pytorch 7 save_reload 保存和提取神经网络

import torch
import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible

# 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)

# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors
# x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)

训练网络，并用两种方式保存网络

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):      # 训练网络
        prediction = net1(x)
        loss = loss_func(prediction, y)
        optimizer.zero_grad()
        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  保存网络的两种方式，第二种只保存参数更快一点
    torch.save(net1, 'net.pkl')                       # save entire net
    torch.save(net1.state_dict(), 'net_params.pkl')   # save only the parameters

加载第一种(含所有信息的)网络：torch.load('net.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)

加载第二种(只含有参数的)网络：net3.load_state_dict(torch.load('net_params.pkl'))

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's 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()

运行上面的函数

# save net1  训练网络，并用两种方式保存网络
save() 

# restore entire net (may slow) 所有信息的网络
restore_net()

# restore only the net parameters 只含参数信息的网络，加载前需要重新构造与之前一模一样的网络
restore_params()