此文章使用Pytorch進行多種優(yōu)化器的性能比較，構(gòu)建兩層神經(jīng)網(wǎng)絡(luò)進行函數(shù)的擬合，使用不同的優(yōu)化器進行優(yōu)化，具體代碼如下：

import torch
import torch.utils.data as Data
import torch.nn.functional as F
import matplotlib.pyplot as plt
%matplotlib inline
LR =0.01
BATCH_SIZE=32
EPOCH=12
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 Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.hidden=torch.nn.Linear(1,20)
        self.predict=torch.nn.Linear(20,1)
    def forward(self,x):
        x=F.relu(self.hidden(x))
        x=self.predict(x)
        return x
    
net_SGD=Net()
net_Momentum=Net()
net_RMSProp=Net()
net_Adam=Net()
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(EPOCH):
    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())
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()

結(jié)果如圖所示：

四種優(yōu)化器實驗結(jié)果