RuntimeError: For unbatched 2-D input, hx should also be 2-D but got 3-D tensor
时间: 2024-05-18 11:17:11 浏览: 41
这个错误通常是由于输入数据和状态的维度不匹配造成的。在 PyTorch 中,RNN(循环神经网络)的状态 `hx` 的维度通常应该与输入数据 `input` 的维度一致。如果 `input` 是一个 2D 张量,则 `hx` 应该也是一个 2D 张量,如果 `input` 是一个 3D 张量,则 `hx` 应该也是一个 3D 张量。
因此,如果你遇到了类似于 "RuntimeError: For unbatched 2-D input, hx should also be 2-D but got 3-D tensor" 的错误,可能是因为你的状态 `hx` 的维度不正确,你需要检查一下它的维度是否正确,并根据 `input` 的维度进行调整。
如果你仍然无法解决这个错误,可以尝试查看输入数据和状态的具体维度,以便找到问题所在。可以使用 `input.shape` 和 `hx.shape` 来获取它们的维度。
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.