h2o deep learning weights and normalization

Question

I'm exploring h2o via the R interface and I'm getting a weird weight matrix. My task is as simple as they get: given x,y compute x+y.
I have 214 rows with 3 columns. The first column(x) was drawn uniformly from (-1000, 1000) and the second one(y) from (-100,100). I just want to combine them so I have a single hidden layer with a single neuron. This is my code:

library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
train <- h2o.importFile(path = "/home/martin/projects/R NN Addition/addition.csv")
model <- h2o.deeplearning(1:2,3,train, hidden = c(1), epochs=200, export_weights_and_biases=T, nfolds=5)
print(h2o.weights(model,1))
print(h2o.weights(model,2))

and the result is

> print(h2o.weights(model,1))
          x          y
1 0.5586579 0.05518193

[1 row x 2 columns] 
> print(h2o.weights(model,2))
        C1
1 1.802469

For some reason the weight value for y is 0.055 - 10 times lower than for x. So, in the end the neural net would compute x+y/10. However, h2o.predict actually returns the correct values (even on a test set).
I'm guessing there's a preprocessing step that's somehow scaling my data. Is there any way I can reproduce the actual weights produced by the model? I would like to be able to visualize some pretty simple neural networks.

Answer 1

Neural networks perform best if all the input features have mean 0 and standard deviation 1. If the features have very different standard deviations, neural networks perform very poorly. Because of that h20 does this normalization for you. In other words, before even training your net it computes mean and standard deviation of all the features you have, and replaces the original values with (x - mean) / stddev. In your case the stddev for the second feature is 10x smaller than for the first, so after the normalization the values end up being 10x more important in terms of how much they contribute to the sum, and the weights heading to the hidden neuron need to cancel it out. That's why the weight for the second feature is 10x smaller.