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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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ge and le in PyTorch

#python #pytorch #ge #le

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*Memos:

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])
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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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