在pytorch中name 'Variable' is not defined

在 PyTorch 的最新版本中，Variable 已经被弃用。如果在使用 PyTorch 1.0 或更高版本的话，你可以使用 torch.Tensor() 来代替 Variable。例如，将代码中的 Variable 替换为 torch.Tensor()，就可以解决这个问题。如果你使用的是旧版本的 PyTorch，你需要导入 Variable，例如：from torch.autograd import Variable。

相关问题

name 'mnist' is not defined

As an AI language model, I cannot determine the context in which this error occurred. However, usually, this error occurs when the code tries to access a variable or object named 'mnist' that has not been defined previously.

If you are working with the MNIST dataset for image recognition tasks, you need to import the dataset from a library like TensorFlow or PyTorch before using it in your code. For example, in TensorFlow, you can import the MNIST dataset as follows:

from tensorflow.keras.datasets import mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

This will import the MNIST dataset and load it into two sets of images and labels for training and testing. Ensure that you have installed and imported the necessary libraries before using them in your code.

Python name 'device' is not defined

The error message "Python name 'device' is not defined" means that the variable "device" has not been defined or declared before it is being used in the code.

To fix this error, you need to declare or define the "device" variable before using it. You can define it using the "torch.device" method, which is commonly used in PyTorch code.

Here's an example code snippet that defines the "device" variable:

import torch

# define device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# use device in code
model.to(device)

In this example, we are using the "torch.cuda.is_available()" method to check if a GPU is available and then defining the "device" variable accordingly. We can then use this variable in our code to move the model to the GPU using the "to()" method.