Softmax
Definition
Softmax converts arbitrary numbers into positive values that sum to one.
Softmax converts a sequence of real values into positive values summing to one and is often used for class probabilities or attention weights.
Simply explained
Several numbers are transformed into shares of a common total; larger inputs receive larger shares.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A classifier computes a distribution over possible product categories from logits.
Limits and pitfalls
- Softmax outputs are not reliable probabilities without calibration; large logits require numerically stable computation.
Origin & history
The function has roots in statistical mechanics and discrete choice models and has become a standard building block of neural networks.
Use in mid-sized companies
- Possible application: A classifier computes a distribution over possible product categories from logits.
Frequently asked questions
What does Softmax mean?
Softmax converts a sequence of real values into positive values summing to one and is often used for class probabilities or attention weights. What should be considered? Softmax outputs are not reliable probabilities without calibration; large logits require numerically stable computation.
Sources
- Deep Learning, Softmax Units · Goodfellow et al., Deep Learning, Softmax Units
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: