reduce(bool)- 返回值是否为标量,默认为True ignore_index(int)- 忽略某一类别,不计算其loss,其loss会为0,并且,在采用size_average时,不会计算那一类的loss,除的时候的分母也不会统计那一类的样本。 实例: /Code/3_optimizer/3_1_lossFunction/3_CroosEntropyLoss.py 补充: output不仅可以是向量,还可以是图...
为True时,返回的loss为除以权重之和的平均值;为False时,返回的各样本的loss之和。 reduce(bool)- 返回值是否为标量,默认为True。 ignore_index(int)- 忽略某一类别,不计算其loss,其loss会为0,并且,在采用size_average时,不会计算那一类的loss,除...
output= loss(input, target)#tensor(1.9424, grad_fn=<L1LossBackward>) output= (|-0.0625-0.6789| + |-2.1603-0.9831|) / 2 =1.9424 7.MSELoss(L2 norm) 创建一个criterion计算input x和target y的每个元素的均方误差(mean absolute error (MAE)) unreduced loss函数(即reduction参数设置为'none')为: ...
fromtorch.nnimportL1Lossinputs=torch.tensor([1,2,3],dtype=torch.float32)targets=torch.tensor([1,2,5],dtype=torch.float32)loss=L1Loss(reduction='mean')result=loss(inputs,targets)print(result)#结果为(|1-1| + |2-2|+ |3-5|)/3 = tensor(0.6667) MSELoss 作用:衡量input X 和target ...
pytorch里的weight decay相当于l2正则 2 常见 Loss L1 loss import torch input = torch.randn(3, 5, requires_grad=True) target = torch.randn(3, 5) mae_loss = torch.nn.L1Loss() output = mae_loss(input, target) 1. 2. 3. 4.
loss_f_sum = nn.CrossEntropyLoss(weight=None, reduction='sum') loss_f_mean = nn.CrossEntropyLoss(weight=None, reduction='mean') # forward loss_none = loss_f_none(inputs, target) loss_sum = loss_f_sum(inputs, target) loss_mean = loss_f_mean(inputs, target) ...
2:torch.nn.MSELoss measures the mean squared error (squared L2 norm) between each element in the inputx and targety. loss =nn.MSELoss() input= torch.randn(1, 2, requires_grad=True) target= torch.randn(1, 2) output= loss(input, target) ...
features=1),torch.nn.ReLU(),)def forward(self,x):return self.layers(x)linear = Linear()opt = torch.optim.Adam(linear.parameters(),lr= 0.005)loss = torch.nn.MSELoss()for epoch in range(3000):l = 0L2 = 0for iter in range(10):for w in linear.parameters():L2 = torch.norm(w,...
(net(train_features),train_labels).mean().item())test_ls.append(loss(net(test_features),test_labels).mean().item())d2l.semilogy(range(1,num_epochs+1),train_ls,'epochs','loss',range(1,num_epochs+1),test_ls,['train','test'])print('L2 norm of w:',net.weight.data.norm()....
1.3 L2正则项——weight_decay 从直观上讲,L2正则化(weight_decay)使得训练的模型在兼顾最小化分类(或其他目标)的Loss的同时,使得权重w尽可能地小,从而将权重约束在一定范围内,减小模型复杂度;同时,如果将w约束在一定范围内,也能够有效防止梯度爆炸。