要點(diǎn)
當(dāng)我們訓(xùn)練好了一個(gè)學(xué)習(xí)模型后骗灶，當(dāng)然是要保存這個(gè)模型，以便下次需要這個(gè)模型的時(shí)候直接提取應(yīng)用耙旦，這一節(jié)中我們就用神經(jīng)網(wǎng)絡(luò)舉例實(shí)現(xiàn)保存提取。
保存
import torch
from torch.autograd import Variable
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)
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()
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
torch.save(net1.state_dict(), 'net_params.pkl') # save only the parameters
