使用 Pytorch 的线性回归：恒定损失

Question

I'm working on a linear regression problem with Pytorch (y=A*x, where the dimensions of A are 2x2). I wrote the following code. I don't know why the loss doesn't change... Can someone help me?

Thanks,

Thomas

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

In my laptop it worked...
since you are running it on just 10 epochs ...and using lr = 0.0001 ,you wont see it in just 10 epochs .

i did this optimizer = torch.optim.SGD(our_model.parameters(), lr = 0.01) (increased lr )which actually decreased the loss in just 10 epochs