使用 pytorch tensorDataset class 时出现“预期一维目标张量”错误

Question

我想知道为什么会发生此错误。 我的直觉告诉我，tensorDataset 读取最后一列作为标签，但我不知道为什么如果我输入一个单独的标签数据集作为第二个参数，它为什么会这样。 另外，有人可以准确解释 one-hot 编码的工作原理以及我如何解决这个问题，因为我每个项目只想要一个 label 吗？

Error: return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: 1D target tensor expected, multi-target not supported

代码：

if __name__ == '__main__':

inputs_file = pd.read_csv('dataset.csv')
targets_file = pd.read_csv('labels.csv')

inputs = inputs_file.iloc[1:1001].values
targets = targets_file.iloc[1:1001].values

inputs = torch.tensor(inputs, dtype=torch.float32)
targets = torch.tensor(targets)

dataset = TensorDataset(inputs, targets)

val_size = 200
test_size = 100
train_size = len(dataset) - (val_size + test_size)

# Divide dataset into 3 unique random subsets
training_data, validation_data, test_data = random_split(dataset, [train_size, val_size, test_size])

batch_size = 50

train_loader = DataLoader(training_data, batch_size, shuffle=True, num_workers=4, pin_memory=True)
valid_loader = DataLoader(validation_data, batch_size*2, num_workers=4, pin_memory=True)

Answer 1

根据我从评论讨论中收集到的信息，错误由以下内容重现。

import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset, random_split

inputs = torch.randn(999, 11, dtype=torch.float32)
targets = torch.randint(5, (999, 1), dtype=torch.long)

# you need this to adapt from pandas, but not for this example code
# inputs = torch.tensor(inputs, dtype=torch.float32)
# targets = torch.tensor(targets)

dataset = TensorDataset(inputs, targets)

val_size = 200
test_size = 100
train_size = len(dataset) - (val_size + test_size)

# Divide dataset into 3 unique random subsets
training_data, validation_data, test_data = random_split(dataset, [train_size, val_size, test_size])

batch_size = 50

train_loader = DataLoader(training_data, batch_size, shuffle=True, num_workers=4, pin_memory=True)
valid_loader = DataLoader(validation_data, batch_size*2, num_workers=4, pin_memory=True)

# guess model. More on this in a moment
model = nn.Sequential(
    nn.Linear(11, 8),
    nn.Linear(8, 5),
)

loss_func = nn.CrossEntropyLoss()

for features, labels in train_loader:
    out = model(features)
    loss = loss_func(out, labels)
    print(f"{loss = }")
    break

解决方案 1

将labels.squeeze(-1)添加到循环体 a la

for features, labels in train_loader:
    out = model(features)
    labels = labels.squeeze()
    loss = loss_func(out, labels)
    print(f"{loss = }")
    break

方案二

最初将您的目标展平

targets = torch.tensor(targets[:, 0])

---

回应

现在我收到这个错误：RuntimeError: mat1 and mat2 shapes cannot be multiplied (11x1 and 11x8) 我还应该补充一点，我使用的是大小为 8 的隐藏层，我有 5 个类

我的架构是对您正在使用的内容的猜测，但由于上面的代码已通过目标重塑解决，我需要更多才能提供更多帮助。

也许有一些文件可以提供帮助？ CrossEntropyLoss示例代码显示目标的预期形状是N ，而不是N, 1或N, classes 。