搭建網路：

torch.manual_seed(1)    # reproducible

# 假資料
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)
y = x.pow(2) + 0.2*torch.rand(x.size())  # noisy y data (tensor), shape=(100, 1)
x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)


def save():
    # 建網路
    net1 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 1)
    )
    optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)
    loss_func = torch.nn.MSELoss()

    # 訓練
    for t in range(100):
        prediction = net1(x)
        loss = loss_func(prediction, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 

兩種儲存途徑：

torch.save(net1, 'net.pkl')  # 儲存整個網路
torch.save(net1.state_dict(), 'net_params.pkl')   # 只儲存網路中的引數 (速度快, 佔記憶體少)

兩種提取方法：

1 提取整個網路：

def restore_net():
    # restore entire net1 to net2
    net2 = torch.load('net.pkl')
    prediction = net2(x)

2 只提取網路引數：

def restore_params():
    # 新建 net3
    net3 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 1)
    )

    # 將儲存的引數複製到 net3
    net3.load_state_dict(torch.load('net_params.pkl'))
    prediction = net3(x)
 

儲存顯示檢視

# 儲存 net1 (1. 整個網路, 2. 只有引數)
save()

# 提取整個網路
restore_net()

# 提取網路引數, 複製到新網路
restore_params(