Softmax Function Definition

A vector of raw, unnormalized scores [1000, 1002, 999] is passed as input to a computational function that converts these scores into a probability distribution. A common technique to prevent numerical errors is to first subtract the maximum value of the vector from every element before applying the main transformation (exponentiation). Why is this subtraction step crucial for handling large input values?

Calculating Output Probabilities from Model Scores

A model outputs the following raw, unnormalized scores for three classes: [2.0, 1.0, 0.1]. If a constant value of 5.0 is added to each of these scores, resulting in a new score vector of [7.0, 6.0, 5.1], how will the resulting probability distribution calculated by the function that converts these scores to probabilities change?

Order Preservation of the Softmax Function

Energy-Based View of Softmax

Output Layer of Softmax Regression

Partition Function in Softmax

Vectorized Minibatch Softmax Regression