Activation Functions Comparison
Compare PyTorch activation functions side by side: ReLU, GELU, SiLU, Sigmoid, Tanh, and more. Interactive plots, formulas, pros/cons, and when to use each one.
Built by Michael Lip
Frequently Asked Questions
Which activation function should I use?
For most cases: ReLU for CNNs and simple networks. GELU for transformers and NLP models (used in BERT, GPT). SiLU/Swish for modern architectures (EfficientNet). Sigmoid for binary output layers. Softmax for multi-class output layers.
What is the dying ReLU problem?
If a ReLU neuron receives large negative inputs, it outputs 0 and its gradient is 0, so it stops learning permanently. Solutions: use LeakyReLU (small negative slope), ELU, or GELU. Proper weight initialization (He init) also helps prevent this.
What is GELU and why do transformers use it?
GELU (Gaussian Error Linear Unit) is x * Phi(x) where Phi is the Gaussian CDF. Unlike ReLU, it's smooth everywhere and allows small negative values. Transformers use it because the smooth gradient flow works well with attention mechanisms and deep architectures.
About This Tool
This tool is part of HeyTensor, a free suite of PyTorch and deep learning utilities. All calculations run entirely in your browser — no data is sent to any server. The source code is open on GitHub.
Contact
HeyTensor is built and maintained by Michael Lip. For questions or feedback, email [email protected].