pytorch实现线性回归例子

import torch
from IPython import display
from matplotlib import pyplot as plt 
import numpy as np
import random

num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2
features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)
labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)

def use_svg_display():   
    # 用矢量图显示
    display.set_matplotlib_formats('svg')
    
def set_figsize(figsize=(3.5, 2.5)):
    use_svg_display()
    # 设置图的尺寸
    plt.rcParams['figure.figsize'] = figsize
    set_figsize()
    plt.scatter(features[:, 1].numpy(), labels.numpy(), 1);
    
def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    random.shuffle(indices)  # 样本的读取顺序是随机的
    for i in range(0, num_examples, batch_size):
        j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)]) # 最后一次可能不足一个batch
        yield  features.index_select(0, j), labels.index_select(0, j)
        
w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, 1)), dtype=torch.float32)
b = torch.zeros(1, dtype=torch.float32)

w.requires_grad_(requires_grad=True)
b.requires_grad_(requires_grad=True) 

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

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

def sgd(params, lr, batch_size):    
    for param in params:      
        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data

 
lr = 0.03
num_epochs = 3
batch_size = 10
net = linreg
loss = squared_loss
for epoch in range(num_epochs):       # 训练模型一共需要num_epochs个迭代周期
        # 在每一个迭代周期中，会使用训练数据集中所有样本一次
        
        for X, y in data_iter(batch_size, features, labels):       # x和y分别是小批量样本的特征和标签
            l = loss(net(X, w, b), y).sum()     # l是有关小批量X和y的损失      
            l.backward()     # 小批量的损失对模型参数求梯度    
            sgd([w, b], lr, batch_size)     # 使用小批量随机梯度下降迭代模型参数   
            w.grad.data.zero_()    # 梯度清零
            b.grad.data.zero_()
            train_l = loss(net(features, w, b), labels)
            print('epoch %d, loss %f' % (epoch + 1, train_l.mean().item()))
            print(true_w, '\n', w)
            print(true_b, '\n', b)

执行结果：