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.]])