min() and max() in PyTorch

*Memos:

My post explains argmin() and argmax().

My post explains aminmax(), amin() and amax().

My post explains minimum(), maximum(). fmin() and fmax().

My post explains kthvalue() and topk().

min() can get the 0D of the 1st one minimum element…


This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

*Memos:

min() can get the 0D of the 1st one minimum element or the 0D or more D tensor of the 1st zero or more minimum elements and their indices from the one or two 0D or more D tensors of zero or more elements as shown below:

*Memos:

  • min() can be used with torch or a tensor.
  • The 1st argument with torch or using a tensor is input(Required-Type:tensor of int, float or bool).
  • The 2nd argument with torch or the 1st argument is dim(Optional-Type:int). *Setting dim can get zero or more 1st minimum elements and their indices.
  • The 2nd argument with torch or the 1st argument is other(Optional-Type:tensor of int, float or bool). *Memos:
    • It can only be used with input.
    • This is the functionality of minimum().
  • The 3rd argument with torch or the 2nd argument is keepdim(Optional-Type:bool). *Memos:
    • It must be used with dim without other.
    • My post explains keepdim argument.
  • There is out argument with torch(Optional-Type:tensor or tuple(tensor, tensor): *Memos:
    • The type of tensor must be used without dim and keepdim.
    • The type of tuple(tensor, tensor) must be used with dim without other.
    • out= must be used.
    • My post explains out argument.
  • Empty 2D or more D input tensor without other tensor doesn't work if not setting dim.
  • Empty 1D input tesnor without other 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.min(input=my_tensor)
my_tensor.min()
# tensor(3)

torch.min(input=my_tensor, dim=0)
torch.min(input=my_tensor, dim=-2)
# torch.return_types.min(
# values=tensor([3, 4, 3, 3]),
# indices=tensor([2, 0, 1, 2]))

torch.min(input=my_tensor, dim=1)
torch.min(input=my_tensor, dim=-1)
# torch.return_types.min(
# values=tensor([4, 3, 3]),
# indices=tensor([1, 2, 0]))

tensor1 = torch.tensor([5, 4, 7, 7])
tensor2 = torch.tensor([[6, 5, 3, 5],
                        [3, 8, 9, 3]])
torch.min(input=tensor1, other=tensor2)
# tensor([[5, 4, 3, 5],
#         [3, 4, 7, 3]])

tensor1 = torch.tensor([5., 4., 7., 7.])
tensor2 = torch.tensor([[6., 5., 3., 5.],
                        [3., 8., 9., 3.]])
torch.min(input=tensor1, other=tensor2)
# tensor([[5., 4., 3., 5.],
#         [3., 4., 7., 3.]])

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

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

torch.min(input=my_tensor) # Error

my_tensor = torch.tensor([])

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

my_tensor = torch.tensor([[]])

torch.min(input=my_tensor, dim=0)
# torch.return_types.min(
# values=tensor([]),
# indices=tensor([], dtype=torch.int64))

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

torch.min(input=my_tensor, dim=0)
# torch.return_types.min(
# values=tensor([], size=(1, 0)),
# indices=tensor([], size=(1, 0), dtype=torch.int64))

max() can get the 0D of the 1st one maximum element or the 0D or more D tensor of the 1st zero or more maximum elements and their indices from the one or two 0D or more D tensors of zero or more elements as shown below:

  • max() can be used with torch or a tensor.
  • The 1st argument with torch or using a tensor is input(Required-Type:tensor of int, float or bool).
  • The 2nd argument with torch or the 1st argument is dim(Optional-Type:int). *Setting dim can get zero or more 1st maximum elements and their indices.
  • The 2nd argument with torch or the 1st argument is other(Optional-Type:tensor of int, float or bool). *Memos:
    • It can only be used with input.
    • This is the functionality of maximum().
  • The 3rd argument with torch or the 2nd argument is keepdim(Optional-Type:bool). *Memos:
    • It must be used with dim without other.
    • My post explains keepdim argument.
  • There is out argument with torch(Optional-Type:tensor or tuple(tensor, tensor): *Memos:
    • The type of tensor must be used without dim and keepdim.
    • The type of tuple(tensor, tensor) must be used with dim without other.
    • out= must be used.
    • My post explains out argument.
  • Empty 2D or more D input tensor without other tensor doesn't work if not setting dim.
  • Empty 1D input tesnor without other 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.max(input=my_tensor)
my_tensor.max()
# tensor(9)

torch.max(input=my_tensor, dim=0)
torch.max(input=my_tensor, dim=-2)
# torch.return_types.max(
# values=tensor([6, 8, 9, 7]),
# indices=tensor([1, 2, 2, 0]))

torch.max(input=my_tensor, dim=1)
torch.max(input=my_tensor, dim=-1)
# torch.return_types.max(
# values=tensor([7, 6, 9]),
# indices=tensor([2, 0, 2]))

tensor1 = torch.tensor([5, 4, 7, 7])
tensor2 = torch.tensor([[6, 5, 3, 5],
                        [3, 8, 9, 3]])
torch.max(input=tensor1, other=tensor2)
# tensor([[6, 5, 7, 7],
#         [5, 8, 9, 7]])

tensor1 = torch.tensor([5., 4., 7., 7.])
tensor2 = torch.tensor([[6., 5., 3., 5.],
                        [3., 8., 9., 3.]])
torch.max(input=tensor1, other=tensor2)
# tensor([[6., 5., 7., 7.],
#         [5., 8., 9., 7.]])

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

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

torch.max(input=my_tensor) # Error

my_tensor = torch.tensor([])

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

my_tensor = torch.tensor([[]])

torch.max(input=my_tensor, dim=0)
# torch.return_types.max(
# values=tensor([]),
# indices=tensor([], dtype=torch.int64))

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

torch.max(input=my_tensor, dim=0)
# torch.return_types.max(
# values=tensor([], size=(1, 0)),
# indices=tensor([], size=(1, 0), dtype=torch.int64))


This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)


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