在 PyTorch 中使用 DataLoaders 进行 k 折交叉验证 [英] k-fold cross validation using DataLoaders in PyTorch
问题描述
我已将我的训练数据集拆分为 80% 的训练数据和 20% 的验证数据,并创建了如下所示的 DataLoaders.但是我不想限制我的模型的训练.所以我想把我的数据分成 K(也许 5)个折叠并进行交叉验证.但是,我不知道如何在拆分数据集后将它们组合到我的数据加载器中.
I have splitted my training dataset into 80% train and 20% validation data and created DataLoaders as shown below. However I do not want to limit my model's training. So I thought of splitting my data into K(maybe 5) folds and performing cross-validation. However I do not know how to combine the datasets to my dataloader after splitting them.
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))
先谢谢你!
推荐答案
我刚刚编写了一个使用数据加载器和数据集的交叉验证函数.这是我的代码,希望对您有所帮助.
I just wrote a cross validation function work with dataloader and dataset. Here is my code, hope this is helpful.
# 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)
为了直观地了解我们正在做的事情的正确性,请参见下面的输出:
In order to give an intuition of correctness for what we are doing, see the output below:
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
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