minimum and maximum in PyTorch

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*Memos:

• My post explains min() and max().
• My post explains fmin() and fmax().
• My post explains argmin() and argmax().
• My post explains aminmax(), amin() and amax().
• My post explains kthvalue() and topk().
• My post explains cummin() and cummax().

minimum() can get the 0D or more D tensor of zero or more minimum elements prioritizing nan from two of the 0D or more D tensors of zero or more elements as shown below:

*Memos:

• minimum() 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 is other(Required-Type:tensor 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.
• nan is taken if there are a number and nan.

import torch

tensor1 = torch.tensor([5., float('nan'), 4., float('nan')])
tensor2 = torch.tensor([[7., 8., float('nan'), float('nan')],
                        [-9., 2., 0., -6.]])
torch.minimum(input=tensor1, other=tensor2)
tensor1.minimum(other=tensor2)
# tensor([[5., nan, nan, nan],
#         [-9., nan, 0., nan]])

tensor1 = torch.tensor(5.)
tensor2 = torch.tensor([[[7., 8.], [float('nan'), float('nan')]],
                        [[-9., 2.], [0., -6.]]])
torch.minimum(input=tensor1, other=tensor2)
# tensor([[[5., 5.], [nan, nan]],
#         [[-9., 2.], [0., -6.]]])

tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[[7, 8], [-5, -1]],
                        [[-9, 2], [0, -6]]])
torch.minimum(input=tensor1, other=tensor2)
# tensor([[[5, 5], [-5, -1]],
#         [[-9, 2], [0, -6]]])

tensor1 = torch.tensor(True)
tensor2 = torch.tensor([[[True, False], [True, False]],
                        [[False, True], [False, True]]])
torch.minimum(input=tensor1, other=tensor2)
# tensor([[[True, False], [True, False]],
#         [[False, True], [False, True]]])

maximum() can get the 0D or more D tensor of zero or more maximum elements prioritizing nan from two of the 0D or more D tensors of zero or more elements as shown below:

*Memos:

• maximum() 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 is other(Required-Type:tensor 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.
• nan is taken if there are a number and nan.

import torch

tensor1 = torch.tensor([5., float('nan'), 4., float('nan')])
tensor2 = torch.tensor([[7., 8., float('nan'), float('nan')],
                        [-9., 2., 0., -6.]])
torch.maximum(input=tensor1, other=tensor2)
tensor1.maximum(other=tensor2)
# tensor([[7., nan, nan, nan],
#         [5., nan, 4., nan]])

tensor1 = torch.tensor(5.)
tensor2 = torch.tensor([[[7., 8.], [float('nan'), float('nan')]],
                        [[-9., 2.], [0., -6.]]])
torch.maximum(input=tensor1, other=tensor2)
# tensor([[[7., 8.], [nan, nan]],
#         [[5., 5.], [5., 5.]]])

tensor1 = torch.tensor(5)
tensor2 = torch.tensor([[[7, 8], [-5, -1]],
                        [[-9, 2], [0, -6]]])
torch.maximum(input=tensor1, other=tensor2)
# tensor([[[7, 8], [5, 5]],
#         [[5, 5], [5, 5]]])

tensor1 = torch.tensor(True)
tensor2 = torch.tensor([[[True, False], [True, False]],
                        [[False, True], [False, True]]])
torch.maximum(input=tensor1, other=tensor2)
# tensor([[[True, True], [True, True]],
#         [[True, True], [True, True]]])