Softmax Function: Turning Scores into Probabilities
The softmax function turns a list of raw scores from a model into a clean probability distribution where all values sum to 1. It's most often the final step in a neural network for multi-class classification, like deciding if an image is a 'cat', 'dog', or 'bird'. The main footgun is mistaking a high softmax probability for high model confidence; it only reflects the score's strength relative to the other scores, not its absolute certainty.
The softmax function acts like a 'winner-takes-most' converter, turning a model's raw, unscaled output scores (logits) into a set of probabilities that sum to 1. This process amplifies the highest score, making it significantly more probable than the others. It's the standard final activation function in neural networks for multi-class classification tasks. For example, it would convert raw sentiment scores like `[1.3, 0.5, -0.8]` into probabilities like `[0.62, 0.28, 0.10]`, clearly pointing to 'positive'. The critical footgun is interpreting a high output (e.g., 95%) as high model confidence. It only reflects the relative difference between scores, not the model's absolute certainty.
Read the original → Wikipedia: Softmax function
- #activation function
- #neural networks
- #classification
- #llms
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