optimizer.py

import torch
import torch.utils.data as Data
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt

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()))

# plt.scatter(x.numpy(),y.numpy())
# plt.show()
#
# optimizer=torch.optim.SGD()
# torch.optim.

torch_dataset=Data.TensorDataset(x,y)
loader=Data.DataLoader(dataset=torch_dataset,batch_size=BATCH_SIZE,shuffle=True,num_workers=2)

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=0.8)
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()
losses_his=[[],[],[],[]]

for epoch in range(EPOCH):
    print(epoch)
    for step,(batch_x,batch_y) in enumerate(loader):
        b_x=Variable(batch_x)
        b_y=Variable(batch_y)

        for net,opt, l_his in zip(nets,optimizers,losses_his):
            output=net(b_x)
            loss=loss_func(output,b_y)
            opt.zero_grad()
            loss.backward()
            opt.step()
            l_his.append(loss.data)

labels=['SGD','Momentum','RMSprop','Adam']
for i, l_his in enumerate(losses_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()