The size of tensor a (10) must match the size of tensor b (128) at non-singleton dimension 1
时间: 2023-08-14 10:36:57 浏览: 77
这个错误通常是因为两个张量的形状不兼容,导致无法执行某些操作。具体地说,错误信息中说,在第1个非单例维度上,张量a的大小为10,而张量b的大小为128,它们的大小不匹配。这意味着你正在尝试执行一个需要两个张量在第1个非单例维度上具有相同大小的操作,但是由于它们的大小不同,所以无法执行。
要解决这个问题,你需要检查代码中使用这两个张量的地方,找出哪个张量的形状不正确。你可以使用 PyTorch 提供的 `size()` 方法来检查张量的形状。例如:
```python
import torch
a = torch.randn(10)
b = torch.randn(128)
print(a.size()) # 输出 torch.Size([10])
print(b.size()) # 输出 torch.Size([128])
```
在上面的示例中,我们定义了两个随机张量 `a` 和 `b`,并使用 `size()` 方法打印它们的形状。你应该检查你的代码,查找哪个张量的形状不正确,并找到一个合适的解决方案。你可以通过调整张量的形状或使用其他方法来解决这个问题。
相关问题
The size of tensor a (10) must match the size of tensor b (196) at non-singleton dimension 1
这个错误提示意味着在某个操作中,两个张量的形状不兼容。具体地说,两个张量的形状必须在某个非单例维度上匹配才能执行该操作。在你的情况下,张量a的大小为10,而张量b的大小为196,它们在第1个维度上的大小不匹配。
要解决这个问题,你需要检查代码中涉及的这两个张量的形状,并确保它们在所有需要匹配的维度上具有相同的大小。你可以使用`torch.Size()`或者`.size()`方法来获取张量的形状,然后进行比较。你还可以使用PyTorch提供的一些操作来改变张量的形状,例如`.view()`或`.reshape()`方法,以满足您的需求。
The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 1
The error message "The size of tensor a (10) must match the size of tensor b (3) at non-singleton dimension 1" indicates that the dimensions of tensor a and tensor b do not match at dimension 1, which prevents the operation from being completed. It seems that the number of elements in tensor a at dimension 1 is 10, while the number of elements in tensor b at dimension 1 is 3.
To fix this issue, you can either resize one of the tensors to match the other tensor's dimension at dimension 1, or reshape one of the tensors to have a different number of dimensions.
Here are some possible solutions:
1. Reshape tensor a: You can reshape tensor a to match the number of elements in tensor b at dimension 1. For example, if tensor a has a shape of (10, 5) and tensor b has a shape of (3, 5), you can reshape tensor a to have a shape of (3, 2, 5) using the reshape() function.
2. Resize tensor b: Alternatively, you can resize tensor b to match the number of elements in tensor a at dimension 1. For example, if tensor a has a shape of (10, 5) and tensor b has a shape of (3, 5), you can resize tensor b to have a shape of (10, 5) using the resize() function.
3. Verify the input data: Double-check the input data for tensor a and tensor b to ensure that they have the correct dimensions and sizes.
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