如何访问和修改PyTorch中张量的值？

使用索引和切片访问张量值。索引用于访问张量的单个元素的值，而切片用于访问一系列元素的值。

我们使用赋值运算符来修改张量的值。使用赋值运算符进行新值分配将用新值修改张量。

步骤

导入所需库。这里，需要使用的库是 torch 。
定义一个 PyTorch 张量。
使用索引访问特定索引处的单个元素的值或使用切片访问元素序列的值。
使用赋值运算符修改访问的值与新值。
最后，打印张量以检查张量是否使用新值进行修改。

例子1

# Python program to access and modify values of a tensor in PyTorch
# Import the libraries
import torch

# Define PyTorch Tensor
a = torch.Tensor([[3, 5],[1, 2],[5, 7]])
print("a:\n",a)

# Access a value at index [1,0]-> 2nd row, 1st Col using indexing
b = a[1,0]
print("a[1,0]:\n", b)

# Other indexing method to access value
c = a[1][0]
print("a[1][0]:\n",c)

# Modifying the value 1 with new value 9
# assignment operator is used to modify with new value
a[1,0] = 9
print("tensor 'a' after modifying value at a[1,0]:")
print("a:\n",a)

输出

a:
tensor([[3., 5.],
        [1., 2.],
        [5., 7.]])
a[1,0]:
  tensor(1.)
a[1][0]:
  tensor(1.)
tensor 'a' after modifying value at a[1,0]:
a:
tensor([[3., 5.],
        [9., 2.],
        [5., 7.]])

例子2

# Python program to access and modify values of a tensor in PyTorch
# Import necessary libraries
import torch

# Define PyTorch Tensor
a = torch.Tensor([[3, 5],[1, 2],[5, 7]])
print("a:\n", a)

# Access all values of 2nd row using slicing
b = a[1]
print("a[1]:\n", a[1])

# Access all values of 1st and 2nd rows
b = a[0:2]
print("a[0:2]:\n" , a[0:2])

# Access all values of 2nd col
c = a[:,1]
print("a[:,1]:\n", a[:,1])

# Access values from first two rows but 2nd col
print("a[0:2, 1]:\n", a[0:2, 1])

# assignment operator is used to modify with new value
# Modifying the values of 2nd row
a[1] = torch.Tensor([9, 9])
print("After modifying a[1]:\n", a)

# Modify values of first two rows but 2nd col
a[0:2, 1] = torch.Tensor([4, 4])
print("After modifying a[0:2, 1]:\n", a)

输出

a:
tensor([[3., 5.],
        [1., 2.],
        [5., 7.]])
a[1]:
  tensor([1., 2.])
a[0:2]:
  tensor([[3., 5.],
        [1., 2.]])
a[:,1]:
  tensor([5., 2., 7.])
a[0:2, 1]:
  tensor([5., 2.])
After modifying a[1]:
tensor([[3., 5.],
        [9., 9.],
        [5., 7.]])
After modifying a[0:2, 1]:
tensor([[3., 4.],
        [9., 4.],
        [5., 7.]])