为什么我们必须标准化神经网络的输入？
我知道有时，例如当输入值是非数字时，必须执行某种转换，但是当我们有数字输入时？为什么数字必须在一定的区间内？
如果数据没有标准化会发生什么？
最佳答案
解释得很好here .
If the input variables are combined linearly, as in an MLP [multilayer perceptron], then it is rarely strictly necessary to standardize the inputs, at least in theory. The reason is that any rescaling of an input vector can be effectively undone by changing the corresponding weights and biases, leaving you with the exact same outputs as you had before. However, there are a variety of practical reasons why standardizing the inputs can make training faster and reduce the chances of getting stuck in local optima. Also, weight decay and Bayesian estimation can be done more conveniently with standardized inputs.
关于machine-learning - 为什么我们必须对人工神经网络的输入进行标准化？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/4674623/