autoencoder - Pytorch中如何简化DataLoader for Autoencoder

Question

有没有更简单的方法来设置数据加载器，因为在自动编码器的情况下输入和目标数据是相同的，并且在训练期间加载数据？ DataLoader总是需要两个输入。

目前我这样定义我的数据加载器:

X_train     = rnd.random((300,100))
X_val       = rnd.random((75,100))
train       = data_utils.TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(X_train).float())
val         = data_utils.TensorDataset(torch.from_numpy(X_val).float(), torch.from_numpy(X_val).float())
train_loader= data_utils.DataLoader(train, batch_size=1)
val_loader  = data_utils.DataLoader(val, batch_size=1)

然后像这样训练:

for epoch in range(50):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = Variable(data), Variable(target).detach()
        optimizer.zero_grad()
        output = model(data, x)
        loss = criterion(output, target)

Answer 1

为什么不对 TensorDataset 进行子类化以使其与未标记数据兼容？

class UnlabeledTensorDataset(TensorDataset):
    """Dataset wrapping unlabeled data tensors.

    Each sample will be retrieved by indexing tensors along the first
    dimension.

    Arguments:
        data_tensor (Tensor): contains sample data.
    """
    def __init__(self, data_tensor):
        self.data_tensor = data_tensor

    def __getitem__(self, index):
        return self.data_tensor[index]

沿着这些思路训练你的自动编码器

X_train     = rnd.random((300,100))
train       = UnlabeledTensorDataset(torch.from_numpy(X_train).float())
train_loader= data_utils.DataLoader(train, batch_size=1)

for epoch in range(50):
    for batch in train_loader:
        data = Variable(batch)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data)