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Sigmoid

Sigmoid is often used as Activation function in an artificial neural network. As a nonlinear function, it transforms any input value into a range between 0 and 1.

The mathematical expression of sigmoid function is defined as below:

$$ \begin{align*} & f(x) = sigmoid(x) = \frac{1}{1 + e^{-x}} = \frac{e^x}{1+e^x} \ \end{align*} $$

Its derivative is $f’(x) = f(x)(1-f(x))$, which can be derived as follow:

$$ \begin{align*} f’(x) &= (\frac{1}{1 + e^{-x}})’ \newline \frac{d}{dx}f(x) &= \frac{d}{dx}(\frac{1}{1 + e^{-x}}) \newline &= \frac{-1}{(1 + e^{-x})^2} \frac{d}{dx}(1 + e^{-x}) \newline &= -(1+e^{-x})^{-2}(\frac{d}{dx}(1) + \frac{d}{dx}(e^{-x})) \newline &= -(1+e^{-x})^{-2}(e^{-x} \frac{d}{dx}(-x)) \newline &= -(1+e^{-x})^{-2}(-e^{-x}) \newline &= \frac{e^{-x}}{(1+e^{-x})^2} \newline &= \frac{1}{(1+e^{-x})} \frac{e^{-x}}{(1+e^{-x})} \newline &= \frac{1}{(1+e^{-x})} \frac{1+e^{-x} - 1}{(1+e^{-x})} \newline &= \frac{1}{(1+e^{-x})} (1 - \frac{1}{(1+e^{-x})}) \newline &= f(x)(1-f(x)) \end{align*} $$

Example of using sigmoid function in PyTorch

import torch
import torch.nn.functional as F

input = torch.tensor([[1,2],[3,4], [5,6]])
output = F.sigmoid(input)
print(output)

# output
tensor([[0.7311, 0.8808],
        [0.9526, 0.9820],
        [0.9933, 0.9975]])

We can also implement the sigmoid function in Python using the math library

import math

def sigmoid(x):
    return 1 / (1 + math.exp(-x))

print([[sigmoid(1), sigmoid(2)], [sigmoid(3), sigmoid(4)], [sigmoid(5), sigmoid(6)]])

# output
[[0.7310585786300049, 0.8807970779778823], 
 [0.9525741268224334, 0.9820137900379085], 
 [0.9933071490757153, 0.9975273768433653]]