set_requires_grad
时间: 2023-12-21 17:06:12 浏览: 149
set_requires_grad是一个函数,用于冻结或解冻神经网络中的参数。在深度学习中,通常使用预训练模型来加速训练过程。但是,预训练模型的参数可能不适用于新的任务,因此需要对模型进行微调。在微调过程中,我们可以选择冻结一些层的参数,只更新一些层的参数。set_requires_grad函数可以帮助我们实现这个目标。通过设置requires_grad属性,我们可以决定哪些层的参数需要更新,哪些层的参数需要冻结。
相关问题
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.
torch.set_grad_enabled(False)
`torch.set_grad_enabled(False)` 是一个函数,它用于在 PyTorch 中关闭自动求导功能,即关闭梯度计算。当我们不需要计算梯度时,可以使用该函数来提高代码的执行效率,减少内存消耗。例如,当我们只是需要使用一个训练好的模型进行推理时,就可以关闭自动求导功能。
在调用 `torch.set_grad_enabled(False)` 后,所有的计算图都不会计算梯度,即使输入的张量有 `requires_grad=True`。因此,在执行代码时,我们需要确保已经计算完成的张量不需要再进行求导操作。