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

We use Indexing and Slicing to access the values of a tensor.Indexing is used to access the value of a single element of the tensor, whereasSlicing is used to access the values of a sequence of elements.

We use the assignment operator to modify the values of a tensor. Assigning new value/s using the assignment operator will modify the tensor with new value/s.

## Steps

• Import the required libraries. Here, the required library is torch.

• Define a PyTorch tensor.

• Access the value of a single element at particular index using indexing or access the values of sequence of elements using slicing.

• Modify the accessed values with new values using the assignment operator.

• Finally, print the tensor to check if the tensor is modified with the new values.

## Example 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)

## Output

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.]])

## Example 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)

## Output

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.]])

Updated on: 06-Nov-2021

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