本文以Paddle为例，torch操作也差不多

我在5年前管这个限制参数过大的东西叫正则化，结果现在叫权重衰减了hhh

有点儿跟不上潮流了，我还以以为这个权重衰减是每次迭代给权重乘以一个0.99999，让他进行衰减呢(这样操作也衰减的太快了吧，显然不是)

然而实际上和正则化是一个东西

都在loss后边加一个参数的 L1/L2 范数，当成损失函数进行优化

$$loss+=0.5\ast coeff\ast reduce\_sum(square(x))$$

$coeff$就是权重衰减系数 或者叫 正则化系数

下面用 L2Decay API来进行一个小实验：

import paddle
from paddle.regularizer import L2Decaypaddle.seed(1107)linear = paddle.nn.Linear(3, 4, bias_attr=False)
old_linear_weight = linear.weight.detach().clone()
# print(old_linear_weight.mean())inp = paddle.rand(shape=[2, 3], dtype="float32")
out = linear(inp)coeff = 0.1
loss = paddle.mean(out)
# loss += 0.5 * coeff  * (linear.weight ** 2).sum()momentum = paddle.optimizer.Momentum(learning_rate=0.1,parameters=linear.parameters(),weight_decay=L2Decay(coeff))loss.backward()
momentum.step()delta_weight = linear.weight - old_linear_weight# print(linear.weight.mean())# print( - delta_weight / linear.weight.grad ) # 学习率
print( delta_weight )momentum.clear_grad()

打印结果：

接下来，不用API L2Decay ，手动用权重的范数来做一下

import paddle
from paddle.regularizer import L2Decaypaddle.seed(1107)linear = paddle.nn.Linear(3, 4, bias_attr=False)
old_linear_weight = linear.weight.detach().clone()
# print(old_linear_weight.mean())inp = paddle.rand(shape=[2, 3], dtype="float32")
out = linear(inp)coeff = 0.1
loss = paddle.mean(out)
loss += 0.5 * coeff  * (linear.weight ** 2).sum()momentum = paddle.optimizer.Momentum(learning_rate=0.1,parameters=linear.parameters(),# weight_decay=L2Decay(coeff))loss.backward()
momentum.step()delta_weight = linear.weight - old_linear_weight# print(linear.weight.mean())# print( - delta_weight / linear.weight.grad ) # 学习率
print( delta_weight )momentum.clear_grad()

API参考地址：

https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/regularizer/L2Decay_cn.html#paddle.regularizer.L2Decay

Paddle中：
对于一个可训练的参数，如果在 ParamAttr 中定义了正则化，那么会忽略 optimizer 中的正则化

my_conv2d = Conv2D(in_channels=10,out_channels=10,kernel_size=1,stride=1,padding=0,weight_attr=ParamAttr(regularizer=L2Decay(coeff=0.01)),  # <----- 此处优先级高bias_attr=False)