model = NeuralNetwork() model.load_state_dict(torch.load("D:/Model_recognation/models/hub/checkpoints/model.pth"))
时间: 2024-05-20 14:13:17 浏览: 17
这段代码是用来加载训练好的神经网络模型的。假设你的模型类名为 NeuralNetwork,模型文件保存在 "D:/Model_recognation/models/hub/checkpoints/model.pth" 这个路径下。首先创建一个 NeuralNetwork 的实例,然后调用它的 load_state_dict 方法,传入 torch.load("D:/Model_recognation/models/hub/checkpoints/model.pth") 加载模型参数。这样就可以使用该模型进行预测或者进行 fine-tune 训练了。
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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 = NeuralNetwork().to(device) print(model)
这段代码是用来创建一个神经网络模型,并将其部署到指定的设备(例如GPU)上。然后打印该模型的架构结构,以便检查和调试。
具体来说,假设我们已经定义了一个名为`NeuralNetwork()`的神经网络类,并在其中实现了前向传播和反向传播的方法。然后,我们可以通过`model = NeuralNetwork().to(device)`来创建一个模型实例,并将其部署到指定的设备上(例如GPU)。最后,我们使用`print(model)`打印模型的结构,以便检查和调试。