model.load_state_dict(pretrained_state_dict)
时间: 2023-06-09 14:06:34 浏览: 62
这是一个加载预训练模型权重的代码段,其中pretrained_state_dict是指先前训练好的模型权重参数字典。通过这段代码,我们可以将这些预训练的权重参数加载到我们当前训练的模型中,以便我们可以在更少的训练时间内取得更好的性能。
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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
model.load_state_dict是PyTorch中用于加载预训练模型参数的函数。它将保存在文件中的模型参数加载到模型中,以便可以在新的数据集上进行微调或进行推理。该函数需要传入一个state_dict参数,它是一个Python字典对象,其中包含了模型的所有参数。通过调用model.load_state_dict(state_dict)函数,可以将这些参数加载到模型中。如果模型结构与保存的参数不匹配,将会抛出一个错误。为了避免这种情况,可以将strict参数设置为False,这样在加载参数时,不会检查模型结构是否匹配。