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GELU is a smooth alternative to ReLU that scales each input by how likely it is to be positive under a Gaussian, rather than cutting negative values to zero. The explainer is a fair introduction to why Transformers like BERT use it, though its figure for GELU(2) is off: the value is about 1.95, not 1. Readers should check numeric claims against the original Hendrycks and Gimpel paper.
GELU Activation Function Explained: How It Works and Why Transformers Use It
Why do Transformer models like BERT use GELU instead of the traditional ReLU activation function? The answer lies in how these functions process information. While ReLU completely removes negative inputs, GELU uses a smooth, input-dependent mechanism to decide how much information should pass through. This small mathematical difference can influence how neural networks learn complex […] The post GELU Activation Function Explained: How It Works and Why Transformers Use It appeared first on Analytics Vidhya.
Why do Transformer models like BERT use GELU instead of the traditional ReLU activation function? The answer lies in how these functions process information. While ReLU completely removes negative inputs, GELU uses a smooth, input-dependent mechanism to decide how much information should pass through. This small mathematical difference can influence how neural networks learn complex patterns. In this article, we will understand the GELU activation function, explore its mathematics, compare it with ReLU, and implement both functions using Python. What Is the GELU Activation Function? Why Does GELU Use a Gaussian Distribution? Why Is GELU Used in Transformers? When Should You Use GELU? What Is the GELU Activation Function? GELU stands for Gaussian Error Linear Unit. It is a nonlinear activation function introduced by Dan Hendrycks and Kevin Gimpel in 2016. An activation function determines how a neuron transforms its…
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