同步本地最新代码

FC_ML_Baseline/FC_ML_Baseline_Predict/Model_Pred.py

@@ -3,6 +3,19 @@ import json
 
 import torch
 
+import sys
+import os
+
+# 获取当前脚本（Data_Load.py）所在的目录
+current_script_dir = os.path.dirname(__file__)  # 结果：/home/app/model/ModelTrainingPython/FC_ML_Baseline/FC_ML_Baseline_Data_Handler
+
+# 从当前目录回退 2 级，得到项目根目录 ModelTrainingPython
+root_path = os.path.abspath(os.path.join(current_script_dir, "..", ".."))
+
+# 将根目录添加到 Python 搜索路径
+sys.path.append(root_path)
+
+
 from FC_ML_Data.FC_ML_Data_Process.Data_Process_Normalization import Normalizer
 from FC_ML_NN_Model.Poly_Model import PolyModel
 from FC_ML_Tool.Serialization import parse_json_file

FC_ML_Baseline/FC_ML_Baseline_Test/Train/param-linux.json

@@ -2,22 +2,22 @@
   "files":["sample1.CSV"],
   "path": "/home/app/model/ModelTrainingPython/FC_ML_Baseline/FC_ML_Baseline_Test/Train/",
   "algorithmParam": {
-    "inputSize": 8,
+    "inputSize": 9,
     "outputSize": 8,
     "algorithm": "多项式拟合",
     "activateFun": "sigmod",
     "lossFun": "l1",
     "optimizeFun": "sgd",
-    "exportFormat": ".onnx",
+    "exportFormat": "bin",
     "trainingRatio": 80,
     "loadSize": 32,
     "studyPercent": 0.001,
     "stepCounts": 3,
-    "roundPrint": 11,
+    "roundPrint": 10,
-    "round": 1001,
+    "round": 300,
-    "preDisposeData": false,
+    "preDisposeData": true,
     "disposeMethod": "minmax",
-    "dataNoOrder": false
+    "dataNoOrder": true
   },
   "algorithm": "基础神经网络NN"
 }

FC_ML_Data/FC_ML_Data_Output/Data_Output_Pytorch.py

@@ -19,4 +19,4 @@ def export_model(model,target,file_name,name,input_tensor):
     if name == 'pth':
         return export_model_pt(model,target,file_name)
     else:
-        raise ValueError(f"不支持的导出类型")
+        raise ValueError(f"不支持的导出类型")