Learn how to identify the sigmoid activation function in neural networks, including its mathematical representation and why it fits the given formula. Essential for CNN certification exams.
Table of Contents
Question
Identify the following activation function :
φ(V) = Z + (1/ 1 + exp (– x * V + Y) ),
Z, X, Y are parameters
A. Step function
B. Ramp function
C. Sigmoid function
D. Gaussian function
Answer
C. Sigmoid function
Explanation
Why It’s Not Other Options
- Step Function: This outputs binary values (0 or 1) based on a threshold, which does not align with the continuous nature of the given formula.
- Ramp Function: This is a piecewise linear function that increases linearly with input, unlike the non-linear sigmoid curve.
- Gaussian Function: This has a bell-shaped curve, which is entirely different from the given formula.
Characteristics of the Sigmoid Function
- Range: Outputs values between 0 and 1.
- Non-linearity: Allows neural networks to model complex relationships.
- Applications: Commonly used in binary classification tasks and as an activation function in neural networks.
The presence of parameters Z,X,Y simply modifies the basic sigmoid behavior but does not alter its fundamental identity. Thus, the formula represents a sigmoid activation function.
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