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openai.com·4h ago
Data Science
Analytics Vidhya·3d ago·5 min read
GELU Activation Function Explained: How It Works and Why Transformers Use It
TL;DR
GELU (Gaussian Error Linear Unit) is preferred over ReLU (Rectified Linear Unit) in Transformer models like BERT due to its smoother, input-dependent activation mechanism. This subtle difference enhances the model's ability to learn complex patterns in data.
✦ Why It Matters
Consider using GELU in your neural network models to improve learning efficiency and performance.
Key Takeaways
- Experiment with GELU for Transformer architectures like BERT.
- Use GELU to retain small negative signals in neural networks.
- Evaluate activation functions based on model architecture and dataset.
- Consider computational trade-offs when choosing between GELU and ReLU.
How It Works
GELU uses the Gaussian cumulative distribution function to smoothly scale inputs, allowing for a probabilistic gating mechanism that retains some negative values while suppressing larger negatives. This contrasts with ReLU's hard thresholding, enabling GELU to better capture complex patterns in data.