L2范数是最熟悉的,它就是欧几里得距离,公式如下: L2范数有很多名称,有人把它的回归叫“岭回归”(Ridge Regression),也有人叫它“权值衰减”(Weight Decay)。以L2范数作为正则项可以得到稠密解,即每个特征对应的参数w L1范数和L2范数的区别 引入PRML一个经典的图来说明下L1和L2范数的区别,如下图所示: 如上图所...
transforms.ToTensor(), # 这表示转成Tensor类型的数据 transforms.Normalize((0.1307,), (0.3081,)) # 这里是进行数据标准化(减去均值除以方差) ])), batch_size=batch_size, shuffle=True) # 按batch_size分出一个batch维度在最前面,shuffle=True打乱顺序 # 测试集 test_loader = torch.utils.data.DataLoade...
pytorch_l2_normalize.py import torch import tensorflow as tf ### PyTorch Version 1 ### x = torch.randn(5, 6) norm_th = x/torch.norm(x, p=2, dim=1, keepdim=True) norm_th[torch.isnan(norm_th)] = 0 # to avoid nan ### PyTorch Version 2 ### norm_th = torch.nn.functional...
我们通过代码来学习pytorch得L2正则项,在pytorch中,L2正则项又叫做weight decay(权值衰减)。我们来看看为啥叫权值衰减,是怎么衰减了。首先,我们原来得时候,参数得更新公式是这样得: 而现在,我们得Obj加上了一个L2正则项Obj = Loss + \frac{\lambda}{2}*\sum_{i}^{N}{w_i}^2,那么参数得更新方式也就变成...
torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W) # Bilinear poolingassert X.size() == (N, D, D)X = torch.reshape(X, (N, D * D))X = torch.sign(X) * torch.sqrt(torch.abs(X) + 1e-5) # Signed-sqrt normalizationX = torch.nn.function...
注意:如果不使用softmax,使用sigmoid归一化分数到了0-1之间,如果想将其变为概率分布,可以使用l1/l2 normalize(we 𝐿1-normalized the aspect weights so that they sum up to one)【Pytorch】F.normalize计算理解 函数定义torch.nn.functional.normalize(input, p=2.0, dim=1, eps=1e-12, out=None) ...
class UTKFace(Dataset): def __init__(self, image_paths): self.transform = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) self.image_paths = image_paths self.images = []...
torch.optimasoptim#优化器的包# 1.prepare dataset#要使用dataset,dataloader所以要设置batch容量#ToTensor讲原始图片转成图像张量(维度1->3,像素值属于【0,1】#Normalize(均值,标准差)像素值切换成0,1分布batch_size =64transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (...
= torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W) # Bilinear poolingassert X.size == (N, D, D)X = torch.reshape(X, (N, D * D))X = torch.sign(X) * torch.sqrt(torch.abs(X) + 1e-5) # Signed-sqrt normalizationX = torch.nn.functional.normalize(X) # L2 ...
transforms.Normalize((0.5,), (0.5,))# 标准化张量 ]) 张量维度不匹配:另一个常见问题是张量维度不匹配,这可能导致模型无法正确处理数据。至关重要的是,要确保输入数据的维度与模型的预期输入大小相匹配。使用 tensor.shape 属性可以在调试过程的早期阶段识别这类问题。