torch.autograd.backward
时间: 2023-04-16 15:02:46 浏览: 63
torch.autograd.backward是PyTorch中的一个函数,用于计算梯度。它接收一个张量作为参数,计算该张量的梯度,并将梯度累加到张量的.grad属性中。该函数通常在反向传播过程中使用,用于计算损失函数对模型参数的梯度。
相关问题
import torch.autograd as autograd
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.
torch.autograd
torch.autograd是PyTorch中的一个自动求导引擎,它能够自动计算张量的梯度。在深度学习中,梯度是非常重要的,因为它可以告诉我们在当前模型参数下,目标函数的变化率最大的方向。在PyTorch中,我们可以使用torch.autograd来自动计算梯度,而不需要手动推导公式。当我们定义一个张量时,如果设置了requires_grad=True,那么这个张量就会被加入到计算图中,计算图会记录所有的计算操作,并根据链式法则自动计算梯度。最终,我们可以使用backward()函数来自动计算梯度。
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