model_state_dict = model.state_dict()
时间: 2023-08-28 09:55:03 浏览: 66
这段代码是用于获取模型的状态字典(state_dict)。在PyTorch中,模型的状态字典包含了模型中所有可学习参数的当前状态。通过调用`state_dict()`方法,我们可以获取模型当前的参数状态,并将其存储在`model_state_dict`变量中。这个状态字典可以用于保存模型或者在需要的时候加载到模型中。
相关问题
model.load_state_dict(pretrained_dict['model_state_dict'], strict=True)怎么删除无用的参数
你可以使用以下代码删除无用的参数:
```
pretrained_dict = torch.load('trained.pth')
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model.load_state_dict(pretrained_dict, strict=True)
```
这段代码将从预训练模型的参数字典中筛选出与新模型对应的参数,并将它们加载到新模型中。通过设置`strict=True`,不匹配的参数将会被抛弃。
model.load_state_dict
(model_state_dict)
This function loads the saved state dictionary of a PyTorch model into the model. The argument `model_state_dict` is a dictionary containing the saved state of the model, which typically includes the weights, biases, and other parameters. The function updates the model with the saved state dictionary, allowing you to resume training or use the saved model for inference.
Example usage:
```python
import torch
# define a simple neural network
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = torch.nn.Linear(10, 5)
self.fc2 = torch.nn.Linear(5, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# create an instance of the network
net = Net()
# save the state dictionary of the model
torch.save(net.state_dict(), 'model.pth')
# create a new instance of the network
net2 = Net()
# load the saved state dictionary into the new network
net2.load_state_dict(torch.load('model.pth'))
# the two networks should have the same state now
assert net.state_dict() == net2.state_dict()
```