What is Softmax?
Softmax
Softmax is an activation function that takes a vector of raw real numbers (logits) and normalizes them into a probability distribution where each value lies between 0 and 1, and all values sum to 1.
Why It Matters for AI Builders
Key to managing sequence memory and token weights during multi-class classification output layers and scaling attention scores in the transformer attention mechanism; optimizing Softmax prevents attention processing bottlenecks and keeps execution latencies low.
Detailed Deep Dive
Softmax is a mathematical function that converts a vector of raw scores (logits) into a probability distribution that sums to 1. Typically applied at the final output layer of classification networks and self-attention models, softmax exponentiates each score and normalizes it, highlighting the dominant output class.
Frequently Asked Questions
Q:Why is Softmax used in classification instead of simple normalization?
Softmax exponentiates the inputs, which exaggerates differences between values—making the largest value closer to 1 and smaller values closer to 0, providing a clear class prediction.
Q:How does temperature scaling affect the Softmax output?
A higher temperature flattens the distribution (increasing diversity/randomness), while a lower temperature makes the distribution sharper and more deterministic.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationMulti-class classification output layers and scaling attention scores in the Transformer attention mechanism.
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