update

Qtorch/Models/Qnn.py

@@ -40,11 +40,11 @@ class Qnn(nn.Module):
   def __prepare_data(self):
 
     # 将data转换为tensor形式
-    X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32).unsqueeze(1)
+    X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
     self.y_train = self.LABEL_ENCODER.fit_transform(self.y_train)
     y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
 
-    X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32).unsqueeze(1)
+    X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32)
     self.y_test = self.LABEL_ENCODER.transform(self.y_test)
     y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
 

main.py

@@ -1,30 +1,34 @@
-from Qtorch.Models.Qcnn import QCNN
+from Qtorch.Models.Qmlp import Qmlp
 from Qfunctions.divSet import divSet
 from Qfunctions.loaData import load_data
 from Qfunctions.saveToxlsx import save_to_xlsx as save_to_xlsx
 
 def main():
   # 输入元数据文件夹名称
-  projet_name = '20250623 FHH-write'                                                                
+  projet_name = '20251214 WZSX'                                                                
   # 请在[]内输入每一个分类的名称
-  label_names = ['5', '2', '0', 'M', 'J', 'U']
+  label_names = ['canvas', 'lambswool', 
+                 'lychee_grain', 'non-woven_fabric', 'nylon',
+                 'PDMS', 'PET', 'PTFE', 'pure_cotton', 'ramie',
+                 'silk_cotton', 'suede'
+                 ]
   print(label_names)
   data = load_data(projet_name, label_names, isDir=False, fileClass='xlsx')
   X_train, X_test, y_train, y_test, encoder = divSet(
     data=data, labels=label_names, test_size= 0.3
   )
   
-  # model = Qmlp(
-  #   X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
-  #   hidden_layers = [16],
-  #   dropout_rate=0      
-  #   )
-
-  model = QCNN(
+  model = Qmlp(
     X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
+    hidden_layers = [1024, 512, 256],
     dropout_rate=0      
     )
 
+  # model = QCNN(
+  #    X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
+  #    dropout_rate=0
+  # )
+  
   pca_2d, pca_3d = model.get_PCA()
 
   model.fit(300)