Deeplearning/main.py

# frofrom Qtorch.Functions import dLoader
from Qtorch.Models.Qmlp import Qmlp
from Qfunctions.divSet import divSet
from Qfunctions.loaData import load_data as dLoader
from sklearn.decomposition import PCA

def main():
  projet_name = '20241009MaterialDiv'
  label_names =["Acrylic", "Ecoflex", "PDMS", "PLA", "Wood"]
  data = dLoader(projet_name, label_names, isDir=True)
  X_train, X_test, y_train, y_test, encoder = divSet(
    data=data, labels=label_names, test_size= 0.3
  )
  
  print(y_train)
  
  import pandas as pd
  pca = PCA(n_components=2)  # 保留两个主成分
  principalComponents = pca.fit_transform(X_train)
  df_pca2d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2'])
  df_pca2d['labels'] = y_train

  pca = PCA(n_components=3)  # 保留三个主成分
  principalComponents = pca.fit_transform(X_train)
  df_pca3d = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2', 'PC3'])
  df_pca3d['labels'] = y_train

  # 保存为xlsx文件
  import os
  folder = os.path.join("./Result", projet_name)
  if not os.path.exists(folder):
    os.makedirs(folder)
  df_pca2d.to_excel(os.path.join(folder, 'pca_2d_points_with_labels.xlsx'), index=False)
  df_pca3d.to_excel(os.path.join(folder, 'pca_3d_points_with_labels.xlsx'), index=False)

  
  model = Qmlp(
    X_train=X_train, X_test=X_test, y_train=y_train, y_test= y_test,
    hidden_layers=[128, 128],
    dropout_rate=0
    )
  model.fit(300)
  
  cm = model.get_cm()
  epoch_data = model.get_epoch_data()

  from Qfunctions.saveToxlsx import save_to_xlsx as stx
  stx(project_name=projet_name, file_name="cm", data=cm )
  stx(project_name=projet_name, file_name="acc_and_loss", data=epoch_data)

  print("Done")
  
if __name__ == '__main__':
  main()