equal(), eq() and ne() in PyTorch

My post explains gt(), lt(), ge() and le().

equal() can check the 2 tensors are the same as shown below:

*Memos:

• equal() can be called both from torch and a tensor.
• The tensors can be 0D or more D tensors.

import torch

tensor1 = torch.tensor([5, 9, 0, 1])
tensor2 = torch.tensor([5, 9, 0, 1])
torch.equal(tensor1, tensor2)
tensor1.equal(tensor2)
torch.equal(tensor2, tensor1)
tensor2.equal(tensor1)
# True

tensor1 = torch.tensor([5, 9, 0, 1])
tensor2 = torch.tensor([5., 9+0j, False, True])
torch.equal(tensor1, tensor2)
tensor1.equal(tensor2)
torch.equal(tensor2, tensor1)
tensor2.equal(tensor1)
# True

tensor1 = torch.tensor([5, 9, 0, 1])
tensor2 = torch.tensor([7, 9, 0, 1])
torch.equal(tensor1, tensor2)
tensor1.equal(tensor2)
torch.equal(tensor2, tensor1)
tensor2.equal(tensor1)
# False

tensor1 = torch.tensor([5, 9, 0, 1])
tensor2 = torch.tensor([[5, 9, 0, 1]])
torch.equal(tensor1, tensor2)
tensor1.equal(tensor2)
torch.equal(tensor2, tensor1)
tensor2.equal(tensor1)
# False

eq() can check the equality of 2 tensors element-wise as shown below:

*Memos:

• eq() can be called both from torch and a tensor.
• The tensors can be 0D or more D tensors.
• The result is the higher D tensor which has more elements.

import torch

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

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

tensor1 = torch.tensor([[5, 0, 3], [6, 9, 1]])
tensor2 = torch.tensor([[[2, 7, 3], [6, 3, 1]],
                        [[5, 0, 4], [8, 9, 7]]])
torch.eq(tensor1, tensor2)
tensor1.eq(tensor2)
torch.eq(tensor2, tensor1)
tensor2.eq(tensor1)
# tensor([[[False, False, True], [True, False, True]],
#         [[True, True, False], [False, True, False]]])

tensor1 = torch.tensor([[5, 0, 3], [6, 9, 1]])
tensor2 = torch.tensor([[[2., 7., 3.], [6., 3., True]],
                        [[5+0j, False, 4+0j], [8+0j, 9+0j, 7+0j]]])
torch.eq(tensor1, tensor2)
tensor1.eq(tensor2)
torch.eq(tensor2, tensor1)
tensor2.eq(tensor1)
# tensor([[[False, False, True], [True, False, True]],
#         [[True, True, False], [False, True, False]]])

tensor1 = torch.tensor([[6, 9, 1], [5, 0, 3]])
tensor2 = torch.tensor([[[2, 7, 3], [6, 3, 1]],
                        [[5, 0, 4], [8, 9, 7]]])
torch.eq(tensor1, tensor2)
tensor1.eq(tensor2)
torch.eq(tensor2, tensor1)
tensor2.eq(tensor1)
# tensor([[[False, False, False], [False, False, False]],
#         [[False, False, False], [False, False, False]]])

ne() can check the inequality of 2 tensors element-wise as shown below:

*Memos:

• ne() can be called both from torch and a tensor.
• The tensors can be 0D or more D tensors.
• The result is the higher D tensor which has more elements.
• ne() and not_equal() are the same because not_equal() is the alias of ne():

import torch

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

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

tensor1 = torch.tensor([[5, 0, 3], [6, 9, 1]])
tensor2 = torch.tensor([[[2, 7, 3], [6, 3, 1]],
                        [[5, 0, 4], [8, 9, 7]]])
torch.ne(tensor1, tensor2)
tensor1.ne(tensor2)
torch.ne(tensor2, tensor1)
tensor2.ne(tensor1)
# tensor([[[True, True, False], [False, True, False]],
#         [[False, False, True], [True, False, True]]])

tensor1 = torch.tensor([[5, 0, 3], [6, 9, 1]])
tensor2 = torch.tensor([[[2., 7., 3.], [6., 3., True]],
                        [[5+0j, False, 4+0j], [8+0j, 9+0j, 7+0j]]])
torch.ne(tensor1, tensor2)
tensor1.ne(tensor2)
torch.ne(tensor2, tensor1)
tensor2.ne(tensor1)
# tensor([[[True, True, False], [False, True, False]],
#         [[False, False, True], [True, False, True]]])

tensor1 = torch.tensor([[6, 9, 1], [5, 0, 3]])
tensor2 = torch.tensor([[[2, 7, 3], [6, 3, 1]],
                        [[5, 0, 4], [8, 9, 7]]])
torch.ne(tensor1, tensor2)
tensor1.ne(tensor2)
torch.ne(tensor2, tensor1)
tensor2.ne(tensor1)
# tensor([[[True, True, True], [True, True, True]],
#         [[True, True, True], [True, True, True]]])