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neural-networknormalization
Shane Smiskol
Shane Smiskol

Reputation: 966

Is it better to individually normalize all inputs for a neural network?

I'm working on a neural network with Keras using TensorFlow as the backend right now, and my model takes 5 inputs, all normalized to 0 to 1. The inputs' units vary from m/s to meters to m/s/s. So, for example, one input could vary from 0 m/s to 30 m/s, while another input could vary from 5 m to 200 m in the training dataset.

Is it better to individually and independently normalize all inputs so that I have different scales for each unit/input? Or would normalizing all inputs to one scale (mapping 0-200 to 0-1 for the example above) be better for accuracy?

Upvotes: 0

Views: 522

Answers (1)

viceriel
viceriel

Reputation: 873

Normalize individualy each input. Because if you normalize everything by dividing 200 some inputs will affect your network less than others. If one input vary between 0-30, after dividing by 200 you get 0-0.15 scale and scale for input which vary 0-200 will be 0-1 after division. So 0-30 input will have less numbers and you tell your network that input is not so relevant as one whith 0-200.

Upvotes: 1

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