PyTorch is an open-source framework available with a Python programming language. We can process the data in PyTorch in the form of a Tensor.
A tensor is a multidimensional array that is used to store the data. So for using a Tensor, we have to import the torch module.
To create a tensor, the method used is tensor()”
Syntax:
Where data is a multi-dimensional array.
torch.logical_not()
torch.logical_not() in PyTorch is performed on a single tensor object. It returns True if the value is False or 0 and returns False if the value is True or not equal to 0. It takes a tensor as a parameter.
Syntax:
Parameter:
tensor_object is the tensor
Example 1
In this example, we will create a one-dimensional tensor – data1 with 5 boolean values and perform logical_not().
import torch
#create a 1D tensor - data1 with 5 boolean values
data1 = torch.tensor([False,True, True, True,False])
#display
print("Tensor: ",data1)
#logical_not on data1
print("Logical NOT on above tensor: ",torch.logical_not(data1))
Output:
Logical NOT on above tensor: tensor([ True, False, False, False, True])
Working:
1. logical_not(False) – True
2. logical_not(True) – False
3. logical_not(True) – False
4. logical_not(True) – False
5. logical_not(False) – True
Example 2
In this example, we will create a two-dimensional tensor – data1 with 5 boolean values in each two rows and perform logical_not().
import torch
#create a 2D tensor - data1 with 5 boolean values each
data1 = torch.tensor([[False,True, True, True,False],[False,True, True, True,False]])
#display
print("Tensor: ",data1)
#logical_not on data1
print("Logical NOT on above tensor: ",torch.logical_not(data1))
Output:
[False, True, True, True, False]])
Logical NOT on above tensor: tensor([[ True, False, False, False, True],
[ True, False, False, False, True]])
Working:
2. logical_not(True) - False, logical_not(True) - False
3. logical_not(True) - False, logical_not(True) - False
4. logical_not(True) - False, logical_not(True) - False
5. logical_not(False) - True, logical_not(False) - True
Example 3
In this example, we will create a one-dimensional tensor – data1 with 5 numeric values and perform logical_not().
import torch
#create a 1D tensor - data1 with 5 numeric values
data1 = torch.tensor([0,1,23,45,56])
#display
print("Tensor: ",data1)
#logical_not on data1
print("Logical NOT on above tensor: ",torch.logical_not(data1))
Output:
Logical NOT on above tensor: tensor([ True, False, False, False, False])
Working:
1. logical_not(0) – True
2. logical_not(1) – False
3. logical_not(23) – False
4. logical_not(45) – False
5. logical_not(56) – False
Example 4
In this example, we will create a two-dimensional tensor – data1 5 boolean values in each two rows and perform logical_not().
import torch
#create a 2D tensor - data1 with 5 boolean values each
data1 = torch.tensor([[12,34,56,78,90],[0,0,1,2,0]])
#display
print("Tensor: ",data1)
#logical_not on data1
print("Logical NOT on above tensor: ",torch.logical_not(data1))
Output:
[ 0, 0, 1, 2, 0]])
Logical NOT on above tensor: tensor([[False, False, False, False, False],
[ True, True, False, False, True]])
Working:
1. logical_not(12) – False, logical_not(0) – True
2. logical_not(34) – False, logical_not(0) – True
3. logical_not(56) – False, logical_not(1) – False
4. logical_not(78) – False, logical_not(2) – False
5. logical_not(90) – False, logical_not(0) – True
Conclusion
In this PyTorch lesson, we discussed how to perform logical NOT operation with a torch.logical_not() method. It returns True if the value is False or 0 and returns False if the value is True or not equal to 0. We discussed 4 examples of boolean values and numeric values with one and 2-dimensional tensors.
