requires_grad=True和requires_grad_=True的区别
时间: 2024-04-26 17:20:28 浏览: 15
`requires_grad=True`和`requires_grad_=True`都是用于设置张量的`requires_grad`属性为`True`,使得该张量可以进行梯度计算和反向传播。
- `requires_grad=True`是张量的构造函数参数,用于在创建张量时设置`requires_grad`属性为`True`。例如:
```python
x = torch.tensor([1, 2, 3], dtype=torch.float32, requires_grad=True)
```
- `requires_grad_()`是张量的原地操作方法,用于将张量的`requires_grad`属性设置为`True`。例如:
```python
x.requires_grad_()
```
两者的区别在于,`requires_grad=True`是在创建张量时设置属性,而`requires_grad_()`是在已有张量上修改属性。同时,需要注意的是,调用`requires_grad_()`方法会改变原始张量,而不是返回一个新的张量。
无论是使用哪种方式,都可以将一个张量标记为需要进行梯度计算和反向传播的张量。
相关问题
怎么设置requires_grad=True
在 PyTorch 中,可以通过设置张量的 `requires_grad` 属性为 `True` 来使其支持梯度计算。对于模型的参数,可以在创建它们时设置 `requires_grad=True`,如下所示:
```python
import torch.nn as nn
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.fc = nn.Linear(10, 1, bias=True)
self.fc.weight.requires_grad = True
self.fc.bias.requires_grad = True
def forward(self, x):
x = self.fc(x)
return x
```
在这个例子中,我们使用了 PyTorch 内置的线性层 `nn.Linear` 创建了一个全连接层,然后将其权重和偏置的 `requires_grad` 设置为 `True`。这样,当我们在模型中前向传播时,这些参数就会被包含在计算图中,以便计算梯度。
对于普通的张量,可以使用 `tensor.requires_grad_()` 方法来将其 `requires_grad` 属性设置为 `True`,如下所示:
```python
import torch
x = torch.randn(10, 10)
x.requires_grad_()
```
这样,当我们在计算图中执行前向传播和反向传播时,张量 `x` 就会被包含在其中,以便计算梯度。
requires_grad=True
In PyTorch, the attribute `requires_grad=True` is used to specify that a tensor requires gradients to be computed during backpropagation.
When a tensor is created with `requires_grad=True`, PyTorch automatically tracks all the operations that involve that tensor and builds a computational graph. This graph is then used during backpropagation to compute the gradients of the loss with respect to the tensor.
By default, the attribute `requires_grad` is set to False when a tensor is created. However, you can set it to True using the following syntax:
```python
import torch
x = torch.ones(3, 3, requires_grad=True)
```
In this example, the tensor `x` is created with a shape of (3, 3) and all its values are set to 1. The attribute `requires_grad` is set to True, which means that gradients will be computed during backpropagation.