IndexError: Target 11 is out of bounds. cross-entropy

Question

如何更改附加模型以适合贝叶斯模型的数据集？我的数据包括 5 个变量和 32 个结果

model = nn.Sequential(
    bnn.BayesLinear(prior_mu=0, prior_sigma=0.1, in_features=5, out_features=100),
    nn.ReLU(),
    bnn.BayesLinear(prior_mu=0, prior_sigma=0.1, in_features=100, out_features=3),
)

cross_entropy_loss = nn.CrossEntropyLoss()
klloss = bnn.BKLLoss(reduction='mean', last_layer_only=False)
klweight = 0.01
optimizer = optim.Adam(model.parameters(), lr=0.01)

接下来是运行模型

for step in range(3000):
    models = model(data_tensor)
    cross_entropy = cross_entropy_loss(models, target_tensor)
    kl = klloss(model)
    total_cost = cross_entropy + klweight*kl

    optimizer.zero_grad()
    total_cost.backward()
    optimizer.step()
  
_,predicted = torch.max(models.data, 1)
final = target_tensor.size(0)
correct = (predicted == target_tensor).sum()
print('- Accuracy: %f %%' % (100 * float(correct) / final))
print('- CE : %2.2f, KL : %2.2f' % (cross_entropy.item(), kl.item()))

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_19752/2114183600.py in <module>
      1 for step in range(4000):
      2     models = model(data_tensor)
----> 3     cross_entropy = cross_entropy_loss(models, target_tensor)
      4     kl = klloss(model)
      5     total_cost = cross_entropy + klweight*kl

~\anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102             return forward_call(*input, **kwargs)
   1103         # Do not call functions when jit is used
   1104         full_backward_hooks, non_full_backward_hooks = [], []

~\anaconda3\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target)
   1148 
   1149     def forward(self, input: Tensor, target: Tensor) -> Tensor:
-> 1150         return F.cross_entropy(input, target, weight=self.weight,
   1151                                ignore_index=self.ignore_index, reduction=self.reduction,
   1152                                label_smoothing=self.label_smoothing)

~\anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
   2844     if size_average is not None or reduce is not None:
   2845         reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2846     return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
   2847 
   2848 

IndexError: Target 11 is out of bounds.

data_tensor.shape:torch.Size([640, 5])

target_tensor.shape: torch.Size([640])

数据属性：

data_tensor=torch.from_numpy(data).float()
target_tensor=torch.from_numpy(target).float()
target_tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  0,  1,  2,  3,
         4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
        22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  0,  1,  2,  3,  4,  5,  6,  7,
         8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,
        26, 27, 28, 29, 30, 31,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
        12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
        30, 31,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,
        16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  0,  1,
         2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  0,  1,  2,  3,  4,  5,
         6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
        24, 25, 26, 27, 28, 29, 30, 31,  0,  1,  2,  3,  4,  5,  6,  7,  8,  9,
        10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
        28, 29, 30, 31])

我不确定我的 target_sensor 和 cross_entropy_Loss 有什么问题。目标传感器是0-31之间的重复数字，20次

Answer 1

我和你遇到了同样的问题，请检查你的模型最终线性部分的输出数是否等于num_classes。我英文不好，请忽略语法问题hhh

IndexError: Target 11 is out of bounds. cross-entropy

IndexError: Target 11 is out of bounds. cross-entropy

python

bayesian

cross-entropy

pytorch