In this article, we are going to see how to compute element-wise logical AND, OR, and NOT of given tensors in PyTorch. We can compute this by using the torch.logical_and(), torch.logical_or(), and torch.logical_not() methods. Let’s discuss all of them one by one.
Compute element-wise with logical AND
torch.logical_and() – This method is used to compute the element-wise logical AND of the given tensor. This method treated the non-zero values as True and zero values as False. The following syntax is used to compute logical AND.
Syntax: torch.logical_and(input, other)
Parameters
- input – This is our input tensor
- other – This tensor is to compute AND with input tensor.
Return : This method returns a tensor with values we get after computing the logical AND.
Example 1:
The following program is to compute element-wise logical AND on two 1D tensors having boolean values.
Python3
# Import the required library import torch # create two tensors having boolean values tens_1 = torch.tensor([True, True, False, False]) tens_2 = torch.tensor([True, False, True, False]) # display the above created tensors print("Input Tensor 1: ", tens_1) print("Input Tensor 2: ", tens_2) # compute the logical AND of input1 and input2 tens = torch.logical_and(tens_1, tens_2) # print result print("\nAfter Compute Logical AND: ", tens) |
Output:
Example 2:
The following program is to understand how to compute element-wise logical AND on two 2D tensors.
Python3
# Import the required library import torch # create two tensors tens_1 = torch.tensor([[10, 0], [0, 20]]) tens_2 = torch.tensor([[0, 30], [0, 40]]) # display the tensors print("Input Tensor 1: \n", tens_1) print("Input Tensor 2: \n", tens_2) # compute the logical AND tens = torch.logical_and(tens_1, tens_2) # print result print("After Compute Logical AND: \n", tens) |
Output:
Compute element-wise with logical OR
torch.logical_or() – This method is used to compute the element-wise logical OR of the given tensor. This method also treated the non-zero values as True and zero values as False. The following syntax is used to compute logical OR.
Syntax: torch.logical_or(input, other)
Parameters
- input – This is our input tensor
- other – This tensor is to compute OR with input tensor.
Return –returns a tensor with values we get after computing the logical OR.
Example 1:
The following program is to know how to compute element-wise logical OR on two 1D tensors.
Python3
# Import the required library import torch # create two tensors tens_1 = torch.tensor([[10, 0, 20, 0]]) tens_2 = torch.tensor([[0, 30, 40, 0]]) # display the tensors print("\n Input Tensor 1: ", tens_1) print("\n Input Tensor 2: ", tens_2) # compute the logical OR tens = torch.logical_or(tens_1, tens_2) # print result print("\n After Compute Logical OR: ", tens) |
Output:
Example 2:
The following program is to understand how to compute element-wise logical OR on two 2D tensors.
Python3
# Import the required library import torch # create two tensors tens_1 = torch.tensor([[11, 0], [0, 12]]) tens_2 = torch.tensor([[0, 13], [0, 14]]) # display the tensors print("\n Input Tensor 1: \n", tens_1) print("\n Input Tensor 2: \n", tens_2) # compute the logical OR tens = torch.logical_or(tens_1, tens_2) # print result print("\n After Compute Logical OR: \n", tens) |
Output:
Compute element-wise with logical NOT
torch.logical_not() – This method is used to compute the element-wise logical NOT of the given input tensor. This method also treated the non-zero values as True and zero values as False. The following syntax is used to compute logical NOT.
Syntax – torch.logical_not(input)
Parameter –
- input – This is our input tensor
Return –This method returns a tensor with values we get after computing the logical NOT.
The following program is to understand how to compute element-wise logical NOT of tensor.
Python3
# import required library import torch # create two tensors tens_1 = torch.tensor([11, 0]) # display the tensors print("\n Input Tensor 1: \n", tens_1) # compute the logical NOT tens = torch.logical_not(tens_1) # display result print("\n After Compute Logical NOT: \n", tens) |
Output:
