ReLU vs Sigmoid vs Tanh: Activation Function Performance
In machine learning, feed-forward neural networks are popular because they use matrix multiplications at each step. when training a model, we can encounter vanishing gradient or exploding gradient problems. These issues arise when numbers either grow too large too quickly, causing errors, or shrink to near zero, making neurons and weights ineffective. To address this, we use activation functions.
ReLU, sigmoid, and tanh are common options. Commenter Nathan Canera prefers ReLU as it helps with vanishing gradients but doesn't fully prevent the exploding gradient issue. ReLU is efficient and preferred in modern models due to its simplicity, it returns the maximum of zero or the input.
Sigmoid and tanh are more complex and computationally expensive due to division and exponent operations. Benchmarks show that ReLU, when optimized, outperforms sigmoid and tanh in speed, making it a top choice. Variants like leaky ReLU allow some data to retain meaning below zero but add computational cost.
ReLU remains the industry standard for activation functions in machine learning.
