课程链接

线性回归的从零开始实现

import random
import torch
from d2l import torch as d2l# 人造数据集
def synthetic_data(w,b,num_examples):X=torch.normal(0,1,(num_examples,len(w)))y=torch.matmul(X,w)+by+=torch.normal(0,0.01,y.shape) # 加入噪声return X,y.reshape(-1,1) # y从行向量转为列向量
true_w=torch.tensor([2,-3.4])
true_b=4.2
features,labels=synthetic_data(true_w,true_b,1000)print('features:',features[0],'\nlabels:',labels[0])#绘图展示
d2l.set_figsize()
d2l.plt.scatter(features[:,1].detach().numpy(),labels.detach().numpy(),1);
d2l.plt.show()
# 读数据集
def data_iter(batch_size,features,labels):num_examples=len(features) #看一下有多少个样本indices=list(range(num_examples))# 生成0-999的元组，然后将range()返回的可迭代对象转为一个列表random.shuffle(indices)# 将序列的所有元素随机排序(打乱下标)for i in range(0,num_examples,batch_size): #从0到最后，每次取batch_size个大小batch_indices=torch.tensor(indices[i:min(i+batch_size,num_examples)]) #超出样本个数没有拿满的话取最小值yield features[batch_indices],labels[batch_indices]batch_size=10
for X,y in data_iter(batch_size,features,labels):#给一些样本标号，每一次随机从里面选取b个样本返回print(X,'\n',y)break#定义初始化模型参数
w=torch.normal(0,0.01,size=(2,1),requires_grad=True)
b=torch.zeros(1,requires_grad=True)
#定义模型
def linreg(X,w,b):return torch.matmul(X,w)+b#定义损失函数
def squared_loss(y_hat,y): #均方损失return (y_hat-y.reshape(y_hat.shape))**2/2
#定义优化算法
def sgd(params,lr,batch_size):with torch.no_grad():for param in params:param-=lr*param.grad/batch_sizeparam.grad.zero_()#训练过程
lr=0.03
num_epochs=3
net=linreg
loss=squared_loss
for epoch in range(num_epochs):for X,y in data_iter(batch_size,features,labels):l=loss(net(X,w,b),y)l.sum().backward()sgd([w,b],lr,batch_size)with torch.no_grad():train_l=loss(net(features,w,b),labels)print(f'epoch{epoch+1},loss{float(train_l.mean()):f}')#比较真实参数和训练得来的参数评估训练的成功程度
print(f'w的估计误差：{true_w-w.reshape(true_w.shape)}')
print(f'b的估计误差：{true_b-b}')

运行结果

线性回归的简洁实现

import random
import numpy as np
import torch
from torch.utils import data
from d2l import torch as d2l
from torch import nn
#使用框架生成数据集
true_w=torch.tensor([2,-3.4])
true_b=4.2
features,labels=d2l.synthetic_data(true_w,true_b,1000)
#使用框架现有的API读取数据
def load_array(data_arrays,batch_size,is_train=True):dataset=data.TensorDataset(*data_arrays)return data.DataLoader(dataset,batch_size,shuffle=is_train)
batch_size=10
data_iter=load_array((features,labels),batch_size)
print(next(iter(data_iter)))
# 模型的定义
#使用框架预定义好的层
net=nn.Sequential(nn.Linear(2,1))
# 初始化模型参数
net[0].weight.data.normal_(0,0.01)
net[0].bias.data.fill_(0)
loss=nn.MSELoss()
trainer=torch.optim.SGD(net.parameters(),lr=0.03)
#训练
num_epochs=3
for epoch in range(num_epochs):for X,y in data_iter:l=loss(net(X),y)trainer.zero_grad()l.backward()trainer.step()l=loss(net(features),labels)print(f'epoch{epoch+1},loss{l:f}')