ge and le in PyTorch

*Memos:

My post explains gt() and lt().
My post explains eq() and ne().
My post explains isclose() and equal().

ge() can check if the zero or more elements of the 1st 0D or more D tensor are greater than or 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:

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

import torch

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([3, 5, 4])

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

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

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[3, 5, 4],
                        [6, 3, 5]])
torch.ge(input=tensor1, other=tensor2)
# tensor([[True, False, False],
#         [False, False, False]])

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

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

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

tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[3., 5., 4.],
                        [6., 3., 5.]])
torch.ge(input=tensor1, other=tensor2)
# tensor([[True, False, False],
#         [False, False, False]])

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

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

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

le() can check if the zero or more elements of the 1st 0D or more D tensor are less than or 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 element as shown below:

*Memos:

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

import torch

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([3, 5, 4])

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

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

tensor1 = torch.tensor([5, 0, 3])
tensor2 = torch.tensor([[3, 5, 4],
                        [6, 3, 5]])
torch.le(input=tensor1, other=tensor2)
# tensor([[False, True, True],
#         [True, True, True]])

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

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

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

tensor1 = torch.tensor([5., 0., 3.])
tensor2 = torch.tensor([[3., 5., 4.],
                        [6., 3., 5.]])
torch.le(input=tensor1, other=tensor2)
# tensor([[False, True, True],
#         [True, True, True]])

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

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

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

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