如何在 PyTorch 中标准化张量？
PyTorch中的张量可以使用torch.nn.functional模块中normalize()提供的函数进行归一化。这是一个非线性激活函数。
它在指定维度上对给定张量执行Lp归一化。
它返回原始张量元素的归一化值的张量。
一维张量可以在0维上归一化，而二维张量可以在0维和1维上归一化，即按列或按行。
n维张量可以在维度(0,1,2,...,n-1)上归一化。
语法
torch.nn.functional.normalize(input, p=2.0, dim = 1)
参数
输入–输入张量
p–规范公式中的幂（指数）值
dim–元素标准化的维度。
脚步
我们可以使用以下步骤来归一化张量-
导入火炬库。确保您已经安装了它。
import torch from torch.nn.functional import normalize
创建一个张量并打印它。
t = torch.tensor([[1.,2.,3.],[4.,5.,6.]])
print("Tensor:", t)使用不同的p值和不同的维度对张量进行归一化。上面定义的张量是一个二维张量，所以我们可以在二维上对其进行归一化。
t1 = normalize(t, p=1.0, dim = 1) t2 = normalize(t, p=2.0, dim = 0)
打印上面计算的归一化张量。
print("Normalized tensor:\n", t1)
print("Normalized tensor:\n", t2)示例1
# import torch library
import torch
from torch.nn.functional import normalize
# define a torch tensor
t = torch.tensor([1., 2., 3., -2., -5.])
# print the above tensor
print("Tensor:\n", t)
# normalize the tensor
t1 = normalize(t, p=1.0, dim = 0)
t2 = normalize(t, p=2.0, dim = 0)
# print normalized tensor
print("Normalized tensor with p=1:\n", t1)
print("Normalized tensor with p=2:\n", t2)输出结果Tensor: tensor([ 1., 2., 3., -2., -5.]) Normalized tensor with p=1: tensor([ 0.0769, 0.1538, 0.2308, -0.1538, -0.3846]) Normalized tensor with p=2: tensor([ 0.1525, 0.3050, 0.4575, -0.3050, -0.7625])
示例2
# import torch library
import torch
from torch.nn.functional import normalize
# define a 2D tensor
t = torch.tensor([[1.,2.,3.],[4.,5.,6.]])
# print the above tensor
print("Tensor:\n", t)
# normalize the tensor
t0 = normalize(t, p=2.0)
# print the normalized tensor
print("Normalized tensor:\n", t0)
# normalize the tensor in dim 0 or column-wise
tc = normalize(t, p=2.0, dim = 0)
# print the normalized tensor
print("Column-wise Normalized tensor:\n", tc)
# normalize the tensor in dim 1 or row-wise
tr = normalize(t, p=2.0, dim = 1)
# print the normalized tensor
print("Row-wise Normalized tensor:\n", tr)输出结果Tensor: tensor([[1., 2., 3.], [4., 5., 6.]]) Normalized tensor: tensor([[0.2673, 0.5345, 0.8018], [0.4558, 0.5698, 0.6838]]) Column-wise Normalized tensor: tensor([[0.2425, 0.3714, 0.4472], [0.9701, 0.9285, 0.8944]]) Row-wise Normalized tensor: tensor([[0.2673, 0.5345, 0.8018], [0.4558, 0.5698, 0.6838]])