torch. logical_ And() method

torch.logical_and() Element logic for computing a given input tensor AND. Zero is treated as False, Nonzero is considered True

Official documentation : https://pytorch.org/docs/stable/generated/torch.logical_and.html#torch.logical_and
torch.logical_and(input, other, *, out=None） → Return tensor

• input（ tensor ） – Input tensor
• other（ tensor ） – Used to calculate AND Tensor
• out （ tensor , Optional ）– Output tensor
  Specific examples : Enter two tensor Variable of type input and other Sum the corresponding position , If it is a tensor internal number type , Not 0 Turn into True, 0 Turn into False

1. No out Parameter type

import torch
#  No out Parameter type 
a = torch.tensor([True, False, True])
b = torch.tensor([True, False, False])
print(torch.logical_and(a, b))
# tensor([ True, False, False])

a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)   #  Not 0 True, 0 False
b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)    #  Not 0 True, 0 False
print(torch.logical_and(a, b))

Output results :

2. belt out Parameter type

Output one more suffix

import torch
#  belt out Parameter type 
a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)

print(torch.logical_and(a, b, out=torch.empty(4, dtype=torch.bool)))    #  return boolean type 
# tensor([False, False, True, False])

print(torch.logical_and(a, b, out=torch.empty(4, dtype=torch.int)))     #  return int32 type 
# tensor([0, 0, 1, 0], dtype=torch.int32)

print(torch.logical_and(a, b, out=torch.empty(4, dtype=torch.double)))  #  return double type 
# tensor([0., 0., 1., 0.], dtype=torch.float64)

Output results :

3. Two dimensional and other examples

import torch
#  Other examples 
a = torch.tensor([0, 1, 3, 2, 2])
b = torch.tensor([1, 1, 0, 1, 2])

print(a == b)       # tensor([False,  True, False, False,  True])
print(b != 2)       # tensor([ True,  True,  True,  True, False])
print(torch.logical_and(a==b, b != 2))
# tensor([False, True, False, False, False])

print("=" * 100)
#  Other examples 
a = torch.tensor([[0, 1, 3, 2, 2],
                  [0, 3, 1, 0, 2]])

b = torch.tensor([[1, 1, 0, 1, 2],
                  [1, 1, 2, 0, 2]])
print(torch.logical_and(a, b))
# tensor([[False, True, False, True, True],
#         [False,  True,  True, False,  True]])

Output results :

For note taking
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