Step Activation Function vs. Sigmoid Activation Function: A Detailed Comparison
Babu Chakraborty
Head of Marketing Technology | AI-Powered Digital Marketing Expert (MTech AI @ IITP) | Branding & Social Media Marketing Strategist
Activation functions are used in artificial neural networks to map the output of a neuron to a desired range.
There are many different activation functions available, each with its own advantages and disadvantages.
In this article, we will compare two of the most common activation functions: the step activation function and the sigmoid activation function.
Step Activation Function
The step activation function is a simple but effective activation function. It takes a single real number as input and outputs a 0 or 1, depending on whether the input is greater than or equal to a threshold value. The equation for the step activation function is as follows:
f(x) = 1 if x >= threshold
f(x) = 0 if x < threshold
The step activation function is often used in binary classification problems, where the goal is to classify an input into one of two categories. For example, the step activation function could be used to classify a tumor as benign or malignant.
Sigmoid Activation Function
The sigmoid activation function is a more complex activation function that produces a smooth, S-shaped curve. The equation for the sigmoid activation function is as follows:
f(x) = 1 / (1 + e^(-x))
The sigmoid activation function is often used in multi-class classification problems, where the goal is to classify an input into one of several categories. For example, the sigmoid activation function could be used to classify a flower as a rose, tulip, or daisy.
Major Differences
The main difference between the step activation function and the sigmoid activation function is the range of outputs that they produce.
The step activation function produces only two outputs, 0 and 1, while the sigmoid activation function produces a continuous range of outputs between 0 and 1.
Another difference between the two activation functions is their use in different types of neural networks.
The step activation function is typically used in feedforward neural networks, while the sigmoid activation function is typically used in recurrent neural networks.
When to Use Each Function
The step activation function is a good choice for binary classification problems where the goal is to produce a binary output. The sigmoid activation function is a good choice for multi-class classification problems where the goal is to produce a continuous output.
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Equations and Sample Python Implementation Code
The following is the equation for the step activation function:
f(x) = 1 if x >= threshold
f(x) = 0 if x < threshold
The following is the sample Python implementation code for the step activation function:
def step_activation(x):
if x >= threshold:
return 1
else:
return 0
The following is the equation for the sigmoid activation function:
f(x) = 1 / (1 + e^(-x))
The following is the sample Python implementation code for the sigmoid activation function:
def sigmoid_activation(x):
return 1 / (1 + math.exp(-x))
Frequently Asked Questions
The main difference between the step function and the sigmoid function is the range of outputs that they produce. The step function produces only two outputs, 0 and 1, while the sigmoid function produces a continuous range of outputs between 0 and 1.
The step function is a good choice for binary classification problems where the goal is to produce a binary output.
The sigmoid function is a good choice for multi-class classification problems where the goal is to produce a continuous output.
Conclusion
The step activation function and the sigmoid activation function are two of the most common activation functions used in artificial neural networks.
Each function has its own advantages and disadvantages, and the best choice for a particular application will depend on the specific requirements of that application.
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