Pytroch报错:UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5.

本文探讨了在使用PyTorch进行深度学习项目时遇到的常见警告:'invalid index of a 0-dim tensor'。该警告将在PyTorch 0.5版本中成为错误。文章详细解释了如何通过使用.tensor.item()方法将0维张量转换为Python数字来避免此警告,确保代码的兼容性和稳定性。

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  print('t=%d,loss=%.4f'%(t+1,loss.data[0]))

问题就出现在这个代码上。
结果还是会照常显示,但是就会有warning。

UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number

loss.data[0] 改成loss.item()就好了
在这里插入图片描述

代码报错UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\ReduceOps.cpp:1808.) rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-7) D:\anaconda\envs\9-25-pytorch\Lib\site-packages\torch\nn\modules\loss.py:538: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. return F.mse_loss(input, target, reduction=self.reduction) Traceback (most recent call last): File "D:\python草稿程序\ppo-uav-5\main.py", line 585, in <module> train_uav_env() File "D:\python草稿程序\ppo-uav-5\main.py", line 483, in train_uav_env ppo.update() File "D:\python草稿程序\ppo-uav-5\PPO.py", line 190, in update logprobs_cont, logprobs_disc, state_values, dist_entropy_cont, dist_entropy_disc = self.policy.evaluate( ^^^^^^^^^^^^^^^^^^^^^ File "D:\python草稿程序\ppo-uav-5\PPO.py", line 110, in evaluate dist_continuous = MultivariateNormal(action_mean, cov_mat) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\envs\9-25-pytorch\Lib\site-packages\torch\distributions\multivariate_normal.py", line 178, in __init__ super().__init__(batch_shape, event_shape, validate_args=validate_args) File "D:\anaconda\envs\9-25-pytorch\Lib\site-packages\torch\distributions\distribution.py", line 70, in __init__ raise ValueError( ValueError: Expected parameter loc (Tensor of shape (2,)) of distribution MultivariateNormal(loc: torch.Size([2]), covariance_matrix: torch.Size([2, 2])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values: tensor([nan, nan], device='cuda:0', grad_fn=<ExpandBackward0>)
03-18
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