(编辑:jimmy 日期: 2024/12/25 浏览:2)
torch.optim集成了很多优化器,如SGD,Adadelta,Adam,Adagrad,RMSprop等,这些优化器自带的一个参数weight_decay,用于指定权值衰减率,相当于L2正则化中的λ参数,注意torch.optim集成的优化器只有L2正则化方法,你可以查看注释,参数weight_decay 的解析是:
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
使用torch.optim的优化器,可如下设置L2正则化
optimizer = optim.Adam(model.parameters(),lr=learning_rate,weight_decay=0.01)
(1)一般正则化,只是对模型的权重W参数进行惩罚,而偏置参数b是不进行惩罚的,而torch.optim的优化器weight_decay参数指定的权值衰减是对网络中的所有参数,包括权值w和偏置b同时进行惩罚。很多时候如果对b 进行L2正则化将会导致严重的欠拟合,因此这个时候一般只需要对权值w进行正则即可。(PS:这个我真不确定,源码解析是 weight decay (L2 penalty) ,但有些网友说这种方法会对参数偏置b也进行惩罚,可解惑的网友给个明确的答复)
(2)缺点:torch.optim的优化器固定实现L2正则化,不能实现L1正则化。如果需要L1正则化,可如下实现:
(3)根据正则化的公式,加入正则化后,loss会变原来大,比如weight_decay=1的loss为10,那么weight_decay=100时,loss输出应该也提高100倍左右。而采用torch.optim的优化器的方法,如果你依然采用loss_fun= nn.CrossEntropyLoss()进行计算loss,你会发现,不管你怎么改变weight_decay的大小,loss会跟之前没有加正则化的大小差不多。这是因为你的loss_fun损失函数没有把权重W的损失加上。
(4)采用torch.optim的优化器实现正则化的方法,是没问题的!只不过很容易让人产生误解,对鄙人而言,我更喜欢TensorFlow的正则化实现方法,只需要tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES),实现过程几乎跟正则化的公式对应的上。
(5)Github项目源码:点击进入
为了,解决这些问题,我特定自定义正则化的方法,类似于TensorFlow正则化实现方法。
一般来说,正则化的主要作用是避免模型产生过拟合,当然啦,过拟合问题,有时候是难以判断的。但是,要判断正则化是否作用了模型,还是很容易的。下面我给出两组训练时产生的loss和Accuracy的log信息,一组是未加入正则化的,一组是加入正则化:
优化器采用Adam,并且设置参数weight_decay=0.0,即无正则化的方法
optimizer = optim.Adam(model.parameters(),lr=learning_rate,weight_decay=0.0)
训练时输出的 loss和Accuracy信息
step/epoch:0/0,Train Loss: 2.418065, Acc: [0.15625] step/epoch:10/0,Train Loss: 5.194936, Acc: [0.34375] step/epoch:20/0,Train Loss: 0.973226, Acc: [0.8125] step/epoch:30/0,Train Loss: 1.215165, Acc: [0.65625] step/epoch:40/0,Train Loss: 1.808068, Acc: [0.65625] step/epoch:50/0,Train Loss: 1.661446, Acc: [0.625] step/epoch:60/0,Train Loss: 1.552345, Acc: [0.6875] step/epoch:70/0,Train Loss: 1.052912, Acc: [0.71875] step/epoch:80/0,Train Loss: 0.910738, Acc: [0.75] step/epoch:90/0,Train Loss: 1.142454, Acc: [0.6875] step/epoch:100/0,Train Loss: 0.546968, Acc: [0.84375] step/epoch:110/0,Train Loss: 0.415631, Acc: [0.9375] step/epoch:120/0,Train Loss: 0.533164, Acc: [0.78125] step/epoch:130/0,Train Loss: 0.956079, Acc: [0.6875] step/epoch:140/0,Train Loss: 0.711397, Acc: [0.8125]
优化器采用Adam,并且设置参数weight_decay=10.0,即正则化的权重lambda =10.0
optimizer = optim.Adam(model.parameters(),lr=learning_rate,weight_decay=10.0)
这时,训练时输出的 loss和Accuracy信息:
step/epoch:0/0,Train Loss: 2.467985, Acc: [0.09375] step/epoch:10/0,Train Loss: 5.435320, Acc: [0.40625] step/epoch:20/0,Train Loss: 1.395482, Acc: [0.625] step/epoch:30/0,Train Loss: 1.128281, Acc: [0.6875] step/epoch:40/0,Train Loss: 1.135289, Acc: [0.6875] step/epoch:50/0,Train Loss: 1.455040, Acc: [0.5625] step/epoch:60/0,Train Loss: 1.023273, Acc: [0.65625] step/epoch:70/0,Train Loss: 0.855008, Acc: [0.65625] step/epoch:80/0,Train Loss: 1.006449, Acc: [0.71875] step/epoch:90/0,Train Loss: 0.939148, Acc: [0.625] step/epoch:100/0,Train Loss: 0.851593, Acc: [0.6875] step/epoch:110/0,Train Loss: 1.093970, Acc: [0.59375] step/epoch:120/0,Train Loss: 1.699520, Acc: [0.625] step/epoch:130/0,Train Loss: 0.861444, Acc: [0.75] step/epoch:140/0,Train Loss: 0.927656, Acc: [0.625]
当weight_decay=10000.0
step/epoch:0/0,Train Loss: 2.337354, Acc: [0.15625] step/epoch:10/0,Train Loss: 2.222203, Acc: [0.125] step/epoch:20/0,Train Loss: 2.184257, Acc: [0.3125] step/epoch:30/0,Train Loss: 2.116977, Acc: [0.5] step/epoch:40/0,Train Loss: 2.168895, Acc: [0.375] step/epoch:50/0,Train Loss: 2.221143, Acc: [0.1875] step/epoch:60/0,Train Loss: 2.189801, Acc: [0.25] step/epoch:70/0,Train Loss: 2.209837, Acc: [0.125] step/epoch:80/0,Train Loss: 2.202038, Acc: [0.34375] step/epoch:90/0,Train Loss: 2.192546, Acc: [0.25] step/epoch:100/0,Train Loss: 2.215488, Acc: [0.25] step/epoch:110/0,Train Loss: 2.169323, Acc: [0.15625] step/epoch:120/0,Train Loss: 2.166457, Acc: [0.3125] step/epoch:130/0,Train Loss: 2.144773, Acc: [0.40625] step/epoch:140/0,Train Loss: 2.173397, Acc: [0.28125]
就整体而言,对比加入正则化和未加入正则化的模型,训练输出的loss和Accuracy信息,我们可以发现,加入正则化后,loss下降的速度会变慢,准确率Accuracy的上升速度会变慢,并且未加入正则化模型的loss和Accuracy的浮动比较大(或者方差比较大),而加入正则化的模型训练loss和Accuracy,表现的比较平滑。
并且随着正则化的权重lambda越大,表现的更加平滑。这其实就是正则化的对模型的惩罚作用,通过正则化可以使得模型表现的更加平滑,即通过正则化可以有效解决模型过拟合的问题。
为了解决torch.optim优化器只能实现L2正则化以及惩罚网络中的所有参数的缺陷,这里实现类似于TensorFlow正则化的方法。
这里封装成一个实现正则化的Regularization类,各个方法都给出了注释,自己慢慢看吧,有问题再留言吧
# 检查GPU是否可用 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # device='cuda' print("-----device:{}".format(device)) print("-----Pytorch version:{}".format(torch.__version__)) class Regularization(torch.nn.Module): def __init__(self,model,weight_decay,p=2): ''' :param model 模型 :param weight_decay:正则化参数 :param p: 范数计算中的幂指数值,默认求2范数, 当p=0为L2正则化,p=1为L1正则化 ''' super(Regularization, self).__init__() if weight_decay <= 0: print("param weight_decay can not <=0") exit(0) self.model=model self.weight_decay=weight_decay self.p=p self.weight_list=self.get_weight(model) self.weight_info(self.weight_list) def to(self,device): ''' 指定运行模式 :param device: cude or cpu :return: ''' self.device=device super().to(device) return self def forward(self, model): self.weight_list=self.get_weight(model)#获得最新的权重 reg_loss = self.regularization_loss(self.weight_list, self.weight_decay, p=self.p) return reg_loss def get_weight(self,model): ''' 获得模型的权重列表 :param model: :return: ''' weight_list = [] for name, param in model.named_parameters(): if 'weight' in name: weight = (name, param) weight_list.append(weight) return weight_list def regularization_loss(self,weight_list, weight_decay, p=2): ''' 计算张量范数 :param weight_list: :param p: 范数计算中的幂指数值,默认求2范数 :param weight_decay: :return: ''' # weight_decay=Variable(torch.FloatTensor([weight_decay]).to(self.device),requires_grad=True) # reg_loss=Variable(torch.FloatTensor([0.]).to(self.device),requires_grad=True) # weight_decay=torch.FloatTensor([weight_decay]).to(self.device) # reg_loss=torch.FloatTensor([0.]).to(self.device) reg_loss=0 for name, w in weight_list: l2_reg = torch.norm(w, p=p) reg_loss = reg_loss + l2_reg reg_loss=weight_decay*reg_loss return reg_loss def weight_info(self,weight_list): ''' 打印权重列表信息 :param weight_list: :return: ''' print("---------------regularization weight---------------") for name ,w in weight_list: print(name) print("---------------------------------------------------")
使用方法很简单,就当一个普通Pytorch模块来使用:例如
# 检查GPU是否可用 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print("-----device:{}".format(device)) print("-----Pytorch version:{}".format(torch.__version__)) weight_decay=100.0 # 正则化参数 model = my_net().to(device) # 初始化正则化 if weight_decay>0: reg_loss=Regularization(model, weight_decay, p=2).to(device) else: print("no regularization") criterion= nn.CrossEntropyLoss().to(device) # CrossEntropyLoss=softmax+cross entropy optimizer = optim.Adam(model.parameters(),lr=learning_rate)#不需要指定参数weight_decay # train batch_train_data=... batch_train_label=... out = model(batch_train_data) # loss and regularization loss = criterion(input=out, target=batch_train_label) if weight_decay > 0: loss = loss + reg_loss(model) total_loss = loss.item() # backprop optimizer.zero_grad()#清除当前所有的累积梯度 total_loss.backward() optimizer.step()
训练时输出的 loss和Accuracy信息:
(1)当weight_decay=0.0时,未使用正则化
step/epoch:0/0,Train Loss: 2.379627, Acc: [0.09375] step/epoch:10/0,Train Loss: 1.473092, Acc: [0.6875] step/epoch:20/0,Train Loss: 0.931847, Acc: [0.8125] step/epoch:30/0,Train Loss: 0.625494, Acc: [0.875] step/epoch:40/0,Train Loss: 2.241885, Acc: [0.53125] step/epoch:50/0,Train Loss: 1.132131, Acc: [0.6875] step/epoch:60/0,Train Loss: 0.493038, Acc: [0.8125] step/epoch:70/0,Train Loss: 0.819410, Acc: [0.78125] step/epoch:80/0,Train Loss: 0.996497, Acc: [0.71875] step/epoch:90/0,Train Loss: 0.474205, Acc: [0.8125] step/epoch:100/0,Train Loss: 0.744587, Acc: [0.8125] step/epoch:110/0,Train Loss: 0.502217, Acc: [0.78125] step/epoch:120/0,Train Loss: 0.531865, Acc: [0.8125] step/epoch:130/0,Train Loss: 1.016807, Acc: [0.875] step/epoch:140/0,Train Loss: 0.411701, Acc: [0.84375]
(2)当weight_decay=10.0时,使用正则化
--------------------------------------------------- step/epoch:0/0,Train Loss: 1563.402832, Acc: [0.09375] step/epoch:10/0,Train Loss: 1530.002686, Acc: [0.53125] step/epoch:20/0,Train Loss: 1495.115234, Acc: [0.71875] step/epoch:30/0,Train Loss: 1461.114136, Acc: [0.78125] step/epoch:40/0,Train Loss: 1427.868164, Acc: [0.6875] step/epoch:50/0,Train Loss: 1395.430054, Acc: [0.6875] step/epoch:60/0,Train Loss: 1363.358154, Acc: [0.5625] step/epoch:70/0,Train Loss: 1331.439697, Acc: [0.75] step/epoch:80/0,Train Loss: 1301.334106, Acc: [0.625] step/epoch:90/0,Train Loss: 1271.505005, Acc: [0.6875] step/epoch:100/0,Train Loss: 1242.488647, Acc: [0.75] step/epoch:110/0,Train Loss: 1214.184204, Acc: [0.59375] step/epoch:120/0,Train Loss: 1186.174561, Acc: [0.71875] step/epoch:130/0,Train Loss: 1159.148438, Acc: [0.78125] step/epoch:140/0,Train Loss: 1133.020020, Acc: [0.65625]
(3)当weight_decay=10000.0时,使用正则化
step/epoch:0/0,Train Loss: 1570211.500000, Acc: [0.09375] step/epoch:10/0,Train Loss: 1522952.125000, Acc: [0.3125] step/epoch:20/0,Train Loss: 1486256.125000, Acc: [0.125] step/epoch:30/0,Train Loss: 1451671.500000, Acc: [0.25] step/epoch:40/0,Train Loss: 1418959.750000, Acc: [0.15625] step/epoch:50/0,Train Loss: 1387154.000000, Acc: [0.125] step/epoch:60/0,Train Loss: 1355917.500000, Acc: [0.125] step/epoch:70/0,Train Loss: 1325379.500000, Acc: [0.125] step/epoch:80/0,Train Loss: 1295454.125000, Acc: [0.3125] step/epoch:90/0,Train Loss: 1266115.375000, Acc: [0.15625] step/epoch:100/0,Train Loss: 1237341.000000, Acc: [0.0625] step/epoch:110/0,Train Loss: 1209186.500000, Acc: [0.125] step/epoch:120/0,Train Loss: 1181584.250000, Acc: [0.125] step/epoch:130/0,Train Loss: 1154600.125000, Acc: [0.1875] step/epoch:140/0,Train Loss: 1128239.875000, Acc: [0.125]
对比torch.optim优化器的实现L2正则化方法,这种Regularization类的方法也同样达到正则化的效果,并且与TensorFlow类似,loss把正则化的损失也计算了。
此外更改参数p,如当p=0表示L2正则化,p=1表示L1正则化。
《Github项目源码》点击进入
以上为个人经验,希望能给大家一个参考,也希望大家多多支持。如有错误或未考虑完全的地方,望不吝赐教。