NOTE: ["binary cross-entropy"](https://en.wikipedia.org/wiki/Cross_entropy) is a terminology used in Machine Learning field.

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交叉熵 seems to mean "cross-entropy" in Mandarin Chinese. [According to this page](https://www.chinatt.news/6401326385628971521.html) however, it expresses the "binary cross-entropy loss" as 交叉熵损失. 

Also, [according to Keras documentation on Chinese](http://keras-cn-docs.readthedocs.io/zh_CN/latest/blog/autoencoder/) (Keras is a popular deep learning library on Python):

> 
> 下面我们训练自编码器，来重构MNIST中的数字，这里使用逐像素的**交叉熵**作为损失函数，优化器为adam
> 
> 
> 

However, it uses "binary cross-entropy" as a loss function.

Now, 交叉 means `cross` and 熵 means `entropy`. So there is no word for `binary` to be fit into. [This page written in Chinese](http://blog.csdn.net/rtygbwwwerr/article/details/50778098) also shows it as "cross-entropy":

> 
> 交叉熵（Cross-Entropy）
> 
> 
> 

So I wonder if it is no problem to just use `交叉熵` to mean "binary cross-entropy". Or is there any other word to specifically mean "binary cross-entropy"? How about other jargons, such as "categorical cross-entropy"? If I use 交叉熵, does it always mean "binary cross-entropy"?