Hi Everyone
If you have some materials for JMP's neural network's algorithm like below, please let me know.
-whether is normalized or not in input
-The kinds of initialization and parameter setting
-Initialization method of W, b
-Learning rate setting
-Epoch No. setting
-how to check learning curve to visualize optimization process of Validation set
-default value of hyper parameter
IkJu,
Chris Gotwalt has written an excellent white paper on the NN algorithm:
https://www.jmp.com/content/dam/jmp/documents/en/white-papers/wp-jmp9-neural-104886.pdf. I base my response below on his paper and JMP documentation.
(SEs: please feel free to chime in. )
Jian
-whether is normalized or not in input
There is an option called Transform Covariate under in the Model Launch window. Refer to page 5 for more details.
-The kinds of initialization and parameter setting
“At the beginning, normally distributed random starting values are generated and the penalty parameter is set to zero. After this initial fit, a nonzero candidate value of the penalty parameter is then chosen, and a univariate line search on the penalty parameter is undertaken.” (Page 7)
-Initialization method of W, b
Normally distributed random values (Pages 4 & 7))
-Learning rate setting
The default is 0.1, but you can choose any value between (0, 1] in the Model Launch window.
-Epoch No. setting
The number of tour can be set in the Model Launch window. It is One by default.
-how to check learning curve to visualize optimization process of Validation set
In NN JMP doesn’t show the cumulative validation. A good suggestion as I’d like to see a report similar to the one in Boosted Trees.
-default value of hyper parameter
These “default” hyper parameter values are displayed in the Model Launch window. For example, one layer, TanH with 3 nodes, 1 tour, etc. I’d call the model using these settings a base model as the best practice is fit several models with different settings and then compare them.
