fastNet.py

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

net2=torch.nn.Sequential(
    torch.nn.Linear(2,10),
    torch.nn.ReLU(),
    torch.nn.Linear(10,2),
)
print(net2)

def save():
    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()
        loss.backward()
        optimizer.step()

    torch.save(net1,'net.pkl')
    torch.save(net1.state_dict(),'net_params.pkl')

    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():
    net2=torch.load('net.pkl')

def restore_params():
    net3==torch.nn.Sequential(
        torch.nn.Linear(1,10),
        torch.nn.ReLU(),
        torch.nn.Linear(10,1)
    )
    net3.load_state_dict(torch.load('net_params.pkl'))