Data Science Asked by user75228 on December 3, 2020
If I am training a deep neural net with input features that are physical in nature (e.g. temperature, precipitation, etc), and I want to be able to perform some kind of transfer learning where I train on multiple instances to see how they perform on a different set of inputs entirely. How do I make sure that the inputs being normalized in each instant don’t conflict with one another?
For example, Mean temperature of 0 degrees with a standard deviation of 10 will be the same as mean temperature of 80 degrees with a standard deviation of 10 after normalization to a mean of 0 and a std dev of 1.
You should select a single preprocessing scheme and keep it constant for all experiments!
In your case, you want to scale your input features to $0$ mean and a standard deviation equal to $1$:
$$x_{scaled} = frac{x-μ}{σ}$$
During the initial training compute the mean $μ$ and standard deviation $σ$ of each feature and store them. Then use the same values to scale the data for all subsequent experiments.
Answered by Djib2011 on December 3, 2020
Get help from others!
Recent Answers
Recent Questions
© 2024 TransWikia.com. All rights reserved. Sites we Love: PCI Database, UKBizDB, Menu Kuliner, Sharing RPP