The Softmax function is a commonly used activation function that is mainly used to convert each element of the input tensor into a probability value between 0 and 1, and the sum of these probability values is 1. This article explains the softmax function in detail. Understanding the softmax function is important for the design and implementation of deep learning models, as it is the key bridge between the neural network output and the probabilistic interpretation. Softmax is generally used as the last layer of a neural network for the output of multi-classification problems. Its essence is an activation function that normalizes a numerical vector into a probability distribution vector, and the sum of each probability is 1.
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