aminmax, amin and amax in PyTorch

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*Memos:

• My post explains min() and max().
• My post explains minimum() and maximum().
• My post explains fmin() and fmax().
• My post explains argmin() and argmax().
• My post explains kthvalue() and topk().
• My post explains cummin() and cummax().

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

*Memos:

• aminmax() 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).
• There is dim argument with torch or a tensor(Optional-Type:int): *Memos:
  • Setting dim can get zero or more 1st minimum and maximum elements.
  • You must use dim=.
• There is keepdim argument with torch or a tensor(Optional-Default:False-Type:bool): *Memos:
  • You must use keepdim=.
  • My post explains keepdim argument.
• There is out argument with torch(Optional-Default:None-Type:tuple(tensor, tensor) or list(tensor, tensor)): *Memos:
  • out= must be used.
  • My post explains out argument.
• The 0D tensor of one complex number with dim=0 or dim=-1 works.
• An empty 2D or more D input tensor or tensor doesn't work if not setting dim.
• An empty 1D input tensor or tensor doesn't work even if setting dim.

import torch

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.aminmax(input=my_tensor)
my_tensor.aminmax()
# torch.return_types.aminmax(
# min=tensor(3),
# max=tensor(9))

torch.aminmax(input=my_tensor, dim=0)
torch.aminmax(input=my_tensor, dim=-2)
# torch.return_types.aminmax(
# min=tensor([3, 4, 3, 3]),
# max=tensor([6, 8, 9, 7]))

torch.aminmax(input=my_tensor, dim=1)
torch.aminmax(input=my_tensor, dim=-1)
# torch.return_types.aminmax(
# min=tensor([4, 3, 3]),
# max=tensor([7, 6, 9]))

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.aminmax(input=my_tensor)
# torch.return_types.aminmax(
# min=tensor(3.),
# max=tensor(9.))

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True],
                          [True, False, True, False]])
torch.aminmax(input=my_tensor)
# torch.return_types.aminmax(
# min=tensor(False),
# max=tensor(True))

my_tensor = torch.tensor(5.+7.j)

torch.aminmax(input=my_tensor, dim=0)
torch.aminmax(input=my_tensor, dim=-1)
# torch.return_types.aminmax(
# min=tensor(5.+7.j),
# max=tensor(5.+7.j))

my_tensor = torch.tensor([])
my_tensor = torch.tensor([[]])
my_tensor = torch.tensor([[[]]])

torch.aminmax(input=my_tensor) # Error

my_tensor = torch.tensor([])

torch.aminmax(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.aminmax(input=my_tensor, dim=0)
# torch.return_types.aminmax(
# min=tensor([]),
# max=tensor([]))

my_tensor = torch.tensor([[[]]])

torch.aminmax(input=my_tensor, dim=0)
# torch.return_types.aminmax(
# min=tensor([], size=(1, 0)),
# max=tensor([], size=(1, 0)))

amin() can get the 0D or more D tensor of zero or more minimum elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

• amin() 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 dim(Optional-Type:int, tuple of int or list of int). *Setting dim can get zero or more 1st minimum elements.
• The 3rd argument with torch or the 2nd argument with a tensor is keepdim(Optional-Default:False-Type:bool). *My post explains keepdim argument.
• There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
  • out= must be used.
  • My post explains out argument.
• An empty 2D or more D input tensor or tensor doesn't work if not setting dim.
• An empty 1D input tensor or tensor doesn't work even if setting dim.

import torch

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.amin(input=my_tensor)
my_tensor.amin()
torch.amin(input=my_tensor, dim=(0, 1))
torch.amin(input=my_tensor, dim=(0, -1))
torch.amin(input=my_tensor, dim=(1, 0))
torch.amin(input=my_tensor, dim=(1, -2))
torch.amin(input=my_tensor, dim=(-1, 0))
torch.amin(input=my_tensor, dim=(-1, -2))
torch.amin(input=my_tensor, dim=(-2, 1))
torch.amin(input=my_tensor, dim=(-2, -1))
# tensor(3)

torch.amin(input=my_tensor, dim=0)
torch.amin(input=my_tensor, dim=-2)
torch.amin(input=my_tensor, dim=(0,))
torch.amin(input=my_tensor, dim=(-2,))
# tensor([3, 4, 3, 3])

torch.amin(input=my_tensor, dim=1)
torch.amin(input=my_tensor, dim=-1)
torch.amin(input=my_tensor, dim=(1,))
torch.amin(input=my_tensor, dim=(-1,))
# tensor([4, 3, 3])

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.amin(input=my_tensor)
# tensor(3.)

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True],
                          [True, False, True, False]])
torch.amin(input=my_tensor)
# tensor(False)

my_tensor = torch.tensor([])
my_tensor = torch.tensor([[]])
my_tensor = torch.tensor([[[]]])

torch.amin(input=my_tensor) # Error

my_tensor = torch.tensor([])

torch.amin(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.amin(input=my_tensor, dim=0)
# tensor([])

my_tensor = torch.tensor([[[]]])

torch.amin(input=my_tensor, dim=0)
# tensor([], size=(1, 0))

amax() can get the 0D or more D tensor of zero or more maximum elements from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

• amax() 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 dim(Optional-Type:int, tuple of int or list of int). *Setting dim can get zero or more 1st maximum elements.
• The 3rd argument with torch or the 2nd argument is keepdim(Optional-Default:False-Type:bool). *My post explains keepdim argument.
• There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
  • out= must be used.
  • My post explains out argument.
• An empty 2D or more D input tensor or tensor doesn't work if not setting dim.
• An empty 1D input tensor or tensor doesn't work even if setting dim.

import torch

my_tensor = torch.tensor([[5, 4, 7, 7],
                          [6, 5, 3, 5],
                          [3, 8, 9, 3]])
torch.amax(input=my_tensor)
my_tensor.amax()
torch.amax(input=my_tensor, dim=(0, 1))
torch.amax(input=my_tensor, dim=(0, -1))
torch.amax(input=my_tensor, dim=(1, 0))
torch.amax(input=my_tensor, dim=(1, -2))
torch.amax(input=my_tensor, dim=(-1, 0))
torch.amax(input=my_tensor, dim=(-1, -2))
torch.amax(input=my_tensor, dim=(-2, 1))
torch.amax(input=my_tensor, dim=(-2, -1))
# tensor(9)

torch.amax(input=my_tensor, dim=0)
torch.amax(input=my_tensor, dim=-2)
torch.amax(input=my_tensor, dim=(0,))
torch.amax(input=my_tensor, dim=(-2,))
# tensor([6, 8, 9, 7])

torch.amax(input=my_tensor, dim=1)
torch.amax(input=my_tensor, dim=-1)
torch.amax(input=my_tensor, dim=(1,))
torch.amax(input=my_tensor, dim=(-1,))
# tensor([7, 6, 9])

my_tensor = torch.tensor([[5., 4., 7., 7.],
                          [6., 5., 3., 5.],
                          [3., 8., 9., 3.]])
torch.amax(input=my_tensor)
# tensor(9.)

my_tensor = torch.tensor([[True, False, True, False],
                          [False, True, False, True],
                          [True, False, True, False]])
torch.amax(input=my_tensor)
# tensor(True)

my_tensor = torch.tensor([])
my_tensor = torch.tensor([[]])
my_tensor = torch.tensor([[[]]])

torch.amax(input=my_tensor) # Error

my_tensor = torch.tensor([])

torch.amax(input=my_tensor, dim=0) # Error

my_tensor = torch.tensor([[]])

torch.amax(input=my_tensor, dim=0)
# tensor([])

my_tensor = torch.tensor([[[]]])

torch.amax(input=my_tensor, dim=0)
# tensor([], size=(1, 0))