The size of tensor a (64) must match the size of tensor b (256) at non-singleton dimension 3
时间: 2023-12-13 07:33:03 浏览: 51
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)
```
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.
resnet The size of tensor a (60) must match the size of tensor b (64) at non-singleton dimension 3
引用和引用[2]中提到的错误是由于张量的大小不匹配导致的。具体而言,引用[1]中的张量a的大小为40,而张量b的大小为41,在非单例维度3上不匹配。引用中的错误也是类似的情况,张量a的大小为22,而张量b的大小为32,在非单例维度3上不匹配。
引用中提到的问题是关于卷积神经网络的输入和输出维度的变化。根据提供的信息,输入张量x的大小为[10, 3, 32, 32],而输出张量out的大小为[10, 300, 22, 22]。这是因为在网络中具有3x3卷积核的卷积层没有添加padding=1,导致每次卷积操作使图像缩小1个单位。
解决这个问题的方法是在具有3x3卷积核的卷积层中添加padding=1参数。这将确保在卷积操作中图像的大小保持不变。
对于你提到的resnet中的错误消息,"The size of tensor a (60) must match the size of tensor b (64) at non-singleton dimension 3",这是一个类似的错误,表示张量a的大小为60,而张量b的大小为64,在非单例维度3上不匹配。解决这个问题的方法是确保在相应的操作中张量的大小匹配。