PyTorch 中的张量运算

在本文中，我们将讨论 PyTorch 中的张量运算。

PyTorch 是一个科学包，用于对给定数据（如Python中的张量）执行操作。张量是数据的集合，如 numpy 数组。我们可以使用张量函数创建张量：

Syntax: torch.tensor([[[element1,element2,.,element n],……,[element1,element2,.,element n]]])

where,

• torch is the module
• tensor is the function
• elements are the data

PyTorch 中应用于张量的操作是：

扩张（）

该操作用于将张量展开为张量数、张量中的行数和张量中的列数。

Syntax: tensor.expand(n,r,c)

where,

• tensor is the input tensor
• n is to return the number of tensors
• r  is the number of rows in each tensor
• c is the number of columns in each tensor

示例：在本例中，我们将张量展开为 4 个张量，每个张量中 2 行 3 列

Python3

# import module
import torch
  
# create a tensor with 2 data
# in 3 three elements each
data = torch.tensor([[10, 20, 30], 
                     [45, 67, 89]])
  
# display
print(data)
  
# expand the tensor into 4 tensors , 2
# rows and 3 columns in each tensor
print(data.expand(4, 2, 3))

输出：

tensor([[10, 20, 30],
        [45, 67, 89]])
tensor([[[10, 20, 30],
         [45, 67, 89]],

        [[10, 20, 30],
         [45, 67, 89]],

        [[10, 20, 30],
         [45, 67, 89]],

        [[10, 20, 30],
         [45, 67, 89]]])

置换（）

这用于使用行和列重新排序张量

Syntax: tensor.permute(a,b,c)

where

• tensor is the input tensor
• permute(1,2,0) is used to permute the tensor by row
• permute(2,1,0) is used to permute the tensor by column

示例：在此示例中，我们将首先按行和按列排列张量。

Python3

# import module
import torch
  
# create a tensor with 2 data
# in 3 three elements each
data = torch.tensor([[[10, 20, 30], 
                      [45, 67, 89]]])
  
# display
print(data)
  
# permute the tensor first by row
print(data.permute(1, 2, 0))
  
# permute the tensor first by column
print(data.permute(2, 1, 0))

输出：

tensor([[[10, 20, 30],
         [45, 67, 89]]])
tensor([[[10],
         [20],
         [30]],

        [[45],
         [67],
         [89]]])
tensor([[[10],
         [45]],

        [[20],
         [67]],

        [[30],
         [89]]])

列表（）

此方法用于从给定张量返回列表或嵌套列表。

Syntax: tensor.tolist()

示例：在此示例中，我们将把给定的张量转换为列表。

Python3

# import module
import torch
  
# create a tensor with 2 data in
# 3 three elements each
data = torch.tensor([[[10, 20, 30], 
                      [45, 67, 89]]])
  
# display
print(data)
  
# convert the tensor to list
print(data.tolist())

输出：

tensor([[[10, 20, 30],
         [45, 67, 89]]])
[[[10, 20, 30], [45, 67, 89]]]

狭窄的（）

此函数用于缩小张量。换句话说，它将根据输入维度扩展张量。

Syntax: torch.narrow(tensor,d,i,l)

where,

• tensor is the input tensor
• d is the dimension to narrow
• i is the starting index of the vector
• l is the length of the new tensor along the dimension – d

示例：在本例中，我们将从第 1 个索引开始的 1 维张量进行缩小，每个维度的长度为 2，我们将从第 0^个索引和长度开始的 1 维张量进行缩小每个维度为 2

Python3

# import module
import torch
  
# create a tensor with 2 data in
# 3 three elements each
data = torch.tensor([[10, 20, 30], 
                     [45, 67, 89], 
                     [23, 45, 67]])
  
# display
print(data)
  
# narrow the tensor
# with 1 dimension
# starting from 1 st index
# length of each dimension is 2
print(torch.narrow(data, 1, 1, 2))
  
# narrow the tensor
# with 1 dimension
# starting from 0 th  index
# length of each dimension is 2
print(torch.narrow(data, 1, 0, 2))

输出：

tensor([[10, 20, 30],
        [45, 67, 89],
        [23, 45, 67]])
tensor([[20, 30],
        [67, 89],
        [45, 67]])
tensor([[10, 20],
        [45, 67],
        [23, 45]])

在哪里（）

此函数用于通过有条件地检查现有张量来返回新张量。

Syntax: torch.where(condition,statement1,statement2)

where,

• condition is used to check the existing tensor condition by applying conditions on the existing tensors
• statememt1 is executed when condition is true
• statememt2 is executed when condition is false

示例：我们将使用不同的关系运算符来检查功能

Python3

# import module
import torch
  
# create a tensor with 3 data in
# 3 three elements each
data = torch.tensor([[[10, 20, 30], 
                      [45, 67, 89],
                      [23, 45, 67]]])
  
# display
print(data)
  
# set the number 100 when the
# number in greater than 45
# otherwise 50
print(torch.where(data > 45, 100, 50))
  
# set the number 100 when the
# number in less than 45
# otherwise 50
print(torch.where(data < 45, 100, 50))
  
# set the number 100 when the number in 
# equal to 23 otherwise 50
print(torch.where(data == 23, 100, 50))

输出：

tensor([[[10, 20, 30],
         [45, 67, 89],
         [23, 45, 67]]])
tensor([[[ 50,  50,  50],
         [ 50, 100, 100],
         [ 50,  50, 100]]])
tensor([[[100, 100, 100],
         [ 50,  50,  50],
         [100,  50,  50]]])
tensor([[[ 50,  50,  50],
         [ 50,  50,  50],
         [100,  50,  50]]])