内部已经包含训练模块

    1. #k折交叉验证部分
    2. def k_fold(k,image_dir,num_epochs,device,batch_size,optimizer,loss,net):
    3.     train_k = './/shallow//train_k.txt'
    4.     test_k = './/shallow//test_k.txt'
    5.     #loss_acc_sum,train_acc_sum, test_acc_sum = 0,0,0
    6.     Ktrain_min_l = []
    7.     Ktrain_acc_max_l = []
    8.     Ktest_acc_max_l = []
    9.     for i in range(k):
    10.         get_k_fold_data(k, i,image_dir)
    11.         #修改train函数，使其返回每一批次的准确率，tarin_ls用列表表示
    12.         train_data = MyDataset(is_train=True, root=train_k)
    13.         test_data = MyDataset(is_train=False, root=test_k)
    15.         train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=10, shuffle=True, num_workers=num_workers)
    16.         test_loader = torch.utils.data.DataLoader(dataset=test_data, batch_size=10, shuffle=True, num_workers=num_workers)
    18.         loss_min,train_acc_max,test_acc_max=train(i,train_loader,test_loader, net, loss, optimizer, device, num_epochs)
    19.         Ktrain_min_l.append(loss_min)
    20.         Ktrain_acc_max_l.append(train_acc_max)
    21.         Ktest_acc_max_l.append(test_acc_max)
    22.         #train_acc_sum += train_acc# train函数epoches（即第k个数据集被测试后）结束后，累加
    23.         #test_acc_sum += test_acc#
    24.         #loss_acc_sum+=loss_acc
    25.         #print('fold %d, lose_rmse_max %.4f, train_rmse_max %.4f, test_rmse_max %.4f ' %(i+1, loss_acc,train_acc, test_acc_max_l[i]))
    26.     return sum(Ktrain_min_l)/len(Ktrain_min_l),sum(Ktrain_acc_max_l)/len(Ktrain_acc_max_l),sum(Ktest_acc_max_l)/len(Ktest_acc_max_l)