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.
Frequently Asked Questions
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.
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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