pytorch白话入门笔记1.7-保存和提取神经网络

目录

 

1.保存和提取神经

（1）代码

（2）运行结果

---

1.保存和提取神经

（1）代码

import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt

torch.manual_seed(1)   #reproducible

#fake data
x = torch.unsqueeze(torch.linspace(-1,1,100),dim = 1)#旧版使用
y = x.pow(2) + 0.2*torch.rand(x.size())
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),       #输入
        torch.nn.ReLU(),             #回归激励函数
        torch.nn.Linear(10,1)        #输出
    )
    optimizer = torch.optim.SGD(net1.parameters(),lr = 0.2)
    loss_func = torch.nn.MSELoss()

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

    torch.save(net1,'net.pkl')    #entire net
    torch.save(net1.state_dict(),'net_params.pkl')  #parameters 保留状态

    #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)



def restore_net():#提取神经网络
    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():
    #net3 = net1
    net3 = torch.nn.Sequential(
        torch.nn.Linear(1, 10),  # 输入
        torch.nn.ReLU(),  # 回归激励函数
        torch.nn.Linear(10, 1)  # 输出
    )
    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()


#save net1
save()
#restore entire net
restore_net()
#restor only the net parameters
restore_params()

（2）运行结果

Process finished with exit code 0