应用梯度下降和随机梯度下降时的几个注意点 - webdancer's Blog
应用梯度下降和随机梯度下降时的几个注意点
webdancer
posted @ 2012年10月17日 04:33
in machine learning
with tags
Machine learning
, 6412 阅读
随机梯度下降算法的基本思想，在前面已经说过了。在应用梯度下降的时候有几个要注意的点，再重新记录一下。
1.收敛
我们在应用梯度下降的时候，一定要关注是否收敛。一般来说，$\mathbb E(\theta)$收敛到一定的值，所求出来的参数也是基本相同的。能使用梯度下降的求解的问题，影响我们求解的是它的学习速率（可以参考前面的内容）。速率过大，可能不收敛；速率过小，收敛速度过慢，所以我们必须恰当的选择学习速率$\eta$。通过下面的例子看以看到[1]：
当学习速率为50时，不收敛；当学习速率为0.01时收敛过慢，所以在训练时候一定要选择恰当的参数。
2.随机shuffle
在随机梯度学习算法中，如果训练集中正例和负例都聚集在一块。不进行shuffle可能会造成'wavy'现象[2]，如下图所示:
[引用]：
[1]自己实现的logistic regression model,然后在上面做的实验，得出的图1。
https://github.com/seaslee/ML_algorithm.git
[2]http://openclassroom.stanford.edu/TestFolder/courses/ufldl/exercises/ex1/ex1.html
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