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

Latest revision as of 23:41, 27 April 2026

Information about message (contribute)

This message has no documentation. If you know where or how this message is used, you can help other translators by adding documentation to this message.

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。

Revision as of 22:03, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Softmax Function unit 1 → zh) Tag: translation ← Older edit		Latest revision as of 23:41, 27 April 2026 (view source) DeployBot (talk \| contribs) (Batch translate Softmax Function unit 1 → zh) Tag: translation
Line 1:		Line 1:
	'''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，在从逻辑回归到大型语言模型的各类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。