gt and lt in PyTorch

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*Memos:

• My post explains ge() and le().
• My post explains eq() and ne().
• My post explains isclose() and equal().

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

• gt() 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() is the alias of gt().

import torch

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

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

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

• lt() 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() is the alias of lt().

import torch

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

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

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原文链接：gt and lt in PyTorch