pytorch数据集的一些问题

UserWarning: train_data has been renamed data
  warnings.warn("train_data has been renamed data")
UserWarning: train_labels has been renamed targets
  warnings.warn("train_labels has been renamed targets")
UserWarning: test_data has been renamed data
  warnings.warn("test_data has been renamed data")
UserWarning: test_labels has been renamed targets
  warnings.warn("test_labels has been renamed targets")
 

按照提示把train_data和test_data改成data，把train_labels和test_labels改成targets即可解决。

这里有点我没想清楚的地方是，data和targets里是怎么区分train和test的？想清楚了再来补充

想清楚了，来更新一下：

我这里的代码是

X_train = train_loader.dataset.data.numpy()
y_train = train_loader.dataset.targets.numpy()
X_test = test_loader.dataset.data[:1000].numpy()
y_test = test_loader.dataset.targets[:1000].numpy()

test_loader和train_loader又是哪里来的呢？

# MNIST dataset
train_dataset = dsets.MNIST(root = '/ml/pymnist', #选择数据的根目录
                           train = True, # 选择训练集
                           transform = None, #不考虑使用任何数据预处理
                           download = True) # 从网络上download图片
test_dataset = dsets.MNIST(root = '/ml/pymnist', #选择数据的根目录
                           train = False, # 选择测试集
                           transform = None, #不考虑使用任何数据预处理
                           download = True) # 从网络上download图片
#加载数据
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True)

train_loader里用的dataset是train_dataset，而test_loader里用的dataset是test_dataset，两个dataset在定义时使用的是训练集和测试集，所以要区分data和targets是属于train还是test只要看前面的loader是train还是test就知道了！