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:

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()