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*My post explains sum() and mean().
min() can get the minimum value as shown below:
*Memos:
-
min()can be called both from torch and a tensor. - Setting a dimension to the 2nd argument with
torchor the 1st argument with a tensor gets zero or more 1st minimum values and the indices of them.
import torch
my_tensor = torch.tensor([[5, 4, 7, 7],
[6, 5, 3, 5],
[3, 8, 9, 3]])
torch.min(my_tensor)
my_tensor.min()
# tensor(3)
torch.min(my_tensor, 0)
my_tensor.min(0)
torch.min(my_tensor, -2)
my_tensor.min(-2)
# torch.return_types.min(
# values=tensor([3, 4, 3, 3]),
# indices=tensor([2, 0, 1, 2]))
torch.min(my_tensor, 1)
my_tensor.min(1)
torch.min(my_tensor, -1)
my_tensor.min(-1)
# torch.return_types.min(
# values=tensor([4, 3, 3]),
# indices=tensor([1, 2, 0]))
max() can get the maximum value as shown below:
*Memos:
-
max()can be called both fromtorchand a tensor. - Setting a dimension to the 2nd argument with
torchor the 1st argument with a tensor gets zero or more 1st maximum values and the indices of them.
import torch
my_tensor = torch.tensor([[5, 4, 7, 7],
[6, 5, 3, 5],
[3, 8, 9, 3]])
torch.max(my_tensor)
my_tensor.max()
# tensor(9)
torch.max(my_tensor, 0)
my_tensor.max(0)
torch.max(my_tensor, -2)
my_tensor.max(-2)
# torch.return_types.max(
# values=tensor([6, 8, 9, 7]),
# indices=tensor([1, 2, 2, 0]))
torch.max(my_tensor, 1)
my_tensor.max(1)
torch.max(my_tensor, -1)
my_tensor.max(-1)
# torch.return_types.max(
# values=tensor([7, 6, 9]),
# indices=tensor([2, 0, 2]))
argmin() can get the indices of the 1st minimum values as shown below:
*Memos:
-
argmin()can be called both fromtorchand a tensor. - The 2nd argument is a dimension with
torch. - The 1st argument is a dimension with a tensor.
import torch
my_tensor = torch.tensor([[5, 4, 7, 7],
[6, 5, 3, 5],
[3, 8, 9, 3]])
torch.argmin(my_tensor)
my_tensor.argmin()
# tensor(6)
torch.argmin(my_tensor, 0)
my_tensor.argmin(0)
torch.argmin(my_tensor, -2)
my_tensor.argmin(-2)
# tensor([2, 0, 1, 2])
torch.argmin(my_tensor, 1)
my_tensor.argmin(1)
torch.argmin(my_tensor, -1)
my_tensor.argmin(-1)
# tensor([1, 2, 0])
argmax() can get the indices of the 1st maximum values:
*Memos:
-
argmax()can be called both fromtorchand a tensor. - The 2nd argument is a dimension with
torch. - The 1st argument is a dimension with a tensor.
import torch
my_tensor = torch.tensor([[5, 4, 7, 7],
[6, 5, 3, 5],
[3, 8, 9, 3]])
torch.argmax(my_tensor)
my_tensor.argmax()
# tensor(10)
torch.argmax(my_tensor, 0)
my_tensor.argmax(0)
torch.argmax(my_tensor, -2)
my_tensor.argmax(-2)
# tensor([1, 2, 2, 0])
torch.argmax(my_tensor, 1)
my_tensor.argmax(1)
torch.argmax(my_tensor, -1)
my_tensor.argmax(-1)
# tensor([2, 0, 2])
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