ctp程序化交易接口-L2权重衰减就是L2正则化的代码实现

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0.9999让他进行衰减呢

L2权重衰减就是L2正则化的代码实现

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∗coeff∗reduce_sum)lossmathrel{+}=0.5*coeff*reduce\_sum)loss+=0.5∗coeff∗reduce_sum)

coeffcoeffcoeff就是权重衰减系数或者叫正则化系数

下面用L2DecayAPI来进行一个小实验：

import paddle
from paddle.regularizer import L2Decay

paddle.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APIL2Decay，手动用权重的范数来做一下

import paddle
from paddle.regularizer import L2Decay

paddle.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()

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)

文章为作者独立观点，不代表股票交易接口观点