pytorch 之 optimizer comparison

 1 import torch
 2 import torch.utils.data as Data
 3 import torch.nn.functional as F
 4 import matplotlib.pyplot as plt
 5 import torch.optim
 6 # torch.manual_seed(1)    # reproducible
 7 
 8 LR = 0.01
 9 BATCH_SIZE = 32
10 EPOCH = 12
11 
12 # fake dataset
13 x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)
14 y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))
15 
16 # plot dataset
17 plt.scatter(x.numpy(), y.numpy())
18 plt.show()
19 
20 # put dateset into torch dataset
21 torch_dataset = Data.TensorDataset(x, y)
22 loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)
23 
24 
25 # default network
26 class Net(torch.nn.Module):
27     def __init__(self):
28         super(Net, self).__init__()
29         self.hidden = torch.nn.Linear(1, 20)   # hidden layer
30         self.predict = torch.nn.Linear(20, 1)   # output layer
31 
32     def forward(self, x):
33         x = F.relu(self.hidden(x))      # activation function for hidden layer
34         x = self.predict(x)             # linear output
35         return x
36 
37 if __name__ == ‘__main__‘:
38     # different nets
39     net_SGD         = Net()
40     net_Momentum    = Net()
41     net_RMSprop     = Net()
42     net_Adam        = Net()
43     nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
44 
45     # different optimizers
46     opt_SGD         = torch.optim.SGD(net_SGD.parameters(), lr=LR)
47     opt_Momentum    = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
48     opt_RMSprop     = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)
49     opt_Adam        = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
50     optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]
51 
52     loss_func = torch.nn.MSELoss()
53     losses_his = [[], [], [], []]   # record loss
54 
55     # training
56     for epoch in range(EPOCH):
57         print(‘Epoch: ‘, epoch)
58         for step, (b_x, b_y) in enumerate(loader):          # for each training step
59             for net, opt, l_his in zip(nets, optimizers, losses_his):
60                 output = net(b_x)              # get output for every net
61                 loss = loss_func(output, b_y)  # compute loss for every net
62                 opt.zero_grad()                # clear gradients for next train
63                 loss.backward()                # backpropagation, compute gradients
64                 opt.step()                     # apply gradients
65                 l_his.append(loss.data.numpy())     # loss recoder
66 
67     labels = [‘SGD‘, ‘Momentum‘, ‘RMSprop‘, ‘Adam‘]
68     for i, l_his in enumerate(losses_his):
69         plt.plot(l_his, label=labels[i])
70     plt.legend(loc=‘best‘)
71     plt.xlabel(‘Steps‘)
72     plt.ylabel(‘Loss‘)
73     plt.ylim((0, 0.2))
74     plt.show()