Input Normalization for Transfer Learning
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.