eq and ne in PyTorch

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*Memos:

• My post explains gt() and lt().
• My post explains ge() and le().
• My post explains isclose() and equal().

eq() can check if the zero or more elements of the 1st 0D or more D tensor are equal to the zero or more elements of the 2nd 0D or more D tensor element-wise, getting the 0D or more D tensor of zero or more elements as shown below:

*Memos:

• eq() can be used with torch or a tensor.
• The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
• The 2nd argument with torch or the 1st argument with a tensor is other(Required-Type:tensor or scalar of int, float, complex or bool).
• There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
  • out= must be used.
  • My post explains out argument.
• The result is the higher D tensor which has more elements.

import torch

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([7, 0, 3])

torch.eq(input=tensor1, other=tensor2)
tensor1.eq(other=tensor2)
torch.eq(input=tensor2, other=tensor1)
# tensor([False, True, True])

tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[3, 5, 4],
                        [6, 3, 5]])
torch.eq(input=tensor1, other=tensor2)
torch.eq(input=tensor2, other=tensor1)
# tensor([[False, True, False],
#         [False, False, True]])

torch.eq(input=tensor1, other=3)
# tensor(False)

torch.eq(input=tensor2, other=3)
# tensor([[True, False, False],
#         [False, True, False]])

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[5, 5, 5],
                        [0, 0, 0],
                        [3, 3, 3]])
torch.eq(input=tensor1, other=tensor2)
torch.eq(input=tensor2, other=tensor1)
# tensor([[True, False, False],
#         [False, True, False], 
#         [False, False, True]])

torch.eq(input=tensor1, other=3)
# tensor([False, False, True])

torch.eq(input=tensor2, other=3)
# tensor([[False, False, False],
#         [False, False, False],
#         [True, True, True]])

tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[5., 5., 5.],
                        [0., 0., 0.],
                        [3., 3., 3.]])
torch.eq(input=tensor1, other=tensor2)
# tensor([[True, False, False],
#         [False, True, False], 
#         [False, False, True]])

torch.eq(input=tensor1, other=3.)
# tensor([False, False, True])

tensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])
tensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],
                        [0.+0.j, 0.+0.j, 0.+0.j],
                        [3.+0.j, 3.+0.j, 3.+.0j]])
torch.eq(input=tensor1, other=tensor2)
# tensor([[True, False, False],
#         [False, True, False],
#         [False, False, True]])

torch.eq(input=tensor1, other=3.+0.j)
# tensor([False, False, True])

tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[True, False, True],
                        [False, True, False],
                        [True, False, True]])
torch.eq(input=tensor1, other=tensor2)
# tensor([[True, True, True],
#         [False, False, False],
#         [True, True, True]])

torch.eq(input=tensor1, other=True)
# tensor([True, False, True])

ne() can check if the zero or more elements of the 1st 0D or more D tensor are not equal to the zero or more elements of the 2nd 0D or more D tensor element-wise, getting the 0D or more D tensor of zero or more elements as shown below:

*Memos:

• ne() can be used with torch or a tensor.
• The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
• The 2nd argument with torch or the 1st argument with a tensor is other(Required-Type:tensor or scalar of int, float, complex or bool).
• There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
  • out= must be used.
  • My post explains out argument.
• not_equal() is the alias of ne().

import torch

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([7, 0, 3])

torch.ne(input=tensor1, other=tensor2)
tensor1.ne(other=tensor2)
torch.ne(input=tensor2, other=tensor1)
# tensor([True, False, False])

tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[3, 5, 4],
                        [6, 3, 5]])
torch.ne(input=tensor1, other=tensor2)
torch.ne(input=tensor2, other=tensor1)
# tensor([[True, False, True],
#         [True, True, False]])

torch.ne(input=tensor1, other=3)
# tensor(True)

torch.ne(input=tensor2, other=3)
# tensor([[False, True, True],
#         [True, False, True]])

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[5, 5, 5],
                        [0, 0, 0],
                        [3, 3, 3]])
torch.ne(input=tensor1, other=tensor2)
torch.ne(input=tensor2, other=tensor1)
# tensor([[False, True, True],
#         [True, False, True],
#         [True, True, False]])

torch.ne(input=tensor1, other=3)
# tensor([True, True, False])

torch.ne(input=tensor2, other=3)
# tensor([[True, True, True],
#         [True, True, True],
#         [False, False, False]])

tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[5., 5., 5.],
                        [0., 0., 0.],
                        [3., 3., 3.]])
torch.ne(input=tensor1, other=tensor2)
# tensor([[False, True, True],
#         [True, False, True],
#         [True, True, False]])

torch.ne(input=tensor1, other=3.)
# tensor([True, True, False])

tensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])
tensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],
                        [0.+0.j, 0.+0.j, 0.+0.j],
                        [3.+0.j, 3.+0.j, 3.+.0j]])
torch.ne(input=tensor1, other=tensor2)
# tensor([[False, True, True],
#         [True, False, True],
#         [True, True, False]])

torch.ne(input=tensor1, other=3.+0.j)
# tensor([True, True, False])

tensor1 = torch.tensor([True, False, True])
tensor2 = torch.tensor([[True, False, True],
                        [False, True, False],
                        [True, False, True]])
torch.ne(input=tensor1, other=tensor2)
# tensor([[False, False, False],
#         [True, True, True],
#         [False, False, False]])

torch.ne(input=tensor1, other=True)
# tensor([False, True, False])