权重衰退——pytroch版

import torch
from torch import nn
from d2l import torch as d2l

# 将数据做的很小，这样容易实现过拟合
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = torch.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter = d2l.load_array(train_data, batch_size)
test_data = d2l.synthetic_data(true_w, true_b, n_test)
test_iter = d2l.load_array(test_data, batch_size, is_train=False)

# 初始化参数模型
def init_params():
    w = torch.normal(0, 1, size=(num_inputs, 1), requires_grad=True)
    b = torch.zeros(1, requires_grad=True)
    return [w, b]

# L2范数惩罚
def l2_penalty(w):
    return torch.sum(w.pow(2))/2

# 训练
def train(lambd):
    # 初始化参数
    w,b=init_params()
    # 线性回归，平方损失函数
    net,loss=lambda x:d2l.linreg(x,w,b),d2l.squared_loss
    num_epochs,lr = 100,0.003
    animator = d2l.Animator(xlabel='epochs',ylabel='loss',yscale='log',
                            xlim=[5,num_epochs],legend=['train','test'])
    for epoch in range(num_epochs):
        for x,y in train_iter:
            # 增加了l2范数惩罚项
            # 广播机制使l2_penalty(w)成为一个长度为batch_size的向量
            l = loss(net(x),y)+lambd*l2_penalty(w)
            l.sum().backward()
            d2l.sgd([w,b],lr,batch_size)
        if (epoch+1)%5==0:
            animator.add(epoch+1,(d2l.evaluate_loss(net,train_iter,loss),
                                  d2l.evaluate_loss(net,test_iter,loss),))
    print('w的L2范数是：',torch.norm(w).item())

# 使用权重参数
# train(lambd=10)
import torch
from torch import nn
from d2l import torch as d2l

# 将数据做的很小，这样容易实现过拟合
n_train, n_test, num_inputs, batch_size = 20, 100, 200, 5
true_w, true_b = torch.ones((num_inputs, 1)) * 0.01, 0.05
train_data = d2l.synthetic_data(true_w, true_b, n_train)
train_iter = d2l.load_array(train_data, batch_size)
test_data = d2l.synthetic_data(true_w, true_b, n_test)
test_iter = d2l.load_array(test_data, batch_size, is_train=False)

# 初始化参数模型
def init_params():
    w = torch.normal(0, 1, size=(num_inputs, 1), requires_grad=True)
    b = torch.zeros(1, requires_grad=True)
    return [w, b]

# L2范数惩罚
def l2_penalty(w):
    return torch.sum(w.pow(2))/2

# 训练
def train(lambd):
    # 初始化参数
    w,b=init_params()
    # 线性回归，平方损失函数
    net,loss=lambda x:d2l.linreg(x,w,b),d2l.squared_loss
    num_epochs,lr = 100,0.003
    animator = d2l.Animator(xlabel='epochs',ylabel='loss',yscale='log',
                            xlim=[5,num_epochs],legend=['train','test'])
    for epoch in range(num_epochs):
        for x,y in train_iter:
            # 增加了l2范数惩罚项
            # 广播机制使l2_penalty(w)成为一个长度为batch_size的向量
            l = loss(net(x),y)+lambd*l2_penalty(w)
            l.sum().backward()
            d2l.sgd([w,b],lr,batch_size)
        if (epoch+1)%5==0:
            animator.add(epoch+1,(d2l.evaluate_loss(net,train_iter,loss),
                                  d2l.evaluate_loss(net,test_iter,loss),))
    print('w的L2范数是：',torch.norm(w).item())

# 使用权重参数
# train(lambd=10)