gt(), lt(), ge() and le() in PyTorch

#pytorch #gt #lt #ge

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

gt() can check the 1st tensor is greater than the 2nd tensor element-wise as shown below:

*Memos:

gt() can be called both from torch and a tensor.
The tensors can be 0D or more D tensors.
One or more complex numbers cannot be used.
gt() and greater() are the same because greater() is the alias of gt():

import torch

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

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

lt() can check the 1st tensor is less than the 2nd tensor element-wise as shown below:

*Memos:

lt() can be called both from torch and a tensor.
The tensors can be 0D or more D tensors.
One or more complex numbers cannot be used.
lt() and less() are the same because less() is the alias of lt():

import torch

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

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

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

ge() can check the 1st tensor is greater than or equal to the 2nd tensor element-wise as shown below:

*Memos:

ge() can be called both from torch and a tensor.
The tensors can be 0D or more D tensors.
One or more complex numbers cannot be used.
ge() and greater_equal() are the same because greater_equal() is the alias of ge():

import torch

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

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

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

le() can check the 1st tensor is less than or equal to the 2nd tensor element-wise as shown below:

*Memos:

le() can be called both from torch and a tensor.
The tensors can be 0D or more D tensors.
One or more complex numbers cannot be used.
le() and less_equal() are the same because less_equal() is the alias of le():

import torch

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

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

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

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gt(), lt(), ge() and le() in PyTorch

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