add weights_only=False, deal torch version warning

api_send_evaluate.py

@@ -18,9 +18,9 @@ def download_file(data_file_info, save_dir):
         log_file_url = data_file_info["log_file_url"]
         log_save_path = os.path.join(save_dir, "log.txt")
         response = requests.get(log_file_url)
-        with open(log_save_path, 'wb') as file:
+        # with open(log_save_path, 'wb') as file:
-            file.write(response.content)
+        #     file.write(response.content)
-        print(f"Log file saved to: {log_save_path}")
+        # print(f"Log file saved to: {log_save_path}")
     else:
         print("无法获取日志文件信息")
 
@@ -29,9 +29,9 @@ def download_file(data_file_info, save_dir):
         data_file_url = data_file_info["data_file_url"]
         data_save_path = os.path.join(save_dir, "data.xlsx")
         response = requests.get(data_file_url)
-        with open(data_save_path, 'wb') as file:
+        # with open(data_save_path, 'wb') as file:
-            file.write(response.content)
+        #     file.write(response.content)
-        print(f"Data file saved to: {data_save_path}")
+        # print(f"Data file saved to: {data_save_path}")
     else:
         print("无法获取数据文件信息")
 

api_send_train.py

@@ -16,9 +16,9 @@ def download_file(data_file_info, save_dir):
         model_file_url = data_file_info["model_file_url"]
         model_save_path = os.path.join(save_dir, "model.pth")
         response = requests.get(model_file_url)
-        with open(model_save_path, 'wb') as file:
+        # with open(model_save_path, 'wb') as file:
-            file.write(response.content)
+        #     file.write(response.content)
-        print(f"Model saved to: {model_save_path}")
+        # print(f"Model saved to: {model_save_path}")
     else:
         print("无法获取模型文件信息")
 
@@ -27,9 +27,9 @@ def download_file(data_file_info, save_dir):
         log_file_url = data_file_info["log_file_url"]
         log_save_path = os.path.join(save_dir, "log.txt")
         response = requests.get(log_file_url)
-        with open(log_save_path, 'wb') as file:
+        # with open(log_save_path, 'wb') as file:
-            file.write(response.content)
+        #     file.write(response.content)
-        print(f"Log file saved to: {log_save_path}")
+        # print(f"Log file saved to: {log_save_path}")
     else:
         print("无法获取日志文件信息")
 
@@ -38,9 +38,9 @@ def download_file(data_file_info, save_dir):
         data_file_url = data_file_info["data_file_url"]
         data_save_path = os.path.join(save_dir, "data.xlsx")
         response = requests.get(data_file_url)
-        with open(data_save_path, 'wb') as file:
+        # with open(data_save_path, 'wb') as file:
-            file.write(response.content)
+        #     file.write(response.content)
-        print(f"Data file saved to: {data_save_path}")
+        # print(f"Data file saved to: {data_save_path}")
     else:
         print("无法获取数据文件信息")
 

client/log.txt

@@ -1,764 +0,0 @@
-2024-05-29 00:58:42 INFO: config: 
-2024-05-29 00:58:45 INFO: Class weights: tensor([21.1878,  3.1149,  0.7420,  0.4378], device='cuda:0')
-2024-05-29 00:58:47 INFO: Epoch 001 | Train Loss: 0.6145 | Train Accuracy: 0.8024 | Validation Loss: 0.3530 | Validation Accuracy: 0.8666 | Validation Mean Precision: 0.7371 | Validation Mean Recall: 0.8382 | Validation Mean F1_score: 0.7780
-2024-05-29 00:58:49 INFO: Epoch 002 | Train Loss: 0.3780 | Train Accuracy: 0.8802 | Validation Loss: 0.4015 | Validation Accuracy: 0.8146 | Validation Mean Precision: 0.6750 | Validation Mean Recall: 0.8230 | Validation Mean F1_score: 0.7243
-2024-05-29 00:58:50 INFO: Epoch 003 | Train Loss: 0.3667 | Train Accuracy: 0.8861 | Validation Loss: 0.3302 | Validation Accuracy: 0.8826 | Validation Mean Precision: 0.7854 | Validation Mean Recall: 0.8465 | Validation Mean F1_score: 0.8125
-2024-05-29 00:58:52 INFO: Epoch 004 | Train Loss: 0.3590 | Train Accuracy: 0.8887 | Validation Loss: 0.3236 | Validation Accuracy: 0.8996 | Validation Mean Precision: 0.8327 | Validation Mean Recall: 0.8399 | Validation Mean F1_score: 0.8350
-2024-05-29 00:58:54 INFO: Epoch 005 | Train Loss: 0.3563 | Train Accuracy: 0.8892 | Validation Loss: 0.3271 | Validation Accuracy: 0.9011 | Validation Mean Precision: 0.7866 | Validation Mean Recall: 0.8377 | Validation Mean F1_score: 0.8014
-2024-05-29 00:58:55 INFO: Epoch 006 | Train Loss: 0.3603 | Train Accuracy: 0.8912 | Validation Loss: 0.3489 | Validation Accuracy: 0.8941 | Validation Mean Precision: 0.7863 | Validation Mean Recall: 0.8253 | Validation Mean F1_score: 0.7933
-2024-05-29 00:58:57 INFO: Epoch 007 | Train Loss: 0.3615 | Train Accuracy: 0.8899 | Validation Loss: 0.3224 | Validation Accuracy: 0.9128 | Validation Mean Precision: 0.8498 | Validation Mean Recall: 0.8398 | Validation Mean F1_score: 0.8445
-2024-05-29 00:58:58 INFO: Epoch 008 | Train Loss: 0.3472 | Train Accuracy: 0.8936 | Validation Loss: 0.3202 | Validation Accuracy: 0.9087 | Validation Mean Precision: 0.8409 | Validation Mean Recall: 0.8421 | Validation Mean F1_score: 0.8408
-2024-05-29 00:59:00 INFO: Epoch 009 | Train Loss: 0.3446 | Train Accuracy: 0.8995 | Validation Loss: 0.3466 | Validation Accuracy: 0.9020 | Validation Mean Precision: 0.7918 | Validation Mean Recall: 0.8389 | Validation Mean F1_score: 0.7942
-2024-05-29 00:59:01 INFO: Epoch 010 | Train Loss: 0.3402 | Train Accuracy: 0.8976 | Validation Loss: 0.3259 | Validation Accuracy: 0.9149 | Validation Mean Precision: 0.8842 | Validation Mean Recall: 0.8261 | Validation Mean F1_score: 0.8511
-2024-05-29 00:59:03 INFO: Epoch 011 | Train Loss: 0.3389 | Train Accuracy: 0.9001 | Validation Loss: 0.3203 | Validation Accuracy: 0.8862 | Validation Mean Precision: 0.7839 | Validation Mean Recall: 0.8474 | Validation Mean F1_score: 0.8105
-2024-05-29 00:59:04 INFO: Epoch 012 | Train Loss: 0.3345 | Train Accuracy: 0.9002 | Validation Loss: 0.3031 | Validation Accuracy: 0.9157 | Validation Mean Precision: 0.8425 | Validation Mean Recall: 0.8488 | Validation Mean F1_score: 0.8452
-2024-05-29 00:59:06 INFO: Epoch 013 | Train Loss: 0.3312 | Train Accuracy: 0.9037 | Validation Loss: 0.3071 | Validation Accuracy: 0.9008 | Validation Mean Precision: 0.7963 | Validation Mean Recall: 0.8467 | Validation Mean F1_score: 0.8113
-2024-05-29 00:59:07 INFO: Epoch 014 | Train Loss: 0.3318 | Train Accuracy: 0.9047 | Validation Loss: 0.3138 | Validation Accuracy: 0.9018 | Validation Mean Precision: 0.7624 | Validation Mean Recall: 0.8601 | Validation Mean F1_score: 0.7981
-2024-05-29 00:59:09 INFO: Epoch 015 | Train Loss: 0.3252 | Train Accuracy: 0.9086 | Validation Loss: 0.2973 | Validation Accuracy: 0.9111 | Validation Mean Precision: 0.8332 | Validation Mean Recall: 0.8528 | Validation Mean F1_score: 0.8409
-2024-05-29 00:59:10 INFO: Epoch 016 | Train Loss: 0.3242 | Train Accuracy: 0.9077 | Validation Loss: 0.2943 | Validation Accuracy: 0.9130 | Validation Mean Precision: 0.8474 | Validation Mean Recall: 0.8502 | Validation Mean F1_score: 0.8474
-2024-05-29 00:59:12 INFO: Epoch 017 | Train Loss: 0.3225 | Train Accuracy: 0.9069 | Validation Loss: 0.3049 | Validation Accuracy: 0.9142 | Validation Mean Precision: 0.8105 | Validation Mean Recall: 0.8539 | Validation Mean F1_score: 0.8275
-2024-05-29 00:59:13 INFO: Epoch 018 | Train Loss: 0.3232 | Train Accuracy: 0.9078 | Validation Loss: 0.3142 | Validation Accuracy: 0.8831 | Validation Mean Precision: 0.7624 | Validation Mean Recall: 0.8533 | Validation Mean F1_score: 0.7997
-2024-05-29 00:59:14 INFO: Epoch 019 | Train Loss: 0.3191 | Train Accuracy: 0.9108 | Validation Loss: 0.2926 | Validation Accuracy: 0.9212 | Validation Mean Precision: 0.8754 | Validation Mean Recall: 0.8612 | Validation Mean F1_score: 0.8681
-2024-05-29 00:59:16 INFO: Epoch 020 | Train Loss: 0.3177 | Train Accuracy: 0.9152 | Validation Loss: 0.2906 | Validation Accuracy: 0.9164 | Validation Mean Precision: 0.7893 | Validation Mean Recall: 0.8658 | Validation Mean F1_score: 0.8178
-2024-05-29 00:59:17 INFO: Epoch 021 | Train Loss: 0.3189 | Train Accuracy: 0.9145 | Validation Loss: 0.2837 | Validation Accuracy: 0.9267 | Validation Mean Precision: 0.8602 | Validation Mean Recall: 0.8654 | Validation Mean F1_score: 0.8618
-2024-05-29 00:59:19 INFO: Epoch 022 | Train Loss: 0.3152 | Train Accuracy: 0.9141 | Validation Loss: 0.2781 | Validation Accuracy: 0.9205 | Validation Mean Precision: 0.8329 | Validation Mean Recall: 0.8657 | Validation Mean F1_score: 0.8480
-2024-05-29 00:59:20 INFO: Epoch 023 | Train Loss: 0.3114 | Train Accuracy: 0.9151 | Validation Loss: 0.2808 | Validation Accuracy: 0.9279 | Validation Mean Precision: 0.8559 | Validation Mean Recall: 0.8620 | Validation Mean F1_score: 0.8584
-2024-05-29 00:59:21 INFO: Epoch 024 | Train Loss: 0.3089 | Train Accuracy: 0.9146 | Validation Loss: 0.3037 | Validation Accuracy: 0.9051 | Validation Mean Precision: 0.7702 | Validation Mean Recall: 0.8667 | Validation Mean F1_score: 0.8087
-2024-05-29 00:59:23 INFO: Epoch 025 | Train Loss: 0.3131 | Train Accuracy: 0.9159 | Validation Loss: 0.2844 | Validation Accuracy: 0.9063 | Validation Mean Precision: 0.8446 | Validation Mean Recall: 0.8616 | Validation Mean F1_score: 0.8520
-2024-05-29 00:59:24 INFO: Epoch 026 | Train Loss: 0.3083 | Train Accuracy: 0.9187 | Validation Loss: 0.2789 | Validation Accuracy: 0.9126 | Validation Mean Precision: 0.8120 | Validation Mean Recall: 0.8610 | Validation Mean F1_score: 0.8344
-2024-05-29 00:59:26 INFO: Epoch 027 | Train Loss: 0.3031 | Train Accuracy: 0.9231 | Validation Loss: 0.2845 | Validation Accuracy: 0.9025 | Validation Mean Precision: 0.7914 | Validation Mean Recall: 0.8660 | Validation Mean F1_score: 0.8225
-2024-05-29 00:59:27 INFO: Epoch 028 | Train Loss: 0.3065 | Train Accuracy: 0.9188 | Validation Loss: 0.2807 | Validation Accuracy: 0.9087 | Validation Mean Precision: 0.8178 | Validation Mean Recall: 0.8595 | Validation Mean F1_score: 0.8364
-2024-05-29 00:59:29 INFO: Epoch 029 | Train Loss: 0.2994 | Train Accuracy: 0.9185 | Validation Loss: 0.2913 | Validation Accuracy: 0.9169 | Validation Mean Precision: 0.7855 | Validation Mean Recall: 0.8691 | Validation Mean F1_score: 0.8162
-2024-05-29 00:59:30 INFO: Epoch 030 | Train Loss: 0.2980 | Train Accuracy: 0.9225 | Validation Loss: 0.2720 | Validation Accuracy: 0.9224 | Validation Mean Precision: 0.8211 | Validation Mean Recall: 0.8657 | Validation Mean F1_score: 0.8414
-2024-05-29 00:59:32 INFO: Epoch 031 | Train Loss: 0.3008 | Train Accuracy: 0.9190 | Validation Loss: 0.2733 | Validation Accuracy: 0.9327 | Validation Mean Precision: 0.8689 | Validation Mean Recall: 0.8712 | Validation Mean F1_score: 0.8697
-2024-05-29 00:59:33 INFO: Epoch 032 | Train Loss: 0.2979 | Train Accuracy: 0.9204 | Validation Loss: 0.2776 | Validation Accuracy: 0.9255 | Validation Mean Precision: 0.8200 | Validation Mean Recall: 0.8756 | Validation Mean F1_score: 0.8420
-2024-05-29 00:59:35 INFO: Epoch 033 | Train Loss: 0.3003 | Train Accuracy: 0.9208 | Validation Loss: 0.2710 | Validation Accuracy: 0.9260 | Validation Mean Precision: 0.8219 | Validation Mean Recall: 0.8711 | Validation Mean F1_score: 0.8420
-2024-05-29 00:59:37 INFO: Epoch 034 | Train Loss: 0.2946 | Train Accuracy: 0.9223 | Validation Loss: 0.2855 | Validation Accuracy: 0.9226 | Validation Mean Precision: 0.8080 | Validation Mean Recall: 0.8608 | Validation Mean F1_score: 0.8287
-2024-05-29 00:59:38 INFO: Epoch 035 | Train Loss: 0.2869 | Train Accuracy: 0.9250 | Validation Loss: 0.2886 | Validation Accuracy: 0.9310 | Validation Mean Precision: 0.8492 | Validation Mean Recall: 0.8662 | Validation Mean F1_score: 0.8544
-2024-05-29 00:59:40 INFO: Epoch 036 | Train Loss: 0.2866 | Train Accuracy: 0.9248 | Validation Loss: 0.3234 | Validation Accuracy: 0.8915 | Validation Mean Precision: 0.7333 | Validation Mean Recall: 0.8630 | Validation Mean F1_score: 0.7779
-2024-05-29 00:59:42 INFO: Epoch 037 | Train Loss: 0.2879 | Train Accuracy: 0.9267 | Validation Loss: 0.2788 | Validation Accuracy: 0.9212 | Validation Mean Precision: 0.8358 | Validation Mean Recall: 0.8697 | Validation Mean F1_score: 0.8511
-2024-05-29 00:59:43 INFO: Epoch 038 | Train Loss: 0.2871 | Train Accuracy: 0.9266 | Validation Loss: 0.2770 | Validation Accuracy: 0.9358 | Validation Mean Precision: 0.8449 | Validation Mean Recall: 0.8763 | Validation Mean F1_score: 0.8522
-2024-05-29 00:59:45 INFO: Epoch 039 | Train Loss: 0.2925 | Train Accuracy: 0.9211 | Validation Loss: 0.2752 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8059 | Validation Mean Recall: 0.8702 | Validation Mean F1_score: 0.8336
-2024-05-29 00:59:47 INFO: Epoch 040 | Train Loss: 0.2828 | Train Accuracy: 0.9284 | Validation Loss: 0.2897 | Validation Accuracy: 0.9023 | Validation Mean Precision: 0.8541 | Validation Mean Recall: 0.8528 | Validation Mean F1_score: 0.8502
-2024-05-29 00:59:48 INFO: Epoch 041 | Train Loss: 0.2808 | Train Accuracy: 0.9286 | Validation Loss: 0.2939 | Validation Accuracy: 0.8967 | Validation Mean Precision: 0.8299 | Validation Mean Recall: 0.8616 | Validation Mean F1_score: 0.8440
-2024-05-29 00:59:50 INFO: Epoch 042 | Train Loss: 0.2822 | Train Accuracy: 0.9250 | Validation Loss: 0.2620 | Validation Accuracy: 0.9276 | Validation Mean Precision: 0.8283 | Validation Mean Recall: 0.8691 | Validation Mean F1_score: 0.8457
-2024-05-29 00:59:52 INFO: Epoch 043 | Train Loss: 0.2771 | Train Accuracy: 0.9282 | Validation Loss: 0.2604 | Validation Accuracy: 0.9288 | Validation Mean Precision: 0.8361 | Validation Mean Recall: 0.8706 | Validation Mean F1_score: 0.8511
-2024-05-29 00:59:53 INFO: Epoch 044 | Train Loss: 0.2765 | Train Accuracy: 0.9276 | Validation Loss: 0.2745 | Validation Accuracy: 0.9250 | Validation Mean Precision: 0.8826 | Validation Mean Recall: 0.8622 | Validation Mean F1_score: 0.8716
-2024-05-29 00:59:55 INFO: Epoch 045 | Train Loss: 0.2796 | Train Accuracy: 0.9262 | Validation Loss: 0.2621 | Validation Accuracy: 0.9322 | Validation Mean Precision: 0.8378 | Validation Mean Recall: 0.8689 | Validation Mean F1_score: 0.8501
-2024-05-29 00:59:56 INFO: Epoch 046 | Train Loss: 0.2721 | Train Accuracy: 0.9300 | Validation Loss: 0.2635 | Validation Accuracy: 0.9238 | Validation Mean Precision: 0.8548 | Validation Mean Recall: 0.8703 | Validation Mean F1_score: 0.8607
-2024-05-29 00:59:58 INFO: Epoch 047 | Train Loss: 0.2794 | Train Accuracy: 0.9229 | Validation Loss: 0.2885 | Validation Accuracy: 0.9106 | Validation Mean Precision: 0.8250 | Validation Mean Recall: 0.8616 | Validation Mean F1_score: 0.8411
-2024-05-29 00:59:59 INFO: Epoch 048 | Train Loss: 0.2702 | Train Accuracy: 0.9275 | Validation Loss: 0.2639 | Validation Accuracy: 0.9241 | Validation Mean Precision: 0.8274 | Validation Mean Recall: 0.8713 | Validation Mean F1_score: 0.8461
-2024-05-29 01:00:01 INFO: Epoch 049 | Train Loss: 0.2695 | Train Accuracy: 0.9298 | Validation Loss: 0.2643 | Validation Accuracy: 0.9248 | Validation Mean Precision: 0.8663 | Validation Mean Recall: 0.8723 | Validation Mean F1_score: 0.8670
-2024-05-29 01:00:02 INFO: Epoch 050 | Train Loss: 0.2681 | Train Accuracy: 0.9298 | Validation Loss: 0.2695 | Validation Accuracy: 0.9320 | Validation Mean Precision: 0.8449 | Validation Mean Recall: 0.8665 | Validation Mean F1_score: 0.8521
-2024-05-29 01:00:03 INFO: Epoch 051 | Train Loss: 0.2759 | Train Accuracy: 0.9291 | Validation Loss: 0.2627 | Validation Accuracy: 0.9312 | Validation Mean Precision: 0.8293 | Validation Mean Recall: 0.8677 | Validation Mean F1_score: 0.8445
-2024-05-29 01:00:05 INFO: Epoch 052 | Train Loss: 0.2605 | Train Accuracy: 0.9304 | Validation Loss: 0.2969 | Validation Accuracy: 0.9006 | Validation Mean Precision: 0.8115 | Validation Mean Recall: 0.8689 | Validation Mean F1_score: 0.8339
-2024-05-29 01:00:06 INFO: Epoch 053 | Train Loss: 0.2672 | Train Accuracy: 0.9291 | Validation Loss: 0.2632 | Validation Accuracy: 0.9205 | Validation Mean Precision: 0.8498 | Validation Mean Recall: 0.8763 | Validation Mean F1_score: 0.8624
-2024-05-29 01:00:08 INFO: Epoch 054 | Train Loss: 0.2725 | Train Accuracy: 0.9272 | Validation Loss: 0.2702 | Validation Accuracy: 0.9188 | Validation Mean Precision: 0.8314 | Validation Mean Recall: 0.8739 | Validation Mean F1_score: 0.8499
-2024-05-29 01:00:09 INFO: Epoch 055 | Train Loss: 0.2600 | Train Accuracy: 0.9307 | Validation Loss: 0.2599 | Validation Accuracy: 0.9327 | Validation Mean Precision: 0.8917 | Validation Mean Recall: 0.8613 | Validation Mean F1_score: 0.8725
-2024-05-29 01:00:11 INFO: Epoch 056 | Train Loss: 0.2596 | Train Accuracy: 0.9298 | Validation Loss: 0.2648 | Validation Accuracy: 0.9327 | Validation Mean Precision: 0.8541 | Validation Mean Recall: 0.8724 | Validation Mean F1_score: 0.8595
-2024-05-29 01:00:13 INFO: Epoch 057 | Train Loss: 0.2652 | Train Accuracy: 0.9296 | Validation Loss: 0.2570 | Validation Accuracy: 0.9356 | Validation Mean Precision: 0.8567 | Validation Mean Recall: 0.8808 | Validation Mean F1_score: 0.8669
-2024-05-29 01:00:15 INFO: Epoch 058 | Train Loss: 0.2580 | Train Accuracy: 0.9300 | Validation Loss: 0.2556 | Validation Accuracy: 0.9368 | Validation Mean Precision: 0.8718 | Validation Mean Recall: 0.8763 | Validation Mean F1_score: 0.8732
-2024-05-29 01:00:16 INFO: Epoch 059 | Train Loss: 0.2627 | Train Accuracy: 0.9309 | Validation Loss: 0.2856 | Validation Accuracy: 0.9133 | Validation Mean Precision: 0.8033 | Validation Mean Recall: 0.8742 | Validation Mean F1_score: 0.8343
-2024-05-29 01:00:18 INFO: Epoch 060 | Train Loss: 0.2592 | Train Accuracy: 0.9309 | Validation Loss: 0.2570 | Validation Accuracy: 0.9351 | Validation Mean Precision: 0.8610 | Validation Mean Recall: 0.8768 | Validation Mean F1_score: 0.8679
-2024-05-29 01:00:19 INFO: Epoch 061 | Train Loss: 0.2617 | Train Accuracy: 0.9296 | Validation Loss: 0.2571 | Validation Accuracy: 0.9279 | Validation Mean Precision: 0.8443 | Validation Mean Recall: 0.8789 | Validation Mean F1_score: 0.8601
-2024-05-29 01:00:21 INFO: Epoch 062 | Train Loss: 0.2633 | Train Accuracy: 0.9292 | Validation Loss: 0.2664 | Validation Accuracy: 0.9233 | Validation Mean Precision: 0.8598 | Validation Mean Recall: 0.8711 | Validation Mean F1_score: 0.8631
-2024-05-29 01:00:22 INFO: Epoch 063 | Train Loss: 0.2592 | Train Accuracy: 0.9284 | Validation Loss: 0.2704 | Validation Accuracy: 0.9336 | Validation Mean Precision: 0.9117 | Validation Mean Recall: 0.8574 | Validation Mean F1_score: 0.8807
-2024-05-29 01:00:23 INFO: Epoch 064 | Train Loss: 0.2568 | Train Accuracy: 0.9311 | Validation Loss: 0.2605 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.9033 | Validation Mean Recall: 0.8751 | Validation Mean F1_score: 0.8886
-2024-05-29 01:00:25 INFO: Epoch 065 | Train Loss: 0.2619 | Train Accuracy: 0.9311 | Validation Loss: 0.2578 | Validation Accuracy: 0.9375 | Validation Mean Precision: 0.8894 | Validation Mean Recall: 0.8710 | Validation Mean F1_score: 0.8791
-2024-05-29 01:00:26 INFO: Epoch 066 | Train Loss: 0.2595 | Train Accuracy: 0.9323 | Validation Loss: 0.2837 | Validation Accuracy: 0.9310 | Validation Mean Precision: 0.8411 | Validation Mean Recall: 0.8659 | Validation Mean F1_score: 0.8461
-2024-05-29 01:00:28 INFO: Epoch 067 | Train Loss: 0.2605 | Train Accuracy: 0.9321 | Validation Loss: 0.2581 | Validation Accuracy: 0.9382 | Validation Mean Precision: 0.8721 | Validation Mean Recall: 0.8798 | Validation Mean F1_score: 0.8747
-2024-05-29 01:00:29 INFO: Epoch 068 | Train Loss: 0.2570 | Train Accuracy: 0.9329 | Validation Loss: 0.2592 | Validation Accuracy: 0.9358 | Validation Mean Precision: 0.8555 | Validation Mean Recall: 0.8761 | Validation Mean F1_score: 0.8629
-2024-05-29 01:00:31 INFO: Epoch 069 | Train Loss: 0.2562 | Train Accuracy: 0.9334 | Validation Loss: 0.2578 | Validation Accuracy: 0.9346 | Validation Mean Precision: 0.8833 | Validation Mean Recall: 0.8799 | Validation Mean F1_score: 0.8811
-2024-05-29 01:00:33 INFO: Epoch 070 | Train Loss: 0.2506 | Train Accuracy: 0.9334 | Validation Loss: 0.2591 | Validation Accuracy: 0.9255 | Validation Mean Precision: 0.8314 | Validation Mean Recall: 0.8705 | Validation Mean F1_score: 0.8485
-2024-05-29 01:00:34 INFO: Epoch 071 | Train Loss: 0.2561 | Train Accuracy: 0.9318 | Validation Loss: 0.2544 | Validation Accuracy: 0.9262 | Validation Mean Precision: 0.8376 | Validation Mean Recall: 0.8759 | Validation Mean F1_score: 0.8546
-2024-05-29 01:00:36 INFO: Epoch 072 | Train Loss: 0.2603 | Train Accuracy: 0.9325 | Validation Loss: 0.2500 | Validation Accuracy: 0.9344 | Validation Mean Precision: 0.8699 | Validation Mean Recall: 0.8731 | Validation Mean F1_score: 0.8713
-2024-05-29 01:00:38 INFO: Epoch 073 | Train Loss: 0.2558 | Train Accuracy: 0.9300 | Validation Loss: 0.2603 | Validation Accuracy: 0.9293 | Validation Mean Precision: 0.8274 | Validation Mean Recall: 0.8768 | Validation Mean F1_score: 0.8475
-2024-05-29 01:00:39 INFO: Epoch 074 | Train Loss: 0.2508 | Train Accuracy: 0.9341 | Validation Loss: 0.2723 | Validation Accuracy: 0.9406 | Validation Mean Precision: 0.8890 | Validation Mean Recall: 0.8800 | Validation Mean F1_score: 0.8838
-2024-05-29 01:00:40 INFO: Epoch 075 | Train Loss: 0.2550 | Train Accuracy: 0.9333 | Validation Loss: 0.2568 | Validation Accuracy: 0.9264 | Validation Mean Precision: 0.8473 | Validation Mean Recall: 0.8822 | Validation Mean F1_score: 0.8633
-2024-05-29 01:00:42 INFO: Epoch 076 | Train Loss: 0.2540 | Train Accuracy: 0.9315 | Validation Loss: 0.2620 | Validation Accuracy: 0.9384 | Validation Mean Precision: 0.8983 | Validation Mean Recall: 0.8661 | Validation Mean F1_score: 0.8805
-2024-05-29 01:00:43 INFO: Epoch 077 | Train Loss: 0.2533 | Train Accuracy: 0.9357 | Validation Loss: 0.2530 | Validation Accuracy: 0.9356 | Validation Mean Precision: 0.8686 | Validation Mean Recall: 0.8737 | Validation Mean F1_score: 0.8705
-2024-05-29 01:00:45 INFO: Epoch 078 | Train Loss: 0.2521 | Train Accuracy: 0.9336 | Validation Loss: 0.2615 | Validation Accuracy: 0.9279 | Validation Mean Precision: 0.8313 | Validation Mean Recall: 0.8729 | Validation Mean F1_score: 0.8497
-2024-05-29 01:00:46 INFO: Epoch 079 | Train Loss: 0.2519 | Train Accuracy: 0.9333 | Validation Loss: 0.2690 | Validation Accuracy: 0.9181 | Validation Mean Precision: 0.8014 | Validation Mean Recall: 0.8720 | Validation Mean F1_score: 0.8309
-2024-05-29 01:00:48 INFO: Epoch 080 | Train Loss: 0.2493 | Train Accuracy: 0.9335 | Validation Loss: 0.2732 | Validation Accuracy: 0.9135 | Validation Mean Precision: 0.8572 | Validation Mean Recall: 0.8625 | Validation Mean F1_score: 0.8575
-2024-05-29 01:00:49 INFO: Epoch 081 | Train Loss: 0.2490 | Train Accuracy: 0.9335 | Validation Loss: 0.2803 | Validation Accuracy: 0.9142 | Validation Mean Precision: 0.7852 | Validation Mean Recall: 0.8712 | Validation Mean F1_score: 0.8198
-2024-05-29 01:00:51 INFO: Epoch 082 | Train Loss: 0.2561 | Train Accuracy: 0.9322 | Validation Loss: 0.2584 | Validation Accuracy: 0.9260 | Validation Mean Precision: 0.8644 | Validation Mean Recall: 0.8733 | Validation Mean F1_score: 0.8670
-2024-05-29 01:00:53 INFO: Epoch 083 | Train Loss: 0.2505 | Train Accuracy: 0.9365 | Validation Loss: 0.2807 | Validation Accuracy: 0.9243 | Validation Mean Precision: 0.7914 | Validation Mean Recall: 0.8726 | Validation Mean F1_score: 0.8221
-2024-05-29 01:00:54 INFO: Epoch 084 | Train Loss: 0.2485 | Train Accuracy: 0.9336 | Validation Loss: 0.2518 | Validation Accuracy: 0.9377 | Validation Mean Precision: 0.8889 | Validation Mean Recall: 0.8652 | Validation Mean F1_score: 0.8755
-2024-05-29 01:00:56 INFO: Epoch 085 | Train Loss: 0.2520 | Train Accuracy: 0.9339 | Validation Loss: 0.2629 | Validation Accuracy: 0.9394 | Validation Mean Precision: 0.8759 | Validation Mean Recall: 0.8828 | Validation Mean F1_score: 0.8783
-2024-05-29 01:00:57 INFO: Epoch 086 | Train Loss: 0.2460 | Train Accuracy: 0.9365 | Validation Loss: 0.2648 | Validation Accuracy: 0.9293 | Validation Mean Precision: 0.8120 | Validation Mean Recall: 0.8745 | Validation Mean F1_score: 0.8352
-2024-05-29 01:00:59 INFO: Epoch 087 | Train Loss: 0.2496 | Train Accuracy: 0.9341 | Validation Loss: 0.2573 | Validation Accuracy: 0.9269 | Validation Mean Precision: 0.8199 | Validation Mean Recall: 0.8756 | Validation Mean F1_score: 0.8432
-2024-05-29 01:01:00 INFO: Epoch 088 | Train Loss: 0.2592 | Train Accuracy: 0.9297 | Validation Loss: 0.2606 | Validation Accuracy: 0.9276 | Validation Mean Precision: 0.8542 | Validation Mean Recall: 0.8776 | Validation Mean F1_score: 0.8643
-2024-05-29 01:01:02 INFO: Epoch 089 | Train Loss: 0.2444 | Train Accuracy: 0.9353 | Validation Loss: 0.2626 | Validation Accuracy: 0.9233 | Validation Mean Precision: 0.8302 | Validation Mean Recall: 0.8795 | Validation Mean F1_score: 0.8527
-2024-05-29 01:01:03 INFO: Epoch 090 | Train Loss: 0.2501 | Train Accuracy: 0.9348 | Validation Loss: 0.2577 | Validation Accuracy: 0.9425 | Validation Mean Precision: 0.8875 | Validation Mean Recall: 0.8787 | Validation Mean F1_score: 0.8821
-2024-05-29 01:01:05 INFO: Epoch 091 | Train Loss: 0.2539 | Train Accuracy: 0.9325 | Validation Loss: 0.2718 | Validation Accuracy: 0.9305 | Validation Mean Precision: 0.8204 | Validation Mean Recall: 0.8721 | Validation Mean F1_score: 0.8400
-2024-05-29 01:01:06 INFO: Epoch 092 | Train Loss: 0.2406 | Train Accuracy: 0.9371 | Validation Loss: 0.2697 | Validation Accuracy: 0.9284 | Validation Mean Precision: 0.8797 | Validation Mean Recall: 0.8662 | Validation Mean F1_score: 0.8709
-2024-05-29 01:01:07 INFO: Epoch 093 | Train Loss: 0.2434 | Train Accuracy: 0.9379 | Validation Loss: 0.2591 | Validation Accuracy: 0.9372 | Validation Mean Precision: 0.8723 | Validation Mean Recall: 0.8627 | Validation Mean F1_score: 0.8666
-2024-05-29 01:01:09 INFO: Epoch 094 | Train Loss: 0.2517 | Train Accuracy: 0.9347 | Validation Loss: 0.2551 | Validation Accuracy: 0.9315 | Validation Mean Precision: 0.8644 | Validation Mean Recall: 0.8765 | Validation Mean F1_score: 0.8699
-2024-05-29 01:01:10 INFO: Epoch 095 | Train Loss: 0.2413 | Train Accuracy: 0.9376 | Validation Loss: 0.2566 | Validation Accuracy: 0.9351 | Validation Mean Precision: 0.8552 | Validation Mean Recall: 0.8802 | Validation Mean F1_score: 0.8648
-2024-05-29 01:01:11 INFO: Epoch 096 | Train Loss: 0.2454 | Train Accuracy: 0.9342 | Validation Loss: 0.2567 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.8657 | Validation Mean Recall: 0.8786 | Validation Mean F1_score: 0.8695
-2024-05-29 01:01:13 INFO: Epoch 097 | Train Loss: 0.2461 | Train Accuracy: 0.9351 | Validation Loss: 0.3030 | Validation Accuracy: 0.9159 | Validation Mean Precision: 0.8856 | Validation Mean Recall: 0.8438 | Validation Mean F1_score: 0.8617
-2024-05-29 01:01:14 INFO: Epoch 098 | Train Loss: 0.2443 | Train Accuracy: 0.9351 | Validation Loss: 0.2589 | Validation Accuracy: 0.9411 | Validation Mean Precision: 0.8834 | Validation Mean Recall: 0.8875 | Validation Mean F1_score: 0.8847
-2024-05-29 01:01:16 INFO: Epoch 099 | Train Loss: 0.2413 | Train Accuracy: 0.9374 | Validation Loss: 0.2525 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8698 | Validation Mean Recall: 0.8768 | Validation Mean F1_score: 0.8722
-2024-05-29 01:01:17 INFO: Epoch 100 | Train Loss: 0.2424 | Train Accuracy: 0.9357 | Validation Loss: 0.2651 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.9286 | Validation Mean Recall: 0.8631 | Validation Mean F1_score: 0.8917
-2024-05-29 01:01:19 INFO: Epoch 101 | Train Loss: 0.2396 | Train Accuracy: 0.9378 | Validation Loss: 0.2523 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8846 | Validation Mean Recall: 0.8693 | Validation Mean F1_score: 0.8761
-2024-05-29 01:01:20 INFO: Epoch 102 | Train Loss: 0.2380 | Train Accuracy: 0.9372 | Validation Loss: 0.2550 | Validation Accuracy: 0.9288 | Validation Mean Precision: 0.8386 | Validation Mean Recall: 0.8814 | Validation Mean F1_score: 0.8580
-2024-05-29 01:01:21 INFO: Epoch 103 | Train Loss: 0.2461 | Train Accuracy: 0.9358 | Validation Loss: 0.2683 | Validation Accuracy: 0.9236 | Validation Mean Precision: 0.8089 | Validation Mean Recall: 0.8717 | Validation Mean F1_score: 0.8343
-2024-05-29 01:01:23 INFO: Epoch 104 | Train Loss: 0.2383 | Train Accuracy: 0.9374 | Validation Loss: 0.2654 | Validation Accuracy: 0.9262 | Validation Mean Precision: 0.8455 | Validation Mean Recall: 0.8824 | Validation Mean F1_score: 0.8628
-2024-05-29 01:01:24 INFO: Epoch 105 | Train Loss: 0.2405 | Train Accuracy: 0.9378 | Validation Loss: 0.2620 | Validation Accuracy: 0.9205 | Validation Mean Precision: 0.8344 | Validation Mean Recall: 0.8782 | Validation Mean F1_score: 0.8540
-2024-05-29 01:01:26 INFO: Epoch 106 | Train Loss: 0.2388 | Train Accuracy: 0.9363 | Validation Loss: 0.2658 | Validation Accuracy: 0.9274 | Validation Mean Precision: 0.8313 | Validation Mean Recall: 0.8789 | Validation Mean F1_score: 0.8508
-2024-05-29 01:01:27 INFO: Epoch 107 | Train Loss: 0.2400 | Train Accuracy: 0.9388 | Validation Loss: 0.2534 | Validation Accuracy: 0.9375 | Validation Mean Precision: 0.8673 | Validation Mean Recall: 0.8875 | Validation Mean F1_score: 0.8765
-2024-05-29 01:01:29 INFO: Epoch 108 | Train Loss: 0.2492 | Train Accuracy: 0.9371 | Validation Loss: 0.3316 | Validation Accuracy: 0.9161 | Validation Mean Precision: 0.7993 | Validation Mean Recall: 0.8617 | Validation Mean F1_score: 0.8212
-2024-05-29 01:01:30 INFO: Epoch 109 | Train Loss: 0.2434 | Train Accuracy: 0.9389 | Validation Loss: 0.2547 | Validation Accuracy: 0.9358 | Validation Mean Precision: 0.8831 | Validation Mean Recall: 0.8750 | Validation Mean F1_score: 0.8783
-2024-05-29 01:01:32 INFO: Epoch 110 | Train Loss: 0.2319 | Train Accuracy: 0.9397 | Validation Loss: 0.2541 | Validation Accuracy: 0.9344 | Validation Mean Precision: 0.8533 | Validation Mean Recall: 0.8842 | Validation Mean F1_score: 0.8674
-2024-05-29 01:01:33 INFO: Epoch 111 | Train Loss: 0.2375 | Train Accuracy: 0.9370 | Validation Loss: 0.2620 | Validation Accuracy: 0.9406 | Validation Mean Precision: 0.8368 | Validation Mean Recall: 0.8768 | Validation Mean F1_score: 0.8466
-2024-05-29 01:01:35 INFO: Epoch 112 | Train Loss: 0.2477 | Train Accuracy: 0.9350 | Validation Loss: 0.2724 | Validation Accuracy: 0.9269 | Validation Mean Precision: 0.8660 | Validation Mean Recall: 0.8627 | Validation Mean F1_score: 0.8613
-2024-05-29 01:01:36 INFO: Epoch 113 | Train Loss: 0.2361 | Train Accuracy: 0.9403 | Validation Loss: 0.2572 | Validation Accuracy: 0.9360 | Validation Mean Precision: 0.8835 | Validation Mean Recall: 0.8674 | Validation Mean F1_score: 0.8739
-2024-05-29 01:01:37 INFO: Epoch 114 | Train Loss: 0.2307 | Train Accuracy: 0.9402 | Validation Loss: 0.2482 | Validation Accuracy: 0.9377 | Validation Mean Precision: 0.8548 | Validation Mean Recall: 0.8835 | Validation Mean F1_score: 0.8673
-2024-05-29 01:01:39 INFO: Epoch 115 | Train Loss: 0.2385 | Train Accuracy: 0.9396 | Validation Loss: 0.2630 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.9123 | Validation Mean Recall: 0.8697 | Validation Mean F1_score: 0.8888
-2024-05-29 01:01:40 INFO: Epoch 116 | Train Loss: 0.2319 | Train Accuracy: 0.9393 | Validation Loss: 0.2572 | Validation Accuracy: 0.9370 | Validation Mean Precision: 0.8816 | Validation Mean Recall: 0.8704 | Validation Mean F1_score: 0.8749
-2024-05-29 01:01:42 INFO: Epoch 117 | Train Loss: 0.2325 | Train Accuracy: 0.9407 | Validation Loss: 0.2674 | Validation Accuracy: 0.9394 | Validation Mean Precision: 0.8710 | Validation Mean Recall: 0.8817 | Validation Mean F1_score: 0.8750
-2024-05-29 01:01:43 INFO: Epoch 118 | Train Loss: 0.2335 | Train Accuracy: 0.9382 | Validation Loss: 0.2692 | Validation Accuracy: 0.9401 | Validation Mean Precision: 0.8907 | Validation Mean Recall: 0.8643 | Validation Mean F1_score: 0.8760
-2024-05-29 01:01:45 INFO: Epoch 119 | Train Loss: 0.2293 | Train Accuracy: 0.9404 | Validation Loss: 0.2504 | Validation Accuracy: 0.9363 | Validation Mean Precision: 0.8421 | Validation Mean Recall: 0.8743 | Validation Mean F1_score: 0.8553
-2024-05-29 01:01:46 INFO: Epoch 120 | Train Loss: 0.2527 | Train Accuracy: 0.9344 | Validation Loss: 0.3017 | Validation Accuracy: 0.9243 | Validation Mean Precision: 0.7946 | Validation Mean Recall: 0.8720 | Validation Mean F1_score: 0.8214
-2024-05-29 01:01:48 INFO: Epoch 121 | Train Loss: 0.2321 | Train Accuracy: 0.9409 | Validation Loss: 0.2578 | Validation Accuracy: 0.9368 | Validation Mean Precision: 0.8683 | Validation Mean Recall: 0.8816 | Validation Mean F1_score: 0.8743
-2024-05-29 01:01:49 INFO: Epoch 122 | Train Loss: 0.2321 | Train Accuracy: 0.9400 | Validation Loss: 0.2715 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8998 | Validation Mean Recall: 0.8672 | Validation Mean F1_score: 0.8825
-2024-05-29 01:01:51 INFO: Epoch 123 | Train Loss: 0.2283 | Train Accuracy: 0.9411 | Validation Loss: 0.2629 | Validation Accuracy: 0.9389 | Validation Mean Precision: 0.8545 | Validation Mean Recall: 0.8817 | Validation Mean F1_score: 0.8652
-2024-05-29 01:01:52 INFO: Epoch 124 | Train Loss: 0.2305 | Train Accuracy: 0.9410 | Validation Loss: 0.2689 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8161 | Validation Mean Recall: 0.8753 | Validation Mean F1_score: 0.8423
-2024-05-29 01:01:54 INFO: Epoch 125 | Train Loss: 0.2371 | Train Accuracy: 0.9391 | Validation Loss: 0.3554 | Validation Accuracy: 0.8994 | Validation Mean Precision: 0.7462 | Validation Mean Recall: 0.8548 | Validation Mean F1_score: 0.7785
-2024-05-29 01:01:55 INFO: Epoch 126 | Train Loss: 0.2330 | Train Accuracy: 0.9403 | Validation Loss: 0.2541 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8837 | Validation Mean Recall: 0.8811 | Validation Mean F1_score: 0.8817
-2024-05-29 01:01:57 INFO: Epoch 127 | Train Loss: 0.2293 | Train Accuracy: 0.9411 | Validation Loss: 0.2689 | Validation Accuracy: 0.9391 | Validation Mean Precision: 0.8538 | Validation Mean Recall: 0.8781 | Validation Mean F1_score: 0.8616
-2024-05-29 01:01:58 INFO: Epoch 128 | Train Loss: 0.2289 | Train Accuracy: 0.9405 | Validation Loss: 0.2716 | Validation Accuracy: 0.9308 | Validation Mean Precision: 0.8678 | Validation Mean Recall: 0.8692 | Validation Mean F1_score: 0.8668
-2024-05-29 01:02:00 INFO: Epoch 129 | Train Loss: 0.2325 | Train Accuracy: 0.9418 | Validation Loss: 0.3116 | Validation Accuracy: 0.9236 | Validation Mean Precision: 0.8135 | Validation Mean Recall: 0.8565 | Validation Mean F1_score: 0.8228
-2024-05-29 01:02:01 INFO: Epoch 130 | Train Loss: 0.2295 | Train Accuracy: 0.9396 | Validation Loss: 0.2617 | Validation Accuracy: 0.9329 | Validation Mean Precision: 0.8443 | Validation Mean Recall: 0.8818 | Validation Mean F1_score: 0.8612
-2024-05-29 01:02:03 INFO: Epoch 131 | Train Loss: 0.2220 | Train Accuracy: 0.9438 | Validation Loss: 0.2818 | Validation Accuracy: 0.9305 | Validation Mean Precision: 0.8444 | Validation Mean Recall: 0.8767 | Validation Mean F1_score: 0.8583
-2024-05-29 01:02:04 INFO: Epoch 132 | Train Loss: 0.2253 | Train Accuracy: 0.9420 | Validation Loss: 0.2535 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8898 | Validation Mean Recall: 0.8748 | Validation Mean F1_score: 0.8813
-2024-05-29 01:02:06 INFO: Epoch 133 | Train Loss: 0.2272 | Train Accuracy: 0.9397 | Validation Loss: 0.2705 | Validation Accuracy: 0.9356 | Validation Mean Precision: 0.8717 | Validation Mean Recall: 0.8769 | Validation Mean F1_score: 0.8730
-2024-05-29 01:02:07 INFO: Epoch 134 | Train Loss: 0.2312 | Train Accuracy: 0.9393 | Validation Loss: 0.2865 | Validation Accuracy: 0.9312 | Validation Mean Precision: 0.8575 | Validation Mean Recall: 0.8658 | Validation Mean F1_score: 0.8603
-2024-05-29 01:02:08 INFO: Epoch 135 | Train Loss: 0.2318 | Train Accuracy: 0.9402 | Validation Loss: 0.2541 | Validation Accuracy: 0.9363 | Validation Mean Precision: 0.8349 | Validation Mean Recall: 0.8838 | Validation Mean F1_score: 0.8546
-2024-05-29 01:02:10 INFO: Epoch 136 | Train Loss: 0.2312 | Train Accuracy: 0.9400 | Validation Loss: 0.2632 | Validation Accuracy: 0.9255 | Validation Mean Precision: 0.8289 | Validation Mean Recall: 0.8741 | Validation Mean F1_score: 0.8495
-2024-05-29 01:02:12 INFO: Epoch 137 | Train Loss: 0.2243 | Train Accuracy: 0.9415 | Validation Loss: 0.2806 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.8307 | Validation Mean Recall: 0.8806 | Validation Mean F1_score: 0.8476
-2024-05-29 01:02:13 INFO: Epoch 138 | Train Loss: 0.2198 | Train Accuracy: 0.9430 | Validation Loss: 0.2474 | Validation Accuracy: 0.9466 | Validation Mean Precision: 0.8796 | Validation Mean Recall: 0.8836 | Validation Mean F1_score: 0.8803
-2024-05-29 01:02:14 INFO: Epoch 139 | Train Loss: 0.2255 | Train Accuracy: 0.9415 | Validation Loss: 0.2539 | Validation Accuracy: 0.9384 | Validation Mean Precision: 0.8582 | Validation Mean Recall: 0.8851 | Validation Mean F1_score: 0.8700
-2024-05-29 01:02:16 INFO: Epoch 140 | Train Loss: 0.2243 | Train Accuracy: 0.9421 | Validation Loss: 0.2745 | Validation Accuracy: 0.9454 | Validation Mean Precision: 0.9037 | Validation Mean Recall: 0.8690 | Validation Mean F1_score: 0.8847
-2024-05-29 01:02:17 INFO: Epoch 141 | Train Loss: 0.2266 | Train Accuracy: 0.9432 | Validation Loss: 0.2637 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.8991 | Validation Mean Recall: 0.8691 | Validation Mean F1_score: 0.8823
-2024-05-29 01:02:19 INFO: Epoch 142 | Train Loss: 0.2293 | Train Accuracy: 0.9433 | Validation Loss: 0.2581 | Validation Accuracy: 0.9324 | Validation Mean Precision: 0.8491 | Validation Mean Recall: 0.8835 | Validation Mean F1_score: 0.8649
-2024-05-29 01:02:20 INFO: Epoch 143 | Train Loss: 0.2344 | Train Accuracy: 0.9409 | Validation Loss: 0.3203 | Validation Accuracy: 0.9375 | Validation Mean Precision: 0.8498 | Validation Mean Recall: 0.8791 | Validation Mean F1_score: 0.8630
-2024-05-29 01:02:22 INFO: Epoch 144 | Train Loss: 0.2378 | Train Accuracy: 0.9412 | Validation Loss: 0.2588 | Validation Accuracy: 0.9341 | Validation Mean Precision: 0.8332 | Validation Mean Recall: 0.8803 | Validation Mean F1_score: 0.8535
-2024-05-29 01:02:23 INFO: Epoch 145 | Train Loss: 0.2172 | Train Accuracy: 0.9448 | Validation Loss: 0.2573 | Validation Accuracy: 0.9468 | Validation Mean Precision: 0.9278 | Validation Mean Recall: 0.8642 | Validation Mean F1_score: 0.8903
-2024-05-29 01:02:24 INFO: Epoch 146 | Train Loss: 0.2384 | Train Accuracy: 0.9400 | Validation Loss: 0.2536 | Validation Accuracy: 0.9466 | Validation Mean Precision: 0.8972 | Validation Mean Recall: 0.8735 | Validation Mean F1_score: 0.8848
-2024-05-29 01:02:26 INFO: Epoch 147 | Train Loss: 0.2204 | Train Accuracy: 0.9432 | Validation Loss: 0.2689 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8888 | Validation Mean Recall: 0.8755 | Validation Mean F1_score: 0.8808
-2024-05-29 01:02:27 INFO: Epoch 148 | Train Loss: 0.2184 | Train Accuracy: 0.9441 | Validation Loss: 0.2586 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8729 | Validation Mean Recall: 0.8793 | Validation Mean F1_score: 0.8736
-2024-05-29 01:02:29 INFO: Epoch 149 | Train Loss: 0.2224 | Train Accuracy: 0.9430 | Validation Loss: 0.2547 | Validation Accuracy: 0.9425 | Validation Mean Precision: 0.8725 | Validation Mean Recall: 0.8788 | Validation Mean F1_score: 0.8726
-2024-05-29 01:02:30 INFO: Epoch 150 | Train Loss: 0.2215 | Train Accuracy: 0.9454 | Validation Loss: 0.2538 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8903 | Validation Mean Recall: 0.8740 | Validation Mean F1_score: 0.8812
-2024-05-29 01:02:31 INFO: Early stopping at epoch 149 | Best epoch : 100
-2024-05-29 01:02:31 INFO: Best Validation F1 Score (Macro): 0.8917
-2024-05-29 01:02:31 INFO: Best Validation Recall (Macro): 0.8631
-2024-05-29 01:02:31 INFO: Best Validation Precision (Macro): 0.9286
-2024-05-29 01:02:31 INFO: Best Epoch: 100
-2024-05-29 01:02:31 INFO: Infer Result: 
-2024-05-29 01:02:31 INFO: Prediction errors: 247
-2024-05-29 01:02:31 INFO: Prediction error percentage: 4.73%
-2024-05-29 01:02:31 INFO: Total samples: 5218
-2024-05-29 01:02:31 INFO: Infer Result End: 
-2024-05-29 01:02:32 INFO: Class weights: tensor([21.1878,  3.1149,  0.7420,  0.4378], device='cuda:0')
-2024-05-29 01:02:34 INFO: Epoch 001 | Train Loss: 0.6230 | Train Accuracy: 0.8188 | Validation Loss: 0.3941 | Validation Accuracy: 0.8261 | Validation Mean Precision: 0.7086 | Validation Mean Recall: 0.8409 | Validation Mean F1_score: 0.7566
-2024-05-29 01:02:35 INFO: Epoch 002 | Train Loss: 0.3746 | Train Accuracy: 0.8786 | Validation Loss: 0.3428 | Validation Accuracy: 0.8807 | Validation Mean Precision: 0.7739 | Validation Mean Recall: 0.8528 | Validation Mean F1_score: 0.8073
-2024-05-29 01:02:37 INFO: Epoch 003 | Train Loss: 0.3755 | Train Accuracy: 0.8800 | Validation Loss: 0.3365 | Validation Accuracy: 0.8793 | Validation Mean Precision: 0.7776 | Validation Mean Recall: 0.8531 | Validation Mean F1_score: 0.8095
-2024-05-29 01:02:38 INFO: Epoch 004 | Train Loss: 0.3689 | Train Accuracy: 0.8843 | Validation Loss: 0.3382 | Validation Accuracy: 0.8790 | Validation Mean Precision: 0.7675 | Validation Mean Recall: 0.8547 | Validation Mean F1_score: 0.8040
-2024-05-29 01:02:40 INFO: Epoch 005 | Train Loss: 0.3610 | Train Accuracy: 0.8857 | Validation Loss: 0.3346 | Validation Accuracy: 0.8898 | Validation Mean Precision: 0.8306 | Validation Mean Recall: 0.8497 | Validation Mean F1_score: 0.8378
-2024-05-29 01:02:41 INFO: Epoch 006 | Train Loss: 0.3629 | Train Accuracy: 0.8899 | Validation Loss: 0.3527 | Validation Accuracy: 0.8948 | Validation Mean Precision: 0.7918 | Validation Mean Recall: 0.8539 | Validation Mean F1_score: 0.8099
-2024-05-29 01:02:42 INFO: Epoch 007 | Train Loss: 0.3564 | Train Accuracy: 0.8890 | Validation Loss: 0.4176 | Validation Accuracy: 0.8773 | Validation Mean Precision: 0.7638 | Validation Mean Recall: 0.8255 | Validation Mean F1_score: 0.7698
-2024-05-29 01:02:44 INFO: Epoch 008 | Train Loss: 0.3575 | Train Accuracy: 0.8914 | Validation Loss: 0.3340 | Validation Accuracy: 0.8946 | Validation Mean Precision: 0.7967 | Validation Mean Recall: 0.8548 | Validation Mean F1_score: 0.8191
-2024-05-29 01:02:45 INFO: Epoch 009 | Train Loss: 0.3616 | Train Accuracy: 0.8893 | Validation Loss: 0.3304 | Validation Accuracy: 0.8896 | Validation Mean Precision: 0.8348 | Validation Mean Recall: 0.8549 | Validation Mean F1_score: 0.8437
-2024-05-29 01:02:47 INFO: Epoch 010 | Train Loss: 0.3497 | Train Accuracy: 0.8881 | Validation Loss: 0.3684 | Validation Accuracy: 0.8807 | Validation Mean Precision: 0.7327 | Validation Mean Recall: 0.8472 | Validation Mean F1_score: 0.7700
-2024-05-29 01:02:48 INFO: Epoch 011 | Train Loss: 0.3529 | Train Accuracy: 0.8885 | Validation Loss: 0.3257 | Validation Accuracy: 0.8979 | Validation Mean Precision: 0.8321 | Validation Mean Recall: 0.8566 | Validation Mean F1_score: 0.8434
-2024-05-29 01:02:50 INFO: Epoch 012 | Train Loss: 0.3521 | Train Accuracy: 0.8906 | Validation Loss: 0.3237 | Validation Accuracy: 0.8977 | Validation Mean Precision: 0.8387 | Validation Mean Recall: 0.8607 | Validation Mean F1_score: 0.8485
-2024-05-29 01:02:51 INFO: Epoch 013 | Train Loss: 0.3535 | Train Accuracy: 0.8934 | Validation Loss: 0.4121 | Validation Accuracy: 0.8991 | Validation Mean Precision: 0.8793 | Validation Mean Recall: 0.8274 | Validation Mean F1_score: 0.8467
-2024-05-29 01:02:53 INFO: Epoch 014 | Train Loss: 0.3501 | Train Accuracy: 0.8945 | Validation Loss: 0.3446 | Validation Accuracy: 0.8955 | Validation Mean Precision: 0.7976 | Validation Mean Recall: 0.8575 | Validation Mean F1_score: 0.8168
-2024-05-29 01:02:54 INFO: Epoch 015 | Train Loss: 0.3449 | Train Accuracy: 0.8937 | Validation Loss: 0.4116 | Validation Accuracy: 0.8819 | Validation Mean Precision: 0.8132 | Validation Mean Recall: 0.8347 | Validation Mean F1_score: 0.8073
-2024-05-29 01:02:56 INFO: Epoch 016 | Train Loss: 0.3417 | Train Accuracy: 0.8954 | Validation Loss: 0.3427 | Validation Accuracy: 0.8939 | Validation Mean Precision: 0.7901 | Validation Mean Recall: 0.8548 | Validation Mean F1_score: 0.8130
-2024-05-29 01:02:57 INFO: Epoch 017 | Train Loss: 0.3482 | Train Accuracy: 0.8916 | Validation Loss: 0.3341 | Validation Accuracy: 0.8960 | Validation Mean Precision: 0.8234 | Validation Mean Recall: 0.8584 | Validation Mean F1_score: 0.8379
-2024-05-29 01:02:59 INFO: Epoch 018 | Train Loss: 0.3493 | Train Accuracy: 0.8918 | Validation Loss: 0.3313 | Validation Accuracy: 0.8678 | Validation Mean Precision: 0.7754 | Validation Mean Recall: 0.8562 | Validation Mean F1_score: 0.8102
-2024-05-29 01:03:00 INFO: Epoch 019 | Train Loss: 0.3495 | Train Accuracy: 0.8912 | Validation Loss: 0.3546 | Validation Accuracy: 0.8977 | Validation Mean Precision: 0.8883 | Validation Mean Recall: 0.7999 | Validation Mean F1_score: 0.8297
-2024-05-29 01:03:02 INFO: Epoch 020 | Train Loss: 0.3496 | Train Accuracy: 0.8928 | Validation Loss: 0.3410 | Validation Accuracy: 0.8814 | Validation Mean Precision: 0.7581 | Validation Mean Recall: 0.8599 | Validation Mean F1_score: 0.7982
-2024-05-29 01:03:03 INFO: Epoch 021 | Train Loss: 0.3415 | Train Accuracy: 0.8946 | Validation Loss: 0.3203 | Validation Accuracy: 0.8987 | Validation Mean Precision: 0.8277 | Validation Mean Recall: 0.8585 | Validation Mean F1_score: 0.8414
-2024-05-29 01:03:05 INFO: Epoch 022 | Train Loss: 0.3433 | Train Accuracy: 0.8942 | Validation Loss: 0.3252 | Validation Accuracy: 0.9025 | Validation Mean Precision: 0.8673 | Validation Mean Recall: 0.8499 | Validation Mean F1_score: 0.8583
-2024-05-29 01:03:06 INFO: Epoch 023 | Train Loss: 0.3400 | Train Accuracy: 0.8917 | Validation Loss: 0.3357 | Validation Accuracy: 0.9018 | Validation Mean Precision: 0.8357 | Validation Mean Recall: 0.8502 | Validation Mean F1_score: 0.8394
-2024-05-29 01:03:08 INFO: Epoch 024 | Train Loss: 0.3426 | Train Accuracy: 0.8960 | Validation Loss: 0.3205 | Validation Accuracy: 0.8924 | Validation Mean Precision: 0.8333 | Validation Mean Recall: 0.8609 | Validation Mean F1_score: 0.8443
-2024-05-29 01:03:09 INFO: Epoch 025 | Train Loss: 0.3368 | Train Accuracy: 0.8960 | Validation Loss: 0.3230 | Validation Accuracy: 0.9023 | Validation Mean Precision: 0.8241 | Validation Mean Recall: 0.8590 | Validation Mean F1_score: 0.8384
-2024-05-29 01:03:10 INFO: Epoch 026 | Train Loss: 0.3417 | Train Accuracy: 0.8957 | Validation Loss: 0.3641 | Validation Accuracy: 0.8402 | Validation Mean Precision: 0.7469 | Validation Mean Recall: 0.8519 | Validation Mean F1_score: 0.7881
-2024-05-29 01:03:12 INFO: Epoch 027 | Train Loss: 0.3293 | Train Accuracy: 0.9018 | Validation Loss: 0.3141 | Validation Accuracy: 0.8955 | Validation Mean Precision: 0.8234 | Validation Mean Recall: 0.8672 | Validation Mean F1_score: 0.8432
-2024-05-29 01:03:13 INFO: Epoch 028 | Train Loss: 0.3328 | Train Accuracy: 0.9007 | Validation Loss: 0.3713 | Validation Accuracy: 0.8529 | Validation Mean Precision: 0.7135 | Validation Mean Recall: 0.8502 | Validation Mean F1_score: 0.7624
-2024-05-29 01:03:15 INFO: Epoch 029 | Train Loss: 0.3296 | Train Accuracy: 0.9051 | Validation Loss: 0.3009 | Validation Accuracy: 0.9114 | Validation Mean Precision: 0.8403 | Validation Mean Recall: 0.8750 | Validation Mean F1_score: 0.8561
-2024-05-29 01:03:16 INFO: Epoch 030 | Train Loss: 0.3205 | Train Accuracy: 0.9056 | Validation Loss: 0.3173 | Validation Accuracy: 0.9080 | Validation Mean Precision: 0.8296 | Validation Mean Recall: 0.8585 | Validation Mean F1_score: 0.8380
-2024-05-29 01:03:18 INFO: Epoch 031 | Train Loss: 0.3179 | Train Accuracy: 0.9112 | Validation Loss: 0.3199 | Validation Accuracy: 0.9140 | Validation Mean Precision: 0.8195 | Validation Mean Recall: 0.8617 | Validation Mean F1_score: 0.8299
-2024-05-29 01:03:19 INFO: Epoch 032 | Train Loss: 0.3183 | Train Accuracy: 0.9083 | Validation Loss: 0.2977 | Validation Accuracy: 0.9051 | Validation Mean Precision: 0.8667 | Validation Mean Recall: 0.8705 | Validation Mean F1_score: 0.8680
-2024-05-29 01:03:21 INFO: Epoch 033 | Train Loss: 0.3155 | Train Accuracy: 0.9120 | Validation Loss: 0.2921 | Validation Accuracy: 0.9284 | Validation Mean Precision: 0.8908 | Validation Mean Recall: 0.8707 | Validation Mean F1_score: 0.8800
-2024-05-29 01:03:22 INFO: Epoch 034 | Train Loss: 0.2983 | Train Accuracy: 0.9138 | Validation Loss: 0.3168 | Validation Accuracy: 0.8816 | Validation Mean Precision: 0.7891 | Validation Mean Recall: 0.8734 | Validation Mean F1_score: 0.8254
-2024-05-29 01:03:23 INFO: Epoch 035 | Train Loss: 0.3106 | Train Accuracy: 0.9117 | Validation Loss: 0.2900 | Validation Accuracy: 0.9310 | Validation Mean Precision: 0.8811 | Validation Mean Recall: 0.8843 | Validation Mean F1_score: 0.8816
-2024-05-29 01:03:25 INFO: Epoch 036 | Train Loss: 0.3020 | Train Accuracy: 0.9173 | Validation Loss: 0.2761 | Validation Accuracy: 0.9226 | Validation Mean Precision: 0.8734 | Validation Mean Recall: 0.8803 | Validation Mean F1_score: 0.8751
-2024-05-29 01:03:26 INFO: Epoch 037 | Train Loss: 0.2974 | Train Accuracy: 0.9162 | Validation Loss: 0.3555 | Validation Accuracy: 0.8989 | Validation Mean Precision: 0.9007 | Validation Mean Recall: 0.8045 | Validation Mean F1_score: 0.8344
-2024-05-29 01:03:28 INFO: Epoch 038 | Train Loss: 0.2953 | Train Accuracy: 0.9161 | Validation Loss: 0.2763 | Validation Accuracy: 0.9310 | Validation Mean Precision: 0.8731 | Validation Mean Recall: 0.8896 | Validation Mean F1_score: 0.8803
-2024-05-29 01:03:30 INFO: Epoch 039 | Train Loss: 0.2964 | Train Accuracy: 0.9166 | Validation Loss: 0.2851 | Validation Accuracy: 0.9329 | Validation Mean Precision: 0.9007 | Validation Mean Recall: 0.8588 | Validation Mean F1_score: 0.8754
-2024-05-29 01:03:32 INFO: Epoch 040 | Train Loss: 0.2976 | Train Accuracy: 0.9191 | Validation Loss: 0.2695 | Validation Accuracy: 0.9281 | Validation Mean Precision: 0.8536 | Validation Mean Recall: 0.8890 | Validation Mean F1_score: 0.8693
-2024-05-29 01:03:33 INFO: Epoch 041 | Train Loss: 0.2938 | Train Accuracy: 0.9177 | Validation Loss: 0.2826 | Validation Accuracy: 0.9142 | Validation Mean Precision: 0.8704 | Validation Mean Recall: 0.8789 | Validation Mean F1_score: 0.8742
-2024-05-29 01:03:35 INFO: Epoch 042 | Train Loss: 0.2920 | Train Accuracy: 0.9186 | Validation Loss: 0.2732 | Validation Accuracy: 0.9126 | Validation Mean Precision: 0.8285 | Validation Mean Recall: 0.8821 | Validation Mean F1_score: 0.8523
-2024-05-29 01:03:36 INFO: Epoch 043 | Train Loss: 0.2856 | Train Accuracy: 0.9218 | Validation Loss: 0.3002 | Validation Accuracy: 0.8833 | Validation Mean Precision: 0.8300 | Validation Mean Recall: 0.8422 | Validation Mean F1_score: 0.8237
-2024-05-29 01:03:38 INFO: Epoch 044 | Train Loss: 0.2905 | Train Accuracy: 0.9187 | Validation Loss: 0.2734 | Validation Accuracy: 0.9166 | Validation Mean Precision: 0.8190 | Validation Mean Recall: 0.8840 | Validation Mean F1_score: 0.8467
-2024-05-29 01:03:40 INFO: Epoch 045 | Train Loss: 0.2862 | Train Accuracy: 0.9202 | Validation Loss: 0.2652 | Validation Accuracy: 0.9229 | Validation Mean Precision: 0.8612 | Validation Mean Recall: 0.8824 | Validation Mean F1_score: 0.8696
-2024-05-29 01:03:41 INFO: Epoch 046 | Train Loss: 0.2861 | Train Accuracy: 0.9209 | Validation Loss: 0.2617 | Validation Accuracy: 0.9264 | Validation Mean Precision: 0.8644 | Validation Mean Recall: 0.8890 | Validation Mean F1_score: 0.8760
-2024-05-29 01:03:43 INFO: Epoch 047 | Train Loss: 0.2799 | Train Accuracy: 0.9230 | Validation Loss: 0.2645 | Validation Accuracy: 0.9219 | Validation Mean Precision: 0.8477 | Validation Mean Recall: 0.8874 | Validation Mean F1_score: 0.8660
-2024-05-29 01:03:45 INFO: Epoch 048 | Train Loss: 0.2787 | Train Accuracy: 0.9235 | Validation Loss: 0.2699 | Validation Accuracy: 0.9365 | Validation Mean Precision: 0.8975 | Validation Mean Recall: 0.8880 | Validation Mean F1_score: 0.8925
-2024-05-29 01:03:46 INFO: Epoch 049 | Train Loss: 0.2814 | Train Accuracy: 0.9218 | Validation Loss: 0.2767 | Validation Accuracy: 0.9274 | Validation Mean Precision: 0.8991 | Validation Mean Recall: 0.8748 | Validation Mean F1_score: 0.8857
-2024-05-29 01:03:48 INFO: Epoch 050 | Train Loss: 0.2829 | Train Accuracy: 0.9229 | Validation Loss: 0.2670 | Validation Accuracy: 0.9329 | Validation Mean Precision: 0.8791 | Validation Mean Recall: 0.8910 | Validation Mean F1_score: 0.8839
-2024-05-29 01:03:50 INFO: Epoch 051 | Train Loss: 0.2736 | Train Accuracy: 0.9251 | Validation Loss: 0.2754 | Validation Accuracy: 0.9368 | Validation Mean Precision: 0.8595 | Validation Mean Recall: 0.8903 | Validation Mean F1_score: 0.8710
-2024-05-29 01:03:51 INFO: Epoch 052 | Train Loss: 0.2820 | Train Accuracy: 0.9217 | Validation Loss: 0.2783 | Validation Accuracy: 0.9322 | Validation Mean Precision: 0.8725 | Validation Mean Recall: 0.8903 | Validation Mean F1_score: 0.8790
-2024-05-29 01:03:53 INFO: Epoch 053 | Train Loss: 0.2775 | Train Accuracy: 0.9254 | Validation Loss: 0.2740 | Validation Accuracy: 0.9185 | Validation Mean Precision: 0.8698 | Validation Mean Recall: 0.8618 | Validation Mean F1_score: 0.8579
-2024-05-29 01:03:54 INFO: Epoch 054 | Train Loss: 0.2703 | Train Accuracy: 0.9263 | Validation Loss: 0.2961 | Validation Accuracy: 0.9118 | Validation Mean Precision: 0.8948 | Validation Mean Recall: 0.8543 | Validation Mean F1_score: 0.8696
-2024-05-29 01:03:56 INFO: Epoch 055 | Train Loss: 0.2801 | Train Accuracy: 0.9256 | Validation Loss: 0.2665 | Validation Accuracy: 0.9324 | Validation Mean Precision: 0.8374 | Validation Mean Recall: 0.8936 | Validation Mean F1_score: 0.8596
-2024-05-29 01:03:58 INFO: Epoch 056 | Train Loss: 0.2735 | Train Accuracy: 0.9246 | Validation Loss: 0.2576 | Validation Accuracy: 0.9403 | Validation Mean Precision: 0.9003 | Validation Mean Recall: 0.8925 | Validation Mean F1_score: 0.8962
-2024-05-29 01:03:59 INFO: Epoch 057 | Train Loss: 0.2720 | Train Accuracy: 0.9288 | Validation Loss: 0.2678 | Validation Accuracy: 0.9138 | Validation Mean Precision: 0.8187 | Validation Mean Recall: 0.8812 | Validation Mean F1_score: 0.8446
-2024-05-29 01:04:01 INFO: Epoch 058 | Train Loss: 0.2688 | Train Accuracy: 0.9264 | Validation Loss: 0.2632 | Validation Accuracy: 0.9219 | Validation Mean Precision: 0.8774 | Validation Mean Recall: 0.8900 | Validation Mean F1_score: 0.8835
-2024-05-29 01:04:02 INFO: Epoch 059 | Train Loss: 0.2655 | Train Accuracy: 0.9280 | Validation Loss: 0.2650 | Validation Accuracy: 0.9305 | Validation Mean Precision: 0.8767 | Validation Mean Recall: 0.8828 | Validation Mean F1_score: 0.8776
-2024-05-29 01:04:04 INFO: Epoch 060 | Train Loss: 0.2720 | Train Accuracy: 0.9267 | Validation Loss: 0.2806 | Validation Accuracy: 0.9322 | Validation Mean Precision: 0.8414 | Validation Mean Recall: 0.8895 | Validation Mean F1_score: 0.8588
-2024-05-29 01:04:05 INFO: Epoch 061 | Train Loss: 0.2681 | Train Accuracy: 0.9302 | Validation Loss: 0.2590 | Validation Accuracy: 0.9269 | Validation Mean Precision: 0.8832 | Validation Mean Recall: 0.8881 | Validation Mean F1_score: 0.8855
-2024-05-29 01:04:06 INFO: Epoch 062 | Train Loss: 0.2662 | Train Accuracy: 0.9279 | Validation Loss: 0.2689 | Validation Accuracy: 0.9391 | Validation Mean Precision: 0.8486 | Validation Mean Recall: 0.8925 | Validation Mean F1_score: 0.8635
-2024-05-29 01:04:08 INFO: Epoch 063 | Train Loss: 0.2689 | Train Accuracy: 0.9278 | Validation Loss: 0.2622 | Validation Accuracy: 0.9413 | Validation Mean Precision: 0.8924 | Validation Mean Recall: 0.8973 | Validation Mean F1_score: 0.8934
-2024-05-29 01:04:09 INFO: Epoch 064 | Train Loss: 0.2718 | Train Accuracy: 0.9299 | Validation Loss: 0.2504 | Validation Accuracy: 0.9365 | Validation Mean Precision: 0.8744 | Validation Mean Recall: 0.8976 | Validation Mean F1_score: 0.8846
-2024-05-29 01:04:11 INFO: Epoch 065 | Train Loss: 0.2694 | Train Accuracy: 0.9299 | Validation Loss: 0.2519 | Validation Accuracy: 0.9298 | Validation Mean Precision: 0.8752 | Validation Mean Recall: 0.8913 | Validation Mean F1_score: 0.8819
-2024-05-29 01:04:12 INFO: Epoch 066 | Train Loss: 0.2643 | Train Accuracy: 0.9305 | Validation Loss: 0.2553 | Validation Accuracy: 0.9181 | Validation Mean Precision: 0.8610 | Validation Mean Recall: 0.8899 | Validation Mean F1_score: 0.8747
-2024-05-29 01:04:13 INFO: Epoch 067 | Train Loss: 0.2688 | Train Accuracy: 0.9282 | Validation Loss: 0.2536 | Validation Accuracy: 0.9303 | Validation Mean Precision: 0.8270 | Validation Mean Recall: 0.8939 | Validation Mean F1_score: 0.8547
-2024-05-29 01:04:15 INFO: Epoch 068 | Train Loss: 0.2645 | Train Accuracy: 0.9294 | Validation Loss: 0.2642 | Validation Accuracy: 0.9166 | Validation Mean Precision: 0.8239 | Validation Mean Recall: 0.8882 | Validation Mean F1_score: 0.8518
-2024-05-29 01:04:16 INFO: Epoch 069 | Train Loss: 0.2713 | Train Accuracy: 0.9269 | Validation Loss: 0.2617 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8426 | Validation Mean Recall: 0.8934 | Validation Mean F1_score: 0.8652
-2024-05-29 01:04:18 INFO: Epoch 070 | Train Loss: 0.2682 | Train Accuracy: 0.9293 | Validation Loss: 0.2513 | Validation Accuracy: 0.9267 | Validation Mean Precision: 0.8489 | Validation Mean Recall: 0.8937 | Validation Mean F1_score: 0.8694
-2024-05-29 01:04:19 INFO: Epoch 071 | Train Loss: 0.2711 | Train Accuracy: 0.9255 | Validation Loss: 0.3099 | Validation Accuracy: 0.8951 | Validation Mean Precision: 0.8093 | Validation Mean Recall: 0.8795 | Validation Mean F1_score: 0.8399
-2024-05-29 01:04:20 INFO: Epoch 072 | Train Loss: 0.2646 | Train Accuracy: 0.9293 | Validation Loss: 0.2576 | Validation Accuracy: 0.9272 | Validation Mean Precision: 0.8917 | Validation Mean Recall: 0.8741 | Validation Mean F1_score: 0.8798
-2024-05-29 01:04:22 INFO: Epoch 073 | Train Loss: 0.2610 | Train Accuracy: 0.9309 | Validation Loss: 0.2520 | Validation Accuracy: 0.9202 | Validation Mean Precision: 0.8384 | Validation Mean Recall: 0.8868 | Validation Mean F1_score: 0.8607
-2024-05-29 01:04:23 INFO: Epoch 074 | Train Loss: 0.2656 | Train Accuracy: 0.9324 | Validation Loss: 0.2593 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.8928 | Validation Mean Recall: 0.8934 | Validation Mean F1_score: 0.8917
-2024-05-29 01:04:25 INFO: Epoch 075 | Train Loss: 0.2658 | Train Accuracy: 0.9341 | Validation Loss: 0.2527 | Validation Accuracy: 0.9320 | Validation Mean Precision: 0.8319 | Validation Mean Recall: 0.8893 | Validation Mean F1_score: 0.8559
-2024-05-29 01:04:26 INFO: Epoch 076 | Train Loss: 0.2576 | Train Accuracy: 0.9326 | Validation Loss: 0.2565 | Validation Accuracy: 0.9298 | Validation Mean Precision: 0.8599 | Validation Mean Recall: 0.8925 | Validation Mean F1_score: 0.8720
-2024-05-29 01:04:28 INFO: Epoch 077 | Train Loss: 0.2562 | Train Accuracy: 0.9329 | Validation Loss: 0.2490 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.8504 | Validation Mean Recall: 0.8994 | Validation Mean F1_score: 0.8703
-2024-05-29 01:04:29 INFO: Epoch 078 | Train Loss: 0.2618 | Train Accuracy: 0.9305 | Validation Loss: 0.2480 | Validation Accuracy: 0.9291 | Validation Mean Precision: 0.8343 | Validation Mean Recall: 0.8897 | Validation Mean F1_score: 0.8582
-2024-05-29 01:04:31 INFO: Epoch 079 | Train Loss: 0.2617 | Train Accuracy: 0.9349 | Validation Loss: 0.2889 | Validation Accuracy: 0.8840 | Validation Mean Precision: 0.7988 | Validation Mean Recall: 0.8796 | Validation Mean F1_score: 0.8339
-2024-05-29 01:04:32 INFO: Epoch 080 | Train Loss: 0.2579 | Train Accuracy: 0.9344 | Validation Loss: 0.2594 | Validation Accuracy: 0.9279 | Validation Mean Precision: 0.8084 | Validation Mean Recall: 0.8874 | Validation Mean F1_score: 0.8391
-2024-05-29 01:04:34 INFO: Epoch 081 | Train Loss: 0.2566 | Train Accuracy: 0.9348 | Validation Loss: 0.2457 | Validation Accuracy: 0.9288 | Validation Mean Precision: 0.8346 | Validation Mean Recall: 0.8909 | Validation Mean F1_score: 0.8589
-2024-05-29 01:04:35 INFO: Epoch 082 | Train Loss: 0.2557 | Train Accuracy: 0.9325 | Validation Loss: 0.2460 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8923 | Validation Mean Recall: 0.8923 | Validation Mean F1_score: 0.8915
-2024-05-29 01:04:37 INFO: Epoch 083 | Train Loss: 0.2624 | Train Accuracy: 0.9323 | Validation Loss: 0.2452 | Validation Accuracy: 0.9406 | Validation Mean Precision: 0.8830 | Validation Mean Recall: 0.9006 | Validation Mean F1_score: 0.8908
-2024-05-29 01:04:38 INFO: Epoch 084 | Train Loss: 0.2540 | Train Accuracy: 0.9347 | Validation Loss: 0.2938 | Validation Accuracy: 0.9118 | Validation Mean Precision: 0.8918 | Validation Mean Recall: 0.8535 | Validation Mean F1_score: 0.8709
-2024-05-29 01:04:40 INFO: Epoch 085 | Train Loss: 0.2563 | Train Accuracy: 0.9332 | Validation Loss: 0.2402 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.8889 | Validation Mean Recall: 0.8983 | Validation Mean F1_score: 0.8928
-2024-05-29 01:04:41 INFO: Epoch 086 | Train Loss: 0.2519 | Train Accuracy: 0.9367 | Validation Loss: 0.2536 | Validation Accuracy: 0.9451 | Validation Mean Precision: 0.8811 | Validation Mean Recall: 0.8987 | Validation Mean F1_score: 0.8873
-2024-05-29 01:04:43 INFO: Epoch 087 | Train Loss: 0.2553 | Train Accuracy: 0.9340 | Validation Loss: 0.2428 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8774 | Validation Mean Recall: 0.8980 | Validation Mean F1_score: 0.8869
-2024-05-29 01:04:44 INFO: Epoch 088 | Train Loss: 0.2584 | Train Accuracy: 0.9344 | Validation Loss: 0.2470 | Validation Accuracy: 0.9248 | Validation Mean Precision: 0.8443 | Validation Mean Recall: 0.8950 | Validation Mean F1_score: 0.8673
-2024-05-29 01:04:45 INFO: Epoch 089 | Train Loss: 0.2556 | Train Accuracy: 0.9326 | Validation Loss: 0.2541 | Validation Accuracy: 0.9456 | Validation Mean Precision: 0.8912 | Validation Mean Recall: 0.8988 | Validation Mean F1_score: 0.8931
-2024-05-29 01:04:47 INFO: Epoch 090 | Train Loss: 0.2609 | Train Accuracy: 0.9334 | Validation Loss: 0.2643 | Validation Accuracy: 0.9219 | Validation Mean Precision: 0.8196 | Validation Mean Recall: 0.8870 | Validation Mean F1_score: 0.8441
-2024-05-29 01:04:48 INFO: Epoch 091 | Train Loss: 0.2539 | Train Accuracy: 0.9358 | Validation Loss: 0.2637 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.9142 | Validation Mean Recall: 0.8870 | Validation Mean F1_score: 0.8993
-2024-05-29 01:04:49 INFO: Epoch 092 | Train Loss: 0.2541 | Train Accuracy: 0.9374 | Validation Loss: 0.2457 | Validation Accuracy: 0.9303 | Validation Mean Precision: 0.8538 | Validation Mean Recall: 0.8972 | Validation Mean F1_score: 0.8726
-2024-05-29 01:04:51 INFO: Epoch 093 | Train Loss: 0.2538 | Train Accuracy: 0.9348 | Validation Loss: 0.2574 | Validation Accuracy: 0.9317 | Validation Mean Precision: 0.8178 | Validation Mean Recall: 0.8883 | Validation Mean F1_score: 0.8441
-2024-05-29 01:04:52 INFO: Epoch 094 | Train Loss: 0.2570 | Train Accuracy: 0.9333 | Validation Loss: 0.2707 | Validation Accuracy: 0.9454 | Validation Mean Precision: 0.9062 | Validation Mean Recall: 0.8899 | Validation Mean F1_score: 0.8964
-2024-05-29 01:04:54 INFO: Epoch 095 | Train Loss: 0.2504 | Train Accuracy: 0.9367 | Validation Loss: 0.2614 | Validation Accuracy: 0.9078 | Validation Mean Precision: 0.8376 | Validation Mean Recall: 0.8897 | Validation Mean F1_score: 0.8612
-2024-05-29 01:04:55 INFO: Epoch 096 | Train Loss: 0.2487 | Train Accuracy: 0.9381 | Validation Loss: 0.2437 | Validation Accuracy: 0.9334 | Validation Mean Precision: 0.8751 | Validation Mean Recall: 0.9001 | Validation Mean F1_score: 0.8870
-2024-05-29 01:04:56 INFO: Epoch 097 | Train Loss: 0.2527 | Train Accuracy: 0.9362 | Validation Loss: 0.2728 | Validation Accuracy: 0.9370 | Validation Mean Precision: 0.8175 | Validation Mean Recall: 0.8876 | Validation Mean F1_score: 0.8417
-2024-05-29 01:04:58 INFO: Epoch 098 | Train Loss: 0.2546 | Train Accuracy: 0.9374 | Validation Loss: 0.2435 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8993 | Validation Mean Recall: 0.9050 | Validation Mean F1_score: 0.9010
-2024-05-29 01:04:59 INFO: Epoch 099 | Train Loss: 0.2485 | Train Accuracy: 0.9376 | Validation Loss: 0.2409 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.9093 | Validation Mean Recall: 0.8978 | Validation Mean F1_score: 0.9021
-2024-05-29 01:05:01 INFO: Epoch 100 | Train Loss: 0.2512 | Train Accuracy: 0.9371 | Validation Loss: 0.2515 | Validation Accuracy: 0.9322 | Validation Mean Precision: 0.8224 | Validation Mean Recall: 0.8932 | Validation Mean F1_score: 0.8509
-2024-05-29 01:05:02 INFO: Epoch 101 | Train Loss: 0.2460 | Train Accuracy: 0.9393 | Validation Loss: 0.2383 | Validation Accuracy: 0.9375 | Validation Mean Precision: 0.8841 | Validation Mean Recall: 0.9019 | Validation Mean F1_score: 0.8926
-2024-05-29 01:05:04 INFO: Epoch 102 | Train Loss: 0.2500 | Train Accuracy: 0.9375 | Validation Loss: 0.2340 | Validation Accuracy: 0.9384 | Validation Mean Precision: 0.8677 | Validation Mean Recall: 0.9005 | Validation Mean F1_score: 0.8822
-2024-05-29 01:05:05 INFO: Epoch 103 | Train Loss: 0.2506 | Train Accuracy: 0.9370 | Validation Loss: 0.2504 | Validation Accuracy: 0.9451 | Validation Mean Precision: 0.8845 | Validation Mean Recall: 0.8995 | Validation Mean F1_score: 0.8897
-2024-05-29 01:05:07 INFO: Epoch 104 | Train Loss: 0.2510 | Train Accuracy: 0.9368 | Validation Loss: 0.2443 | Validation Accuracy: 0.9276 | Validation Mean Precision: 0.8389 | Validation Mean Recall: 0.8981 | Validation Mean F1_score: 0.8649
-2024-05-29 01:05:08 INFO: Epoch 105 | Train Loss: 0.2527 | Train Accuracy: 0.9376 | Validation Loss: 0.2544 | Validation Accuracy: 0.9334 | Validation Mean Precision: 0.8985 | Validation Mean Recall: 0.8926 | Validation Mean F1_score: 0.8944
-2024-05-29 01:05:09 INFO: Epoch 106 | Train Loss: 0.2500 | Train Accuracy: 0.9364 | Validation Loss: 0.2334 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8886 | Validation Mean Recall: 0.9002 | Validation Mean F1_score: 0.8940
-2024-05-29 01:05:11 INFO: Epoch 107 | Train Loss: 0.2552 | Train Accuracy: 0.9357 | Validation Loss: 0.2488 | Validation Accuracy: 0.9257 | Validation Mean Precision: 0.8337 | Validation Mean Recall: 0.8966 | Validation Mean F1_score: 0.8611
-2024-05-29 01:05:12 INFO: Epoch 108 | Train Loss: 0.2436 | Train Accuracy: 0.9412 | Validation Loss: 0.2525 | Validation Accuracy: 0.9377 | Validation Mean Precision: 0.8642 | Validation Mean Recall: 0.8972 | Validation Mean F1_score: 0.8751
-2024-05-29 01:05:14 INFO: Epoch 109 | Train Loss: 0.2450 | Train Accuracy: 0.9388 | Validation Loss: 0.2574 | Validation Accuracy: 0.9348 | Validation Mean Precision: 0.8378 | Validation Mean Recall: 0.8865 | Validation Mean F1_score: 0.8565
-2024-05-29 01:05:15 INFO: Epoch 110 | Train Loss: 0.2485 | Train Accuracy: 0.9380 | Validation Loss: 0.2350 | Validation Accuracy: 0.9324 | Validation Mean Precision: 0.8607 | Validation Mean Recall: 0.8979 | Validation Mean F1_score: 0.8778
-2024-05-29 01:05:16 INFO: Epoch 111 | Train Loss: 0.2430 | Train Accuracy: 0.9386 | Validation Loss: 0.2396 | Validation Accuracy: 0.9370 | Validation Mean Precision: 0.8582 | Validation Mean Recall: 0.8986 | Validation Mean F1_score: 0.8758
-2024-05-29 01:05:18 INFO: Epoch 112 | Train Loss: 0.2450 | Train Accuracy: 0.9401 | Validation Loss: 0.2348 | Validation Accuracy: 0.9483 | Validation Mean Precision: 0.9050 | Validation Mean Recall: 0.9019 | Validation Mean F1_score: 0.9032
-2024-05-29 01:05:19 INFO: Epoch 113 | Train Loss: 0.2453 | Train Accuracy: 0.9393 | Validation Loss: 0.2299 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.9008 | Validation Mean Recall: 0.9071 | Validation Mean F1_score: 0.9035
-2024-05-29 01:05:21 INFO: Epoch 114 | Train Loss: 0.2426 | Train Accuracy: 0.9391 | Validation Loss: 0.2407 | Validation Accuracy: 0.9459 | Validation Mean Precision: 0.8603 | Validation Mean Recall: 0.9017 | Validation Mean F1_score: 0.8755
-2024-05-29 01:05:22 INFO: Epoch 115 | Train Loss: 0.2450 | Train Accuracy: 0.9390 | Validation Loss: 0.2473 | Validation Accuracy: 0.9336 | Validation Mean Precision: 0.8894 | Validation Mean Recall: 0.8754 | Validation Mean F1_score: 0.8798
-2024-05-29 01:05:23 INFO: Epoch 116 | Train Loss: 0.2412 | Train Accuracy: 0.9404 | Validation Loss: 0.2356 | Validation Accuracy: 0.9461 | Validation Mean Precision: 0.8769 | Validation Mean Recall: 0.9003 | Validation Mean F1_score: 0.8863
-2024-05-29 01:05:25 INFO: Epoch 117 | Train Loss: 0.2419 | Train Accuracy: 0.9392 | Validation Loss: 0.2348 | Validation Accuracy: 0.9315 | Validation Mean Precision: 0.8703 | Validation Mean Recall: 0.8954 | Validation Mean F1_score: 0.8820
-2024-05-29 01:05:26 INFO: Epoch 118 | Train Loss: 0.2409 | Train Accuracy: 0.9394 | Validation Loss: 0.2333 | Validation Accuracy: 0.9430 | Validation Mean Precision: 0.8961 | Validation Mean Recall: 0.8900 | Validation Mean F1_score: 0.8926
-2024-05-29 01:05:27 INFO: Epoch 119 | Train Loss: 0.2395 | Train Accuracy: 0.9397 | Validation Loss: 0.2334 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.8885 | Validation Mean Recall: 0.9005 | Validation Mean F1_score: 0.8940
-2024-05-29 01:05:29 INFO: Epoch 120 | Train Loss: 0.2379 | Train Accuracy: 0.9411 | Validation Loss: 0.2319 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8558 | Validation Mean Recall: 0.8983 | Validation Mean F1_score: 0.8747
-2024-05-29 01:05:30 INFO: Epoch 121 | Train Loss: 0.2440 | Train Accuracy: 0.9397 | Validation Loss: 0.2303 | Validation Accuracy: 0.9391 | Validation Mean Precision: 0.9018 | Validation Mean Recall: 0.8980 | Validation Mean F1_score: 0.8995
-2024-05-29 01:05:31 INFO: Epoch 122 | Train Loss: 0.2410 | Train Accuracy: 0.9395 | Validation Loss: 0.2416 | Validation Accuracy: 0.9494 | Validation Mean Precision: 0.8874 | Validation Mean Recall: 0.8995 | Validation Mean F1_score: 0.8906
-2024-05-29 01:05:33 INFO: Epoch 123 | Train Loss: 0.2388 | Train Accuracy: 0.9400 | Validation Loss: 0.2333 | Validation Accuracy: 0.9516 | Validation Mean Precision: 0.8943 | Validation Mean Recall: 0.9036 | Validation Mean F1_score: 0.8971
-2024-05-29 01:05:34 INFO: Epoch 124 | Train Loss: 0.2442 | Train Accuracy: 0.9407 | Validation Loss: 0.2335 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8646 | Validation Mean Recall: 0.9003 | Validation Mean F1_score: 0.8805
-2024-05-29 01:05:35 INFO: Epoch 125 | Train Loss: 0.2356 | Train Accuracy: 0.9429 | Validation Loss: 0.2293 | Validation Accuracy: 0.9363 | Validation Mean Precision: 0.8749 | Validation Mean Recall: 0.9021 | Validation Mean F1_score: 0.8872
-2024-05-29 01:05:37 INFO: Epoch 126 | Train Loss: 0.2379 | Train Accuracy: 0.9382 | Validation Loss: 0.2344 | Validation Accuracy: 0.9444 | Validation Mean Precision: 0.8703 | Validation Mean Recall: 0.9002 | Validation Mean F1_score: 0.8834
-2024-05-29 01:05:38 INFO: Epoch 127 | Train Loss: 0.2384 | Train Accuracy: 0.9420 | Validation Loss: 0.2281 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8800 | Validation Mean Recall: 0.9004 | Validation Mean F1_score: 0.8892
-2024-05-29 01:05:40 INFO: Epoch 128 | Train Loss: 0.2382 | Train Accuracy: 0.9422 | Validation Loss: 0.2430 | Validation Accuracy: 0.9480 | Validation Mean Precision: 0.9093 | Validation Mean Recall: 0.8987 | Validation Mean F1_score: 0.9022
-2024-05-29 01:05:41 INFO: Epoch 129 | Train Loss: 0.2411 | Train Accuracy: 0.9413 | Validation Loss: 0.2267 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8863 | Validation Mean Recall: 0.9044 | Validation Mean F1_score: 0.8948
-2024-05-29 01:05:42 INFO: Epoch 130 | Train Loss: 0.2401 | Train Accuracy: 0.9397 | Validation Loss: 0.2340 | Validation Accuracy: 0.9353 | Validation Mean Precision: 0.8694 | Validation Mean Recall: 0.8974 | Validation Mean F1_score: 0.8809
-2024-05-29 01:05:44 INFO: Epoch 131 | Train Loss: 0.2399 | Train Accuracy: 0.9400 | Validation Loss: 0.2628 | Validation Accuracy: 0.9346 | Validation Mean Precision: 0.8641 | Validation Mean Recall: 0.8938 | Validation Mean F1_score: 0.8763
-2024-05-29 01:05:45 INFO: Epoch 132 | Train Loss: 0.2416 | Train Accuracy: 0.9410 | Validation Loss: 0.2332 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.9088 | Validation Mean Recall: 0.9019 | Validation Mean F1_score: 0.9047
-2024-05-29 01:05:47 INFO: Epoch 133 | Train Loss: 0.2354 | Train Accuracy: 0.9399 | Validation Loss: 0.2508 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8577 | Validation Mean Recall: 0.8996 | Validation Mean F1_score: 0.8727
-2024-05-29 01:05:48 INFO: Epoch 134 | Train Loss: 0.2424 | Train Accuracy: 0.9403 | Validation Loss: 0.2310 | Validation Accuracy: 0.9360 | Validation Mean Precision: 0.8762 | Validation Mean Recall: 0.9007 | Validation Mean F1_score: 0.8873
-2024-05-29 01:05:50 INFO: Epoch 135 | Train Loss: 0.2397 | Train Accuracy: 0.9411 | Validation Loss: 0.2372 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8788 | Validation Mean Recall: 0.9016 | Validation Mean F1_score: 0.8888
-2024-05-29 01:05:51 INFO: Epoch 136 | Train Loss: 0.2378 | Train Accuracy: 0.9414 | Validation Loss: 0.2496 | Validation Accuracy: 0.9243 | Validation Mean Precision: 0.8301 | Validation Mean Recall: 0.8939 | Validation Mean F1_score: 0.8578
-2024-05-29 01:05:53 INFO: Epoch 137 | Train Loss: 0.2341 | Train Accuracy: 0.9442 | Validation Loss: 0.2389 | Validation Accuracy: 0.9487 | Validation Mean Precision: 0.8830 | Validation Mean Recall: 0.9058 | Validation Mean F1_score: 0.8911
-2024-05-29 01:05:54 INFO: Epoch 138 | Train Loss: 0.2340 | Train Accuracy: 0.9429 | Validation Loss: 0.2345 | Validation Accuracy: 0.9358 | Validation Mean Precision: 0.8733 | Validation Mean Recall: 0.8916 | Validation Mean F1_score: 0.8820
-2024-05-29 01:05:55 INFO: Epoch 139 | Train Loss: 0.2312 | Train Accuracy: 0.9432 | Validation Loss: 0.2445 | Validation Accuracy: 0.9497 | Validation Mean Precision: 0.8943 | Validation Mean Recall: 0.9006 | Validation Mean F1_score: 0.8951
-2024-05-29 01:05:57 INFO: Epoch 140 | Train Loss: 0.2446 | Train Accuracy: 0.9409 | Validation Loss: 0.2427 | Validation Accuracy: 0.9444 | Validation Mean Precision: 0.8703 | Validation Mean Recall: 0.9028 | Validation Mean F1_score: 0.8839
-2024-05-29 01:05:58 INFO: Epoch 141 | Train Loss: 0.2346 | Train Accuracy: 0.9425 | Validation Loss: 0.2412 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.9138 | Validation Mean Recall: 0.8791 | Validation Mean F1_score: 0.8943
-2024-05-29 01:06:00 INFO: Epoch 142 | Train Loss: 0.2318 | Train Accuracy: 0.9446 | Validation Loss: 0.2428 | Validation Accuracy: 0.9255 | Validation Mean Precision: 0.8446 | Validation Mean Recall: 0.8889 | Validation Mean F1_score: 0.8619
-2024-05-29 01:06:01 INFO: Epoch 143 | Train Loss: 0.2327 | Train Accuracy: 0.9433 | Validation Loss: 0.2254 | Validation Accuracy: 0.9480 | Validation Mean Precision: 0.8936 | Validation Mean Recall: 0.9038 | Validation Mean F1_score: 0.8979
-2024-05-29 01:06:03 INFO: Epoch 144 | Train Loss: 0.2414 | Train Accuracy: 0.9421 | Validation Loss: 0.2236 | Validation Accuracy: 0.9420 | Validation Mean Precision: 0.8841 | Validation Mean Recall: 0.9018 | Validation Mean F1_score: 0.8921
-2024-05-29 01:06:04 INFO: Epoch 145 | Train Loss: 0.2356 | Train Accuracy: 0.9423 | Validation Loss: 0.2346 | Validation Accuracy: 0.9370 | Validation Mean Precision: 0.8650 | Validation Mean Recall: 0.9031 | Validation Mean F1_score: 0.8818
-2024-05-29 01:06:06 INFO: Epoch 146 | Train Loss: 0.2340 | Train Accuracy: 0.9427 | Validation Loss: 0.2265 | Validation Accuracy: 0.9475 | Validation Mean Precision: 0.8958 | Validation Mean Recall: 0.9004 | Validation Mean F1_score: 0.8976
-2024-05-29 01:06:07 INFO: Epoch 147 | Train Loss: 0.2277 | Train Accuracy: 0.9432 | Validation Loss: 0.3076 | Validation Accuracy: 0.9152 | Validation Mean Precision: 0.8523 | Validation Mean Recall: 0.8642 | Validation Mean F1_score: 0.8505
-2024-05-29 01:06:09 INFO: Epoch 148 | Train Loss: 0.2410 | Train Accuracy: 0.9401 | Validation Loss: 0.2299 | Validation Accuracy: 0.9528 | Validation Mean Precision: 0.9296 | Validation Mean Recall: 0.8966 | Validation Mean F1_score: 0.9120
-2024-05-29 01:06:10 INFO: Epoch 149 | Train Loss: 0.2286 | Train Accuracy: 0.9441 | Validation Loss: 0.2286 | Validation Accuracy: 0.9468 | Validation Mean Precision: 0.8873 | Validation Mean Recall: 0.9005 | Validation Mean F1_score: 0.8931
-2024-05-29 01:06:12 INFO: Epoch 150 | Train Loss: 0.2282 | Train Accuracy: 0.9451 | Validation Loss: 0.2408 | Validation Accuracy: 0.9348 | Validation Mean Precision: 0.8572 | Validation Mean Recall: 0.8970 | Validation Mean F1_score: 0.8735
-2024-05-29 01:06:12 INFO: Best Validation F1 Score (Macro): 0.9120
-2024-05-29 01:06:12 INFO: Best Validation Recall (Macro): 0.8966
-2024-05-29 01:06:12 INFO: Best Validation Precision (Macro): 0.9296
-2024-05-29 01:06:12 INFO: Best Epoch: 148
-2024-05-29 01:06:13 INFO: Infer Result: 
-2024-05-29 01:06:13 INFO: Prediction errors: 328
-2024-05-29 01:06:13 INFO: Prediction error percentage: 6.29%
-2024-05-29 01:06:13 INFO: Total samples: 5218
-2024-05-29 01:06:13 INFO: Infer Result End: 
-2024-05-29 01:06:14 INFO: Class weights: tensor([21.1878,  3.1149,  0.7420,  0.4378], device='cuda:0')
-2024-05-29 01:06:15 INFO: Epoch 001 | Train Loss: 0.5638 | Train Accuracy: 0.8236 | Validation Loss: 0.3719 | Validation Accuracy: 0.8728 | Validation Mean Precision: 0.7790 | Validation Mean Recall: 0.8359 | Validation Mean F1_score: 0.8044
-2024-05-29 01:06:16 INFO: Epoch 002 | Train Loss: 0.3773 | Train Accuracy: 0.8733 | Validation Loss: 0.3723 | Validation Accuracy: 0.8685 | Validation Mean Precision: 0.7648 | Validation Mean Recall: 0.8368 | Validation Mean F1_score: 0.7960
-2024-05-29 01:06:18 INFO: Epoch 003 | Train Loss: 0.3640 | Train Accuracy: 0.8830 | Validation Loss: 0.3758 | Validation Accuracy: 0.8723 | Validation Mean Precision: 0.8008 | Validation Mean Recall: 0.8216 | Validation Mean F1_score: 0.8066
-2024-05-29 01:06:19 INFO: Epoch 004 | Train Loss: 0.3688 | Train Accuracy: 0.8807 | Validation Loss: 0.3599 | Validation Accuracy: 0.8967 | Validation Mean Precision: 0.7897 | Validation Mean Recall: 0.8373 | Validation Mean F1_score: 0.8084
-2024-05-29 01:06:21 INFO: Epoch 005 | Train Loss: 0.3604 | Train Accuracy: 0.8866 | Validation Loss: 0.3584 | Validation Accuracy: 0.8898 | Validation Mean Precision: 0.7989 | Validation Mean Recall: 0.8367 | Validation Mean F1_score: 0.8152
-2024-05-29 01:06:22 INFO: Epoch 006 | Train Loss: 0.3510 | Train Accuracy: 0.8886 | Validation Loss: 0.3690 | Validation Accuracy: 0.9078 | Validation Mean Precision: 0.8460 | Validation Mean Recall: 0.8215 | Validation Mean F1_score: 0.8331
-2024-05-29 01:06:23 INFO: Epoch 007 | Train Loss: 0.3486 | Train Accuracy: 0.8885 | Validation Loss: 0.3636 | Validation Accuracy: 0.8814 | Validation Mean Precision: 0.7616 | Validation Mean Recall: 0.8338 | Validation Mean F1_score: 0.7866
-2024-05-29 01:06:25 INFO: Epoch 008 | Train Loss: 0.3586 | Train Accuracy: 0.8884 | Validation Loss: 0.3995 | Validation Accuracy: 0.8584 | Validation Mean Precision: 0.6917 | Validation Mean Recall: 0.8266 | Validation Mean F1_score: 0.7349
-2024-05-29 01:06:27 INFO: Epoch 009 | Train Loss: 0.3516 | Train Accuracy: 0.8884 | Validation Loss: 0.3793 | Validation Accuracy: 0.9039 | Validation Mean Precision: 0.8564 | Validation Mean Recall: 0.7745 | Validation Mean F1_score: 0.8028
-2024-05-29 01:06:28 INFO: Epoch 010 | Train Loss: 0.3557 | Train Accuracy: 0.8906 | Validation Loss: 0.3648 | Validation Accuracy: 0.9109 | Validation Mean Precision: 0.8370 | Validation Mean Recall: 0.8359 | Validation Mean F1_score: 0.8339
-2024-05-29 01:06:30 INFO: Epoch 011 | Train Loss: 0.3502 | Train Accuracy: 0.8914 | Validation Loss: 0.3655 | Validation Accuracy: 0.8991 | Validation Mean Precision: 0.8369 | Validation Mean Recall: 0.8220 | Validation Mean F1_score: 0.8266
-2024-05-29 01:06:31 INFO: Epoch 012 | Train Loss: 0.3512 | Train Accuracy: 0.8910 | Validation Loss: 0.3420 | Validation Accuracy: 0.8987 | Validation Mean Precision: 0.7998 | Validation Mean Recall: 0.8412 | Validation Mean F1_score: 0.8182
-2024-05-29 01:06:33 INFO: Epoch 013 | Train Loss: 0.3408 | Train Accuracy: 0.8918 | Validation Loss: 0.3465 | Validation Accuracy: 0.8860 | Validation Mean Precision: 0.7539 | Validation Mean Recall: 0.8438 | Validation Mean F1_score: 0.7906
-2024-05-29 01:06:34 INFO: Epoch 014 | Train Loss: 0.3465 | Train Accuracy: 0.8917 | Validation Loss: 0.3924 | Validation Accuracy: 0.9097 | Validation Mean Precision: 0.8766 | Validation Mean Recall: 0.7619 | Validation Mean F1_score: 0.7906
-2024-05-29 01:06:36 INFO: Epoch 015 | Train Loss: 0.3374 | Train Accuracy: 0.8964 | Validation Loss: 0.3431 | Validation Accuracy: 0.9142 | Validation Mean Precision: 0.8497 | Validation Mean Recall: 0.8311 | Validation Mean F1_score: 0.8401
-2024-05-29 01:06:37 INFO: Epoch 016 | Train Loss: 0.3372 | Train Accuracy: 0.8997 | Validation Loss: 0.4301 | Validation Accuracy: 0.8781 | Validation Mean Precision: 0.8042 | Validation Mean Recall: 0.8120 | Validation Mean F1_score: 0.7946
-2024-05-29 01:06:39 INFO: Epoch 017 | Train Loss: 0.3339 | Train Accuracy: 0.9015 | Validation Loss: 0.3258 | Validation Accuracy: 0.9233 | Validation Mean Precision: 0.8430 | Validation Mean Recall: 0.8493 | Validation Mean F1_score: 0.8455
-2024-05-29 01:06:40 INFO: Epoch 018 | Train Loss: 0.3233 | Train Accuracy: 0.9027 | Validation Loss: 0.3266 | Validation Accuracy: 0.9305 | Validation Mean Precision: 0.8529 | Validation Mean Recall: 0.8462 | Validation Mean F1_score: 0.8489
-2024-05-29 01:06:42 INFO: Epoch 019 | Train Loss: 0.3175 | Train Accuracy: 0.9093 | Validation Loss: 0.3345 | Validation Accuracy: 0.8824 | Validation Mean Precision: 0.7621 | Validation Mean Recall: 0.8447 | Validation Mean F1_score: 0.7971
-2024-05-29 01:06:43 INFO: Epoch 020 | Train Loss: 0.3114 | Train Accuracy: 0.9082 | Validation Loss: 0.3168 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8144 | Validation Mean Recall: 0.8507 | Validation Mean F1_score: 0.8309
-2024-05-29 01:06:44 INFO: Epoch 021 | Train Loss: 0.3086 | Train Accuracy: 0.9090 | Validation Loss: 0.3441 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8698 | Validation Mean Recall: 0.8049 | Validation Mean F1_score: 0.8254
-2024-05-29 01:06:46 INFO: Epoch 022 | Train Loss: 0.3085 | Train Accuracy: 0.9097 | Validation Loss: 0.3579 | Validation Accuracy: 0.8594 | Validation Mean Precision: 0.7556 | Validation Mean Recall: 0.8371 | Validation Mean F1_score: 0.7891
-2024-05-29 01:06:47 INFO: Epoch 023 | Train Loss: 0.3063 | Train Accuracy: 0.9116 | Validation Loss: 0.3301 | Validation Accuracy: 0.9085 | Validation Mean Precision: 0.8656 | Validation Mean Recall: 0.8126 | Validation Mean F1_score: 0.8296
-2024-05-29 01:06:48 INFO: Epoch 024 | Train Loss: 0.3002 | Train Accuracy: 0.9138 | Validation Loss: 0.2995 | Validation Accuracy: 0.9346 | Validation Mean Precision: 0.8294 | Validation Mean Recall: 0.8622 | Validation Mean F1_score: 0.8429
-2024-05-29 01:06:50 INFO: Epoch 025 | Train Loss: 0.2980 | Train Accuracy: 0.9166 | Validation Loss: 0.3655 | Validation Accuracy: 0.8539 | Validation Mean Precision: 0.7530 | Validation Mean Recall: 0.8401 | Validation Mean F1_score: 0.7886
-2024-05-29 01:06:51 INFO: Epoch 026 | Train Loss: 0.2954 | Train Accuracy: 0.9175 | Validation Loss: 0.3148 | Validation Accuracy: 0.9046 | Validation Mean Precision: 0.7703 | Validation Mean Recall: 0.8515 | Validation Mean F1_score: 0.8023
-2024-05-29 01:06:53 INFO: Epoch 027 | Train Loss: 0.2933 | Train Accuracy: 0.9172 | Validation Loss: 0.3090 | Validation Accuracy: 0.9013 | Validation Mean Precision: 0.7660 | Validation Mean Recall: 0.8597 | Validation Mean F1_score: 0.8033
-2024-05-29 01:06:54 INFO: Epoch 028 | Train Loss: 0.2911 | Train Accuracy: 0.9197 | Validation Loss: 0.3001 | Validation Accuracy: 0.9149 | Validation Mean Precision: 0.7834 | Validation Mean Recall: 0.8644 | Validation Mean F1_score: 0.8166
-2024-05-29 01:06:56 INFO: Epoch 029 | Train Loss: 0.2878 | Train Accuracy: 0.9215 | Validation Loss: 0.2957 | Validation Accuracy: 0.9126 | Validation Mean Precision: 0.8348 | Validation Mean Recall: 0.8613 | Validation Mean F1_score: 0.8473
-2024-05-29 01:06:57 INFO: Epoch 030 | Train Loss: 0.2930 | Train Accuracy: 0.9195 | Validation Loss: 0.2962 | Validation Accuracy: 0.9427 | Validation Mean Precision: 0.8938 | Validation Mean Recall: 0.8603 | Validation Mean F1_score: 0.8759
-2024-05-29 01:06:59 INFO: Epoch 031 | Train Loss: 0.2880 | Train Accuracy: 0.9211 | Validation Loss: 0.2935 | Validation Accuracy: 0.9219 | Validation Mean Precision: 0.8076 | Validation Mean Recall: 0.8677 | Validation Mean F1_score: 0.8338
-2024-05-29 01:07:00 INFO: Epoch 032 | Train Loss: 0.2824 | Train Accuracy: 0.9232 | Validation Loss: 0.3298 | Validation Accuracy: 0.9447 | Validation Mean Precision: 0.9091 | Validation Mean Recall: 0.8462 | Validation Mean F1_score: 0.8727
-2024-05-29 01:07:02 INFO: Epoch 033 | Train Loss: 0.2837 | Train Accuracy: 0.9224 | Validation Loss: 0.2909 | Validation Accuracy: 0.9401 | Validation Mean Precision: 0.8588 | Validation Mean Recall: 0.8793 | Validation Mean F1_score: 0.8661
-2024-05-29 01:07:03 INFO: Epoch 034 | Train Loss: 0.2817 | Train Accuracy: 0.9224 | Validation Loss: 0.2925 | Validation Accuracy: 0.9286 | Validation Mean Precision: 0.8098 | Validation Mean Recall: 0.8709 | Validation Mean F1_score: 0.8354
-2024-05-29 01:07:04 INFO: Epoch 035 | Train Loss: 0.2828 | Train Accuracy: 0.9257 | Validation Loss: 0.3143 | Validation Accuracy: 0.9459 | Validation Mean Precision: 0.9279 | Validation Mean Recall: 0.8317 | Validation Mean F1_score: 0.8661
-2024-05-29 01:07:06 INFO: Epoch 036 | Train Loss: 0.2807 | Train Accuracy: 0.9251 | Validation Loss: 0.2908 | Validation Accuracy: 0.9142 | Validation Mean Precision: 0.7866 | Validation Mean Recall: 0.8656 | Validation Mean F1_score: 0.8195
-2024-05-29 01:07:08 INFO: Epoch 037 | Train Loss: 0.2707 | Train Accuracy: 0.9263 | Validation Loss: 0.2948 | Validation Accuracy: 0.8996 | Validation Mean Precision: 0.8118 | Validation Mean Recall: 0.8603 | Validation Mean F1_score: 0.8336
-2024-05-29 01:07:09 INFO: Epoch 038 | Train Loss: 0.2781 | Train Accuracy: 0.9269 | Validation Loss: 0.3149 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8591 | Validation Mean Recall: 0.8720 | Validation Mean F1_score: 0.8644
-2024-05-29 01:07:11 INFO: Epoch 039 | Train Loss: 0.2825 | Train Accuracy: 0.9238 | Validation Loss: 0.2745 | Validation Accuracy: 0.9372 | Validation Mean Precision: 0.8428 | Validation Mean Recall: 0.8778 | Validation Mean F1_score: 0.8585
-2024-05-29 01:07:12 INFO: Epoch 040 | Train Loss: 0.2710 | Train Accuracy: 0.9251 | Validation Loss: 0.2770 | Validation Accuracy: 0.9382 | Validation Mean Precision: 0.8456 | Validation Mean Recall: 0.8808 | Validation Mean F1_score: 0.8608
-2024-05-29 01:07:14 INFO: Epoch 041 | Train Loss: 0.2737 | Train Accuracy: 0.9256 | Validation Loss: 0.2823 | Validation Accuracy: 0.9272 | Validation Mean Precision: 0.8186 | Validation Mean Recall: 0.8739 | Validation Mean F1_score: 0.8417
-2024-05-29 01:07:15 INFO: Epoch 042 | Train Loss: 0.2737 | Train Accuracy: 0.9262 | Validation Loss: 0.2772 | Validation Accuracy: 0.9332 | Validation Mean Precision: 0.8278 | Validation Mean Recall: 0.8764 | Validation Mean F1_score: 0.8484
-2024-05-29 01:07:17 INFO: Epoch 043 | Train Loss: 0.2708 | Train Accuracy: 0.9291 | Validation Loss: 0.2925 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.7864 | Validation Mean Recall: 0.8704 | Validation Mean F1_score: 0.8201
-2024-05-29 01:07:19 INFO: Epoch 044 | Train Loss: 0.2712 | Train Accuracy: 0.9275 | Validation Loss: 0.2971 | Validation Accuracy: 0.9413 | Validation Mean Precision: 0.8440 | Validation Mean Recall: 0.8748 | Validation Mean F1_score: 0.8531
-2024-05-29 01:07:20 INFO: Epoch 045 | Train Loss: 0.2637 | Train Accuracy: 0.9281 | Validation Loss: 0.2773 | Validation Accuracy: 0.9257 | Validation Mean Precision: 0.8128 | Validation Mean Recall: 0.8746 | Validation Mean F1_score: 0.8390
-2024-05-29 01:07:22 INFO: Epoch 046 | Train Loss: 0.2646 | Train Accuracy: 0.9314 | Validation Loss: 0.3151 | Validation Accuracy: 0.9384 | Validation Mean Precision: 0.8973 | Validation Mean Recall: 0.8465 | Validation Mean F1_score: 0.8643
-2024-05-29 01:07:24 INFO: Epoch 047 | Train Loss: 0.2746 | Train Accuracy: 0.9307 | Validation Loss: 0.2830 | Validation Accuracy: 0.9202 | Validation Mean Precision: 0.8222 | Validation Mean Recall: 0.8750 | Validation Mean F1_score: 0.8462
-2024-05-29 01:07:25 INFO: Epoch 048 | Train Loss: 0.2710 | Train Accuracy: 0.9300 | Validation Loss: 0.3276 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8877 | Validation Mean Recall: 0.8459 | Validation Mean F1_score: 0.8653
-2024-05-29 01:07:27 INFO: Epoch 049 | Train Loss: 0.2664 | Train Accuracy: 0.9273 | Validation Loss: 0.2763 | Validation Accuracy: 0.9468 | Validation Mean Precision: 0.8783 | Validation Mean Recall: 0.8811 | Validation Mean F1_score: 0.8785
-2024-05-29 01:07:28 INFO: Epoch 050 | Train Loss: 0.2672 | Train Accuracy: 0.9310 | Validation Loss: 0.2751 | Validation Accuracy: 0.9360 | Validation Mean Precision: 0.8619 | Validation Mean Recall: 0.8758 | Validation Mean F1_score: 0.8685
-2024-05-29 01:07:30 INFO: Epoch 051 | Train Loss: 0.2631 | Train Accuracy: 0.9305 | Validation Loss: 0.3099 | Validation Accuracy: 0.9427 | Validation Mean Precision: 0.8857 | Validation Mean Recall: 0.8576 | Validation Mean F1_score: 0.8705
-2024-05-29 01:07:31 INFO: Epoch 052 | Train Loss: 0.2644 | Train Accuracy: 0.9319 | Validation Loss: 0.2810 | Validation Accuracy: 0.9281 | Validation Mean Precision: 0.8176 | Validation Mean Recall: 0.8737 | Validation Mean F1_score: 0.8406
-2024-05-29 01:07:32 INFO: Epoch 053 | Train Loss: 0.2651 | Train Accuracy: 0.9327 | Validation Loss: 0.2926 | Validation Accuracy: 0.9461 | Validation Mean Precision: 0.9038 | Validation Mean Recall: 0.8609 | Validation Mean F1_score: 0.8805
-2024-05-29 01:07:34 INFO: Epoch 054 | Train Loss: 0.2667 | Train Accuracy: 0.9330 | Validation Loss: 0.2811 | Validation Accuracy: 0.9267 | Validation Mean Precision: 0.8414 | Validation Mean Recall: 0.8662 | Validation Mean F1_score: 0.8523
-2024-05-29 01:07:35 INFO: Epoch 055 | Train Loss: 0.2633 | Train Accuracy: 0.9303 | Validation Loss: 0.2722 | Validation Accuracy: 0.9300 | Validation Mean Precision: 0.8459 | Validation Mean Recall: 0.8767 | Validation Mean F1_score: 0.8604
-2024-05-29 01:07:37 INFO: Epoch 056 | Train Loss: 0.2631 | Train Accuracy: 0.9333 | Validation Loss: 0.2820 | Validation Accuracy: 0.9406 | Validation Mean Precision: 0.8851 | Validation Mean Recall: 0.8645 | Validation Mean F1_score: 0.8727
-2024-05-29 01:07:38 INFO: Epoch 057 | Train Loss: 0.2571 | Train Accuracy: 0.9340 | Validation Loss: 0.2712 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8676 | Validation Mean Recall: 0.8853 | Validation Mean F1_score: 0.8736
-2024-05-29 01:07:40 INFO: Epoch 058 | Train Loss: 0.2627 | Train Accuracy: 0.9320 | Validation Loss: 0.2894 | Validation Accuracy: 0.9403 | Validation Mean Precision: 0.8377 | Validation Mean Recall: 0.8681 | Validation Mean F1_score: 0.8471
-2024-05-29 01:07:41 INFO: Epoch 059 | Train Loss: 0.2638 | Train Accuracy: 0.9315 | Validation Loss: 0.2959 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8363 | Validation Mean Recall: 0.8684 | Validation Mean F1_score: 0.8494
-2024-05-29 01:07:43 INFO: Epoch 060 | Train Loss: 0.2573 | Train Accuracy: 0.9353 | Validation Loss: 0.2799 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8791 | Validation Mean Recall: 0.8676 | Validation Mean F1_score: 0.8729
-2024-05-29 01:07:45 INFO: Epoch 061 | Train Loss: 0.2565 | Train Accuracy: 0.9344 | Validation Loss: 0.2700 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.8607 | Validation Mean Recall: 0.8765 | Validation Mean F1_score: 0.8679
-2024-05-29 01:07:46 INFO: Epoch 062 | Train Loss: 0.2567 | Train Accuracy: 0.9355 | Validation Loss: 0.2701 | Validation Accuracy: 0.9329 | Validation Mean Precision: 0.8125 | Validation Mean Recall: 0.8743 | Validation Mean F1_score: 0.8380
-2024-05-29 01:07:48 INFO: Epoch 063 | Train Loss: 0.2568 | Train Accuracy: 0.9347 | Validation Loss: 0.2848 | Validation Accuracy: 0.9308 | Validation Mean Precision: 0.8642 | Validation Mean Recall: 0.8640 | Validation Mean F1_score: 0.8640
-2024-05-29 01:07:49 INFO: Epoch 064 | Train Loss: 0.2592 | Train Accuracy: 0.9347 | Validation Loss: 0.3028 | Validation Accuracy: 0.9502 | Validation Mean Precision: 0.9341 | Validation Mean Recall: 0.8443 | Validation Mean F1_score: 0.8780
-2024-05-29 01:07:51 INFO: Epoch 065 | Train Loss: 0.2563 | Train Accuracy: 0.9364 | Validation Loss: 0.2770 | Validation Accuracy: 0.9344 | Validation Mean Precision: 0.8393 | Validation Mean Recall: 0.8752 | Validation Mean F1_score: 0.8552
-2024-05-29 01:07:52 INFO: Epoch 066 | Train Loss: 0.2573 | Train Accuracy: 0.9366 | Validation Loss: 0.2776 | Validation Accuracy: 0.9377 | Validation Mean Precision: 0.8700 | Validation Mean Recall: 0.8737 | Validation Mean F1_score: 0.8718
-2024-05-29 01:07:54 INFO: Epoch 067 | Train Loss: 0.2579 | Train Accuracy: 0.9347 | Validation Loss: 0.2781 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8977 | Validation Mean Recall: 0.8684 | Validation Mean F1_score: 0.8821
-2024-05-29 01:07:55 INFO: Epoch 068 | Train Loss: 0.2532 | Train Accuracy: 0.9356 | Validation Loss: 0.2768 | Validation Accuracy: 0.9327 | Validation Mean Precision: 0.8469 | Validation Mean Recall: 0.8583 | Validation Mean F1_score: 0.8522
-2024-05-29 01:07:57 INFO: Epoch 069 | Train Loss: 0.2602 | Train Accuracy: 0.9335 | Validation Loss: 0.2614 | Validation Accuracy: 0.9387 | Validation Mean Precision: 0.8516 | Validation Mean Recall: 0.8835 | Validation Mean F1_score: 0.8659
-2024-05-29 01:07:58 INFO: Epoch 070 | Train Loss: 0.2517 | Train Accuracy: 0.9378 | Validation Loss: 0.2627 | Validation Accuracy: 0.9451 | Validation Mean Precision: 0.8909 | Validation Mean Recall: 0.8638 | Validation Mean F1_score: 0.8762
-2024-05-29 01:08:00 INFO: Epoch 071 | Train Loss: 0.2524 | Train Accuracy: 0.9378 | Validation Loss: 0.3027 | Validation Accuracy: 0.9370 | Validation Mean Precision: 0.9020 | Validation Mean Recall: 0.8489 | Validation Mean F1_score: 0.8703
-2024-05-29 01:08:01 INFO: Epoch 072 | Train Loss: 0.2508 | Train Accuracy: 0.9387 | Validation Loss: 0.2622 | Validation Accuracy: 0.9339 | Validation Mean Precision: 0.8282 | Validation Mean Recall: 0.8786 | Validation Mean F1_score: 0.8501
-2024-05-29 01:08:02 INFO: Epoch 073 | Train Loss: 0.2510 | Train Accuracy: 0.9374 | Validation Loss: 0.3073 | Validation Accuracy: 0.9001 | Validation Mean Precision: 0.8304 | Validation Mean Recall: 0.8477 | Validation Mean F1_score: 0.8362
-2024-05-29 01:08:04 INFO: Epoch 074 | Train Loss: 0.2538 | Train Accuracy: 0.9381 | Validation Loss: 0.2730 | Validation Accuracy: 0.9241 | Validation Mean Precision: 0.8150 | Validation Mean Recall: 0.8734 | Validation Mean F1_score: 0.8408
-2024-05-29 01:08:05 INFO: Epoch 075 | Train Loss: 0.2461 | Train Accuracy: 0.9397 | Validation Loss: 0.2752 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8718 | Validation Mean Recall: 0.8853 | Validation Mean F1_score: 0.8759
-2024-05-29 01:08:07 INFO: Epoch 076 | Train Loss: 0.2501 | Train Accuracy: 0.9390 | Validation Loss: 0.2890 | Validation Accuracy: 0.9511 | Validation Mean Precision: 0.8746 | Validation Mean Recall: 0.8827 | Validation Mean F1_score: 0.8767
-2024-05-29 01:08:08 INFO: Epoch 077 | Train Loss: 0.2499 | Train Accuracy: 0.9379 | Validation Loss: 0.2729 | Validation Accuracy: 0.9437 | Validation Mean Precision: 0.8866 | Validation Mean Recall: 0.8568 | Validation Mean F1_score: 0.8679
-2024-05-29 01:08:09 INFO: Epoch 078 | Train Loss: 0.2495 | Train Accuracy: 0.9379 | Validation Loss: 0.3090 | Validation Accuracy: 0.9430 | Validation Mean Precision: 0.8976 | Validation Mean Recall: 0.8496 | Validation Mean F1_score: 0.8678
-2024-05-29 01:08:11 INFO: Epoch 079 | Train Loss: 0.2597 | Train Accuracy: 0.9355 | Validation Loss: 0.2853 | Validation Accuracy: 0.9420 | Validation Mean Precision: 0.8788 | Validation Mean Recall: 0.8599 | Validation Mean F1_score: 0.8684
-2024-05-29 01:08:12 INFO: Epoch 080 | Train Loss: 0.2516 | Train Accuracy: 0.9388 | Validation Loss: 0.3153 | Validation Accuracy: 0.9459 | Validation Mean Precision: 0.8786 | Validation Mean Recall: 0.8645 | Validation Mean F1_score: 0.8669
-2024-05-29 01:08:14 INFO: Epoch 081 | Train Loss: 0.2436 | Train Accuracy: 0.9395 | Validation Loss: 0.2982 | Validation Accuracy: 0.9061 | Validation Mean Precision: 0.8083 | Validation Mean Recall: 0.8647 | Validation Mean F1_score: 0.8334
-2024-05-29 01:08:15 INFO: Epoch 082 | Train Loss: 0.2471 | Train Accuracy: 0.9388 | Validation Loss: 0.2890 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.8683 | Validation Mean Recall: 0.8676 | Validation Mean F1_score: 0.8639
-2024-05-29 01:08:17 INFO: Epoch 083 | Train Loss: 0.2451 | Train Accuracy: 0.9402 | Validation Loss: 0.2967 | Validation Accuracy: 0.9027 | Validation Mean Precision: 0.8015 | Validation Mean Recall: 0.8642 | Validation Mean F1_score: 0.8289
-2024-05-29 01:08:18 INFO: Epoch 084 | Train Loss: 0.2420 | Train Accuracy: 0.9386 | Validation Loss: 0.2831 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.8775 | Validation Mean Recall: 0.8854 | Validation Mean F1_score: 0.8786
-2024-05-29 01:08:20 INFO: Epoch 085 | Train Loss: 0.2547 | Train Accuracy: 0.9369 | Validation Loss: 0.2654 | Validation Accuracy: 0.9346 | Validation Mean Precision: 0.8498 | Validation Mean Recall: 0.8797 | Validation Mean F1_score: 0.8636
-2024-05-29 01:08:21 INFO: Epoch 086 | Train Loss: 0.2514 | Train Accuracy: 0.9384 | Validation Loss: 0.3007 | Validation Accuracy: 0.9082 | Validation Mean Precision: 0.8393 | Validation Mean Recall: 0.8697 | Validation Mean F1_score: 0.8536
-2024-05-29 01:08:22 INFO: Epoch 087 | Train Loss: 0.2424 | Train Accuracy: 0.9400 | Validation Loss: 0.3106 | Validation Accuracy: 0.9075 | Validation Mean Precision: 0.7938 | Validation Mean Recall: 0.8659 | Validation Mean F1_score: 0.8248
-2024-05-29 01:08:24 INFO: Epoch 088 | Train Loss: 0.2446 | Train Accuracy: 0.9409 | Validation Loss: 0.2821 | Validation Accuracy: 0.9511 | Validation Mean Precision: 0.8834 | Validation Mean Recall: 0.8685 | Validation Mean F1_score: 0.8756
-2024-05-29 01:08:25 INFO: Epoch 089 | Train Loss: 0.2482 | Train Accuracy: 0.9385 | Validation Loss: 0.2714 | Validation Accuracy: 0.9300 | Validation Mean Precision: 0.8420 | Validation Mean Recall: 0.8810 | Validation Mean F1_score: 0.8599
-2024-05-29 01:08:26 INFO: Epoch 090 | Train Loss: 0.2391 | Train Accuracy: 0.9412 | Validation Loss: 0.2766 | Validation Accuracy: 0.9253 | Validation Mean Precision: 0.8245 | Validation Mean Recall: 0.8741 | Validation Mean F1_score: 0.8466
-2024-05-29 01:08:28 INFO: Epoch 091 | Train Loss: 0.2444 | Train Accuracy: 0.9385 | Validation Loss: 0.2952 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8953 | Validation Mean Recall: 0.8605 | Validation Mean F1_score: 0.8764
-2024-05-29 01:08:29 INFO: Epoch 092 | Train Loss: 0.2455 | Train Accuracy: 0.9415 | Validation Loss: 0.3181 | Validation Accuracy: 0.9097 | Validation Mean Precision: 0.8257 | Validation Mean Recall: 0.8678 | Validation Mean F1_score: 0.8453
-2024-05-29 01:08:31 INFO: Epoch 093 | Train Loss: 0.2396 | Train Accuracy: 0.9411 | Validation Loss: 0.2680 | Validation Accuracy: 0.9447 | Validation Mean Precision: 0.8448 | Validation Mean Recall: 0.8827 | Validation Mean F1_score: 0.8604
-2024-05-29 01:08:32 INFO: Epoch 094 | Train Loss: 0.2380 | Train Accuracy: 0.9434 | Validation Loss: 0.2805 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8437 | Validation Mean Recall: 0.8829 | Validation Mean F1_score: 0.8607
-2024-05-29 01:08:34 INFO: Epoch 095 | Train Loss: 0.2412 | Train Accuracy: 0.9411 | Validation Loss: 0.2776 | Validation Accuracy: 0.9267 | Validation Mean Precision: 0.8410 | Validation Mean Recall: 0.8749 | Validation Mean F1_score: 0.8569
-2024-05-29 01:08:35 INFO: Epoch 096 | Train Loss: 0.2447 | Train Accuracy: 0.9393 | Validation Loss: 0.2868 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.8860 | Validation Mean Recall: 0.8736 | Validation Mean F1_score: 0.8790
-2024-05-29 01:08:37 INFO: Epoch 097 | Train Loss: 0.2447 | Train Accuracy: 0.9392 | Validation Loss: 0.2930 | Validation Accuracy: 0.9276 | Validation Mean Precision: 0.8495 | Validation Mean Recall: 0.8744 | Validation Mean F1_score: 0.8609
-2024-05-29 01:08:38 INFO: Epoch 098 | Train Loss: 0.2377 | Train Accuracy: 0.9417 | Validation Loss: 0.2798 | Validation Accuracy: 0.9483 | Validation Mean Precision: 0.8845 | Validation Mean Recall: 0.8865 | Validation Mean F1_score: 0.8851
-2024-05-29 01:08:40 INFO: Epoch 099 | Train Loss: 0.2369 | Train Accuracy: 0.9421 | Validation Loss: 0.2916 | Validation Accuracy: 0.9279 | Validation Mean Precision: 0.8261 | Validation Mean Recall: 0.8778 | Validation Mean F1_score: 0.8490
-2024-05-29 01:08:41 INFO: Epoch 100 | Train Loss: 0.2441 | Train Accuracy: 0.9405 | Validation Loss: 0.2857 | Validation Accuracy: 0.9497 | Validation Mean Precision: 0.9044 | Validation Mean Recall: 0.8552 | Validation Mean F1_score: 0.8778
-2024-05-29 01:08:42 INFO: Epoch 101 | Train Loss: 0.2394 | Train Accuracy: 0.9421 | Validation Loss: 0.2720 | Validation Accuracy: 0.9427 | Validation Mean Precision: 0.8477 | Validation Mean Recall: 0.8843 | Validation Mean F1_score: 0.8634
-2024-05-29 01:08:44 INFO: Epoch 102 | Train Loss: 0.2360 | Train Accuracy: 0.9436 | Validation Loss: 0.3173 | Validation Accuracy: 0.9554 | Validation Mean Precision: 0.9357 | Validation Mean Recall: 0.8516 | Validation Mean F1_score: 0.8851
-2024-05-29 01:08:45 INFO: Epoch 103 | Train Loss: 0.2453 | Train Accuracy: 0.9418 | Validation Loss: 0.2888 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8438 | Validation Mean Recall: 0.8632 | Validation Mean F1_score: 0.8490
-2024-05-29 01:08:47 INFO: Epoch 104 | Train Loss: 0.2416 | Train Accuracy: 0.9428 | Validation Loss: 0.2814 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8923 | Validation Mean Recall: 0.8646 | Validation Mean F1_score: 0.8769
-2024-05-29 01:08:48 INFO: Epoch 105 | Train Loss: 0.2352 | Train Accuracy: 0.9433 | Validation Loss: 0.2688 | Validation Accuracy: 0.9518 | Validation Mean Precision: 0.8917 | Validation Mean Recall: 0.8902 | Validation Mean F1_score: 0.8906
-2024-05-29 01:08:50 INFO: Epoch 106 | Train Loss: 0.2389 | Train Accuracy: 0.9411 | Validation Loss: 0.2652 | Validation Accuracy: 0.9430 | Validation Mean Precision: 0.8431 | Validation Mean Recall: 0.8823 | Validation Mean F1_score: 0.8598
-2024-05-29 01:08:51 INFO: Epoch 107 | Train Loss: 0.2376 | Train Accuracy: 0.9431 | Validation Loss: 0.3083 | Validation Accuracy: 0.9526 | Validation Mean Precision: 0.8946 | Validation Mean Recall: 0.8753 | Validation Mean F1_score: 0.8840
-2024-05-29 01:08:52 INFO: Epoch 108 | Train Loss: 0.2360 | Train Accuracy: 0.9435 | Validation Loss: 0.2626 | Validation Accuracy: 0.9471 | Validation Mean Precision: 0.8882 | Validation Mean Recall: 0.8715 | Validation Mean F1_score: 0.8792
-2024-05-29 01:08:54 INFO: Epoch 109 | Train Loss: 0.2354 | Train Accuracy: 0.9424 | Validation Loss: 0.2737 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.8633 | Validation Mean Recall: 0.8868 | Validation Mean F1_score: 0.8737
-2024-05-29 01:08:55 INFO: Epoch 110 | Train Loss: 0.2351 | Train Accuracy: 0.9440 | Validation Loss: 0.2690 | Validation Accuracy: 0.9533 | Validation Mean Precision: 0.8975 | Validation Mean Recall: 0.8797 | Validation Mean F1_score: 0.8882
-2024-05-29 01:08:57 INFO: Epoch 111 | Train Loss: 0.2378 | Train Accuracy: 0.9420 | Validation Loss: 0.3163 | Validation Accuracy: 0.9463 | Validation Mean Precision: 0.8949 | Validation Mean Recall: 0.8634 | Validation Mean F1_score: 0.8783
-2024-05-29 01:08:58 INFO: Epoch 112 | Train Loss: 0.2336 | Train Accuracy: 0.9429 | Validation Loss: 0.2854 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8654 | Validation Mean Recall: 0.8679 | Validation Mean F1_score: 0.8661
-2024-05-29 01:08:59 INFO: Epoch 113 | Train Loss: 0.2371 | Train Accuracy: 0.9418 | Validation Loss: 0.2602 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.8678 | Validation Mean Recall: 0.8832 | Validation Mean F1_score: 0.8750
-2024-05-29 01:09:01 INFO: Epoch 114 | Train Loss: 0.2405 | Train Accuracy: 0.9430 | Validation Loss: 0.2923 | Validation Accuracy: 0.9526 | Validation Mean Precision: 0.9054 | Validation Mean Recall: 0.8668 | Validation Mean F1_score: 0.8843
-2024-05-29 01:09:02 INFO: Epoch 115 | Train Loss: 0.2383 | Train Accuracy: 0.9420 | Validation Loss: 0.2974 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.8905 | Validation Mean Recall: 0.8809 | Validation Mean F1_score: 0.8850
-2024-05-29 01:09:04 INFO: Epoch 116 | Train Loss: 0.2352 | Train Accuracy: 0.9421 | Validation Loss: 0.2949 | Validation Accuracy: 0.9483 | Validation Mean Precision: 0.8926 | Validation Mean Recall: 0.8773 | Validation Mean F1_score: 0.8845
-2024-05-29 01:09:05 INFO: Epoch 117 | Train Loss: 0.2295 | Train Accuracy: 0.9423 | Validation Loss: 0.2957 | Validation Accuracy: 0.9475 | Validation Mean Precision: 0.8692 | Validation Mean Recall: 0.8806 | Validation Mean F1_score: 0.8743
-2024-05-29 01:09:07 INFO: Epoch 118 | Train Loss: 0.2352 | Train Accuracy: 0.9419 | Validation Loss: 0.2967 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8815 | Validation Mean Recall: 0.8578 | Validation Mean F1_score: 0.8684
-2024-05-29 01:09:08 INFO: Epoch 119 | Train Loss: 0.2330 | Train Accuracy: 0.9426 | Validation Loss: 0.2782 | Validation Accuracy: 0.9267 | Validation Mean Precision: 0.7955 | Validation Mean Recall: 0.8685 | Validation Mean F1_score: 0.8241
-2024-05-29 01:09:09 INFO: Epoch 120 | Train Loss: 0.2346 | Train Accuracy: 0.9435 | Validation Loss: 0.2898 | Validation Accuracy: 0.9509 | Validation Mean Precision: 0.8836 | Validation Mean Recall: 0.8824 | Validation Mean F1_score: 0.8825
-2024-05-29 01:09:11 INFO: Epoch 121 | Train Loss: 0.2317 | Train Accuracy: 0.9406 | Validation Loss: 0.2599 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8615 | Validation Mean Recall: 0.8849 | Validation Mean F1_score: 0.8718
-2024-05-29 01:09:12 INFO: Epoch 122 | Train Loss: 0.2317 | Train Accuracy: 0.9433 | Validation Loss: 0.3046 | Validation Accuracy: 0.9538 | Validation Mean Precision: 0.9172 | Validation Mean Recall: 0.8648 | Validation Mean F1_score: 0.8887
-2024-05-29 01:09:14 INFO: Epoch 123 | Train Loss: 0.2369 | Train Accuracy: 0.9432 | Validation Loss: 0.2878 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8843 | Validation Mean Recall: 0.8609 | Validation Mean F1_score: 0.8721
-2024-05-29 01:09:15 INFO: Epoch 124 | Train Loss: 0.2359 | Train Accuracy: 0.9421 | Validation Loss: 0.2757 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8707 | Validation Mean Recall: 0.8850 | Validation Mean F1_score: 0.8775
-2024-05-29 01:09:16 INFO: Epoch 125 | Train Loss: 0.2291 | Train Accuracy: 0.9441 | Validation Loss: 0.2829 | Validation Accuracy: 0.9528 | Validation Mean Precision: 0.8847 | Validation Mean Recall: 0.8695 | Validation Mean F1_score: 0.8768
-2024-05-29 01:09:18 INFO: Epoch 126 | Train Loss: 0.2311 | Train Accuracy: 0.9438 | Validation Loss: 0.2883 | Validation Accuracy: 0.9516 | Validation Mean Precision: 0.8827 | Validation Mean Recall: 0.8722 | Validation Mean F1_score: 0.8769
-2024-05-29 01:09:19 INFO: Epoch 127 | Train Loss: 0.2359 | Train Accuracy: 0.9426 | Validation Loss: 0.2760 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8837 | Validation Mean Recall: 0.8745 | Validation Mean F1_score: 0.8789
-2024-05-29 01:09:21 INFO: Epoch 128 | Train Loss: 0.2295 | Train Accuracy: 0.9463 | Validation Loss: 0.2796 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.8730 | Validation Mean Recall: 0.8767 | Validation Mean F1_score: 0.8746
-2024-05-29 01:09:22 INFO: Epoch 129 | Train Loss: 0.2364 | Train Accuracy: 0.9432 | Validation Loss: 0.2764 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.9033 | Validation Mean Recall: 0.8750 | Validation Mean F1_score: 0.8878
-2024-05-29 01:09:24 INFO: Epoch 130 | Train Loss: 0.2364 | Train Accuracy: 0.9417 | Validation Loss: 0.2771 | Validation Accuracy: 0.9494 | Validation Mean Precision: 0.8728 | Validation Mean Recall: 0.8905 | Validation Mean F1_score: 0.8803
-2024-05-29 01:09:25 INFO: Epoch 131 | Train Loss: 0.2376 | Train Accuracy: 0.9442 | Validation Loss: 0.2667 | Validation Accuracy: 0.9490 | Validation Mean Precision: 0.8756 | Validation Mean Recall: 0.8862 | Validation Mean F1_score: 0.8801
-2024-05-29 01:09:27 INFO: Epoch 132 | Train Loss: 0.2263 | Train Accuracy: 0.9450 | Validation Loss: 0.3103 | Validation Accuracy: 0.9535 | Validation Mean Precision: 0.8947 | Validation Mean Recall: 0.8818 | Validation Mean F1_score: 0.8875
-2024-05-29 01:09:28 INFO: Epoch 133 | Train Loss: 0.2348 | Train Accuracy: 0.9429 | Validation Loss: 0.2708 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8562 | Validation Mean Recall: 0.8815 | Validation Mean F1_score: 0.8666
-2024-05-29 01:09:30 INFO: Epoch 134 | Train Loss: 0.2355 | Train Accuracy: 0.9433 | Validation Loss: 0.3033 | Validation Accuracy: 0.9356 | Validation Mean Precision: 0.8828 | Validation Mean Recall: 0.8483 | Validation Mean F1_score: 0.8617
-2024-05-29 01:09:31 INFO: Epoch 135 | Train Loss: 0.2343 | Train Accuracy: 0.9430 | Validation Loss: 0.2929 | Validation Accuracy: 0.9554 | Validation Mean Precision: 0.9127 | Validation Mean Recall: 0.8739 | Validation Mean F1_score: 0.8918
-2024-05-29 01:09:33 INFO: Epoch 136 | Train Loss: 0.2328 | Train Accuracy: 0.9437 | Validation Loss: 0.2694 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.8926 | Validation Mean Recall: 0.8659 | Validation Mean F1_score: 0.8773
-2024-05-29 01:09:34 INFO: Epoch 137 | Train Loss: 0.2263 | Train Accuracy: 0.9457 | Validation Loss: 0.3113 | Validation Accuracy: 0.9533 | Validation Mean Precision: 0.9083 | Validation Mean Recall: 0.8547 | Validation Mean F1_score: 0.8789
-2024-05-29 01:09:35 INFO: Epoch 138 | Train Loss: 0.2282 | Train Accuracy: 0.9449 | Validation Loss: 0.3070 | Validation Accuracy: 0.9540 | Validation Mean Precision: 0.8865 | Validation Mean Recall: 0.8901 | Validation Mean F1_score: 0.8864
-2024-05-29 01:09:37 INFO: Epoch 139 | Train Loss: 0.2246 | Train Accuracy: 0.9457 | Validation Loss: 0.2966 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8537 | Validation Mean Recall: 0.8867 | Validation Mean F1_score: 0.8683
-2024-05-29 01:09:38 INFO: Epoch 140 | Train Loss: 0.2394 | Train Accuracy: 0.9415 | Validation Loss: 0.3025 | Validation Accuracy: 0.9459 | Validation Mean Precision: 0.8786 | Validation Mean Recall: 0.8632 | Validation Mean F1_score: 0.8692
-2024-05-29 01:09:39 INFO: Epoch 141 | Train Loss: 0.2264 | Train Accuracy: 0.9451 | Validation Loss: 0.3347 | Validation Accuracy: 0.9142 | Validation Mean Precision: 0.8398 | Validation Mean Recall: 0.8613 | Validation Mean F1_score: 0.8476
-2024-05-29 01:09:41 INFO: Epoch 142 | Train Loss: 0.2311 | Train Accuracy: 0.9445 | Validation Loss: 0.3079 | Validation Accuracy: 0.9411 | Validation Mean Precision: 0.8551 | Validation Mean Recall: 0.8717 | Validation Mean F1_score: 0.8614
-2024-05-29 01:09:42 INFO: Epoch 143 | Train Loss: 0.2273 | Train Accuracy: 0.9443 | Validation Loss: 0.2940 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.8405 | Validation Mean Recall: 0.8832 | Validation Mean F1_score: 0.8584
-2024-05-29 01:09:44 INFO: Epoch 144 | Train Loss: 0.2301 | Train Accuracy: 0.9426 | Validation Loss: 0.2795 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8480 | Validation Mean Recall: 0.8835 | Validation Mean F1_score: 0.8631
-2024-05-29 01:09:45 INFO: Epoch 145 | Train Loss: 0.2287 | Train Accuracy: 0.9466 | Validation Loss: 0.2916 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8783 | Validation Mean Recall: 0.8773 | Validation Mean F1_score: 0.8776
-2024-05-29 01:09:46 INFO: Epoch 146 | Train Loss: 0.2211 | Train Accuracy: 0.9451 | Validation Loss: 0.2923 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.8744 | Validation Mean Recall: 0.8823 | Validation Mean F1_score: 0.8773
-2024-05-29 01:09:48 INFO: Epoch 147 | Train Loss: 0.2251 | Train Accuracy: 0.9453 | Validation Loss: 0.2950 | Validation Accuracy: 0.9389 | Validation Mean Precision: 0.8420 | Validation Mean Recall: 0.8840 | Validation Mean F1_score: 0.8605
-2024-05-29 01:09:49 INFO: Epoch 148 | Train Loss: 0.2266 | Train Accuracy: 0.9455 | Validation Loss: 0.3134 | Validation Accuracy: 0.9547 | Validation Mean Precision: 0.9031 | Validation Mean Recall: 0.8579 | Validation Mean F1_score: 0.8784
-2024-05-29 01:09:51 INFO: Epoch 149 | Train Loss: 0.2201 | Train Accuracy: 0.9451 | Validation Loss: 0.3001 | Validation Accuracy: 0.9509 | Validation Mean Precision: 0.8832 | Validation Mean Recall: 0.8781 | Validation Mean F1_score: 0.8803
-2024-05-29 01:09:52 INFO: Epoch 150 | Train Loss: 0.2248 | Train Accuracy: 0.9472 | Validation Loss: 0.2832 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8776 | Validation Mean Recall: 0.8634 | Validation Mean F1_score: 0.8686
-2024-05-29 01:09:52 INFO: Best Validation F1 Score (Macro): 0.8918
-2024-05-29 01:09:52 INFO: Best Validation Recall (Macro): 0.8739
-2024-05-29 01:09:52 INFO: Best Validation Precision (Macro): 0.9127
-2024-05-29 01:09:52 INFO: Best Epoch: 135
-2024-05-29 01:09:53 INFO: Infer Result: 
-2024-05-29 01:09:53 INFO: Prediction errors: 276
-2024-05-29 01:09:53 INFO: Prediction error percentage: 5.29%
-2024-05-29 01:09:53 INFO: Total samples: 5218
-2024-05-29 01:09:53 INFO: Infer Result End: 
-2024-05-29 01:09:54 INFO: Class weights: tensor([21.1878,  3.1149,  0.7420,  0.4378], device='cuda:0')
-2024-05-29 01:09:55 INFO: Epoch 001 | Train Loss: 0.7153 | Train Accuracy: 0.7340 | Validation Loss: 0.3855 | Validation Accuracy: 0.8579 | Validation Mean Precision: 0.7364 | Validation Mean Recall: 0.8465 | Validation Mean F1_score: 0.7803
-2024-05-29 01:09:57 INFO: Epoch 002 | Train Loss: 0.3869 | Train Accuracy: 0.8727 | Validation Loss: 0.3771 | Validation Accuracy: 0.8960 | Validation Mean Precision: 0.8236 | Validation Mean Recall: 0.8232 | Validation Mean F1_score: 0.8229
-2024-05-29 01:09:58 INFO: Epoch 003 | Train Loss: 0.3729 | Train Accuracy: 0.8802 | Validation Loss: 0.3644 | Validation Accuracy: 0.8915 | Validation Mean Precision: 0.8278 | Validation Mean Recall: 0.8135 | Validation Mean F1_score: 0.8188
-2024-05-29 01:10:00 INFO: Epoch 004 | Train Loss: 0.3626 | Train Accuracy: 0.8838 | Validation Loss: 0.3687 | Validation Accuracy: 0.8912 | Validation Mean Precision: 0.8143 | Validation Mean Recall: 0.8141 | Validation Mean F1_score: 0.8088
-2024-05-29 01:10:01 INFO: Epoch 005 | Train Loss: 0.3617 | Train Accuracy: 0.8846 | Validation Loss: 0.3838 | Validation Accuracy: 0.8994 | Validation Mean Precision: 0.8640 | Validation Mean Recall: 0.7868 | Validation Mean F1_score: 0.8173
-2024-05-29 01:10:03 INFO: Epoch 006 | Train Loss: 0.3598 | Train Accuracy: 0.8884 | Validation Loss: 0.3625 | Validation Accuracy: 0.8797 | Validation Mean Precision: 0.8158 | Validation Mean Recall: 0.8171 | Validation Mean F1_score: 0.8122
-2024-05-29 01:10:04 INFO: Epoch 007 | Train Loss: 0.3538 | Train Accuracy: 0.8877 | Validation Loss: 0.3807 | Validation Accuracy: 0.9011 | Validation Mean Precision: 0.8244 | Validation Mean Recall: 0.8377 | Validation Mean F1_score: 0.8253
-2024-05-29 01:10:06 INFO: Epoch 008 | Train Loss: 0.3549 | Train Accuracy: 0.8872 | Validation Loss: 0.3486 | Validation Accuracy: 0.8977 | Validation Mean Precision: 0.8069 | Validation Mean Recall: 0.8500 | Validation Mean F1_score: 0.8255
-2024-05-29 01:10:07 INFO: Epoch 009 | Train Loss: 0.3569 | Train Accuracy: 0.8903 | Validation Loss: 0.3481 | Validation Accuracy: 0.9042 | Validation Mean Precision: 0.8344 | Validation Mean Recall: 0.8363 | Validation Mean F1_score: 0.8353
-2024-05-29 01:10:09 INFO: Epoch 010 | Train Loss: 0.3561 | Train Accuracy: 0.8857 | Validation Loss: 0.3606 | Validation Accuracy: 0.8939 | Validation Mean Precision: 0.7999 | Validation Mean Recall: 0.8533 | Validation Mean F1_score: 0.8184
-2024-05-29 01:10:10 INFO: Epoch 011 | Train Loss: 0.3514 | Train Accuracy: 0.8919 | Validation Loss: 0.3448 | Validation Accuracy: 0.8963 | Validation Mean Precision: 0.8194 | Validation Mean Recall: 0.8499 | Validation Mean F1_score: 0.8338
-2024-05-29 01:10:11 INFO: Epoch 012 | Train Loss: 0.3464 | Train Accuracy: 0.8920 | Validation Loss: 0.4253 | Validation Accuracy: 0.8244 | Validation Mean Precision: 0.7776 | Validation Mean Recall: 0.8241 | Validation Mean F1_score: 0.7930
-2024-05-29 01:10:13 INFO: Epoch 013 | Train Loss: 0.3534 | Train Accuracy: 0.8903 | Validation Loss: 0.3526 | Validation Accuracy: 0.9063 | Validation Mean Precision: 0.8406 | Validation Mean Recall: 0.8238 | Validation Mean F1_score: 0.8318
-2024-05-29 01:10:14 INFO: Epoch 014 | Train Loss: 0.3552 | Train Accuracy: 0.8912 | Validation Loss: 0.3485 | Validation Accuracy: 0.8864 | Validation Mean Precision: 0.8004 | Validation Mean Recall: 0.8515 | Validation Mean F1_score: 0.8229
-2024-05-29 01:10:16 INFO: Epoch 015 | Train Loss: 0.3518 | Train Accuracy: 0.8903 | Validation Loss: 0.3456 | Validation Accuracy: 0.8970 | Validation Mean Precision: 0.8218 | Validation Mean Recall: 0.8341 | Validation Mean F1_score: 0.8272
-2024-05-29 01:10:17 INFO: Epoch 016 | Train Loss: 0.3447 | Train Accuracy: 0.8915 | Validation Loss: 0.3723 | Validation Accuracy: 0.8953 | Validation Mean Precision: 0.8107 | Validation Mean Recall: 0.8414 | Validation Mean F1_score: 0.8198
-2024-05-29 01:10:19 INFO: Epoch 017 | Train Loss: 0.3465 | Train Accuracy: 0.8955 | Validation Loss: 0.3739 | Validation Accuracy: 0.9003 | Validation Mean Precision: 0.8512 | Validation Mean Recall: 0.8323 | Validation Mean F1_score: 0.8394
-2024-05-29 01:10:20 INFO: Epoch 018 | Train Loss: 0.3487 | Train Accuracy: 0.8912 | Validation Loss: 0.3505 | Validation Accuracy: 0.9015 | Validation Mean Precision: 0.8415 | Validation Mean Recall: 0.8071 | Validation Mean F1_score: 0.8215
-2024-05-29 01:10:22 INFO: Epoch 019 | Train Loss: 0.3456 | Train Accuracy: 0.8959 | Validation Loss: 0.3628 | Validation Accuracy: 0.8893 | Validation Mean Precision: 0.8132 | Validation Mean Recall: 0.8352 | Validation Mean F1_score: 0.8177
-2024-05-29 01:10:23 INFO: Epoch 020 | Train Loss: 0.3460 | Train Accuracy: 0.8949 | Validation Loss: 0.3566 | Validation Accuracy: 0.8860 | Validation Mean Precision: 0.7617 | Validation Mean Recall: 0.8656 | Validation Mean F1_score: 0.8009
-2024-05-29 01:10:25 INFO: Epoch 021 | Train Loss: 0.3447 | Train Accuracy: 0.8946 | Validation Loss: 0.3573 | Validation Accuracy: 0.9051 | Validation Mean Precision: 0.8354 | Validation Mean Recall: 0.8056 | Validation Mean F1_score: 0.8175
-2024-05-29 01:10:26 INFO: Epoch 022 | Train Loss: 0.3391 | Train Accuracy: 0.8954 | Validation Loss: 0.3559 | Validation Accuracy: 0.9034 | Validation Mean Precision: 0.7991 | Validation Mean Recall: 0.8563 | Validation Mean F1_score: 0.8178
-2024-05-29 01:10:28 INFO: Epoch 023 | Train Loss: 0.3366 | Train Accuracy: 0.8990 | Validation Loss: 0.3500 | Validation Accuracy: 0.9116 | Validation Mean Precision: 0.8469 | Validation Mean Recall: 0.8488 | Validation Mean F1_score: 0.8458
-2024-05-29 01:10:30 INFO: Epoch 024 | Train Loss: 0.3260 | Train Accuracy: 0.9006 | Validation Loss: 0.3413 | Validation Accuracy: 0.8886 | Validation Mean Precision: 0.8000 | Validation Mean Recall: 0.8478 | Validation Mean F1_score: 0.8180
-2024-05-29 01:10:31 INFO: Epoch 025 | Train Loss: 0.3264 | Train Accuracy: 0.9026 | Validation Loss: 0.3468 | Validation Accuracy: 0.8840 | Validation Mean Precision: 0.8166 | Validation Mean Recall: 0.8548 | Validation Mean F1_score: 0.8311
-2024-05-29 01:10:33 INFO: Epoch 026 | Train Loss: 0.3199 | Train Accuracy: 0.9057 | Validation Loss: 0.3456 | Validation Accuracy: 0.9188 | Validation Mean Precision: 0.8186 | Validation Mean Recall: 0.8632 | Validation Mean F1_score: 0.8298
-2024-05-29 01:10:34 INFO: Epoch 027 | Train Loss: 0.3121 | Train Accuracy: 0.9085 | Validation Loss: 0.3176 | Validation Accuracy: 0.9229 | Validation Mean Precision: 0.8549 | Validation Mean Recall: 0.8609 | Validation Mean F1_score: 0.8577
-2024-05-29 01:10:36 INFO: Epoch 028 | Train Loss: 0.3128 | Train Accuracy: 0.9088 | Validation Loss: 0.3313 | Validation Accuracy: 0.9030 | Validation Mean Precision: 0.8433 | Validation Mean Recall: 0.8171 | Validation Mean F1_score: 0.8277
-2024-05-29 01:10:37 INFO: Epoch 029 | Train Loss: 0.3048 | Train Accuracy: 0.9121 | Validation Loss: 0.3351 | Validation Accuracy: 0.8896 | Validation Mean Precision: 0.8207 | Validation Mean Recall: 0.8580 | Validation Mean F1_score: 0.8375
-2024-05-29 01:10:39 INFO: Epoch 030 | Train Loss: 0.3070 | Train Accuracy: 0.9118 | Validation Loss: 0.3896 | Validation Accuracy: 0.9308 | Validation Mean Precision: 0.9174 | Validation Mean Recall: 0.7874 | Validation Mean F1_score: 0.8335
-2024-05-29 01:10:41 INFO: Epoch 031 | Train Loss: 0.3086 | Train Accuracy: 0.9126 | Validation Loss: 0.3215 | Validation Accuracy: 0.9272 | Validation Mean Precision: 0.8323 | Validation Mean Recall: 0.8757 | Validation Mean F1_score: 0.8484
-2024-05-29 01:10:42 INFO: Epoch 032 | Train Loss: 0.2959 | Train Accuracy: 0.9132 | Validation Loss: 0.3049 | Validation Accuracy: 0.9166 | Validation Mean Precision: 0.8345 | Validation Mean Recall: 0.8784 | Validation Mean F1_score: 0.8547
-2024-05-29 01:10:44 INFO: Epoch 033 | Train Loss: 0.3047 | Train Accuracy: 0.9145 | Validation Loss: 0.3062 | Validation Accuracy: 0.9281 | Validation Mean Precision: 0.8512 | Validation Mean Recall: 0.8437 | Validation Mean F1_score: 0.8453
-2024-05-29 01:10:45 INFO: Epoch 034 | Train Loss: 0.2985 | Train Accuracy: 0.9163 | Validation Loss: 0.3060 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8429 | Validation Mean Recall: 0.8732 | Validation Mean F1_score: 0.8570
-2024-05-29 01:10:47 INFO: Epoch 035 | Train Loss: 0.2927 | Train Accuracy: 0.9151 | Validation Loss: 0.3039 | Validation Accuracy: 0.9138 | Validation Mean Precision: 0.8351 | Validation Mean Recall: 0.8775 | Validation Mean F1_score: 0.8543
-2024-05-29 01:10:48 INFO: Epoch 036 | Train Loss: 0.2896 | Train Accuracy: 0.9177 | Validation Loss: 0.3140 | Validation Accuracy: 0.9322 | Validation Mean Precision: 0.8434 | Validation Mean Recall: 0.8705 | Validation Mean F1_score: 0.8519
-2024-05-29 01:10:50 INFO: Epoch 037 | Train Loss: 0.2899 | Train Accuracy: 0.9188 | Validation Loss: 0.3012 | Validation Accuracy: 0.9176 | Validation Mean Precision: 0.8340 | Validation Mean Recall: 0.8794 | Validation Mean F1_score: 0.8539
-2024-05-29 01:10:51 INFO: Epoch 038 | Train Loss: 0.2863 | Train Accuracy: 0.9207 | Validation Loss: 0.3030 | Validation Accuracy: 0.9231 | Validation Mean Precision: 0.8070 | Validation Mean Recall: 0.8945 | Validation Mean F1_score: 0.8400
-2024-05-29 01:10:53 INFO: Epoch 039 | Train Loss: 0.2907 | Train Accuracy: 0.9194 | Validation Loss: 0.3020 | Validation Accuracy: 0.9250 | Validation Mean Precision: 0.8040 | Validation Mean Recall: 0.8742 | Validation Mean F1_score: 0.8310
-2024-05-29 01:10:54 INFO: Epoch 040 | Train Loss: 0.2924 | Train Accuracy: 0.9205 | Validation Loss: 0.2926 | Validation Accuracy: 0.9190 | Validation Mean Precision: 0.8339 | Validation Mean Recall: 0.8848 | Validation Mean F1_score: 0.8564
-2024-05-29 01:10:55 INFO: Epoch 041 | Train Loss: 0.2851 | Train Accuracy: 0.9194 | Validation Loss: 0.2905 | Validation Accuracy: 0.9372 | Validation Mean Precision: 0.8701 | Validation Mean Recall: 0.8803 | Validation Mean F1_score: 0.8746
-2024-05-29 01:10:57 INFO: Epoch 042 | Train Loss: 0.2851 | Train Accuracy: 0.9224 | Validation Loss: 0.2880 | Validation Accuracy: 0.9351 | Validation Mean Precision: 0.8657 | Validation Mean Recall: 0.8688 | Validation Mean F1_score: 0.8664
-2024-05-29 01:10:58 INFO: Epoch 043 | Train Loss: 0.2797 | Train Accuracy: 0.9250 | Validation Loss: 0.3238 | Validation Accuracy: 0.8979 | Validation Mean Precision: 0.8217 | Validation Mean Recall: 0.8565 | Validation Mean F1_score: 0.8367
-2024-05-29 01:11:00 INFO: Epoch 044 | Train Loss: 0.2775 | Train Accuracy: 0.9228 | Validation Loss: 0.2947 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8022 | Validation Mean Recall: 0.8744 | Validation Mean F1_score: 0.8306
-2024-05-29 01:11:02 INFO: Epoch 045 | Train Loss: 0.2753 | Train Accuracy: 0.9261 | Validation Loss: 0.2843 | Validation Accuracy: 0.9363 | Validation Mean Precision: 0.8575 | Validation Mean Recall: 0.8772 | Validation Mean F1_score: 0.8659
-2024-05-29 01:11:03 INFO: Epoch 046 | Train Loss: 0.2702 | Train Accuracy: 0.9260 | Validation Loss: 0.3121 | Validation Accuracy: 0.9334 | Validation Mean Precision: 0.8922 | Validation Mean Recall: 0.8340 | Validation Mean F1_score: 0.8572
-2024-05-29 01:11:05 INFO: Epoch 047 | Train Loss: 0.2709 | Train Accuracy: 0.9273 | Validation Loss: 0.2895 | Validation Accuracy: 0.9391 | Validation Mean Precision: 0.8918 | Validation Mean Recall: 0.8594 | Validation Mean F1_score: 0.8749
-2024-05-29 01:11:06 INFO: Epoch 048 | Train Loss: 0.2790 | Train Accuracy: 0.9240 | Validation Loss: 0.2837 | Validation Accuracy: 0.9425 | Validation Mean Precision: 0.8962 | Validation Mean Recall: 0.8756 | Validation Mean F1_score: 0.8851
-2024-05-29 01:11:08 INFO: Epoch 049 | Train Loss: 0.2739 | Train Accuracy: 0.9266 | Validation Loss: 0.2945 | Validation Accuracy: 0.9123 | Validation Mean Precision: 0.7993 | Validation Mean Recall: 0.8767 | Validation Mean F1_score: 0.8307
-2024-05-29 01:11:09 INFO: Epoch 050 | Train Loss: 0.2743 | Train Accuracy: 0.9259 | Validation Loss: 0.2828 | Validation Accuracy: 0.9217 | Validation Mean Precision: 0.8427 | Validation Mean Recall: 0.8726 | Validation Mean F1_score: 0.8566
-2024-05-29 01:11:11 INFO: Epoch 051 | Train Loss: 0.2698 | Train Accuracy: 0.9280 | Validation Loss: 0.2861 | Validation Accuracy: 0.9152 | Validation Mean Precision: 0.8464 | Validation Mean Recall: 0.8744 | Validation Mean F1_score: 0.8596
-2024-05-29 01:11:12 INFO: Epoch 052 | Train Loss: 0.2723 | Train Accuracy: 0.9254 | Validation Loss: 0.2761 | Validation Accuracy: 0.9341 | Validation Mean Precision: 0.8384 | Validation Mean Recall: 0.9002 | Validation Mean F1_score: 0.8632
-2024-05-29 01:11:14 INFO: Epoch 053 | Train Loss: 0.2654 | Train Accuracy: 0.9287 | Validation Loss: 0.2775 | Validation Accuracy: 0.9439 | Validation Mean Precision: 0.9045 | Validation Mean Recall: 0.8643 | Validation Mean F1_score: 0.8826
-2024-05-29 01:11:15 INFO: Epoch 054 | Train Loss: 0.2657 | Train Accuracy: 0.9287 | Validation Loss: 0.2999 | Validation Accuracy: 0.9044 | Validation Mean Precision: 0.8346 | Validation Mean Recall: 0.8505 | Validation Mean F1_score: 0.8404
-2024-05-29 01:11:17 INFO: Epoch 055 | Train Loss: 0.2673 | Train Accuracy: 0.9313 | Validation Loss: 0.2771 | Validation Accuracy: 0.9401 | Validation Mean Precision: 0.8600 | Validation Mean Recall: 0.8788 | Validation Mean F1_score: 0.8675
-2024-05-29 01:11:18 INFO: Epoch 056 | Train Loss: 0.2655 | Train Accuracy: 0.9294 | Validation Loss: 0.2879 | Validation Accuracy: 0.9420 | Validation Mean Precision: 0.8785 | Validation Mean Recall: 0.8743 | Validation Mean F1_score: 0.8739
-2024-05-29 01:11:19 INFO: Epoch 057 | Train Loss: 0.2641 | Train Accuracy: 0.9294 | Validation Loss: 0.2677 | Validation Accuracy: 0.9305 | Validation Mean Precision: 0.8363 | Validation Mean Recall: 0.8826 | Validation Mean F1_score: 0.8563
-2024-05-29 01:11:21 INFO: Epoch 058 | Train Loss: 0.2594 | Train Accuracy: 0.9315 | Validation Loss: 0.2707 | Validation Accuracy: 0.9260 | Validation Mean Precision: 0.8238 | Validation Mean Recall: 0.8940 | Validation Mean F1_score: 0.8536
-2024-05-29 01:11:22 INFO: Epoch 059 | Train Loss: 0.2617 | Train Accuracy: 0.9338 | Validation Loss: 0.2643 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8479 | Validation Mean Recall: 0.8942 | Validation Mean F1_score: 0.8659
-2024-05-29 01:11:24 INFO: Epoch 060 | Train Loss: 0.2569 | Train Accuracy: 0.9321 | Validation Loss: 0.2638 | Validation Accuracy: 0.9430 | Validation Mean Precision: 0.8675 | Validation Mean Recall: 0.8817 | Validation Mean F1_score: 0.8726
-2024-05-29 01:11:26 INFO: Epoch 061 | Train Loss: 0.2625 | Train Accuracy: 0.9306 | Validation Loss: 0.3039 | Validation Accuracy: 0.9336 | Validation Mean Precision: 0.8855 | Validation Mean Recall: 0.8657 | Validation Mean F1_score: 0.8732
-2024-05-29 01:11:27 INFO: Epoch 062 | Train Loss: 0.2603 | Train Accuracy: 0.9309 | Validation Loss: 0.2673 | Validation Accuracy: 0.9274 | Validation Mean Precision: 0.8403 | Validation Mean Recall: 0.8833 | Validation Mean F1_score: 0.8599
-2024-05-29 01:11:29 INFO: Epoch 063 | Train Loss: 0.2590 | Train Accuracy: 0.9335 | Validation Loss: 0.2725 | Validation Accuracy: 0.9439 | Validation Mean Precision: 0.9112 | Validation Mean Recall: 0.8662 | Validation Mean F1_score: 0.8867
-2024-05-29 01:11:30 INFO: Epoch 064 | Train Loss: 0.2616 | Train Accuracy: 0.9327 | Validation Loss: 0.2650 | Validation Accuracy: 0.9365 | Validation Mean Precision: 0.8602 | Validation Mean Recall: 0.8991 | Validation Mean F1_score: 0.8761
-2024-05-29 01:11:32 INFO: Epoch 065 | Train Loss: 0.2493 | Train Accuracy: 0.9362 | Validation Loss: 0.2712 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.8534 | Validation Mean Recall: 0.8945 | Validation Mean F1_score: 0.8651
-2024-05-29 01:11:33 INFO: Epoch 066 | Train Loss: 0.2582 | Train Accuracy: 0.9297 | Validation Loss: 0.2670 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.8514 | Validation Mean Recall: 0.8869 | Validation Mean F1_score: 0.8645
-2024-05-29 01:11:35 INFO: Epoch 067 | Train Loss: 0.2598 | Train Accuracy: 0.9332 | Validation Loss: 0.2765 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8676 | Validation Mean Recall: 0.8667 | Validation Mean F1_score: 0.8670
-2024-05-29 01:11:36 INFO: Epoch 068 | Train Loss: 0.2601 | Train Accuracy: 0.9302 | Validation Loss: 0.2669 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.8963 | Validation Mean Recall: 0.8715 | Validation Mean F1_score: 0.8824
-2024-05-29 01:11:38 INFO: Epoch 069 | Train Loss: 0.2609 | Train Accuracy: 0.9323 | Validation Loss: 0.2747 | Validation Accuracy: 0.9305 | Validation Mean Precision: 0.8149 | Validation Mean Recall: 0.8885 | Validation Mean F1_score: 0.8423
-2024-05-29 01:11:40 INFO: Epoch 070 | Train Loss: 0.2543 | Train Accuracy: 0.9336 | Validation Loss: 0.2802 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8160 | Validation Mean Recall: 0.8889 | Validation Mean F1_score: 0.8465
-2024-05-29 01:11:42 INFO: Epoch 071 | Train Loss: 0.2506 | Train Accuracy: 0.9366 | Validation Loss: 0.2599 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.9094 | Validation Mean Recall: 0.8801 | Validation Mean F1_score: 0.8939
-2024-05-29 01:11:43 INFO: Epoch 072 | Train Loss: 0.2561 | Train Accuracy: 0.9327 | Validation Loss: 0.2603 | Validation Accuracy: 0.9444 | Validation Mean Precision: 0.8659 | Validation Mean Recall: 0.8944 | Validation Mean F1_score: 0.8757
-2024-05-29 01:11:45 INFO: Epoch 073 | Train Loss: 0.2520 | Train Accuracy: 0.9353 | Validation Loss: 0.2702 | Validation Accuracy: 0.9377 | Validation Mean Precision: 0.8264 | Validation Mean Recall: 0.8977 | Validation Mean F1_score: 0.8513
-2024-05-29 01:11:46 INFO: Epoch 074 | Train Loss: 0.2481 | Train Accuracy: 0.9343 | Validation Loss: 0.2680 | Validation Accuracy: 0.9274 | Validation Mean Precision: 0.8202 | Validation Mean Recall: 0.8893 | Validation Mean F1_score: 0.8472
-2024-05-29 01:11:48 INFO: Epoch 075 | Train Loss: 0.2522 | Train Accuracy: 0.9351 | Validation Loss: 0.2735 | Validation Accuracy: 0.9193 | Validation Mean Precision: 0.8245 | Validation Mean Recall: 0.8841 | Validation Mean F1_score: 0.8512
-2024-05-29 01:11:49 INFO: Epoch 076 | Train Loss: 0.2547 | Train Accuracy: 0.9346 | Validation Loss: 0.3240 | Validation Accuracy: 0.9123 | Validation Mean Precision: 0.8894 | Validation Mean Recall: 0.8398 | Validation Mean F1_score: 0.8617
-2024-05-29 01:11:51 INFO: Epoch 077 | Train Loss: 0.2498 | Train Accuracy: 0.9393 | Validation Loss: 0.2805 | Validation Accuracy: 0.9188 | Validation Mean Precision: 0.7940 | Validation Mean Recall: 0.8892 | Validation Mean F1_score: 0.8308
-2024-05-29 01:11:52 INFO: Epoch 078 | Train Loss: 0.2471 | Train Accuracy: 0.9360 | Validation Loss: 0.2699 | Validation Accuracy: 0.9439 | Validation Mean Precision: 0.8825 | Validation Mean Recall: 0.8944 | Validation Mean F1_score: 0.8848
-2024-05-29 01:11:54 INFO: Epoch 079 | Train Loss: 0.2487 | Train Accuracy: 0.9356 | Validation Loss: 0.2776 | Validation Accuracy: 0.9339 | Validation Mean Precision: 0.8538 | Validation Mean Recall: 0.8796 | Validation Mean F1_score: 0.8654
-2024-05-29 01:11:55 INFO: Epoch 080 | Train Loss: 0.2572 | Train Accuracy: 0.9366 | Validation Loss: 0.2511 | Validation Accuracy: 0.9406 | Validation Mean Precision: 0.8664 | Validation Mean Recall: 0.8982 | Validation Mean F1_score: 0.8802
-2024-05-29 01:11:57 INFO: Epoch 081 | Train Loss: 0.2506 | Train Accuracy: 0.9360 | Validation Loss: 0.2938 | Validation Accuracy: 0.9248 | Validation Mean Precision: 0.7877 | Validation Mean Recall: 0.8899 | Validation Mean F1_score: 0.8225
-2024-05-29 01:11:58 INFO: Epoch 082 | Train Loss: 0.2437 | Train Accuracy: 0.9394 | Validation Loss: 0.2564 | Validation Accuracy: 0.9372 | Validation Mean Precision: 0.8497 | Validation Mean Recall: 0.8953 | Validation Mean F1_score: 0.8694
-2024-05-29 01:12:00 INFO: Epoch 083 | Train Loss: 0.2443 | Train Accuracy: 0.9384 | Validation Loss: 0.2602 | Validation Accuracy: 0.9363 | Validation Mean Precision: 0.8785 | Validation Mean Recall: 0.8850 | Validation Mean F1_score: 0.8812
-2024-05-29 01:12:01 INFO: Epoch 084 | Train Loss: 0.2452 | Train Accuracy: 0.9362 | Validation Loss: 0.2597 | Validation Accuracy: 0.9396 | Validation Mean Precision: 0.8585 | Validation Mean Recall: 0.8910 | Validation Mean F1_score: 0.8725
-2024-05-29 01:12:03 INFO: Epoch 085 | Train Loss: 0.2435 | Train Accuracy: 0.9352 | Validation Loss: 0.2677 | Validation Accuracy: 0.9444 | Validation Mean Precision: 0.9117 | Validation Mean Recall: 0.8634 | Validation Mean F1_score: 0.8844
-2024-05-29 01:12:04 INFO: Epoch 086 | Train Loss: 0.2476 | Train Accuracy: 0.9402 | Validation Loss: 0.2685 | Validation Accuracy: 0.9339 | Validation Mean Precision: 0.8227 | Validation Mean Recall: 0.8931 | Validation Mean F1_score: 0.8450
-2024-05-29 01:12:06 INFO: Epoch 087 | Train Loss: 0.2483 | Train Accuracy: 0.9378 | Validation Loss: 0.2700 | Validation Accuracy: 0.9459 | Validation Mean Precision: 0.8503 | Validation Mean Recall: 0.8925 | Validation Mean F1_score: 0.8636
-2024-05-29 01:12:08 INFO: Epoch 088 | Train Loss: 0.2434 | Train Accuracy: 0.9364 | Validation Loss: 0.2546 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.9009 | Validation Mean Recall: 0.8806 | Validation Mean F1_score: 0.8901
-2024-05-29 01:12:09 INFO: Epoch 089 | Train Loss: 0.2387 | Train Accuracy: 0.9374 | Validation Loss: 0.2575 | Validation Accuracy: 0.9447 | Validation Mean Precision: 0.8725 | Validation Mean Recall: 0.8967 | Validation Mean F1_score: 0.8812
-2024-05-29 01:12:11 INFO: Epoch 090 | Train Loss: 0.2502 | Train Accuracy: 0.9339 | Validation Loss: 0.2569 | Validation Accuracy: 0.9423 | Validation Mean Precision: 0.8874 | Validation Mean Recall: 0.8838 | Validation Mean F1_score: 0.8856
-2024-05-29 01:12:12 INFO: Epoch 091 | Train Loss: 0.2408 | Train Accuracy: 0.9385 | Validation Loss: 0.2585 | Validation Accuracy: 0.9459 | Validation Mean Precision: 0.8800 | Validation Mean Recall: 0.8921 | Validation Mean F1_score: 0.8820
-2024-05-29 01:12:14 INFO: Epoch 092 | Train Loss: 0.2420 | Train Accuracy: 0.9387 | Validation Loss: 0.2552 | Validation Accuracy: 0.9480 | Validation Mean Precision: 0.8807 | Validation Mean Recall: 0.8985 | Validation Mean F1_score: 0.8866
-2024-05-29 01:12:15 INFO: Epoch 093 | Train Loss: 0.2379 | Train Accuracy: 0.9394 | Validation Loss: 0.2599 | Validation Accuracy: 0.9411 | Validation Mean Precision: 0.8927 | Validation Mean Recall: 0.8822 | Validation Mean F1_score: 0.8868
-2024-05-29 01:12:16 INFO: Epoch 094 | Train Loss: 0.2454 | Train Accuracy: 0.9393 | Validation Loss: 0.2661 | Validation Accuracy: 0.9339 | Validation Mean Precision: 0.8435 | Validation Mean Recall: 0.8907 | Validation Mean F1_score: 0.8642
-2024-05-29 01:12:18 INFO: Epoch 095 | Train Loss: 0.2451 | Train Accuracy: 0.9379 | Validation Loss: 0.2583 | Validation Accuracy: 0.9365 | Validation Mean Precision: 0.8730 | Validation Mean Recall: 0.8885 | Validation Mean F1_score: 0.8802
-2024-05-29 01:12:19 INFO: Epoch 096 | Train Loss: 0.2467 | Train Accuracy: 0.9383 | Validation Loss: 0.2601 | Validation Accuracy: 0.9439 | Validation Mean Precision: 0.8610 | Validation Mean Recall: 0.8937 | Validation Mean F1_score: 0.8725
-2024-05-29 01:12:21 INFO: Epoch 097 | Train Loss: 0.2420 | Train Accuracy: 0.9386 | Validation Loss: 0.2540 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.8324 | Validation Mean Recall: 0.8900 | Validation Mean F1_score: 0.8544
-2024-05-29 01:12:22 INFO: Epoch 098 | Train Loss: 0.2386 | Train Accuracy: 0.9376 | Validation Loss: 0.3058 | Validation Accuracy: 0.8941 | Validation Mean Precision: 0.7867 | Validation Mean Recall: 0.8765 | Validation Mean F1_score: 0.8249
-2024-05-29 01:12:24 INFO: Epoch 099 | Train Loss: 0.2474 | Train Accuracy: 0.9330 | Validation Loss: 0.2698 | Validation Accuracy: 0.9451 | Validation Mean Precision: 0.8972 | Validation Mean Recall: 0.8685 | Validation Mean F1_score: 0.8821
-2024-05-29 01:12:25 INFO: Epoch 100 | Train Loss: 0.2386 | Train Accuracy: 0.9400 | Validation Loss: 0.2691 | Validation Accuracy: 0.9456 | Validation Mean Precision: 0.8807 | Validation Mean Recall: 0.8983 | Validation Mean F1_score: 0.8867
-2024-05-29 01:12:26 INFO: Epoch 101 | Train Loss: 0.2379 | Train Accuracy: 0.9388 | Validation Loss: 0.2913 | Validation Accuracy: 0.9118 | Validation Mean Precision: 0.8662 | Validation Mean Recall: 0.8597 | Validation Mean F1_score: 0.8601
-2024-05-29 01:12:28 INFO: Epoch 102 | Train Loss: 0.2417 | Train Accuracy: 0.9402 | Validation Loss: 0.2545 | Validation Accuracy: 0.9492 | Validation Mean Precision: 0.8994 | Validation Mean Recall: 0.8907 | Validation Mean F1_score: 0.8947
-2024-05-29 01:12:29 INFO: Epoch 103 | Train Loss: 0.2373 | Train Accuracy: 0.9405 | Validation Loss: 0.2559 | Validation Accuracy: 0.9468 | Validation Mean Precision: 0.8630 | Validation Mean Recall: 0.8946 | Validation Mean F1_score: 0.8734
-2024-05-29 01:12:31 INFO: Epoch 104 | Train Loss: 0.2386 | Train Accuracy: 0.9402 | Validation Loss: 0.2571 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8940 | Validation Mean Recall: 0.8795 | Validation Mean F1_score: 0.8862
-2024-05-29 01:12:32 INFO: Epoch 105 | Train Loss: 0.2383 | Train Accuracy: 0.9399 | Validation Loss: 0.2599 | Validation Accuracy: 0.9473 | Validation Mean Precision: 0.9270 | Validation Mean Recall: 0.8749 | Validation Mean F1_score: 0.8987
-2024-05-29 01:12:34 INFO: Epoch 106 | Train Loss: 0.2351 | Train Accuracy: 0.9403 | Validation Loss: 0.2768 | Validation Accuracy: 0.9394 | Validation Mean Precision: 0.8365 | Validation Mean Recall: 0.8886 | Validation Mean F1_score: 0.8547
-2024-05-29 01:12:35 INFO: Epoch 107 | Train Loss: 0.2368 | Train Accuracy: 0.9414 | Validation Loss: 0.2615 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8621 | Validation Mean Recall: 0.8880 | Validation Mean F1_score: 0.8737
-2024-05-29 01:12:37 INFO: Epoch 108 | Train Loss: 0.2389 | Train Accuracy: 0.9409 | Validation Loss: 0.2585 | Validation Accuracy: 0.9298 | Validation Mean Precision: 0.8401 | Validation Mean Recall: 0.8891 | Validation Mean F1_score: 0.8622
-2024-05-29 01:12:38 INFO: Epoch 109 | Train Loss: 0.2375 | Train Accuracy: 0.9400 | Validation Loss: 0.2929 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.8369 | Validation Mean Recall: 0.8850 | Validation Mean F1_score: 0.8461
-2024-05-29 01:12:40 INFO: Epoch 110 | Train Loss: 0.2347 | Train Accuracy: 0.9405 | Validation Loss: 0.3048 | Validation Accuracy: 0.8991 | Validation Mean Precision: 0.8031 | Validation Mean Recall: 0.8789 | Validation Mean F1_score: 0.8356
-2024-05-29 01:12:42 INFO: Epoch 111 | Train Loss: 0.2366 | Train Accuracy: 0.9398 | Validation Loss: 0.2538 | Validation Accuracy: 0.9475 | Validation Mean Precision: 0.8843 | Validation Mean Recall: 0.8884 | Validation Mean F1_score: 0.8856
-2024-05-29 01:12:44 INFO: Epoch 112 | Train Loss: 0.2364 | Train Accuracy: 0.9410 | Validation Loss: 0.2593 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.9128 | Validation Mean Recall: 0.8733 | Validation Mean F1_score: 0.8917
-2024-05-29 01:12:45 INFO: Epoch 113 | Train Loss: 0.2355 | Train Accuracy: 0.9414 | Validation Loss: 0.2456 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.8732 | Validation Mean Recall: 0.8889 | Validation Mean F1_score: 0.8805
-2024-05-29 01:12:47 INFO: Epoch 114 | Train Loss: 0.2308 | Train Accuracy: 0.9405 | Validation Loss: 0.2547 | Validation Accuracy: 0.9509 | Validation Mean Precision: 0.8926 | Validation Mean Recall: 0.8934 | Validation Mean F1_score: 0.8917
-2024-05-29 01:12:49 INFO: Epoch 115 | Train Loss: 0.2352 | Train Accuracy: 0.9398 | Validation Loss: 0.2585 | Validation Accuracy: 0.9516 | Validation Mean Precision: 0.9059 | Validation Mean Recall: 0.8819 | Validation Mean F1_score: 0.8934
-2024-05-29 01:12:50 INFO: Epoch 116 | Train Loss: 0.2350 | Train Accuracy: 0.9418 | Validation Loss: 0.2582 | Validation Accuracy: 0.9437 | Validation Mean Precision: 0.8530 | Validation Mean Recall: 0.8864 | Validation Mean F1_score: 0.8667
-2024-05-29 01:12:52 INFO: Epoch 117 | Train Loss: 0.2365 | Train Accuracy: 0.9412 | Validation Loss: 0.2547 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8572 | Validation Mean Recall: 0.8984 | Validation Mean F1_score: 0.8743
-2024-05-29 01:12:54 INFO: Epoch 118 | Train Loss: 0.2359 | Train Accuracy: 0.9421 | Validation Loss: 0.2676 | Validation Accuracy: 0.9226 | Validation Mean Precision: 0.8458 | Validation Mean Recall: 0.8865 | Validation Mean F1_score: 0.8648
-2024-05-29 01:12:55 INFO: Epoch 119 | Train Loss: 0.2350 | Train Accuracy: 0.9417 | Validation Loss: 0.2670 | Validation Accuracy: 0.9233 | Validation Mean Precision: 0.8406 | Validation Mean Recall: 0.8663 | Validation Mean F1_score: 0.8529
-2024-05-29 01:12:57 INFO: Epoch 120 | Train Loss: 0.2361 | Train Accuracy: 0.9419 | Validation Loss: 0.2471 | Validation Accuracy: 0.9454 | Validation Mean Precision: 0.9043 | Validation Mean Recall: 0.8730 | Validation Mean F1_score: 0.8868
-2024-05-29 01:12:58 INFO: Epoch 121 | Train Loss: 0.2301 | Train Accuracy: 0.9425 | Validation Loss: 0.2528 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8586 | Validation Mean Recall: 0.8952 | Validation Mean F1_score: 0.8735
-2024-05-29 01:13:00 INFO: Epoch 122 | Train Loss: 0.2301 | Train Accuracy: 0.9428 | Validation Loss: 0.2522 | Validation Accuracy: 0.9368 | Validation Mean Precision: 0.8629 | Validation Mean Recall: 0.8920 | Validation Mean F1_score: 0.8765
-2024-05-29 01:13:01 INFO: Epoch 123 | Train Loss: 0.2270 | Train Accuracy: 0.9430 | Validation Loss: 0.2535 | Validation Accuracy: 0.9483 | Validation Mean Precision: 0.8869 | Validation Mean Recall: 0.8861 | Validation Mean F1_score: 0.8854
-2024-05-29 01:13:03 INFO: Epoch 124 | Train Loss: 0.2373 | Train Accuracy: 0.9407 | Validation Loss: 0.2443 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.9040 | Validation Mean Recall: 0.8877 | Validation Mean F1_score: 0.8953
-2024-05-29 01:13:04 INFO: Epoch 125 | Train Loss: 0.2369 | Train Accuracy: 0.9408 | Validation Loss: 0.2487 | Validation Accuracy: 0.9408 | Validation Mean Precision: 0.8470 | Validation Mean Recall: 0.8950 | Validation Mean F1_score: 0.8669
-2024-05-29 01:13:05 INFO: Epoch 126 | Train Loss: 0.2325 | Train Accuracy: 0.9406 | Validation Loss: 0.2493 | Validation Accuracy: 0.9451 | Validation Mean Precision: 0.8878 | Validation Mean Recall: 0.8862 | Validation Mean F1_score: 0.8862
-2024-05-29 01:13:07 INFO: Epoch 127 | Train Loss: 0.2287 | Train Accuracy: 0.9436 | Validation Loss: 0.2560 | Validation Accuracy: 0.9497 | Validation Mean Precision: 0.9214 | Validation Mean Recall: 0.8633 | Validation Mean F1_score: 0.8888
-2024-05-29 01:13:08 INFO: Epoch 128 | Train Loss: 0.2326 | Train Accuracy: 0.9425 | Validation Loss: 0.2499 | Validation Accuracy: 0.9473 | Validation Mean Precision: 0.9066 | Validation Mean Recall: 0.8545 | Validation Mean F1_score: 0.8769
-2024-05-29 01:13:10 INFO: Epoch 129 | Train Loss: 0.2261 | Train Accuracy: 0.9439 | Validation Loss: 0.2410 | Validation Accuracy: 0.9468 | Validation Mean Precision: 0.8777 | Validation Mean Recall: 0.8924 | Validation Mean F1_score: 0.8840
-2024-05-29 01:13:11 INFO: Epoch 130 | Train Loss: 0.2302 | Train Accuracy: 0.9420 | Validation Loss: 0.2591 | Validation Accuracy: 0.9468 | Validation Mean Precision: 0.9059 | Validation Mean Recall: 0.8826 | Validation Mean F1_score: 0.8934
-2024-05-29 01:13:13 INFO: Epoch 131 | Train Loss: 0.2277 | Train Accuracy: 0.9424 | Validation Loss: 0.2433 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8739 | Validation Mean Recall: 0.8906 | Validation Mean F1_score: 0.8817
-2024-05-29 01:13:14 INFO: Epoch 132 | Train Loss: 0.2311 | Train Accuracy: 0.9432 | Validation Loss: 0.2515 | Validation Accuracy: 0.9483 | Validation Mean Precision: 0.8859 | Validation Mean Recall: 0.8938 | Validation Mean F1_score: 0.8871
-2024-05-29 01:13:15 INFO: Epoch 133 | Train Loss: 0.2321 | Train Accuracy: 0.9453 | Validation Loss: 0.2674 | Validation Accuracy: 0.9391 | Validation Mean Precision: 0.8580 | Validation Mean Recall: 0.8718 | Validation Mean F1_score: 0.8639
-2024-05-29 01:13:17 INFO: Epoch 134 | Train Loss: 0.2290 | Train Accuracy: 0.9418 | Validation Loss: 0.2519 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.8650 | Validation Mean Recall: 0.8955 | Validation Mean F1_score: 0.8749
-2024-05-29 01:13:18 INFO: Epoch 135 | Train Loss: 0.2315 | Train Accuracy: 0.9430 | Validation Loss: 0.2515 | Validation Accuracy: 0.9430 | Validation Mean Precision: 0.8731 | Validation Mean Recall: 0.8855 | Validation Mean F1_score: 0.8788
-2024-05-29 01:13:20 INFO: Epoch 136 | Train Loss: 0.2241 | Train Accuracy: 0.9444 | Validation Loss: 0.2534 | Validation Accuracy: 0.9384 | Validation Mean Precision: 0.8605 | Validation Mean Recall: 0.8888 | Validation Mean F1_score: 0.8722
-2024-05-29 01:13:22 INFO: Epoch 137 | Train Loss: 0.2242 | Train Accuracy: 0.9451 | Validation Loss: 0.2517 | Validation Accuracy: 0.9466 | Validation Mean Precision: 0.8789 | Validation Mean Recall: 0.8929 | Validation Mean F1_score: 0.8835
-2024-05-29 01:13:24 INFO: Epoch 138 | Train Loss: 0.2232 | Train Accuracy: 0.9460 | Validation Loss: 0.2462 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8552 | Validation Mean Recall: 0.8826 | Validation Mean F1_score: 0.8662
-2024-05-29 01:13:26 INFO: Epoch 139 | Train Loss: 0.2217 | Train Accuracy: 0.9437 | Validation Loss: 0.2574 | Validation Accuracy: 0.9406 | Validation Mean Precision: 0.8720 | Validation Mean Recall: 0.8893 | Validation Mean F1_score: 0.8796
-2024-05-29 01:13:27 INFO: Epoch 140 | Train Loss: 0.2289 | Train Accuracy: 0.9464 | Validation Loss: 0.2516 | Validation Accuracy: 0.9415 | Validation Mean Precision: 0.9035 | Validation Mean Recall: 0.8865 | Validation Mean F1_score: 0.8946
-2024-05-29 01:13:29 INFO: Epoch 141 | Train Loss: 0.2312 | Train Accuracy: 0.9439 | Validation Loss: 0.2439 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8684 | Validation Mean Recall: 0.8967 | Validation Mean F1_score: 0.8771
-2024-05-29 01:13:30 INFO: Epoch 142 | Train Loss: 0.2297 | Train Accuracy: 0.9418 | Validation Loss: 0.2441 | Validation Accuracy: 0.9447 | Validation Mean Precision: 0.8890 | Validation Mean Recall: 0.8807 | Validation Mean F1_score: 0.8845
-2024-05-29 01:13:32 INFO: Epoch 143 | Train Loss: 0.2305 | Train Accuracy: 0.9445 | Validation Loss: 0.2535 | Validation Accuracy: 0.9454 | Validation Mean Precision: 0.8631 | Validation Mean Recall: 0.8980 | Validation Mean F1_score: 0.8765
-2024-05-29 01:13:33 INFO: Epoch 144 | Train Loss: 0.2258 | Train Accuracy: 0.9448 | Validation Loss: 0.2426 | Validation Accuracy: 0.9487 | Validation Mean Precision: 0.8848 | Validation Mean Recall: 0.8961 | Validation Mean F1_score: 0.8885
-2024-05-29 01:13:35 INFO: Epoch 145 | Train Loss: 0.2177 | Train Accuracy: 0.9453 | Validation Loss: 0.2586 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.8581 | Validation Mean Recall: 0.8820 | Validation Mean F1_score: 0.8683
-2024-05-29 01:13:37 INFO: Epoch 146 | Train Loss: 0.2191 | Train Accuracy: 0.9460 | Validation Loss: 0.2532 | Validation Accuracy: 0.9368 | Validation Mean Precision: 0.8530 | Validation Mean Recall: 0.8699 | Validation Mean F1_score: 0.8604
-2024-05-29 01:13:38 INFO: Epoch 147 | Train Loss: 0.2292 | Train Accuracy: 0.9462 | Validation Loss: 0.2493 | Validation Accuracy: 0.9456 | Validation Mean Precision: 0.8658 | Validation Mean Recall: 0.8877 | Validation Mean F1_score: 0.8744
-2024-05-29 01:13:40 INFO: Epoch 148 | Train Loss: 0.2264 | Train Accuracy: 0.9430 | Validation Loss: 0.2436 | Validation Accuracy: 0.9471 | Validation Mean Precision: 0.8962 | Validation Mean Recall: 0.8813 | Validation Mean F1_score: 0.8883
-2024-05-29 01:13:41 INFO: Epoch 149 | Train Loss: 0.2257 | Train Accuracy: 0.9436 | Validation Loss: 0.2408 | Validation Accuracy: 0.9502 | Validation Mean Precision: 0.8866 | Validation Mean Recall: 0.8838 | Validation Mean F1_score: 0.8844
-2024-05-29 01:13:43 INFO: Epoch 150 | Train Loss: 0.2259 | Train Accuracy: 0.9453 | Validation Loss: 0.2507 | Validation Accuracy: 0.9447 | Validation Mean Precision: 0.9128 | Validation Mean Recall: 0.8688 | Validation Mean F1_score: 0.8888
-2024-05-29 01:13:43 INFO: Best Validation F1 Score (Macro): 0.8987
-2024-05-29 01:13:43 INFO: Best Validation Recall (Macro): 0.8749
-2024-05-29 01:13:43 INFO: Best Validation Precision (Macro): 0.9270
-2024-05-29 01:13:43 INFO: Best Epoch: 105
-2024-05-29 01:13:44 INFO: Infer Result: 
-2024-05-29 01:13:44 INFO: Prediction errors: 277
-2024-05-29 01:13:44 INFO: Prediction error percentage: 5.31%
-2024-05-29 01:13:44 INFO: Total samples: 5218
-2024-05-29 01:13:44 INFO: Infer Result End: 
-2024-05-29 01:13:45 INFO: Class weights: tensor([21.1878,  3.1149,  0.7420,  0.4378], device='cuda:0')
-2024-05-29 01:13:46 INFO: Epoch 001 | Train Loss: 0.5522 | Train Accuracy: 0.8203 | Validation Loss: 0.3500 | Validation Accuracy: 0.8654 | Validation Mean Precision: 0.7424 | Validation Mean Recall: 0.8555 | Validation Mean F1_score: 0.7875
-2024-05-29 01:13:48 INFO: Epoch 002 | Train Loss: 0.3739 | Train Accuracy: 0.8840 | Validation Loss: 0.3281 | Validation Accuracy: 0.8884 | Validation Mean Precision: 0.7683 | Validation Mean Recall: 0.8636 | Validation Mean F1_score: 0.8060
-2024-05-29 01:13:49 INFO: Epoch 003 | Train Loss: 0.3690 | Train Accuracy: 0.8832 | Validation Loss: 0.3156 | Validation Accuracy: 0.8948 | Validation Mean Precision: 0.8094 | Validation Mean Recall: 0.8525 | Validation Mean F1_score: 0.8294
-2024-05-29 01:13:50 INFO: Epoch 004 | Train Loss: 0.3699 | Train Accuracy: 0.8828 | Validation Loss: 0.3496 | Validation Accuracy: 0.8752 | Validation Mean Precision: 0.7393 | Validation Mean Recall: 0.8699 | Validation Mean F1_score: 0.7881
-2024-05-29 01:13:52 INFO: Epoch 005 | Train Loss: 0.3555 | Train Accuracy: 0.8906 | Validation Loss: 0.3119 | Validation Accuracy: 0.8970 | Validation Mean Precision: 0.8134 | Validation Mean Recall: 0.8354 | Validation Mean F1_score: 0.8235
-2024-05-29 01:13:53 INFO: Epoch 006 | Train Loss: 0.3598 | Train Accuracy: 0.8899 | Validation Loss: 0.3153 | Validation Accuracy: 0.8915 | Validation Mean Precision: 0.7934 | Validation Mean Recall: 0.8546 | Validation Mean F1_score: 0.8209
-2024-05-29 01:13:55 INFO: Epoch 007 | Train Loss: 0.3670 | Train Accuracy: 0.8904 | Validation Loss: 0.3234 | Validation Accuracy: 0.8924 | Validation Mean Precision: 0.7900 | Validation Mean Recall: 0.8508 | Validation Mean F1_score: 0.8137
-2024-05-29 01:13:56 INFO: Epoch 008 | Train Loss: 0.3572 | Train Accuracy: 0.8905 | Validation Loss: 0.3167 | Validation Accuracy: 0.8936 | Validation Mean Precision: 0.7821 | Validation Mean Recall: 0.8500 | Validation Mean F1_score: 0.8108
-2024-05-29 01:13:58 INFO: Epoch 009 | Train Loss: 0.3542 | Train Accuracy: 0.8954 | Validation Loss: 0.3912 | Validation Accuracy: 0.8977 | Validation Mean Precision: 0.8861 | Validation Mean Recall: 0.6835 | Validation Mean F1_score: 0.6724
-2024-05-29 01:14:00 INFO: Epoch 010 | Train Loss: 0.3517 | Train Accuracy: 0.8951 | Validation Loss: 0.3090 | Validation Accuracy: 0.9154 | Validation Mean Precision: 0.8555 | Validation Mean Recall: 0.8375 | Validation Mean F1_score: 0.8456
-2024-05-29 01:14:02 INFO: Epoch 011 | Train Loss: 0.3468 | Train Accuracy: 0.8996 | Validation Loss: 0.3001 | Validation Accuracy: 0.9090 | Validation Mean Precision: 0.8131 | Validation Mean Recall: 0.8506 | Validation Mean F1_score: 0.8292
-2024-05-29 01:14:04 INFO: Epoch 012 | Train Loss: 0.3399 | Train Accuracy: 0.9003 | Validation Loss: 0.3083 | Validation Accuracy: 0.9015 | Validation Mean Precision: 0.7884 | Validation Mean Recall: 0.8673 | Validation Mean F1_score: 0.8189
-2024-05-29 01:14:05 INFO: Epoch 013 | Train Loss: 0.3385 | Train Accuracy: 0.9004 | Validation Loss: 0.3251 | Validation Accuracy: 0.8757 | Validation Mean Precision: 0.7619 | Validation Mean Recall: 0.8754 | Validation Mean F1_score: 0.8048
-2024-05-29 01:14:07 INFO: Epoch 014 | Train Loss: 0.3400 | Train Accuracy: 0.8993 | Validation Loss: 0.3105 | Validation Accuracy: 0.8975 | Validation Mean Precision: 0.7679 | Validation Mean Recall: 0.8820 | Validation Mean F1_score: 0.8111
-2024-05-29 01:14:08 INFO: Epoch 015 | Train Loss: 0.3263 | Train Accuracy: 0.9047 | Validation Loss: 0.2822 | Validation Accuracy: 0.9212 | Validation Mean Precision: 0.8312 | Validation Mean Recall: 0.8591 | Validation Mean F1_score: 0.8429
-2024-05-29 01:14:09 INFO: Epoch 016 | Train Loss: 0.3287 | Train Accuracy: 0.9078 | Validation Loss: 0.2929 | Validation Accuracy: 0.9276 | Validation Mean Precision: 0.8541 | Validation Mean Recall: 0.8508 | Validation Mean F1_score: 0.8517
-2024-05-29 01:14:11 INFO: Epoch 017 | Train Loss: 0.3217 | Train Accuracy: 0.9075 | Validation Loss: 0.2738 | Validation Accuracy: 0.9253 | Validation Mean Precision: 0.8276 | Validation Mean Recall: 0.8760 | Validation Mean F1_score: 0.8480
-2024-05-29 01:14:12 INFO: Epoch 018 | Train Loss: 0.3195 | Train Accuracy: 0.9144 | Validation Loss: 0.2735 | Validation Accuracy: 0.9269 | Validation Mean Precision: 0.8196 | Validation Mean Recall: 0.8776 | Validation Mean F1_score: 0.8433
-2024-05-29 01:14:14 INFO: Epoch 019 | Train Loss: 0.3118 | Train Accuracy: 0.9167 | Validation Loss: 0.2774 | Validation Accuracy: 0.9138 | Validation Mean Precision: 0.7965 | Validation Mean Recall: 0.8827 | Validation Mean F1_score: 0.8314
-2024-05-29 01:14:15 INFO: Epoch 020 | Train Loss: 0.3158 | Train Accuracy: 0.9158 | Validation Loss: 0.2708 | Validation Accuracy: 0.9221 | Validation Mean Precision: 0.8245 | Validation Mean Recall: 0.8754 | Validation Mean F1_score: 0.8471
-2024-05-29 01:14:16 INFO: Epoch 021 | Train Loss: 0.3059 | Train Accuracy: 0.9145 | Validation Loss: 0.2673 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8241 | Validation Mean Recall: 0.8800 | Validation Mean F1_score: 0.8487
-2024-05-29 01:14:18 INFO: Epoch 022 | Train Loss: 0.3047 | Train Accuracy: 0.9197 | Validation Loss: 0.3004 | Validation Accuracy: 0.9152 | Validation Mean Precision: 0.8557 | Validation Mean Recall: 0.8587 | Validation Mean F1_score: 0.8480
-2024-05-29 01:14:19 INFO: Epoch 023 | Train Loss: 0.3043 | Train Accuracy: 0.9167 | Validation Loss: 0.2625 | Validation Accuracy: 0.9358 | Validation Mean Precision: 0.8606 | Validation Mean Recall: 0.8653 | Validation Mean F1_score: 0.8607
-2024-05-29 01:14:21 INFO: Epoch 024 | Train Loss: 0.2983 | Train Accuracy: 0.9209 | Validation Loss: 0.2709 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8613 | Validation Mean Recall: 0.8708 | Validation Mean F1_score: 0.8633
-2024-05-29 01:14:22 INFO: Epoch 025 | Train Loss: 0.2962 | Train Accuracy: 0.9197 | Validation Loss: 0.2698 | Validation Accuracy: 0.9411 | Validation Mean Precision: 0.8537 | Validation Mean Recall: 0.8783 | Validation Mean F1_score: 0.8642
-2024-05-29 01:14:24 INFO: Epoch 026 | Train Loss: 0.3008 | Train Accuracy: 0.9197 | Validation Loss: 0.3021 | Validation Accuracy: 0.8917 | Validation Mean Precision: 0.8151 | Validation Mean Recall: 0.8501 | Validation Mean F1_score: 0.8305
-2024-05-29 01:14:25 INFO: Epoch 027 | Train Loss: 0.2928 | Train Accuracy: 0.9239 | Validation Loss: 0.2605 | Validation Accuracy: 0.9353 | Validation Mean Precision: 0.8407 | Validation Mean Recall: 0.8948 | Validation Mean F1_score: 0.8608
-2024-05-29 01:14:27 INFO: Epoch 028 | Train Loss: 0.2858 | Train Accuracy: 0.9230 | Validation Loss: 0.2611 | Validation Accuracy: 0.9365 | Validation Mean Precision: 0.8308 | Validation Mean Recall: 0.8963 | Validation Mean F1_score: 0.8562
-2024-05-29 01:14:28 INFO: Epoch 029 | Train Loss: 0.2883 | Train Accuracy: 0.9275 | Validation Loss: 0.2759 | Validation Accuracy: 0.9068 | Validation Mean Precision: 0.7824 | Validation Mean Recall: 0.8876 | Validation Mean F1_score: 0.8235
-2024-05-29 01:14:29 INFO: Epoch 030 | Train Loss: 0.2825 | Train Accuracy: 0.9279 | Validation Loss: 0.2701 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8755 | Validation Mean Recall: 0.8559 | Validation Mean F1_score: 0.8630
-2024-05-29 01:14:31 INFO: Epoch 031 | Train Loss: 0.2863 | Train Accuracy: 0.9243 | Validation Loss: 0.2559 | Validation Accuracy: 0.9358 | Validation Mean Precision: 0.8351 | Validation Mean Recall: 0.8988 | Validation Mean F1_score: 0.8585
-2024-05-29 01:14:32 INFO: Epoch 032 | Train Loss: 0.2789 | Train Accuracy: 0.9287 | Validation Loss: 0.2467 | Validation Accuracy: 0.9463 | Validation Mean Precision: 0.8719 | Validation Mean Recall: 0.8786 | Validation Mean F1_score: 0.8740
-2024-05-29 01:14:34 INFO: Epoch 033 | Train Loss: 0.2783 | Train Accuracy: 0.9295 | Validation Loss: 0.2501 | Validation Accuracy: 0.9379 | Validation Mean Precision: 0.8656 | Validation Mean Recall: 0.8665 | Validation Mean F1_score: 0.8658
-2024-05-29 01:14:35 INFO: Epoch 034 | Train Loss: 0.2727 | Train Accuracy: 0.9312 | Validation Loss: 0.2493 | Validation Accuracy: 0.9298 | Validation Mean Precision: 0.8434 | Validation Mean Recall: 0.8546 | Validation Mean F1_score: 0.8484
-2024-05-29 01:14:37 INFO: Epoch 035 | Train Loss: 0.2779 | Train Accuracy: 0.9296 | Validation Loss: 0.2492 | Validation Accuracy: 0.9224 | Validation Mean Precision: 0.8464 | Validation Mean Recall: 0.8520 | Validation Mean F1_score: 0.8489
-2024-05-29 01:14:39 INFO: Epoch 036 | Train Loss: 0.2733 | Train Accuracy: 0.9327 | Validation Loss: 0.2885 | Validation Accuracy: 0.9279 | Validation Mean Precision: 0.9080 | Validation Mean Recall: 0.8273 | Validation Mean F1_score: 0.8616
-2024-05-29 01:14:40 INFO: Epoch 037 | Train Loss: 0.2775 | Train Accuracy: 0.9303 | Validation Loss: 0.2454 | Validation Accuracy: 0.9399 | Validation Mean Precision: 0.8496 | Validation Mean Recall: 0.8985 | Validation Mean F1_score: 0.8704
-2024-05-29 01:14:42 INFO: Epoch 038 | Train Loss: 0.2728 | Train Accuracy: 0.9313 | Validation Loss: 0.2623 | Validation Accuracy: 0.9121 | Validation Mean Precision: 0.8160 | Validation Mean Recall: 0.8964 | Validation Mean F1_score: 0.8509
-2024-05-29 01:14:43 INFO: Epoch 039 | Train Loss: 0.2680 | Train Accuracy: 0.9321 | Validation Loss: 0.2379 | Validation Accuracy: 0.9471 | Validation Mean Precision: 0.8742 | Validation Mean Recall: 0.8566 | Validation Mean F1_score: 0.8649
-2024-05-29 01:14:45 INFO: Epoch 040 | Train Loss: 0.2686 | Train Accuracy: 0.9324 | Validation Loss: 0.2637 | Validation Accuracy: 0.9487 | Validation Mean Precision: 0.8879 | Validation Mean Recall: 0.8862 | Validation Mean F1_score: 0.8837
-2024-05-29 01:14:46 INFO: Epoch 041 | Train Loss: 0.2633 | Train Accuracy: 0.9357 | Validation Loss: 0.2428 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8923 | Validation Mean Recall: 0.8526 | Validation Mean F1_score: 0.8701
-2024-05-29 01:14:48 INFO: Epoch 042 | Train Loss: 0.2661 | Train Accuracy: 0.9352 | Validation Loss: 0.2480 | Validation Accuracy: 0.9281 | Validation Mean Precision: 0.8271 | Validation Mean Recall: 0.8921 | Validation Mean F1_score: 0.8525
-2024-05-29 01:14:49 INFO: Epoch 043 | Train Loss: 0.2624 | Train Accuracy: 0.9364 | Validation Loss: 0.2365 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8922 | Validation Mean Recall: 0.8856 | Validation Mean F1_score: 0.8863
-2024-05-29 01:14:51 INFO: Epoch 044 | Train Loss: 0.2619 | Train Accuracy: 0.9357 | Validation Loss: 0.2507 | Validation Accuracy: 0.9375 | Validation Mean Precision: 0.8640 | Validation Mean Recall: 0.8634 | Validation Mean F1_score: 0.8619
-2024-05-29 01:14:52 INFO: Epoch 045 | Train Loss: 0.2611 | Train Accuracy: 0.9391 | Validation Loss: 0.2320 | Validation Accuracy: 0.9449 | Validation Mean Precision: 0.8784 | Validation Mean Recall: 0.8908 | Validation Mean F1_score: 0.8828
-2024-05-29 01:14:54 INFO: Epoch 046 | Train Loss: 0.2609 | Train Accuracy: 0.9356 | Validation Loss: 0.2472 | Validation Accuracy: 0.9255 | Validation Mean Precision: 0.8256 | Validation Mean Recall: 0.8952 | Validation Mean F1_score: 0.8548
-2024-05-29 01:14:55 INFO: Epoch 047 | Train Loss: 0.2604 | Train Accuracy: 0.9354 | Validation Loss: 0.2942 | Validation Accuracy: 0.9097 | Validation Mean Precision: 0.8179 | Validation Mean Recall: 0.8810 | Validation Mean F1_score: 0.8430
-2024-05-29 01:14:57 INFO: Epoch 048 | Train Loss: 0.2584 | Train Accuracy: 0.9365 | Validation Loss: 0.2268 | Validation Accuracy: 0.9425 | Validation Mean Precision: 0.8596 | Validation Mean Recall: 0.9028 | Validation Mean F1_score: 0.8763
-2024-05-29 01:14:58 INFO: Epoch 049 | Train Loss: 0.2583 | Train Accuracy: 0.9348 | Validation Loss: 0.2326 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8569 | Validation Mean Recall: 0.8761 | Validation Mean F1_score: 0.8655
-2024-05-29 01:15:00 INFO: Epoch 050 | Train Loss: 0.2558 | Train Accuracy: 0.9369 | Validation Loss: 0.2437 | Validation Accuracy: 0.9327 | Validation Mean Precision: 0.8721 | Validation Mean Recall: 0.8755 | Validation Mean F1_score: 0.8734
-2024-05-29 01:15:01 INFO: Epoch 051 | Train Loss: 0.2592 | Train Accuracy: 0.9356 | Validation Loss: 0.2396 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.9035 | Validation Mean Recall: 0.8756 | Validation Mean F1_score: 0.8885
-2024-05-29 01:15:03 INFO: Epoch 052 | Train Loss: 0.2561 | Train Accuracy: 0.9392 | Validation Loss: 0.2425 | Validation Accuracy: 0.9207 | Validation Mean Precision: 0.8424 | Validation Mean Recall: 0.8876 | Validation Mean F1_score: 0.8620
-2024-05-29 01:15:04 INFO: Epoch 053 | Train Loss: 0.2649 | Train Accuracy: 0.9364 | Validation Loss: 0.2449 | Validation Accuracy: 0.9293 | Validation Mean Precision: 0.8623 | Validation Mean Recall: 0.8785 | Validation Mean F1_score: 0.8700
-2024-05-29 01:15:06 INFO: Epoch 054 | Train Loss: 0.2537 | Train Accuracy: 0.9384 | Validation Loss: 0.2415 | Validation Accuracy: 0.9502 | Validation Mean Precision: 0.8928 | Validation Mean Recall: 0.8738 | Validation Mean F1_score: 0.8826
-2024-05-29 01:15:07 INFO: Epoch 055 | Train Loss: 0.2535 | Train Accuracy: 0.9378 | Validation Loss: 0.2268 | Validation Accuracy: 0.9490 | Validation Mean Precision: 0.8852 | Validation Mean Recall: 0.8867 | Validation Mean F1_score: 0.8844
-2024-05-29 01:15:09 INFO: Epoch 056 | Train Loss: 0.2523 | Train Accuracy: 0.9385 | Validation Loss: 0.2487 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.8939 | Validation Mean Recall: 0.8531 | Validation Mean F1_score: 0.8709
-2024-05-29 01:15:10 INFO: Epoch 057 | Train Loss: 0.2543 | Train Accuracy: 0.9369 | Validation Loss: 0.2358 | Validation Accuracy: 0.9506 | Validation Mean Precision: 0.9005 | Validation Mean Recall: 0.8847 | Validation Mean F1_score: 0.8918
-2024-05-29 01:15:12 INFO: Epoch 058 | Train Loss: 0.2486 | Train Accuracy: 0.9423 | Validation Loss: 0.2328 | Validation Accuracy: 0.9418 | Validation Mean Precision: 0.8817 | Validation Mean Recall: 0.8804 | Validation Mean F1_score: 0.8804
-2024-05-29 01:15:13 INFO: Epoch 059 | Train Loss: 0.2492 | Train Accuracy: 0.9417 | Validation Loss: 0.2293 | Validation Accuracy: 0.9447 | Validation Mean Precision: 0.8670 | Validation Mean Recall: 0.8881 | Validation Mean F1_score: 0.8757
-2024-05-29 01:15:14 INFO: Epoch 060 | Train Loss: 0.2520 | Train Accuracy: 0.9357 | Validation Loss: 0.2328 | Validation Accuracy: 0.9442 | Validation Mean Precision: 0.8621 | Validation Mean Recall: 0.8965 | Validation Mean F1_score: 0.8711
-2024-05-29 01:15:16 INFO: Epoch 061 | Train Loss: 0.2513 | Train Accuracy: 0.9388 | Validation Loss: 0.2349 | Validation Accuracy: 0.9344 | Validation Mean Precision: 0.8586 | Validation Mean Recall: 0.8796 | Validation Mean F1_score: 0.8686
-2024-05-29 01:15:18 INFO: Epoch 062 | Train Loss: 0.2477 | Train Accuracy: 0.9411 | Validation Loss: 0.2338 | Validation Accuracy: 0.9494 | Validation Mean Precision: 0.8695 | Validation Mean Recall: 0.8990 | Validation Mean F1_score: 0.8757
-2024-05-29 01:15:19 INFO: Epoch 063 | Train Loss: 0.2536 | Train Accuracy: 0.9403 | Validation Loss: 0.2277 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8602 | Validation Mean Recall: 0.8964 | Validation Mean F1_score: 0.8733
-2024-05-29 01:15:21 INFO: Epoch 064 | Train Loss: 0.2574 | Train Accuracy: 0.9391 | Validation Loss: 0.2368 | Validation Accuracy: 0.9518 | Validation Mean Precision: 0.9008 | Validation Mean Recall: 0.8799 | Validation Mean F1_score: 0.8888
-2024-05-29 01:15:22 INFO: Epoch 065 | Train Loss: 0.2454 | Train Accuracy: 0.9426 | Validation Loss: 0.2489 | Validation Accuracy: 0.9430 | Validation Mean Precision: 0.8678 | Validation Mean Recall: 0.8740 | Validation Mean F1_score: 0.8679
-2024-05-29 01:15:24 INFO: Epoch 066 | Train Loss: 0.2527 | Train Accuracy: 0.9414 | Validation Loss: 0.2262 | Validation Accuracy: 0.9471 | Validation Mean Precision: 0.8680 | Validation Mean Recall: 0.8970 | Validation Mean F1_score: 0.8770
-2024-05-29 01:15:25 INFO: Epoch 067 | Train Loss: 0.2477 | Train Accuracy: 0.9420 | Validation Loss: 0.2258 | Validation Accuracy: 0.9528 | Validation Mean Precision: 0.8917 | Validation Mean Recall: 0.8876 | Validation Mean F1_score: 0.8877
-2024-05-29 01:15:27 INFO: Epoch 068 | Train Loss: 0.2420 | Train Accuracy: 0.9434 | Validation Loss: 0.2347 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8667 | Validation Mean Recall: 0.8981 | Validation Mean F1_score: 0.8763
-2024-05-29 01:15:28 INFO: Epoch 069 | Train Loss: 0.2491 | Train Accuracy: 0.9409 | Validation Loss: 0.2411 | Validation Accuracy: 0.9253 | Validation Mean Precision: 0.8311 | Validation Mean Recall: 0.8892 | Validation Mean F1_score: 0.8557
-2024-05-29 01:15:30 INFO: Epoch 070 | Train Loss: 0.2476 | Train Accuracy: 0.9410 | Validation Loss: 0.2438 | Validation Accuracy: 0.9310 | Validation Mean Precision: 0.8419 | Validation Mean Recall: 0.8923 | Validation Mean F1_score: 0.8622
-2024-05-29 01:15:31 INFO: Epoch 071 | Train Loss: 0.2451 | Train Accuracy: 0.9432 | Validation Loss: 0.2347 | Validation Accuracy: 0.9526 | Validation Mean Precision: 0.8871 | Validation Mean Recall: 0.8912 | Validation Mean F1_score: 0.8864
-2024-05-29 01:15:33 INFO: Epoch 072 | Train Loss: 0.2509 | Train Accuracy: 0.9420 | Validation Loss: 0.2302 | Validation Accuracy: 0.9370 | Validation Mean Precision: 0.8412 | Validation Mean Recall: 0.8959 | Validation Mean F1_score: 0.8639
-2024-05-29 01:15:34 INFO: Epoch 073 | Train Loss: 0.2429 | Train Accuracy: 0.9423 | Validation Loss: 0.2365 | Validation Accuracy: 0.9461 | Validation Mean Precision: 0.8589 | Validation Mean Recall: 0.8936 | Validation Mean F1_score: 0.8718
-2024-05-29 01:15:36 INFO: Epoch 074 | Train Loss: 0.2398 | Train Accuracy: 0.9436 | Validation Loss: 0.2248 | Validation Accuracy: 0.9499 | Validation Mean Precision: 0.8697 | Validation Mean Recall: 0.8973 | Validation Mean F1_score: 0.8780
-2024-05-29 01:15:37 INFO: Epoch 075 | Train Loss: 0.2439 | Train Accuracy: 0.9429 | Validation Loss: 0.2445 | Validation Accuracy: 0.9341 | Validation Mean Precision: 0.8606 | Validation Mean Recall: 0.8901 | Validation Mean F1_score: 0.8703
-2024-05-29 01:15:38 INFO: Epoch 076 | Train Loss: 0.2503 | Train Accuracy: 0.9416 | Validation Loss: 0.2321 | Validation Accuracy: 0.9516 | Validation Mean Precision: 0.8785 | Validation Mean Recall: 0.9019 | Validation Mean F1_score: 0.8835
-2024-05-29 01:15:40 INFO: Epoch 077 | Train Loss: 0.2388 | Train Accuracy: 0.9443 | Validation Loss: 0.2253 | Validation Accuracy: 0.9492 | Validation Mean Precision: 0.8923 | Validation Mean Recall: 0.8838 | Validation Mean F1_score: 0.8871
-2024-05-29 01:15:41 INFO: Epoch 078 | Train Loss: 0.2395 | Train Accuracy: 0.9445 | Validation Loss: 0.2290 | Validation Accuracy: 0.9483 | Validation Mean Precision: 0.8846 | Validation Mean Recall: 0.8879 | Validation Mean F1_score: 0.8834
-2024-05-29 01:15:43 INFO: Epoch 079 | Train Loss: 0.2402 | Train Accuracy: 0.9431 | Validation Loss: 0.2307 | Validation Accuracy: 0.9475 | Validation Mean Precision: 0.8603 | Validation Mean Recall: 0.8771 | Validation Mean F1_score: 0.8656
-2024-05-29 01:15:44 INFO: Epoch 080 | Train Loss: 0.2382 | Train Accuracy: 0.9441 | Validation Loss: 0.2315 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.8613 | Validation Mean Recall: 0.8771 | Validation Mean F1_score: 0.8660
-2024-05-29 01:15:46 INFO: Epoch 081 | Train Loss: 0.2442 | Train Accuracy: 0.9447 | Validation Loss: 0.2756 | Validation Accuracy: 0.9173 | Validation Mean Precision: 0.8645 | Validation Mean Recall: 0.8634 | Validation Mean F1_score: 0.8637
-2024-05-29 01:15:47 INFO: Epoch 082 | Train Loss: 0.2427 | Train Accuracy: 0.9418 | Validation Loss: 0.2658 | Validation Accuracy: 0.9061 | Validation Mean Precision: 0.8135 | Validation Mean Recall: 0.8797 | Validation Mean F1_score: 0.8424
-2024-05-29 01:15:49 INFO: Epoch 083 | Train Loss: 0.2400 | Train Accuracy: 0.9437 | Validation Loss: 0.2399 | Validation Accuracy: 0.9365 | Validation Mean Precision: 0.8269 | Validation Mean Recall: 0.8993 | Validation Mean F1_score: 0.8505
-2024-05-29 01:15:50 INFO: Epoch 084 | Train Loss: 0.2394 | Train Accuracy: 0.9458 | Validation Loss: 0.2312 | Validation Accuracy: 0.9435 | Validation Mean Precision: 0.8488 | Validation Mean Recall: 0.8906 | Validation Mean F1_score: 0.8652
-2024-05-29 01:15:52 INFO: Epoch 085 | Train Loss: 0.2399 | Train Accuracy: 0.9439 | Validation Loss: 0.2269 | Validation Accuracy: 0.9492 | Validation Mean Precision: 0.8641 | Validation Mean Recall: 0.9015 | Validation Mean F1_score: 0.8755
-2024-05-29 01:15:54 INFO: Epoch 086 | Train Loss: 0.2386 | Train Accuracy: 0.9461 | Validation Loss: 0.2290 | Validation Accuracy: 0.9504 | Validation Mean Precision: 0.8869 | Validation Mean Recall: 0.8848 | Validation Mean F1_score: 0.8851
-2024-05-29 01:15:55 INFO: Epoch 087 | Train Loss: 0.2349 | Train Accuracy: 0.9438 | Validation Loss: 0.2372 | Validation Accuracy: 0.9471 | Validation Mean Precision: 0.8773 | Validation Mean Recall: 0.8883 | Validation Mean F1_score: 0.8772
-2024-05-29 01:15:56 INFO: Epoch 088 | Train Loss: 0.2403 | Train Accuracy: 0.9450 | Validation Loss: 0.2286 | Validation Accuracy: 0.9478 | Validation Mean Precision: 0.8763 | Validation Mean Recall: 0.8840 | Validation Mean F1_score: 0.8773
-2024-05-29 01:15:58 INFO: Epoch 089 | Train Loss: 0.2317 | Train Accuracy: 0.9470 | Validation Loss: 0.2379 | Validation Accuracy: 0.9490 | Validation Mean Precision: 0.8656 | Validation Mean Recall: 0.8911 | Validation Mean F1_score: 0.8728
-2024-05-29 01:15:59 INFO: Epoch 090 | Train Loss: 0.2358 | Train Accuracy: 0.9441 | Validation Loss: 0.3328 | Validation Accuracy: 0.8857 | Validation Mean Precision: 0.7890 | Validation Mean Recall: 0.8822 | Validation Mean F1_score: 0.8281
-2024-05-29 01:16:01 INFO: Epoch 091 | Train Loss: 0.2360 | Train Accuracy: 0.9441 | Validation Loss: 0.2315 | Validation Accuracy: 0.9485 | Validation Mean Precision: 0.8613 | Validation Mean Recall: 0.9039 | Validation Mean F1_score: 0.8736
-2024-05-29 01:16:03 INFO: Epoch 092 | Train Loss: 0.2379 | Train Accuracy: 0.9424 | Validation Loss: 0.2375 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.8939 | Validation Mean Recall: 0.8746 | Validation Mean F1_score: 0.8835
-2024-05-29 01:16:04 INFO: Epoch 093 | Train Loss: 0.2345 | Train Accuracy: 0.9463 | Validation Loss: 0.2396 | Validation Accuracy: 0.9506 | Validation Mean Precision: 0.8930 | Validation Mean Recall: 0.8806 | Validation Mean F1_score: 0.8851
-2024-05-29 01:16:05 INFO: Epoch 094 | Train Loss: 0.2340 | Train Accuracy: 0.9457 | Validation Loss: 0.2291 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.8789 | Validation Mean Recall: 0.8975 | Validation Mean F1_score: 0.8825
-2024-05-29 01:16:07 INFO: Epoch 095 | Train Loss: 0.2359 | Train Accuracy: 0.9465 | Validation Loss: 0.2341 | Validation Accuracy: 0.9492 | Validation Mean Precision: 0.8710 | Validation Mean Recall: 0.9028 | Validation Mean F1_score: 0.8809
-2024-05-29 01:16:08 INFO: Epoch 096 | Train Loss: 0.2328 | Train Accuracy: 0.9440 | Validation Loss: 0.2341 | Validation Accuracy: 0.9487 | Validation Mean Precision: 0.8756 | Validation Mean Recall: 0.9014 | Validation Mean F1_score: 0.8792
-2024-05-29 01:16:10 INFO: Epoch 097 | Train Loss: 0.2368 | Train Accuracy: 0.9475 | Validation Loss: 0.2284 | Validation Accuracy: 0.9432 | Validation Mean Precision: 0.8534 | Validation Mean Recall: 0.8919 | Validation Mean F1_score: 0.8662
-2024-05-29 01:16:11 INFO: Epoch 098 | Train Loss: 0.2383 | Train Accuracy: 0.9458 | Validation Loss: 0.2287 | Validation Accuracy: 0.9492 | Validation Mean Precision: 0.8593 | Validation Mean Recall: 0.9070 | Validation Mean F1_score: 0.8711
-2024-05-29 01:16:13 INFO: Epoch 099 | Train Loss: 0.2322 | Train Accuracy: 0.9465 | Validation Loss: 0.2415 | Validation Accuracy: 0.9542 | Validation Mean Precision: 0.9140 | Validation Mean Recall: 0.8597 | Validation Mean F1_score: 0.8844
-2024-05-29 01:16:14 INFO: Epoch 100 | Train Loss: 0.2398 | Train Accuracy: 0.9429 | Validation Loss: 0.2266 | Validation Accuracy: 0.9540 | Validation Mean Precision: 0.8987 | Validation Mean Recall: 0.8803 | Validation Mean F1_score: 0.8885
-2024-05-29 01:16:16 INFO: Epoch 101 | Train Loss: 0.2326 | Train Accuracy: 0.9475 | Validation Loss: 0.2360 | Validation Accuracy: 0.9492 | Validation Mean Precision: 0.8990 | Validation Mean Recall: 0.8713 | Validation Mean F1_score: 0.8846
-2024-05-29 01:16:18 INFO: Epoch 102 | Train Loss: 0.2255 | Train Accuracy: 0.9474 | Validation Loss: 0.2285 | Validation Accuracy: 0.9535 | Validation Mean Precision: 0.8878 | Validation Mean Recall: 0.8961 | Validation Mean F1_score: 0.8883
-2024-05-29 01:16:19 INFO: Epoch 103 | Train Loss: 0.2353 | Train Accuracy: 0.9448 | Validation Loss: 0.2224 | Validation Accuracy: 0.9533 | Validation Mean Precision: 0.8830 | Validation Mean Recall: 0.8941 | Validation Mean F1_score: 0.8862
-2024-05-29 01:16:21 INFO: Epoch 104 | Train Loss: 0.2271 | Train Accuracy: 0.9485 | Validation Loss: 0.2401 | Validation Accuracy: 0.9523 | Validation Mean Precision: 0.8855 | Validation Mean Recall: 0.8681 | Validation Mean F1_score: 0.8759
-2024-05-29 01:16:22 INFO: Epoch 105 | Train Loss: 0.2360 | Train Accuracy: 0.9412 | Validation Loss: 0.2309 | Validation Accuracy: 0.9502 | Validation Mean Precision: 0.8945 | Validation Mean Recall: 0.8722 | Validation Mean F1_score: 0.8824
-2024-05-29 01:16:24 INFO: Epoch 106 | Train Loss: 0.2348 | Train Accuracy: 0.9449 | Validation Loss: 0.2282 | Validation Accuracy: 0.9487 | Validation Mean Precision: 0.8652 | Validation Mean Recall: 0.9024 | Validation Mean F1_score: 0.8771
-2024-05-29 01:16:25 INFO: Epoch 107 | Train Loss: 0.2368 | Train Accuracy: 0.9457 | Validation Loss: 0.2383 | Validation Accuracy: 0.9197 | Validation Mean Precision: 0.8275 | Validation Mean Recall: 0.8991 | Validation Mean F1_score: 0.8548
-2024-05-29 01:16:26 INFO: Early stopping at epoch 106 | Best epoch : 57
-2024-05-29 01:16:26 INFO: Best Validation F1 Score (Macro): 0.8918
-2024-05-29 01:16:26 INFO: Best Validation Recall (Macro): 0.8847
-2024-05-29 01:16:26 INFO: Best Validation Precision (Macro): 0.9005
-2024-05-29 01:16:26 INFO: Best Epoch: 57
-2024-05-29 01:16:26 INFO: Infer Result: 
-2024-05-29 01:16:26 INFO: Prediction errors: 434
-2024-05-29 01:16:26 INFO: Prediction error percentage: 8.32%
-2024-05-29 01:16:26 INFO: Total samples: 5218
-2024-05-29 01:16:26 INFO: Infer Result End: 
-2024-05-29 01:16:26 INFO: Result: Avg F1: 0.8822 Avg Wrong Percentage: 5.99%
-2024-05-29 01:16:26 INFO: Result: Avg Precision: [0.7481330358677849, 0.8581788688175195, 0.949279701595013, 0.9525727597360033] | 0.87704109150408
-2024-05-29 01:16:26 INFO: Result: Avg Recall: [0.8096774193548386, 0.8906921241050119, 0.8885600455321571, 0.9802013422818792] | 0.8922827328184718
-2024-05-29 01:16:26 INFO: Result: Avg F1: [0.7714399759907937, 0.8738125884545493, 0.9175302382524844, 0.9660268498572094] | 0.8822024131387591

config/config.yaml

@@ -1,127 +1,128 @@
-#---设备配置---#
+#---系统配置---#
-device: cpu 
+system:
-# device: cuda
+  print_log: true
+  log_retention_days: 1
+  clean_dirs:
+    - "train_api"
+    - "inference_api"
+    - "evaluate_api"
+  host: "0.0.0.0"
+  port: 8088
+  device: "cpu"  # 可选: "cpu", "cuda"
 
-#---训练配置---#
+#---路径配置---#
-n_epochs: 150
+paths:
-batch_size: 16
+  model:
-learning_rate: 0.001
+    train: "/data/model/psychology.pth"
-nc: 4
+    default: "model/psychology.pth"
-#data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
+  normalizer:
-data_train: train_val
+    train: "/data/model/normalizer.pkl"
-early_stop_patience: 50
+    default: "model/normalizer.pkl"
-gamma: 0.98
+  fonts:
-step_size: 10
+    chinese: "fonts/simhei.ttf"
-experimental_mode: false
-experiments_count: 50
-replace_model: true  # 是否替换现有模型
-
-#---检测和推理配置---#
-# 检测和推理使用模型路径
-model_path: /data/model/psychology.pth
-default_model_path: model/psychology.pth
 
-#---样本特征---#
+#---训练配置---#
-# 标签名称
+training:
-label_name: 类别
+  n_epochs: 500
-# 特征名称
+  batch_size: 1024
-feature_names:
+  learning_rate: 0.001
-  - "强迫症状数字化"
+  early_stop_patience: 100
-  - "人际关系敏感数字化" 
+  scheduler:
-  - "抑郁数字化"
+    gamma: 0.98
-  - "多因子症状"
+    step_size: 20
-  - "母亲教养方式数字化"
+  regularization:
-  - "父亲教养方式数字化"
+    l1_lambda: 1e-6 # 默认1e-5, 降低L1，因为特征已经通过权重控制
-  - "自评家庭经济条件数字化"
+    l2_lambda: 5e-4 # 默认1e-4, 增加L2，加强过拟合控制
-  - "有无心理治疗（咨询）史数字化"
+  dropout_rate: 0.2
-  - "学业情况数字化"
+  experimental_mode: true
-  - "出勤情况数字化"
+  experiments_count: 10
-# 定义特征权重列表
+  replace_model: true
-feature_weights:
+  data_mode: "train_val"  # 可选: "train", "train_val", "all"
-  - 0.135
+  class_weights:
-  - 0.085
+    balance_mode: "balanced"  # 可选: "balanced", "balanced_subsample", "none"
-  - 0.08
+    # recall_weights: [1.4, 1.5, 1.1, 0.5]  # 召回率调节权重 值更大则更容易检测出来
-  - 0.2
+    recall_weights: [1, 1, 1, 1]  # 召回率调节权重 整体平衡 整体错误率更低
-  - 0.09
+    weight_normalize: "max"    # 可选: "max", "sum", "none"
-  - 0.09
-  - 0.06
-  - 0.06
-  - 0.08
-  - 0.12
-
-#---网络结构---#
-# MLP configuration
-mlp:
-  input_dim: 10  # Number of input features
-  layers:
-    - output_dim: 32
-      activation: relu
-    - output_dim: 128
-      activation: relu
-    - output_dim: 32
-      activation: relu
-  output_dim: 4  # Number of classes
 
-# Transformer configuration
+#---模型配置---#
-transformer:
+model:
-  d_model: 32  # Reduced embedding dimension
+  num_classes: 4  # nc
-  nhead: 4  # Reduced number of attention heads
+  input_dim: 10
-  num_encoder_layers: 2  # Reduced number of encoder layers
+  architecture: "mlp"  # 可选: "mlp", "transformer"
-  num_decoder_layers: 2  # Reduced number of decoder layers
+  mlp:
-  dim_feedforward: 128  # Reduced feedforward network dimension
+    layers:
-  dropout: 0.1  # Dropout probability
+      - output_dim: 32
-  input_dim: 10  # Number of input features
+        activation: "relu"
-  output_dim: 4  # Number of classes
+      - output_dim: 128
+        activation: "relu"
+      - output_dim: 32
+        activation: "relu"
+    output_dim: 4
 
-#---训练配置备份---#
+#---特征配置---#
-# MLP good train param 1
+features:
-# #---训练配置---#
+  label_name: "类别"
-# n_epochs: 150
+  groups:
-# batch_size: 16
+    核心症状:
-# learning_rate: 0.001
+      - "强迫症状数字化"
-# nc: 4
+      - "人际关系敏感数字化" 
-# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
+      - "抑郁数字化"
-# data_train: train_val
+    父亲教养:
-# early_stop_patience: 50
+      - "父亲教养方式数字化"
-# gamma: 0.98
+    母亲教养:
-# step_size: 10
+      - "母亲教养方式数字化"
-# experiments_count: 1
+    家庭经济:
+      - "自评家庭经济条件数字化"
+    心理咨询:
+      - "有无心理治疗（咨询）史数字化"
+    多因子:
+      - "多因子症状"
+    出勤:
+      - "出勤情况数字化"
+    学业:
+      - "学业情况数字化"
 
-# MLP good train param 2
+  feature_names:
-# #---训练配置---#
+    - "强迫症状数字化"
-# n_epochs: 300
+    - "人际关系敏感数字化" 
-# batch_size: 8
+    - "抑郁数字化"
-# learning_rate: 0.0005
+    - "多因子症状"
-# nc: 4
+    - "母亲教养方式数字化"
-# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
+    - "父亲教养方式数字化"
-# data_train: train_val
+    - "自评家庭经济条件数字化"
-# early_stop_patience: 50
+    - "有无心理治疗（咨询）史数字化"
-# gamma: 0.98
+    - "学业情况数字化"
-# step_size: 10
+    - "出勤情况数字化"
-# experiments_count: 1
 
-# Transformer good train param 1
+  feature_weights:
-# #---训练配置---#
+    - 0.135  # 强迫症状
-# n_epochs: 150
+    - 0.085  # 人际关系敏感
-# batch_size: 64
+    - 0.08   # 抑郁
-# learning_rate: 0.001
+    - 0.2    # 多因子症状
-# nc: 4
+    - 0.09   # 母亲教养
-# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
+    - 0.09   # 父亲教养
-# data_train: train_val
+    - 0.06   # 家庭经济
-# early_stop_patience: 50
+    - 0.06   # 心理治疗史
-# gamma: 0.98
+    - 0.08   # 学业情况
-# step_size: 10
+    - 0.12   # 出勤情况
-# experiments_count: 1
 
-# Transformer good train param 2
+  feature_mapping:
-# #---训练配置---#
+    父亲教养方式:
-# n_epochs: 300
+      1: 0.59  # 温暖与理解
-# batch_size: 8
+      0: 0.46  # 其他
-# learning_rate: 0.0005
+    母亲教养方式:
-# nc: 4
+      1: 0.69  # 温暖与理解
-# #data_train: train_val # train: 只用train训练，val做验证， infer做测试；train_val: 用train和val做训练，infer做验证， infer做测试；all: 全部训练，全部验证，全部测试（数据先1/5作为infer，剩下的再1/5作为val，剩下的4/5作为训练）
+      0: 0.56  # 其他
-# data_train: train_val
+    家庭经济条件:
-# early_stop_patience: 50
+      2: 0.54  # 贫困
-# gamma: 0.98
+      1: 0.54  # 较差
-# step_size: 10
+      0: 0.47  # 其他
-# experiments_count: 1
+    心理咨询史:
+      true: 0.21  # 有
+      false: 0.09  # 无
+    出勤情况:
+      true: 0.74   # 高于平均
+      false: 0.67  # 低于平均
+    学业情况:
+      true: 0.59   # 有预警
+      false: 0.50  # 无预警

log.txt

@@ -1,10 +0,0 @@
-2024-05-29 01:46:51 INFO: config: 
-2024-05-29 01:46:52 INFO: Infer Result: 
-2024-05-29 01:46:52 INFO: Prediction errors: 1070
-2024-05-29 01:46:52 INFO: Prediction error percentage: 4.10%
-2024-05-29 01:46:52 INFO: Total samples: 26088
-2024-05-29 01:46:52 INFO: Infer Result End: 
-2024-05-29 01:46:52 INFO: Result: Avg F1: 0.9233 Avg Wrong Percentage: 4.10%
-2024-05-29 01:46:52 INFO: Result: Avg Precision: [0.8276923076923077, 0.9590373783922171, 0.9740385781875531, 0.9537483137406051] | 0.9286291445031708
-2024-05-29 01:46:52 INFO: Result: Avg Recall: [0.8733766233766234, 0.8944603629417383, 0.9136322257624033, 0.9965767217076118] | 0.9195114834470941
-2024-05-29 01:46:52 INFO: Result: Avg F1: [0.8499210110584519, 0.9256239189523102, 0.9428688861487876, 0.9746922698178238] | 0.9232765214943434

psy_api.py

@@ -1,3 +1,7 @@
+"""
+FastAPI 服务主模块
+提供训练、评估和推理的 HTTP 接口
+"""
 import os
 import time
 import datetime
@@ -7,417 +11,345 @@ import uvicorn
 import schedule
 import threading
 import yaml
-import numpy as np
 from fastapi import FastAPI, Request, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
 from typing import List
 import atexit
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
-from utils.feature_process import create_feature_df, apply_feature_weights, Features
-from utils.common import MLModel
-
-app = FastAPI()
-
-# 控制是否打印的宏定义
-PRINT_LOG = True
-
-# 日志保留天数
-DAYS = 1
-
-# 清理文件夹
-DIRS = ["train_api", "inference_api", "evaluate_api"]
-
-# 初始化配置文件
-config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "config/config.yaml"))
-
-# 初始化日志配置
-log_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "logfile.log"))
-logging.basicConfig(filename=log_path, level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
 
-def log_print(message):
+from utils.feature_processor import (
-    logging.info(message)
+    Features, FeatureProcessor, FeatureWeightApplier, normalize_features
-    if PRINT_LOG:
+)
-        print(message)
+from utils.model_trainer import MLModel
-
-# 保证日志写到文件
-def flush_log():
-    for handler in logging.getLogger().handlers:
-        handler.flush()
 
-# 定义fastapi返回类 inference
+# API 响应模型
 class PredictionResult(BaseModel):
-    predictions: list
+    """推理结果模型"""
+    predictions: List[int]
 
-# 定义fastapi返回类
 class ClassificationResult(BaseModel):
-    precision: list
+    """分类评估结果模型"""
-    recall: list
+    precision: List[float]
-    f1: list
+    recall: List[float]
+    f1: List[float]
     wrong_percentage: float
 
-# 允许所有域名的跨域请求
+class APIResponse(BaseModel):
+    """API 通用响应模型"""
+    classification_result: ClassificationResult
+    data_file: dict
+
+class APIConfig:
+    """API 配置类"""
+    def __init__(self):
+        # 初始化配置文件路径
+        self.config_path = os.path.abspath(os.path.join(
+            os.path.dirname(__file__), "config/config.yaml"))
+        
+        # 加载配置
+        with open(self.config_path, 'r') as f:
+            self.config = yaml.safe_load(f)
+            
+        # 初始化日志
+        self.log_path = os.path.abspath(os.path.join(
+            os.path.dirname(__file__), "logfile.log"))
+        self._setup_logging()
+        
+        # 初始化目录
+        self._setup_directories()
+
+    def _setup_logging(self):
+        """配置日志"""
+        logging.basicConfig(
+            filename=self.log_path,
+            level=logging.INFO,
+            format='%(asctime)s %(levelname)s: %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S'
+        )
+
+    def _setup_directories(self):
+        """创建必要的目录"""
+        for dir_name in self.config['system']['clean_dirs']:
+            dir_path = os.path.abspath(os.path.join(
+                os.path.dirname(__file__), dir_name))
+            os.makedirs(dir_path, exist_ok=True)
+
+    def update_log_handler(self, log_path: str):
+        """更新日志处理器"""
+        logger = logging.getLogger()
+        for handler in logger.handlers[:]:
+            logger.removeHandler(handler)
+        file_handler = logging.FileHandler(log_path)
+        file_handler.setLevel(logging.INFO)
+        file_handler.setFormatter(logging.Formatter(
+            '%(asctime)s %(levelname)s: %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S'
+        ))
+        logger.addHandler(file_handler)
+
+class FileCleanup:
+    """文件清理类"""
+    @staticmethod
+    def clean_old_files(directory: str, days: int):
+        """清理指定天数前的文件"""
+        now = time.time()
+        cutoff = now - (days * 86400)
+        
+        for root, _, files in os.walk(directory):
+            for file in files:
+                file_path = os.path.join(root, file)
+                if os.path.getmtime(file_path) < cutoff:
+                    os.remove(file_path)
+                    logging.info(f"Removed old file: {file_path}")
+
+    @staticmethod
+    def schedule_cleanup(config: dict):
+        """定期清理任务"""
+        for directory in config['system']['clean_dirs']:
+            abs_directory = os.path.abspath(os.path.join(
+                os.path.dirname(__file__), directory))
+            FileCleanup.clean_old_files(
+                abs_directory, 
+                config['system']['log_retention_days']
+            )
+
+# 初始化 FastAPI 应用
+app = FastAPI()
+api_config = APIConfig()
+
+# 配置 CORS
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
     allow_credentials=True,
-    allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+    allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# 定义一个函数来动态更新日志处理器
+# 挂载静态文件目录
-def update_log_handler(log_path):
+for dir_name in api_config.config['system']['clean_dirs']:
-    logger = logging.getLogger()
+    dir_path = os.path.abspath(os.path.join(os.path.dirname(__file__), dir_name))
-    # 移除所有现有的处理器
+    app.mount(f"/{dir_name}", StaticFiles(directory=dir_path), name=dir_name)
-    for handler in logger.handlers[:]:
-        logger.removeHandler(handler)
-    # 添加新的文件处理器
-    file_handler = logging.FileHandler(log_path)
-    file_handler.setLevel(logging.INFO)
-    file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
-    logger.addHandler(file_handler)
-
-# 定义清理旧文件的函数
-def clean_old_files(directory, days=DAYS):
-    now = time.time()
-    cutoff = now - (days * 86400)  # 1天前的时间戳
-
-    for root, dirs, files in os.walk(directory):
-        for file in files:
-            file_path = os.path.join(root, file)
-            if os.path.getmtime(file_path) < cutoff:
-                os.remove(file_path)
-                print(f"Removed old file: {file_path}")
 
-# 定期清理任务
-def schedule_cleanup():
-    directories = DIRS
-    for directory in directories:
-        abs_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), directory))
-        clean_old_files(abs_directory)
-
-# 启动定时任务
-def start_scheduler():
-    schedule.every(1).hours.do(schedule_cleanup)
-    while True:
-        schedule.run_pending()
-        time.sleep(1)
-
-# 定义训练接口
 @app.post("/train/")
-async def train_model(request: Request, features_list: List[Features]):
+async def train_model(request: Request, features_list: List[Features]) -> APIResponse:
-    global config_path
+    """训练模型接口"""
-    # 遍历每个特征对象，并将其添加到 all_features 中
+    # 创建特征处理器
-    all_features = create_feature_df(features_list)
+    processor = FeatureProcessor(api_config.config)
-
+    all_features = processor.create_feature_df(features_list)
-    # 读取 YAML 配置文件
+    
-    with open(config_path, 'r') as f:
-        config = yaml.load(f, Loader=yaml.FullLoader)
-        feature_names = config['feature_names']
-        feature_weights = config['feature_weights']
-
     # 应用特征权重
-    feature_label_weighted = apply_feature_weights(all_features, feature_names, feature_weights)
+    feature_label_weighted = FeatureWeightApplier.apply_weights(
-
+        all_features,
-    start_time = time.time()  # 记录开始时间
+        api_config.config['features']['feature_names'],
-
+        api_config.config['features']['feature_weights']
-    # 创建静态文件存放文件夹
+    )
-    static_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "train_api"))  # 设置模型文件和配置文件的存放目录，和本py同级
+    
-    os.makedirs(static_dir, exist_ok=True)
+    # 特征归一化
-
+    feature_label_weighted = normalize_features(
-    # 训练前设置
+        feature_label_weighted,
+        api_config.config['features']['feature_names'],
+        is_train=True,
+        config=api_config.config
+    )
+    
+    # 记录开始时间
+    start_time = time.time()
+    
+    # 设置训练相关路径
     now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    data_path = os.path.abspath(os.path.join(static_dir, f"train_feature_label_weighted_{now}.xlsx"))
+    train_dir = os.path.abspath(os.path.join(
-    config['data_path'] = data_path
+        os.path.dirname(__file__), "train_api"))
+    
+    # 更新配置中的路径
+    api_config.config['paths']['model'].update({
+        'train_process': os.path.join(train_dir, f"train_process_{now}.png"),
+        'evaluate_result_path': os.path.join(train_dir, f"evaluate_result_{now}.png")
+    })
+    
+    # 保存训练数据
+    data_path = os.path.join(train_dir, f"train_feature_label_weighted_{now}.xlsx")
     feature_label_weighted.to_excel(data_path, index=False)
-
+    
-    # 添加模型保存路径
+    # 更新配置
-    train_model_path = os.path.abspath(os.path.join(static_dir, f"train_model_{now}.pth"))
+    api_config.config['data_path'] = data_path
-    config['train_model_path'] = train_model_path
+    api_config.config['train_model_path'] = os.path.join(
-
+        train_dir, f"train_model_{now}.pth")
+    # 添加相关性矩阵图路径
+    api_config.config['correlation_matrix_path'] = os.path.join(
+        train_dir, f"correlation_matrix_{now}.png")
+    
     # 配置日志
-    log_path = os.path.abspath(os.path.join(static_dir, f"train_log_{now}.log"))
+    log_path = os.path.join(train_dir, f"train_log_{now}.log")
-    update_log_handler(log_path)
+    api_config.update_log_handler(log_path)
-
+    
-    # 配置训练和验证结果图片路径
+    # 训练模型
-    train_process_path = os.path.abspath(os.path.join(static_dir, f"train_progress_img_{now}.png"))
+    ml_model = MLModel(api_config.config)
-    config['train_process_path'] = train_process_path
+    avg_f1, wrong_percentage, precision, recall, f1 = ml_model.train_detect()
-    evaluate_result_path = os.path.abspath(os.path.join(static_dir, f"evaluate_result_img_{now}.png"))
+    
-    config['evaluate_result_path'] = evaluate_result_path
+    # 如果配置允许，复制最佳模型到指定位置
-
+    if api_config.config['training']['replace_model']:
-    log_print("config: " + str(config))
+        shutil.copyfile(
-
+            api_config.config['train_model_path'],  # 不带序号的版本（最佳模型）
-    # 开始训练
+            api_config.config['paths']['model']['train']  # 复制到目标位置
-    # 初始化 MLModel 实例
+        )
-    ml_model = MLModel(config)
+    
-    list_avg_f1 = []
+    # 记录结束时间
-    list_wrong_percentage = []
+    end_time = time.time()
-    list_precision = []
+    logging.info(f"训练耗时: {end_time - start_time} 秒")
-    list_recall = []
+    
-    list_f1 = []
+    # 构建响应
-    train_times = 1 if config['experimental_mode'] == False else config["experiments_count"]
+    return APIResponse(
-    for _ in range(train_times):
+        classification_result=ClassificationResult(
-        avg_f1, wrong_percentage, precision, recall, f1 = ml_model.train_detect()
-        list_avg_f1.append(avg_f1)
-        list_wrong_percentage.append(wrong_percentage)
-        list_precision.append(precision)
-        list_recall.append(recall)
-        list_f1.append(f1)
-
-    log_print(f"Result: Avg F1: {sum(list_avg_f1) / len(list_avg_f1):.4f} Avg Wrong Percentage: {sum(list_wrong_percentage) / len(list_wrong_percentage):.2f}%")
-    log_print(f"Result: Avg Precision: {[sum(p[i] for p in list_precision) / len(list_precision) for i in range(len(list_precision[0]))]} | {np.mean(list_precision)}")
-    log_print(f"Result: Avg Recall: {[sum(r[i] for r in list_recall) / len(list_recall) for i in range(len(list_recall[0]))]} | {np.mean(list_recall)}")
-    log_print(f"Result: Avg F1: {[sum(f1[i] for f1 in list_f1) / len(list_f1) for i in range(len(list_f1[0]))]} | {np.mean(list_f1)}")
-
-    end_time = time.time()  # 记录结束时间
-    log_print("预测耗时: " + str(end_time - start_time) + " 秒")  # 打印执行时间
-
-    # 替换现有检测模型
-    if(config["replace_model"] == True):
-        # 如果模型路径不是绝对路径，则转换为绝对路径
-        if not os.path.isabs(config["model_path"]):
-            abs_model_path = os.path.abspath(os.path.join(os.path.dirname(__file__), config["model_path"]))
-            config["model_path"] = abs_model_path
-        shutil.copyfile(config["train_model_path"], config["model_path"])
-        log_print(f"Model file has been copied from {config['train_model_path']} to {config['model_path']}")
-
-    # 保证日志写到文件
-    atexit.register(flush_log)
-
-    # 返回分类结果和模型文件下载 URL，static不是程序执行路径，而是app.mount的静态文件夹
-    model_file_url = f"{request.base_url}train_api/train_model_{now}.pth"
-    log_file_url = f"{request.base_url}train_api/train_log_{now}.log"
-    data_file_url = f"{request.base_url}train_api/train_feature_label_weighted_{now}.xlsx"
-    train_process_img_url = f"{request.base_url}train_api/train_progress_img_{now}.png"
-    evaluate_result_img_url = f"{request.base_url}train_api/evaluate_result_img_{now}.png"
-
-    # 返回分类结果和模型文件
-    return {
-        "classification_result": ClassificationResult(
             precision=precision,
             recall=recall,
             f1=f1,
             wrong_percentage=wrong_percentage
         ),
-        "data_file": {
+        data_file={
-            "model_file_url": model_file_url,
+            "model_file_url": f"{request.base_url}train_api/train_model_{now}.pth",
-            "log_file_url": log_file_url,
+            "log_file_url": f"{request.base_url}train_api/train_log_{now}.log",
-            "data_file_url": data_file_url,
+            "data_file_url": f"{request.base_url}train_api/train_feature_label_weighted_{now}.xlsx",
-            "train_process_img_url": train_process_img_url,
+            "train_process_img_url": f"{request.base_url}train_api/train_model_{now}_training_process.png",
-            "evaluate_result_img_url": evaluate_result_img_url
+            "evaluate_result_img_url": f"{request.base_url}train_api/train_model_{now}_evaluate_result.png"
         }
-    }
+    )
 
-# 定义验证接口
 @app.post("/evaluate/")
-async def evaluate_model(request: Request, features_list: List[Features]):
+async def evaluate_model(request: Request, features_list: List[Features]) -> APIResponse:
-    global config_path
+    """评估模型接口"""
-    # 遍历每个特征对象，并将其添加到 all_features 中
+    # 特征处理
-    all_features = create_feature_df(features_list)
+    processor = FeatureProcessor(api_config.config)
-
+    all_features = processor.create_feature_df(features_list)
-    # 读取 YAML 配置文件
+    
-    with open(config_path, 'r') as f:
+    feature_label_weighted = FeatureWeightApplier.apply_weights(
-        config = yaml.load(f, Loader=yaml.FullLoader)
+        all_features,
-        feature_names = config['feature_names']
+        api_config.config['features']['feature_names'],
-        feature_weights = config['feature_weights']
+        api_config.config['features']['feature_weights']
-
+    )
-    # 应用特征权重
+    
-    feature_label_weighted = apply_feature_weights(all_features, feature_names, feature_weights)
+    feature_label_weighted = normalize_features(
-
+        feature_label_weighted,
-    start_time = time.time()  # 记录开始时间
+        api_config.config['features']['feature_names'],
-
+        is_train=False,
-    # 创建静态文件存放文件夹
+        config=api_config.config
-    static_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "evaluate_api"))  # 设置模型文件和配置文件的存放目录，和本py同级
+    )
-    os.makedirs(static_dir, exist_ok=True)
+    
-
+    # 评估
-    # 训练前设置
+    start_time = time.time()
     now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    data_path = os.path.abspath(os.path.join(static_dir, f"evaluate_feature_label_weighted_{now}.xlsx"))
+    evaluate_dir = os.path.abspath(os.path.join(
-    config['data_path'] = data_path
+        os.path.dirname(__file__), "evaluate_api"))
+    
+    # 更新配置中的评估结果图路径
+    api_config.config['paths']['model']['evaluate_result_path'] = os.path.join(
+        evaluate_dir, f"evaluate_result_{now}.png")
+    
+    data_path = os.path.join(
+        evaluate_dir, f"evaluate_feature_label_weighted_{now}.xlsx")
     feature_label_weighted.to_excel(data_path, index=False)
-
+    
-    # 配置验证结果图片路径
+    log_path = os.path.join(evaluate_dir, f"evaluate_log_{now}.log")
-    evaluate_result_path = os.path.abspath(os.path.join(static_dir, f"evaluate_result_img_{now}.png"))
+    api_config.update_log_handler(log_path)
-    config['evaluate_result_path'] = evaluate_result_path
+    
-
+    ml_model = MLModel(api_config.config)
-    # 如果模型路径不是绝对路径，则转换为绝对路径
+    avg_f1, wrong_percentage, precision, recall, f1 = ml_model.evaluate_model(
-    if not os.path.isabs(config["model_path"]):
+        feature_label_weighted[api_config.config['features']['feature_names']].values,
-        abs_model_path = os.path.abspath(os.path.join(os.path.dirname(__file__), config["model_path"]))
+        feature_label_weighted[api_config.config['features']['label_name']].values
-        config["model_path"] = abs_model_path
+    )
-
+    
-    # 检查模型文件是否存在，如果不存在则复制模型文件
+    end_time = time.time()
-    if not os.path.exists(config["model_path"]):
+    logging.info(f"评估耗时: {end_time - start_time} 秒")
-        if not os.path.isabs(config['default_model_path']):
+    
-            config['default_model_path'] = os.path.join(os.path.dirname(os.path.abspath(__file__)), config['default_model_path'])
+    return APIResponse(
-        os.makedirs(os.path.dirname(config["model_path"]), exist_ok=True)
+        classification_result=ClassificationResult(
-        shutil.copyfile(config['default_model_path'], config["model_path"])
-        log_print(f"Model file not found. Copied default model from {config['default_model_path']} to {config['model_path']}")
-
-    # 配置日志
-    log_path = os.path.abspath(os.path.join(static_dir, f"evaluate_log_{now}.log"))
-    update_log_handler(log_path)
-
-    # 特征和标签
-    X = feature_label_weighted[config['feature_names']].values
-    y = feature_label_weighted[config['label_name']].values
-
-    # 初始化 MLModel 实例
-    ml_model = MLModel(config)
-
-    # 加载模型
-    ml_model.load_model()
-
-    avg_f1, wrong_percentage, precision, recall, f1 = ml_model.evaluate_model(X, y)
-
-    end_time = time.time()  # 记录结束时间
-    log_print("预测耗时: " + str(end_time - start_time) + " 秒")  # 打印执行时间
-
-    # 保证日志写到文件
-    atexit.register(flush_log)
-
-    # 返回分类结果和模型文件下载 URL，static不是程序执行路径，而是app.mount的静态文件夹
-    log_file_url = f"{request.base_url}evaluate_api/evaluate_log_{now}.log"
-    data_file_url = f"{request.base_url}evaluate_api/evaluate_feature_label_weighted_{now}.xlsx"
-    evaluate_result_img_url = f"{request.base_url}evaluate_api/evaluate_result_img_{now}.png"
-
-    # 返回分类结果和模型文件
-    return {
-        "classification_result": ClassificationResult(
             precision=precision,
             recall=recall,
             f1=f1,
             wrong_percentage=wrong_percentage
         ),
-        "data_file": {
+        data_file={
-            "log_file_url": log_file_url,
+            "log_file_url": f"{request.base_url}evaluate_api/evaluate_log_{now}.log",
-            "data_file_url": data_file_url,
+            "data_file_url": f"{request.base_url}evaluate_api/evaluate_feature_label_weighted_{now}.xlsx",
-            "evaluate_result_img_url": evaluate_result_img_url
+            "evaluate_result_img_url": f"{request.base_url}evaluate_api/evaluate_model_{now}_evaluate_result.png"
         }
-    }
+    )
 
-# 定义推理接口
 @app.post("/inference/")
-async def inference_model(request: Request, features_list: List[Features]):
+async def inference_model(request: Request, features_list: List[Features]) -> PredictionResult:
-    global config_path
+    """推理接口"""
-    # 遍历每个特征对象，并将其添加到 all_features 中
+    processor = FeatureProcessor(api_config.config)
-    all_features = create_feature_df(features_list)
+    all_features = processor.create_feature_df(features_list)
-
+    
-    # 读取 YAML 配置文件
+    feature_label_weighted = FeatureWeightApplier.apply_weights(
-    with open(config_path, 'r') as f:
+        all_features,
-        config = yaml.load(f, Loader=yaml.FullLoader)
+        api_config.config['features']['feature_names'],
-        feature_names = config['feature_names']
+        api_config.config['features']['feature_weights']
-        feature_weights = config['feature_weights']
+    )
-
+    
-    # 应用特征权重
+    feature_label_weighted = normalize_features(
-    feature_label_weighted = apply_feature_weights(all_features, feature_names, feature_weights)
+        feature_label_weighted,
-
+        api_config.config['features']['feature_names'],
-    start_time = time.time()  # 记录开始时间
+        is_train=False,
-
+        config=api_config.config
-    # 创建静态文件存放文件夹
+    )
-    static_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "inference_api"))  # 设置模型文件和配置文件的存放目录，和本py同级
+    
-    os.makedirs(static_dir, exist_ok=True)
+    start_time = time.time()
-
-    # 训练前设置
     now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-    data_path = os.path.abspath(os.path.join(static_dir, f"inference_feature_label_weighted_{now}.xlsx"))
+    inference_dir = os.path.abspath(os.path.join(
-    config['data_path'] = data_path
+        os.path.dirname(__file__), "inference_api"))
+    
+    data_path = os.path.join(
+        inference_dir, f"inference_feature_label_weighted_{now}.xlsx")
     feature_label_weighted.to_excel(data_path, index=False)
-
+    
-    # 配置日志
+    log_path = os.path.join(inference_dir, f"inference_log_{now}.log")
-    log_path = os.path.abspath(os.path.join(static_dir, f"inference_log_{now}.log"))
+    api_config.update_log_handler(log_path)
-    update_log_handler(log_path)
+    
-
+    ml_model = MLModel(api_config.config)
-    # 如果模型路径不是绝对路径，则转换为绝对路径
+    predictions = ml_model.inference_model(
-    if not os.path.isabs(config["model_path"]):
+        feature_label_weighted[api_config.config['features']['feature_names']].values
-        abs_model_path = os.path.abspath(os.path.join(os.path.dirname(__file__), config["model_path"]))
+    )
-        config["model_path"] = abs_model_path
+    
-
+    end_time = time.time()
-    # 检查模型文件是否存在，如果不存在则复制模型文件
+    logging.info(f"推理耗时: {end_time - start_time} 秒")
-    if not os.path.exists(config["model_path"]):
+    
-        if not os.path.isabs(config['default_model_path']):
-            config['default_model_path'] = os.path.join(os.path.dirname(os.path.abspath(__file__)), config['default_model_path'])
-        os.makedirs(os.path.dirname(config["model_path"]), exist_ok=True)
-        shutil.copyfile(config['default_model_path'], config["model_path"])
-        log_print(f"Model file not found. Copied default model from {config['default_model_path']} to {config['model_path']}")
-
-    # 特征和标签
-    X = feature_label_weighted[config['feature_names']].values
-
-    # 初始化 MLModel 实例
-    ml_model = MLModel(config)
-
-    # 加载模型
-    ml_model.load_model()
-
-    predictions = ml_model.inference_model(X)
-
-    end_time = time.time()  # 记录结束时间
-    log_print("预测耗时: " + str(end_time - start_time) + " 秒")  # 打印执行时间
-
-    log_print("预测结果: " + str(predictions))
-
-    # 保证日志写到文件
-    atexit.register(flush_log)
-
-    # 返回预测结果
     return PredictionResult(predictions=predictions)
 
-# 定义模型上传接口
 @app.post("/upload_model/")
 async def upload_model(file: UploadFile = File(...)):
-    global config_path
+    """上传模型接口"""
-    # 创建模型存放文件夹
+    models_dir = os.path.abspath(os.path.join(
-    models_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "models"))
+        os.path.dirname(__file__), "models"))
     os.makedirs(models_dir, exist_ok=True)
-
+    
-    # 保存模型文件
     file_path = os.path.join(models_dir, "psy.pth")
     with open(file_path, "wb") as buffer:
         buffer.write(await file.read())
-
+    
     return {"message": "模型上传成功", "file_path": file_path}
 
+def start_scheduler():
+    """启动定时任务"""
+    schedule.every(1).hours.do(
+        FileCleanup.schedule_cleanup, 
+        config=api_config.config
+    )
+    while True:
+        schedule.run_pending()
+        time.sleep(1)
 
-# 以下是fastapi启动配置
 if __name__ == "__main__":
-    # 获取当前时间并格式化为字符串
+    # 启动定时清理任务
-    current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    # 打印程序启动时间
-    log_print(f"Program started at {current_time}")
-
-    name_app = os.path.basename(__file__)[0:-3]  # Get the name of the script
-    log_config = {
-        "version": 1,
-        "disable_existing_loggers": True,
-        "handlers": {
-            "file_handler": {
-                "class": "logging.FileHandler",
-                "filename": "logfile.log",
-            },
-        },
-        "root": {
-            "handlers": ["file_handler"],
-            "level": "INFO",
-        },
-    }
-    # 创建静态文件存放文件夹
-    static_dir_train = os.path.abspath(os.path.join(os.path.dirname(__file__), "train_api"))  # 设置模型文件和配置文件的存放目录，和本py同级
-    static_dir_evaluate = os.path.abspath(os.path.join(os.path.dirname(__file__), "evaluate_api"))
-    static_dir_inference = os.path.abspath(os.path.join(os.path.dirname(__file__), "inference_api"))
-    static_dir_models = os.path.abspath(os.path.join(os.path.dirname(__file__), "models"))
-    os.makedirs(static_dir_train, exist_ok=True)
-    os.makedirs(static_dir_evaluate, exist_ok=True)
-    os.makedirs(static_dir_inference, exist_ok=True)
-    os.makedirs(static_dir_models, exist_ok=True)
-    # 同级目录下的static文件夹
-    app.mount("/train_api", StaticFiles(directory=static_dir_train), name="static_dir_train")
-    app.mount("/evaluate_api", StaticFiles(directory=static_dir_evaluate), name="static_dir_evaluate")
-    app.mount("/inference_api", StaticFiles(directory=static_dir_inference), name="static_dir_inference")
-    app.mount("/models", StaticFiles(directory=static_dir_models), name="static_dir_models")
-
-    # 启动定时任务
     scheduler_thread = threading.Thread(target=start_scheduler)
     scheduler_thread.daemon = True
     scheduler_thread.start()
-
+    
-    uvicorn.run(app, host="0.0.0.0", port=8088, reload=False)
+    # 启动服务
+    uvicorn.run(
+        app,
+        host=api_config.config['system']['host'],
+        port=api_config.config['system']['port'],
+        reload=False
+    )

utils/common.py

@@ -1,318 +0,0 @@
-import os
-import yaml
-import pandas as pd
-import numpy as np
-import torch
-from torch import nn
-from torch.utils.data import DataLoader, TensorDataset
-from sklearn.model_selection import StratifiedKFold
-from sklearn.metrics import precision_score, recall_score, f1_score
-from sklearn.utils.class_weight import compute_class_weight
-import logging
-import matplotlib.pyplot as plt
-from matplotlib.font_manager import FontProperties
-
-# 控制是否打印的宏定义
-PRINT_LOG = True
-
-# 指定字体路径
-font_path = os.path.join(os.path.dirname(__file__), '../fonts', 'simhei.ttf')
-font_prop = FontProperties(fname=font_path)
-
-def log_print(message):
-    logging.info(message)
-    if PRINT_LOG:
-        print(message)
-
-class MLModel:
-    def __init__(self, model_config):
-        self.config = model_config
-        self.model = None
-
-    def create_model(self):
-        self.model = MLP(self.config).to(self.config['device'])
-        # self.model = TransformerModel(self.config).to(self.config['device'])
-
-    def load_model(self):
-        self.create_model()
-        self.model.load_state_dict(torch.load(self.config['model_path'], map_location=self.config['device']))
-
-    def load_and_split_data(self):
-        parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-        file_path = os.path.join(parent_dir, self.config['data_path'])
-
-        data = pd.read_excel(file_path)
-
-        X = data[self.config['feature_names']].values
-        y = data[self.config['label_name']].values
-
-        skf_outer = StratifiedKFold(n_splits=5, shuffle=True)
-        train_index_outer, test_index_outer = next(skf_outer.split(X, y))
-        X_train_val, X_infer = X[train_index_outer], X[test_index_outer]
-        y_train_val, y_infer = y[train_index_outer], y[test_index_outer]
-
-        skf_inner = StratifiedKFold(n_splits=5, shuffle=True)
-        train_index_inner, test_index_inner = next(skf_inner.split(X_train_val, y_train_val))
-        X_train, X_val = X_train_val[train_index_inner], X_train_val[test_index_inner]
-        y_train, y_val = y_train_val[train_index_inner], y_train_val[test_index_inner]
-
-        return X, y, X_train_val, y_train_val, X_train, y_train, X_val, y_val, X_infer, y_infer
-
-    def save_model(self, model_path):
-        torch.save(self.model.state_dict(), model_path)
-
-    def evaluate_model(self, X_infer, y_infer):
-        self.model.eval()
-        with torch.no_grad():
-            outputs = self.model(torch.from_numpy(X_infer).float().to(self.config['device']))
-
-        _, predictions = torch.max(outputs, 1)
-        precision = precision_score(y_infer, predictions.cpu().numpy(), average=None)
-        recall = recall_score(y_infer, predictions.cpu().numpy(), average=None)
-        f1 = f1_score(y_infer, predictions.cpu().numpy(), average=None)
-        wrong_count = len(np.where(y_infer != predictions.cpu().numpy())[0])
-        total_count = len(y_infer)
-        wrong_percentage = (wrong_count / total_count) * 100
-
-        log_print("Evaluate Result: ")
-
-        log_print(f"Prediction errors: {wrong_count}")
-        log_print(f"Prediction error percentage: {wrong_percentage:.2f}%")
-        log_print(f"Total samples: {total_count}")
-
-        avg_precision = np.mean(precision)
-        avg_recall = np.mean(recall)
-        avg_f1 = np.mean(f1)
-
-        for i in range(len(precision)):
-            log_print(f"Class {i} Precision: {precision[i]:.4f}, Recall: {recall[i]:.4f}, F1: {f1[i]:.4f}")
-
-        log_print("精确率:" + str(precision))
-        log_print("召回率:" + str(recall))
-        log_print("F1得分:" + str(f1))
-        log_print("平均精确率:" + str(avg_precision))
-        log_print("平均召回率:" + str(avg_recall))
-        log_print("平均F1得分:" + str(avg_f1))
-        log_print("Evaluate Result End: ")
-
-        fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
-        ax1.bar(np.arange(len(precision)), precision)
-        ax1.set_title('Precision(精确率)', fontproperties=font_prop)
-        ax2.bar(np.arange(len(recall)), recall)
-        ax2.set_title('Recall(召回率)', fontproperties=font_prop)
-        ax3.bar(np.arange(len(f1)), f1)
-        ax3.set_title('F1 Score(F1得分)', fontproperties=font_prop)
-        # 保存图片
-        parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-        evaluate_result_path = os.path.join(parent_dir, self.config['evaluate_result_path'])
-        plt.savefig(evaluate_result_path)
-
-        return np.mean(f1), wrong_percentage, precision, recall, f1
-
-    def inference_model(self, X_infer):
-        self.model.eval()
-        with torch.no_grad():
-            outputs = self.model(torch.from_numpy(X_infer).float().to(self.config['device']))
-
-        _, predictions = torch.max(outputs, 1)
-        return predictions.cpu().numpy().tolist()
-
-    def train_model(self, train_loader, val_loader, criterion, optimizer, scheduler):
-        n_epochs = self.config['n_epochs']
-        best_val_f1 = 0.0
-        best_val_recall = 0.0
-        best_val_precision = 0.0
-        best_epoch = -1
-        best_model = None
-        patience = self.config['early_stop_patience']
-        trigger_times = 0
-
-        train_loss_history, train_acc_history, val_loss_history, val_acc_history, val_f1_history, val_precision_history, val_recall_history = [[] for _ in range(7)]
-
-        plt.rcParams['figure.max_open_warning'] = 50
-
-        for epoch in range(n_epochs):
-            # Training phase
-            self.model.train()
-            train_loss, train_acc = 0, 0
-            for inputs, targets in train_loader:
-                optimizer.zero_grad()
-                outputs = self.model(inputs)
-                loss = criterion(outputs, targets)
-                loss.backward()
-                optimizer.step()
-                train_loss += loss.item() * inputs.size(0)
-                _, preds = torch.max(outputs, 1)
-                train_acc += torch.sum(preds == targets.data)
-
-            train_loss /= len(train_loader.dataset)
-            train_acc = train_acc.double().cpu() / len(train_loader.dataset)
-
-            # 更新学习率
-            scheduler.step()
-
-            # Validation phase
-            val_loss, val_acc, all_preds, all_targets = 0, 0, [], []
-            self.model.eval()
-            with torch.no_grad():
-                for inputs, targets in val_loader:
-                    outputs = self.model(inputs)
-                    loss = criterion(outputs, targets)
-                    val_loss += loss.item() * inputs.size(0)
-                    _, preds = torch.max(outputs, 1)
-                    val_acc += torch.sum(preds == targets.data)
-                    all_preds.extend(preds.cpu().numpy())
-                    all_targets.extend(targets.cpu().numpy())
-
-            val_loss /= len(val_loader.dataset)
-            val_acc = val_acc.double().cpu() / len(val_loader.dataset)
-
-            class_precisions_m = precision_score(all_targets, all_preds, average='macro')
-            class_recalls_m = recall_score(all_targets, all_preds, average='macro')
-            class_f1_scores_m = f1_score(all_targets, all_preds, average='macro')
-
-            log_print(f'Epoch {epoch+1:0{3}d} | Train Loss: {train_loss:.4f} | Train Accuracy: {train_acc:.4f} | Validation Loss: {val_loss:.4f} | Validation Accuracy: {val_acc:.4f} | Validation Mean Precision: {class_precisions_m:.4f} | Validation Mean Recall: {class_recalls_m:.4f} | Validation Mean F1_score: {class_f1_scores_m:.4f}')
-
-            train_loss_history.append(train_loss)
-            train_acc_history.append(train_acc)
-            val_loss_history.append(val_loss)
-            val_acc_history.append(val_acc)
-            val_f1_history.append(class_f1_scores_m)
-            val_precision_history.append(class_precisions_m)
-            val_recall_history.append(class_recalls_m)
-
-            # 打印训练和验证过程的可视化图片
-            plt.close('all')
-            fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
-            ax1.plot(train_loss_history, label='Train Loss(训练损失)')
-            ax1.plot(val_loss_history, label='Validation Loss(验证损失)')
-            ax1.set_title('Loss(损失)', fontproperties=font_prop)
-            ax1.legend(prop=font_prop)
-            ax2.plot(train_acc_history, label='Train Accuracy(训练正确率)')
-            ax2.plot(val_acc_history, label='Validation Accuracy(验证正确率)')
-            ax2.set_title('Accuracy(正确率)', fontproperties=font_prop)
-            ax2.legend(prop=font_prop)
-            ax3.plot(val_f1_history, label='Validation F1(验证F1得分)')
-            ax3.plot(val_precision_history, label='Validation Precision(验证精确率)')
-            ax3.plot(val_recall_history, label='Validation Recall(验证召回率)')
-            ax3.set_title('Precision Recall F1-Score (Macro Mean)(宏平均)', fontproperties=font_prop)
-            ax3.legend(prop=font_prop)
-            # 保存图片
-            parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-            train_process_path = os.path.join(parent_dir, self.config['train_process_path'])
-            plt.savefig(train_process_path)
-
-            if class_f1_scores_m > best_val_f1:
-                best_val_f1 = class_f1_scores_m
-                best_val_recall = class_recalls_m
-                best_val_precision = class_precisions_m
-                best_epoch = epoch
-                best_model = self.model.state_dict()
-                trigger_times = 0
-            else:
-                trigger_times += 1
-                if trigger_times >= patience:
-                    log_print(f'Early stopping at epoch {epoch} | Best epoch : {best_epoch + 1}')
-                    break
-
-        return best_val_f1, best_val_recall, best_val_precision, best_epoch, best_model
-
-    def train_detect(self):
-        X, y, X_train_val, y_train_val, X_train, y_train, X_val, y_val, X_infer, y_infer = self.load_and_split_data()
-
-        if self.config['data_train'] == 'train_val':
-            train_dataset = TensorDataset(torch.from_numpy(X_train_val).float().to(self.config['device']), torch.from_numpy(y_train_val).long().to(self.config['device']))
-            val_dataset = TensorDataset(torch.from_numpy(X_infer).float().to(self.config['device']), torch.from_numpy(y_infer).long().to(self.config['device']))
-            class_weights = torch.tensor(compute_class_weight('balanced', classes=np.unique(y_train_val), y=y_train_val), dtype=torch.float32).to(self.config['device'])
-        elif self.config['data_train'] == 'train':
-            train_dataset = TensorDataset(torch.from_numpy(X_train).float().to(self.config['device']), torch.from_numpy(y_train).long().to(self.config['device']))
-            val_dataset = TensorDataset(torch.from_numpy(X_val).float().to(self.config['device']), torch.from_numpy(y_val).long().to(self.config['device']))
-            class_weights = torch.tensor(compute_class_weight('balanced', classes=np.unique(y_train), y=y_train), dtype=torch.float32).to(self.config['device'])
-        elif self.config['data_train'] == 'all':
-            train_dataset = TensorDataset(torch.from_numpy(X).float().to(self.config['device']), torch.from_numpy(y).long().to(self.config['device']))
-            val_dataset = TensorDataset(torch.from_numpy(X).float().to(self.config['device']), torch.from_numpy(y).long().to(self.config['device']))
-            X_infer = X
-            y_infer = y
-            class_weights = torch.tensor(compute_class_weight('balanced', classes=np.unique(y), y=y), dtype=torch.float32).to(self.config['device'])
-        else:
-            logging.error("Error: Set data_train first in yaml!")
-            raise ValueError("Error: Set data_train first in yaml!")
-        
-        log_print(f"Class weights: {class_weights}")
-
-        train_loader = DataLoader(train_dataset, batch_size=self.config['batch_size'], shuffle=True)
-        val_loader = DataLoader(val_dataset, batch_size=self.config['batch_size'])
-
-        self.create_model()
-
-        criterion = nn.CrossEntropyLoss(weight=class_weights)
-        optimizer = torch.optim.Adam(self.model.parameters(), lr=self.config['learning_rate'])
-        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, self.config['step_size'], self.config['gamma'])
-
-        best_val_f1, best_val_recall, best_val_precision, best_epoch, best_model = self.train_model(train_loader, val_loader, criterion, optimizer, scheduler)
-
-        # Save the best model
-        self.save_model(self.config['train_model_path'])
-
-        log_print(f"Best Validation F1 Score (Macro): {best_val_f1:.4f}")
-        log_print(f"Best Validation Recall (Macro): {best_val_recall:.4f}")
-        log_print(f"Best Validation Precision (Macro): {best_val_precision:.4f}")
-        log_print(f"Best Epoch: {best_epoch + 1}")
-
-        avg_f1, wrong_percentage, precision, recall, f1 = self.evaluate_model(X_infer, y_infer)
-
-        return avg_f1, wrong_percentage, precision, recall, f1
-
-
-# class MLP(nn.Module):
-#     def __init__(self, config):
-#         super(MLP, self).__init__()
-#         self.model = nn.Sequential(
-#             nn.Linear(len(config['feature_names']), 32),
-#             nn.ReLU(),
-#             nn.Linear(32, 128),
-#             nn.ReLU(),
-#             nn.Linear(128, 32),
-#             nn.ReLU(),
-#             nn.Linear(32, config['nc']),
-#         )
-
-#     def forward(self, x):
-#         return self.model(x)
-#     20260605
-
-class MLP(nn.Module):
-    def __init__(self, config):
-        super(MLP, self).__init__()
-        layers = []
-        input_dim = config['mlp']['input_dim']
-        for layer_cfg in config['mlp']['layers']:
-            layers.append(nn.Linear(input_dim, layer_cfg['output_dim']))
-            if layer_cfg.get('activation', None) == 'relu':
-                layers.append(nn.ReLU())
-            input_dim = layer_cfg['output_dim']
-        layers.append(nn.Linear(input_dim, config['mlp']['output_dim']))
-        self.model = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.model(x)
-    
-class TransformerModel(nn.Module):
-    def __init__(self, config):
-        super(TransformerModel, self).__init__()
-        self.embedding = nn.Linear(config['transformer']['input_dim'], config['transformer']['d_model'])
-        self.transformer = nn.Transformer(
-            d_model=config['transformer']['d_model'],
-            nhead=config['transformer']['nhead'],
-            num_encoder_layers=config['transformer']['num_encoder_layers'],
-            num_decoder_layers=config['transformer']['num_decoder_layers'],
-            dim_feedforward=config['transformer']['dim_feedforward'],
-            dropout=config['transformer']['dropout']
-        )
-        self.fc = nn.Linear(config['transformer']['d_model'], config['transformer']['output_dim'])
-
-    def forward(self, x):
-        x = self.embedding(x).unsqueeze(1)  # Add sequence dimension
-        transformer_output = self.transformer(x, x)
-        output = self.fc(transformer_output.squeeze(1))  # Remove sequence dimension
-        return output

utils/feature_process.py

@@ -1,134 +0,0 @@
-import os
-import sys
-root_path = os.getcwd()
-sys.path.append(root_path)
-
-import time
-import datetime
-import signal
-import uvicorn
-import pandas as pd
-from fastapi import FastAPI, Request
-from pydantic import BaseModel
-from typing import List
-from fastapi.middleware.cors import CORSMiddleware
-import logging
-import matplotlib.pyplot as plt
-import argparse
-import numpy as np
-import yaml
-import threading
-import pickle
-from fastapi.responses import FileResponse
-from fastapi.staticfiles import StaticFiles
-
-# 定义单个样本的特征类
-class Features(BaseModel):
-    # 10个SCL评测量（后续再处理）范围0-4
-    somatization: float
-    obsessive_compulsive: float
-    interpersonal_sensitivity: float
-    depression: float
-    anxiety: float
-    hostility: float
-    terror: float
-    paranoia: float
-    psychoticism: float
-    other: float
-    # 基本信息特征量
-    father_parenting_style: int # 温暖与理解：1；其他：0
-    mother_parenting_style: int # 温暖与理解：1；其他：0
-    self_assessed_family_economic_condition: int # 贫困：2；较差：1；其他：0
-    history_of_psychological_counseling: bool # 有：1；无：0
-    # 日常行为特征量
-    absenteeism_above_average: bool # 大于平均次数：1；小于等于：0
-    academic_warning: bool # 有预警：1；无预警：0
-    # 标签
-    label: int # 0-3 共4个类别
-
-def process_features_list(features_list: List[dict]) -> List[Features]:
-    """
-    将包含字典的列表转换为包含 Features 实例的列表
-    """
-    return [Features(**features_dict) for features_dict in features_list]
-
-def create_feature_df(features_list):
-    """
-    根据features_list对象创建一个DataFrame。
-    
-    参数:
-    features_list (Features): 包含特征值的对象的数组
-    
-    返回:
-    pandas.DataFrame: 包含特征和标签的DataFrame
-    """
-
-    # 定义一个空的 DataFrame 用于存储所有样本特征
-    all_features = pd.DataFrame()
-
-    for features in features_list:
-        relevant_features = {
-            "somatization": features.somatization,
-            "obsessive_compulsive": features.obsessive_compulsive,
-            "interpersonal_sensitivity": features.interpersonal_sensitivity,
-            "depression": features.depression,
-            "anxiety": features.anxiety,
-            "hostility": features.hostility,
-            "terror": features.terror,
-            "paranoia": features.paranoia,
-            "psychoticism": features.psychoticism,
-            "other": features.other
-        }
-        df_feature = pd.DataFrame({
-            # 数字化特征--基本信息
-            '父亲教养方式数字化': [(lambda x: 0.59 if x == 1 else 0.46)(features.father_parenting_style)],
-            '母亲教养方式数字化': [(lambda x: 0.69 if x == 1 else 0.56)(features.mother_parenting_style)],
-            '自评家庭经济条件数字化': [(lambda x: 0.54 if x in [2, 1] else 0.47)(features.self_assessed_family_economic_condition)],
-            '有无心理治疗（咨询）史数字化': [(lambda x: 0.21 if x else 0.09)(features.history_of_psychological_counseling)],
-            # 数字化特征--症状因子
-            '强迫症状数字化': [features.obsessive_compulsive / 4],
-            '人际关系敏感数字化': [features.interpersonal_sensitivity / 4],
-            '抑郁数字化': [features.depression / 4],
-            '多因子症状': [(lambda x: sum(1 for value in x.values() if value > 3.0) / 10)(relevant_features)],
-            # 数字化特征--日常行为
-            '出勤情况数字化': [0.74 if features.absenteeism_above_average else 0.67],
-            '学业情况数字化': [0.59 if features.academic_warning else 0.50],
-            "类别": [features.label]
-        })
-        all_features = pd.concat([all_features, df_feature], ignore_index=True)
-
-    return all_features
-
-def apply_feature_weights(df, feature_names, feature_weights):
-    """
-    将特征权重应用到DataFrame中。
-    
-    参数:
-    df (pandas.DataFrame): 包含特征和标签的DataFrame
-    feature_names (list): 特征名称列表
-    feature_weights (list): 特征权重列表
-    
-    返回:
-    pandas.DataFrame: 应用权重后的DataFrame
-    """
-    # 找到最大值
-    max_value = max(feature_weights)
-
-    # 缩放权重
-    feature_weights_scaled = [x / max_value for x in feature_weights]
-
-    # 打印缩放后的特征权重
-    # print("Scaled Feature Weights:", feature_weights_scaled)
-
-    # 将特征和标签分开，并做归一化处理
-    X = df[feature_names].values
-    y = df['类别'].values
-
-    # 分别乘以权重，放在归一化后
-    for i in range(len(feature_names)):
-        X[:, i] = X[:, i] * feature_weights_scaled[i]
-
-    feature_label_weighted = pd.DataFrame(X, columns=feature_names)
-    feature_label_weighted['类别'] = y
-
-    return feature_label_weighted

utils/feature_processor.py

@@ -0,0 +1,274 @@
+"""
+特征处理模块
+包含特征数据结构定义、特征转换、权重应用和归一化等功能
+"""
+from typing import List, Dict, Any, Union, Optional
+import os
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+import pickle
+from pydantic import BaseModel, Field
+import logging
+import shutil
+
+class Features(BaseModel):
+    """
+    单个样本的特征数据结构
+    
+    Attributes:
+        somatization: 躯体化症状得分
+        obsessive_compulsive: 强迫症状得分
+        interpersonal_sensitivity: 人际关系敏感得分
+        depression: 抑郁症状得分
+        anxiety: 焦虑症状得分
+        hostility: 敌对症状得分
+        terror: 恐怖症状得分
+        paranoia: 偏执症状得分
+        psychoticism: 精神病性得分
+        other: 其他症状得分
+        father_parenting_style: 父亲教养方式 (1: 温暖与理解, 0: 其他)
+        mother_parenting_style: 母亲教养方式 (1: 温暖与理解, 0: 其他)
+        self_assessed_family_economic_condition: 家庭经济状况 (2: 贫困, 1: 较差, 0: 其他)
+        history_of_psychological_counseling: 心理咨询史 (True: 有, False: 无)
+        absenteeism_above_average: 出勤情况 (True: 高于平均, False: 低于平均)
+        academic_warning: 学业预警 (True: 有预警, False: 无预警)
+        label: 分类标签 (0-3)
+    """
+    # SCL评测量（范围0-4）
+    somatization: float = Field(..., description="躯体化症状得分")
+    obsessive_compulsive: float = Field(..., description="强迫症状得分")
+    interpersonal_sensitivity: float = Field(..., description="人际关系敏感得分")
+    depression: float = Field(..., description="抑郁症状得分")
+    anxiety: float = Field(..., description="焦虑症状得分")
+    hostility: float = Field(..., description="敌对症状得分")
+    terror: float = Field(..., description="恐怖症状得分")
+    paranoia: float = Field(..., description="偏执症状得分")
+    psychoticism: float = Field(..., description="精神病性得分")
+    other: float = Field(..., description="其他症状得分")
+    
+    # 基本信息特征
+    father_parenting_style: int = Field(..., description="父亲教养方式")
+    mother_parenting_style: int = Field(..., description="母亲教养方式")
+    self_assessed_family_economic_condition: int = Field(..., description="家庭经济状况")
+    history_of_psychological_counseling: bool = Field(..., description="心理咨询史")
+    
+    # 日常行为特征
+    absenteeism_above_average: bool = Field(..., description="出勤情况")
+    academic_warning: bool = Field(..., description="学业预警")
+    
+    # 标签
+    label: int = Field(..., description="分类标签")
+
+class FeatureProcessor:
+    """
+    特征处理类，负责特征的转换和处理
+    
+    Attributes:
+        config: 配置信息字典
+        feature_mapping: 特征映射规则
+        feature_names: 特征名称列表
+    """
+    def __init__(self, config: Dict[str, Any]) -> None:
+        self.config = config
+        self.feature_mapping = config['features']['feature_mapping']
+        self.feature_names = config['features']['feature_names']
+
+    def _process_scl_features(self, features: Features) -> Dict[str, float]:
+        """
+        处理SCL相关特征
+        
+        Args:
+            features: 特征对象
+            
+        Returns:
+            处理后的SCL特征字典
+        """
+        return {
+            "somatization": features.somatization,
+            "obsessive_compulsive": features.obsessive_compulsive,
+            "interpersonal_sensitivity": features.interpersonal_sensitivity,
+            "depression": features.depression,
+            "anxiety": features.anxiety,
+            "hostility": features.hostility,
+            "terror": features.terror,
+            "paranoia": features.paranoia,
+            "psychoticism": features.psychoticism,
+            "other": features.other
+        }
+
+    def _calculate_multi_factor(self, scl_features: Dict[str, float]) -> float:
+        """
+        计算多因子症状得分
+        
+        Args:
+            scl_features: SCL特征字典
+            
+        Returns:
+            多因子症状得分
+        """
+        severe_symptoms_count = sum(1 for value in scl_features.values() if value > 3.0)
+        return severe_symptoms_count / len(scl_features)
+
+    def create_feature_df(self, features_list: List[Features]) -> pd.DataFrame:
+        """
+        创建特征DataFrame
+        
+        Args:
+            features_list: 特征对象列表
+            
+        Returns:
+            处理后的特征DataFrame
+        """
+        processed_features = []
+        
+        for features in features_list:
+            scl_features = self._process_scl_features(features)
+            
+            try:
+                feature_dict = {
+                    '父亲教养方式数字化': self.feature_mapping['父亲教养方式'].get(
+                        features.father_parenting_style, 
+                        self.feature_mapping['父亲教养方式'][0]  # 默认值
+                    ),
+                    '母亲教养方式数字化': self.feature_mapping['母亲教养方式'].get(
+                        features.mother_parenting_style,
+                        self.feature_mapping['母亲教养方式'][0]  # 默认值
+                    ),
+                    '自评家庭经济条件数字化': self.feature_mapping['家庭经济条件'].get(
+                        features.self_assessed_family_economic_condition,
+                        self.feature_mapping['家庭经济条件'][0]  # 默认值
+                    ),
+                    '有无心理治疗（咨询）史数字化': self.feature_mapping['心理咨询史'][features.history_of_psychological_counseling],
+                    '强迫症状数字化': features.obsessive_compulsive / 4,
+                    '人际关系敏感数字化': features.interpersonal_sensitivity / 4,
+                    '抑郁数字化': features.depression / 4,
+                    '多因子症状': self._calculate_multi_factor(scl_features),
+                    '出勤情况数字化': self.feature_mapping['出勤情况'][features.absenteeism_above_average],
+                    '学业情况数字化': self.feature_mapping['学业情况'][features.academic_warning],
+                    self.config['features']['label_name']: features.label
+                }
+                processed_features.append(feature_dict)
+            except KeyError as e:
+                logging.error(f"Invalid feature value: {e}")
+                # 使用默认值继续处理
+                feature_dict = {
+                    '父亲教养方式数字化': self.feature_mapping['父亲教养方式'][0],
+                    '母亲教养方式数字化': self.feature_mapping['母亲教养方式'][0],
+                    '自评家庭经济条件数字化': self.feature_mapping['家庭经济条件'][0],
+                    '有无心理治疗（咨询）史数字化': self.feature_mapping['心理咨询史'][False],
+                    '强迫症状数字化': features.obsessive_compulsive / 4,
+                    '人际关系敏感数字化': features.interpersonal_sensitivity / 4,
+                    '抑郁数字化': features.depression / 4,
+                    '多因子症状': self._calculate_multi_factor(scl_features),
+                    '出勤情况数字化': self.feature_mapping['出勤情况'][False],
+                    '学业情况数字化': self.feature_mapping['学业情况'][False],
+                    self.config['features']['label_name']: features.label
+                }
+                processed_features.append(feature_dict)
+            
+        return pd.DataFrame(processed_features)
+
+class FeatureWeightApplier:
+    """特征权重应用类"""
+    @staticmethod
+    def apply_weights(df: pd.DataFrame, feature_names: List[str], 
+                     feature_weights: List[float]) -> pd.DataFrame:
+        """应用特征权重"""
+        max_weight = max(feature_weights)
+        weights_scaled = [w / max_weight for w in feature_weights]
+        
+        features_data = df[feature_names].values
+        labels = df['类别'].values
+        
+        features_weighted = features_data * np.array(weights_scaled)
+        result_df = pd.DataFrame(features_weighted, columns=feature_names)
+        result_df['类别'] = labels
+        
+        return result_df
+
+class FeatureNormalizer:
+    """特征归一化处理类"""
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.feature_groups = {}
+        self.scalers = {}
+        
+        for group_name, features in config['features']['groups'].items():
+            indices = [config['features']['feature_names'].index(feature) 
+                      for feature in features]
+            self.feature_groups[group_name] = indices
+
+    def fit_transform(self, features_data: np.ndarray) -> np.ndarray:
+        """训练时的归一化转换"""
+        features_scaled = features_data.copy()
+        for group_name, indices in self.feature_groups.items():
+            self.scalers[group_name] = StandardScaler()
+            features_scaled[:, indices] = self.scalers[group_name].fit_transform(features_data[:, indices])
+        return features_scaled
+
+    def transform(self, features_data: np.ndarray) -> np.ndarray:
+        """预测时的归一化转换"""
+        features_scaled = features_data.copy()
+        for group_name, indices in self.feature_groups.items():
+            features_scaled[:, indices] = self.scalers[group_name].transform(features_data[:, indices])
+        return features_scaled
+
+    def save(self, path: str) -> None:
+        """
+        保存归一化参数
+        
+        Args:
+            path: 保存路径
+        """
+        # 确保目录存在
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        
+        with open(path, 'wb') as f:
+            pickle.dump(self.scalers, f)
+
+    def load(self, path: str) -> None:
+        """
+        加载归一化参数
+        
+        Args:
+            path: 加载路径
+        """
+        # 如果主路径不存在，尝试从默认路径复制
+        if not os.path.exists(path):
+            default_path = os.path.join(
+                os.path.dirname(__file__), 
+                '..', 
+                self.config['paths']['normalizer']['default']
+            )
+            if os.path.exists(default_path):
+                # 确保目标目录存在
+                os.makedirs(os.path.dirname(path), exist_ok=True)
+                shutil.copyfile(default_path, path)
+                logging.info(f"Copied normalizer from default path: {default_path} to {path}")
+            else:
+                raise FileNotFoundError(f"No normalizer found in either path: {path} or {default_path}")
+            
+        with open(path, 'rb') as f:
+            self.scalers = pickle.load(f)
+
+def normalize_features(df: pd.DataFrame, feature_names: List[str], 
+                      is_train: bool, config: Dict[str, Any]) -> pd.DataFrame:
+    """特征归一化处理入口函数"""
+    features_data = df[feature_names].values
+    labels = df[config['features']['label_name']].values
+    
+    normalizer = FeatureNormalizer(config)
+    
+    if is_train:
+        features_normalized = normalizer.fit_transform(features_data)
+        if config.get('paths', {}).get('normalizer', {}).get('train'):
+            normalizer.save(config['paths']['normalizer']['train'])
+    else:
+        normalizer.load(config['paths']['normalizer']['train'])
+        features_normalized = normalizer.transform(features_data)
+    
+    result_df = pd.DataFrame(features_normalized, columns=feature_names)
+    result_df[config['features']['label_name']] = labels
+    
+    return result_df

utils/model_trainer.py

@@ -0,0 +1,719 @@
+"""
+模型定义和训练模块
+包含神经网络模型定义、训练和评估功能
+"""
+import os
+import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import DataLoader, TensorDataset
+from sklearn.model_selection import StratifiedKFold
+from sklearn.metrics import precision_score, recall_score, f1_score
+from sklearn.utils.class_weight import compute_class_weight
+import logging
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+import seaborn as sns
+import warnings
+from typing import Dict, Any, Tuple, List
+import pandas as pd
+import datetime
+import shutil
+import copy
+
+# 屏蔽警告
+warnings.filterwarnings("ignore", category=UserWarning, module="matplotlib.font_manager")
+warnings.filterwarnings("ignore", category=UserWarning, module="seaborn.utils")
+
+class BaseModel(nn.Module):
+    """神经网络基类"""
+    def get_l1_loss(self) -> torch.Tensor:
+        """计算L1正则化损失"""
+        l1_loss = torch.tensor(0., device=next(self.parameters()).device)
+        for param in self.parameters():
+            l1_loss += torch.norm(param, p=1)
+        return l1_loss
+
+    def get_l2_loss(self) -> torch.Tensor:
+        """计算L2正则化损失"""
+        l2_loss = torch.tensor(0., device=next(self.parameters()).device)
+        for param in self.parameters():
+            l2_loss += torch.norm(param, p=2)
+        return l2_loss
+
+class MLP(BaseModel):
+    """多层感知机模型"""
+    def __init__(self, config: Dict[str, Any]):
+        super(MLP, self).__init__()
+        layers = []
+        input_dim = config['model']['input_dim']
+        self.dropout = nn.Dropout(p=config['training']['dropout_rate'])
+        
+        for layer_cfg in config['model']['mlp']['layers']:
+            linear = nn.Linear(input_dim, layer_cfg['output_dim'])
+            nn.init.xavier_normal_(linear.weight)
+            layers.append(linear)
+            
+            if layer_cfg.get('activation') == 'relu':
+                layers.append(nn.ReLU())
+                layers.append(self.dropout)
+            
+            input_dim = layer_cfg['output_dim']
+            
+        self.output = nn.Linear(input_dim, config['model']['mlp']['output_dim'])
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """前向传播"""
+        x = self.model(x)
+        return self.output(x)
+
+class MLModel:
+    """模型训练和评估类"""
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.model = None
+        # 修改设备初始化
+        self.device = torch.device(
+            self.config['system']['device'] 
+            if torch.cuda.is_available() and self.config['system']['device'] == 'cuda'
+            else 'cpu'
+        )
+        logging.info(f"Using device: {self.device}")  # 记录使用的设备
+        print(f"Using device: {self.device}")  # 打印使用的设备
+        
+        # 设置字体
+        font_path = os.path.join(os.path.dirname(__file__), '..', 
+                               config['paths']['fonts']['chinese'])
+        self.font_prop = FontProperties(fname=font_path)
+
+    def create_model(self) -> None:
+        """创建模型实例"""
+        self.model = MLP(self.config).to(self.device)
+
+    def load_model(self) -> None:
+        """加载已训练的模型"""
+        self.create_model()
+        model_path = self.config['paths']['model']['train']
+        
+        # 如果主路径不存在，尝试从默认路径复制
+        if not os.path.exists(model_path):
+            default_model_path = os.path.join(
+                os.path.dirname(__file__), 
+                '..', 
+                self.config['paths']['model']['default']
+            )
+            if os.path.exists(default_model_path):
+                # 确保目标目录存在
+                os.makedirs(os.path.dirname(model_path), exist_ok=True)
+                shutil.copyfile(default_model_path, model_path)
+                logging.info(f"Copied model from default path: {default_model_path} to {model_path}")
+            else:
+                raise FileNotFoundError(f"No model found in either path: {model_path} or {default_model_path}")
+        
+        self.model.load_state_dict(torch.load(model_path, map_location=self.device, weights_only=False))
+
+    def plot_correlation(self, X: np.ndarray, feature_names: List[str]) -> None:
+        """绘制特征相关性矩阵"""
+        corr_matrix = np.corrcoef(X.T)
+        plt.figure(figsize=(12, 10))
+        
+        sns.heatmap(corr_matrix, 
+                   annot=True, 
+                   cmap='coolwarm', 
+                   xticklabels=feature_names,
+                   yticklabels=feature_names,
+                   fmt='.2f')
+        
+        plt.xticks(rotation=45, ha='right', fontproperties=self.font_prop)
+        plt.yticks(rotation=0, fontproperties=self.font_prop)
+        plt.title('特征相关性矩阵', fontproperties=self.font_prop)
+        plt.tight_layout()
+        
+        plt.savefig(self.config['correlation_matrix_path'], bbox_inches='tight', dpi=300)
+        plt.close()
+
+    def train_model(self, train_loader: DataLoader, val_loader: DataLoader, 
+                   criterion: nn.Module, optimizer: torch.optim.Optimizer, 
+                   scheduler: torch.optim.lr_scheduler._LRScheduler) -> Tuple:
+        """训练模型"""
+        n_epochs = self.config['training']['n_epochs']
+        best_val_f1 = 0.0
+        best_model = None
+        best_epoch = -1  # 记录最佳epoch
+        patience = self.config['training']['early_stop_patience']
+        trigger_times = 0
+        
+        l1_lambda = float(self.config['training']['regularization']['l1_lambda'])
+        l2_lambda = float(self.config['training']['regularization']['l2_lambda'])
+
+        # 初始化指标记录字典
+        train_metrics = {'loss': [], 'acc': []}
+        val_metrics = {'loss': [], 'acc': [], 'f1': [], 'precision': [], 'recall': []}
+
+        for epoch in range(n_epochs):
+            # 训练阶段
+            self.model.train()
+            train_loss, train_acc = self._train_epoch(train_loader, criterion, 
+                                                    optimizer, l1_lambda, l2_lambda)
+            
+            # 验证阶段
+            val_loss, val_acc, val_f1, val_precision, val_recall = self._validate_epoch(val_loader, criterion)
+            
+            # 更新学习率
+            scheduler.step()
+            
+            # 记录指标
+            train_metrics['loss'].append(train_loss)
+            train_metrics['acc'].append(train_acc)
+            val_metrics['loss'].append(val_loss)
+            val_metrics['acc'].append(val_acc)
+            val_metrics['f1'].append(val_f1)
+            val_metrics['precision'].append(val_precision)
+            val_metrics['recall'].append(val_recall)
+            
+            # 打印训练信息
+            log_message = (
+                f'Epoch {epoch+1:03d} | '
+                f'Train Loss: {train_loss:.4f} | '
+                f'Train Acc: {train_acc:.4f} | '
+                f'Val Loss: {val_loss:.4f} | '
+                f'Val Acc: {val_acc:.4f} | '
+                f'Val F1: {val_f1:.4f} | '
+                f'Val Precision: {val_precision:.4f} | '
+                f'Val Recall: {val_recall:.4f}'
+            )
+            logging.info(log_message)
+            print(log_message)
+            
+            # 更新最佳模型信息
+            if val_f1 > best_val_f1:
+                best_val_f1 = val_f1
+                # 创建模型状态的深拷贝
+                best_model = copy.deepcopy(self.model.state_dict())
+                best_epoch = epoch + 1
+                trigger_times = 0
+            else:
+                trigger_times += 1
+                if trigger_times >= patience:
+                    log_message = (
+                        f'Early stopping at epoch {epoch+1}\n'
+                        f'Best model was saved at epoch {best_epoch} with F1: {best_val_f1:.4f}'
+                    )
+                    logging.info(log_message)
+                    print(log_message)
+                    break
+
+        # 绘制训练过程图
+        self._plot_training_process(train_metrics, val_metrics)
+        
+        # 打印最佳模型信息
+        log_message = f'Training completed. Best model at epoch {best_epoch} with F1: {best_val_f1:.4f}'
+        logging.info(log_message)
+        print(log_message)
+
+        return best_val_f1, best_model, best_epoch
+
+    def _train_epoch(self, train_loader: DataLoader, criterion: nn.Module, 
+                    optimizer: torch.optim.Optimizer, l1_lambda: float, 
+                    l2_lambda: float) -> Tuple[float, float]:
+        """训练一个epoch"""
+        train_loss = 0
+        train_acc = 0
+        
+        for inputs, targets in train_loader:
+            optimizer.zero_grad()
+            outputs = self.model(inputs)
+            
+            # 计算各种损失
+            ce_loss = criterion(outputs, targets)
+            l1_loss = self.model.get_l1_loss()
+            l2_loss = self.model.get_l2_loss()
+            
+            # 转换正则化系数为tensor并移动到正确的设备
+            l1_lambda_tensor = torch.tensor(l1_lambda, device=ce_loss.device)
+            l2_lambda_tensor = torch.tensor(l2_lambda, device=ce_loss.device)
+            
+            # 计算总损失
+            loss = ce_loss + l1_lambda_tensor * l1_loss + l2_lambda_tensor * l2_loss
+            
+            loss.backward()
+            optimizer.step()
+            
+            train_loss += loss.item() * inputs.size(0)
+            _, preds = torch.max(outputs, 1)
+            train_acc += torch.sum(preds == targets.data)
+            
+        return train_loss / len(train_loader.dataset), train_acc.double() / len(train_loader.dataset)
+
+    def _validate_epoch(self, val_loader: DataLoader, criterion: nn.Module) -> Tuple[float, float, float, float, float]:
+        """验证一个epoch"""
+        self.model.eval()
+        val_loss = 0
+        val_acc = 0
+        all_preds = []
+        all_targets = []
+        
+        with torch.no_grad():
+            for inputs, targets in val_loader:
+                outputs = self.model(inputs)
+                loss = criterion(outputs, targets)
+                
+                val_loss += loss.item() * inputs.size(0)
+                _, preds = torch.max(outputs, 1)
+                val_acc += torch.sum(preds == targets.data)
+                
+                all_preds.extend(preds.cpu().numpy())
+                all_targets.extend(targets.cpu().numpy())
+        
+        # 计算各项指标
+        val_f1 = f1_score(all_targets, all_preds, average='macro')
+        val_precision = precision_score(all_targets, all_preds, average='macro')
+        val_recall = recall_score(all_targets, all_preds, average='macro')
+        
+        return (val_loss / len(val_loader.dataset), 
+                val_acc.double() / len(val_loader.dataset), 
+                val_f1,
+                val_precision,
+                val_recall)
+
+    def evaluate_model(self, features_data: np.ndarray, labels: np.ndarray, is_training: bool = False) -> Tuple:
+        """
+        评估模型
+        
+        Args:
+            features_data: 输入特征数据
+            labels: 真实标签
+            is_training: 是否在训练过程中调用
+            
+        Returns:
+            Tuple[float, float, List[float], List[float], List[float]]: 
+                (平均F1值, 错误率, 精确率列表, 召回率列表, F1值列表)
+        """
+        # 只有在非训练过程中才加载模型
+        if not is_training:
+            self.load_model()
+        
+        self.model.eval()
+        with torch.no_grad():
+            outputs = self.model(torch.from_numpy(features_data).float().to(self.device))
+            _, predictions = torch.max(outputs, 1)
+        
+        predictions = predictions.cpu().numpy()
+        precision = precision_score(labels, predictions, average=None)
+        recall = recall_score(labels, predictions, average=None)
+        f1 = f1_score(labels, predictions, average=None)
+        
+        wrong_count = np.sum(labels != predictions)
+        wrong_percentage = (wrong_count / len(labels)) * 100
+        
+        # 打印评估结果
+        log_message = (
+            f"\nEvaluation Results:\n"
+            f"Average F1: {np.mean(f1):.4f}\n"
+            f"Wrong Percentage: {wrong_percentage:.2f}%\n"
+            f"Precision: {precision.tolist()} | {np.mean(precision):.4f}\n"
+            f"Recall: {recall.tolist()} | {np.mean(recall):.4f}\n"
+            f"F1: {f1.tolist()} | {np.mean(f1):.4f}\n"
+            f"Total samples: {len(labels)}\n"
+            f"Wrong predictions: {wrong_count}"
+        )
+        logging.info(log_message)
+        print(log_message)
+        
+        # 添加评估结果可视化
+        plt.figure(figsize=(15, 5))
+        
+        # 设置类别标签
+        class_labels = [f'类别{i}' for i in range(len(precision))]
+        x = np.arange(len(class_labels))
+        width = 0.35  # 柱状图的宽度
+        
+        # 精确率柱状图
+        plt.subplot(131)
+        plt.bar(x, precision, width)
+        plt.title('Precision(精确率)', fontproperties=self.font_prop)
+        plt.xticks(x, class_labels, fontproperties=self.font_prop)
+        plt.ylim(0, 1)
+        
+        # 召回率柱状图
+        plt.subplot(132)
+        plt.bar(x, recall, width)
+        plt.title('Recall(召回率)', fontproperties=self.font_prop)
+        plt.xticks(x, class_labels, fontproperties=self.font_prop)
+        plt.ylim(0, 1)
+        
+        # F1分数柱状图
+        plt.subplot(133)
+        plt.bar(x, f1, width)
+        plt.title('F1 Score(F1得分)', fontproperties=self.font_prop)
+        plt.xticks(x, class_labels, fontproperties=self.font_prop)
+        plt.ylim(0, 1)
+        
+        plt.tight_layout()
+        if 'evaluate_result_path' in self.config['paths']['model']:
+            plt.savefig(self.config['paths']['model']['evaluate_result_path'])
+        plt.close()
+        
+        return np.mean(f1), wrong_percentage, precision.tolist(), recall.tolist(), f1.tolist()
+
+    def inference_model(self, features_data: np.ndarray) -> List[int]:
+        """
+        模型推理
+        
+        Args:
+            features_data: 输入特征数据
+            
+        Returns:
+            预测结果列表
+        """
+        # 先加载模型
+        self.load_model()
+        
+        self.model.eval()
+        with torch.no_grad():
+            outputs = self.model(torch.from_numpy(features_data).float().to(self.device))
+            _, predictions = torch.max(outputs, 1)
+        return predictions.cpu().numpy().tolist()
+
+    def save_model(self, path: str) -> None:
+        """
+        保存模型
+        
+        Args:
+            path: 保存路径
+        """
+        # 确保目录存在
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        
+        torch.save(self.model.state_dict(), path)
+
+    def _compute_class_weights(self, y_train: np.ndarray) -> torch.Tensor:
+        """计算类别权重"""
+        weight_config = self.config['training'].get('class_weights', {})
+        balance_mode = weight_config.get('balance_mode', 'balanced')
+        normalize_mode = weight_config.get('weight_normalize', 'max')
+        
+        # 计算基础平衡权重
+        if balance_mode != 'none':
+            balanced_weights = compute_class_weight(
+                balance_mode, 
+                classes=np.unique(y_train), 
+                y=y_train
+            )
+        else:
+            balanced_weights = np.ones(len(np.unique(y_train)))
+        
+        # 获取召回率调节权重
+        recall_adjustment = np.array(
+            weight_config.get('recall_weights', [1.0] * len(np.unique(y_train)))
+        )
+        
+        # 归一化调节权重
+        if normalize_mode == 'max':
+            normalized_adjustment = recall_adjustment / np.max(recall_adjustment)
+        elif normalize_mode == 'sum':
+            normalized_adjustment = recall_adjustment / np.sum(recall_adjustment) * len(recall_adjustment)
+        else:
+            normalized_adjustment = recall_adjustment
+        
+        # 计算最终权重
+        final_weights = balanced_weights * normalized_adjustment
+        
+        # 记录权重计算过程
+        log_message = (
+            f"\nClass weights calculation:\n"
+            f"Balance mode: {balance_mode}\n"
+            f"Normalize mode: {normalize_mode}\n"
+            f"Class distribution: {np.bincount(y_train)}\n"
+            f"Balanced weights: {balanced_weights}\n"
+            f"Recall adjustment: {recall_adjustment}\n"
+            f"Normalized adjustment: {normalized_adjustment}\n"
+            f"Final weights: {final_weights}"
+        )
+        logging.info(log_message)
+        print(log_message)
+        
+        return torch.tensor(final_weights, dtype=torch.float32).to(self.device)
+
+    def train_detect(self) -> Tuple[float, float, List[float], List[float], List[float]]:
+        """
+        训练和检测模型
+        
+        Returns:
+            Tuple[float, float, List[float], List[float], List[float]]:
+                - average_f1: 平均F1分数
+                - wrong_percentage: 错误率
+                - precision_scores: 各类别精确率
+                - recall_scores: 各类别召回率
+                - f1_scores: 各类别F1分数
+        """
+        # 从配置文件中读取数据路径
+        data = pd.read_excel(self.config['data_path'])
+        
+        # 获取特征和标签
+        X = data[self.config['features']['feature_names']].values
+        y = data[self.config['features']['label_name']].values
+        
+        # 显示特征相关性
+        self.plot_correlation(X, self.config['features']['feature_names'])
+        
+        # 根据配置选择数据模式
+        if self.config['training']['data_mode'] == 'train_val':
+            # 使用 StratifiedKFold 进行划分
+            skf = StratifiedKFold(n_splits=5, shuffle=True)
+            train_idx, val_idx = next(skf.split(X, y))
+            X_train, X_val = X[train_idx], X[val_idx]
+            y_train, y_val = y[train_idx], y[val_idx]
+        else:
+            X_train, X_val = X, X
+            y_train, y_val = y, y
+        
+        # 创建数据加载器
+        train_dataset = TensorDataset(
+            torch.from_numpy(X_train).float().to(self.device),
+            torch.from_numpy(y_train).long().to(self.device)
+        )
+        val_dataset = TensorDataset(
+            torch.from_numpy(X_val).float().to(self.device),
+            torch.from_numpy(y_val).long().to(self.device)
+        )
+        
+        train_loader = DataLoader(
+            train_dataset, 
+            batch_size=self.config['training']['batch_size'], 
+            shuffle=True
+        )
+        val_loader = DataLoader(
+            val_dataset,
+            batch_size=self.config['training']['batch_size']
+        )
+        
+        # 使用新的权重计算方法
+        class_weights = self._compute_class_weights(y_train)
+        
+        if self.config['training']['experimental_mode']:
+            return self._run_experiments(X_train, y_train, X_val, y_val, class_weights)
+        else:
+            results = self._single_train_detect(X_train, y_train, X_val, y_val, class_weights)
+            # 只返回前5个值，去掉 best_epoch
+            return results[:-1]
+
+    def _update_metrics(self, train_metrics: Dict, val_metrics: Dict,
+                       train_loss: float, train_acc: float,
+                       val_loss: float, val_acc: float,
+                       val_f1: float, epoch: int) -> None:
+        """更新训练和验证指标"""
+        train_metrics['loss'].append(train_loss)
+        train_metrics['acc'].append(train_acc)
+        
+        val_metrics['loss'].append(val_loss)
+        val_metrics['acc'].append(val_acc)
+        val_metrics['f1'].append(val_f1)
+        
+        # 同时记录到日志和打印到控制台
+        log_message = (
+            f'Epoch {epoch+1:03d} | '  # 添加 epoch 信息，使用3位数字格式
+            f'Train Loss: {train_loss:.4f} | '
+            f'Train Acc: {train_acc:.4f} | '
+            f'Val Loss: {val_loss:.4f} | '
+            f'Val Acc: {val_acc:.4f} | '
+            f'Val F1: {val_f1:.4f}'
+        )
+        logging.info(log_message)
+        print(log_message)  # 打印到控制台
+
+    def _run_experiments(self, X_train, y_train, X_val, y_val, class_weights):
+        """运行多次实验"""
+        all_results = []
+        list_avg_f1 = []
+        list_wrong_percentage = []
+        list_precision = []
+        list_recall = []
+        list_f1 = []
+        
+        best_experiment_f1 = 0
+        best_experiment_results = None
+        best_experiment_model = None
+        best_experiment_num = -1
+        best_experiment_epoch = -1
+        best_model_path = None
+        best_train_process_path = None
+        best_evaluate_result_path = None
+        
+        base_model_path = self.config['train_model_path']
+        base_name = os.path.splitext(base_model_path)[0]
+        ext = os.path.splitext(base_model_path)[1]
+        
+        for i in range(self.config['training']['experiments_count']):
+            exp_num = i + 1
+            logging.info(f"Starting experiment {exp_num}/{self.config['training']['experiments_count']}")
+            print(f"Starting experiment {exp_num}/{self.config['training']['experiments_count']}")
+            
+            # 为每次实验创建模型和图片保存路径
+            exp_model_path = f"{base_name}_exp{exp_num}{ext}"
+            exp_train_process_path = exp_model_path.replace('.pth', '_training_process.png')
+            exp_evaluate_result_path = exp_model_path.replace('.pth', '_evaluate_result.png')
+            
+            # 更新配置中的路径
+            self.config['paths']['model'].update({
+                'train_process': exp_train_process_path,
+                'evaluate_result_path': exp_evaluate_result_path
+            })
+            
+            results = self._single_train_detect(X_train, y_train, X_val, y_val, class_weights, exp_model_path)
+            avg_f1, wrong_percentage, precision, recall, f1, best_epoch = results
+            
+            # 记录每次实验的结果
+            list_avg_f1.append(avg_f1)
+            list_wrong_percentage.append(wrong_percentage)
+            list_precision.append(precision)
+            list_recall.append(recall)
+            list_f1.append(f1)
+            
+            # 如果是最佳模型
+            if avg_f1 > best_experiment_f1:
+                best_experiment_f1 = avg_f1
+                best_experiment_results = results
+                best_experiment_model = self.model.state_dict()
+                best_experiment_num = exp_num
+                best_experiment_epoch = best_epoch
+                best_model_path = exp_model_path
+                best_train_process_path = exp_train_process_path
+                best_evaluate_result_path = exp_evaluate_result_path
+        
+        # 复制最佳实验的文件到目标路径（不带exp序号的版本）
+        base_model_path = self.config['train_model_path']
+        base_train_process_path = base_model_path.replace('.pth', '_training_process.png')
+        base_evaluate_result_path = base_model_path.replace('.pth', '_evaluate_result.png')
+        
+        shutil.copyfile(best_model_path, base_model_path)
+        shutil.copyfile(best_train_process_path, base_train_process_path)
+        shutil.copyfile(best_evaluate_result_path, base_evaluate_result_path)
+        
+        # 更新配置中的路径为不带序号的版本
+        self.config['paths']['model'].update({
+            'train_process': base_train_process_path,
+            'evaluate_result_path': base_evaluate_result_path
+        })
+        
+        if self.config['training']['replace_model']:
+            target_model_path = self.config['paths']['model']['train']
+            target_train_process_path = target_model_path.replace('.pth', '_training_process.png')
+            target_evaluate_result_path = target_model_path.replace('.pth', '_evaluate_result.png')
+            
+            shutil.copyfile(best_model_path, target_model_path)
+            shutil.copyfile(best_train_process_path, target_train_process_path)
+            shutil.copyfile(best_evaluate_result_path, target_evaluate_result_path)
+        
+        # 打印日志信息
+        log_message = (
+            f"\nExperiments Summary:\n"
+            f"Mean F1: {np.mean(list_avg_f1):.4f} "
+            f"Mean Wrong Percentage: {np.mean(list_wrong_percentage):.2f}%\n"
+            f"Mean Precision: {[np.mean([p[i] for p in list_precision]) for i in range(len(list_precision[0]))]} | {np.mean(list_precision)}\n"
+            f"Mean Recall: {[np.mean([r[i] for r in list_recall]) for i in range(len(list_recall[0]))]} | {np.mean(list_recall)}\n"
+            f"Mean F1: {[np.mean([f1[i] for f1 in list_f1]) for i in range(len(list_f1[0]))]} | {np.mean(list_f1)}\n"
+            f"\nBest Model Details:\n"
+            f"Best Model Path: {os.path.basename(best_model_path)}\n"
+            f"From Experiment: {best_experiment_num}/{self.config['training']['experiments_count']}\n"
+            f"Best Epoch: {best_experiment_epoch}\n"
+            f"Best F1: {best_experiment_f1:.4f} "
+            f"Wrong Percentage: {best_experiment_results[1]:.2f}%\n"
+            f"Precision: {best_experiment_results[2]} | {np.mean(best_experiment_results[2])}\n"
+            f"Recall: {best_experiment_results[3]} | {np.mean(best_experiment_results[3])}\n"
+            f"F1: {best_experiment_results[4]} | {np.mean(best_experiment_results[4])}"
+        )
+        logging.info(log_message)
+        print(log_message)
+        
+        return (best_experiment_results[0],  # average_f1
+                best_experiment_results[1],  # wrong_percentage
+                best_experiment_results[2],  # precision
+                best_experiment_results[3],  # recall
+                best_experiment_results[4])  # f1
+
+    def _single_train_detect(self, X_train, y_train, X_val, y_val, class_weights, model_path=None):
+        """单次训练过程"""
+        # 创建数据加载器
+        train_dataset = TensorDataset(
+            torch.from_numpy(X_train).float().to(self.device),
+            torch.from_numpy(y_train).long().to(self.device)
+        )
+        val_dataset = TensorDataset(
+            torch.from_numpy(X_val).float().to(self.device),
+            torch.from_numpy(y_val).long().to(self.device)
+        )
+        
+        train_loader = DataLoader(train_dataset, batch_size=self.config['training']['batch_size'], shuffle=True)
+        val_loader = DataLoader(val_dataset, batch_size=self.config['training']['batch_size'])
+        
+        # 创建模型和优化器
+        self.create_model()
+        criterion = nn.CrossEntropyLoss(weight=class_weights)
+        optimizer = torch.optim.Adam(
+            self.model.parameters(), 
+            lr=self.config['training']['learning_rate']
+        )
+        scheduler = torch.optim.lr_scheduler.StepLR(
+            optimizer,
+            self.config['training']['scheduler']['step_size'],
+            self.config['training']['scheduler']['gamma']
+        )
+        
+        # 设置训练过程图的保存路径
+        if model_path:
+            train_process_path = model_path.replace('.pth', '_training_process.png')
+            self.config['paths']['model']['train_process'] = train_process_path
+        else:
+            train_process_path = self.config['train_model_path'].replace('.pth', '_training_process.png')
+            self.config['paths']['model']['train_process'] = train_process_path
+        
+        # 训练模型
+        best_val_f1, best_model, best_epoch = self.train_model(
+            train_loader, val_loader, criterion, optimizer, scheduler
+        )
+        
+        # 加载最佳模型
+        self.model.load_state_dict(best_model)
+        
+        # 保存模型
+        if model_path:
+            self.save_model(model_path)
+        else:
+            # 如果是单次训练模式，直接保存到原始路径
+            self.save_model(self.config['train_model_path'])
+        
+        # 使用最佳模型进行评估
+        eval_results = self.evaluate_model(X_val, y_val, is_training=True)
+        return eval_results + (best_epoch,)
+
+    def _plot_training_process(self, train_metrics: Dict, val_metrics: Dict) -> None:
+        """绘制训练过程图"""
+        plt.figure(figsize=(15, 5))
+        
+        # 损失曲线
+        plt.subplot(131)
+        plt.plot(train_metrics['loss'], label='Train Loss(训练损失)')
+        plt.plot(val_metrics['loss'], label='Val Loss(验证损失)')
+        plt.title('Loss(损失)', fontproperties=self.font_prop)
+        plt.legend(prop=self.font_prop)
+        
+        # 准确率曲线
+        plt.subplot(132)
+        plt.plot(train_metrics['acc'], label='Train Accuracy(训练正确率)')
+        plt.plot(val_metrics['acc'], label='Val Accuracy(验证正确率)')
+        plt.title('Accuracy(正确率)', fontproperties=self.font_prop)
+        plt.legend(prop=self.font_prop)
+        
+        # F1分数、精确率和召回率曲线
+        plt.subplot(133)
+        plt.plot(val_metrics['f1'], label='Validation F1(验证F1得分)')
+        plt.plot(val_metrics['precision'], label='Validation Precision(验证精确率)')
+        plt.plot(val_metrics['recall'], label='Validation Recall(验证召回率)')
+        plt.title('Metrics(评估指标)', fontproperties=self.font_prop)
+        plt.legend(prop=self.font_prop)
+        
+        plt.tight_layout()
+        plt.savefig(self.config['paths']['model']['train_process'])
+        plt.close()