import torch.autograd as autograd
时间: 2023-08-19 14:58:34 浏览: 56
The `torch.autograd` module in PyTorch provides classes and functions for automatic differentiation. It allows you to compute gradients of tensors with respect to other tensors, which is essential for training neural networks using techniques like backpropagation.
To use `torch.autograd`, you typically import it as `autograd`:
```python
import torch.autograd as autograd
```
Some commonly used classes and functions from `autograd` include:
- `Variable`: This class is deprecated in recent versions of PyTorch. You can directly work with tensors and enable gradient computation using the `requires_grad=True` flag.
- `grad`: This function computes the gradients of tensors with respect to other tensors.
- `backward`: This function computes the gradients for all tensors involved in a computation graph, starting from a scalar value.
- `Function`: This class is used to define custom autograd functions for operations that are not natively supported by PyTorch.
These are just a few examples of what you can do with `torch.autograd`. It provides a powerful mechanism for automatic differentiation in PyTorch.