sparse_categorical_crossentropy和categorical_crossentropy有什么区别？ [英] What is the difference between sparse_categorical_crossentropy and categorical_crossentropy?

问题描述

sparse_categorical_crossentropy 和 categorical_crossentropy 有什么区别？什么时候应该使用一种损失而不是另一种？例如，这些损失是否适合线性回归？

What is the difference between sparse_categorical_crossentropy and categorical_crossentropy? When should one loss be used as opposed to the other? For example, are these losses suitable for linear regression?

推荐答案

简单地：

• categorical_crossentropy （ cce ）使用单热点数组来计算概率，


• sparse_categorical_crossentropy （ scce ）使用类别索引

• categorical_crossentropy (cce) uses a one-hot array to calculate the probability,
• sparse_categorical_crossentropy (scce) uses a category index

考虑具有5个类别（或类）的分类问题。

Consider a classification problem with 5 categories (or classes).

• 在 cce 的情况下，一个热门目标可能是 [0，1，0，0，0] ，该模型可能会预测 [。2，.5，.1，.1，.1] （可能正确）

• In the case of cce, the one-hot target may be [0, 1, 0, 0, 0] and the model may predict [.2, .5, .1, .1, .1] (probably right)

在 scce 的情况下，目标索引可能为[1]，并且模型可以预测为：[.5]。

In the case of scce, the target index may be [1] and the model may predict: [.5].

现在考虑3类分类问题。

Consider now a classification problem with 3 classes.

• 对于 cce ，一个热门目标可能是 [0，0，1] ，模型可以预测 [。5，。 1，.4] （可能不准确，因为它给头等舱带来了更多的可能性）


• 对于 scce ，目标索引可能是 [0] ，并且模型可能会预测 [。5]

• In the case of cce, the one-hot target might be [0, 0, 1] and the model may predict [.5, .1, .4] (probably inaccurate, given that it gives more probability to the first class)
• In the case of scce, the target index might be [0], and the model may predict [.5]

大多数分类模型会产生一次性熵和分类熵，因为您可以节省空间，但会丢失大量信息（例如，第二种情况，使用稀疏时索引2也非常接近。我总是使用一口气并研究 cce 输出的模型可靠性。

Most categorical models produce one-hot and categorical entropy because you save space, but lose A LOT of information (for example, in the 2nd case, index 2 was also very close) when using sparse. I always use one-hot and study the cce output for model reliability.

简而言之，请使用 sparse_categorical_crossentropy ，如果您的类是互斥的，即您根本不在乎其他足够接近的预测。

In short, use sparse_categorical_crossentropy when your classes are mutually exclusive, i.e. you don't care at all about other close enough predictions.

这篇关于sparse_categorical_crossentropy和categorical_crossentropy有什么区别？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！