How does NN prevent completion of training by Mu value?

Question

Kyeonghwan Kim 2017-8-9

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/352081-how-does-nn-prevent-completion-of-training-by-mu-value

评论： Noor Thamir 2022-5-17

采纳的回答： Amy

example.JPG

When you do NN, the situation is that the training is completed by Mu value. How can I prevent it?

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Noor Thamir 2022-5-17

Hello Doctors:

please, I need clarification regarding the parameters in the attached image: performance, gradient, mu, and validation check.

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Amy 2017-8-11

10
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/352081-how-does-nn-prevent-completion-of-training-by-mu-value#answer_277714

Hi Keonghwan,

The MU value is used to control the weights of the neurons updating process (back propagation) during training. If your training stops with the message "Maximum MU reached", it is a sign that additional training will not improve learning.

If you are reaching a maximum MU value too quickly, it might be because the data set you are using for training is too complex for the number of neurons in your network to successfully model. You can increase the number of neurons by increasing the value of 'hiddenLayerSize' in the script you used for training.

It is possible to set your own initial and maximum MU values for the 'trainlm' training function. See http://www.mathworks.com/help/nnet/ref/trainlm.html for information on the initial parameters and how they are updated.

You might also find the discussions of regularization and early stopping on this page helpful: http://www.mathworks.com/help/nnet/ug/improve-neural-network-generalization-and-avoid-overfitting.html

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Chris P 2020-8-9

编辑：Chris P 2020-8-9

Finally, a helpful answer to a neural network-related question. A lot better than most of the answers. Not going to name any names but you all know who I'm talking about.

请先登录，再进行评论。