machine-learning - 在PyTorch中使用DataLoaders进行k折交叉验证

Question

我将训练数据集分为80％的训练和20％的验证数据，并创建了DataLoader，如下所示。但是，我不想限制模型的训练。因此，我想到了将数据拆分为K(可能是5)折叠并执行交叉验证的方法。但是，我不知道如何在拆分后将数据集组合到我的数据加载器中。

train_size = int(0.8 * len(full_dataset))
validation_size = len(full_dataset) - train_size
train_dataset, validation_dataset = random_split(full_dataset, [train_size, validation_size])

full_loader = DataLoader(full_dataset, batch_size=4,sampler = sampler_(full_dataset), pin_memory=True) 
train_loader = DataLoader(train_dataset, batch_size=4, sampler = sampler_(train_dataset))
val_loader = DataLoader(validation_dataset, batch_size=1, sampler = sampler_(validation_dataset))

先感谢您 !

Answer 1

我刚刚编写了一个与数据加载器和数据集一起使用的交叉验证功能。
这是我的代码，希望对您有所帮助。

# define a cross validation function
def crossvalid(model=None,criterion=None,optimizer=None,dataset=None,k_fold=5):
    
    train_score = pd.Series()
    val_score = pd.Series()
    
    total_size = len(dataset)
    fraction = 1/k_fold
    seg = int(total_size * fraction)
    # tr:train,val:valid; r:right,l:left;  eg: trrr: right index of right side train subset 
    # index: [trll,trlr],[vall,valr],[trrl,trrr]
    for i in range(k_fold):
        trll = 0
        trlr = i * seg
        vall = trlr
        valr = i * seg + seg
        trrl = valr
        trrr = total_size
        # msg
#         print("train indices: [%d,%d),[%d,%d), test indices: [%d,%d)" 
#               % (trll,trlr,trrl,trrr,vall,valr))
        
        train_left_indices = list(range(trll,trlr))
        train_right_indices = list(range(trrl,trrr))
        
        train_indices = train_left_indices + train_right_indices
        val_indices = list(range(vall,valr))
        
        train_set = torch.utils.data.dataset.Subset(dataset,train_indices)
        val_set = torch.utils.data.dataset.Subset(dataset,val_indices)
        
#         print(len(train_set),len(val_set))
#         print()
        
        train_loader = torch.utils.data.DataLoader(train_set, batch_size=50,
                                          shuffle=True, num_workers=4)
        val_loader = torch.utils.data.DataLoader(val_set, batch_size=50,
                                          shuffle=True, num_workers=4)
        train_acc = train(res_model,criterion,optimizer,train_loader,epoch=1)
        train_score.at[i] = train_acc
        val_acc = valid(res_model,criterion,optimizer,val_loader)
        val_score.at[i] = val_acc
    
    return train_score,val_score
        

train_score,val_score = crossvalid(res_model,criterion,optimizer,dataset=tiny_dataset)

为了对我们所做的事情有一个正确的直觉，请参阅以下输出:

train indices: [0,0),[3600,18000), test indices: [0,3600)
14400 3600

train indices: [0,3600),[7200,18000), test indices: [3600,7200)
14400 3600

train indices: [0,7200),[10800,18000), test indices: [7200,10800)
14400 3600

train indices: [0,10800),[14400,18000), test indices: [10800,14400)
14400 3600

train indices: [0,14400),[18000,18000), test indices: [14400,18000)
14400 3600