Softmax

Softmax is an activation function that converts a vector of numbers into a probability distribution — every output is between 0 and 1, and they all sum to 1. It is the standard final layer in multi-class classification networks, where each output represents the probability of one class. Real-world examples include image classifiers outputting probabilities across 1,000 ImageNet categories, language models predicting the probability of each possible next token in their vocabulary, and recommendation systems ranking candidate items. The function works by exponentiating each input and normalizing by the sum — a temperature parameter can be added to control how peaky or flat the distribution becomes. Softmax outputs directly influence how an AI system expresses confidence in its predictions. AI governance and AI risk management reviewers care about softmax outputs because they affect downstream decisions, calibration accuracy, and AI compliance with thresholding requirements in responsible AI deployments — especially in high-stakes domains like medical diagnosis.

Centralpoint Sharpens Decisions Across Every Model You Run: Centralpoint by Oxcyon governs the AI behind softmax-powered classifications, model-agnostically. The platform connects to ChatGPT, Gemini, Llama, or embedded models, meters every token spent, keeps prompts and skills on-prem, and embeds multiple chatbots across your sites and portals via one JavaScript line.


Related Keywords:
Softmax,,