newbie
/
Deeplearning
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
29 Commits 1 Branch 0 Tags 157 KiB
Python 96.7%
Shell 3.3%
Go to file
newbie 4fea502e6b Update Qnn model data preparation to include unsqueeze for tensor dimensions; replace Qmlp with QCNN in main.py; add diagram generation script 2025-12-14 23:24:52 +08:00
.vscode update README 2024-12-28 19:37:22 +08:00
Qfunctions Refactor data saving and visualization functions; add metrics tracking in Qnn model 2025-06-24 23:10:53 +08:00
Qtorch Update Qnn model data preparation to include unsqueeze for tensor dimensions; replace Qmlp with QCNN in main.py; add diagram generation script 2025-12-14 23:24:52 +08:00
Scripts Add confusion matrix attributes to Qnn model; remove unused test.py file; comment out QCNN model instantiation in main.py 2025-06-24 23:31:19 +08:00
.gitignore update .gitignore 2024-12-28 20:34:31 +08:00
README.md update README 2024-12-28 20:29:16 +08:00
main.py Update Qnn model data preparation to include unsqueeze for tensor dimensions; replace Qmlp with QCNN in main.py; add diagram generation script 2025-12-14 23:24:52 +08:00
test.py Update Qnn model data preparation to include unsqueeze for tensor dimensions; replace Qmlp with QCNN in main.py; add diagram generation script 2025-12-14 23:24:52 +08:00

README.md

#README

Quickly Start

  1. 将项目文件夹编辑成日期+项目名
  2. 编辑好label名称，label名称命名变成英文或者数字

例如：”PDMS“， ”1“ 等, , 如果你的每一个类，下面又多个子特征则可以建立一个文件夹，在创建神经网络类的时候将isDir参数改成True即可.
详细如下图：
2.1. 如果只有一类特征
image.png
2.2. 如果有多类特征
image.png

  1. 将准备好的文件夹移动到Static文件夹中（没有就建立），如果没有 Result 建立一个Result文件夹用来存放结果

  2. 读取数据：

  # 以MaterialDiv为例
  projet_name = '20241009MaterialDiv'                                           
  label_names = ['Acrlic', 'Ecoflex', 'PDMS', 'PLA', 'Wood']
  # 使用库 divSet 划分训练集和数据集
  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
  )
  1. 创建神经网络类
   model = Qmlp(
     X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
     hidden_layers = [128],
     dropout_rate=0
   )
  1. 训练并获取数据
  pca_2d, pca_3d = model.get_PCA()

  model.fit(300)
  
  cm = model.get_cm()
  epoch_data = model.get_epoch_data()

  save_to_xlsx(project_name=projet_name, file_name="pca_2d", data=pca_2d)
  save_to_xlsx(project_name=projet_name, file_name="pca_3d", data=pca_3d)
  save_to_xlsx(project_name=projet_name, file_name="cm", data=cm )
  save_to_xlsx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)