RuntimeError: For unbatched 2-D input, hx should also be 2-D but got 3-D tensor
时间: 2024-05-18 11:14:44 浏览: 34
这个错误通常是由于输入的tensor和hidden state的维度不匹配导致的。在Pytorch中,hidden state的维度要求和输入tensor的维度相同。因此,如果你的输入是一个2-D tensor,那么hidden state也应该是一个2-D tensor。如果你的hidden state是一个3-D tensor,那么它的第一个维度应该是batch size,第二个维度应该是num layers,第三个维度应该是hidden size。你可以通过调整hidden state的维度来解决这个错误。
相关问题
RuntimeError: For unbatched 2-D input, hx and cx should also be 2-D but got (3-D, 3-D) tensors
这个错误通常发生在使用LSTM或GRU等循环神经网络时,当输入数据是2D的,而初始隐藏状态(hx)和细胞状态(cx)是3D的时候会出现。
为了解决这个问题,你可以尝试将初始隐藏状态和细胞状态的维度改为2D,以与输入数据的维度匹配。你可以使用`unsqueeze`函数来添加一个新的维度。
例如,如果你的初始隐藏状态(hx)和细胞状态(cx)的形状为`(batch_size, hidden_size)`,你可以使用以下代码将维度改为`(1, batch_size, hidden_size)`:
```
hx = hx.unsqueeze(0)
cx = cx.unsqueeze(0)
```
这将在第一个维度上添加一个新的维度,将它们扩展为3D张量。这样,它们就可以与输入数据的维度匹配了。
RuntimeError: shape '[-1, 3, 2]' is invalid for input of size 8
This error message usually occurs when the size of the given input is not compatible with the specified shape. Specifically, the shape '[-1, 3, 2]' means that the tensor has an unknown batch size, 3 rows, and 2 columns. However, the input size of 8 is not compatible with this shape.
To resolve this error, you need to adjust either the shape or the input size. One possible solution is to reshape the input tensor to have the same number of elements as the specified shape. For example, if you have an input tensor of size (4, 3, 2), you can reshape it to (-1, 3, 2) using the following code:
```python
import torch
x = torch.randn(4, 3, 2)
x = x.view(-1, 3, 2)
```
Alternatively, you can adjust the shape to match the input size. For example, if you have an input tensor of size (8,), you can reshape it to (4, 3, 2) using the following code:
```python
import torch
x = torch.randn(8)
x = x.view(4, 3, 2)
```
In either case, make sure that the shape and input size are compatible before running your code.
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