Pytorch 的线性回归：常数损失

Question

我正在使用 Pytorch 处理线性回归问题（y=A*x，其中 A 的维度为 2x2）。我写了下面的代码。我不知道为什么损失没有改变...有人可以帮助我吗？

谢谢，

托马斯

import torch
import numpy as np
from scipy.integrate import odeint
from matplotlib import pyplot as plt
from torch.autograd import Variable

def EDP(X,t):
    X_0=-2*X[0]
    X_1=-2*X[1]
    grad=np.array([X_0,X_1])
    return grad

T=np.arange(0,10,0.1)
X_train=odeint(EDP,[10,20],T)

Y_train=np.zeros_like(X_train)
for i in range(Y_train.shape[0]):
    Y_train[i,:]=np.dot(np.array([[2,0],[0,2]]),X_train[i,:])
print(X_train,Y_train)

X_train=torch.Tensor(X_train)
torch.transpose(X_train,0,1)
Y_train=torch.Tensor(Y_train)
print(X_train.shape)

import torch.nn as nn

class LinearRegression(torch.nn.Module): 
    
    def __init__(self):
        super(LinearRegression, self).__init__() 
        self.linear = torch.nn.Linear(2,2,bias = False) # bias is default True

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred

criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(our_model.parameters(), lr = 0.0001)

our_model = LinearRegression()

x_train = X_train
y_train = Y_train
#x_train.requires_grad=True
print(x_train.shape)
print(y_train.shape)
ntrain=10

for t in range(ntrain):
    
    y_pred=our_model(x_train)
    loss=criterion(y_train,y_pred)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()
    print(t,loss)

print(our_model.linear.weight)

Answer 1

在我的笔记本电脑上它工作...
因为你运行它只在 10 epochs ... 并且使用 lr = 0.0001 ，你不会在 10 epochs.

中看到它

我这样做了 optimizer = torch.optim.SGD(our_model.parameters(), lr = 0.01)（增加了 lr ）实际上只用了 10 个周期就减少了损失

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