PyTorch tutorial ：保存提取

保存方法

先建立一个网络 net1

torch.save(net1, 'net.pkl')  #保存全部网络
torch.save(net1.state_dict(), 'net_params.pkl')  #保存参数

提取方法1 提取全部网络

def restore_net():
    # restore entire net1 to net2
    net2 = torch.load('net.pkl')   #直接提取net1
    prediction2 = net2(x)

提取方法2 提取参数

需要自己再建立一个跟要提取的网络一模一样的网络（本文是net3）

然后提取参数

 net3 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 1)
    )

    # copy net1'parameters into net3
    net3.load_state_dict(torch.load('net_params.pkl'))
    prediction3 = net3(x)

全部代码如下

import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt

# torch.manual_seed(1)

# fake data
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():
    # save net1
    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()

    for t in range(100):
        prediction = net1(x)
        loss = loss_func(prediction, y)
        optimizer.zero_grad()  # 实验的时候，刚开始，忘了这个，拟合完全失败啊
        loss.backward()
        optimizer.step()

    # plot result
    plt.figure(1, figsize=(10, 3))
    plt.subplot(131)
    plt.title('Net1')
    plt.scatter(x.data.numpy(), y.data.numpy())
    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)

    # !! 2 ways to save the net
    torch.save(net1, 'net.pkl')
    torch.save(net1.state_dict(), 'net_params.pkl')


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

    # plot result
    plt.subplot(132)
    plt.title('Net2')
    plt.scatter(x.data.numpy(), y.data.numpy())
    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)


def restore_params():
    # restore only the parameters in net1 to net3
    net3 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),
        torch.nn.ReLU(),
        torch.nn.Linear(10, 1)
    )

    # copy net1'parameters into net3
    net3.load_state_dict(torch.load('net_params.pkl'))
    prediction = net3(x)

    # plot result
    plt.subplot(133)
    plt.title('Net3')
    plt.scatter(x.data.numpy(), y.data.numpy())
    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
    plt.show()


# swve net1
save()

# restore entire net(may slow)
restore_net()

# restore only the net parameters
restore_params()

PyTorch tutorial ：保存提取

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