Buy Me a Coffee☕ *Memos: My post explains equal(), eq() and ne(). My post explains gt() and lt(). My post explains ge() and le(). isclose() can check if the zero or more elements of the 1st 0D or more D tensor are equal or nearly e…

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*Memos:

My post explains equal(), eq() and ne().

My post explains gt() and lt().

My post explains ge() and le().

isclose() can check if the zero or more elements of the 1st 0D or more D tensor are equal or nearly 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:

isclose() 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 of int, float, complex or bool).

The 3rd argument with torch or the 2nd argument with a tensor is rtol(Optional-Default:1e-05-Type:float).

The 4th argument with torch or the 3rd argument with a tensor is atol(Optional-Default:1e-08-Type:float).

The 5th argument with torch or the 4th argument with a tensor is equal_nan(Optional-Default:False-Type:bool).

The formula is |input - other| <= rtol x |other| + atol.

import torch



tensor1 = torch.tensor([1.00001001, 1.00000996, 1.00000995, torch.nan])

tensor2 = torch.tensor([1., 1., 1., torch.nan])



torch.isclose(input=tensor1, other=tensor2)

torch.isclose(input=tensor1, other=tensor2,

              rtol=1e-05, atol=1e-08, equal_nan=False)

            # 0.00001   # 0.00000001

tensor1.isclose(other=tensor2)

torch.isclose(input=tensor2, other=tensor1)

# tensor([False, False, True, False])



torch.isclose(input=tensor1, other=tensor2, equal_nan=True)

# tensor([False, False, True, True])



tensor1 = torch.tensor([[1.00001001, 1.00000996],

                        [1.00000995, torch.nan]])

tensor2 = torch.tensor([[1., 1.],

                        [1., torch.nan]])

torch.isclose(input=tensor1, other=tensor2)

# tensor([[False, False],

#         [True, False]])



tensor1 = torch.tensor([[[1.00001001],

                         [1.00000996]],

                        [[1.00000995],

                         [torch.nan]]])

tensor2 = torch.tensor([[[1.], [1.]],

                        [[1.], [torch.nan]]])

torch.isclose(input=tensor1, other=tensor2)

# tensor([[[False], [False]],

#         [[True], [False]]])



tensor1 = torch.tensor([[1.00001001, 1.00000996],

                        [1.00000995, torch.nan]])

tensor2 = torch.tensor([1., 1.])



torch.isclose(input=tensor1, other=tensor2)

# tensor([[False, False],

#         [True, False]])



tensor1 = torch.tensor([[1.00001001, 1.00000996],

                        [1.00000995, torch.nan]])

tensor2 = torch.tensor(1.)



torch.isclose(input=tensor1, other=tensor2)

# tensor([[False, False],

#         [True, False]])



tensor1 = torch.tensor([])

tensor2 = torch.tensor([])



torch.equal(input=tensor1, other=tensor2)

# True



tensor1 = torch.tensor([])

tensor2 = torch.tensor([[]])



torch.equal(input=tensor1, other=tensor2)

# False



tensor1 = torch.tensor([0, 1, 2])

tensor2 = torch.tensor(1)



torch.isclose(input=tensor1, other=tensor2)

# tensor([False, True, False])



tensor1 = torch.tensor([0.+0.j, 1.+0.j, 2.+0.j])

tensor2 = torch.tensor(1.+0.j)



torch.isclose(input=tensor1, other=tensor2)

# tensor([False, True, False])



tensor1 = torch.tensor([False, True, False])

tensor2 = torch.tensor(True)



torch.isclose(input=tensor1, other=tensor2)

# tensor([False, True, False])