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Posted 02-28-2024 12:14 PM
(341 views)

This is a sample question from the certification preparation for advanced predictive modeling:

Consider a Generalized Additive Neural Network (GANN) with 3 continuous inputs and 2 hidden nodes

for each input.

How many parameters do you need to estimate when training the neural network?

A. 19

B. 21

C. 22

D. 25

correct_answer = "C"

I would like to know why the correct answer is C? Is there a formula that could be used to calculate the parameters estimated in GANN or can someone please draw a diagram to illustrate the correct answer? Thanks.

2 REPLIES 2

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Hello,

* I think (!)* it's like this :

- The basic architecture for a generalized additive neural network (GANN) has a separate MLP (multilayer perceptron) with a single hidden layer of h units for each input variable (h=2 in your example).
- Each individual univariate function has 3*h parameters (where h could vary across inputs).
- The individual output bias terms are absorbed into the overall bias
- An enhanced architecture includes an additional parameter for a direct connection (skip layer)

So, 3 continuous inputs with each of them having two hidden nodes (in one hidden layer) and a skip layer ... that makes:**3 * (3 * 2 + 1) + 1 = 22**

- First 3 due to 3 continuous inputs
- 3 * 2 = 3 * h (see point 2 above)
- + 1 between the brackets is for the direct connection (skip layer)
- the last + 1 (outside the brackets) is for the overall bias (see point 3 above)

Koen

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Thanks for the explanation!

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