

  251. 2025-05-26 10:55:46,112 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    252. 

  252. 2025-05-26 10:55:46,139 - 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
    253. 

  253. 2025-05-26 10:55:46,139 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    254. 

  254. 2025-05-26 10:55:46,139 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    255. 

  255. 2025-05-26 10:55:46,140 - 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
    256. 

  256. 2025-05-26 10:55:46,140 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    257. 

  257. 2025-05-26 10:55:46,141 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    258. 

  258. 2025-05-26 10:55:47,068 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    259. 

  259. 2025-05-26 10:55:47,068 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    260. 

  260. 2025-05-26 10:55:47,068 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    261. 

  261. 2025-05-26 10:55:47,068 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    262. 

  262. 2025-05-26 10:55:59,678 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    263. 

  263. 2025-05-26 10:55:59,678 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    264. 

  264. 2025-05-26 10:55:59,678 - tf_lstm.py - INFO - 训练集损失函数为：[9.0739e-01 3.1934e-01 9.9630e-02 2.7470e-02 6.6900e-03 1.4800e-03
    265. 

  265.  3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
    266. 

  266.  6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    267. 

  267.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    268. 

  268.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    269. 

  269.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    270. 

  270.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    271. 

  271.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    272. 

  272.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    273. 

  273.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    274. 

  274.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    275. 

  275.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    276. 

  276.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    277. 

  277.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    278. 

  278.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    279. 

  279.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
    280. 

  280.  5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
    281. 

  281. 2025-05-26 10:55:59,678 - tf_lstm.py - INFO - 训练集损失函数为：[9.0427e-01 3.1717e-01 9.8750e-02 2.7500e-02 7.0700e-03 1.9500e-03
    282. 

  282.  8.2000e-04 5.9000e-04 5.5000e-04 5.4000e-04 5.4000e-04 5.4000e-04
    283. 

  283.  5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    284. 

  284.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    285. 

  285.  5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04 5.3000e-04
    286. 

  286.  5.3000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    287. 

  287.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    288. 

  288.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    289. 

  289.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    290. 

  290.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    291. 

  291.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    292. 

  292.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    293. 

  293.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    294. 

  294.  5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04 5.2000e-04
    295. 

  295.  5.2000e-04 5.2000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    296. 

  296.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04
    297. 

  297.  5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
    298. 

  298. 2025-05-26 10:55:59,679 - tf_lstm.py - INFO - 验证集损失函数为：[5.1485e-01 1.6816e-01 4.8690e-02 1.2410e-02 2.8400e-03 6.3000e-04
    299. 

  299.  1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    300. 

  300.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    301. 

  301.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    302. 

  302.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    303. 

  303.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    304. 

  304.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    305. 

  305.  8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
    306. 

  306.  8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    307. 

  307.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    308. 

  308.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    309. 

  309.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    310. 

  310.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    311. 

  311.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    312. 

  312.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    313. 

  313.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
    314. 

  314.  7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
    315. 

  315. 2025-05-26 10:55:59,679 - tf_lstm.py - INFO - 验证集损失函数为：[0.51265 0.16698 0.04873 0.01305 0.00364 0.00146 0.001   0.00092 0.0009
    316. 

  316.  0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
    317. 

  317.  0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
    318. 

  318.  0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
    319. 

  319.  0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
    320. 

  320.  0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
    321. 

  321.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    322. 

  322.  0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
    323. 

  323.  0.00083 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    324. 

  324.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    325. 

  325.  0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
    326. 

  326.  0.00082] - training
    327. 

  327. 2025-05-26 10:55:59,716 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 6833d83f6162e6249af02c4c - insert_trained_model_into_mongo
    328. 

  328. 2025-05-26 10:55:59,716 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 6833d83f7a77586f9dc561a2 - insert_trained_model_into_mongo
    329. 

  329. 2025-05-26 10:55:59,723 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 6833d83f7a77586f9dc561a4 - insert_scaler_model_into_mongo
    330. 

  330. 2025-05-26 10:55:59,759 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 6833d83f6162e6249af02c4e - insert_scaler_model_into_mongo
    331. 

  331. 2025-05-26 10:56:01,061 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
    332. 

  332. 2025-05-26 10:56:01,078 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
    333. 

  333. 2025-05-26 10:56:01,087 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
    334. 

  334. 2025-05-26 10:56:01,112 - 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
    335. 

  335. 2025-05-26 10:56:01,112 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
    336. 

  336. 2025-05-26 10:56:01,113 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
    337. 

  337. 2025-05-26 10:56:02,019 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
    338. 

  338. 2025-05-26 10:56:02,019 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
    339. 

  339. 2025-05-26 10:56:13,741 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
    340. 

  340. 2025-05-26 10:56:13,742 - tf_lstm.py - INFO - 训练集损失函数为：[8.9571e-01 3.1434e-01 9.7760e-02 2.6850e-02 6.4800e-03 1.3800e-03
    341. 

  341.  2.6000e-04 5.0000e-05 1.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00
    342. 

  342.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    343. 

  343.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    344. 

  344.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    345. 

  345.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    346. 

  346.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    347. 

  347.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    348. 

  348.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    349. 

  349.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    350. 

  350.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    351. 

  351.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    352. 

  352.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    353. 

  353.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    354. 

  354.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    355. 

  355.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    356. 

  356.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
    357. 

  357. 2025-05-26 10:56:13,742 - tf_lstm.py - INFO - 验证集损失函数为：[5.0752e-01 1.6519e-01 4.7670e-02 1.2060e-02 2.6900e-03 5.3000e-04
    358. 

  358.  9.0000e-05 2.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    359. 

  359.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    360. 

  360.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    361. 

  361.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    362. 

  362.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    363. 

  363.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    364. 

  364.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    365. 

  365.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    366. 

  366.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    367. 

  367.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    368. 

  368.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    369. 

  369.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    370. 

  370.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    371. 

  371.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    372. 

  372.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
    373. 

  373.  0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
    374. 

  374. 2025-05-26 10:56:13,779 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 6833d84d8a59ed23c934ddf3 - insert_trained_model_into_mongo
    375. 

  375. 2025-05-26 10:56:13,800 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 6833d84d8a59ed23c934ddf5 - insert_scaler_model_into_mongo
    376.