*Memos:
- My post explains log() and log1p().
- My post explains exp() and exp2().
- My post explains expm1() and sigmoid().
log2() can get the 0D or more D tensor of the zero or more elements by log2(x) from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
log2()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-Default:None-Type:tensor): *Memos:-
out=must be used. - My post explains
outargument.
-
- *A
floattensor is returned unless an input tensor iscomplextensor. - The formula is y = log2(x).
- The graph in Desmos:
import torch my_tensor = torch.tensor([-0.1, 0.0, 0.1, 0.9, 1.0, 1.1, 2.0, 100.0]) torch.log2(input=my_tensor) my_tensor.log2() # tensor([nan, -inf, -3.3219, -0.1520, 0.0000, 0.1375, 1.0000, 6.6439]) my_tensor = torch.tensor([[-0.1, 0.0, 0.1, 0.9], [1.0, 1.1, 2.0, 100.0]]) torch.log2(input=my_tensor) # tensor([[nan, -inf, -3.3219, -0.1520], # [0.0000, 0.1375, 1.0000, 6.6439]]) my_tensor = torch.tensor([[[-0.1, 0.0], [0.1, 0.9]], [[1.0, 1.1], [2.0, 100.0]]]) torch.log2(input=my_tensor) # tensor([[[nan, -inf], [-3.3219, -0.1520]], # [[0.0000, 0.1375], [1.0000, 6.6439]]]) 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], [2.0+0.j, 100.0+0.j]]]) torch.log2(input=my_tensor) # tensor([[[-3.3219+4.5324j, -inf+0.0000j], # [-3.3219+0.0000j, -0.1520+0.0000j]], # [[0.0000+0.0000j, 0.1375+0.0000j], # [1.0000+0.0000j, 6.6439+0.0000j]]]) my_tensor = torch.tensor([[[-1, 0], [1, 2]], [[5, 8], [10, 100]]]) torch.log2(input=my_tensor) # tensor([[[nan, -inf], [0.0000, 1.0000]], # [[2.3219, 3.0000], [3.3219, 6.6439]]]) my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.log2(input=my_tensor) # tensor([[[0., -inf], [0., -inf]], # [[-inf, 0.], [-inf, 0.]]]) log10() can get the 0D or more D tensor of the zero or more elements by log10(x) from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
log10()can be used withtorchor a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - There is
outargument withtorch(Optional-Default:None-Type:tensor): *Memos:-
out=must be used. - My post explains
outargument.
-
- *A
floattensor is returned unless an input tensor iscomplextensor. - The formula is y = log10(x).
- The graph in Desmos:
import torch my_tensor = torch.tensor([-0.1, 0.0, 0.1, 0.9, 1.0, 1.1, 10.0, 100.0]) torch.log10(input=my_tensor) my_tensor.log10() # tensor([nan, -inf, -1.0000, -0.0458, 0.0000, 0.0414, 1.0000, 2.0000]) my_tensor = torch.tensor([[-0.1, 0.0, 0.1, 0.9], [1.0, 1.1, 10.0, 100.0]]) torch.log10(input=my_tensor) # tensor([[nan, -inf, -1.0000, -0.0458], # [0.0000, 0.0414, 1.0000, 2.0000]]) my_tensor = torch.tensor([[[-0.1, 0.0], [0.1, 0.9]], [[1.0, 1.1], [10.0, 100.0]]]) torch.log10(input=my_tensor) # tensor([[[nan, -inf], # [-1.0000, -0.0458]], # [[0.0000, 0.0414], # [1.0000, 2.0000]]]) 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], [10.0+0.j, 100.0+0.j]]]) torch.log10(input=my_tensor) # tensor([[[-1.0000+1.3644j, -inf+0.0000j], # [-1.0000+0.0000j, -0.0458+0.0000j]], # [[0.0000+0.0000j, 0.0414+0.0000j], # [1.0000+0.0000j, 2.0000+0.0000j]]]) my_tensor = torch.tensor([[[-1, 0], [1, 2]], [[5, 8], [10, 100]]]) torch.log10(input=my_tensor) # tensor([[[nan, -inf], [0.0000, 0.3010]], # [[0.6990, 0.9031], [1.0000, 2.0000]]]) my_tensor = torch.tensor([[[True, False], [True, False]], [[False, True], [False, True]]]) torch.log10(input=my_tensor) # tensor([[[0., -inf], [0., -inf]], # [[-inf, 0.], [-inf, 0.]]]) 
Top comments (0)