ValueError: Expected parameter loc (Tensor of shape (1, 2)) of distribution Normal(loc: torch.Size([1, 2]), scale: torch.Size([1, 2])) to satisfy the constraint Real(), but found invalid values: tensor([[nan, nan]], device='cuda:0', grad_fn=<AddmmBackward

这个错误提示表明在使用PyTorch中的正态分布函数`torch.distributions.Normal`时，输入的参数`loc`包含了NaN值，这是不被允许的。在概率论中，正态分布的均值是一个实数，因此`loc`参数必须是一个实数，不能包含NaN值。

你需要检查传递给`loc`参数的张量，确保其中不包含NaN值。你可以使用`torch.isnan()`函数检查张量中是否有NaN值，然后使用`torch.where()`函数将NaN值替换为0或其他你认为合适的值，例如：

import torch

loc = torch.tensor([[1.0, float('nan')]])
loc = torch.where(torch.isnan(loc), torch.zeros_like(loc), loc)
print(loc)

这里将NaN值替换为了0，如果你的应用场景中不适合使用0，你可以将`torch.zeros_like(loc)`替换为其他你认为合适的张量，例如`torch.full_like(loc, 1.0)`表示用1.0来替换NaN值。

相关问题

valueerror: size shape must match input shape. input is 1d, size is 2

ValueError: size shape must match input shape. input is 1d, size is 2.

回答：这个错误提示是因为输入的形状是1维的，但是在程序中指定了2维的大小（size），所以两者不匹配，导致出现了错误。需要检查程序中的代码，确保输入的形状和大小是一致的。

builtins.ValueError: Expected 2D array, got 1D array instead:

To fix the `ValueError` you encountered, you need to reshape your 1D array into a 2D array. You can use the `reshape` method from NumPy to accomplish this. Here's an example of how to do it:

import numpy as np

# Assuming your 1D array is called 'arr'
arr_2d = np.reshape(arr, (-1, 1))

# Now 'arr_2d' is a 2D array with a single column

In this example, `arr` is your 1D array and `arr_2d` is the reshaped 2D array. The `-1` in the `reshape` method's argument means that the size of that dimension will be inferred based on the size of the original array. The `1` specifies that the reshaped array should have a single column.

Make sure to replace `'arr'` with the actual name of your 1D array in your code.