----------------------------- Save and reload -------------------------
##################################################################################################################################
# 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()
------------------------batch train---------------------------------------------
for step, (batch_x, batch_y) in enumerate(loader):
這個循環不能暴露在外面，一定要寫個方法調用
torch.manual_seed(1) # reproducible
BATCH_SIZE = 5
# BATCH_SIZE = 8
x = torch.linspace(1, 10, 10) # this is x data (torch tensor)
y = torch.linspace(10, 1, 10) # this is y data (torch tensor)
torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
dataset=torch_dataset, # torch TensorDataset format
batch_size=BATCH_SIZE, # mini batch size
shuffle=True, # random shuffle for training
num_workers=2, # subprocesses for loading data
)
def show_batch():
for epoch in range(3): # train entire dataset 3 times
for step, (batch_x, batch_y) in enumerate(loader): # for each training step
# train your data...
print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',
batch_x.numpy(), '| batch y: ', batch_y.numpy())
if __name__ == '__main__':
show_batch()