The size of tensor a (5) must match the size of tensor b (256) at non-singleton dimension 3、
时间: 2024-04-06 14:26:21 浏览: 147
这个错误提示是由于在进行某种操作时,要求两个张量在非单一维度上具有相同的大小,但是在维度3上,张量a的大小为5,而张量b的大小为256,因此不匹配。
可能的解决方法是调整张量a或张量b的形状,使它们在维度3上具有相同的大小。你可以使用torch.reshape()函数来改变张量的形状,或者使用torch.unsqueeze()函数在维度3上添加一个维度。
相关问题
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.
The size of tensor a (64) must match the size of tensor b (256) at non-singleton dimension 3
这个错误通常是由于两个张量在某个维度上的大小不匹配导致的。在这种情况下,您需要检查两个张量的形状,并确保它们在所有维度上都匹配。如果您需要更改张量的形状,可以使用PyTorch中的view()函数。
以下是一个示例,展示如何使用view()函数将一个形状为[64, 1, 28, 28]的张量调整为形状为[64, 784]的张量,以便与另一个形状为[64, 784, 256]的张量匹配:
```python
import torch
a = torch.randn(64, 1, 28, 28)
b = torch.randn(64, 784, 256)
# 将a张量的形状调整为[64, 784]
a = a.view(64, 784)
# 现在a和b在所有维度上都匹配
c = torch.matmul(a, b)
```
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