您的神经网络至少存在一个问题，该问题会使您的结果概率产生偏差:模型输出是最后一层的sigmoid本身是.

There's at least one problem with your neural network that skews your result probabilities: the model output is the sigmoid of the last layer which itself is a sigmoid.

这意味着您的登录信息(即原始分数)在[0, 1]中，因此最终概率是在[0, 1]范围而不是[-inf, inf]上计算的.

This means that your logit (i.e., the raw score) is in [0, 1], so the final probability is computed on a [0, 1] range, not [-inf, inf].

如上图所示，这使得结果概率为 大于0.5.

As you can see from the graph above, this makes the results probability to be greater than 0.5.

解决方案:尝试使用没有最后一个sigmoid的同一网络.

Solution: try the same network without the last sigmoid.