eq and ne in PyTorch

*Memos:

• 
  ne() 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 or scalar of int, float, complex or bool).


• There is out argument with torch(Optional-Default:None-Type:tensor):
  *Memos:
  
  
  
  
  • 
    out= must be used.
  
  
  • 
    is the alias of ne().
    
  
  
  
  
  
  
  
  

  CODE Kopieren

  import torch
  
  
  
  tensor1 = torch.tensor([5, 0, 3])
  
  tensor2 = torch.tensor([7, 0, 3])
  
  
  
  torch.ne(input=tensor1, other=tensor2)
  
  tensor1.ne(other=tensor2)
  
  torch.ne(input=tensor2, other=tensor1)
  
  # tensor([True, False, False])
  
  
  
  tensor1 = torch.tensor(5)
  
  tensor2 = torch.tensor([[3, 5, 4],
  
                          [6, 3, 5]])
  
  torch.ne(input=tensor1, other=tensor2)
  
  torch.ne(input=tensor2, other=tensor1)
  
  # tensor([[True, False, True],
  
  #         [True, True, False]])
  
  
  
  torch.ne(input=tensor1, other=3)
  
  # tensor(True)
  
  
  
  torch.ne(input=tensor2, other=3)
  
  # tensor([[False, True, True],
  
  #         [True, False, True]])
  
  
  
  tensor1 = torch.tensor([5, 0, 3])
  
  tensor2 = torch.tensor([[5, 5, 5],
  
                          [0, 0, 0],
  
                          [3, 3, 3]])
  
  torch.ne(input=tensor1, other=tensor2)
  
  torch.ne(input=tensor2, other=tensor1)
  
  # tensor([[False, True, True],
  
  #         [True, False, True],
  
  #         [True, True, False]])
  
  
  
  torch.ne(input=tensor1, other=3)
  
  # tensor([True, True, False])
  
  
  
  torch.ne(input=tensor2, other=3)
  
  # tensor([[True, True, True],
  
  #         [True, True, True],
  
  #         [False, False, False]])
  
  
  
  tensor1 = torch.tensor([5., 0., 3.])
  
  tensor2 = torch.tensor([[5., 5., 5.],
  
                          [0., 0., 0.],
  
                          [3., 3., 3.]])
  
  torch.ne(input=tensor1, other=tensor2)
  
  # tensor([[False, True, True],
  
  #         [True, False, True],
  
  #         [True, True, False]])
  
  
  
  torch.ne(input=tensor1, other=3.)
  
  # tensor([True, True, False])
  
  
  
  tensor1 = torch.tensor([5.+0.j, 0.+0.j, 3.+0.j])
  
  tensor2 = torch.tensor([[5.+0.j, 5.+0.j, 5.+0.j],
  
                          [0.+0.j, 0.+0.j, 0.+0.j],
  
                          [3.+0.j, 3.+0.j, 3.+.0j]])
  
  torch.ne(input=tensor1, other=tensor2)
  
  # tensor([[False, True, True],
  
  #         [True, False, True],
  
  #         [True, True, False]])
  
  
  
  torch.ne(input=tensor1, other=3.+0.j)
  
  # tensor([True, True, False])
  
  
  
  tensor1 = torch.tensor([True, False, True])
  
  tensor2 = torch.tensor([[True, False, True],
  
                          [False, True, False],
  
                          [True, False, True]])
  
  torch.ne(input=tensor1, other=tensor2)
  
  # tensor([[False, False, False],
  
  #         [True, True, True],
  
  #         [False, False, False]])
  
  
  
  torch.ne(input=tensor1, other=True)
  
  # tensor([False, True, False])
  
  

  
  
  
  Vollständiger Original-Bericht

  Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf dev.to.

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