input_=torch.randn((3,4))a=torch.nn.Softmax()(input_)b=torch.nn.functional.normalize(a)a的结果为:tensor([[0.2074,0.2850,0.1973,0.3103],[0.2773,0.1442,0.3652,0.2132],[0.3244,0.3206,0.0216,0.3334]])b的结果为:tensor([[0.4071,0.5595,0.3874,0.6092],[0.5274,0.2743,0.6945,0.4054],[0.5738,0.56...
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 ...
randn(batch, sentence_length, embedding_dim) layer_norm = nn.LayerNorm(embedding_dim) # Activate module layer_norm(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...
(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....
nn/Normalize.lua Newer Older RawNormal viewHistory 100644150 lines (132 sloc)4.22 KB Adds Normalize Jul 31, 2015 1 localNormalize, parent=torch.class('nn.Normalize','nn.Module') 2 3 functionNormalize:__init(p,eps) 4 parent.__init(self) ...
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_...
ToTensor(), normalize, ])), batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) model.eval() validate_loss_fn = nn.CrossEntropyLoss().cuda() eval_metrics = validate(model, loader, validate_loss_fn, args) print(eval_metrics) ...
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) #默认的值 train_augs = transforms.Compose([ transforms.RandomResizedCrop(size=224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ...