分析一下,当预测值f(xi)和真实值yi差别较小的时候(绝对值差小于1),其实使用的是L2 loss;差别大的时候,使用的是L1 loss的平移。因此,Smooth L1 loss其实是L1 loss 和L2 loss的结合,同时拥有两者的部分优点: 真实值和预测值差别较小时(绝对值差小于1),梯度也会比较小(损失函数比普通L1 loss...
# 需要导入模块: import torch [as 别名]# 或者: from torch importnorm[as 别名]defloss_l2(self, l2=0):"""L2 loss centered around mu_init, scaled optionally per-source. In other words, diagonal Tikhonov regularization, ||D(\mu-\mu_{init})||_2^2 where D is diagonal. Args: - l2: ...
2022.6 torch中使用l1 l2正则的写法 直接自己写 model是要正则的模型,reg_type选择'l1'还是l2,coef是系数。 def regularization(model:nn.Module, reg_type,coef): int_type=int(reg_type[1]) reg_loss = 0 for module in model.modules(): for param in module.parameters(): reg_loss+=torch.norm(par...
loss = torch.mean(loss)ifl2 !=0: loss += l2 * (torch.norm(z_p) + torch.norm(z_n) + torch.norm(z_d))returnloss, l_n, l_d, l_nd 开发者ID:ml-lab,项目名称:tile2vec,代码行数:12,代码来源:tilenet.py 示例12: th_pearsonr ▲点赞 1▼ defth_pearsonr(x, y):""" mimics ...
pytorch中梯度剪裁方法为 **torch.nn.utils.clip_grad_norm_(parameters, max_norm, norm_type=2)**。三个参数: _parameters:希望实施梯度裁剪的可迭代网络参数 max_norm:该组网络参数梯度的范数上限 norm_type:范数类型 L1/L2/L3 官方对该方法的描述为: ...
(1)# 用索引列表获取耦合通道对应的参数,并展开成2维local_norm=w.abs().sum(1)# 计算每个通道参数子矩阵的 L1 Normgroup_imp.append(local_norm)# 将其保存在列表中iflen(group_imp)==0:returnNone# 跳过不包含卷积层的分组# 4. 按通道计算平均重要性group_imp=torch.stack(group_imp,dim=0).mean(...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/nn/modules/loss.py at main · pytorch/pytorch
torch.norm() 求模/求范数 模就是二范数 torch.norm(input, p='fro', dim=None, keepdim=False, out=None, dtype=None) 输入代码 import torch rectangle_height = 3 rectangle_width = 4 inputs = torch.randn(rectangle_height, rectangle_width) for i in range(rectangle_height): for j in range...
norm:范数 mean: sum prod max,min, argmin,argmax:返回索引,打平;可以加维度 kthvalue topk 2.api norm-p 保证不被打平: 十:激活函数与loss 1.sigmoid 求导情况: 两边接近0,1: # sigmoid a= torch.linspace(-100,100,10) b=torch.sigmoid(a) ...
def sisnr(x, s, eps=1e-8): """ calculate training loss input: x: separated signal, N x S tensor s: reference signal, N x S tensor Return: sisnr: N tensor """ def l2norm(mat, keepdim=False): return torch.norm(mat, dim=-1, keepdim=keepdim) if x.shape != s.shape: rai...