(embedding) # Image Example N, C, H, W = 20, 5, 10, 10 input = torch.randn(N, C, H, W) # Normalize over the last three dimensions (i.e. the channel and spatial dimensions) # as shown in the image below layer_norm = nn.LayerNorm([C, H, W]) output = layer_norm(input...
torch.nn.functional.normalize torch.nn.functional.normalize(input, p=2, dim=1, eps=1e-12, out=None) 功能:将某一个维度除以那个维度对应的范数(默认是2范数)。 主要讲以下三种情况: 输入为一维Tensor a = torch.Tensor([1,2,3]) torch.nn.functional.normalize(a, dim=0) tensor([0.2673, 0.5345...
torch.nn.functional.normalize(input, p=2.0, dim=1, eps=1e-12, out=None) # type: (Tensor, float, int, float, Optional[Tensor]) -> Tensor 1. 2. 公式为 参数及功能 F.normalize(data, p=2/1, dim=0/1/-1) 将某一个维度除以那个维度对应的范数(默认是2范数) input:输入的数据(tensor)...
torch.nn.functional.normalize(input, p=2.0, dim=1, eps=1e-12, out=None) Parameters: input: Input tensor of any shape p: 计算p范数。 dim: 计算范数的维度。 eps: 很小的数,防止分母为0。 out: The output tensor。 源码: def normalize(input, p=2, dim=1, eps=1e-12, out=None): #...
torch.nn中的normalize函数,包含以下参数: 1. dim:在哪个维度上进行归一化,一般是在数据的通道维度上进行; 2. p:归一化指数,默认为2,即欧几里得距离; 3. eps:避免除以0的小数,建议设为较小的值。 例如,对于形状为(N,C,H,W)的输入张量,可以使用以下代码进行通道归一化: ``` import torch.nn.functional ...
torch.nn.functional.normalize torch.nn.functional.normalize(input, p=2, dim=1, eps=1e-12, out=None)功能：将某⼀个维度除以那个维度对应的范数(默认是2范数)。主要讲以下三种情况：输⼊为⼀维Tensor a = torch.Tensor([1,2,3])torch.nn.functional.normalize(a, dim=0)tensor([0.2673, 0....
F.normalize(torch.nn.functional) 和 torch.norm 在维度操作方面,两者的操作一致。前者在后者求向量L2范数的基础上,增加了标准化 来源https://blog.csdn.net/nyist_yangguang/article/details/126449306 a = image_features / image_features.norm(dim=1, keepdim=True) b = F.normalize(image_features,dim=1...
import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms from torch.utils.data import DataLoader import matplotlib.pyplot as plt 定义数据集和数据加载器 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5...
In[1]:elfin=nn.BatchNorm1d(2,affine=False) In[2]:data=torch.tensor([[1,2], [3,4], [5,6], [7,8], [9,10]],dtype=torch.float32) In[3]:elfin(data) Out[3]: tensor([[-1.4142,-1.4142], [-0.7071,-0.7071], [0.0000,0.0000], ...
weight, -0.1, 0.1) nn.init.constant_(self.weight[padding_idx], 0.0) if freeze_embed: self.weight.requires_grad = False assert 0.0 < normalize_decay_rate < 1.0 self.normalize = normalize_embed self.normalize_decay_rate = normalize_decay_rate self.mean = None self.var = None self.init_...