PyTorch四种常用优化器测试

PyTorch四种常用优化器测试SGD、SGD（Momentum)、RMSprop、Adam

import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
import  torch
import  torch.utils.data as Data
import torch.nn.functional as F
import matplotlib.pyplot as plt

#超参数
LR =0.001
Batch_Size = 32
Epochs = 12

#生成训练数据
x  = torch.unsqueeze(torch.linspace(-1,1,1000),dim=1)

y = x.pow(2) + 0.1 * torch.normal(torch.zeros(*x.size()))

torch_dataset = Data.TensorDataset(x,y)
loader = Data.DataLoader(dataset=torch_dataset,batch_size=Batch_Size,shuffle=True)

class Net2(torch.nn.Module):
    def __init__(self):
        super(Net2,self).__init__()
        self.hidden = torch.nn.Linear(1,20)
        self.predict = torch.nn.Linear(20,1)

    #前向传递
    def forward(self,x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

net_SGD = Net2()
net_Momentum =Net2()
net_RMSprop = Net2()
net_Adam = Net2()

nets = [net_SGD,net_Momentum,net_RMSprop,net_Adam]

opt_SGD = torch.optim.SGD(net_SGD.parameters(),lr=LR)
opt_Momentum = torch.optim.SGD(net_Momentum.parameters(),lr=LR,momentum=0.9)
opt_RMSProp = torch.optim.RMSprop(net_RMSprop.parameters(),lr=LR,alpha=0.9)
opt_Adam = torch.optim.Adam(net_Adam.parameters(),lr=LR,betas=(0.9,0.99))
optimizers = [opt_SGD,opt_Momentum,opt_RMSProp,opt_Adam]

loss_func = torch.nn.MSELoss()
loss_his = [[],[],[],[]]

for epoch in range(Epochs):
    for step,(batch_x,batch_y) in enumerate(loader):
        for net,opt,l_his in zip(nets,optimizers,loss_his):
            output = net(batch_x)
            loss = loss_func(output,batch_y)
            opt.zero_grad()
            loss.backward()
            opt.step()
            l_his.append(loss.data.numpy())  #loss recoder
labels = ['SGD','Momentum','RMsprop','Adam']
for i ,l_his in enumerate(loss_his):
    plt.plot(l_his, label=labels[i])
plt.legend(loc='best')
plt.xlabel('Steps')
plt.ylabel('Loss')
plt.ylim((0, 0.2))
plt.show()