DEV Community

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

Posted on • Edited on

gt and lt in PyTorch

Buy Me a Coffee

*Memos:

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])
Enter fullscreen mode Exit fullscreen mode

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])
Enter fullscreen mode Exit fullscreen mode

Heroku

This site is built on Heroku

Join the ranks of developers at Salesforce, Airbase, DEV, and more who deploy their mission critical applications on Heroku. Sign up today and launch your first app!

Get Started

Top comments (0)

Billboard image

The Next Generation Developer Platform

Coherence is the first Platform-as-a-Service you can control. Unlike "black-box" platforms that are opinionated about the infra you can deploy, Coherence is powered by CNC, the open-source IaC framework, which offers limitless customization.

Learn more