MLP拟合数据效果不佳，调整参数后仍无改善求解决方案

我尝试使用MLP拟合我的数据，但效果未达预期。当前设置的MLP是一个4层网络，每个隐藏层的隐藏单元数为100。

import torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
import numpy as np
import pandas as pd

sg = pd.read_csv("/Users/xxxxxx/Desktop/sgf.csv",header=None)
sg = np.array(sg)
t = np.linspace(0,1500,51)
sg1 = sg[0,:]

X = np.expand_dims(t,axis=1)
Y = sg1.reshape(51,-1)

dataset = TensorDataset(torch.tensor(X,dtype=torch.float),torch.tensor(Y,dtype=torch.float))
dataloader = DataLoader(dataset,batch_size=51,shuffle=True)

class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.net = nn.Sequential(nn.Linear(in_features=1,out_features=100),nn.Sigmoid(),
                                 nn.Linear(100,100),nn.Sigmoid(),
                                 nn.Linear(100,100),nn.Sigmoid(),
                                 nn.Linear(100,100),nn.Sigmoid(),
                                 nn.Linear(100,1))
    def forward(self,input):
        return self.net(input)

net = Net()
optim = torch.optim.Adam(net.parameters(),lr=0.0001)
loss = nn.MSELoss()

for epoch in range(1000):
    ls = None
    for bx,by in dataloader:
        optim.zero_grad()
        y_hat = net(bx)
        ls = loss(y_hat,by)
        ls.backward()
        optim.step()
    
    if (epoch+1) % 500 == 0:
        print("step: {0}, loss: {1}".format(epoch+1,ls.item()))

pred = net(torch.tensor(X,dtype = torch.float))

%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(t,sg1,label="fact")
plt.plot(t,pred.detach().numpy(),label='predict')
plt.title("sin function")
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.legend()
plt.show()

数据示例 #

sg1 = [ 0.08196445,  0.03914855,  0.00515015, -0.02003076, -0.03639418,
       -0.03993232, -0.05617677, -0.04173793, -0.02260713, -0.00150733,
        0.00902901,  0.023227  ,  0.0238875 ,  0.02220099,  0.01067397,
       -0.00998361, -0.03089296, -0.02376404, -0.0205146 , -0.01143468,
       -0.01102645,  0.00288759,  0.02869272,  0.03123309,  0.03807579,
        0.02819487,  0.0163046 ,  0.00416688, -0.00608408, -0.00566473,
        0.00194873,  0.01498575,  0.00616927, -0.01562626, -0.01950474,
       -0.00951809, -0.00754946, -0.01478931, -0.01092594,  0.00464668,
        0.00144806,  0.00459074,  0.0138626 ,  0.0161684 , -0.00040095,
       -0.00075886, -0.00839019, -0.00918888, -0.0072783 , -0.00265845,
        0.00467066]

拟合结果图像 #

我尝试过调整隐藏单元数、网络层数及学习率，但结果仍不理想，怀疑存在欠拟合问题，却不知道该如何解决。

内容的提问来源于stack exchange，提问作者BonHaha