PyTorch – 如何获得张量元素的指数？

2026-06-02 1 花语

要找到输入张量元素的指数，我们可以应用或。这里，输入是计算指数的输入张量。这两种方法都返回一个新的张量，其中包含输入张量元素的指数值。Tensor.exp()torch.exp(input)

语法

Tensor.exp()

或者

torch.exp(input)

脚步

我们可以使用以下步骤来计算输入张量元素的指数-

导入火炬库。确保您已经安装了它。

import torch

创建一个张量并打印它。

t1 = torch.rand(4,3) print("Tensor:", t1)

计算张量元素的指数。为此，请使用并可选地将此值分配给新变量。torch.exp(input)

exp_t1 = torch.exp(t1)

打印结果张量。

print("Exponentials of elements:\n", exp_t1)

示例1

以下Python程序展示了如何计算输入张量元素的指数。

# import torch library import torch # create a tensor t1 = torch.tensor([1,2,3,4,5]) # display the tensor print("Tensor t1:\n", t1) # Compute the exponential of the elements of the tensor exp_t1 = t1.exp() print("Exponential of t1:\n",exp_t1) # create another tensor t2 = torch.randn(2,3) print("Tensor t2:\n", t2) # Compute the exponential of the elements of the above tensor exp_t2 = t2.exp() print("Exponential of t2:\n",exp_t2)输出结果Tensor t1: tensor([1, 2, 3, 4, 5]) Exponential of t1: tensor([ 2.7183, 7.3891, 20.0855, 54.5981, 148.4132]) Tensor t2: tensor([[ 0.2986, 0.0348, 2.1201], [-0.4488, -0.2205, 0.5417]]) Exponential of t2: tensor([[1.3480, 1.0354, 8.3319], [0.6384, 0.8021, 1.7189]])

示例2

# import torch library import torch # create a tensor t1 = torch.tensor([[1,2,3],[4,5,6]]) # display the tensor print("Tensor t1:\n", t1) # Other way to compute the exponential of the elements exp_t1 = torch.exp(t1) print("Exponential of t1:\n",exp_t1) # create another tensor t2 = torch.randn(4,3) print("Tensor t2:\n", t2) # compute the exponential of the elements of the tensor exp_t2 = torch.exp(t2) print("Exponential of t2:\n",exp_t2)输出结果Tensor t1: tensor([[1, 2, 3], [4, 5, 6]]) Exponential of t1: tensor([[ 2.7183, 7.3891, 20.0855], [ 54.5981, 148.4132, 403.4288]]) Tensor t2: tensor([[ 1.3574, -0.3132, 0.9117], [-0.4421, 1.4100, -0.9875], [ 0.1515, 0.1374, -0.6713], [ 1.1636, -0.1663, -1.1224]]) Exponential of t2: tensor([[3.8862, 0.7311, 2.4884], [0.6427, 4.0959, 0.3725], [1.1636, 1.1473, 0.5110], [3.2014, 0.8468, 0.3255]])