How to Compute the Logistic Sigmoid Function of Tensor Elements in PyTorch
In this article, we will see how to compute the logistic sigmoid function of Tensor Elements in PyTorch.
The torch.special.expit() & torch.sigmoid() methods are logistic functions in a tensor. torch.sigmoid() is an alias of torch.special.expit() method. So, these methods will take the torch tensor as input and compute the logistic function element-wise of the tensor.
Syntax:
torch.special.expit(tensor)
torch.sigmoid(tensor)Parameter:
- tensor is the input tensor
Return: Return the logistic function of elements with new tensor.
Example 1:
In this example, we are creating a one-dimensional tensor with 6 elements and returning the logistic sigmoid function of elements using the sigmoid() method.
Python3
import torch # create 1D tensor with 6 elements t1 = torch.arange(1, 13) # display print(t1) # Compute the logistic sigmoid # function of elements in the # above tensor print(torch.sigmoid(t1)) |
Output:
tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
tensor([0.7311, 0.8808, 0.9526, 0.9820, 0.9933, 0.9975, 0.9991, 0.9997, 0.9999,
1.0000, 1.0000, 1.0000])
Example 2:
In this example, we are creating a one-dimensional tensor with 5 elements and returning the logistic sigmoid function of elements using torch.special.expit() method.
Python3
import torch # create 1D tensor with 5 elements t1 = torch.arange(1, 6) # display print(t1) # Compute the logistic sigmoid # function of elements in the # above tensor print(torch.special.expit(t1)) |
Output:
tensor([1, 2, 3, 4, 5])
tensor([0.7311, 0.8808, 0.9526, 0.9820, 0.9933])
Example 3:
In this example, we are creating a two-dimensional tensor with 3×3 elements, and returning the logistic sigmoid function of elements using sigmoid() method.
Python3
import torch # create 2D tensor with 3 elements each t1 = torch.tensor([[-20, 34, 56], [6, -9, 8]]) # display print(t1) # Compute the logistic sigmoid function # of elements in the above tensor print(torch.sigmoid(t1)) |
Output:
tensor([[-20, 34, 56],
[ 6, -9, 8]])
tensor([[2.0612e-09, 1.0000e+00, 1.0000e+00],
[9.9753e-01, 1.2339e-04, 9.9966e-01]])
Example 4:
In this example, we are creating a two-dimensional tensor with 3×3 elements each and, returning the logistic sigmoid function of elements using torch.special.expit() method.
Python3
import torch # create 2D tensor with 3 elements each t1 = torch.tensor([[-20, 34, 56, ], [78, 90, 8]]) # display print(t1) # Compute the logistic sigmoid # function of elements in the # above tensor print(torch.special.expit(t1)) |
Output:
tensor([[-20, 34, 56],
[ 6, -9, 8]])
tensor([[2.0612e-09, 1.0000e+00, 1.0000e+00],
[9.9753e-01, 1.2339e-04, 9.9966e-01]])
