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000动手从0实现线性回归

0. 背景介绍

我们构造一个简单的人工训练数据集，它可以使我们能够直观比较学到的参数和真实的模型参数的区别。
设训练数据集样本数为1000，输入个数（特征数）为2。给定随机生成的批量样本特征 X∈R1000×2
X∈R 1000×2 ，我们使用线性回归模型真实权重 w=[2,−3.4]⊤ 和偏差 b=4.2以及一个随机噪声项 ϵϵ 来生成标签

# 需要导入的包
import numpy as np
import torch
import random
from d2l import torch as d2l
from IPython import display
from matplotlib import pyplot as plt

1. 生成数据集合（待拟合）

使用python生成待拟合的数据

num_input = 2
num_example = 1000
w_true = [2,-3.4]
b_true = 4.2
features = torch.randn(num_example,num_input)
print('features.shape = '+ str(features.shape) )
labels =  w_true[0] * features[:,0] + w_true[1] * features[:,1] + b_true
labels += torch.tensor(np.random.normal(0,0.01 , size = labels.size() ),dtype = torch.float32)
print(features[0],labels[0])

2.数据的分批量处理

def data_iter(batch_size, features, labels):num_example = len(labels)indices = list(range(num_example))random.shuffle(indices)for i in range(0, num_example, batch_size):j = torch.tensor( indices[i:min(i+ batch_size,num_example)])yield features.index_select(0,j) ,labels.index_select(0,j)

3. 模型构建及训练

3.1 定义模型：

def linreg(X, w, b):return torch.mm(X,w)+b

3.2 定义损失函数

def square_loss(y, y_hat):return (y_hat - y.view(y_hat.size()))**2/2

3.3 定义优化算法

def sgd(params , lr ,batch_size):for param in params:param.data  -= lr * param.grad / batch_size

3.4 模型训练

# 设置超参数
lr = 0.03
num_epochs =5
net = linreg
loss = square_loss
batch_size = 10
for epoch in range(num_epochs):for X,y in data_iter(batch_size= batch_size,features=features,labels= labels):l = loss(net(X,w,b),y).sum()l.backward()sgd([w,b],lr,batch_size=batch_size)#梯度清零避免梯度累加w.grad.data.zero_()b.grad.data.zero_()train_l = loss(net(features,w,b),labels)print('epoch %d, loss %f' %(epoch +1 ,train_l.mean().item()))

epoch 1, loss 0.032550
epoch 2, loss 0.000133
epoch 3, loss 0.000053
epoch 4, loss 0.000053
epoch 5, loss 0.000053

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基于pytorch的线性模型的实现

1. 相关数据和初始化与上面构建相同
2. 定义模型

import torch
from torch import nn
class LinearNet(nn.Module):def __init__(self, n_feature):# 调用父类的初始化super(LinearNet,self).__init__()# Linear(输入特征数，输出特征的数量，是否含有偏置项)self.linera = nn.Linear(n_feature,1)def forward(self,x):y = self.linera(x)return y
#打印模型的结构：
net = LinearNet(num_input)
print(net) 
# LinearNet( (linera): Linear(in_features=2, out_features=1, bias=True)
)

3. 初始化模型的参数

from torch.nn import init
init.normal_(net.linera.weight,mean=0,std= 0.1)
init.constant_(net.linera.bias ,val=0)

4. 定义损失函数

loss = nn.MSELoss()

5.定义优化算法

import torch.optim as optim
optimizer =  optim.SGD(net.parameters(),lr = 0.03)
print(optimizer)

5. 训练模型：

num_epochs = 3
for epoch in range(1,num_epochs+1):for X,y in data_iter(batch_size= batch_size,features=features,labels= labels):output= net(X)l = loss(output,y.view(-1,1))optimizer.zero_grad()l.backward()optimizer.step()print('epoch %d ,loss: %f' %(epoch,l.item()) )

epoch 1 ,loss: 0.000159
epoch 2 ,loss: 0.000089
epoch 3 ,loss: 0.000066