Translations:Softmax Function/1/zh: Difference between revisions

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Message definition (Softmax Function)

The '''softmax function''' (also called the '''normalized exponential function''') is a mathematical function that converts a vector of real numbers ('''{{Term|logits}}''') into a probability distribution. It is the standard output {{Term|activation function|activation}} for multi-class classification in neural networks and plays a central role in models ranging from {{Term|logistic regression}} to large language models.

softmax 函数（也称为归一化指数函数）是一种将实数向量（logits）转换为概率分布的数学函数。它是神经网络中多类分类的标准输出激活函数，在从逻辑回归到大语言模型的各种模型中扮演核心角色。

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	'''softmax 函数'''（也称为'''归一化指数函数'''）是一种将实数向量（'''logits'''~~）转换为概率分布的数学函数。它是神经网络中多类别分类的标准输出激活函数，在从逻辑回归到大型语言模型的各种模型中都发挥着核心作用。~~		'''softmax 函数'''（也称为'''归一化指数函数'''）是一种将实数向量（'''{{Term\|logits}}'''）转换为概率分布的数学函数。它是神经网络中多类分类的标准输出{{Term\|activation function\|激活函数}}，在从{{Term\|logistic regression\|逻辑回归}}到大语言模型的各种模型中扮演核心角色。