【PyTorch】sgd，momentum，adam对损失函数的影响

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

LR = 0.01
BATCH_SIZE = 32
EPOCH = 12

# fake dataset
x = torch.unsqueeze(torch.linspace(-1, 1, 300), 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,
    num_workers=2
)

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.hidden = nn.Linear(1,20)
        self.prediction = nn.Linear(20,1)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.prediction(x)
        return x
def main():
    net_SGD         = Net()
    net_Momentum    = Net()
    net_Adam        = Net()
    nets = [net_SGD, net_Momentum, net_Adam]
    opt_SGD         = optim.SGD(net_SGD.parameters(), lr=LR)
    opt_Momentum    = optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.9)
    opt_Adam        = optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
    optimizers = [opt_SGD, opt_Momentum, opt_Adam]
    loss_func = nn.MSELoss()
    losses_his = [[], [], []]
    #training
    for epoch in range(EPOCH):
        print('EPOCH:',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):
                out = net(b_x)
                loss = loss_func(out,b_y)
                opt.zero_grad()
                loss.backward()
                opt.step()
                l_his.append(loss.item())
    labels = ['SGD', 'Momentum', '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()

if __name__ == '__main__':
    main()

结果图：