pytorch1.0神经网络保存、提取、加载

pytorch1.0网络保存、提取、加载

import torch
import torch.nn.functional as F  # 包含激励函数
import matplotlib.pyplot as plt

# 假数据
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)

def save():
    # save net1
    # 建网络
    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()
    # 训练
    for t in range(100):
        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
    torch.save(net1, 'net.pkl')  # save entire net # 保存整个网络
    torch.save(net1.state_dict(), 'net_params.pkl')   # save only the parameters # 只保存网络中的参数 (速度快, 占内存少)


# 提取网络
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)

# 只提取网络参数
def restore_params():
    # 新建 net3
    # 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)
    )
    # 将保存的参数复制到 net3
    # 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()

# 保存 net1 (1. 整个网络, 2. 只有参数)
# save net1
save()
# 提取整个网络
# restore entire net (may slow)
restore_net()
# 提取网络参数, 复制到新网络
# restore only the net parameters
restore_params()

 

转载于:https://www.cnblogs.com/jeshy/p/11199820.html