Understanding of the layers parameter in get_tabular_learner

(Tony Hung) #1

I haven’t been able to find any documentation on the layer parameter in get_tabular_learner function

i.e
learn = get_tabular_learner(data, layers=[500,100], metrics=accuracy)

does anyone know what layers is and what it does

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(Jan) #2

It refers to the sizes of the hidden fully connected layers between the input (after embedding) and before the classification layer. The number of hidden layers is determined by the length of the list. So in your case you’ll have two hidden fully connected layers of size 500 and 100 respectively.

Edit: This snippet from tabular/models.py should make it clearer …

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Tabular_learner layers=[] parameter guidelines?
(Aaron) #3

@Jan I think you answered what it is but still remains what it does.

I n reference to the list of layers that @tbass134 used below, is there any rule of thumb for how many layers one should have or how large the layers should be for a given problem? I think this would help me to understand what it does better.

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(Abu Fadl) #4

I am also looking for tips on this.

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(Paul Xuereb) #5

Bump. Also looking for advice on rules of thumb for how many layers to use

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(blissweb) #6

Same issue. Documentation and courses even completely gloss over it. I’m working with tabular data.

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(Andrei Lyskov) #7

Also curious to know whether there’s a general heuristic to determine size of layers

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