TensorFlow中logits一词的含义是什么? [英] What is the meaning of the word logits in TensorFlow?

问题描述

在以下TensorFlow函数中，我们必须在最后一层中提供人工神经元的激活.我了解.但是我不明白为什么叫logits?那不是数学函数吗?

In the following TensorFlow function, we must feed the activation of artificial neurons in the final layer. That I understand. But I don't understand why it is called logits? Isn't that a mathematical function?

loss_function = tf.nn.softmax_cross_entropy_with_logits(
     logits = last_layer,
     labels = target_output
)

推荐答案

登录是一个重载术语，可能意味着很多不同的东西:

Logits is an overloaded term which can mean many different things:

在数学中， Logit 是一种映射概率的函数( [0, 1])到R((-inf, inf))

In Math, Logit is a function that maps probabilities ([0, 1]) to R ((-inf, inf))

0.5的概率对应于0的对数.负的logit对应于小于0.5的概率，对大于0.5的正数.

Probability of 0.5 corresponds to a logit of 0. Negative logit correspond to probabilities less than 0.5, positive to > 0.5.

在ML 中，可以

分类的原始(非标准化)预测的向量 模型生成，通常将其传递给规范化 功能.如果模型正在求解多类分类 问题，logits通常成为softmax函数的输入.这 然后，softmax函数生成(归一化)概率的向量 每个可能的类别都有一个值.

the vector of raw (non-normalized) predictions that a classification model generates, which is ordinarily then passed to a normalization function. If the model is solving a multi-class classification problem, logits typically become an input to the softmax function. The softmax function then generates a vector of (normalized) probabilities with one value for each possible class.

也要登录 有时是指sigmoid函数的逐元素逆.

Logits also sometimes refer to the element-wise inverse of the sigmoid function.

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