Multivariate regression gradient descent algorithm implementation (SGD optimization) (data set randomly generated)

Multivariate regression gradient descent algorithm implementation (SGD optimization) (data set randomly generated)

Below is the code. In fact, the blogger has done a lot of experiments. Whether the experimental results are good or not depends on the quality of the data set, the selection of the learning rate, and the selection of the SGD optimizer batch.

Let's take a look at the code:

import torch
from torch.autograd import Variable
from torch.utils import data
X =torch.normal(0,100,(100,4))
w=torch.tensor([1,2,3,4])


Y =torch.matmul(X, w.type(dtype=torch.float))  + torch.normal(0, 1, (100, ))
print(Y)
Y=Y.reshape((-1, 1))
print(Y.type())
print(w.type())
print(X.type())
#将X，Y转成200 batch大小，1维度的数据

def load_array(data_arrays, batch_size, is_train=True):
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)

data_iter = load_array((X, Y), 32)


model = torch.nn.Sequential(torch.nn.Linear(4, 1))


w=torch.tensor([1,0.1,0.2,0.3])
b=torch.randn(1)
w=Variable(w,requires_grad=True)

b=Variable(b,requires_grad=True)
print(w)



def loss_function(w,x,y,choice,b):
    if choice==1:
        return torch.abs(torch.sum(w@x)+b-y)
    else:
      #    print("fdasf:",torch.sum(w@x),y)
       #   print(torch.pow(torch.sum(w@x)-y,2))
          return torch.sum(w@x)-y
index=0

n=1000
batch=32
learning_rating=0.000001
def SGD(batch):
    grad=Variable(torch.tensor([0.0]),requires_grad=True)
  
   
    for j in range(batch):
        try:
           #  print(w,X[index],Y[index],b,)
         #    print(loss_function(w,X[index],Y[index],b,2))
           #  print(torch.sum(w@X[index]),Y[index])
             grad=(torch.sum(w@X[index])-Y[index])*(-1)*X[index]+grad
            
             loss=loss_function(w,X[index],Y[index],2,b)
             index=index+1
        except:
             index=0
        
    return  grad/batch,loss/batch

while n:
    n=n-1
  
    grad,loss=SGD(batch)
    w.data=w.data+learning_rating*grad*w.data
   # print("b",b)
    print("w:",w)
    print("loss:",loss)
  #  b.data=b.data-(learning_rating*b.grad.data)
 #   print("b",b)