How to access and modify the values of a Tensor in PyTorch?

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In this article, we are going to see how to access and modify the value of a tensor in PyTorch using Python.

We can access the value of a tensor by using indexing and slicing. Indexing is used to access a single value in the tensor. slicing is used to access the sequence of values in a tensor. we can modify a tensor by using the assignment operator. Assigning a new value in the tensor will modify the tensor with the new value.

Import the torch libraries and then create a PyTorch tensor. Access values of the tensor. Modify a value with a new value by using the assignment operator.

Example 1: Access and modify value using indexing. in the below example, we are accessing and modifying the value of a tensor.

Python

# Import torch libraries
import torch
 
# create PyTorch tensor
tens = torch.Tensor([1, 2, 3, 4, 5])
 
# print tensor
print("Original tensor:", tens)
 
# access a value by their index
temp = tens[2]
print("value of tens[2]:", temp)
 
 
# modify a value.
tens[2] = 10
 
# print tensor after modify the value
print("After modify the value:", tens)

Output:

Example 2: Access and modify the sequence of values in tensor using slicing.

Python

# Import torch libraries
import torch
 
# create PyTorch Tensor
tens = torch.Tensor([[1, 2, 3], [4, 5, 6]])
 
# Print the tensor
print("Original tensor: ", tens)
 
 
# Access all values of only second row
# using slicing
a = tens[1]
print("values of only second row: ", a)
 
# Access all values of only third column
b = tens[:, 2]
print("values of only third column: ", b)
 
# Access values of second row and first
# two column
c = tens[1, 0:2]
print("values of second row and first two column: ", c)
 
# Modifying all the values of second row
tens[1] = torch.Tensor([40, 50, 60])
print("After modifying second row:  ", tens)
 
# Modify values of first rows and last
# two column
tens[0, 1:3] = torch.Tensor([20, 30])
print("After modifying first rows and last two column ", tens)