解释model.load_state_dict(weights_dict)
时间: 2023-10-30 07:24:03 浏览: 32
这行代码的作用是将预训练模型的权重参数加载到当前的模型中。具体来说,`weights_dict`是一个包含预训练模型权重的字典,通过调用当前模型的`load_state_dict`方法,将`weights_dict`作为参数传入,以覆盖当前模型中对应参数的值。这样就可以将预训练模型的参数应用于当前模型,从而可以在当前模型的基础上进行微调或其他操作。
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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()
```
model.load_state_dict(weights_dict)
`model.load_state_dict(weights_dict)` 是一个 PyTorch 中用来加载预训练模型权重的函数。它将一个保存了模型参数的字典加载到模型中,使得模型的参数与预训练模型的参数一致。这个函数通常用于迁移学习,将预训练模型的参数加载到新的模型中,以便利用预训练模型在新的任务上的表现。需要注意的是,预训练模型和新的模型的结构必须相同,否则加载参数会出错。