Super Kai (Kazuya Ito)*Memos:
- My post explains log2() and log10().
- My post explains exp() and exp2().
- My post explains expm1() and sigmoid().
log() can get the 0D or more D tensor of the zero or more elements by ln(x) which is the natural logarithm based on e, from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
log()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - There is
outargument withtorch(Optional-Type:tensor): *Memos:-
out=must be used. -
My post explains
outargument.
-
- *A
floattensor is returned unless an input tensor iscomplextensor. - The formula is y = ln(x) or y = loge(x).
- The graph in Desmos:
import torch
my_tensor = torch.tensor([-0.1, 0.0, 0.1, 0.9, 1.0, 1.1, torch.e, 100.0])
torch.log(input=my_tensor)
my_tensor.log()
# tensor([nan, -inf, -2.3026, -0.1054, 0.0000, 0.0953, 1.0000, 4.6052])
my_tensor = torch.tensor([[-0.1, 0.0, 0.1, 0.9],
[1.0, 1.1, torch.e, 100.0]])
torch.log(input=my_tensor)
# tensor([[nan, -inf, -2.3026, -0.1054],
# [0.0000, 0.0953, 1.0000, 4.6052]])
my_tensor = torch.tensor([[[-0.1, 0.0], [0.1, 0.9]],
[[1.0, 1.1], [torch.e, 100.0]]])
torch.log(input=my_tensor)
# tensor([[[nan, -inf], [-2.3026, -0.1054]],
# [[0.0000, 0.0953], [1.0000, 4.6052]]])
my_tensor = torch.tensor([[[-0.1+0.j, 0.0+0.j], [0.1+0.j, 0.9+0.j]],
[[1.0+0.j, 1.1+0.j], [torch.e+0.j, 100.0+0.j]]])
torch.log(input=my_tensor)
# tensor([[[-2.3026+3.1416j, -inf+0.0000j],
# [-2.3026+0.0000j, -0.1054+0.0000j]],
# [[0.0000+0.0000j, 0.0953+0.0000j],
# [1.0000+0.0000j, 4.6052+0.0000j]]])
my_tensor = torch.tensor([[[-1, 0], [1, 2]],
[[5, 8], [10, 100]]])
torch.log(input=my_tensor)
# tensor([[[nan, -inf], [0.0000, 0.6931]],
# [[1.6094, 2.0794], [2.3026, 4.6052]]])
my_tensor = torch.tensor([[[True, False], [True, False]],
[[False, True], [False, True]]])
torch.log(input=my_tensor)
# tensor([[[0., -inf], [0., -inf]],
# [[-inf, 0.], [-inf, 0.]]])
log1p() can get the 0D or more D tensor of the zero or more elements by ln(x + 1) which is the natural logarithm based on e, from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
log1p()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - There is
outargument withtorch(Optional-Type:tensor): *Memos:-
out=must be used. -
My post explains
outargument.
-
- *A
floattensor is returned unless an input tensor iscomplextensor. - The formula is y = ln(x + 1) or y = loge(x + 1).
- The graph in Desmos:
import torch
my_tensor = torch.tensor([-1.1, -1.0, -0.9, -0.1, 0.0, 0.1, torch.e, 10.0])
torch.log1p(input=my_tensor)
my_tensor.log1p()
# tensor([nan, -inf, -2.3026, -0.1054, 0.0000, 0.0953, 1.3133, 2.3979])
my_tensor = torch.tensor([[-1.1, -1.0, -0.9, -0.1],
[0.0, 0.1, torch.e, 10.0]])
torch.log1p(input=my_tensor)
# tensor([[nan, -inf, -2.3026, -0.1054],
# [0.0000, 0.0953, 1.3133, 2.3979]])
my_tensor = torch.tensor([[[-1.1, -1.0], [-0.9, -0.1]],
[[0.0, 0.1], [torch.e, 10.0]]])
torch.log1p(input=my_tensor)
# tensor([[[nan, -inf], [-2.3026, -0.1054]],
# [[0.0000, 0.0953], [1.3133, 2.3979]]])
my_tensor = torch.tensor([[[-1.1+0.j, -1.0+0.j], [-0.9+0.j, -0.1+0.j]],
[[0.0+0.j, 0.1+0.j], [torch.e+0.j, 10.0+0.j]]])
torch.log1p(input=my_tensor)
# tensor([[[-2.3026+3.1416j, -inf+0.0000j],
# [-2.3026+0.0000j, -0.1054+0.0000j]],
# [[0.0000+0.0000j, 0.0953+0.0000j],
# [1.3133+0.0000j, 2.3979+0.0000j]]])
my_tensor = torch.tensor([[[-1, 0], [1, 2]],
[[5, 8], [10, 100]]])
my_tensor = torch.tensor([[[-2, -1], [0, 1]],
[[2, 5], [8, 10]]])
torch.log1p(input=my_tensor)
# tensor([[[nan, -inf], [0.0000, 0.6931]],
# [[1.0986, 1.7918], [2.1972, 2.3979]]])
my_tensor = torch.tensor([[[True, False], [True, False]],
[[False, True], [False, True]]])
torch.log1p(input=my_tensor)
# tensor([[[0.6931, 0.0000], [0.6931, 0.0000]],
# [[0.0000, 0.6931], [0.0000, 0.6931]]])
