90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
"""
|
|
View more, visit my tutorial page: https://mofanpy.com/tutorials/
|
|
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
|
|
|
|
Dependencies:
|
|
torch: 0.4
|
|
matplotlib
|
|
"""
|
|
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)
|
|
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
|
|
|
|
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()
|
|
|
|
# save net1
|
|
save()
|
|
|
|
# restore entire net (may slow)
|
|
restore_net()
|
|
|
|
# restore only the net parameters
|
|
restore_params()
|